A national approach to health risk assessment, risk communication and management of chemical hazards...

NATIONAL WATER COMMISSION — WATERLINES i

A national approach to health risk assessment, risk

communication and management of chemical

hazards from recycled water

C hapman HF , L eus c h F DL , P roc hazka E , C umming J , R os s V

Griffith University

Humpage A , F ros c io S , L aingam S Australian Water Quality Centre

K han S J , T rinh T , Mc Donald J A

UNSW Water Research Centre

Waterlines Report Series No 48, May 2011

Waterlines

This paper is part of a series of works commissioned by the National Water Commission on key water issues. This work has been undertaken by Water Quality Research Australia on behalf of the National Water Commission.

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© Commonwealth of Australia 2011

This work is copyright.

Apart from any use as permitted under the Copyright Act 1968, no part may be reproduced by any process without prior written permission from the Commonwealth.

Requests and enquiries concerning reproduction and rights should be addressed to the Commonwealth Copyright Administration, Attorney General’s Department, Robert Garran Offices, National Circuit, Barton ACT 2600 or posted at www.ag.gov.au/cca.

Online/print: ISBN: 978-1-921853-19-7 A national approach to health risk assessment, risk communication and management of chemical hazards from recycled water, May 2011 Authors: HF Chapman, FDL Leusch, E Prochazka, J Cumming, V Ross, A Humpage, S Froscio, S Laingam, SJ Khan, T Trinh and JA McDonald

Published by the National Water Commission 95 Northbourne Avenue Canberra ACT 2600 Tel: 02 6102 6000 Email: [email protected]

Date of publication: May 2011

Cover design by: Angelink

An appropriate citation for this report is: Chapman HF, Leusch FDL, Prochazka E, Cumming J, Ross V; Humpage A, Froscio S, Laingam S, Khan SJ, Trinh T, McDonald JA, 2011, A national approach to health risk assessment, risk communication and management of chemical hazards from recycled water, Waterlines report, National Water Commission, Canberra

Disclaimer

This paper is presented by the National Water Commission for the purpose of informing discussion and does not necessarily reflect the views or opinions of the Commission.

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Foreword

The National Water Commission is pleased to release this Waterlines report, A national approach to the risk assessment, risk communication and management of chemicals in recycled water. The report is part of a suite of work undertaken by the Commission to support its work on water quality regulation and inform its wider urban water future directions agenda.

Drinking water is a critical resource and must be safe for consumers. As the way we use and manage water is changing, we are faced with new challenges. In the past, our supplies have typically come from protected catchments and required little treatment for drinking. These times have now changed – all state and territories have invested in diversifying their supply portfolios. There are thousands of chemicals potentially present in wastewater that need to be managed and addressed including in recycling schemes and we must continue to look for innovative and cost effective approaches to manage risks.

This Waterlines report demonstrates innovative approaches to the comprehensive analysis of contaminants in water sources. These new approaches overcome significant cost and process limitations relative to traditional methods, and are shown to be effective in the detection of risks in alternate water supplies, including recycled water. The report demonstrates it is possible to characterise and manage the risks of all chemicals to levels below thresholds of concerns for human and environmental safety.

The methods developed and validated in this report have direct application in water planning, management and regulation of risks in our water supplies. The Commission commends the research team in their development and testing of the analytical toolbox and encourages governments and practitioners to consider its use when validating risks in higher-risk alternate water supply schemes.

This project has demonstrated that sound science can result in the ability to recognise and manage hazards in water recycling schemes using a preventative approach as adopted by the Australian Guidelines for Water Recycling (2006). The Commission supports the view that through increased whole of government collaboration, research and development activities can be optimised nationally. In this context, the recommended actions provided in the report provide a starting point for greater inter-jurisdictional collaboration.

The Commission encourages discussion of the recommendations presented and looks forward to working with governments and other stakeholders to advance this important aspect of water management in Australia. James Cameron Chief Executive Officer National Water Commission

May 2011

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Contents Foreword iv Executive summary xiii 1 Introduction 1

1.1 Background 1 1.2 The challenge 1 1.3 The solution 2

2 Risk assessment and risk management of chemicals in recycled water: current practice and future needs 4 2.1 Introduction 4 2.2 Part A: Risk assessment and the regulation of chemicals in Australia 4 2.3 Part B: Alternative methods in the risk framework 18

3 Review of potential toxicological outcomes associated with exposure to chemical contaminants from drinking water 32 3.1 Scope 32 3.2 Potential health outcomes associated with exposure to contaminants in drinking water 32 3.3 Considerations for in vitro testing 45 3.4 Conclusions 46

4 Chemical analysis and validation of extraction process 48 4.1 Summary 48 4.2 Aim and objectives 48 4.3 Materials 49 4.4 Sample collection and solid phase extraction 50 4.5 Analysis of PPCPs by HPLC-MS/MS 51 4.6 Analysis of nitrosamines by GC-MS/MS 57 4.7 Analysis of trihalomethanes by GC-ECD 60 4.8 Analysis of steroid hormones by GC-MS/MS 61 4.9 Solid phase extraction optimisation experiments 64 4.10 Conclusion 70

5 Bioanalytical and chemical screening of Australian recycled water 71 5.1 Site selection and sampling 71 5.2 Priority chemicals list 74 5.3 Bioanalytical tools 77 5.4 Effects fingerprint 85 5.5 Results 91 5.6 Discussion 110 5.7 Conclusions 121

6 Enhancing risk communication from science to policy and regulation and implementation of recycled water in Australia 122 6.1 Introduction 122 6.2 Risk perceptions and the rejection of water recycling schemes 122 6.3 Science, policy and practice 123 6.4 A theoretical approach 124 6.5 Empirical research 124 6.6 This research 126 6.7 Method 127 6.8 Discussion 136

7 Conclusions and recommendations 138 7.1 Risk assessment (Chapter 2) 138 7.2 Results of the monitoring program (Chapter 5) 139 7.3 Role of bioassays in risk assessment 141

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7.4 Importance of communication and management of knowledge (Chapter 6) 142 7.5 Recommendations 142

8 Bibliography 144 9 Appendices 153

9.1 Appendix I: Detailed results from solid phase extraction experiments 153 9.2 Appendix II: Chemical selection matrices 167 9.3 Appendix III – Abridged SOP for collection and extraction of water samples (full SOP available upon request) 189 9.4 Appendix IV – Priority chemicals factsheets 192

Tables Table 1: Key elements of the regulatory and management structure of chemicals

in Australia (adapted from DEH (1998)). ............................................................................ 8Table 2: Approaches to risk assessment by the Australian regulatory authorities .................. 10Table 3: Consultative committee structures of the four chemical regulators ........................... 17Table 4: Structural classes of chemicals as determined by Cramer et al. (1978). .................. 21Table 5: Classification of presumable risk showing ‘protection index’ (PI) and

categories of safety .......................................................................................................... 21Table 6: Tabulated version of FDA’s plot to determine level of concern (class)

based on chemical structure (category) and exposure threshold .................................... 22Table 7: The level of toxicity tests required for each class increased from the low

concern class I to the high concern class III .................................................................... 22Table 8: Fifth percentile NOELs and human exposure thresholds (Munro et al

1996) based on structural class (Cramer 1978) ............................................................... 23Table 9: Suggested tiered structure for the risk management of chemicals

detected in source water in Australia ............................................................................... 30Table 10: Prioritisation of health outcomes based on relevance, feasibility of in

vitro testing systems, and previous deployment in a validation period ............................ 47Table 11: Transitions for PPCP compounds using ESI positive mode. .................................. 53Table 12: Transitions for PPCP compounds using ESI negative mode. ................................. 56Table 13: GC-MS/MS method parameters. ............................................................................. 59Table 14: Optimal analyte dependent parameters for tandem mass spectrometry

(steroidal hormones) ......................................................................................................... 63Table 15: Analyte groups used in the 4-L extraction experiments .......................................... 65Table 16: SPE configurations tested. ...................................................................................... 65Table 17: Description of sites and water samples taken in this study ..................................... 72Table 18: Model compounds used in this study ...................................................................... 75Table 19: Bioassay battery. ..................................................................................................... 78Table 20: Relative potency (expressed as log RP) of model compounds – 1 of 2

– in the basal and reactive toxicity assays ....................................................................... 86Table 21. Relative potency (expressed as log RP) of model compounds - 2 of 2 -

in the specific toxicity assays. .......................................................................................... 88Table 22: Summary of bioassay results 1 of 2 – basal and reactive toxicity ........................... 92Table 23: Summary of bioassay results 2 of 2 - specific toxicity ............................................. 94Table 24: Summary of chemical analysis results – 1 of 5 – Hormones,

pharmaceuticals, industrial compounds ........................................................................... 97Table 25: Summary of chemical analysis results – 2 of 5 – Pharmaceuticals and

personal care products ..................................................................................................... 99Table 26: Summary of chemical analysis results – 3 of 5 – Pesticides and

disinfection by-products .................................................................................................. 101Table 27: Additional compounds analysed. ........................................................................... 104

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Table 28: Summary of chemical analysis results – 4 of 5 – Additional pharmaceutical compounds ........................................................................................... 106

Table 29: Summary of chemical analysis results – 5 of 5 – Additional pharmaceutical, personal care, industrial and pesticide compounds ............................. 108

Table 30. Bioassays as indicators of classes of pollutants. .................................................. 119 Figures Figure 1: An example of the risk assessment framework .......................................................... 6Figure 2: Decision tree approach for determining data requirements based on

Cramer class .................................................................................................................... 24Figure 3: Conceptual model of traditional risk assessment and TTC assessment

highlighting the difference in the role of toxicity threshold determined from toxicity testing and human exposure threshold ................................................................ 28

Figure 4: Model of the risk assessment framework incorporating TTC as a screening tool ................................................................................................................... 30

Figure 5: The HPLC-MS/MS instrument used for PPCPs analysis. ........................................ 51Figure 6: The GC-MS/MS instrument used for N-nitrosamines and hormones

analysis ............................................................................................................................. 57Figure 7: The GC-ECD instrument used for THMs analysis ................................................... 60Figure 8: Four-litre extraction experiment ................................................................................ 66Figure 9: Labelled tubes for elution ......................................................................................... 66Figure 10: Drying cartridges under nitrogen ............................................................................ 66Figure 11: Elution of large volume SPE cartridges .................................................................. 67Figure 12: SPE recoveries of steroids hormones .................................................................... 68Figure 13: SPE recoveries of pharmaceuticals ....................................................................... 69Figure 14: Priority chemical classes. ....................................................................................... 74Figure 15. Relationship between Kow and removal efficacy during dissolved air

floatation/filtration and chlorination (WRP 1), for compounds with a concentration of at least 30 ng/L in the source water ..................................................... 115

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Abbreviations and acronyms

µL Microlitre (1 000 000th of a litre) µM Micromolar (a unit of concentration) 4NP 4-Nonylphenol 4-NPD 4-Nitro-o-phenyldiamine 4tOP 4-t-Octylphenol AChE Acetylcholinesterase ACHHRA Australian Centre for Human Health Risk Assessment ACN Acetonitrile ADEC Australian Drug Evaluation Committee ADI Acceptable daily intake ADWG Australian drinking water guidelines AICS Australian Inventory of Chemical Substances ANZECC Australian and New Zealand Environment and Conservation Council APVMA Australian Pesticides and Veterinary Medicines Authority AR Androgen Receptor AR-CALUX AR-CALUX bioassay to measure (anti)androgenic activity ARMCANZ Agriculture and Resource Management Council of Australia and New Zealand ARTG Australian Register of Therapeutic Goods ATCC American Type Culture Collection ATP Adenosine triphosphate, a coenzyme used as energy carrier in cells AWA Australian Water Association AWQC Australian Water Quality Centre BaP Benzo(a)pyrene BPA Bisphenol A BSTFA N, O-bis(trimethylsilyl)trifluoro-acetamide C3A Human hepatocellular carcinoma cell line Caco2 Human epithelial colorectal adenocarcinoma cell line Caco2-NRU Caco2-NRU assay to measure cytotoxicity to gastro-intestinal cells CASRN Chemical Abstract Service Registry Number CCME Canadian Council of Ministers of the Environment CD-FBS Charcoal-dextran stripped fetal bovine serum CDTA Trans-1,2-Cyclohexanediaminetetraacetic Acid CNS Central nervous system CPA Cytokine production assay CRCWQT Cooperative Research Centre for Water Quality and Treatment CYP1A2 Cytochrome P450 1A2 CYP450 Cytochrome P450 DBP Disinfection by-product DCM Dichloromethane DDE Dichlorodiphenyldichloroethylene DDT Dichlorodiphenyltrichloroethane DEET Diethyltoluamide DHT Dihydrotestosterone DMEM Dulbecco's modified eagle medium DNA Deoxyribonucleic acid DoHA Department of Health and Ageing DSEWPaC Department of Sustainability, Environment Water, Population and

Communities DTNB 5,5’-Dithio-bis(2-Nitrobenzoic Acid) E1 Estrone

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αE2 17α-Estradiol βE2 17β-Estradiol E3 Estriol ECx Effect Concentration “x”, the concentration required to affect x% of the

population under study ECD Electron capture detector ECVAM European Centre for Validation of Alternative Methods EDC Endocrine disrupting compound EDRF Endothelium-derived relaxing factor EE2 17α-Ethinylestradiol EI Electron impact ELISA Enzyme-linked immunosorbent assay EPHC Environmental Protection and Heritage Council ERFF Environment Research Funder’s Forum ERα Estrogen receptor α isomer ERα-CALUX ERα-CALUX bioassay to measure (anti)estrogenic activity ESI Electrospray ionisation FAO Food and Agriculture Organisation FB Field blank FBS Fetal bovine serum FCMN Flow-cytometry-based micronucleus FDA Food and Drugs Administration FSA Food Science Australia FSANZ Food Standards Australia New Zealand GC/MS Gas chromatrography/mass spectrometry GC-ECD Gas chromatrography – electron capture detection GHS Globally Harmonized System of Classification and Labelling of Chemicals GR Glucocorticoid receptor GR-CALUX GR-CALUX assay to measure glucocorticoid activity GWRC Global Water Research Coalition HACCP Hazard analysis and critical control points HepaTOX HepaTOX assay to measure cytotoxicity to liver cells HepCYP1A2 HepCYP1A2 assay to measure induction of cytochrome P450 1A2 enzyme HEPES 4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid HPLC High-performance liquid chromatography I.D Internal diameter ICx Inhibition concentration “x”, the concentration required to inhibit x% of the

population under study ICNA Industrial Chemicals Notification Act IL1β Interleukin 1β ILSI International Life Sciences Institute IPCS International program on chemical safety IPR Indirect potable reuse ITC Industry technical committee JECFA Joint FAO/WHO Expert Committee on Food Additives LC-MS Liquid chromatography mass spectrometry LOD Limit of detection LOQ Limit of quantification LPS Lipopolysaccharide LRCC Low regulatory concern chemicals MDL Method detection limit MeOH Methanol MFO Multi-function oxidase MiliQ Ultrapure water mL Millilitre (1000th of a litre)

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mM Millimolar (a concentration) MMS Methyl methanesulfonate MRM Multiple reaction monitoring MS Mass spectrometry MS/MS Tandem mass spectrometry MTBE Methyl tert-butyl ether MTS 3-(4,5-Dimethylthiazol-2-yl)-5-(3-carboxymethoxyphenyl)-2-(4-sulfophenyl)-

2H-tetrazolium ND Not detected NDBA N-Nitrosodi-n-butylamine NDEA N-Nitrosodiethylamine NDMA N-Nitrosodimethylamine NDPA N-Nitrosodipropylamine NHMRC National Health and Medical Research Council NICNAS National Industrial Chemicals Notification and Assessment Scheme NMEA N-Nitrosomethylethylamine NMor, or NMorph N-Nitrosomorpholine NOEL No observable effects concentration NOHSC National Occupational Health and Safety Council NPip N-Nitrosopiperidine NPyr N-Nitrosopyrrolidine NRC National Research Council NRMMC Natural Resource Management Ministerial Council NRU Neutral red uptake NSAID Non-steroidal anti-inflammatory drug NWC National Water Commission OCSEH Office of Chemical Safety and Environmental Health OECD Organisation for Economic Cooperation and Development OH&S Operational health and safety Org2058 16α-ethyl-21-hydroxyl-19-norpregn-4-ene-3,20-dione PAH Polyaromatic Hydrocarbon PBS Phosphate buffered saline PCB Polychlorinated Biphenyl PMA Phorbol-12-myristate-13-acetate PPCP Pharmaceutical and personal care product PR Progesterone receptor PR-CALUX PR-CALUX bioassay to measure progesterone-like activity PUBCRIS Public Chemical Registration Information System QAQC Quality assurance/quality control QLD Queensland RFU Relative fluorescence unit rGTU Relative genotoxic unit REF Relative enrichment factor RNS Raising National Standard Program RO Reverse osmosis RPMI Royal Park Memorial Institute cell culture media rTU Relative toxic unit S9 Rat liver homogenate containing phase I and phase II enzymes SD Standard deviation SDC Standards Development Committee SEM Standard error of the mean SEQ South-east Queensland SOP Standard operating protocol SPE Solid phase extraction SUSDP Standard for Uniform Scheduling of Drugs and Poisons

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SWRC Smart Water Research Centre T3 Triiodothyronine T4 Thyroxine TA100 Ames Salmonella typhimurium tester strain 100 TA98 Ames Salmonella typhimurium tester strain 98 TCDD 2,3,7,8-Tetrachlorodibenzodioxin TCEP Tris(2-Chloroethyl)phosphate TDI Tolerable daily intake TGA Therapeutic Goods Authority THM Trihalomethane THP1 Human acute monocytic leukemia cell line THP1-CPA THP1 CPA assay to measure interference with cytokine production (a

measure of immunotoxicity) TICC TGA – Industry Consultative Committee TIE Toxicity identification evaluation TMCS Trimethylchlorosilane TMS Trimethylsilyl TMX Tamoxifen, an anti-estrogenic pharmaceutical TRβ Thyroid Receptor β isomer TRβ-CALUX TRβ-CALUX bioassay to measure thyroid-like activity TTC Threshold of toxicological concern U2OS Human osteosarcoma cell line UNSW University of New South Wales USA United States of America USEPA United States Environmental Protection Agency UV Ultraviolet UWSRA Urban Water Security Research Alliance WA Western Australia WHO World Health Organization WIL2NS Human lymphoblastoid cell line WIL2NS TOX WIL2NS TOX assay to measure cytotoxicity to lymphocytes WQRA Water Quality Research Australia WRP Water reclamation plant WSAA Water Services Association of Australia WWTP Wastewater treatment plant

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Acknowledgments

Funding and support for this work was provided by the National Water Commission (NWC) under the Raising National Standard Program (RNWS), Water Quality Research Australia (WQRA), ACTEW/Ecowise, Melbourne Water, Urban Water Security Research Alliance (UWSRA), SA Water, Sydney Water, United Water, Water Corporation and the Western Australian Department of Water. The project was a collaboration between Griffith University (GU), The University of New South Wales (UNSW) and the Australian Water Quality Centre (AWQC), Adelaide and the industry partners.

Adam Lovell is thanked for his role in defining the project in the early stages and for his ongoing role as industry advisor to the project. We thank Pam Quayle, Tarren Reitsema, Dan Inglis, Melody Lau, Jackson Wong, Nhat Le Minh and Heather Coleman for their assistance in the laboratory and all industry partner staff for their assistance in the field work components. We thank Jane-Louise Lampard and David Halliwell for editorial comments.

The project was overseen by a steering committee comprised of Andrew Humpage (AWQC), Stuart Khan (UNSW), Malcolm Warnecke (Ecowise), Judy Blackbeard (Melbourne Water), Peter Cox (Sydney Water), Simon Toze (UWSRA), Adam Lovell (WSAA), David Halliwell (WQRA), Brian Priestley (ACHHRA), Tarren Reitsema (Dept of Water, WA) and Paul Rasmussen (United Water). The research team would like to thank the steering committee for their input and guidance.

Paul Smith of the National Water Commission (NWC) is thanked for his role in ensuring the project met its milestones and for promoting the work when possible. Paul is also thanked for his input into the steering committee meetings. David Halliwell is also thanked for his role in managing the project overall on behalf of Water Quality Research Australia.

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Executive summary Drinking water is a critical resource and must be safe for consumers. As the availability of drinking water in the world comes under increased pressure from extreme weather events and a growing population, alternative sources of water such as recycled water become viable supply options. Of primary concern is the risk posed by pathogens due to the immediate impact of disease outbreaks. Therefore risk management for drinking water has focused more on harm from pathogens rather than chemicals. We are faced with new challenges as the way we use and manage water is changing. The presence of biologically-active chemicals in water is of interest because of potential adverse effects on wildlife and humans. There are thousands of chemicals potentially present in wastewater. Additionally new chemicals may be formed during the treatment processes (e.g. disinfection by-products). We cannot measure all chemicals present, therefore alternative methods are required. Bioanalytical methods or bioassays relevant to human health are evaluated in this research. This project was undertaken subsequent to those conducted previously under Water Quality Research Australia’s (WQRA) predecessor the Cooperative Research Centre for Water Quality and Treatment (CRCWQT), Global Water Research Coalition (GWRC 2008) and Uniquest (EPHC/NRW/NWC 2008) and in parallel with a partner project conducted in Western Australia (Reitsema et al. 2010). This project extends the previous work by including additional endpoints, primarily of human health relevance. The broad aim of the study was to adopt and validate methods or tools for assessing the potential for impacts to humans.

Chapter 2 reviews the risk assessment and regulation of chemicals in Australia. In addition, the history and application of the concept of ‘thresholds of toxicological concern’ (TTC) as used in the EPHC/NHMRC/NRMMC water recycling guidelines (2008) is reviewed and its relevance to chemical regulation and water quality risk assessment is addressed.

Chapter 3 provides an overview of the health outcomes that need to be considered in relation to exposure to contaminants from drinking water sources. Available in vitro bioassay methods for each of these health outcomes are then presented. Most of the relevant endpoints can be measured to a certain extent using in vitro tools, except reproductive and development toxicity (which are events that involve complex biological interactions that are not currently modelled in vitro). This chapter was the basis for the selection of the methods for the bioassay toolbox, which provide a measure of cytotoxicity, genotoxicity, mutagenicity, liver enzyme induction, endocrine (receptor-mediated) effects, immunotoxicity and neurotoxicity.

Chapter 4 summarises the validation of the extraction and chemical methods used during this project. After sample collection, all samples underwent solid phase extraction (SPE) on two different types of SPE cartridges. The selection of these cartridges was made to maximise the range of chemicals that could be effectively extracted from the water matrices. The eluted samples were divided into subcomponents, which were then sent to the Smart Water Research Centre (SWRC) and the Australian Water Quality Centre (AWQC) for bioassay analysis or kept at UNSW for chemical analysis. The trace chemical analysis was undertaken at the University of New South Wales (UNSW) and used state-of-the-art analytical methods including high performance liquid chromatography-tandem mass spectrometry (HPLC/MS-MS), gas chromatography-tandem mass spectrometry (GC-MS/MS) and gas chromatography-electron capture detection (GC-ECD).

Chapter 5 presents the bioassay and chemical analysis data of the monitoring program. Nine water reclamation plants throughout Australia were sampled, five with reverse osmosis (RO) and four with other treatment technologies (providing class A recycled water). Many of the monitored chemicals were detected in treated sewage, and RO removed most chemicals to below detection limit. All compounds measured in RO-treated water were several orders of magnitude below Australian guidelines for water recycling. Non-RO treatment stages were

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less effective, with concentrations in class A water only slightly lower than source waters. Bioassay results were in line with chemical trends, and biological activity was well correlated with chemical groups (e.g. hormones and endocrine activity, insecticides and neurotoxicity, disinfection by-products and genotoxicity/mutagenicity/immunotoxicity). Although unlikely to be a health concern, low level (anti)estrogenic activity was detected in some of the RO-treated samples. It is hypothesised that plasticisers from RO membranes could be responsible, although this remains to be demonstrated.

Chapter 6 examined issues regarding communication between research scientists, policy officers in government, regulators and the water industry. As public perceptions of risk have been shown to be influenced by perceptions of the credibility of the responsible agency (policy maker/regulator or water manager) it is vital that decisions made on water supply are based on the best-available science and communicated effectively. The research comprised semi-structured interviews with stakeholders and the data was analysed using qualitative methods. The issues identified included (among others) a need for a more strategic approach, structures to improve dialogue, issues with science to policy translation, consistency of guidelines and regulation and political nervousness around recycled water.

In conclusion this project has demonstrated that sound scientific evidence and good communication can contribute significantly to water reform in Australia. The methods developed and validated have direct application in water planning and management including safe management of priority substances and waters. Correctly managed and safe use of water sources has significant economic benefit both in Australia and in other water-scarce countries. This aim is consistent with the objectives of the National Water Initiative as outlined by the National Water Commission. The following are recommendations from this project.

Multiple barriers should always be deployed in water recycling schemes.

The focus on multiple lines of evidence through the addition of robust bioanalytical tools to the chemical and modelling methods enables the efficacy of treatment technologies to be more reliably assessed. This project has demonstrated that sound science can result in the ability to recognise and manage hazards in water recycling schemes using a preventative approach as adopted by the Australian guidelines for water recycling (2006).

The ability of in vitro bioassays to predict in vivo outcomes should be further investigated.

While the utility of using in vitro assays in monitoring schemes is demonstrated, there are knowledge gaps relating to how this translates to population impacts for wildlife and humans. Further knowledge on the predictive capacity of the bioassays will greatly enhance understanding and use of the techniques.

More chemicals should be tested using the bioassay battery developed in this project and the effects fingerprint examined to enhance the identification of priority chemicals for analysis.

A range of new chemicals were tested in the bioassay suite and the effects fingerprint is a useful tools in determining priorities for which chemicals should be investigated further in the risk assessments or those that do not need immediate investigation.

The range of endpoints included in the bioassay battery should be continually reviewed to incorporate new scientific developments.

This is a rapidly evolving field of science and the research has demonstrated that there are now a range of bioassays that can be used for some endpoints (in particular estrogenic and

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androgenic); however, the range of assays available should be further expanded as the science progresses to address a greater range of specific health outcomes.

The source of the low-level (anti)estrogenic activity in RO samples should be investigated.

This project has demonstrated that nearly all chemical activity is removed using a multiple-barrier approach. Some low level of endocrine activity was still detectable in a few RO-treated samples. If the source of the activity can be identified then source control practices could be employed.

The source of pesticides, and particularly the herbicides atrazine, diuron and simazine, in treated sewage should be investigated.

There were some specific chemicals identified during this project that require further investigation to ensure that human and environmental health is maintained. This is particularly important in some regions of Australia where discharge to natural environments is practised, as well as when the water is used as the source water for water reclamation plants.

Knowledge transfer and uptake into practice requires active communication between all water stakeholders.

Applied research projects can deliver valuable knowledge and commercially-focused outcomes for the water industry and for government. Chapter 6 of this report has identified some of the barriers to communication between stakeholders associated with water recycling schemes. Opportunities to pursue knowledge transfer will greatly assist with the adoption of research outcomes.

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1 Introduction 1.1 B ac kground With a climate of extreme weather events and ongoing population growth in Australia, there is a need to investigate and characterise public health risks associated with alternative water sources such as recycled/reclaimed water for domestic use including drinking water. One of the major impediments to water recycling for domestic use to date has been lack of public acceptance and clear regulatory guidance relating to chemicals that potentially remain in treated water. Important among these concerns is the perceived safety of the water for users and the environment. Reasons for this include our poor understanding of the long-term health consequences of exposure to chemicals in water at trace concentrations, and the unknown effects of chemical mixtures and/or unregulated chemicals.

This project sought to address these issues by providing new tools for the measurement of chemicals in water and guidance on how to use the emerging science in risk assessment. The outcomes have significant value in terms of providing assurance to the community and to regulators and practitioners that through the use of sound science we can adequately assess, communicate and manage risk and communicate complex issues in a useful way. The methods developed will provide powerful tools for use in hazard assessment critical control point (HACCP) programs and inform future reviews of national guidelines to protect the environment and water users.

The result of the field and laboratory work is more accurate methods for monitoring and risk assessment of chemicals for the protection of public health. Some of the same bioassays can also be used to monitor discharged wastewater and/or treatment efficacy before discharge, thus preserving the quality of both Australia’s environment and the health of the human population. A significant strength in this approach is that the information can be applied to both ecosystem effects and to human health impacts because we are dealing with effects at a sub-organism level.

The broad aims of the study were to adopt and validate methods or tools for assessing the potential for human health impacts from drinking water that contains a significant proportion of recycled water and to encourage the use of the tools and the knowledge in management of water safety. Public perceptions of risk have been shown to be influenced by the credibility of the responsible authority (e.g. regulator or water providers) and so it is vital that these practitioners provide assurance to the community that they are making decisions based on the best-available science. To achieve this it is necessary to establish and maintain strong communication between scientists, policy makers, regulators and managers of recycled water.

1.2 T he c hallenge As the way we use and manage water is changing, we are faced with new challenges. One of these is the reclamation of wastewater to augment existing drinking water supplies. The presence of biologically-active chemicals in water is of international interest because of potential adverse effects on wildlife and humans. Concerns remain, even in the absence of clear evidence of human health impacts, and it is now imperative to have risk assessment and risk communication tools to manage perception as well as the science. In the development of guidelines for drinking water it is necessary to extrapolate from animal studies (usually mice or rats), or other wildlife such as fish, and then to apply safety factors to the data to determine guideline values for humans or trigger values for ecosystems. It can take


years or decades to develop the necessary information and so the new methods are a valuable source of additional knowledge that is useful in the short term.

Chemical analysis alone has been problematic due to the large number of compounds that can be in the water and the ultra-low concentrations that can be biologically active. Chemical tests also have to be constantly updated to detect new chemicals on the market, existing chemicals with new uses, or illegal use or disposal of chemicals that may enter water sources. Chemical measurements also only account for those we know to test for and therefore new or unexpected chemicals may be missed altogether. With the increasing number of new chemicals developed and eventually released into water, it is becoming increasingly difficult to measure all possible contaminants using standard chemical techniques (such as gas or liquid chromatography - mass spectrometry). “As any analytical chemist knows, what you see depends on what you look for” (Lynn Roberts, Johns Hopkins University). Also important is that chemicals do not occur in isolation in reclaimed source water and present mostly as a complex mixture. In our efforts to ensure users are safe from pathogenic organisms, high levels of disinfection are used. This can result in a new hazard from treatment or disinfection by-products (DBPs). DBPs are known and/or suspected to be a hazard and therefore need to be fully understood and managed to protect public health. Microbial degradation products of organics in water can also be bioactive.

Short term in vitro tests can aid predicting the likelihood of effect after long-term exposure. These methods, previously developed for the pharmaceutical industry, provide a much-needed new approach to contaminant testing in water. Bioassays can detect multiple contaminants in one test because they do not measure chemicals by their structure but by their effect on biological systems (molecules or cells).

1.3 T he s olution Biological tests (‘bioassays’) are ideal screening tools because they can detect a wide range of contaminants based on their biological effect. The methods allow for high throughput screening and the information from this can be a powerful tool in risk assessment and communication. The availability of these tools will allow water utilities and regulators to test the quality of different types of water, such as sewage, river, tap and recycled water. This proposed toolbox will help water utilities and environmental agencies to monitor water quality as it impacts water resources, including all life that depends on it. This project aimed to apply a suite of reliable bioassays to screen water for chemical contaminants potentially harmful to humans.

An obvious advantage of toxicological testing is that it is not necessary to know which chemicals may be responsible for a specific toxicity to measure it. After careful validation it may be possible that the full additive or synergistic effects of unknown chemicals in mixtures can be attributed.

However, toxicity testing also has a number of limitations that must be recognised. One is that no matter how complex a bioassay system (or organism) is, it will never be identical to a human being and so some uncertainty will remain in terms of extrapolation of results to public health implications. A further complication is that bioassays are sometimes prone to ‘false positives’ due to other sub-optimal conditions for organism survival (e.g. temperature, oxygen availability, nutrient deficiency and salinity). For this reason, careful interpretation of results is necessary and in some cases it may be difficult to convince a sceptical public that assay results indicate a satisfactory water quality.

While chemical toxicity assessment by chemical analysis alone would rarely be considered adequate for new potable recycling schemes, the choice between in vitro bioassay testing


and live animal testing should be determined based on the detail of international experience and knowledge of the specific water treatment processes to be used.

One approach would be to screen water samples with in vitro toxicity tests for a wide range of potential human health endpoints. Samples resulting in positive biological responses in vitro would be subjected to a thorough targeted chemical analysis to determine the causative chemical(s). If no clear link between chemical and bioassay data could be established, then a full toxicity identification evaluation (TIE) may be necessary, where the sample is fractionated and then re-analysed using both bioassay and chemical methods to identify the class of the causative chemical(s). This process is repeated until the identity of the chemical(s) can be determined. For ethical reasons, live animal testing should only be considered where a strong case can be made that such testing has a reasonable likelihood of leading to significantly beneficial public health outcomes.


2 Risk assessment and risk management of chemicals in recycled water: current practice and future needs

2.1 Introduc tion The current crisis in providing water to Australian cities presents challenges to both regulators and managers of urban water. Water providers are facing the challenge of providing safe water from non-traditional sources, such as recycled or reclaimed waters. Federal regulators of chemicals face the challenge of adapting risk assessment strategies to account for the reuse of water (and biosolids) as sorption to waste solids and dilution to receiving waters can no longer be assumed to be a route of loss. Developing water quality guidelines for human consumption of recycled water must keep pace with a growing list of new chemicals that may enter the waste stream. State regulators of water quality must determine and implement guidelines and standards that not only ensure public safety, but can be monitored and enforced with available technologies.

The focus of this research was the investigation and characterisation of human health risks related to the use of alternative water sources such as recycled or reclaimed water for domestic use. Ecosystem risk assessment methods differ somewhat but that is outside the scope of this project and will not be considered further here.

In this chapter, the role of regulatory risk assessment in Australia is examined. In the first part of this chapter, issues concerning the structure and workability of the risk framework are addressed, particularly as it affects the ability of regulators to set guidelines and manage risks associated with chemical release.

The second part of the chapter reviews the history and application of the concept of threshold of toxicological concern (TTC) and its relevance to chemical regulation and water quality risk assessment. Issues of science and policy arising in both sections will be addressed, and a workable way forward appropriate to water management is discussed.

2.2 P art A : R is k as s es s ment and the regulation of c hemic als in A us tralia

2.2.1 The risk assessment framework

Regulatory risk assessment of chemicals in most jurisdictions follows the framework developed by the United States Environment Protection Agency (USEPA) in the early 1980s. This process, known as the four-step paradigm, was first outlined by the National Research Council (NRC 1983). The four steps of the process are:

1. hazard identification

2. dose-response assessment

3. exposure assessment

4. risk characterisation.


There are many published versions of the four-step framework for risk assessment, and many are focused on human health impacts, either from direct exposure (i.e. from food and water consumption) or indirect exposure from the environment (i.e. air toxics, recreational water use). From the basic framework, variations of the method, and of terminology, have developed. Some of this variation results from the purpose of the risk assessment; for example, whether the risk assessment is remedial (for contaminated sites) or predictive (for new chemicals) and whether the assessment is conducted for the purpose of protecting human health, endangered species or ecosystems. The enHealth model (Source: enHealth 2004) is typical (enHealth 2004). The models generally give the impression of a linear process from problem to solution, however some models include a feedback loop from risk management to either risk assessment or issue identification (or both), implying a circular process of continued refinement.


Figure 1: An example of the risk assessment framework

Issue Identification- Identification of key issues amenable to risk assessment

Hazard AssessmentHazard Identification

-Collection and analysis of relevant data

-Uncertainty analysis for hazard identification stepDose-response Assessment

-Collection of relevant data

-Uncertainty analysis for dose response assessment step

Exposure Assessment-analysis of hazard locations

-Identification of exposed populations

-Identification of potential exposure pathways

-Estimate of contaminant intakes for pathways

-Uncertainty analysis for exposure assessment step

Risk Characterisation-Characterise potential for adverse health effects to occur

-Evaluate uncertainty

-Summarise risk information

Risk Management-Define options and evaluate the environmental health, social and political aspects of the options

-Make informed decisions

-Take actions to implement the decisions

-Monitor and evaluate the effectiveness of the action taken

Review and

Reality Check

Review and

Reality Check

Engage Stakeholders, Risk Communicators and Community Consultation

Issue Identification- Identification of key issues amenable to risk assessment

Hazard AssessmentHazard Identification

-Collection and analysis of relevant data

-Uncertainty analysis for hazard identification stepDose-response Assessment

-Collection of relevant data

-Uncertainty analysis for dose response assessment step

Exposure Assessment-analysis of hazard locations

-Identification of exposed populations

-Identification of potential exposure pathways

-Estimate of contaminant intakes for pathways

-Uncertainty analysis for exposure assessment step

Risk Characterisation-Characterise potential for adverse health effects to occur

-Evaluate uncertainty

-Summarise risk information

Risk Management-Define options and evaluate the environmental health, social and political aspects of the options

-Make informed decisions

-Take actions to implement the decisions

-Monitor and evaluate the effectiveness of the action taken

Review and

Reality Check

Review and

Reality Check

Engage Stakeholders, Risk Communicators and Community Consultation

Source: enHealth 2004


2.2.2 Regulatory risk assessment in Australia

In the Australian regulatory environment, the risk assessment model is invoked for the manufacture/importation of all new and many existing chemicals (DEH 1998). For regulatory purposes, chemicals are classified into four distinct groups:

• therapeutic chemicals for human use

• chemicals for use in agriculture and veterinary medicine

• food additives and contaminants

• industrial chemicals.

Risk assessment of each group of chemicals is the subject of separate legislation and regulated by a different agency (Table 1). Consequently, there are differences in the method of assessment, the data required for assessment, and the information published following assessment.


Table 1: Key elements of the regulatory and management structure of chemicals in Australia (adapted from DEH (1998)).

Element Industrial chemicals Agricultural and veterinary products Pharmaceuticals Food additives

Scheme responsible for assessment

National Industrial Chemicals Notification and Assessment Scheme (NICNAS)

Australian Pesticides and Veterinary Medicines Authority (APVMA)

Therapeutic Goods Administration (TGA)

Food Standards Australia New Zealand (FSANZ)

Ministry Health and Ageing Agriculture, Fisheries and Forestry Health and Ageing Health and Ageing Assessment and/or registration

All new chemicals are assessed before use. Existing chemicals are reviewed on a priority basis.

All new products and new uses of products must be assessed and registered before use. Existing chemicals and products may be reviewed.

All new therapeutic goods must be registered on the Australian Register of Therapeutic Goods.

Stipulates food standards to protect public health.

Scope and definition Assessment of a chemical entity (not product) – any chemical that has an industrial use may be included i.e. dyes, solvents, adhesives, plastics, laboratory chemicals, paints, cleaning products, cosmetics and toiletries. Excludes articles, radioactive chemicals or chemicals solely in other schemes.

Registration of products includes: an agricultural product used to stupefy, repel, inhibit the feeding of pests on plants or other things; destroy a plant or modify physiology; or attract a pest to destroy it. Veterinary product includes a substance for preventing, diagnosing curing or alleviating disease in animals. Excludes fertilisers.

Assessment and registration of therapeutic goods

Control of contaminants and food additives that are added to food to assist in food processing or to achieve a technological purpose in the food, for example, colouring or flavouring.

Controls of use If not on inventory (or has an assessment certificate or permit issued) may not be used commercially; may be removed from the Australian Inventory of Chemicals Substances (AICS). Application of assessment report recommendations by legislation at state and territory level through adoption of National Occupational Health and Safety Council (NOHSC) National Model Regulations for Hazardous Workplaces (OH&S only).

If not registered may not be used; registration may specify how used; registration can be cancelled; use controlled by state and territory by application of registration advice. (Labels must be complied with.)

If not registered, may not be used. Licensing of Manufacturer; Standard for Uniform Scheduling of Drugs and Poisons (SUSDP); controls use with Poison Schedule Classification.

Food Standards Code (A14); control permissible additives, preservatives and colours; application of Maximum Residue Limits.


Element Industrial chemicals Agricultural and veterinary products Pharmaceuticals Food additives

Supporting legislation

Industrial Chemicals (Notification and Assessment) Act 1989, Various state legislation on OH&S and poisons.

Agricultural and Veterinary Chemicals (Code) Act 1994 and Administration Act 1994, state and territory complementary legislation control of use legislation including pesticides, poisons and food Acts.

Therapeutic Goods Act 1989, Poisons Act (various).

Food, stock and medicines Acts.

Advisory products –including labels, assessment reports, safe use advice

Chemical Assessment Reports, (NICNAS) Exposure Standards, Labelling, Material Safety Data Sheets (ASCC) SUSDP.

Product Assessment Labelling Maximum Residue Limits, SUSDP – Poison Schedule Classification.

SUSDP – Poison Schedule Classification.

Labelling Maximum Residue Limits SUSDP – Poison Schedule Classification.


All four agencies use a risk assessment approach to the management of chemicals in Australia (Table 2). FSANZ and NICNAS both outline this procedure on their websites. TGA applies a risk management approach to assessing products for safety, efficacy and quality. APVMA uses a ‘decision tree’ approach to determine the nature and extent of assessment that is required for active constituents and/or products, where the governing factors in selection are potential for exposure and ‘novelty’. While the APVMA approach is ‘risk based’, their reports have a significant hazard/safety focus due to occupational health and safety and labelling requirements. Regardless of the approach to risk assessment taken by the regulatory authorities, the outcomes of the process, in terms of labelling, control etc, are essentially hazard based, as expressed in risk and safety phrases, poison scheduling and the Globally Harmonized System of Classification and Labelling of Chemicals (GHS).

Table 2: Approaches to risk assessment by the Australian regulatory authorities

FSANZ

In the first step of risk assessment (hazard identification) potential adverse effects from the chemical are assessed. In the second step, (hazard characterisation) the dose of the chemical that is a problem is determined, and reference health standards may be set. The third step (exposure evaluation) determines the amount of the chemical that populations may be exposed to from all sources, including diet, water and the environment. In the fourth step (risk characterisation) the estimated exposure, including dietary exposure, is compared with the reference health standard for the particular food chemical.

NICNAS

NICNAS conducts chemical risk assessment according to the type of chemical notification received. For a chemical already in use in Australia, a quantitative risk assessment measures the risk posed by the chemical, while for a new chemical, a qualitative assessment describes the risk. NICNAS uses the four step paradigm and assesses risk in occupational health and safety, public health and the environment.

TGA

The TGA uses a 'risk-management' approach to regulating medicines supplied in Australia. This refers to the level of scrutiny applied to individual applications for inclusion in the Australian Register of Therapeutic Goods (ARTG). Medicines used to treat serious conditions, or which need to be used under a doctor's supervision, are subjected to a high level of scrutiny and evaluation to determine their quality, safety and efficacy. Other products, for example many complementary medicines (such as herbal, vitamin and mineral products), are not generally subject to the same level of evaluation and are assessed only for quality and safety.

APVMA

Registration of agricultural or veterinary chemical products also provides a stamp of security that those products have been scientifically assessed to ensure that, when used as directed by the label, they pose no unacceptable risk to human health, worker safety, the environment or trade and that the products work effectively. Registration also helps to ensure that unacceptable residues from the chemicals used in agriculture do not appear in food for human or animal consumption in Australia or in Australia’s export markets. This information is as detailed on their respective websites.

2.2.3 Current challenges to the risk assessment framework

Conflicting and confusing definitions of risk

As mentioned previously, there are many published guidelines on the risk assessment process based on the original NRC model. In these guidelines, there is a reasonable consistency as to what the risk characterisation process is, however, there is often no clear concept of ‘risk’. In fact, many guidelines for risk characterisation have been published without including a definition of risk; for example, the USEPA Guidelines for carcinogen risk assessment (USEPA 1996b), Guidelines for reproductive toxicity assessment (USEPA 1996a), and even the Risk characterisation handbook (USEPA 2000a). A definition of risk as a ‘characteristic of a situation or action wherein two or more outcomes are possible, the particular outcome that will occur is unknown, and at least one of the possibilities is undesired’ was included in the ERA Guidance for Superfund (USEPA 1997).


The lack of clarity in the definition of risk is evident in the various meanings that are assigned to the word risk in the scientific literature, for example: risk = hazard; risk = likelihood, probability; risk = damage, loss, consequence; risk = relative frequency; which can lead to ambiguity in the outcome of the risk assessment, and difficulties in interpretation and communication of the risk (Malmfors & Rosing 2002). Fairly standard definitions of risk are given on the websites of FSANZ (‘a function of the probability of an adverse health effect and the severity of that effect, consequential to a hazard(s) in food’) and NICNAS (‘the probability or likelihood of harm and the likely extent of the harm’). TGA explains risk more specifically for pharmaceuticals as a potential to do harm:

‘Risk is not an absolute concept. It is an assessment of the potential of a product to do harm to those it is intended to help, or to others (such as children) who may come in contact with it regardless of whether the harm results from following or disregarding the directions for use.’

In Australia risk from chemicals is managed by the setting of a guideline for the concentration of a known (chemical) hazard, and ensuring that treatment options ensure that the concentration is not exceeded. Water quality guidelines are estimated based on lifetime exposures, and few allowances are made for exceedance. This is in contrast with the air quality standard, which includes concentration, an averaging period, and number of days of exceedance allowed. In terms of the water quality guidelines, risk can be expressed either as an estimate of the likelihood that the water quality guideline will be exceeded and the consequence of that exceedance, or as providing the level of protection/safety intended, and the consequences of it failing to do so.

Current issues in the debate surrounding the risk assessment framework, and the (consequent paradigm shifts that may be the consequence of them) may have important implications for urban water providers.

Issue identification

The existence of the regulatory structures for chemicals in Australia indicated that the importation and manufacture of chemicals is already identified as an issue amenable to risk assessment. That the issues may be different for different types of chemicals is reflected in the variety of agencies that are involved, and the different approaches taken.

For example in the registration of therapeutics, agricultural chemicals and veterinary medicines, the focus is on occupational health and safety. Not all chemicals are considered equal and this is reflected in the different procedures within each agency that determines the level of assessment that various chemicals, chemical uses or chemical volumes, are subjected.

The differing emphasis placed on the environmental fate (i.e. the fate of the chemical subsequent to use) in the risk assessment process is important for urban water providers. This process is most thoroughly covered in the risk assessment of agricultural chemicals and veterinary medicines, where the manner of use of the chemical may result in wide dispersion of the chemical with consequences to water catchments, ecosystems, human health and even trade. The issue of release to environmental waters via wastewater discharge is also considered in the assessment of industrial chemicals, although the indirect impacts on human health from these releases does not normally form part of the public health section of the risk assessment process. The environmental fate of pharmaceuticals is not considered in the regulatory risk assessment process. The focus of TGA assessments is on the safety and efficacy of pharmaceuticals in approved uses only. Human health consequences from

Issue identification - identification of key issues amenable to risk assessment


secondary exposure scenarios are not normally part of the TGA risk assessment processes, despite the strong emphasis on public health. The absence of environmental impact assessment and subsequent secondary human exposure for pharmaceuticals results from the failure of issue identification at the time legislation for regulation of their use was enacted.

Regardless of the inclusion and importance of the environmental and indirect public health exposure in the risk assessments carried out by agencies in the past, the consideration of the risks resulting from direct or indirect potable reuse of water (and until recently, from the recycling of biosolids) has not yet formed a significant part of the risk assessment process. Generally, it has been assumed that treated wastewater is released to and diluted in environmental waters and all solids from wastewater treatment plants (WWTPs) are disposed to landfill,incinerated or reclaimed for beneficial reuse. These assumptions are no longer valid due to beneficial use of biosolids and planned reuse of water, and this has implications for both past and future risk assessments.

Effects and exposure assessment

Exposure assessment examines the contact between an organism and a chemical in terms of the intensity, frequency and duration of the contact (USEPA 1998). Where the concentration of the chemical in the environment can be directly measured, effects assessment need only consider the interaction between the organism and the environment and the uptake through exchange boundaries where absorption takes place (skin, lung, gastrointestinal tract) (USEPA 1992). However, in some cases, such as the assessment of a new chemical or where analytical methods are not available, the exposure must be estimated by means of models. The exposure, in terms of concentration in the exposure medium, can be estimated with fate and transport models based on use and release data and the physico-chemical properties of the chemical. This assessment takes into account relevant properties of the chemical that may determine fate (volatility, aqueous solubility etc.), use patterns and potential release estimates to predict an environmental concentration. These estimates also take into account basic (human) population data and the water-use patterns of the population.

The assessment of the effects of a chemical are often regarded as comprising two components: hazard identification and dose (concentration)–response assessment (ECB 2003). Hazard relates to the capacity of a specific agent to produce a particular type of adverse health or environmental effect, for example the capacity of benzene to cause cancer or the capacity of solar radiation to cause skin cancer (enHealth 2004). Dose-response assessment explores the relationship between the amount of exposure to the hazard and the level of effect observed (Neely 1994), which can be estimated or determined from toxicological studies, epidemiological studies and, for environmental impacts, ecological studies. As a result the effects assessment is a two-step process comprising the identification of the hazard, and the capacity of the hazard to cause the response, that is the level of exposure, or dose required (enHealth 2004).

Effect assessment Hazard identification - collection and analysis of relevant data

- uncertainty analysis for hazard identification step

Dose-response assessment - collection of relevant data

- uncertainty analysis for dose response assessment step

Exposure assessment - analysis of hazard locations

- identification of exposed populations

- identification of potential exposure pathways

- estimate of contaminant intakes for pathways

- uncertainty analysis for exposure assessment step


Effects and exposure assessment requires the collection and analysis of data, and is the scientific centre of the risk assessment process. Data can either be collected specifically for a particular risk assessment, or drawn from the body of scientific knowledge, or a combination of both. The important last step in the data analysis process for both effects and exposure assessment is uncertainty analysis. The NRC ‘red book’ (NRC 1983) describes uncertainty as ‘pervasive’ and the ‘dominant analytical difficulty’ in risk assessment. Uncertainty and variability exist in all risk assessments (USEPA 2004) to an often unknown degree (Malmfors & Rosing 2002). An examination of the sources of uncertainty, either qualitatively or quantitatively, can be a productive means of refining a risk assessment, or to find solutions to the problems at issue (Verdonck et al. 2007). In many discussions of uncertainty and variability in risk characterisation, the distinction between the two terms is often blurred, and the terms even used interchangeably. Variability (stochastic uncertainty) is generally considered to be the measured and therefore known variation among members of a defined population, which can potentially lead to variations in risk (Wilson & Shlyakhter 1997). For Verdonk et al. (2007), ontological uncertainty (variability) is the inherent randomness in the parameter, but also includes variability in human behaviour and variability in societal factors such as the development of technological systems. Variability in this sense is a fundamental property of nature and is irreducible through further measurement. Uncertainty (epistemological uncertainty, uncertainty of fact), however, arises from a lack of knowledge which can be reduced through additional investigation. Uncertainty in risk assessment can arise from many factors, and has been broadly classified into three main areas (USEPA 1992):

• scenario uncertainty, arising from missing or incomplete information e.g. descriptive errors, aggregation errors, errors in professional judgement, and incomplete analysis

• parameter uncertainty, which affects a particular parameter e.g. measurement errors, sampling errors, variability, and use of generic or surrogate data

• model uncertainty, arising from the scientific theory affecting the ability of a model to make predictions.

Uncertainty can be reduced by the addition of new methods in either hazard or exposure assessment. Uncertainty is not always reduced by the collection of additional data for example, if the uncertainty arises from the methodology, or if the new data collected differs from existing data or knowledge. Analysis of the uncertainty in the estimation of the effects and exposure must therefore be considered in the risk characterisation.


Risk characterisation

The risk characterisation integrates the information from effects and exposure assessment (enHealth 2004) to provide a description of the nature and magnitude of the risk, including uncertainty (NRC 1983). The outcome of the risk characterisation can be qualitative or quantitative. Qualitative risk characterisation provides a descriptive indication of risk (e.g. high, medium, low), usually determined against a pre-existing set of criteria or guidelines developed for that purpose (NRC 1994). A quantitative risk characterisation attempts to put a numeric value to the risk; for example, lifetime and population risks that have been determined for some carcinogens. However, the use of numerical methods in the risk characterisation does not indicate that the outcome will be a quantitative risk characterisation that is a measure of the risk. For example, the hazard index used for non-carcinogens in humans is a benchmark used in the estimation of risk, but is not a quantitative measure of risk (NRC 1994).

The most common approach to dealing with uncertainty in the risk assessment process is to adopt a ‘conservative’ or ‘precautionary’ strategy. Examples of such conservatism can be seen in risk assessments where, for example assumptions of 100 per cent release or no adsorption in WWTP are adopted. The addition of ‘safety factors’ or ‘assessment factors’ are examples of a conservative approach to risk characterisation. According to the USEPA staff paper (USEPA 2004), the approach adopted should ‘not underestimate risk in the face of uncertainty and variability’ and the default values used should ‘guard against underestimating risk while also being scientifically plausible given existing uncertainty.’ However, this conservative approach has been widely criticised among industry stakeholders, earning such titles as ‘unnecessary conservatism’ (Conolly et al. 1999) and ‘compounding conservatism’ (Stahl et al. 2005). The use of ‘intentionally protective’ assumptions results in ‘biased’ risk characterisation (DeMott et al. 2005), and has the potential to mask uncertainty in the risk characterisation (Verdonck et al. 2007). The overstating of risks in conservative risk assessments is thought to lead to inappropriate risk management decisions (Stahl et al. 2005), unless risk managers are aware that the risk characterisation is not a real risk measure, but a measure to avoid false negatives (unsafe chemicals that are assessed as safe) (Verdonck et al. 2007). Further, the method may lead to more stringent regulatory controls and standards than are necessary (Conolly et al. 1999).

Other stakeholders may take the position that the conservative approach is not protective or precautionary enough. It is possible that in individual cases the final overall risk estimate will underestimate the true risk, even though the tendency is for the estimate to be higher than the true risk rather than lower (USEPA 2004). In contrast to the position taken by industry stakeholders, it has been argued that uncertainty in the data and scientific evidence in the risk assessment process may create a bias in favour of regulatory inaction (Latin 1997; Wilson et al. 2000), as regulators may be reluctant to enforce restrictions or controls without good science and a degree of certainty.

Theoretically, the assessment reports of the regulatory agencies are intended to provide sufficient characterisation of risk to enable the management of the risk by appropriate agencies. This works well in occupational health and safety (OH&S) areas, where many years of experience has led to the

Review

and reality check

Review

and reality check

Risk characterisation - characterise potential for adverse health effects to occur

- evaluate uncertainty

- summarise risk information


development of standardised risk and safety implementation guidelines, and the development of safety equipment and procedures. Environmental risk phrases are now being introduced under the globally harmonised system (GHS), and may eventually reach a level where they function as well as OH&S system. However, there is no public health equivalent, beyond poisons scheduling that can be applied or adapted to secondary exposure through recycling of reclaimed water or solids.

Risk management

The decision to adopt a precautionary approach to deal with uncertainty is a policy decision, not a scientific one. According to the USEPA staff paper (USEPA 2004), policy provides the fundamental framework of the risk assessment, and informs the default assumptions used within the framework. These policy positions are developed outside the risk assessment. It is not universally accepted, however, that the role of policy is one of framing and informing the risk assessment process, or of filling in the gaps left by scientific knowledge. Science and policy are sometimes viewed as belonging to risk assessment and risk management respectively.

According to Verdonck et al. (2005), risk assessment is a science-based process which identifies and characterises hazard, exposure and risk (including uncertainty), while risk management is a ‘decision-making process used for deciding between policy alternatives in consultation with stakeholders, taking into account both the estimated risk and its uncertainty, the precautionary principle, the cost and other factors (e.g. social, economic and legal considerations)’. Risk assessments, in this framework, should ‘aspire to the greatest extent to be objective scientific exercises that seek realistically to estimate risk’ while subsequent risk management ‘must be fully and transparently distinguished from risk assessment if the practice of risk assessment is to have scientific credibility’ (ACC 2003).

Using conservatism to deal with uncertainty is not the only policy decision that impacts on the ability of risk managers to make and implement defendible decisions. A crucial factor for urban water providers in the development of the regulatory system and the framing of the legislation has been regulating the information that is made available subsequent to the assessment of new chemicals.

NICNAS and APVMA both publish reports for chemicals that are assessed. All NICNAS assessment reports are available on the web in a searchable database. Summary reports are also available. Not all APVMA reports are available online, but may be obtained by request. APVMA reports are generally comprehensive, and include detailed information on the chemistry and manufacture, toxicological assessment, residues assessment, occupational health and safety, environmental assessment, efficacy and safety assessment, and all labelling requirements; however, they do not include a risk characterisation. NICNAS reports include hazard and exposure assessments and risk characterisation for occupational health and safety, and environment and public health. However, some items relating to the chemical identity, including the chemical name and molecular formula, can be withheld at the request of the notifier. The import volume may also be confidential, and a ‘sanitised’ volume used in the assessment. While the chemical identity can sometimes be determined from a search of the trade name, risk managers are often presented with a risk assessment of a chemical they have no means of identifying. One example of this is the assessment report of Tinosan HP100, which recommends that a concentration limit of 0.22 µg/L ‘be implemented by state regulators for the

Risk management - define options and evaluate the environmental health, social and political aspects of the options

- make informed decisions

- take actions to implement the decisions

- monitor and evaluate the effectiveness of the action(s) taken


release of the notified chemical to the environment’, although the chemical name, chemical abstract service registry number (CASRN), molecular and structural formulae, and molecular weight are all exempt information and not included in the report.

NICNAS, TGA and APVMA all maintain searchable online databases of their inventories. Food Science Australia (FSA) publishes lists of permitted additives and their concentration levels, but does not have an on-line, searchable database. The APVMA is probably the most comprehensive of these, with a variety of search options, but the listings do not include CASRN. It is necessary, therefore to know the name of the chemical that is in use in Australia when searching the database. For example, the pesticide Malathion is listed on the APVMA database (PUBCRIS) as Maldison. Agricultural chemicals and veterinary medicines are added to PUBCRIS immediately after they are assessed. There is significantly less information on the NICNAS inventory, the Australian Inventory of Chemical Substances (AICS), although this has been recently improved. Chemicals are identified by their International Union of Pure and Applied Chemistry name and CASRN. New chemicals are generally added to AICS five years after they are assessed. However, although the AICS listing includes the information that the chemical has been assessed by NICNAS, unless the chemical assessment report was published under the same name as the AICS listing, it may not be possible to identify the assessment report for a newly listed chemical. For example, of the 13 chemicals added to the inventory in April 2008, only one had the assessment report published under the same name as its subsequent AICS listing. One other was identifiable because the name on the assessment report was listed as an alternative name in the CASRN database. It is not possible to identify the assessment reports for the remaining 11 chemicals. The AICS database has the facility for the inclusion of associated names, but it would appear that it is not thought necessary for the name under which the chemical has been assessed to be included.

The reasons given for withholding or restricting information supplied to or produced during the risk assessment process generally relate to commercial confidentiality and competitive advantage of the chemical manufacturer/importer. In the case of Industrial Chemicals Notification Act (ICNA), this protection via confidentiality was intended to give advantage for the five years between notification of the chemical and public listing of the chemical on AICS. However, the manner in which the scheme is being implemented is providing indefinite market protection to the chemical manufacturer/importer at the expense of making information available to risk managers and the public.

Stakeholders

All four regulatory authorities have a structure of consultative committees in place to facilitate the input of stakeholders from industry, government and the community (Table 3). In addition to standing committees, ad hoc consultative committees may be formed for specific reviews of policy and/or procedure, for example, the NICNAS Low Regulatory Concern Chemicals (LRCC) Task Force, or TGA’s consultative committee on ‘Guidelines for Tamper-evident Packaging of Medicines, Complementary Healthcare Products and Medical Devices’.


Table 3: Consultative committee structures of the four chemical regulators

TGA NICNAS APVMA FSANZ

Industry TGA-Industry Consultative Committee (TICC).

Industry Government Consultative Committee

Industry Liaison Committee

Retailers and Manufacturers Liaison Committee

Industry – Expert

Several, including Australian Drug Evaluation Committee (ADEC) and Drugs and Poisons Schedule Committee.

Industry Technical Committee

Standards Development Committee (SDC)

Community None, but Consumer Health Forum has representatives on other committees including TICC and ADEC.

Community Engagement Forum

Community Consultation Committee

Standard Development Advisory Committee, Community representatives may also nominate for SDC positions.

Government Memorandum of Understanding

Registration Liaison Committee

Food Regulation Standing Committee (of the Ministerial Council)

The membership and activities of these committees are detailed in the annual reports of the organisations. A review of the membership of all these committees indicate that one group of stakeholders has been excluded from participation at any level – those concerned with the disposal and fate of chemicals subsequent to use, including urban water providers.

2.2.4 Conclusion to Part A

Federal government regulatory agencies are responsible for the risk assessment of chemicals introduced into Australia, but only for some agricultural chemicals are exposure guidelines set (i.e. ADIs). There is no environmental assessment of pharmaceuticals or food additives, and there is no assumption of water reuse in the environmental assessment of industrial chemicals. Incomplete data is made available so that providers of reclaimed water are not able to carry out their own risk assessments.

In addition to the above agencies:

• the Office of Chemical Safety and Environmental Health (OCSEH) in the Department of Health and Aging (DoHA) has a group of specialised scientists that provide human health RA advice for the Australian Pesticides and Veterinary and Medicines Authority (APVMA)

• the National Industrial Chemical Notification and Assessment Scheme (NICNAS) has its own group of specialised scientists to undertake human health RA of industrial chemicals

• the Department of Sustainability, Environment Water, Population and Communities (DSEWPaC) has a group of specialised scientists that provide environmental RA advice for the APVMA and NICNAS.

The current system is based on the responsibility of the chemical producers to prove to the federal agencies that the proposed use of the chemical is not hazardous, and the follow-up and management is the responsibility of state environment departments. The system needs to adapt to the new


requirements and take into account water reuse. To continue the current practice shifts the burden of responsibility away from the chemical producers to urban water providers.

The move to water reuse poses some challenges to the current risk assessment of new and existing chemicals in Australia, and raises questions regarding the current procedures. For example:

• Should water managers be included in the stakeholder groups of the regulatory agencies and have an input into the way risk assessment of chemicals is carried out?

• Should there be environmental risk assessment of pharmaceuticals and food additives, and their metabolites by TGA and FSANZ?

• Should the risk assessments carried out by the regulatory agencies take into account reuse of water for human consumption?

• Should the risk assessment include determination of an Acceptable Daily Intake (ADI) and the Australian drinking water guidelines (ADWG) where chemicals are released to water, and where appropriate based on volume and toxicology or structure?

• Should there be a review of the confidentiality arrangements (i.e. identity and volumes) for chemicals that are released to water?

• Is there a requirement for a process of review for all chemicals already assessed under existing schemes where there is either no risk assessment or where release to wastewater was treated as a sink?

2.3 P art B : A lternative methods in the ris k framework

2.3.1 Introduction

In addition to challenges described above in the science of risk assessment, concerns within toxicological science and environmental chemistry also impact on the risk assessment framework. The number of chemicals in commercial use is large and increasing, exposure pathways are complex and there are increasing costs in testing and monitoring all chemicals in a variety of risk scenarios.

In environmental chemistry, there have been significant improvements in the ability to detect many chemicals at ever lower concentrations, which in itself presents further challenges to toxicology. However, many chemicals are still difficult to analyse in environmental samples (polymers, surfactants). Fate pathways once thought of as sinks, such as wastewater and biosolids, are now recycled back into exposure pathways. And while the ability to detect and quantify many chemicals in the environment has improved, the ability to predict the fate of new chemicals, or even existing chemicals in new uses, is still somewhat limited.

The increasing demand for greater certainty in chemical safety in toxicology is challenged by difficulties such as problems in extrapolating from adverse findings in the laboratory to human health effects (Cramer et al. 1978), and is now further confronted with changes in community acceptance of animal testing. Methodological responses to these pressures have included the development of improved pharmaco-kinetic modelling and in vitro testing. These methods are intended generally to supplement and improve the prediction of hazards while reducing the need for animal testing. Perhaps more important to risk managers, however, is the development of concepts to address the ‘long standing principle that resources should be directed towards the testing and evaluation of those substances with the greatest potential to produce human risk and away from those with low potential for risk’ (Munro 1996). One of these concepts is the use of structural alerts to screen chemicals whose structure suggests that biological activity may occur, thus setting a more directed focus for determining which chemicals should be the subject of further research. The other is the de minimis concept: that there is a level of exposure to a substance of known structure so low as to be of little or no toxicological concern. Although these concepts have a different premise, a different historical


development and a different function within the risk framework, they are unfortunately both generally described by same term, threshold of toxicological concern (TTC).

2.3.2 Threshold of toxicological concern

De minimis concept and threshold of regulation

The concept that there is an exposure threshold that is so trivial as to be below the need for regulation was first proposed by Frawley (1967). At the time, considerable resources were being devoted to the testing and regulation of chemicals used for food packaging. Frawley wanted to ‘try to determine a level of use of any food-packaging component which could be considered safe regardless of its degree of toxicity’. To test the hypothesis, Frawley examined both the potential toxicity of chemicals that might be used in packaging and the exposure likely to result from the migration of the chemical from the container to food. Frawley studied the potential migration of chemicals from the container to the food, by testing the migration of the most extreme example – that of rosin size from paper. Rosin, a resin extracted from conifer plants, is used to coat, or size paper used in food packaging. It was concluded that 0.2 per cent of any chemical in any container type can only be at 0.1 ppm in the diet, and could be considered as safe. It was established from the tabulated results of 220 long-term (two year) toxicity studies with no observed effect levels (NOELs) that, apart from pesticides and heavy metals, only one chemical, acrylamide, had a NOEL < 100 ppm. Frawley concluded that by applying a safety factor of 100, ‘all 132 of the non-pesticidal chemicals are safe for man's diet at a dietary concentration of 0.1 ppm or higher’. Many of these chemicals are found at much higher concentrations in food, however, Frawley maintains ‘had we assumed that they were all safe at 0.1 ppm and permitted their use up to that level without toxicity studies, we would have been correct in 100 per cent of the cases and would not have exposed the public to any health hazard’.

Initially, threshold of regulation decisions were made by the US Food and Drug Administration (FDA) on a case-by-case basis, and perceived inconsistencies led to the requirement for a general policy that would be both consistent and scientifically credible (Rulis 1987). The solution was to introduce a threshold of regulation based on lifetime risk of cancer estimated from the carcinogenicity potency database that had been published by Gold et al. (1984). This data was selected as it was thought likely that at exceedingly low exposure levels, potential carcinogenesis was the only phenomenon capable of producing any concern (Rulis, 1987). The methodology used by FDA, and reported by Rulis (1987), involved transforming the carcinogenicity potency database to an exposure distribution at a constant assumed risk of 1x10-6 per lifetime. The plotted curve of the results, called a ‘risk equivalent exposure distribution’, describes the relative probability that a carcinogen selected at random from the universe of known carcinogens will be one that presents a risk of 1 x 10-6 per lifetime at a given exposure level. According to Rulis (1987), if it is conservatively assumed that one in five chemicals of unknown carcinogenicity assessed under a threshold of regulation policy at an exposure level of 1 ppb is in fact carcinogenic, then it can be argued on a purely probabilistic basis that 95 out of every hundred such decisions will result in no more risk than 1 x 10-6 per lifetime, with the vast majority yielding far lower levels of risk. The method was intended only for chemicals of unknown carcinogenicity. Known carcinogens would still be subject to formal risk assessment.

Although some concern was expressed at the assumption that as many as one in five chemicals may be carcinogenic, the proposed threshold of regulation received general support of participants at a workshop on safety assessment procedures for indirect food additives (Munro 1990). According to Munro (1990), the advantages of the FDA method were:

• the need to subject the chemical of interest to extensive toxicological testing is obviated since inferences on the potential toxicity of the substance in question are made indirectly in relation to the toxicity of other chemicals

• the same threshold of regulation is applied to all new chemicals presented for consideration, providing an element of simplicity in practice.


The method was also highly conservative, due to the assumption that a new chemical is as potent as the most potent substance in the chosen reference base. The weakness of the approach is the possibility, although remote, that the new chemical is more potent than any in the database. The threshold of 1 ppb as suggested by Frawley was supported by the workshop. However, when codified, the threshold of regulation was more conservative than proposed by either Rulis (1987) or Munro (1990).

The de minimis concept has been applied by the US FDA since 1995, as a threshold of regulation for indirect food additives (Munro et al. 1996). The code states:

A substance used in a food-contact article (e.g. food-packaging or food-processing equipment) that migrates, or that may be expected to migrate, into food will be exempted from regulation as a food additive because it becomes a component of food at levels that are below the threshold of regulation if the substance presents no other health or safety concerns because:

‘The use in question has been shown to result in or may be expected to result in dietary concentrations at or below 0.5 parts per billion, corresponding to dietary exposure levels at or below 1.5 micrograms/person/day (based on a diet of 1500 grams of solid food and 1500 grams of liquid food per person per day’ (CFR 2008)

Other conditions contributing to the exemption of indirect additives from regulations include that the substance has no technical effect in or on the food into which it migrates, and that it has no significant adverse impact on the environment. The exemption does not apply to known carcinogens.

Structural alerts and human exposure thresholds

The use of structural-alerts in toxicology arose largely from the same concerns about the allocation of resources as the de minimis concept. Like Frawley (1967), Cramer et al. (1978) was concerned that the priority in toxicological research was an ‘extremely coarse screen’, which failed to differentiate at all among less serious sources of potential hazard. Using Cramer’s methods, priority was given first to substances whose structures or known biological effects ‘raise questions’, and then to those used in ‘largest quantity’. What was required, they suggested, was a method for the ‘preliminary assessment of probable risk’ (sic). The method would not be a substitute for data, but a guide to ‘the priority and scope of the effort required to acquire additional information’.

The first stage of the method outlined by Cramer et al. (1978) involved the formalising of the judgements already used by toxicologists based on knowledge or assumptions of structure-activity relationships and metabolic fate ‘to classify every structurally defined organic or metallo-organic chemical by criteria that are based largely on structure or on widely known facts of biochemistry and physiological chemistry’. The result is a decision tree which enables most (but not all) organic compounds to be classified into three classes of likely oral toxicity based on their structure (Table 4).


Table 4: Structural classes of chemicals as determined by Cramer et al. (1978).

Class Description

Class I Substances are those with structures and related data suggesting a low order of oral toxicity. If combined with low human exposure, they should enjoy an extremely low priority for investigation. The criteria for adequate evidence of safety would also be minimal. Greater exposures would require proportionately higher priority for more exhaustive study.

Class II Substances are simply intermediate. They are less clearly innocuous than those of class I, but do not offer the basis either of the positive indication of toxicity or of the lack of knowledge characteristic of those in class III.

Class III Substances are those that permit no strong initial presumptions of safety, or that may even suggest significant toxicity. They thus deserve the highest priority for investigation. Particularly when per capita intake is high or a significant subsection of the population has a high intake, the implied hazard would then require the most extensive evidence for safety in use.

Not all chemicals can be classified according to Cramer’s decision tree. Inorganic chemicals are obviously excluded. Further, although Cramer’s first rule places most biological substances into class I, there are some exclusions. The first rule of the decision tree states ‘Is the substance a normal constituent of the body (F) or an optical isomer of such?’

This question throws into class I all normal constituents of body tissues and fluids, including normal metabolites. Hormones are excluded, as are, by implication, the metabolites of environmental and food contaminants or those resulting from disease states.

The second stage in Cramer’s methodology required that the estimate of ‘toxic threat’ obtained from the decision tree be combined with exposure data to obtain an ‘estimate of presumable risk’, which could be used to establish the criteria of priority for toxicity testing (Cramer et al. 1978). The measure of exposure used was daily per capita intake (in milligrams). The ratio of the exposure measure with the lowest no-effect level for each compound (multiplied by 50 kilograms body weight) gave a ‘protection index’ (PI). The PI was further classified into four categories of safety, from A–D (Table 5). A safety category of ‘A’ represented the lowest priority for toxicity testing, with ‘no present need for the acquisition of actual animal data’. Classifications ‘B’ and ‘C’ represent progressively higher priority for testing, with classification ‘C’ requiring at least sub-chronic studies, while classification ‘D’ would indicate the need for chronic studies and, where appropriate, other supporting data.

Table 5: Classification of presumable risk showing ‘protection index’ (PI) and categories of safety

Source: Cramer et al. 1978

Other factors, not covered in the methodology, that could affect the direction and extent of testing included the ‘importance (irreplace-ability) of a substance, the nature of the anticipated toxic effect, the level of organisational or social concern, the sensitivity of particular groups of users, more refined data on the distribution of exposure and, not least, the 'gut feeling' of the professional toxicologist


must be involved’ (Cramer et al. 1978). Further, the method requires both knowledge of chemical structure and reasonably accurate estimates of intake. According to Cramer et al. (1978) if either is missing, ‘this method of estimating hazard must not be applied’ (their emphasis).

A structural alerts method of establishing levels of concern, similar to Cramer’s protection index was adopted by the FDA for the assessment of food additives and colourings (Rulis 1990). The scheme combined structural alerts with exposure data to group chemicals into classes for a tiered toxicity testing regime. In this scheme the chemicals were separated into three categories based on chemical structure (A–C), and then into classes (I–III) based on potential exposure (Table 6). The level of toxicity tests required for each class increased from the low concern class I to the high concern class III (Table 7) (CFSAN 2000). Although considerable overlap occurs between the FDA scheme and Cramer’s, there are differences. One such difference is that the FDA scheme allows for the classification of inorganic chemicals, which Cramer’s does not.

Table 6: Tabulated version of FDA’s plot to determine level of concern (class) based on chemical structure (category) and exposure threshold

Category Class I Class II Class III

A 2.5 50 > 50

B 1.25 25 > 25

C 0.62 12.5 > 12.5 Source: Rulis 1990

Table 7: The level of toxicity tests required for each class increased from the low concern class I to the high concern class III

Toxicity studies 1 Concern levels

I II III

Short-term tests for genetic toxicity X X X

Metabolism and pharmacokinetic studies X X

Short-term toxicity studies with rodents X 2

Sub-chronic toxicity studies with rodents X 2 X 2

Sub-chronic toxicity studies with non-rodents X 2

Reproduction studies with teratology phase X 2 X 2

One-year toxicity studies with non-rodents X

Carcinogenicity studies with rodents X 3

Chronic toxicity/carcinogenicity studies with rodents X 3,4 1 Not including dose range-finding studies, if appropriate; 2 Including neurotoxicity and immunotoxicity screens; 3 An in utero phase is recommended for one of the two recommended carcinogenicity studies with rodents, preferably the study with rats; 4 Combined study may be performed as separate studies. Source: CFSAN 2000

The FDA adopted different systems for the regulation of chemicals deliberately added to foods (structural alerts), and accidental contaminants resulting from contact with food packaging.

Munro et al. (1996) recognised the potential of structural class to further extend the concept of toxicological threshold from the work of Frawley (1967) and Rulis (1987, 1990), which established a framework for defining thresholds for chemicals in general. The authors proposed that by correlating structural class with toxicity, an acceptable exposure threshold for each structural class could be determined. According to Munro et al. (1996):

‘the most comprehensive efforts at correlation of structure with toxic potential is the decision tree approach of Cramer et al. (1978), in which a series of 33 questions primarily about the structure but, in some cases, about other properties, such as propensity for hydrolysis and


physiological occurrence – lead to one of three classes reflecting a presumption of low, moderate or serious toxicity’.

Using less stringent selection criteria than originally used by Cramer (1978), i.e. inclusion of sub-chronic NOELs, and therefore a larger database of chemicals (612) Munro et al. (1996) classified each chemical according to the Cramer decision tree, and plotted the NOELs for each class. Taking the fifth percentile of each class, and applying a safety factor of 100, a ‘human exposure threshold’ was determined for each of the three classes (Table 8). The human exposure thresholds were ‘intended to apply to chemically-defined substances for which there is no presumption of genotoxic carcinogenicity’; otherwise the FDA threshold of regulation, 1.5 µg/day would be a more appropriate value (Munro et al. 1996). However, human exposure thresholds based on structural class should be incorporated in regulatory proposals which are aimed at establishing priorities for, and extent of, testing and evaluation of chemical substances.

Table 8: Fifth percentile NOELs and human exposure thresholds (Munro et al 1996) based on structural class (Cramer 1978)

The application of structural alerts and human exposure threshold was then applied to the assessment of flavouring substances in food (Munro et al. 1999). The method was adopted by Joint FAO/WHO Expert Committee on Food Additives (JECFA) ‘in order to provide a practical solution for evaluating such a large number of low-exposure substances in a timely manner’. The safety evaluation procedure described incorporates knowledge of toxicology, chemical structure, metabolism and intake. Munro et al. (1999) outlined a decision tree approach, the first step of which required the determination of the structural class of the substance according to Cramer’s method. The decision tree, with five steps (Source: Munro et al. 1999

) has only two outcomes, dividing the substances into ‘(i) those substances whose structure, metabolism, and relevant toxicity data clearly indicate that the substance would be expected not to be a safety concern under current conditions of intended use; and (ii) those substances which may require additional data to perform an adequate safety evaluation’.


Figure 2: Decision tree approach for determining data requirements based on Cramer class

Source: Munro et al. 1999

The database on which human exposure thresholds and the threshold of regulatory concern were based was expanded in a discussion paper presented at a workshop on ‘Threshold of Toxicological Concern for Chemical Substances Present in the Diet’, held in Paris in October 1999, in which possible inclusion of specific additional endpoints, such as neurotoxicity and developmental neurotoxicity, immunotoxicity and developmental toxicity, as well as general toxicity and carcinogenicity were considered (Kroes et al. 2000). In the workshop, a database of NOELs for the specific endpoints was compiled from screened oral toxicity studies. The outcomes of the workshop included:

• NOELs for immunotoxicity did not differ from the distribution of non-specific endpoint NOELs for the same compounds

• the cumulative distribution of NOELs for neurotoxic substances endpoints was significantly lower than the those for other non-cancer endpoints, however, were more than adequately covered by a threshold of 1.5 μg/person/day

• the exposure threshold value of 1.5 μg/person/day would be sufficiently low to provide an adequate margin of safety against any adverse endocrine effect associated with dietary environmental estrogenic compounds of anthropogenic origin (e.g. pesticides, packaging materials)

• for compounds which do not possess structural alerts for genotoxicity and carcinogenicity further analysis may indicate that a higher threshold may be appropriate.

The final point (4) was taken up in a workshop of the Expert Group of the European branch of the International Life Sciences Institute (ILSI) on structure-based TTC in March 2003, which examined the use of structural alerts for a more tiered approach to human exposure thresholds (Kroes et al. 2004). The workshop group concluded that, provided there is a sound estimate of intake, the TTC principle could be applied for low concentrations in food of substances that lack toxicity data.


In addition to the human exposure thresholds for Cramer class as proposed by Munro et al. (1996), the workshop considered:

• the possibility of increased safety assurance by the identification of structural alerts for high potency carcinogens

• the possibility of separate classes for neurotoxins, teratogens or endocrine disrupting chemicals

• the possible inclusion of food allergies, hypersensitivity reactions and intolerances in the TTC concept.

In respect to the first issue, a TTC of 0.15 μg/person/day was proposed for compounds with some structural alerts for carcinogenicity, using a highly conservative approach based on linear extrapolation of the animal dose-response data down to a theoretical risk of one in a million (Kroes et al. 2004). The workshop identified five structural groups with high potency genotoxic potential which it termed the ‘cohort of concern’: aflatoxin-like compounds, N-nitroso-compounds, azoxy-compounds, steroids, and polyhalogenated dibenzo-p-dioxins and -dibenzofurans. The workshop suggested that a TTC concept could not be appropriate for these chemicals, which may still be a concern at intakes of 0.15 μg/person/day (Kroes et al. 2004).

It was also recommended that neurotoxins be separated into two groups, with organophosphates having a threshold of 18 μg/person/day, and non-organophosphate neurotoxins remaining in Cramer class III. The workshop concluded that there was no need for a separate class for teratogens, and that there was not enough consistent data to consider a separate class for endocrine disruptors (Kroes et al. 2004).

To facilitate the understanding of the increasing complexity of the classification of chemicals using TTC concepts, a new decision tree was proposed (Kroes et al. 2004). The output of the decision process is either:

• that anticipated exposure would not be predicted to represent a safety concern

• that risk assessment is not appropriate without toxicity data on the compound.

It was suggested that the decision tree, in addition to being applied to indicate analytical needs or to set priorities in toxicity testing, may be used to advise risk managers about the extent to which exposure would have to be reduced to give negligible risk (Kroes et al. 2004). However, the decision tree was not intended to replace conventional approaches to risk characterisation for established and well-studied chemicals such as food additives and pesticides. In addition, the decision tree specifically excluded:

• heavy metals, such as arsenic, cadmium, lead and mercury

• compounds with extremely long half-lives that show very large species differences in bioaccumulation, such as TCDD and structural analogues

• proteins.

In step-wise fashion, the decision tree eliminates first those chemicals for which TTC is not appropriate, that is, non-essential metal or metal containing compounds and poly-halogenated dibenzodioxins, -dibenzofurans, and –biphenyls; and aflatoxin-like-, azoxy-, and N-nitroso- compounds. This is followed by the elimination of compounds that are estimated to be consumed at less than 0.15 μg/day for chemicals with structural alerts for genotoxicity or 1.5 μg/day for other chemicals; that is, at levels so low they would not raise concerns irrespective of the functional groups present in the molecule and therefore are not a safety concern. Non-genotoxic chemicals with exposures greater than the threshold of 1.5 µg/day are finally sorted by identified Cramer class and eliminated if the estimated intake is lower than the exposure threshold for the class (Kroes et al. 2004).

In summary, the process of the decision tree is to:


• eliminate those chemicals for which a TTC is not applicable; that is, metals and the cohort of concern structures

• eliminate those chemicals whose exposure is less than the de minimis threshold of regulation

• apply Cramer structures and Munro’s human exposure thresholds to the remaining chemicals.

2.3.3 Discussion

The origin of the concept is very much tied to the regulation of chemicals in food. While this may seem helpful as potable water is part of the diet, there are some obvious problems. Frawley (1967), for example, was able to rely on the general good sense of industry not to want to put potent toxicants into food:

‘It is, of course, possible that some chemical may be synthesized at some time in the future which would be toxic at 0.1 ppm in the diet. However, it is almost impossible for such a compound to become an intentional component of a food container. For a compound to be toxic for man at 0.1 ppm presumes that it will be as toxic or more toxic than any commercial pesticide. For it to be used as a component of a food container presumes that it must be manufactured, packaged, distributed, and in other ways handled several times before contacting the food. It is inconceivable that a compound as toxic as this could pass through so many hands, in an industry not accustomed to handling highly toxic substances, without revealing its toxicity through injury to personnel. Once recognized, safe handling of such a compound would require such extreme industrial hygiene precautions as to be incompatible with converting operations and food-packaging practices.'

Rulis (1987) was similarly able to discount ‘pesticides, economic poisons, (and) some exquisitely toxic substances’ in assessing threshold of regulation, because for all other substances there was ‘no sizable probability of effects occurring in rodents at doses lower than about 1 mg/kg.bw/day’. In assessing the range of toxicities in the database on which he based his structural class and decision table, Cramer (1978) noted that most of the NOEL values fell within the range 10 and 100 mg/kg.bw/day. Substances with toxicities above 1000 mg/kg.bw/day were so ‘patently harmless’ that they would likely not be tested to the point of establishment of a NOEL, but substances with NOELs less than 1 mg/kg.bw/day were unlikely to be used unless they were ‘useful economic poisons, drugs or chemical warfare agents’ and data on them unlikely to be published (Cramer et al. 1978).

While we remain hopeful about the absence of chemical warfare agents from food, it is clear that pesticides, useful economic poisons, drugs and other exquisitely toxic substances can and do find their way into water. Fortunately, published data on such exquisitely toxic substances is increasingly available. Increasingly, the methodological framework begun by pioneers such Frawley, Cramer and Munro has been extended to include a greater number of chemicals, endpoints and alerts. The development of TTC beyond the threshold of regulation to an integrated part of the risk assessment process is nevertheless important to the risk assessment and management of urban water. To fully understand its usefulness, advantages and possible pitfalls, it is appropriate to critically examine the process of this development.

The initial aims in developing the threshold of regulation were to untangle a mess of regulations, streamline the regulatory process and free up resources for more important work in toxicology and chemistry (Frawley 1967). According to Frawley (1967), more time and money had been spent by government, industry and universities on food packaging than on any other area of environmental health other than pesticides and drugs, and to waste such resources on trivial matters was to gamble with public health. Concerned that the demand for data had outstripped the ability of toxicologists to supply it, Cramer (1978) wanted to codify the decisions already being made by toxicologists in prioritising research. For Cramer (1978), the millions spent on cyclamates, saccharin, Red no 2 and monosodium glutamate, seemingly without resolution, could not possibly be replicated for every substance of ‘present and possible’ concern. While the costs in terms of time, money and expertise are still relevant two decades later, increasingly the political cost in animal use is also raising


concerns (Kroes & Kozianowski 2002). Avoiding unnecessary extensive toxicity testing by the development of ‘widely accepted TTC values’ is seen to be of benefit to industry, regulators and consumers (Kroes & Kozianowski 2002).

The process that evolved is exemplified by the decision tree of Kroes et al. (2004), where the likelihood that a particular level of exposure to a chemical will be without toxic effects in the absence of chemical-specific toxicity data, based on the available toxicity data for a wide range of chemicals, is balanced against very low levels of intake of the chemical under evaluation. In other words using a threshold of toxicological concern was a trade-off of the increased uncertainty resulting from the absence of chemical-specific toxicity data against very low exposure (Renwick 2004).

It is important to compare the concept of a threshold, as it is developed in the TTC concept, with that of threshold as it occurs in the traditional risk assessment process. It is often stated in recent literature on TTC, that the setting of ADIs (and tolerable daily intakes) is based on the concept of a threshold at which risk of exposure to a given substance becomes non-existent or trivial, and that this is a justification for the use of the more general approach to thresholds (Kroes et al. 2004; Renwick, 2005). However, as stated clearly by Munro et al. (1999), ADIs are based on toxicological data and the establishment of a NOEL, an approach that differs from the concept that a safety evaluation can be performed in many instances on the basis of intake and structure-activity relationships in the absence of toxicological data about the chemical. Structural classes are only one part of the TTC method. Central to the method is reasonable certainty in the exposure side of the risk equation. Appropriate human exposure estimates are necessary for applying TTC, including:

• consideration of combining estimates of exposure to substances with common mechanisms, such as organophosphates

• consideration of total human exposure from all sources (Kroes et al. 2004).

The different role of threshold in traditional risk assessment and in TTC methods is shown schematically in Error! Reference source not found.. The two schemes have different starting points, but except in circumstances where exposure is less than the TTC value (Munro’s human exposure threshold), the two methods have the same endpoint. The important difference between the schemes is the possibility of making a ‘safety evaluation’ conclusion without toxicity data in situations where the exposure is less than the threshold value. If exposure is greater, or estimated to be greater, than the threshold value, the normal process of risk assessment, with the determination of a chemical-specific threshold based on a NOEL determined in standardised toxicity tests is required for the establishment of an ADI or tolerable daily intake (TDI) and the completion of full risk assessment process (Munro et al. 1999).


Figure 3: Conceptual model of traditional risk assessment and TTC assessment highlighting the difference in the role of toxicity threshold determined from toxicity testing and human exposure threshold

Exposure > Threshold

Toxicity Testing

ADI

ThresholdExposure

Structural Categories

NOEL

Safety Evaluation

UVCBs

UVCB: substances of unknown or variable composition; complex reaction products; biological material.

Human Exposure Threshold

Risk Assessment

Known Structure

Exposure < Threshold

Traditional Risk Assessment

Threshold of Toxicological Concern

Specific Substance

Exposure > Threshold

Toxicity Testing

ADI

ThresholdExposure

Structural Categories

NOEL

Safety Evaluation

UVCBs

UVCB: substances of unknown or variable composition; complex reaction products; biological material.

Human Exposure Threshold

Risk Assessment

Known Structure

Exposure < Threshold

Traditional Risk Assessment

Threshold of Toxicological Concern

Specific Substance

It is useful here to examine the results of threshold of regulation decisions by FDA, and threshold of toxicological concern decisions by JECFA – two assessment schemes where the method has been applied. In both cases, the process is called a ‘safety assessment’, not a ‘risk assessment’. In FDA, the result is a decision not to require a full notification package, including toxicity data before permitting the use of the chemical in the manufacture of food packaging material (Rulis 1987). According to the ILSI working group, it is important that the US FDA threshold of regulation policy did not mean that indirect food additives with exposures below the threshold were not regulated at all, but that for chemicals with such low exposures, it was ‘justified to regulate them with less expenditure of resources, commensurate with the lesser potential for risk’ (Barlow et al. 2001). In the case of food flavourings assessed by JECFA, the result is ‘ADI not specified’, as the total daily intake of the substance does not constitute a hazard to human health (IPCS 1987). The use of the structure-based threshold and low exposure is just one of several ways JECFA may reach an ‘ADI not specified’ decision.

During the development of the TTC approach, from Frawley (1967) to the present, there has been a change in the terminology used in its description. The ILSI Expert Group Working Paper defined TTC as ‘a level of exposure to chemicals below which no significant risk is expected to exist’ (Kroes et al. 2000), a definition which remains consistent with the concept as originally proposed by Rulis, Munro etc. At the workshop at which the paper was discussed, concern was expressed at the inclusion of ‘any chemical’, and the imprecise nature of the term ‘significant risk’. However, even though the importance of clear and consistent terminology to the understanding of the science by risk managers and the public is raised, no alternative definition was offered. Munro (1996) regarded TTC as a concept, and called values determined using the method ‘human exposure thresholds’. Over time, ‘human exposure threshold’ has become ‘threshold of toxicological concern’, where the latter term begins to refer not only to the concept or method, but also the values determined by the method. Similarly, ‘threshold of regulation’ becomes first ‘threshold of toxicological concern’, then ‘generic threshold of toxicological concern’. The concept of de minimis and structure-based thresholds become confused, for example, according to Kroes and Kozianowski (2002):

However, the TTC concept goes further than this in proposing that a de minimis value can be identified for many chemicals, including those of unknown toxicity, based on consideration of their chemical structures. The de minimis concept accepts that human exposure threshold levels exist for different types of chemicals/structures.


This argument confuses the de minimis concept, which is a regulatory position not a scientific one, even though it is backed by good scientific research, with structural alerts for prioritising toxicity testing. Renwick (2004) further suggested that TTC-enabled risk assessment (that is, not just safety assessment) can be undertaken in the absence of toxicity data on the compound under evaluation, providing that the intake is sufficiently low. Finally, TTC comes to be defined in terms of structure: ‘threshold of toxicological concern (TTC) is a level of intake predicted to be without adverse effects based on the toxicity of structurally-related compounds’ (Renwick 2005).

Since the proposal of the TTC concept by Munro et al. (1996) there has been an increase in the number of chemicals in the database, and the number of endpoints considered, providing greater clarity to the identity and exclusion of the ‘exquisite poisons’. Further studies have endeavoured to use a TTC approach for chemicals other than food additives, for example, cosmetics (Kroes et al. 2007), personal and household care products (Blackburn et al. 2005), and in pharmaceutical manufacturing operations (Dolan et al. 2005). While Dolan et al. (2005) proposed categories and thresholds specific to the risk situation being studied; both Blackburn et al. (2005) and Kroes et al. (2007) proposed to establish that the Cramer/Munro method was suitable for the assessment of cosmetic and personal care and household products. Blackburn et al. (2005) established that chemicals used in personal care and household products could both be classified by Cramer class and have NOELs that fitted within the range of the original dataset of Munro et al. (1996). Kroes et al. (2007) sought to establish that oral TTCs were valid for the topical exposures typical of cosmetic products. However, in reviewing the probabilistic approach of Rulis (1986), Munro (1990) was able to show that threshold results could vary depending on the chemicals selected for the database. Therefore ‘great care must be taken to ensure that the animal test databases use to establish the threshold are valid and appropriate for use in low dose risk assessment’.

2.3.4 Conclusion to Part B

Structure-activity relationships should be regarded only as a guide, not a ‘channel’ that replaces the judgement of the toxicologist (Cramer 1978). Only the determination of a chemical-specific threshold based on a NOEL determined in standardised toxicity tests can lead to the establishment of an ADI and the completion of a full risk assessment process (Munro 1990). Mindful of these caveats, the incorporation of the TTC method into the risk framework needs to be carefully considered. Care must be taken that the trade-off described by Renwick (2004) does not lead to increased uncertainty and conservatism without a commensurate gain. One view of the process is that the application of the TTC principle acts as a screening process in the risk framework (Kroes 2005). As can be seen in Source: Kroes et al. 2005

, this screening process requires the exposure assessment to be displaced in the framework. Rather than being conducted concurrently with hazard assessment, it must now precede it. This reflects the fact that reliable estimates or measurements of exposure are required for the application of structural class (Cramer 1978) and human exposure thresholds (Munro et al. 1996).


Figure 4: Model of the risk assessment framework incorporating TTC as a screening tool

Source: Kroes et al. 2005

The TTC methodology could allow a tiered approach to the management of chemical risk in water by water providers. The tiers would correspond to chemicals for which guidelines already exist, chemicals with published risk assessments, which include ADIs, chemicals for which suitable published toxicity data exists, but for which no formal risk assessment has been published, and chemicals for which there is no published toxicity data and where the establishment of a TTC is appropriate (Table 9). The cohort of concern is of course excluded.

Table 9: Suggested tiered structure for the risk management of chemicals detected in source water in Australia

Tier Situation Guideline Response

1 Chemicals for which current Australian guideline exists

ADWG Exceedance would require intervention.

2 Chemicals for which there are appropriate ADIs resulting from peer-reviewed risk assessments, for example, pesticides assessed by APVMA and TGA, WHO etc.

Proposed DWG based on ADI

A value which would reasonably be expected to be accepted as an ADWG in the normal review process. Exceedance requires intervention.

3 Chemicals for which there is toxicity data published in peer-reviewed scientific journals

Interim DWG based on published NOEL (and/or Cramer Class, if at variance)

Detection would require a formal risk assessment process, leading to a proposed guideline. Exceedance requires intervention.

4 Chemicals for which no reliable toxicity data exists.

Exposure Trigger Value based on Cramer Class

Exceedance of the TTC trigger value appropriate to the chemical’s classification would require risk assessment, including initiating appropriate toxicity studies.


Based on the proposed risk assessment methodology using TTC (Kroes et al. 2005), chemicals in tiers 3 and 4, require no action unless the chemicals are detected in drinking water at levels above the trigger values suggested by their structure and/or published toxicity data. For chemicals in class 2, it would reasonably be expected that an ADWG would be determined formally from the peer-reviewed ADI in the normal course of events, subject to consideration of any caveats or uncertainty in the risk assessment report.

The tiered approach requires clearly defined terminology, so that there is no confusion as to which type of guideline values are being applied, as the response required is dependent on the type of exposure guideline to which measured concentration is being compared. It would be excessive, for example, if mitigation controls were implemented for exceedance concentrations of tier 3 or 4 chemicals had been recorded. It is a necessary priority for the urban water providers to develop a protocol for response to exceedance of guidelines values in consultation with state health authorities.

The use of human exposure thresholds base on the TTC approach developed by Cramer, Munro and others and described here provides a useful tool for risk managers faced with the detection of chemicals for which no formal guideline exists. While these values do not substitute for properly determined drinking-water guidelines, they can serve to prioritise chemicals for further risk assessment, and if necessary, toxicity and fate testing. As drinking water guidelines are based on lifetime exposure, the use of the human exposure threshold can also be useful in the monitoring of chemicals that occur only intermittently in water. The major limitation of human exposure threshold is that it can only be used where it is possible to determine the exposure to the chemical; that is, it can be detected and quantified in water, and its fate downstream of that detection (in further storage/treatment) can be estimated. It is important for the management of risk in reclaimed water that a procedure for the determining of water quality guidelines, perhaps similar to that used for poisons scheduling, should be developed and adopted. Until then, the use of human exposure threshold could be an important risk management strategy for helping prioritise effort in performing the risk assessments of chemicals of concern in reclaimed water.


3 Review of potential toxicological outcomes associated with exposure to chemical contaminants from drinking water

3.1 S c ope In broad terms, toxicity is an adverse effect on the production, function and/or survival of cells. A thorough understanding of these three aspects for each potential site of toxicity is critical to developing a comprehensive screening battery for risk assessment. This chapter explores the different types of toxicity that may be expected to be associated with drinking water.

The purpose of this chapter is:

• to give the reader an appreciation of the normal function and significance of different organs and organ systems in the human body

• to describe toxic effects and define mechanisms of toxicity (when known)

• to provide a list of in vitro methods that are available to measure toxic effects to these tissues.

Note that unless otherwise indicated the information in this review is taken from Ballantyne et al. (1995), Fox (1991) and Klaassen et al. (2008).

3.2 P otential health outc omes as s oc iated with expos ure to c ontaminants in drinking water

The route of exposure can significantly affect toxicity. In the case of drinking water, oral ingestion is the main route of exposure and the digestive system will be the focus of entry into the organism. The great majority of ingested toxicants will be absorbed in the small intestine, which has a very large specialised surface area making it very efficient at absorbing nutrients from food, but also toxicants. Absorption can be via active transporters but is usually a passive process, where toxicants traverse the epithelial barriers and reach blood capillaries by diffusing through cells. Lipid solubility is usually the most important property influencing absorption with lipophilic chemicals more readily absorbed than hydrophilic (more water-soluble) substances. Absorbed compounds are transported to the liver via the hepatic portal vein.

Once in the liver, the compounds will undergo ‘first-pass metabolism’ and be biotransformed by cytochrome P450 enzymes and conjugated with large hydrophilic molecules (such as glucuronide). The main purpose of first-pass metabolism is to make small lipophilic xenobiotics larger and more water soluble, which will facilitate their excretion. After biotransformation, the compounds can travel either of two routes: large water-soluble compounds are excreted back into the small intestine via the bile duct and eventually excreted from the body in faeces, but if the compound is still sufficiently small and lipophilic, it can enter the systemic blood circulation. In case of the former, the compound no longer poses a risk to other organs, as it does not come into further contact with them. In case of the latter, however, the compound can then affect any tissue perfused by blood – in other words all tissues – particularly if it is lipid soluble. If the contaminant is relatively hydrophilic, it will eventually be excreted into bile by the liver or into urine by the kidneys. Highly lipophilic compounds that are resistant to biotransformation (such as polyhalogenated biphenyls and chlorinated hydrocarbon insecticides) are very hard to eliminate and tend to accumulate in the body upon repeated exposure.


Exposure to environmental pollutants can result in a variety of effects in the exposed organism. Some of these toxic effects are very general and can potentially affect all cells, while others are specific to certain tissues due to their specific structure and/or function. Some biological functions are fulfilled by systems composed of multiple organs (e.g. the immune system), and toxicity to any of the organs involved may result in failure of the whole system.

The following paragraphs are organised by effect, starting with basal toxic effects that may affect all cells, to non-organ directed toxicity (e.g. carcinogenicity and developmental toxicity), to toxicity to organ systems (e.g. hematotoxicity, immunotoxicity, neurotoxicity, endocrine effects, reproductive toxicity), and finally to organ-specific toxicity (e.g. kidney, liver and cardiovascular toxicity).

3.2.1 Basal toxicity

Basal toxicity can be mediated by reaction of the toxicant with a target molecule, resulting in dysfunction/injury of the molecule, whole cell, or whole organism; or by alteration of the biological microenvironment required for the proper function of molecules, organelles, cells and organs. Impaired cell function can lead to cell and tissue death, either from necrosis or apoptosis (unplanned and planned cell death, respectively).

At the molecular level

There are several mechanisms of binding of a toxicant to the target molecule, including non-covalent and covalent binding, hydrogen abstraction, electron transfer, and enzymatic reactions. Once bound, the toxicant can be toxic by causing dysfunction or destruction of the target molecule. Dysfunction is the most common form of toxicity, where a toxicant inhibits the normal function of the molecule. For example, pyrethroid insecticides inhibit closure of sodium channels involved in neurotransmission, and many toxicants inhibit enzyme activity (often simply by altering their three-dimensional structure). Destruction is less common but can also occur when toxicants fragment or cross-link the primary structure of the molecule, which can then lead to free radical and electrophile formation.

Some toxicants also alter the biological microenvironment, either by altering the pH (which can dissipate the proton gradient that drives ATP synthesis; e.g. pentachlorophenol), destroying transmembrane gradients that are essential to cell function or altering the lipid phase of cellular membranes (e.g. detergents and solvents).

At the cellular level

Toxicity at the molecular level can result in impaired cellular function or alteration of cell maintenance mechanisms that can lead to necrosis or apoptosis, such as:

• dysregulation of on-going cellular activity

• impairment of cellular maintenance by interfering with ATP synthesis and regeneration (e.g. pentachlorophenol, DDT, ethanol), interfering with intra/extra-cellular calcium ion regulation (e.g. lindane, acetaminophen, chloroform) or causing excessive production of highly reactive oxygen and nitrogen species

• increasing mitochondrial membrane permeability, causing general cell failure

• dysregulation of gene expression by disruption of either DNA transcription (e.g. dexamethasone, PCBs, PAHs), intracellular signalling (e.g. lead), or extracellular signalling

• physical damage to cell structures such as the cell membrane (e.g. solvents, detergents), lysosomal membranes (e.g. hydrocarbons), the cytoskeleton, microfilament structure (e.g. microcystin) or protein synthesis (e.g. ricin).

General (basal) cytotoxicity involves one or more of the above-mentioned processes and affects all cell types.


Measuring basal toxicity in-vitro

There are several well-validated methods to determine acute toxicity from many of these mechanisms (Siebert et al. 1996; Gennari et al. 2004; Ukelis et al. 2008):

• caco-2 cells: membrane functions and transport systems

• ATP chemiluminescence assay: energy production and metabolism

• intracellular [Ca2+] measurement and gap junction assays: effects on intracellular signalling.

There are a wide range of commercially available assays to measure cell viability in any type of cell (e.g. neutral red uptake, MTS assay), the difficulty is in choosing a representative cell line. One cell line that is commonly used is the normal human epidermal keratinocytes (NHEK). A proposed tier testing scheme for using in vitro cytotoxicity data to determine acute toxicity suggests using an undifferentiated cell line such as 3T3 cells in mouse to determine basal cytotoxicity (stage 1), in co-culture with hepatocytes to determine the impact of biotransformation on toxicity (stage 2), and finally with differentiated cell lines to determine selective cytotoxicity (stage 3) (Siebert et al. 1996).

3.2.2 Non-organ-directed toxicity: carcinogenicity

Chemicals that induce cancer have been broadly classified in two categories: genotoxic carcinogens (e.g. PAHs) that interact physically with DNA to alter or damage its structure, and epigenetic carcinogens that impact DNA expression without directly affecting DNA structure through DNA methylation and receptor-mediated effects.

Carcinogenesis develops over three stages:

• initiation is the introduction of a ‘mistake’ (mutagenesis) in the DNA sequence (Initiation can be caused by genotoxic carcinogens binding to DNA and causing errors during DNA synthesis. Initiation on its own is not sufficient to cause abnormal cell growth because DNA damage can sometimes be repaired or the cell can become unviable due to the mutation.)

• promotion is the selective expansion of initiated cells

• progression involves the conversion of unstable promoted cells into stable malignant tumours. (Due to the increased DNA synthesis, additional genotoxic events may occur at this stage resulting in additional DNA damage including chromosomal aberrations and translocations.)

Complete carcinogens have the ability to function at initiation, promotion and progression. Many carcinogens are not intrinsically carcinogenic but require metabolic activation to become carcinogenic. This may also result in tissue-specific effects, as different tissues can have different levels of enzyme expression.

Measuring carcinogenicity in-vitro

There are several in vitro bioassays to measure carcinogenicity (reviewed in Combes et al. 1999; Kowalski 2001). Genotoxic assays are in vitro testing systems that measure mutagenicity and are often presented as a surrogate for carcinogenicity. However, while they are usually very predictive of direct and indirect acting genotoxic carcinogens, they fail to detect epigenetic carcinogens. They include:

• gene mutation, including:

– mouse lymphoma (L5178Y) or human cells thymidine kinase (tk) mutations

– hamster (CHO and V79) and human (TK6) hypoxanthine guanine phosphoribosyltransferase (hpgrt) mutation

– bacterial mutagenesis (e.g. Ames test)


• DNA damage, including:

– comet assay for DNA strand breakage

– gammaH2AX assay

– bacterial genotoxicity (e.g. SOS/umu test)

– cytogenetic assays

• visible alterations in karyotype

• sister chromatid exchange (SCE) (usually with hamster CHO or V79 cells)

• chromosomal aberration assay (ABS) (usually with hamster CHO or V79 cells)

• micronucleus assay, which may be performed in many types of cells (e.g. human HepG2 cells).

Other assays include cell transformation assays (currently based on rodent immortal cells), which may provide an integration of carcinogenicity via multiple mechanisms including both genotoxic and non-genotoxic pathways

3.2.3 Non-organ-directed toxicity: developmental toxicology

This section describes developmental toxicity, i.e. adverse effects on development due to exposure to xenobiotics. Development is characterised by changes that are orchestrated by a cascade of factors regulating gene transcription. A particularity of developmental toxicology is that the sensitivity of the organism to toxicants can vary depending on its developmental stage.

Embryotoxic chemicals affect the conceptus prior to the fetal stage (usually up to eight weeks in humans). Imprinting, implantation, gastrulation and organogenesis all occur during embryo development, and toxicants that interference with cell proliferation, differentiation and/or apoptosis often lead to embryotoxicity (e.g. cyclophosphamide).

Fetotoxic chemicals affect the conceptus from the fetal stage onwards (usually after eight weeks in humans).

Teratogens are compounds that cause birth defects and can lead to pre- and post-natal mortality. Gastrulation and organogenesis during embryo development and the subsequent tissue differentiation and growth during fetal development are particularly sensitive to teratogens. Toxicants that can affect cell migration, cell–cell interactions, differentiation, morphogenesis and energy metabolism are often teratogens.

Exposure to developmental toxicants can result in death of the embryo, death of the fetus, or teratogenesis (birth defects, some of which may lead to pre- and post-natal mortality). Endocrine-disrupting compounds (EDCs) may also negatively affect development of the conceptus (e.g. diethylstilbestrol).

Measuring developmental toxicity in vitro

In view of the complexity of reproduction and development, the design of in vitro alternatives that are able to predict in vivo effects is especially challenging (Piersma 2006). Bioassays can detect some developmental toxicant by focusing on specific developmental steps that can be modelled in vitro. There are a few assays for specific developmental stages only, such as implantation success and stem cell development and differentiation. Many of these assays are based on non-human cells. Examples of these bioassays are listed below:

• Effects on implantation success (reviewed in Bremer et al. 2007):

– BeWo or JEG3 cells (human choriocarcinoma cells)


– HTR-8/SVneo cells (human trophoblast cells).

• Embryotoxicity tests (not performed on human cells for ethical reasons) (reviewed in Spielmann et al. 2006):

– mouse embryonic stem cell test

– [pre-implantation whole embryo culture]

– [post-implantation whole embryo culture].

• Teratogenicity (reviewed in Spielmann et al. 2006):

– [micromass test].

• Differentiation of cells:

– stem cell differentiation (embryonic and non-embryonic)

– neuroblastoma cell differentiation.

Please note that assays in square brackets require whole animals for sourcing of the test system (e.g. embryo).

There are also in vitro assays for developmental toxicology of individual organs, such as developmental neurotoxicity (reviewed in Coecke et al. 2007).

3.2.4 System toxicity: haematotoxicity

The production of blood cells (haematopoiesis) is a highly regulated sequence of events by which blood cell precursors proliferate and differentiate to meet the relentless needs of oxygen transport, host defence and repair and blood homeostasis. The main organs involved in haematopoiesis are the bone marrow and the spleen. A haematotoxicant is a toxicant that either interferes with haematopoiesis or the affects the viability of red blood cells, which can result in anaemia and hypoxia (lack of oxygen). The effect on white blood cell viability is covered in Section 3.2.5.

Haematopoiesis requires carefully orchestrated cell maturation and differentiation, and is particularly sensitive to cytoreductive or antimitotic agents and toxicants that can interfere with differentiation and maturation of blood cell precursors.

The viability of red blood cells can be affected by oxidative damage (which can interfere with the oxygen-carrying capacity of haemoglobin) or by modification of cell surface proteins (e.g. mefenamic acid), which can then lead to loss of ‘self’ antigens and subsequent destruction by white blood cells.

Measuring haematotoxicity in vitro

In vitro methods currently used in haematotoxicology usually measure effects on development of progenitor cells and stromal cells, which support those cells (reviewed in Gribaldo et al. 1996; Parent-Massin 2001; Rich 2003) include:

• Colony formation in clonogenic assays with haematopoietic progenitor cells:

– erythroid lineage:

BFU-E (burst-forming unit erythroid)

CFU-E (colony-forming unit erythroid)

– myeloid lineage:

CFU-GM (colony-forming unit – granulocyte/macrophage)

– megakaryocyte lineage:


CFU-MK (colony-forming unit – megakaryocyte)

BFU-MK (burst-forming unit – megakaryocyte)

– multiple lineages:

CFU-GEMM (colony-forming unit – granulocyte/erythroid/monocyte/megakaryocyte)

• assays on stromal cells using CFU-F (colony-forming units of fibroblasts).

Some method development is still required for many of these endpoints to produce a reliable in vitro test system of haematotoxicity.

3.2.5 System toxicity: immunotoxicity

Broadly defined, immunotoxic agents adversely affect the immune system, which protects the organism against pathogens and tumours. The immune system comprises numerous lymphoid organs (including the bone marrow, thymus, spleen, lymph nodes etc) and numerous cell populations with a variety of functions. Antigen recognition is the cornerstone of the immune system: antigens, usually protein or polysaccharide ‘signatures’ of nonself material, are recognised by specific antibodies, which then initiates an immune response. The two types of immune response are innate and adaptive.

The innate immune system is non-specific and is the body’s primary defence mechanism. It relies on a variety of proteins (called the complement system) and involves several immune cells, such as natural killer cells, macrophages and neutrophils. Natural killer cells release cytokines and cytolytic compounds that destroy the target cell. Macrophages and neutrophils are phagocytic cells and eliminate most microorganisms through the release of reactive oxygen species.

The adaptive (or ‘acquired’) immune system is an antigen-specific response triggered by the innate immune system. In simple terms, immune cells learn to recognise the invading pathogen and deploy a more sophisticated set of specifically-targeted cells, such helper T-cells and killer T-cells. Helper T-cells secrete cytokines and help direct the immune response depending on the nature of the threat. Killer T-cells bind to the target cell and release the content of cytolytic granules (containing cytokines, perforins and other enzymes) on the target cell, a process called degranulation. Once degranulated, the killer T-cell releases the dying target cell and moves on to kill other target cells.

The immune system can also call upon other specialised cells when fighting inflammation, such as basophils and mastocytes. When stimulated, these cells degranulate to release histamine, proteoglycans, proteolytic enzymes, leukotrienes and cytokines. These chemicals attract other immune cells.

The immune system must strike a delicate balance between excessive and insufficient immune response. Toxicants may cause malfunction of the immune system, which can result in dysfunctions of the immune system such as:

• Immunosuppression, which results in a reduction in the efficacy of the immune response (i.e, impaired resistance), while immunostimulation stimulates the immune system, which may result in excessive immune response. A very wide range of xenobiotics have been show to suppress or stimulate the immune system, including PCBs, PAHs, pesticides, metals, solvents, hormones, pharmaceuticals and UV radiation. Some toxicants (e.g. sulfamethoxazole) can stimulate immune cells directly by binding to their membrane receptors.

• Hypersensitivity reactions, which result from the immune system responding in an exaggerated or inappropriate manner (e.g. penicillin). Hypersensitivity has been linked with exposure to industrial compounds, metals, solvents and pharmaceuticals.


• Autoimmune disease, occurs when the reactions of the immune system are directed at the body’s own tissues (‘self’). It is more difficult to establish a clear link between xenobiotic exposure and autoimmunity, though some chemicals have been implicated in chemical-induced autoimmunity such as some pharmaceuticals, plastics (vinyl chloride), mercury, and some pesticides (hexachlorobenzene). Interaction between toxicants and endogenous proteins can sometimes result in the altered protein no longer recognised as ‘self’ (e.g. penicillin).

Measuring immunotoxicity in vitro

Several assays already exist to measure immunotoxicity in vitro:

• toxicity to immune cells or cytokine production in:

– jurkat E6 cells (human T lymphoblasts)

– U937 cells (human leukemic monocyte lymphoma cell)

– cytokine expression protein array (CD4+ T lymphocyte)

• antibody production in SKW cells (B cells).

Furthermore, a recent review and preliminary study (Gennari et al. 2005; Carfi et al. 2007) have identified several potential in vitro systems to more comprehensively examine immunotoxicity:

• immunosuppression:

– lymphocyte viability assay

– expanded cytokine assay

– cytolytic function assay

• hypersensitivity:

– human dendritic cell assay.

3.2.6 System toxicity: neurotoxicity

A neurotoxicant is a toxic compound that affects the development, function or viability of neurons and the nervous system. The nervous system coordinates numerous functions in the organism via neurons and neurotransmitters. There are two cell populations in nervous tissues: the neurons, which specialise in generation, reception and transfer of information (transmitted by neurotransmitters such as acetylcholine and epinephrine); and glial cells with provide support and nutrition to neurons.

The four most common targets of neurotoxicants are the neuron, the axon (the neuron’s projection towards other neurons), the myelinating cell and the neurotransmitter system.

1. Neuronopathy: Although the neuron is similar to other types of cells in many respects, some features of the neuron are unique, and provide peculiar vulnerabilities. Some of those unique features are a high metabolic rate, a long cellular process supported by the cell body and an excitable membrane that is rapidly depolarised and repolarised. A large number of chemicals are known to result in toxic neuronopathy, including metals (aluminum, arsenic, lead, manganese, mercury, methyl mercury), industrial compounds (trimethyltin), pharmaceuticals and solvents.

2. Axonopathy: Some toxicants can ‘cut’ the axon, resulting in a physical break in neuron transmission. Many chemicals have been linked to axonopathy, including metals (gold and platinum), alkaloids, pharmaceuticals, industrial compounds (acrylamide), solvents and pesticides.

3. Myelinopathy: Myelin provides electrical insulation of neuronal processes, and its absence leads to a slowing and/or aberrant conduction of electrical impulses. Some toxicants can interfere with myelin maintenance or function.


4. Neurotransmitter-associated toxicity: A wide range of naturally-occurring toxins as well as pesticides and pharmaceuticals can inhibit proper neurotransmitter function. For example, organophosphorous and carbamate pesticides inhibit the enzyme acetylcholinesterase, responsible for recycling the neurotransmitter acetylcholine.

There are several morphological peculiarities of the nervous system that make it particularly sensitive to some toxicants. These include the blood-brain barrier (a mesh of endothelial cells that provides an additional barrier to toxicants reaching the central nervous system), the unusual cell morphology of neurons (which are very elongated rather than small and spherical, and creates extraordinary demands on protein synthesis and transport of vesicles and organelles), the myelin sheet (which is rich in lipids and dependent on the proper function of a number of membrane-associated proteins, making it a sensitive site for toxicant action) and the high energy requirements of neurons (which makes them extremely sensitive to interruptions in the supply or oxygen or glucose that can be caused by toxicants such as cyanide and carbon monoxide).

Astrocytes (a type of glial cell) are thought to play a significant role in defense to neurotoxicants, though their exact function is still unclear.

Measuring neurotoxicity in vitro

The following in vitro neurotoxicity assays are currently in use (reviewed in Atterwill et al. 1994; Costa 1998; Tiffany-Castiglioni et al. 2006; Coecke et al. 2007):

• human neuronal and glial viability test (SK-N-SH or derivatives such as SH-SY5Y, and C6 cells, respectively)

• precursor cell differentiation (using neuroblastoma cells)

• glial maturation (myelination) in U-373MG cells (human astrocytoma (CNS tumour) cells)

• apoptosis of neuroblastoma cells

• neurotransmitter receptor profiles in neuroblastoma cells

• interference with neurotransmitter enzymes (e.g. acetylcholinesterase inhibition) or postsynaptic receptors.

To be toxic to the central nervous system (i.e. the brain), xenobiotics first have to cross the blood-brain barrier. This process can be modelled in vitro using immortalised brain endothelial cells (e.g. SV-HCEC, HBEC-51 or BB19 cells) (Prieto et al. 2004).

3.2.7 System toxicity: endocrine effects

Endocrine glands are collections of specialised cells that synthesise, store, and release their secretions (hormones) directly into the bloodstream. They are sensing and signalling devices capable of responding to changes in the internal and external environments to coordinate a multiplicity of activities that maintain homeostasis. Disruption of normal endocrine function can have a wide range of effects, potentially affecting many different organ systems. The main endocrine glands of the body are listed below.

• The pituitary is a small protrusion off the hypothalamus at the base of the brain, and secretes hormones (including hormones to other endocrine glands called trophic hormones) under stimulation from the hypothalamus. The pituitary releases hormones related to growth (growth hormone), lactation (prolactin), reproductive functions (gonadotropins and corticotropic hormones) and thyroid activity (thyroid-stimulating hormone).


• The adrenal glands, which are located on top of both kidneys and are mainly responsible for regulating the stress response through the synthesis of corticosteroids (cortisol and aldosterone) and catecholamines (epinephrine, norepinephrine, and dopamine), glucose metabolism (glucocorticoids), and reproduction (androgens, estrogens and progestins).

• The pancreas, which produces digestive enzymes and hormones that regulate glucose metabolism (insulin, glucagon and somatostatin).

• The thyroid gland, which secretes the thyroid hormones thyroxine (T4) and triiodothyronine (T3) under stimulation of the pituitary. Thyroid hormones increase metabolic rate, increase glucose availability, stimulate new protein synthesis, stimulate heart rate, cardiac output and blood flow, and increase neuronal development in young animals. The thyroid gland also produces calcitonin, involved in calcium homeostasis.

• The parathyroid glands produce hormones involved in calcium homeostasis (parathyroid hormone, calcitonin and vitamin D) under stimulation of calcium-sensing receptors. This unique feedback system is sensitive to toxicants similar to calcium ions (e.g. aluminium).

• The gonads: testes in males, ovaries in females. The gonads produce sex hormones (androgens, estrogens) and progestogens (progesterone). Hormone production and gametogenesis in the gonads is under direct pituitary hormonal control. Gonads are sensitive to toxic substances because gametogenesis relies on rapidly dividing cells, which are often vulnerable to chemical destruction. The blood-testes barrier controls the entry of large molecules and toxicants into the seminiferous tubules, where gametogenesis occurs.

There are four main mechanisms of endocrine toxicity:

1. Excessive stimulation, which can cause hyperplasia (excessive cellular development) and hypertrophy (gross enlargement) of the endocrine organ, and eventually lead to tumour development.

2. Interference with hormone synthesis or secretion. For example some pharmaceuticals (e.g. sulfonamides, 2,4-dihydroxybenzoic acid, aminotriazole, antipyrine, amitrole) interfere with thyroid hormone synthesis.

3. Increased hormone catabolism (destruction). Toxicants that induce liver enzymes (e.g. phenobarbital, benzodiazepines, DDT, chlorinated hydrocarbons) can increase the rate of conjugation and excretion of hormones such as T3 and T4.

4. Interference with hormone signalling (endocrine disruption). Hormones act by binding to specific hormone receptors (often compared to a lock and key), which then results in a biochemical cascade that eventually triggers the intended effect. Toxicant-induced interference with hormone signalling can result in erroneous endocrine communication and/or interfere with the complex system of hormonal feedback loops. Some toxicants can mimic hormone activity (‘agonists’) while others can inhibit normal hormonal function (‘antagonists’). In some instances, this effect is intentional (e.g. pharmaceuticals used for birth control), but xenobiotics can also be un-intentionally endocrine disruptors (e.g. industrial compounds like bisphenol A, phthalates). Interference or mimicry of sex steroids (estrogens and androgens) can also significantly affect reproduction (described in more detail in Section 3.2.8). Most of the activity of the endocrine system has sensitive feedback loops, and toxicants that affect or mimic hormones often affect multiple endocrine glands.

Measuring endocrine toxicity in vitro

The endocrine system is a complex interaction of multiple tissues, so in vitro assays are still limited. There are many assays available to measure interference with hormone receptors however, and some to measure interference with hormone synthesis including:

• interference via hormone receptors, including estrogen, androgen, thyroid and glucocorticoid receptors (endocrine disruption). There are many in vitro assays for these endpoints including:


– ,receptor binding, reporter gene and cell proliferation assays (reviewed in NIEHS 2002; Charles 2004; Soto et al. 2006; GWRC 2008)

• steroidogenesis in the H295R human adrenocortical carcinoma cell line (Sanderson & van den Berg 2003)

• endocrine pancreas, including:

– insulinoma cell lines.

3.2.8 System toxicity: reproductive

The purpose of the reproductive system is to produce good quality gametes, capable of fertilisation and producing a viable offspring, which in turn can successfully reproduce. This requires a large number of complex processes, orchestrated in a precise order for optimal performance at different life stages. That chemicals can adversely affect reproduction in males and females is not a new notion, one only has to look at the importance of drugs as contraceptives to realise how sensitive the reproductive system can be to external chemical influences. Endocrine communication is critical to proper reproductive function, and xenobiotics that can adversely affect endocrine glands (see Section 3.2.7) generally also generally result in reproductive toxicity. A wide range of environmental chemicals are known to mimic (e.g. trenbolone) or inhibit androgens (e.g. vinclozolin, procymidone, linuron, p,p’-DDE, phthalates) or mimic/inhibit estrogens (e.g. methoxychlor metabolites, ethynylestradiol, bisphenol A, nonylphenol, DDT), and exposure to these hormone mimics can adversely affect reproductive function(s).

Sex hormones (androgens and estrogens) are particularly important in fetal reproductive organ development, puberty and sexual maturation, and these stages are thus inherently susceptible to endocrine disruption. Toxicants such as PCBs, DDT/DDE, brominated flame retardants, dioxins, hexachlorobenzene, personal care compounds and heavy metals have been linked to reproductive abnormalities, although their exact mechanism is often unknown.

The female reproductive cycle relies on subtle hormonal communication between the pituitary and ovarian secretions of progesterone and estrogens. These hormones determine ovulation and prepare the female accessory organs to receive male sperm. Disruption of these hormonal cues can lead to infertility (e.g. disruption of the LH surge by the pesticides chlordimeform and N-methyldithiocarbamate prevents or delays ovulation, which results in infertility in laboratory animals).

Male reproductive processes likewise rely on carefully orchestrated hormonal communication through the hypothalamo-pituitary-testes axis, and endocrine disruption can also affect male reproduction. The vast majority of male reproductive toxicants however affect sperm production (spermatogenesis), some via indirect routes (e.g. nutrient disruption after exposure to zinc, or increase steroid clearance by the liver due to carbon tetrachloride exposure) but most via a direct effect on testis or spermatogenesis itself by interfering or damaging supporting (Sertoli) cells (e.g. phthalate ester metabolites, dibromoacetic acid, m-dinitrobenzene) or interfering energy production in sperm cells (e.g. chlorosugars, epichlohydrin).

Reproductive toxicants could also affect fertilisation and implantation, and successful pregnancy depends heavily on complex and subtle hormonal communication – and so is also susceptible to endocrine disruption (e.g. some pharmaceuticals used to terminate pregnancies interfere with progesterone synthesis).

Measuring reproductive toxicity in vitro

As described above, reproduction is a complex process relying on the successful completion of multiple individual processes. Many of these processes (e.g. puberty, secondary sexual characteristics and behaviour) cannot be modelled in vitro, but a few others can (reviewed in Brown et al. 1995; Bremer et al. 2005) including:


• effects on fertility:

– H295R cell line (steroidogenesis)

– Leydig cell lines (progesterone production and cytotoxicity)

– KK1 granulosa cell line (progesterone and estrogen production)

– enzymatic assays (e.g. aromatase assay)

• endocrine assays (already discussed in the section on endocrine effects)

– receptor binding assays and cellular assays for androgens / anti-androgens

– receptor binding assays and cellular assays for estrogens / anti-estrogens

• placenta cell line function assay (aromatase activity and cytotoxicity)

– JEG-3 and JAR human choriocarcinoma cells

– BeWo cells (human placental choriocarcinoma cells).

3.2.9 Target organ toxicity: hepatotoxicity (liver toxicity).

The liver is the main organ where exogenous chemicals are metabolised and prepared for excretion. As a consequence, liver cells can be exposed to significant concentrations of toxicants. However the liver has an immense capacity for self-repair, and recovery is usually quick once the toxicant is removed.

After absorption by the small intestine, ingested nutrients, vitamins, metals, drugs and environmental toxicants are all sent to the liver via the hepatic portal vein. Efficient scavenging or uptake processes extract these absorbed materials from the blood for catabolism, storage, and/or excretion into bile. Hepatocytes, or liver cells, are rich in mitochondria (to provide for their high energy needs) and cytochrome P450 enzymes (which are involved in metabolism and detoxification). Hepatocytes also have a significant role in protein synthesis (by recycling all major plasma proteins), carbohydrate and lipid metabolism, cholesterol production and bile secretion (and important detoxification mechanism).

There are several key factors that modulate hepatotoxicity:

• Uptake and concentration: the liver is immediately ‘downstream’ of the gastrointestinal tract, and as such receives the highest concentrations of lipophilic drugs and environmental pollutants. Other toxins are rapidly extracted from the blood into hepatocytes via active transport mechanisms.

• Bioactivation and detoxification: one of the vital functions of the liver is to eliminate exogenous chemicals and endogenous intermediates. Biotransformation however can generate reactive electrophilic metabolites, which can react with proteins and other target molecules.

• Regeneration: the liver has a high capacity to restore lost tissue and function by regeneration. Loss of hepatocytes triggers proliferation of mature hepatocytes to replace the lost tissue (this is initiated by cytokines and growth factors). Nevertheless, chemicals that can interfere with the cell cycle (e.g. colchicine) can block that regenerative ability.

Hepatotoxicity results in impaired liver function and the potential build-up of toxic by-products of cellular metabolism. There are several known mechanisms of toxicity to liver cells, including direct cytotoxicity to hepatocytes (e.g. acetaminophen, carbon tetrachloride, microcystin), damage to epithelial cells of liver capillaries (e.g. after excessive dose of acetaminophen, endotoxin, microcystin), impaired bile excretion (usually from interference of bile salt export pumps by toxicants such as pharmaceuticals, hormones and metals) and excessive cell proliferation to replace dead cells (hyperplasia; e.g. after chronic exposure to excess androgens, alcohol and aflatoxin).


Measuring hepatotoxicity in vitro

There are several immortalised liver cancer cell lines (e.g. HepG2, human hepatoma BC2) available as in vitro models for hepatotoxicity, however these cancerous cell lines exhibit differences in gene and protein expression as well as reduced metabolising capacity compared with primary hepatocytes (Donato et al. 2008). Non-cancerous immortalised human hepatocytes (Fa2N-4 cell) closely resembling primary hepatocytes have recently become available commercially (Steen 2004), and these may be useful in vitro models to study the effect of toxicants on hepatocyte viability (cytotoxicity) and P450 metabolic activity.

The metabolising function of hepatocytes is of course critical to toxicology, and there are many instances where primary hepatocytes have been co-incubated with other cells to incorporate metabolism (Coecke et al. 1999).

3.2.10 Target organ toxicity: nephrotoxicity (kidney toxicity)

The kidney’s principal role is to filter the blood and maintain total body homeostasis. It plays a principal role in excretion of metabolic wastes (such as urea) and in the regulation of extracellular fluid volume, electrolyte composition and blood pH. The kidneys also produce hormones to regulate extracellular volume and red blood cell production (renin and erythropoietin, respectively) and metabolise vitamin D3 to its active form. Similar to the liver, the kidneys are equipped with a variety of detoxification mechanisms and have considerable functional reserve and regenerative capacities.

The kidneys are particularly sensitive to blood-borne toxicants as they receive about a quarter of the cardiac output. The processes involved in forming urine may also concentrate potential toxicants in the tubular fluid. A wide variety of pharmaceuticals (e.g. antibiotics, analgesics, radiocontrast media, anti-cancer agents and angiotensin inhibitors and blockers), environmental chemicals and metals can cause nephrotoxicity via structural and/or functional damage. Proper kidney function is highly dependent passive and active (ATP-driven) transport mechanisms and toxicant-induced interruptions in energy production for any of these active transport mechanisms or interference with critical membrane-bound enzymes or transporters can seriously impact its function. The efficiency of the kidneys is also dependent on tight control of capillary pressure, and the kidneys are particularly sensitive to vasoactive substances.

Measuring nephrotoxicity in vitro

Models for in vitro nephrotoxicity are usually based on primary cultures, but several assay using immortal renal cell lines do exist (Hawksworth et al. 1995; Morin et al. 1997; Pfaller & Gstraunthaler 1998) including:

• cytotoxicity (neutral red uptake assay with human kidney cells HK-2)

• cell proliferation (clonogenic assays)

• glucose uptake.

3.2.11 Target organ toxicity: cardiovascular toxicity

Cardiovascular toxicology focuses on adverse effects on the heart and the vascular system. Exposure to toxic compounds can result in alterations of biochemical pathways, defects in cellular structure and function, and pathogenesis of the affected cardiovascular system.

Cardiotoxicity

Heartbeat is controlled by specialised pacemaker cells and cardiac electrophysiology and function is under neurohormonal regulation. The primary contractile unit in the heart is the cardiac muscle cell, or


cardiomyocyte. Stimulation of cardiomyocytes through bioelectricity is due to carefully orchestrated transport of three positively charged ions: calcium, sodium and potassium. Each of the ions has specific channels and pumps on the membrane of cardiac myocytes.

The very high energy requirements of the heart muscle (continuous synthesis of ATP via mitochondrial oxidative phosphorylation is required for cardiomyocyte function) and heavy reliance on ion channels and pumps are specific consideration for cardiotoxicity. Not surprisingly then, many substances can cause cardiac toxic responses, mostly by affecting ion channels (e.g. the antiarrhythmic drugs verapamil and quinidine), calcium ion homeostasis (e.g. pharmaceuticals such as ouabain, some antimicrobial and antiviral agents, aldehydes, halogenated alkanes and metals), and electrical excitability and action potential generation (e.g. local anesthetics like benzocaine or proainamide).

Vascular toxicity

Toxic responses of the vascular system include changes in blood pressure and damage to blood vessels. The main function of the vascular system is to provide oxygen and nutrients to and remove carbon dioxide and metabolic products from organ systems. It also delivers hormones and cytokines to target organs. Dilation and constriction of blood vessels is controlled remotely by neurons and hormones (such as epinephrine, norepinephrine and angiotensin) and locally by oxygen supply and endothelium-derived relaxing factor (EDRF). Thus neurotoxicity or endocrine disruption may also affect vascular function.

Blood vessels are mostly composed of epithelial cells enveloped by smooth muscle cells. All chemicals, after absorption, contact the vascular system. Specific vascular toxicity can occur from damage to either epithelial cells (e.g. aspirin, endotoxins, carbon monoxide) or smooth muscle cells (e.g. metals interfering with calcium homeostasis) or from vasoactive compounds (e.g. cocaine, nicotine, metals). It is not entirely clear how (and if) toxic responses of the vascular system affect physiological function and toxicity to other organs, but damage to vascular epithelial cells could produce reactive oxygen species and subsequent oxidative injury.

Measuring cardiovascular toxicity in vitro

The cardiac muscle cell line HL-1 (Claycomb et al. 1998) can be used to test the effects of toxicants on:

• cardiomyocyte viability (using the neutral red uptake assay)

• electrophysiology (using membrane potential).

Other in vitro tests (reviewed in Netzer et al. 2001) may also work with HL-1 cells but this remains to be tested.

3.2.12 Health outcomes not considered in this review

Some health outcomes of relevance to risk assessment of new chemicals have not been included in this review. The following health effects were not reviewed because they were deemed unlikely to result from exposure to chemical contaminants from drinking water:

• sensory organ toxicity, including ocular toxicity

• respiratory toxicity

• cutaneous toxicity

• musculoskeletal toxicity (myotoxicity).


The following health effects were not reviewed because the main mechanism leading to these types of toxicity is already discussed under a different section of this review (e.g. basal toxicity, carcinogenicity):

• bladder toxicity

• gastrointestinal tract toxicity.

3.3 C ons iderations for in vitro tes ting In vitro methods have been used for decades in the field of toxicology, and they can provide a substantial advantage over in vivo testing: use of human cells, lower variability, better experimental control, more sensitivity, shorter duration, lower financial and ethical cost etc. However there are limitations to using in vitro models for developing a human health risk assessment, in particular a lack of understanding of bioavailability and metabolism. These limitations need to be clearly understood and stated to achieve a meaningful risk assessment. Specifically, in vitro data may not accurately predict human in vivo effects when:

• the chemical is not well absorbed into the body

• the chemical is subject to first-pass metabolism by the liver, in which case the metabolite may have a different toxicity profile

• the chemical is distributed so that less (or more) reaches the receptor than would be predicted based on its absorption

• the response measured in vitro is an artefact of the in vitro culture conditions (e.g. pH)

• the chemical is eliminated from the body rapidly

• the toxic effect is an integration of higher-order effects (e.g. combination of effects from multiple organs)

• pharmacokinetic parameters (e.g. bioavailability, half life, peak plasma concentration) can affect the toxicity of chemicals and cannot readily be modelled in vitro.

The limitations do not mean that in vitro method cannot be used for human health risk assessment, but that care must be taken when relying on in vitro data for in vivo extrapolation.

The current model of deriving human health risk assessment from animal in vivo data is equally affected by similar limitations, as the structural and biochemical differences (e.g. differences in metabolism, toxicokinetics, morphology, and molecular mechanisms) between test animals and humans make extrapolation from one to the other difficult. The use of ‘safety factors’ is often assumed to be adequate to buffer any uncertainty in extrapolating from animal in vivo data to potential human effect, but there is little biological basis behind their assigned numeric value (e.g. 10 for interspecies differences, 10 for pharmacokinetic differences, etc). A preliminary health risk assessment model carefully derived from in vitro data is arguably just as valid as one derived from animal in vivo data, as long as limitations are understood and accounted for using adequate uncertainty factors.

New developments in the field of microfluidics have great implications to the potential of in vitro methods (e.g. ‘human on a chip’) and might significantly enhance the predictive capabilities of in vitro systems (Baudoin et al. 2007). The European Centre for the Validation of Alternative Methods (ECVAM) Integrated Testing Strategies Task Force has published a proposed framework to rely on in vitro and in silico methods to produce meaningful risk assessments (Blaauboer et al. 1999) and future developments in this field may further improve the reliability of preliminary risk assessments based on these techniques.


3.3.1 Culture methods

A technical limitation of in vitro systems is the fact that the artificial cell culture conditions modify the basal gene expression profile and may influence the response of cells to a chemical. To reduce this artefact, cell culture conditions will need to be carefully developed for each test system while keeping in mind good cell culture and in vitro practices (Cooper-Hannan et al. 1999; Coecke et al. 2005). It goes without saying that strict standards laboratory standards and quality assurance/quality control methods must be adhered to ensure the production of high-quality in vitro data. The Organisation for Economic Cooperation and Development (OECD) provides guidance documents on good laboratory practice for in vitro studies (OECD 2004).

3.3.2 Metabolism-mediated toxicity

Metabolism (biotransformation) can be a crucial factor in toxicity. Many xenobiotics become less toxic after biotransformation, a process called ‘detoxification’, but some can be metabolised into more toxic compounds, called ‘toxification' or ‘metabolic activation’. In most cases, this increased toxicity from biotransformation is due to the creation of electrophiles, free radicals, nucleophiles or redox-active reactants. However in some cases the increased toxicity can be due to the change in molecular structure, which allows the metabolite to better interact with specific receptors or enzymes; for example, the organophosphate insecticide parathion is biotransformed into paraoxon, a potent inhibitor of cholinesterase. Metabolic activation is often incorporated into drug design, with the metabolite often more potent than the ingested drug. Biotransformation is therefore a crucial consideration when developing in vitro tests.

Concern for possible metabolic activation can be addressed by pre-incubating the samples with metabolising systems in a cell-free extract (S9 fraction) or culturing the target cells with isolated hepatocytes or other CYP450-expressing cell types (Coecke et al. 2006). In silico models are also available (Coecke et al. 2006).

3.3.3 Bioavailability

Bioavailability is another concern for in vitro studies (Nielsen 2008). In silico mathematical models are often used and there is an in vitro model of the intestinal barrier (Le Ferrec et al. 2001), which may help further refine estimates of bioavailability.

3.4 C onc lus ions After reviewing an extended version of this chapter, the participants of the National Water Commission project ‘Health risk assessment, communication and management of chemical hazards from recycled water’ met for a half-day workshop on 8 August 2008 in Sydney, Australia.

During the workshop, the 13 endpoints presented in this review were prioritised based on relevance of the endpoint to health risk assessment of chemicals in purified recycled water for potable use, feasibility and availability of in vitro assays to measure that particular endpoint, and previous deployment of bioassays for that endpoint in the validation of an existing successful recycled water scheme for potable use. Table 10 summarises the consensus of the participants at the workshop.


Table 10: Prioritisation of health outcomes based on relevance, feasibility of in vitro testing systems, and previous deployment in a validation period

Health focus Relevance Feasibility in vitro

Previous use?

Average Priority for this project

Carcinogenicity 4 4 4 4.0 High Basal toxicity 4 4 3 3.7 High Endocrine effects 4 2 4 3.3 High Hepatotoxicity 4 3 2 3.0 High Immunotoxicity 3 3 1 2.3 Med Neurotoxicity 3 3 1 2.3 Med Hematotoxicity 2 3 1 2.0 Low Developmental toxicity* 4 1 1 2.0 Low Reproductive toxicity* 4 1 1 2.0 Low Nephrotoxicity 2 2 1 1.7 Low Cardiovascular toxicity 1 2 1 1.3 Low Notes: Values were assigned from 1 to 4, with 1 the lowest and 4 the highest. * The project participants acknowledged that developmental and reproductive toxicity are important health outcomes to examine for health risk assessment of recycled water for potable use. Due to the lack of in vitro methods to adequately measure these outcomes in humans, they have been assigned a low priority for this particular project (which focuses on in vitro methods).

The following health outcomes were identified as the focus of the project: carcinogenicity, basal toxicity, endocrine effects, and hepatoxicity. Immunotoxicity and neurotoxicity were also identified as feasible and relevant; however, no single assay can encompass all mechanisms leading to these types of toxicity. Therefore, one bioassay was selected for each endpoint as indicative of this type of toxicity.


4 Chemical analysis and validation of extraction process

4.1 S ummary This chapter describes the methods that were developed and deployed for sample collection, extraction and chemical analysis for this project. Samples were collected from various recycled water (and other) sources. In most cases, the samples were adsorbed to solid phase extraction (SPE) cartridges onsite. Two types of SPE cartridges were used in series to maximise the range of analytes that could be successfully captured. These were Waters Oasis HLB SPE cartridges and coconut charcoal SPE cartridges. The adsorbed cartridges were then transported to the UNSW laboratory for elution. The eluted samples were divided into subsamples and distributed as follows:

• 400 µL to be sent to AWQC (SA Water) for bioassay analysis

• 300 µL to be sent to SWRC (Griffith University) for bioassay analysis

• 300 µL kept at UNSW for chemical analysis.

A list of priority chemicals was developed based on a set of pre-determined criteria. The list is discussed further in Chapter 5 (Section 5.2). Trace chemical analysis undertaken at UNSW included four distinct analytical methods, all run on the same sample sequentially as follows:

• HPLC-MS/MS for PPCPs

• GC-MS/MS for N-nitrosamines

• GC-ECD for trihalomethanes

• GC-MS/MS for steroid hormones.

This chapter also presents the results of some SPE optimisation and validation experiments that were undertaken as a means of determining a final extraction protocol. A series of SPE recovery experiments were undertaken to identify an optimum SPE procedure. SPE phases trialled included Waters Oasis HLB, Supelclean LC-18 and Coconut charcoal cartridges. For two of these phases, large size cartridges (35 mL) were used to facilitate extraction of large water samples (4L). Experiments were undertaken with the three cartridges including single cartridges and various arrangements of the HLB, C18 and charcoal cartridges in series. Synthetically prepared standard solutions containing a mixture of trace chemical contaminants (including pharmaceuticals, personal care products, steroid hormones and nitrosamines) were extracted and the recovered solutions were analysed by HPLC-MS/MS and GC-MS/MS. The results of these experiments indicate that Oasis HLB cartridges provided the optimum SPE phase. In most cases, effective elution was achieved with up to 100 mL methanol (MeOH) when using the large cartridges (35 mL). However, in a few cases, improved recoveries could be achieved by an additional washing with methyltert-butylether (MTBE).

Based on the above results, a final extraction protocol was developed and is presented in the conclusions section of this chapter.

4.2 A im and objec tives The aims of the work presented in this chapter were:

• to optimise and test validate a suitable sample collection and extraction protocol

• to undertake trace chemical analysis of all samples for selected chemical analytes based on the priority chemicals list


• to determine the approximate method recovery for the target chemicals based on the optimised procedure.

4.3 Materials 17α-estradiol, 17β-estradiol, estrone, estriol, 17α-ethynylestradiol, levonorgestrel, mestranol, testosterone, etiocholanolone, androstenedione, androsterone, dihydrotestosterone, pyridine and 99% N, O-bis(trimethylsilyl)trifluoro-acetamide (BSTFA) with 1% trimethylchlorosilane (TMCS) (all analytical grade), Whatman glass fibre filters and filtering system were purchased from Sigma-Aldrich (Castle Hill, NSW, Australia). D3-estriol, D3-dihydrotestosterone, D2-testosterone, D4-17α-ethynylestradiol, D4-estrone, D4-17β-estradiol, D2-etiocholanolone were purchased from CDN isotopes Inc., Canada and D3-androstenedione was purchased from National Measurement Institute, Australia.

N-nitrosodimethylamine (NDMA), N-nitrosomethyethylamine (NMEA), N-nitrosodiethylamine (NDEA), N-nitrosodipropylamine (NDPA), N-nitrosodi-n-butylamine (NDBA), N-nitrosopyrrolidine (NPyr), N-nitrosopiperidine (NPip), N-nitrosomorpholine (NMorph), sodium thiosulphate (reagent grade), dichloromethane (DCM) (spectroscopic grade) and methanol (HPLC grade) were purchased from Supelco (St Louis, MO, USA). N-nitrosodimethylamine-D6, N-nitrosodiethylamine-D10, N-nitrosomethylethylamine-D3, N-nitrosodipropylamine-D14, N-nitrosodi-n-butylamine-D9, N-nitrosopyrrolidine-D8, N-nitrosopiperidine-D10, N-nitrosomorpholine-D8 were purchased from CDN isotopes (Pointe-Claire, Quebec, Canada).

Atenolol, paracetamol, sulfamethoxazole, caffeine, trimethoprim, TCEP, dilantin, carbamazepine, norfluoxetine, fluoxetine, enalapril, risperidone, atrazine, linuron, omeprazole, clozapine, amitriptyline, DEET, primidone, verapamil, triclocarban, triamterene, polyparaben, metformin, meprobamate, hydroxyzine, diazepam, chlorpyrifos, diazinon, methotrexate, simazine, trifluralin, ketoprofen ,naproxen, bisphenol A, ibuprofen, gemfibrozil, triclosan, simvastatin, diclofenac, triclocarban, t-octylphenol, polyparaben, phenylphenol, 4-n-nonylphenol, diuron, pentachlorophenol and salicylic acid were purchased from Sigma-Aldrich (Castle Hill, NSW, Australia). o-Hydroxyatorvastatin, p-hydroxyatorvastatin, atorvastatin, simvastatinhydroxyacid, D5-p-hydroxyatorvastatin, D5-enalapril, D4-risperidone, D3-triclosan, D6-simvastatinhydroxyacid, D6-simvastatin, D6-verapamil, D9-trimethoprim, D3-meprobamate, D8-hydroxyzine, D5-triamterene, D4-sulfamethoxazole, D5-o-hydroxyatorvastatin and D5-atorvastatin were purchased from Toronto Research Chemicals (North York, Ontario, Canada). D7-Atenolol, 15N13C-paracetamol, D9-caffeine, D10-dilantin, D10-carbamazepine, D5-norfluoxetine, D5-fluoxetine, D5-atrazine, D5-linuron, D3-omeprazole, D4-clozapine, D6-amtriptyline, D7-DEET, D5-primidone, D4-triclocarban, D6-metformin, D10-diazinon, D10-simazine, D14-trifluralin, D3-ketoprofen, D3-naproxen, D6-bisphenol A, D3-ibuprofen, D6-gemfibrozil, D4-diclofenac, D4-triclocarban, D17-n-octylphenol, 13C6-phenylphenol, D4-4-n-nonylphenol, D6-diuron and D6-salicilic acid were purchased from CDN isotopes (Pointe-Claire, Quebec, Canada). D5-diazepam was purchased from Cambridge Isotope Laboratories (Andover, Massachusetts, USA)

Acetonitrile and methanol (anhydrous spectroscopy grade) were purchased from Ajax Finechem (Tarron Point, NSW, Australia). Ultrapure water was produced using a Driec-Q filtering system from Millipore (North Ryde, NSW, Australia). Kimble culture tubes (13 mm I.D. x 100 mm) and a Thermo Speedvac concentrator (model no. SPD121P) were purchased from Biolab (Clayton, Vic, Australia).

Stock standard solutions of analytes and isotope labelled standards were initially prepared in acetronitrile (500 mg.L-1, 20 mL) in amber vials and then further serial diluted with acetonitrile to obtain working standard solutions of lower concentrations. All standard solutions were stored at -18oC

nd prepared freshly every three months. Working solutions of analytes and isotope labelled standards at lower concentrations were stored at 4ºC and freshly prepared from concentrated stock standards monthly.


Supelclean Coconut charcoal SPE cartridges (2 g bed weight, particle size: 80/120 mesh) were purchased from Supelco (St Louis, MO, USA). Oasis hydrophilic lipophilic balance (HLB) solid phase extraction cartridges (6 mL, 500 mg) were purchased from Waters (Rydalmere, NSW, Australia).

Large Oasis HLB SPE cartridges (6 g sorbent/35 mL) were purchased from Waters (10/box; product no. 186000118). Specialised large adaptors for joining 35 mL SPE tubes in series (10/box; product no. WAT048160) and regular size HLB cartridges (0.5 g/6 mL, 30/box, product no.186000115) were also purchased from Waters. Large Supelclean LC-18 SPE cartridges (10 g sorbent/60 mL) were purchased from Sigma Aldrich (16/box, product no. 57136). Specialised large adaptors for joining 60 mL SPE tubes in series were also purchased from Sigma Aldrich (6/box; product no. 57267). Coconut charcoal SPE cartridges (2 g/6 mL) were purchased from Sigma Aldrich (30/box, product no. 57144). Adaptors for joining 6 mL SPE tubes in series were also purchased from Sigma Aldrich (12/box, product no. 57020-U). SPE processes were carried out on a 12-port SPE manifold (Sigma Aldrich) under vacuum from a diaphragm pump.

4.4 S ample c ollec tion and s olid phas e extrac tion Grab samples (two × 1 L) were taken in methanol-rinsed glass bottles. All samples were extracted onsite or kept on ice until brought back to the laboratory. Sampling sites are described in Chapter 5 (Table 17).

Samples were extracted by passage through two 6cc solid-phase extraction cartridges in series, first an Oasis HLB (Waters Corp) and then a Supelclean coconut charcoal cartridge (Sigma-Aldrich).

All Oasis HLB and coconut charcoal SPE cartridges were individually pre-conditioned prior to extraction with methanol (5 mL), followed by ultrapure water (5 mL). Two pairs of HLB and coconut charcoal cartridges were then joined in series with the HLB cartridge on top of the coconut charcoal cartridge. Each of these pairs were used to extract 1 L of the sample, giving a total sample volume of 2 L.

SPE cartridges were loaded by drawing through 1 L of the aqueous samples under vacuum, maintaining a consistent loading flow rate of less than 5 mL.min-1. The SPE cartridges were rinsed with 10 mL of ultrapure water before drying by passing through a flow of air for approximately 30 minutes. The dried SPE cartridges were then packaged and sent to the UNSW laboratory.

After arriving at the laboratory, the cartridges were immediately further dried by passing through a flow of nitrogen gas. If required, dried cartridges were stored at -18 ºC before elution and quantitative analysis.

Each of the four SPE cartridges (two x HLB, two x coconut charcoal) from each sample were eluted into the same 25 mL glass vial. The HLB cartridges were first eluted with methanol (two x 5 mL) and the eluted samples were evaporated down to approximately 2 mL under a stream of nitrogen using a Turbovap LV (Caliper Life Sciences, Hopkinton, MA, USA). The coconut charcoal SPE cartridges were then eluted into the same vial and further evaporated down to approximately 1 mL. These final one mL eluents were then split and distributed as follows:

• 400 µL to be sent to AWQC (SA Water) for bioassay analysis

• 300 µL to be sent to SWRC (Griffith University) for bioassay analysis

• 300 µL kept at UNSW for chemical analysis.


The fractions retained for chemical analysis were then spiked with concentrated solutions of isotopically labelled standards including nitrosamines, hormones and PPCPs (50 µL, 1ppm stock) for accurate quantitation by isotope dilution. Finally, additional methanol (150 µL) was added to provide a final sample volume of 600 µL for analysis. These samples were analysed by a number of instrumental methods in the following order:

• HPLC-MS/MS for PPCPs

• GC-MS/MS for N-nitrosamines

• GC-ECD for trihalomethanes

• GC-MS/MS for steroid hormones.

4.5 A nalys is of P P C P s by HP L C -MS /MS This analytical method was originally based on the method developed by Southern Nevada Water Authority (USA) and validated in a peer-reviewed journal publication (Vanderford & Snyder 2006). We have used the same materials and instrumentation, with the only significant difference being that we have now added numerous additional analytes to the method to be able to test for a wider range of potential contaminants. The full details of the revised method are provided in the following sections.

Figure 5: The HPLC-MS/MS instrument used for PPCPs analysis.

4.5.1 Liquid chromatography

Analytes were separated using an Agilent (Palo Alto, CA, USA) 1200 series high performance liquid chromatography (HPLC) system equipped with a 150 x 4.6 mm, 5 µm particle size, Luna C18 (2) column (Phenomenex, Torrence CA, USA). A binary gradient consisting of 5 mM ammonium acetate in water (A) and 100% methanol (B) at a flow rate of 800 µL/min was used. For electrospray ionisation (ESI) positive analyses, the gradient was as follows: 10% B held for 0.50 minutes, stepped to 50% B at 0.51 minutes and increased linearly to 100% B at eight minutes, then held at 100% B for two minutes. For ESI negative analyses, the gradient was as follows: 10% B held for 0.50 minutes, stepped to 60% B at 0.51 minutes and increased linearly to 100% B at eight minutes, then held at


100% B for three minutes. A five minutes equilibration step at 10% B was used at the start of each run. An injection volume of 10 µL was used for all methods.

4.5.2 Mass spectrometry

Mass spectrometry was performed using an API 4000 triple quadrupole mass spectrometer (Applied Biosystems, Foster City, CA, USA) equipped with a turbo-V ion source employed in both positive and negative electro-spray modes. Using multiple reaction monitoring (MRM) two mass transitions for all but triclosan was monitored for unequivocal confirmation. One mass transition for the labelled internal standard was monitored. Only the first transition was used for quantitation. Relative retention times of the analyte and isotopically labelled internal standard were also monitored to ensure correct identification. All transitions of analytes in ESI positive mode and ESI negative mode are presented in Table 11 and Table 12, respectively.


Table 11: Transitions for PPCP compounds using ESI positive mode.

Analyte Retention

time (min)

MRM Declust. potential (V)

Collis. energy (V)

Collis. cell exit potential (V)

Atenolol 4.2 267.2→145.1 267.2→190.2

41 41

37 27

24 10

Atenolol-D7 4.2 274.1→145.1 46 37 24

Paracetamol 4.5 152.1→110.1 36 23 18

Paracetamol-15N13C 4.5 155.0→111.0 61 25 18

Sulfamethoxazole 4.5 254.0→156.1 254.0→92

51 51

23 39

26 16

sulfamethoxazole-D4 4.5 258.1→160.1 51 23 26

Caffeine 5 195.0→138.1 195.0→110.1

56 56

27 33

22 18

Caffeine-D9 5 204.1→144.2 51 29 24

Trimethoprim 5.3 291.1→230.2 291.1→261.1

41 41

33 35

12 14

Trimethoprim-D9 5.3 300.3→234.15 71 35 16

TCEP 7.2 284.9→223 284.9→62.9

61 61

45 19

10 12

Dilantin 7.1 253.1→182.1 253.1→104.1

61 61

27 47

10 16

Dilantin-D10 7.1 263.1→192.2 71 29 30

Carbamazepine 7.4 237.0→194.2 237.0→192.1

66 66

27 33

12 36

Carbamazepine-D10 7.4 247.1→204.3 51 29 10

p-Hydroxyatorvastatin 6.9 575.2→440.2 575.2→466.1

51 51

33 15

26 14

p-Hydroxyatorvastatin-D5 6.9 580.2→445.1 61 35 26

Norfluoxetine 8.3 296.0→134 296.0→30.2

26 26

11 33

12 4

Norfluoxetine-D5 8.3 301.0→139 26 11 22

Fluoxetine 8.4 310.0→44.1 310.0→148.2

36 36

37 13

6 14

Fluoxetine-D5 8.4 315.1→44.2 46 43 6

Enalapril 6.8 377.1→234.1 377.1→91.1

56 56

27 87

12 14

Enalapril-D5 6.8 382.2→239.2 66 29 12

Risperidone 8 411.1→191.2 411.3→109.95

66 76

39 69

10 6

Risperidone-D4 8 415.1→195.2 66 39 10

Atrazine 8.2 216.0→174.2 216.0→96.1

36 36

25 35

30 16


Analyte Retention

time (min)


Collis. energy (V)


Atrazine-d5 8.3 221.3→179.05 56 27 12

o-Hydroxyatorvastatin 7.9 575.2→440.2 575.2→466.1

51 51

33 15

26 14

o-Hydroxyatorvast-D5 7.9 580.2→445.1 61 35 26

Linuron 8.9 249.0→182.2 249.0→160.1

46 46

23 23

10 28

Linuron-D6 8.9 255.0→160.1 51 27 28

Atorvastatin 8.2 559.1→440.1 559.1→250.3

61 61

31 61

28 20

Atorvastatin-D5 8.2 564.2→445.4 71 31 10

Omeprazole 7.6 346.2→198.2 346.2→136.1

51 51

17 49

12 12

Omeprazole-D3 7.6 349.2→198 46 17 12

Clozapine 9.5 327.1→270.2 327.1→192.1

76 76

33 59

16 32

Clozapine-D4 9.5 331.2→272 81 35 16

Amitriptyline 9.5 278.2→233 278.2→117.1

56 56

25 35

20 20

Amitriptyline-D6 9.5 284.4→233.1 6 25 14

DEET 8.1 192.2→119 192.2→108.9

71 71

27 37

20 18

DEET-D7 8.1 199.2→126.1 66 19 14

Primidone 5.8 219.2→162.2 219.2→119

66 56

25 19

20 10

Primidone-D5 5.8 224.2→167 76 27 22

Verapamil 10.2 455.4→165.1 455.4→150

106 106

41 59

28 26

Verapamil-D6 10.2 461.4→165.2 111 41 8

Triclocarban 10.6 315.0→127 315.0→162.1

76 31

49 45

22 22

Triclocarban-D4 10.6 319.1→128 31 29 26

Triamterene 6.3 254.2→237 254.2→104

96 96

39 57

14 18

Triamterene-D5 6.3 259.2→242.2 106 39 14

Polyparaben 8.2 181.2→139.1 181.2→121

31 31

15 29

24 6

Metformin 2.5 130.1→113.1 51 19 18

Metformin-D6 2.5 136.1→119.2 51 21 20

Meprobamate 6.3 218.9→158.2 218.9→115.1

66 66

13 19

26 20


Analyte Retention

time (min)


Collis. energy (V)


Meprobamate-D3 6.3 221.9→161.2 66 15 34

Hydroxyzine 9.9 375.3→201.1 375.3→165.1

61 61

27 83

36 28

Hydroxyzine-D8 9.8 383.3→201.1 66 27 12

Diazepam 8.9 285.1→193.1 285.1→154.2

96 96

45 41

32 26

Diazepam-D5 8.9 290.9→198.1 86 47 10

Chlorpyrifos 11.1 349.9→197.9 349.9→115

71 71

29 37

34 20

Diazinon 10.1 305.1→169.1 305.1→115

66 66

33 45

30 20

Diazinon-D10 10 315.2→170.1 76 35 30

Methotraxate 4.2 455.2→308.2 455.2→175

81 81

31 55

18 30

Simazine 7.4 202.1→132.1 202.1→124.1

81 81

29 27

22 22

Simazine-D10 7.3 212.2→137.1 71 29 24

Trifluralin 11.1 336.2→236.1 336.2→251.8

71 71

23 25

22 44

Trifluralin-D14 11.1 350.2→238 66 25 42


Table 12: Transitions for PPCP compounds using ESI negative mode.

Analyte Retention

time (min)


Collis. energy (V)


Ketoprofen 5.7 252.8→208.8 -25 -10 -11

Ketoprofen-D3 5.7 255.6→211.7 -35 -10 -11

Naproxen 5.9 228.9→184.6 228.9→169.8

-25 -25

-10 -20

-31 -7

Naproxen-D3 5.9 231.9→187.8 -20 -8 -17

Bisphenol A 7.2 226.9→211.8 226.9→132.9

-65 -65

-26 -36

-11 -21

Bisphenol A-D6 7.2 232.9→214.9 -70 -26 -17

Ibuprofen 7.7 204.9→160.8 204.9→158.8

-30 -30

-10 -8

-13 -13

Ibuprofen-D3 7.7 208.0→163.9 -20 -10 -13

Gemfibrozil 9.3 248.9→120.8 248.9→126.8

-30 -30

-16 -14

-19 -7

Gemfibrozil-D6 9.3 254.9→120.9 -40 -18 -19

Triclosan 10 286.6→35 -30 -36 -3

Triclosan-D3 10 289.7→34.9 -30 -36 -3

Sim-hydroxyacid 9.2 435.1→318.9 435.1→114.9

-50 -50

-22 -40

-7 -19

Sim-hydxyacid-D6 9.2 441.1→319 -55 -22 -5

Simvastatin 10.5 399.0→114.9 399.0→282.8

-65 -65

-34 -20

-19 -15

Simvastatin-D6 10.5 405.4→121.1 -70 -36 -7

Diclofenac 7 293.9→249.7 293.9→213.7

-30 -30

-14 -28

-11 -11

Diclofenac-D4 7 297.9→253.8 -35 -16 -13

Triclocarban 10 312.8→159.8 312.8→125.8

-60 -55

-20 -18

-9 -27

Triclocarban-D4 10 317.0→159.8 -55 -34 -9

t-Octylphenol 10.4 205.2→132.9 205.2→134

-90 -90

-40 -26

-11 -11

n-OP-D17 10.9 222.1→108 -70 -24 -1

Polyparaben 7.3 179→135.7 179→136.9

-70 -75

-22 -42

-1 -5

Phenylphenol 7.9 168.9→114.8 168.9→140.8

-75 -85

-36 -46

-9 -19

Phenylphenol-13C6 7.9 174.9→120.9 -80 -30 -19

Nonylphenol 11.4 219.0→106 -80 -32 -7

Nonylphenol-D4 11.4 223.1→110 223.1→109.4

-90 -90

-28 -32

-17 -17


Analyte Retention

time (min)


Collis. energy (V)


Diuron 7.5 230.9→185.7 230.9→149.7

-75 -75

-26 -34

-11 -9

Diuron-D6 7.5 236.9→185.8 -30 -24 -15

Salicylic Acid 4.4 136.9→92.8 136.9→65

-70 -50

-26 -24

-9 -15

Salicylic Acid-D6 4.4 140.9→96.9 -50 -42 -1

Pentachlorophenol 8.4 262.7→35 266.7→37

-75 -70

-54 -54

-15 -15

4.6 A nalys is of nitros amines by G C -MS /MS This analytical method was recently developed at UNSW and has now been fully optimised and validated. The final methodology has been submitted for publication in the Journal of Chromatography. It is expected to be available in that journal subsequent to peer review and acceptance for publication. The full method validation details are included in that paper (McDonald et al. 2011).

Figure 6: The GC-MS/MS instrument used for N-nitrosamines and hormones analysis

4.6.1 Gas chromatography-tandem mass spectrometry

Samples were analysed on an Agilent 7890A gas chromatograph (GC) coupled with an Agilent 7000B triple quadrupole mass spectrometer (MS/MS).

The GC inlet was operated in splitless mode, held at a temperature of 280ºC and lined with a single tapered deactivated inlet liner (4 mm, Aglient Technology). An injection volume of 1 µL was used. Analytes were separated on an Agilent DB-1701P, (30 m x 0.25 mm, 0.25 mm film thickness) column using a 1.2 mL.min-1 ultrahigh purity helium flow. An injection volume of 1 µL was used and the oven temperature program was as follows; 50°C held for one minute then raised to 80ºC at a rate of


10ºC/min, increased to 180ºC at 15ºC/min, increased to 260ºC at 35ºC/min and held for five minutes (total run time 15 minutes). The GC/MS-MS interface temperature was maintained at 260ºC.

Mass spectrometric ionisation was undertaken in electron impact (EI) ionisation mode with an EI voltage of 70 eV and a source temperature of 280ºC. The triple quadrupole MS detector was operated in multiple reaction monitoring (MRM) mode. To identify the most suitable transitions for MRM, analytical standards were initially analysed in scan mode to identify suitable precursor ions in MS1 with a scan range of m/z 30 to m/z M+10 (where M is the mass of the compound of interest). Fragmentation of the precursor ions in the collision cell was assessed by performing a product ion scan using the same mass range and scan time. Product ion intensity was optimised for each transition by repeated injections at different collision energies. All samples were run with a solvent delay of five minutes and the analytes were separated into five discrete time segments for MRM monitoring with dwell times ranging from 10 to 80 ms, depending on the time segment, to achieve 10–20 cycles across each peak for good quantification.

The ion transitions monitored for all analytes and isotope standards, as well as the specific dwell times and collision energies for the method are presented in Table 13. The first MRM transition shown for each molecule was used for quantification, while the second transition shown was monitored only for confirmation of molecular identification. Isotopically labelled surrogate standards were observed to consistently elute before the native analyte by 0.01–0.09 seconds. This is in accordance with the reverse isotopic effect for chromatographic separation of molecules in the gas phase (Possanzini et al. 1968).


Table 13: GC-MS/MS method parameters.

Segment start time (min)

Analytes and isotope standards

Retention time (min)

MRM transitions (m/z)

Collision energy (V)

Dwell time (ms)

4.30 NDMA 4.56 74.0 → 44.1 74.0 → 42.1

3 7

20 10

NDMA-D6 4.55 80.0 →50.1 80.0 →48.1

3 7

20 10

NMEA 5.62 88.0 →71.0 88.0 →43.0

3 5

20 10

NMEA-D3 5.6 91.0 →74.0 91.0 →46.0

3 5

20 10

NDEA 6.44 102.0 →85.0 102.0 →56.1

5 10

20 10

NDEA-D10 6.39 112.1 →94.1 112.1 →62.0

5 10

20 10

8.20 NDPA 8.38 130.1 →113.0 130.1 →43.0

0 10

20 10

NDPA-D14 8.31 144.0 →126.1 144.0 →50.1

0 10

20 10

NMorph 8.72 116.0 →86.0 116.0 →56.1

0 10

20 10

NMorph-D8 8.7 124.0 →94.0 124.0 →62.0

0 10

20 10

NPyr 8.9 100.0 →70.0 100.0 →55.0

5 5

20 10

NPyr-D8 8.86 108.0 →78.1 108.0 →62.1

5 7

20 10

9.15 NPip 9.11 114.0 →97.0 114.0 →84.0

5 5

20 10

NPip-D10 9.07 124.1 →106.0 124.1 →94.0

5 5

20 10

10.00 NDBA 10.26 158.0 →141.1 158.0 →99.0

3 5

20 10

NDBA-D18 10.17 176.2 →158.0 176.2 →110.0

0 5

20 10

4.6.2 Identification and quantification

As described in the previous section, two MRM transitions of a single precursor ion were monitored for each target compound. Analysis of the acquired data was undertaken using Agilent MassHunter software. The confirmed identification of a target compound was only established once the analysis met all of the identification criteria. These included the observed presence of the two expected transitions at the same retention time, the area ratio of two transitions within a range of 20 per cent variability with respect to the mean area ratio of all calibration solutions, and a consistent analyte-surrogate relative retention time as that of calibration solutions with relative standard deviation of less than 0.1 minute.


4.6.3 Calibration

Quantitative determination of the target analytes was undertaken using external calibration principles combined with the isotope dilution technique. Calibration curves were comprised of at least five points out of nine calibration points for the non-labelled standards (0.5, 1, 5, 10, 50, 100 and 200 ng.mL-1 in DCM) prepared in GC autosampler vials. The lowest calibration point used for each analyte was that corresponding to the lowest concentration above the analyte-specific method detection limit (MDL). Isotopically labelled internal standards (50 ng.mL-1) were added to each calibration standard. A calibration curve of relative response ratio vs relative concentration ratio of the analyte to internal standard was generated from these standards. A minimum of five calibration points was used in all cases, depending on the concentrations of various samples. All calibration curves had a minimum correlation coefficient of 0.99 and the calculated concentration of each calibration standard was required to be within 80–120 per cent of its true value in order for the sample batch to be considered to have passed quality control criteria.

4.7 A nalys is of trihalomethanes by G C -E C D Figure 7: The GC-ECD instrument used for THMs analysis

4.7.1 Gas chromatography – electron capture detection

The levels of trihalomethanes (THMs) in samples were detected and quantified using an HP 6890 gas chromatography coupled (GC) with an Agilent electron capture detector (ECD). The GC inlet was operated in splitless mode, held at a temperature of 200°C and lined with a single tapered deactivated inlet liner (4mm, Aglient Technology). An injection volume of 1 µL was used. Analytes were separated on an Agilent HP-5MS (30 m x 0.25 mm, 0.25 µm nominal film thickness) column using a 0.9 mL.min-1 ultrahigh purity helium flow as carrier gas. Oven temperature started at 40°C and increased to 45ºC at a rate of 2ºC/min before rising to 280ºC at a rate of 40ºC/min and staying at 280ºC for four minutes. Total run time was about 12.88 minutes. ECD temperature was maintained at 300ºC. Ultrahigh purity mixture of argon methane 5% was used as a makeup gas for the ECD detector. The combined flow rate of constant column and makeup flows was maintained at 60 mL/min. Retention times of four trihalomethanes including CHCl3, CHCl2Br, CHClBr2 and CHBr3 were 3.30, 4.02, 4.79 and 5.45 minutes (± 0.02 min) respectively.



Calibration curves consisted of five points of standard solutions which were prepared in GC autosampler vials by diluting analytical grade thrihalomethane stocks in methanol (5 x 104, 5 x 105, 5 x 106, 1 x 107 and 5 x 107 dilution ratios). Methanol was dehydrated using anhydrous sodium sulphite power to remove the moisture before use. Correlation coefficient (r2) of all calibration curves were greater than 0.98. Identifications of four targeted trihalomethanes in samples were confirmed based on the retention times of the GC peaks matched with those in the standard solutions. To minimise the false positive detection, replicate sets of standard solutions were frequently analysed between sample runs to detect any shifts in the retention time. No significant shift in retention time (i.e. > 0.02 min) was observed during the sample analysis. Method quantitation limits were 100 ng/mL for CHCl3 and 20 ng/mL for the other trihalomethanes (e.g. CHCl2Br, CHClBr2 and CHBr3).

4.8 A nalys is of s teroid hormones by G C -MS /MS The methodology used for the analysis of steroid hormones was developed at UNSW. The full method, including full method validation has recently been published in the Journal of Chromatography A (Trinh et al. 2011).

4.8.1 Trimethylsilyl (TMS) derivatisation

In preparation for GC-MS/MS analysis, all samples were evaporated to remove the methanol solvent prior to reconstitution and chemical derivatisation. 50 µL of BSTFA (99%)-TCMS (1%), 50 µL of pyridine and 400 µL of acetonitrile (anhydrous grade) were added to the dried samples, then the vials were sealed and heated at 60ºC for 30 minutes. The derivatised samples were then allowed to cool to room temperature.

It should be noted that this derivatisation process is sensitive to the presence of any moisture. Accordingly, it is important to ensure that the samples are fully dried (as described in the previous section) before the addition of the derivatising reagents and anhydrous acetonitrile. The smallest commercially available bottles of pyridine (100 mL) and anhydrous acetonitrile (100 mL) were also used to avoid long storage times of these moderately hygroscopic solvents. Similarly, the mixed derivatising reagent was purchased in 1 mL packs and used only on the same day that they were opened.

4.8.2 Gas chromatography-tandem mass spectrometry

Samples were analysed on an Agilent 7890A gas chromatograph (GC) coupled with an Agilent 7000B triple quadrupole mass spectrometer (MS/MS). The GC injection port was operated in splitless mode. The inlet temperature and the GC/MS interface temperature were maintained at 250ºC. An injection volume of 1µL was used. The inlet was used in splitless mode with a purge time of 1.5 minutes. Analytes were separated on an Agilent HP5-MS (30 m x 250 µm x 0.25 µm) column using a 0.8 mL.min-1 helium flow. The GC oven temperature was initiated at 130ºC and held for 0.5 minutes, then increased by 40ºC.min-1 to 240ºC, and increased by 5˚C.min-1 to 280ºC and held at 280ºC for 3.75 minutes. The total run time was 15 minutes.

Mass spectrometric ionisation was undertaken in electron impact (EI) ionisation mode with an EI voltage of 70 eV and a source temperature of 280ºC. The triple quadrupole MS detector was operated in multiple reaction monitoring (MRM) mode with the gain set to 100 for all analytes. To identify the most suitable transitions for MRM, analytical standards were initially analysed in scan mode to identify suitable precursor ions in MS1 with a scan range of m/z 30 to m/z M+10 (where M is the derivatised mass of the compound of interest). Fragmentation of the precursor ions in the collision cell was assessed by performing a product ion scan using the same mass range and scan time. All samples were run with a solvent delay of five minutes and the analytes were separated into three discrete time


segments for MRM monitoring with dwell times ranging from 3 to 25 ms, depending on the time segment, to achieve 10–20 cycles across each peak for good quantification. All ions were monitored at wide resolution (1.2 amu at half height).

The ion transitions monitored for all analytes and isotope standards, as well as the specific dwell times and collision energies for the method are presented in Table 14. The first MRM transition shown for each molecule was used for quantification, while the second transition shown was monitored only for confirmation of molecular identification.


Table 14: Optimal analyte dependent parameters for tandem mass spectrometry (steroidal hormones)

Segment start time (min)

Analytes and isotope labelled standards

Retention time

(min)

MRM transitions Dwell time

(ms)

Collis. energy (V)

7.00 Androsterone 8.58 347.2 → 271.2 347.2 → 175.1

25 25

6 8

Etiocholanolone 8.70 347.2 → 271.2 347.2 → 175.1

25 25

6 8

D2-Etiocholanolone 8.68 349.2 → 273.3 349.2 → 175.0

25 25

6 8

9.20 Dihydrotestosterone 9.70 347.2 → 213.2 347.2 → 271.2

3 3

10 10

D3-Dihydrotestosterone 9.67 350.1 → 215.1 350.1 → 273.2

3 3

10 10

17α-Estradiol 9.79 416.0 → 285.1 416.0 → 326.2

3 3

10 5

17ß-Estradiol 10.25 416.0 → 285.1 416.0 → 326.2

3 3

10 5

D4-17ß-Estradiol 10.23 420.0 → 287.2 420.0 → 330.3

3 3

10 5

Estrone 9.82 342.1 → 257.1 342.1 → 243.9

3 3

15 15

D4-Estrone 9.79 346.3 → 261.2 346.3 → 246.2

3 3

15 15

Androstenedione 10.10 286.1 → 109.1 286.1 → 124.1

3 3

5 5

D3-Androstenedione 10.07 289.3 → 110.0 289.3 → 127.0

3 3

5 5

Testosterone 10.41 360.2 → 174.1 360.2 → 162.1

3 3

11 11

D2-Testosterone 10.40 362.1 → 176.1 362.1 → 164.1

3 3

11 11

Mestranol 10.82 367.0 → 193.2 367.0 → 173.1

3 3

17 17

11.15 17α-Ethynylestradiol 11.45 425.0 → 193.1 425.0 → 231.2

9 9

20 20

D4-17α-Ethynylestradiol 11.43 429.1 → 195.1 429.1 → 233.1

9 9

20 20

Levonorgestrel 12.13 355.0 → 167.0 355.0 → 193.0

9 9

20 20

Estriol 12.58 504.2 → 324.3 504.2 → 386.3

9 9

11 9

D3-Estriol 12.55 507.3 → 327.0 507.3 → 389.4

9 9

11 9



As described in the previous section, two MRM transitions of a single precursor ion were monitored for each target compound. Analysis of the acquired data was undertaken using Agilent MassHunter software. The confirmed identification of a target compound was only established once the analysis met all of the identification criteria. These included the observed presence of the two expected transitions at the same retention time, the area ratio of two transitions within a range of 20 per cent variability with respect to the mean area ratio of all calibration solutions, and a consistent analyte-surrogate relative retention time as that of calibration solutions with relative standard deviation of less than 0.1 minutes.

4.9 S olid phas e extrac tion optimis ation experiments Two different experimental designs were assessed in the early stages of this study. These included a series of one-litre extraction experiments (for steroidal hormones only) and a series of four-litre extraction experiments (for all analytes). Both experimental designs are described below.

4.9.1 One-litre extraction experiments (steroidal hormones only)

A detailed recovery analysis was assessed for a previously-optimised SPE method for steroidal hormones. This method involves the extraction of 1 L water samples by regular size HLB SPE cartridges (0.5 g/6 mL). The steroid hormones assessed in these experiments were 17α-estradiol, 17β-estradiol, estrone, estriol, 17α-ethynylestradiol, levonorgestrel, mestranol, testosterone, dihydrotestosterone (DHT) and trenbolone. MiliQ water (1 L) was spiked with a mixture of steroid hormones to make up individual chemical concentrations of 1000 ng/L.

Prior to SPE, all cartridges were pre-conditioned with MeOH (5 mL) followed by MilliQ water (5 mL). The steroid hormone solutions were then drawn through the HLB cartridges at a flow rate not exceeding 5 mL/min. Cartridges were dried under a nitrogen gas flow until visibly dry (up to 1 hour). Three types of elution solvents were tested in these experiments as follows:

• 20% MeOH and 80% ACN (10 mL)

• 50% MeOH and 50% ACN (10 mL)

• 100% MeOH (10 mL)

Each of these three elution experiments was undertaken in nine replications to confirm the mean value as well as the recovery variability.

After elution, the eluants were centrifugally evaporated under vacuum at 35ºC using a Thermo Speedvac concentrator until visibly dry. They were then reconstituted in acetonitrile (1 mL) and transferred to amber GC autosampler vials. The samples were dried under a gentle nitrogen stream until visibly dry before being derivatised and analysed by GC-MS/MS (see details below).

4.9.2 Four-litre extraction experiments

The four-litre extraction experiments were undertaken with 4 L synthetic solutions prepared with chemicals from four analyte groups (PPCPs group 1, PPCPs group 2, steroid hormones and nitrosamines) as summarised in Table 15. These solutions were prepared in MilliQ water at individual chemical concentrations of 1000 ng/L. The cartridges used in this experiment were large HLB, large C18 and normal size charcoal.


Table 15: Analyte groups used in the 4-L extraction experiments

Analyte group Analytes Analytical method

PPCPs (1) Atenolol, paracetamol, sulfamethoxazole, caffeine, carbamazepine, atrazine, DEET, diazepam,

LC-MS/MS ESI positive

PPCPs (2) Bisphenol A, gemfibrozil, triclosan, diclofenac, T-octylphenol, nonylphenol

LC-MS/MS ESI negative

Steroid hormones 17α-estradiol, 17ß-estradiol, estrone, estriol, 17α-ethynylestradiol, levonorgestrel, mestranol, testosterone, dihydrotestosterone (DHT) and trenbolone

TMS derivatisation and quantitative analysis on GC-MS/MS

Nitrosamines NDMA etc. Quantitative analysis on GC-MS/MS without derivatisation

Six separate four-litre extraction experiments were undertaken with the SPE tubes arranged in the configurations shown in Table 16.

Table 16: SPE configurations tested.

Experiment SPE configuration

1 HLB

2 C18

3 Charcoal

4 HLB C18 Charcoal

5 C18 HLB Charcoal

6 HLB Charcoal

In all cases, the (large) HLB and C-18 SPE cartridges were preconditioned with MeOH (20 mL) followed by MilliQ water (20 mL). The (normal size) charcoal SPE cartridges were preconditioned with MeOH (5 mL) followed by MilliQ water (5 mL). The 4 L solutions were then drawn through the SPE cartridges at flow rates not exceeding 5 mL/min. Cartridges were dried under a nitrogen gas flow until visibly dry (typically 3-4 hours).

All SPE cartridges were eluted separately as follows:

• HLB: MeOH (1 x 20 mL, 9 x 9 mL) followed by MTBE (10 x 9 mL)

• C18: MeOH (1 x 20 mL, 9 x 9 mL) followed by MTBE (10 x 9 mL)

• Charcoal: MeOH (5 mL) followed by MTBE (5 mL) and DCM (5 mL).

After elution, the eluants were centrifugally evaporated under vacuum at 35ºC using the Thermo Speedvac concentrator until dry. They were then reconstituted with methanol (1 mL) and divided into two sub-fractions (0.5 mL each) in amber GC autosampler vials. The first sub-fraction was analysed for PPCPs (group 1), PPCPs (group 2), and stored for future nitrosamine analysis. The second sub-fraction was stored for future steroid hormones analysis.

From the six experiments described in Table 16, this process led to a total of 152 fractions x 2 sub-fractions = 304 individual samples to be analysed. For the purposes of this work, only the PPCPs (group 1) and PPCPs (group 2) analytical groups were analysed from these experiments (i.e. 152 x 2 = 304 sample runs). Full data are provided in Appendix I. Images of this extraction process are presented in Figure 9: Labelled tubes for elutionError! Reference source not found..


Figure 8: Four-litre extraction experiment

Figure 9: Labelled tubes for elution

Figure 10: Drying cartridges under nitrogen


Figure 11: Elution of large volume SPE cartridges

4.9.3 Results

The results of the one-litre extraction experiments and four-litre extraction experiments are provided below. The four-litre extraction experiment results are for the PPCPs (group 1) and PPCPs (group 2). One-litre extraction experiment results are for the steroid hormones.

4.9.4 One-litre extraction experiments (steroidal hormones)

The results of one-litre extraction experiment for steroidal hormones are presented in Error! Reference source not found.. In general, the three tested solvent formulations achieved similar recoveries for most of the analytes. The exception was levonorgestrel, for which the recoveries were about 20 per cent lower in 20%MeOH+80%ACN elution solvent than that of other solvent mixes. High recoveries of 70–120 per cent were achieved for all compounds through the three elution solvent types.


Figure 12: SPE recoveries of steroids hormones

Four-litre extraction experiments

Only the PPCPs (group 1) and PPCPs (group 2) analytes have currently been analysed for the four-litre extraction experiments. Concentrations of all fractions are presented in Appendix I. It was clearly observed from this data that the HLB cartridges were by far the most effective for the extraction of these analytes. The detailed variability of extraction performance with various eluted fractions is presented in Error! Reference source not found.. In these figures, cumulative elution volumes are presented on the horizontal axis and cumulative per cent recovery is presented on the vertical axis.

The first 100 mL elution volume (i.e. the first 10 fractions) in each case was derived from methanol elution, while the remaining 90 mL (i.e. the next 10 fractions) was derived from MTBE (note that the SPE tubes themselves retain approx. 15 mL of solvent). Three data points for each fraction represent the HLB cartridges from Experiment 1, Experiment 4 and Experiment 6. These results indicate that very good recoveries were generally achieved in the range of 70–95 per cent. Most of the analytes


were effectively eluted using MeOH (40-100 mL). However nonylphenol was only recovered ~50 per cent by 100 mL MeOH and required the additional MTBE elution to achieve up to ~80 per cent recovery.

Figure 13: SPE recoveries of pharmaceuticals


4.10 C onc lus ion Comprehensive analytical methods were developed, optimised and validated for this research project. These methods are suitable for a wide range of trace organic analytes likely to be present in various recycled water samples. These include pharmaceuticals, personal care products, pesticides, estrogenic hormones, androgenic hormones, N-nitrosamine disinfection byproducts and trihalomethane disinfection byproducts.

A solid phase extraction protocol was optimised for the effective recovery of a broad range of analytes. This procedure involved extraction of two x 1 L water sequentially through Oasis HLB SPE cartridges and coconut charcoal SPE cartridges.

A final standard operating procedure (SOP) was developed and provided to all personnel undertaking sample collection, extraction and/or analysis.


5 Bioanalytical and chemical screening of Australian recycled water

There is a need to thoroughly characterise the human and ecological risks associated with new water sources, particularly water reclaimed from wastewater for potable use. Analysis of these complex mixtures of trace chemicals presents challenges for standard chemical analysis methods, which require foreknowledge of the likely contaminants. Bioanalytical methods such as in vitro bioassays are ideal screening tools that can detect a wide range of contaminants based on their biological effect rather than their chemical structures, which means that no expectation bias is introduced in the analysis. In combination with chemical analysis, ‘unknown’ biologically-active contaminants can be detected and sometimes identified. This project will apply a combination of in vitro bioassay and chemical methods to screen water produced from several Australian water recycling schemes for potentially harmful chemicals.

5.1 S ite s elec tion and s ampling Nine water reclamation plants in six Australian states/territories were sampled. These plants were selected to provide a variety of treatment technologies (from pond- to membrane-based systems) in a range of climatic conditions. Samples were taken in the morning between 7am and 1pm. Sample types and a brief description of each site is provided in Table 17 below.


Table 17: Description of sites and water samples taken in this study

ID Dates Sample type n Sample description

WRP 1 May & Jul 2010

Treated sewage 8 Large urban wastewater plant, > 500 000 EP. Has gone through conventional sedimentation, activated sludge digestion (with aerobic and anaerobic zones) and several days’ retention in stabilisation lagoons.

Class A 8 Coagulation, dissolved air floatation/filtration and chlorination.

WRP 2 Apr & Jul 2010 Treated sewage 6 Large urban wastewater source, > 500 000 EP. Has gone through conventional activated sludge treatment, settlement tanks and ultrafiltration.

RO 6 Reverse osmosis, UV disinfection.

WRP 3 Jul 2010 Treated sewage 3 Large urban wastewater source (mostly domestic), > 500 000 EP. Has gone through conventional activated sludge treatment and settlement tanks, followed by chloramine disinfection.

Class A 3 Coagulation, ultrafiltration and further chloramine disinfection.

WRP 4

Apr 2010 Treated sewage 4 Large urban wastewater source, > 500 000 EP. Has gone through engineered anaerobic/aerobic lagoons, including activated sludge treatment, with a total residence time of up to 30 days.

Class A 4 UV and chlorine disinfection.

WRP 5 Apr & Jul 2010 Treated sewage 8 Combined from large urban wastewater plants, > 500 000 EP. Has gone through conventional activated sludge treatment and settlement tanks.

RO 8 Flocculation, chloramination, microfiltration, reverse osmosis, advanced oxidation.

WRP 6 Apr 2010 Treated sewage 1 Combined from large urban wastewater plants, approximately 100 000 EP. Has gone through conventional activated sludge treatment and settlement tanks.

RO 1 Flocculation, chlorination, ultrafiltration, reverse osmosis, advanced oxidation.

WRP 7

Apr & May 2010

Treated sewage 4 Large urban wastewater source with a significant industrial component, approximately 200 000 EP. Has gone through conventional aerobic/anaerobic activated sludge treatment, clarification and sand filtration.

RO 4 Microfiltration, reverse osmosis, chlorination and pH adjustment.

WRP 8

Jul 2010 Treated sewage 4 Combined from large urban wastewater plants, approximately 400 000 EP. Has gone through conventional aerobic/anaerobic activated sludge treatment, clarification and chlorination.

RO 4 Chloramination, ultrafiltration, reverse osmosis, chlorination. Trials of different disinfection regimes were underway at the time of sampling.

WRP 9

Apr & Jul 2010 Treated sewage 8 Medium municipal wastewater, mostly residential sewage, < 50 000 EP. Has gone through trickling filters and settlement lagoons.

Class A 8 Ultrafiltration and chlorination.

B Jul 2010 Bottled water 1 Standard bottled drinking water.

T Jul 2010 Tap water 5 Tap water from 5 different Australian capital cities.


ID Dates Sample type n Sample description

R Jul 2010 Rainwater 1 Water from a private rainwater tank in QLD.

FB Apr, May & Jul 2010

Field Blank 11 Laboratory ultrapure water extracted in same environment and conditions as the field samples.

“EP” = Equivalent population.


Grab samples (two × 2L) were taken of the source water (usually treated sewage) and the final recycled water in methanol-rinsed glass bottles. Metropolitan tap water, bottled and ultrapure water field samples were also taken as negative control. All samples were kept on ice until brought back to the laboratory. Samples were processed on the same day by passage through two 6cc solid-phase extraction cartridges in series, first an Oasis HLB (Waters Corp) and then a Supelclean coconut charcoal cartridge (Sigma-Aldrich). Once dried, the cartridges were eluted with 100% methanol, the extracts blown down to dryness under gentle nitrogen stream, and reconstituted to 1 mL (i.e. sample enrichment factor from SPE was 2000×). The same aliquots were used for chemical and bioassay analysis.

5.2 P riority c hemic als lis t A list of 39 priority chemicals for screening analysis was narrowed down from an initial list of 342 chemicals from a variety of sources (including scientific literature, Australian guidelines and other reports) based on criteria such as the availability of chemical analysis methods, predicted biological activity, actual and perceived toxicity, presence on industrial inventories and likelihood of occurrence in recycled water sources (see Appendix II for the full table, Section 9.2). The priority list includes chlorinated and brominated disinfection by-products, natural hormones (e.g. estrogens, androgens), industrial compounds (e.g. bisphenol A, nonylphenol), a personal care product (DEET), pesticides (e.g. atrazine, diuron, pentachlorophenol), pharmaceuticals (e.g. caffeine, carbamazepine, ethynylestradiol) and a veterinary drug (trenbolone) (Error! Reference source not found. and Table 18). Fact sheets for these compounds are included in Appendix IV.

Figure 14: Priority chemical classes.

‘DBP’ = disinfection by-product; ‘PCP’ = personal care product, ‘Vet Drug’ = veterinary drug.


Table 18: Model compounds used in this study

Chemical name CASRN Class Comment AGWR1 (ng/L)

ANZECC irrigation (ng/L)

17β-Estradiol (βE2) 50-28-2 Hormone Natural estrogen, main estrogenic hormone in humans 175

Estrone (E1) 53-16-7 Hormone Natural estrogen , principal metabolite of estrogen hormones 30

17α-Estradiol (αE2) 57-91-0 Hormone Natural estrogen, more common in non-human mammals 175

Estriol (E3) 50-27-1 Hormone Natural estrogen, mostly produced during pregnancy 50

17α-Ethynylestradiol (EE2) 57-63-6 Pharmaceutical Used in contraceptive pills 1.5

Mestranol 72-33-3 Pharmaceutical Used in contraceptive pills, metabolised to ethynylestradiol 2.5

Testosterone 58-22-0 Hormone Natural androgen, main androgenic hormone in mammals 7000

5α-Dihydrotestosterone (DHT) 521-18-6 Hormone Natural androgen, very potent androgenic hormone

17β-Trenbolone 10161-33-8 Pharmaceutical (vet) Growth promoter used in livestock management

Levonorgestrel 797-63-7 Pharmaceutical Progestogen, used as a contraceptive

Bisphenol A 80-05-7 Industrial compound Plasticizer 200 000

4-Nonylphenol 104-40-5 Industrial compound Degradation of alkylphenol ethoxylates, used in the manufacture of plastics, detergents, paints and pesticides

500 000

4-t-Octylphenol 140-66-9 Industrial compound 50 000

Atenolol 56715-13-0 Pharmaceutical β-Blocker, used to treat cardiovascular disease 25 0002

Caffeine 58-08-2 Pharmaceutical Psychoactive simulant found in coffee, tea and other drinks Provisional [350]

Carbamazepine 298-46-4 Pharmaceutical Anticonvulsant and mood stabiliser 100 000

Diethyltoluamide (DEET) 134-62-3 Pharmaceutical Active ingredient in insect repellents 2 500 000

Diazepam 439-14-5 Pharmaceutical Benzodiazepine derivative, used as mood stabiliser 2500

Diclofenac 15307-79-64 Pharmaceutical Non-steroidal anti-inflammatory (NSAID), used as analgesic 1800

Gemfibrozil 25812-30-0 Pharmaceutical Lipid regulator 600 000

Indomethacin 53-86-1 Pharmaceutical Non-steroidal anti-inflammatory (NSAID), used as analgesic 25 000

Methotrexate 59-05-2 Pharmaceutical Cytotoxic drug used to treat cancer and autoimmune disease 52



ANZECC irrigation (ng/L)

Paracetamol (Acetaminophen) 103-90-2 Pharmaceutical Over-the-counter analgesic and antipyretic 175 000

Salicylic acid 69-72-7 Pharmaceutical Aspirin metabolite (analgesic, antipyretic and anti-inflammatory) 105 000

Sulfamethoxazole 723-46-6 Pharmaceutical Antibiotic 35 000

Triclosan 3380-34-5 Personal care product Antibiotic and antifungal Provisional

[350]

Atrazine 1912-24-9 Pesticide Triazine herbicide, photosynthesis inhibitor 40 000 [10 000]3

Chlorpyrifos 2921-88-2 Pesticide Organophosphate insecticide 10 000

Diazinon 333-41-5 Pesticide Organophosphate insecticide 3000

Diuron 330-54-1 Pesticide Herbicide, photosystem II inhibitor 30 000 2000

Pentachlorophenol 87-86-5 Pesticide Organochlorine pesticide and disinfectant 10 000

Simazine 122-34-9 Pesticide Triazine herbicide, photosynthesis inhibitor 20 000 [10 000]3

Trifluralin 1582-09-8 Pesticide Pre-emergence herbicide 50 000 [10 000]3

Bromochloroacetic acid 5589-96-8 Disinfection by-product Disinfection by-product Provisional

[14]2

Bromodichloromethane 75-27-4 Disinfection by-product THM disinfection by-product, also used as a flame retardant 6000

Bromoform 75-25-2 Disinfection by-product Trihalomethane disinfection by-product 100 000

Chloroform 67-66-3 Disinfection by-product Trihalomethane disinfection by-product 200 000

Dibromochloromethane 124-48-1 Disinfection by-product Trihalomethane disinfection by-product 100 000

N-Nitrosodimethylamine (NDMA) 62-75-9 Disinfection by-

product Disinfection by-product, also present in industrial wastewater 10

na = not available. 1 Australian guidelines for water recycling, Phase 2 – augmentation of drinking water supplies (EPHC/NHRMC/NRMMC 2008); 2 Queensland Public Health Regulation 2005 Schedule 3B (QG 2005); 3 General limit for all herbicides in NSW (ANZECC/ARMCANZ 2000); 4 the CASRN is for diclofenac sodium.


These priority chemicals were analysed in the SPE extracts by a combination of high performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS) and gas chromatography-tandem mass spectrometry (GC-MS/MS) techniques. Analytical quantification was undertaken with isotope dilution to the SPE extracts to control for any matrix-effects variability.

5.3 B ioanalytic al tools Following a review of potential human health effects from drinking water exposure to toxic chemicals and the current state-of-the-science of bioanalytical methods (Chapter 3), twelve in vitro bioassays were selected for this project. The selected assays provide measures of primary non-specific (basal cytotoxicity), specific (endocrine effects, hepatoxicity, and limited measures of immunotoxicity and neurotoxicity) and reactive toxicity (mutagenicity and genotoxicity) (Table 19).


Table 19: Bioassay battery.

Mode of toxicity *

Endpoint Mechanism modelled Bioassay

Non-specific

Cytotoxicity Basal cytotoxicity to gastro-intestinal cells

Caco2-NRU (1)

Cytotoxicity Basal cytotoxicity to white blood cells WIL2NS TOX

Cytotoxicity Basal cytotoxicity to liver cells HepaTOX

Reactive Mutagenicity Mutagenic potential Ames TA98 and TA100 test (6)

Genotoxicity Micronucleus formation WIL2NS FCMN (7)

Specific Hepatotoxicity Multi-function oxidase (MFO: CYP1A2) induction in liver cells

HepCYP1A2

Endocrine: estrogenic ER-mediated transcriptional activation

ERα-CALUX “+” mode (2)

Endocrine: anti-estrogenic

Inhibition of ER-mediated transcriptional activation

ERα-CALUX “-“ mode (2)

Endocrine: androgenic AR-mediated transcriptional activation

AR-CALUX “+” mode (2)

Endocrine: anti-androgenic

Inhibition of AR-mediated transcriptional activation

AR-CALUX “-“ mode (2)

Endocrine: glucocorticoid

GR-mediated transcriptional activation

GR-CALUX (2)

Endocrine: progesteronic

PR-mediated transcriptional activation

PR-CALUX (2)

Endocrine: thyroid receptor

TRβ-mediated transcriptional activation

TRβ-CALUX (2)

Immunostimulation Stimulation of cytokine production by monocytes

THP1 CPA “+” mode (4)

Immunosuppression Inhibition of cytokine production by monocytes

THP1 CPA “-” mode (4)

( Neurotoxicity ) Inhibition of acetylcholinesterase AChE assay (5) * Classification based on Escher and Hermens 2002. (1) Konsoula and Barile 2005; (2) van der Linden et al. 2008; (4) Baqui et al. 1998; (5) Hamers et al. 2000; (6) Ames et al. 1973; (7) Laingam et al. 2008.

The priority chemicals and field samples were tested in all bioassays by adding an aliquot of the compound or SPE extracts, respectively, to the incubation medium, ensuring the final carrier solvent concentration did not result in toxicity (usually ≤ 0.1 per cent).

5.3.1 Methodology for bioanalytical tools

The following section briefly describes the methods for the bioanalytical tools used in this project. Up-to-date standard operating protocols (SOPs) are available upon request.

Non-specific toxicity

C aco2-NR U

The Caco2-NRU (neutral red uptake) test is a measure of non-specific cytotoxicity. It is used to determine if the test sample impacts on the viability of Caco2 (human epithelial colorectal adenocarcinoma) cells after 21h of exposure. Cell viability at the end of the incubation period is determined by adding neutral red, a dye that stains only live cells, and measuring the amount dye taken up by the cell culture.


The methods were adapted from Konsoula and Barile (2005). In brief, Caco2 cells are grown in growth medium (DMEM/F12 with phenol red supplemented with 8% fetal bovine serum (FBS) and 100 μM non-essential amino acids). For the assay, cells are seeded at 20 000 cells/well in 100 μL assay medium (DMEM/F12 medium without phenol red supplemented with 5% stripped fetal bovine serum (CD-FBS) and 100 μM non-essential amino acids) in 96-well plates and incubated for approximately 24h at 37ºC 5% CO2. When the cells reach confluency (usually about 24h), the medium is removed and replaced by 150 μL of fresh assay medium and 50 μL of assay medium containing the model compound or water extract to be tested (final methanol concentration in the assay plate ≤0.1%). After 21h of incubation at 37ºC 5% CO2, the medium is removed, the wells rinsed with 150 μL phosphate buffered saline (PBS), and the PBS is replaced by 150 μL of neutral red media (50 μg/mL neutral red in assay media, made fresh). After a further 3h incubation at 37ºC 5% CO2, the medium is aspirated and replaced with 150 μL of neutral red desorbing fixative (1% acetic acid, 50% ethanol, in deionised water). The plate is placed on an orbital shaker at 600rpm for 10 min at room temperature and absorbance was read in a plate absorbance reader (BMG FluoSTAR Omega) at 540 nm.

Average and standard deviation of the replicates were calculated. A sample was considered cytotoxic if it decreased Caco2 viability by more than 20 per cent (a threshold identified as >3×SD from baseline). The toxic activity in a sample was expressed as toxic units, calculated as 1/REFEC20, where REFEC20 is the relative enrichment factor of the water sample needed to achieve a 20 per cent decrease in viability. REF is computed by dividing the sample enrichment factor (from SPE extraction, generally 2000× in this project) with the dilution in the assay (generally 1000× for this assay).

For QAQC, a nine-point methanol standard curve was run on every plate to ensure the standard of the response for each run. The EC50 and maximal absorbance of each plate was compared to control charts and the entire plate rejected if these values were more than three standard deviations away from the historical averages.

WIL 2NS T OX

The WIL2NS TOX test is a general cytotoxicity test using WIL2NS lymphoblast cells. Cytotoxicity testing was carried out using the resazurin assay as measure of cell viability following 24-hour exposure to the test compound. Resazurin is a nonfluorescent dye which is metabolised to a highly fluorescent product, resorufin, by mitochondrial activity in viable cells (Nociari et al. 1998). The level of fluorescence produced is directly proportional to the number of viable cells present.

The WIL2-NS human lymphoblastoid cell line was a gift from Dr B. Sanderson (Flinders University, South Australia). The cells were cultured in RPMI-1640medium (Sigma–Aldrich) supplemented with 10% foetal bovine serum (FBS), 10 mM HEPES, 1.5 g/l sodium bicarbonate, 0.06 mg/ml penicillin G and 0.1 mg/ml streptomycin. Cells were culture at 37°C, 5% CO2. To maintain log-phase growth, cell passages were conducted every two-to-three days maintaining a cell density between 1×105 and 1×106 cells/ml.

WIL2-NS cells were prepared in culture medium at a density of 5.0 x 105cells/ml. Cells were seeded into a 96-well tissue culture plate (100µL/well). Samples to be tested were prepared by dilution into culture medium (1/50). Then 100 µL was added into each well containing cells. Samples were plated in duplicate. A solvent control (1% methanol) and high (272 µM), medium (182 µM) and low (91 µM) concentrations of the positive control methyl methane sulfonate (MMS) were included with each experimental batch. The plate was gently mixed and cells were incubated for 24 hours. Following the 24-hour exposure, 0.1 mg/ml of resazurin was added into each well. The plate was incubated in cell culture incubator for 2 hr and fluorescence (530/580; excitation/emission) was measured using a Wallac VICTOR3 1420 Multilabel Counter, PerkinElmer™.

Cytotoxicity was expressed as percentage reduction of cell viability compared with the solvent control using the formula below.


Statistical analyses were carried out to determine which samples induced significant cytotoxic effects. Each sample was tested in ≥2 independent cytotoxicity experiments. Average and standard deviations of the control (solvent control) were calculated from all screening experiments. A sample was considered cytotoxic if it decreased WIL2NS viability by more than 12 per cent (a threshold identified as >3×SD from baseline)

HepaT OX

Basal cytotoxicity in liver cells was determined by using the resazurin assay as measure of cell viability in C3A liver cells following exposure to the test sample. The level of fluorescence produced is directly proportional to the number of viable cells present.

The C3A human hepatocellular carcinoma cell line was obtained from the American Type Culture Collection (ATCC) (Manassas, VA, USA). C3A cells were maintained in minimal essential medium (MEM) with Earle’s salts supplemented with 2 mM L-glutamine, 1.5 g/L sodium bicarbonate, 1 mM sodium pyruvate, 0.1 mM non-essential amino acids, 0.06 mg/ml streptomycin, 0.1 mg/ml penicillin and 10% foetal bovine serum and incubated at 37°C, 5% CO2. Tissue culture flasks (75 cm2) were obtained from Greiner Bio-one (CELLSTAR, Germany).

Cells were seeded into 96-well flat bottom plates (Sarstedt, Australia) at a density of 30 000 cells/well. Following incubation at 37°C, 5% CO2 for 24 hours, cells were treated with chemicals or water samples as follows. Samples were prepared by dilution in cell culture medium. Model compounds prepared in methanol were diluted 1/100 into cell culture medium while organic compounds were diluted 1/1000. All water samples were diluted 1/100 into cell culture medium (maximum methanol concentration in the assay of 1%). For treatment, culture media was removed from microplate wells and replaced with culture media containing the sample to be tested. Each sample was dispensed in triplicate. A positive control, 30 µg/mL methyl methane sulfonate (MMS), and a solvent control (1% methanol) were included on each microplate. Plates were incubated at 37°C, 5% CO2 for 24 hours. All samples were assayed in ≥2 independent experiments.

Cytotoxicity was assessed by the resazurin reduction assay. Resazurin stock was obtained from Sigma-Aldrich in powder form. For assay, culture media was removed from microplate wells, washed with phosphate-buffered saline (PBS) and replaced with 100 µL of fresh media containing 0.1 mg/mL resazurin. The plate was returned to the incubator (37°C, 5% CO2) for two hours. Fluorescence of samples was determined at 530/580 nm (excitation/emission) using a Perkin Elmer VICTOR3 (Wellesley, MA) plate reader. The % control fluorescence was determined for each sample. Results were expressed as viability (% control).

Statistical analyses were carried out to determine if any of the samples were cytotoxic, that is, had significantly reduced cell viability as compared to the control samples. Control data (vehicle control) was collated from all cytotoxicity screening experiments and the mean and SD determined. The limit of detection (LOD) was defined as 3×SD (≥20 per cent reduction in viability) and the limit of quantification (LOQ) was defined as 10×SD (≥68 per cent reduction in viability). In this case samples were considered to have a cytotoxic response when the reduction in viability was greater than the LOD.

A mes T A 98 and T A 100

The Ames fluctuation test is a modified version of the conventional Ames mutagenicity test. The test is carried out in liquid medium using a microplate format (i.e. 384-well plates) and mutagenicity is scored by counting number of wells that turn yellow as result of the growth of bacteria. It has been reported that the test has greater sensitivity when compared with the conventional Ames test and hence suitable for the testing of aqueous samples with low level of mutagens (Bridges 1980). Two


Salmonella typhimurium tester strains, TA98 and TA100 were used in these experiments. Both TA98 and TA100 are commonly used for mutagenicity testing of contaminants in drinking water (Meier 1988).

Each sample was tested in triplicate in a sterile 24-well tissue culture plate. Samples were diluted 1/100 in the exposure medium (liquid Vogel-Bonner medium supplemented with 100 µM Histidine/Biotin solution) either with or without S9 (microsomal fraction of rat liver, containing essential metabolic enzyme for assessment of pro- mutagens). Then 25µL of tester strain (either TA98 or TA100, 18h culture) was added to each well followed by the sample to give a total volume of 300 µL. The exposure was continued in a rotating incubator (37ºC, 110 rpm) in the dark for 90 minutes.

Before a sample could be assessed for mutagenicity it was important to screen for cytotoxic effects that could interfere with the interpretation of results. Cytotoxicity was determined using the resazurin reduction assay. Resazurin (Sigma-Aldrich, MO, USA) is a nonfluorescent dye, which is metabolised to a highly fluorescent product, resorufin, by viable cells. The level of fluorescence produced is directly proportional to the number of viable cells. Briefly, following the 90 minute exposure, 50 µL of solution from each well (of the 24-well plate) was transferred into a well in a new 96-well plate, containing 50 µL resazurin medium (100 µg/mL resazurin in Vogel-Bonner medium), mixed and incubated at 37ºC for 30 minutes. Fluorescence (RFU) at 530/580 (excitation/emission) was measured using a Wallac VICTOR3 1420 Multilabel Counter, PerkinElmer™. Cytotoxicity was expressed as percentage reduction of cell viability compared with the solvent control using the formula below.

To determine mutagenicity of the test compound, 2.75 mL of detection medium (Vogel-Bonner and Bromcresol purple without histidine supplement) was mixed with the remaining cells in each well (250 µl) of the 24-well exposure plate. Using multichannel electronic pipette, the mixture from each well were transferred into 48 wells (50 µL/well) in a 384-well plate. The plate was then incubated in a 37ºC incubator for 48 hr. Mutagenicity of the test compound was determined by observing growth of the tester strain in the histidine deficient medium, which was indicated by the change of colour from purple to yellow. Solvent controls (1% methanol) and positive controls (6 µg/ml 4-nitro-o-phenylenediamine for TA98(-S9), 5µg/mL 2-aminofluorene for TA98 (+S9)), 0.3 µg/,mL sodium azide for TA100 (-S9) and 2-aminofluorene for TA100 (+S9) were included as appropriate. For each sample, the number of wells that turned yellow were recorded in an Excel spreadsheet. The average and the standard deviation value of 3 replicates were calculated. Each sample was analysed in ≥ 2 independent experiments. The baseline (BL) value was calculated from the average numbers of yellow wells of the no-treatment control + 1 SD. The test compound was considered mutagenic if viability ≥ 50% and the average numbers of yellow wells of the test > 2 fold the baseline.

WIL 2NS F C MN

Genotoxicity of the model chemicals and concentrated water samples was assessed using the flow cytometry based micronucleus (FCMN) assay. The protocol followed was adapted from Laingam et al. (2008). The assay can detect both chromosome breakage and chromosome loss following exposure of the cells to a test chemical. Following incubation of the cells for one cell division, the formation of micronuclei greater than baseline levels indicates DNA damage.

The WIL2-NS human lymphoblastoid cell line was a gift from Dr B Sanderson (Flinders University, South Australia). The cells were cultured in RPMI-1640medium (Sigma–Aldrich) supplemented with 10% foetal bovine serum (FBS), 10 mM HEPES, 1.5 g/l sodium bicarbonate, 0.06 mg/ml penicillin G and 0.1 mg/mL streptomycin. Cells were culture at 37ºC, 5% CO2. To maintain log-phase growth, cell passages were conducted every two-to-three days maintaining a cell density between 1×105 and 1×106 cells/ml.


The first part of the genotoxicity assay was to determine if any of the test compounds had cytotoxic effects in WIL2-NS cells that may interfere with genotoxicity determinations (performed as described above in WIL2NS TOX). For genotoxicity assessment using the FCMN, WIL2-NS cells were prepared in culture medium at a density of 5.0 x 105cells/ml. Cells were seeded into a 96-well tissue culture plate (100µL/well). Samples to be tested were prepared by dilution into culture medium (1/50). Then 100 µL was added into each well containing cells. The plate was gently mixed and cells were incubated for 24 hours. Following the 24-hour exposure, cells were transferred to 1.5 mL Eppendorf tubes, centrifuged (100 rpm, five minutes) and the supernatant removed. To each tube, 200 µL of the FCM solution 1 (referred to Laingam et al. 2008) was added. The tubes were gently mixed and incubated at room temperature in the dark for 60 minutes. Following the 60-minute incubation, 200 µL of the FCM solution 2 (Laingam et al. 2008) was added forcefully into each tube and incubation continued at room temperature in the dark for 30 minutes. The suspension (cell lysate containing nuclei and micronuclei) was then transferred in to 2.5 ml plastic tubes. Both nuclei and micronuclei were quantified using the flow cytometry method as described in Laingam et al. (2008). Each sample was tested in duplicate and the experiment was repeated at least once (total ≥2 individual experiments).

The average and the standard deviation values of replicates were calculated. The test compound was considered genotoxic if: viability was >70 per cent (by the resazurin reduction assay) and MN value was >3×SD over control.

Specific toxicity

HepC Y P 1A 2

This assay specifically measures the level of cytochrome P450 enzyme 1A2 (CYP1A2) in liver cells, an enzyme that plays a key role in the biotransformation of chemicals in the liver. CYP1A2 enzyme activity is induced in response to exposure to number of chemicals including polycyclic aromatic hydrocarbons and some pharmaceuticals. The substrate used in this assay is converted by CYP1A2 to a luciferin product that generates light when a detection agent is added. The amount of luciferin produced is proportional to CYP1A2 enzyme activity.

The C3A human hepatocellular carcinoma cell line was maintained as described above in Section 0. Cells were seeded into 96-well flat bottom plates (Sarstedt, Australia) at a density of 120 000 cells/well. Following incubation at 37ºC, 5% CO2 for 24 hours to produce a monolayer, cells were treated with chemicals or water samples as follows. Samples were prepared by dilution in cell culture medium. Model compounds prepared in methanol were diluted 1/100 into cell culture medium while organic compounds were diluted 1/1000. All water samples were diluted 1/100 into cell culture medium. For treatment, the media was removed from microplate wells and replaced with culture media containing the sample. Each sample was dispensed in triplicate. A positive control, 6 µM benzo-a-pyrene, and a solvent control (1% methanol) were included on each microplate.

For treatment, the media was removed from microplate wells and replaced with culture media containing the sample. The plate was returned to the incubator at 37°C, 5% CO2. The medium containing test compound was replaced after 24 hours and incubation continued for a further 24 hours to give a total exposure period of 48 hours.

For assay the treatment was removed from the microplate wells, cells were washed two times with CC-PBS and replaced with 60 µL of basic media containing 100µM Luciferin-ME (P450-Glo™ CYP1A2 Assay, Promega, Australia). Following incubation for three hours at 37ºC, 5% CO2, 50µL was transferred to a 96-well opaque plate (Optiplate, PerkinElmer, Australia). Then 50µL of detection reagent was added as supplied by Promega, mixed and incubated at room temperature for 20 minutes. Luminescence of samples was determined using a Perkin Elmer VICTOR3 (Wellesley, MA) plate reader. Results were expressed as fold induction of CYP1A2 activity over the vehicle control.


Statistical analyses were carried out to determine the level of CYP1A2 induction that was significantly different from the vehicle control. The control data for CYP1A2 levels was collated from all experiments, and the mean and SD determined. The limit of detection (LOD) was defined as 3×SD and the limit of quantification (LOQ) was defined as 10×SD. Samples with a fold induction ≥ 1.68 (LOQ) were considered positive for induction of CYP1A2 activity.

C A L UX

The CALUX battery of assays measures the endocrine activity of a sample. Specifically, the assays are based on U2OS (human osteosarcoma) cells genetically-engineered with a luciferase reporter gene to measure genomic estrogen receptor alpha (ERα), androgen receptor (AR), glucocorticoid receptor (GR), progesterone receptor (PR) and thyroid receptor beta (TRβ)-mediated activity. Upon exposure to endocrine-active compounds, the genetically-modified cells produce the enzyme luciferase, which can then be quantified by adding the substrate luciferin and measuring light production.

The assays were run as described in van der Linden et al. (2008). In brief, CALUX cells are grown in growth medium (DMEM/F12 medium with phenol red supplemented with 8% fetal bovine serum (FBS) and 100 μM non-essential amino acids). The cells are seeded at 8000–12 000 cells/well (depending on the specific assay) in 100 μL assay medium (DMEM/F12 medium without phenol red supplemented with 5% stripped fetal bovine serum (CD-FBS) and 100 μM non-essential amino acids) in 96-well plates and incubated for 24 hours at 37ºC 5% CO2 (the ERα-CALUX cells are incubated a further 24h, with a change of medium after 24h). The medium is then replaced with 150 μL of fresh assay medium and 50 μL of assay medium containing the model compound or water extract to be tested (final methanol concentration in the assay plate ≤0.1%). After 24 hours of incubation at 37ºC 5% CO2, the medium is removed and replaced by 30 μL of lysis reagent (25 mM Tris, 2 mM dithiothreitol, 2 mM CDTA, 10% glycerol and 1% triton X-100 in deionised water). After a short incubation of 15 minutes at room temperature, luciferase activity is read in a plate luminescence reader (BMG FluoSTAR Omega) immediately after injection of the glowmix (prepared as detailed in van der Linden et al. 2008).

Average and standard deviation of the replicates were calculated. A sample was considered biologically-active if it increased luminescence more than 10 standard deviations above the blank baseline in agonist mode or decreased luminescence by more than 20 per cent of the maximal decrease (IC20) in antagonist mode. The activity in a biologically-active sample was calculated by interpolation from the standard curve using a response between the quantification level (10×SD above baseline in agonist mode and IC20 in antagonist mode) and the EC50, with a coefficient of variability of <15 per cent. The standard compound was dependent on the CALUX assay: 17β-estradiol and tamoxifen in the agonist and antagonist modes of the ERα-CALUX, respectively; dihydrotestosterone and flutamide in the agonist and antagonist modes of the AR-CALUX, respectively; dexamethasone in the GR-CALUX; Org2058 in the PR-CALUX; and T3 in the TRβ-CALUX.

For QAQC, a six-point standard curve was run on every plate to ensure standard response for each run. The EC50 was compared to control charts and the entire plate rejected if it was more than three standard deviations away from the historical average. The data was also rejected if the fold-induction between blank and maximal dose was < 6 (< 3 for the ERα-CALUX).

T HP 1 c ytokine production as s ay

The THP1-CPA (cytokine production assay) provides a measure of immunotoxicity. Like most cells of the immune system, THP1 (human acute monocytic leukemia) cells produce small molecules called ‘cytokines’ to effectively coordinate the immune response against the presence of foreign microorganisms or compounds. There are many different cytokines with different functions. For this assay, we monitored interleukin 1β (IL1β), a cytokine mainly produced by macrophages after stimulation by microorganisms, immune complexes or particular compounds. IL1β has multiple effects in an acute immune response, including stimulation of other immune cells, increasing the temperature


set-point in the hypothalamus (fever) and stimulating blood cell formation (WHO 1996). The assay was run in two modes: agonist and antagonist mode. In agonist mode, the production of IL1β by THP1 cells after 24 hours’ exposure to the sample was measured. In antagonist mode, the inhibition of the normal production of IL1β by THP1 cells exposed to E. coli lipopolysaccharide (LPS) and the sample was measured.

The methods were adapted from Baqui et al. (1998). In brief, THP1 cells were cultured in growth medium (DMEM/F12 medium with phenol red supplemented with 8% fetal bovine serum (FBS) and 100 μM non-essential amino acids). For the assay, cells were seeded at 200 000 cells/well in 200 μL of growth media (with 1 μg/mL LPS in antagonist mode) and 50 μL of assay medium containing the model compound or water extract to be tested (final methanol concentration in the assay plate ≤0.1%). After 24 hours incubation at 37ºC 5% CO2, cells were transferred to a V-bottom 96-well plate, centrifuged at 300×g for five minutes, and the supernatant was transferred to a fresh 96-well plate. IL1β concentration in the supernatant was assayed by commercially-available ELISA, following the supplier’s instructions (Human IL1β quantikine ELISA, RnD Systems).

Average and standard deviation of the replicates were calculated. A sample was considered biologically-active if it increased IL1β concentration by more than 10 standard deviations above the blank baseline in agonist mode and decreased IL1β concentration by more than 25 per cent below the antagonist blank baseline (1 μg/mL LPS). The activity in a biologically-active sample was calculated by interpolation from the standard curve using a response between the quantification level (10×SD above baseline in agonist mode and IC25 in antagonist mode) and the EC50. The standard compound was dependent on the assay mode, with PMA (phorbol-12-myristate-13-acetate) and dexamethasone for the agonist and antagonist modes, respectively.

For QAQC, a six-point standard curve (PMA in agonist mode, dexamethasone in antagonist mode) was run on every plate to ensure a standard response for each run. The EC50 was compared to control charts and the entire plate rejected if it was more than three standard deviations away from the historical average.

A C hE as s ay

Acetylcholine esterase (AChE) activity was measured using a colorimetric assay obtained from Abraxis LLC (US). AChE is an enzyme involved in the proper functioning of the neurotransmitter acetylcholine at neuromuscular junctions. Interference with AChE is one measure of neurotoxicity. A number of organophosphate and carbamate pesticides inhibit AChE activity. The assay will only detect neurotoxins that act via this mechanism.

The assay is based on a modification of the Ellman method (Ellman et al. 1961). In this assay AChE is incubated with the sample and the compound acetylthiocholine (ATC) which reacts with 5,5’-Dithio-bis(2-Nitrobenzoic Acid) (DTNB) to produce a yellow colour read at 405 nm. AChE inhibitors such as organophosphate or carbamate pesticides inhibit the enzyme AChE, causing a reduction in colour development.

Samples were diluted 1/100 in 50% MeOH (aqueous). Samples (25µl) were processed in duplicate following instructions of the Abraxis OP/Carbamate kit (PN550055). A positive control, 5µg/L Diazinon in 50% MeOH, (provided with the kit) was run in duplicate in each assay. A negative control (no treatment, 50% MeOH) was also included. The absorbance of DTNB in each of the reactions was determined at 405 nm on a using a Perkin Elmer VICTOR3 (Wellesley, MA) plate reader. Results were expressed as % inhibition of control absorbance within each assay. All samples were assayed in ≥2 independent experiments.

Statistical analyses were carried out to determine which of the samples significantly inhibited AChE activity. Control data (expressed as % inhibition) was collated, and the mean and SD determined. The limit of detection (LOD) was defined as 3×SD and the limit of quantification (LOQ) was defined as


10×SD. Samples with % inhibition greater than the LOQ (≥27% inhibition) were considered positive in the AChE assay.

5.4 E ffec ts fingerprint All model compounds were tested in the bioassay battery (Table 20). The activity of each compound is expressed as the log value of its potency relative to the standard for that assay: methotrexate for cytotoxicity (Caco2NRU, WIL2NS cytotox and HepaTOX), methyl methanesulfonate (MMS) for the genotoxicity in the WIL2NS FCMN, 4-nitro-o-phenyldiamine (4-NPD) for genotoxicity in the Ames TA98-S9, sodium azide for genotoxicity in the Ames TA100-S9, 2-aminofluorene for genotoxicity in the Ames TA98+S9 and Ames TA100+S9, benzo-a-pyrene for cytochrome P450 induction in the HepCYP1A2, dihydrotestosterone for androgenic activity in the AR-CALUX, flutamide for anti-androgenic activity in the AR-CALUX, 17β-estradiol for estrogenic activity in the ERα-CALUX, tamoxifen for anti-estrogenic activity in the ERα-CALUX, dexamethasone for glucocorticoid activity in the GR-CALUX, Org 2058 (16α-ethyl-21-hydroxyl-19-norpregn-4-ene-3,20-dione) for progresterone activity in the PR-CALUX, triiodothyronine (T3) for thyroid activity in the TRβ-CALUX, phorbol-12-myristate-13-acetate (PMA) for cytokine induction in the THP1 assay, dexamethasone for inhibition of cytokine induction in the THP1 assay, and chlorpyrifos for acetylcholinesterase inhibition in the AChE assay.

To calculate relative potency, the concentration of the standard to achieve a specific response (usually EC50, but sometimes EC20-50, depending on the assay) was divided by the concentration of the model compound required to achieve the same response in the assay. The value was then transformed to log10, and is reported in the table below. For example, 3.8 pM of 17β-estradiol had the same effect as 270 pM of estrone in the ERα-CALUX assay, hence the relative potency of estrone in that assay is RP = 3.8 / 270 = 0.014, or logRP = log(0.014) = -1.85.

If the compound did not result in a response at the highest concentration tested, then the compound is identified as ‘ND’ (for not detected), followed by ‘<’ the log RP that the chemical would have had if it had produced a response at the highest dose tested. This acknowledges that the compound may produce a response for that particular endpoint if it were ever tested at higher concentrations.


Table 20: Relative potency (expressed as log RP) of model compounds – 1 of 2 – in the basal and reactive toxicity assays

Chemical name Cytotoxicity Genotoxicity Mutagenicity

Caco2-NRU WIL2NS TOX HepaTOX WIL2NS FCMN Ames TA98 –S9 Ames TA98 +S9 Ames TA100 –

S9

Ames TA100 +S9

Methotrexate Methotrexate Methotrexate MMS 4-NPD 2-Aminofluorene Sodium azide 2-Aminofluorene

17β-Estradiol (βE2) -1.09 -1.39 -1.21 ND (<1.56) ND (<-1.29) ND (<-1.22) ND (<-2.38) ND (<-1.04)

Estrone (E1) ND (<-0.53) ND (<-0.87) ND (<-0.53) ND (<1.30) ND (<-0.60) ND (<-0.52) ND (<-1.68) ND (<-0.34)

17α-Estradiol (αE2) -1.13 -1.25 -1.05 ND (<1.56) ND (<-1.29) ND (<-1.22) ND (<-2.38) ND (<-1.04)

Estriol (E3) ND (<-0.90) -1.19 ND (<-0.90) ND (<0.93) ND (<-0.97) ND (<-0.89) ND (<-2.05) ND (<-0.71)

17α-Ethynylestradiol (EE2) -1.04 -1.20 -0.92 1.62 ND (<-1.26) ND (<-1.18) ND (<-2.34) ND (<-1.00)

Mestranol ND (<-0.86) ND (<-1.20) ND (<-0.87) ND (<0.96) ND (<-0.94) ND (<-0.86) ND (<-2.02) ND (<-0.68)

Testosterone ND (<-1.20) ND (<-1.54) ND (<-1.20) 0.65 ND (<-1.27) ND (<-1.19) ND (<-2.35) ND (<-1.01)

5α-Dihydrotestosterone (DHT) ND (<-1.15) ND (<-1.53) -1.19 0.65 ND (<-1.27) ND (<-1.19) ND (<-2.35) ND (<-1.01)

17β-Trenbolone ND (<-1.23) ND (<-1.56) ND (<-1.23) 0.62 ND (<-1.30) ND (<-1.22) ND (<-2.38) ND (<-1.04)

Levonorgestrel ND (<-1.16) ND (<-1.50) -1.15 0.69 ND (<-1.23) ND (<-1.16) ND (<-2.32) ND (<-0.98)

Bisphenol A (BPA) -1.21 -1.61 -1.28 ND (<0.53) ND (<-1.37) ND (<-1.29) ND (<-2.45) ND (<-1.11)

4-Nonylphenol (4NP) -1.10 -1.53 ND (<-1.32) ND (<0.99) ND (<-1.39) ND (<-1.31) ND (<-2.47) ND (<-1.13)

4-t-Octylphenol (4tOP) -1.09 -1.14 ND (<-1.35) ND (<1.44) ND (<-1.41) ND (<-1.34) ND (<-2.50) ND (<-1.16)

Atenolol ND (<-1.19) ND (<-1.57) ND (<-1.24) ND (<0.60) ND (<-1.30) ND (<-1.23) ND (<-2.39) ND (<-1.05)

Caffeine ND (<-1.33) ND (<-1.71) ND (<-1.37) ND (<0.46) ND (<-1.44) ND (<-1.37) ND (<-2.52) ND (<-1.18)

Carbamazepine ND (<-1.24) ND (<-1.62) ND (<-1.29) ND (<0.54) ND (<-1.36) ND (<-1.28) ND (<-2.44) ND (<-1.10)

DEET ND (<-2.07) ND (<-2.41) ND (<-2.08) ND (<-0.25) ND (<-2.15) ND (<-2.07) ND (<-3.23) ND (<-1.89)

Diazepam ND (<-1.50) ND (<-1.84) ND (<-1.51) ND (<0.32) ND (<-1.58) ND (<-1.50) ND (<-2.66) ND (<-1.32)

Diclofenac ND (<-1.15) ND (<-1.49) ND (<-1.16) ND (<0.67) ND (<-1.23) ND (<-1.15) ND (<-2.31) ND (<-0.97)

Gemfibrozil ND (<-1.22) ND (<-1.60) ND (<-1.26) ND (<0.57) ND (<-1.33) ND (<-1.25) ND (<-2.41) ND (<-1.07)

Indomethacin ND (<-1.07) ND (<-1.44) ND (<-1.11) ND (<0.72) ND (<-1.17) ND (<-1.10) ND (<-2.26) ND (<-0.92)

Methotrexate 0.00 0.00 0.00 3.43 ND (<-0.07) ND (<0.00) ND (<-1.16) ND (<0.19)

Paracetamol ND (<-1.44) ND (<-1.82) ND (<-1.48) ND (<0.35) ND (<-1.55) ND (<-1.47) ND (<-2.63) ND (<-1.29)

Salicylic acid ND (<-1.48) ND (<-1.86) ND (<-1.52) ND (<0.31) ND (<-1.59) ND (<-1.51) ND (<-2.67) ND (<-1.33)

Sulfamethoxazole ND (<-1.25) ND (<-1.59) ND (<-1.26) ND (<0.57) ND (<-1.33) ND (<-1.25) ND (<-2.41) ND (<-1.07)

Triclosan -0.96 -0.90 -0.83 ND (<1.59) ND (<-0.79) ND (<-0.71) ND (<-1.87) ND (<-0.53)


Chemical name Cytotoxicity Genotoxicity Mutagenicity


S9

Ames TA100 +S9

Methotrexate Methotrexate Methotrexate MMS 4-NPD 2-Aminofluorene Sodium azide 2-Aminofluorene

Atrazine ND (<-1.33) ND (<-1.66) ND (<-1.33) ND (<0.50) ND (<-1.40) ND (<-1.32) ND (<-2.48) ND (<-1.14)

Chlorpyrifos ND (<-1.38) ND (<-1.75) ND (<-1.42) ND (<0.41) ND (<-1.48) ND (<-1.41) ND (<-2.57) ND (<-1.23)

Diazinon -1.79 -2.07 -1.71 ND (<0.43) ND (<-1.94) ND (<-1.87) ND (<-3.03) ND (<-1.69)

Diuron ND (<-1.29) ND (<-1.63) -1.26 ND (<0.54) ND (<-1.36) ND (<-1.29) ND (<-2.45) ND (<-1.10)

Pentachlorophenol -1.10 ND (<-1.57) ND (<-1.24) ND (<0.60) ND (<-1.30) ND (<-1.23) ND (<-2.39) ND (<-1.05)

Simazine ND (<-1.05) ND (<-1.39) ND (<-1.06) ND (<0.78) ND (<-1.12) ND (<-1.05) ND (<-2.21) ND (<-0.87)

Trifluralin ND (<-0.73) ND (<-1.07) ND (<-0.74) ND (<1.09) ND (<-0.80) ND (<-0.73) ND (<-1.89) ND (<-0.55)

Bromochloroacetic acid ND (<-2.08) -2.40 ND (<-2.12) -0.26 ND (<-2.19) ND (<-2.11) -3.05 ND (<-1.93)

Bromodichloromethane -4.20 -4.05 ND (<-3.74) ND (<-1.91) ND (<-3.81) ND (<-3.74) ND (<-4.90) ND (<-3.55)

Bromoform ND (<-4.68) -4.00 -3.12 -1.88 ND (<-3.79) ND (<-3.71) ND (<-4.87) ND (<-3.53)

Chloroform ND (<-4.75) ND (<-4.09) -3.86 ND (<-1.92) ND (<-3.82) ND (<-3.75) ND (<-4.91) ND (<-3.57)

Dibromochloromethane -4.68 -3.82 -3.12 ND (<-1.61) ND (<-3.82) ND (<-3.74) ND (<-4.90) ND (<-3.56)

NDMA ND (<-2.49) ND (<-2.83) ND (<-2.49) ND (<-0.66) ND (<-2.56) ND (<-2.48) ND (<-3.64) ND (<-2.30) ‘4-NPD’ = 4-Nitro-o-phenyldiamine; ‘MMS’ = Methyl methanesulfonate; ‘S9’ = liver homogenate containing phase I and phase II enzymes (‘S9 fraction’)


Table 21. Relative potency (expressed as log RP) of model compounds - 2 of 2 - in the specific toxicity assays.

Chemical name Liver

toxicity

Endocrine effects Immunotoxicity Neurotox.

HepCYP1

A2

CALUX THP1 + THP1 - AChE

AR + AR - ERα + ERα - GR PR TRβ

BaP DHT Flutamide βE2 Tamoxifen Dexa Org 2058 T3 PMA Dexa Chlorpy

17β-Estradiol (βE2) ND (<-1.39)

-4.40 1.86 0.00 Agonist ND (<-5.03)

ND (<-5.56)

ND (<-4.32)

ND (<-3.05)

ND (<-3.61)

ND (<-2.65)

Estrone (E1) ND (<-0.69)

ND (<-4.56)

1.17 -1.85 Agonist ND (<-4.34)

ND (<-4.74)

ND (<-3.63)

ND (<-2.39)

-2.83 ND (<-2.65)

17α-Estradiol (αE2) ND (<-1.39)

ND (<-5.26)

1.14 -2.69 Agonist ND (<-5.03)

ND (<-5.44)

ND (<-4.32)

ND (<-3.09)

ND (<-3.61)

ND (<-2.65)

Estriol (E3) ND (<-1.06)

ND (<-5.21)

-0.26 -1.77 Agonist ND (<-4.71)

ND (<-5.11)

ND (<-4.00)

ND (<-2.76)

ND (<-3.28)

ND (<-2.62)

17α-Ethynylestradiol (EE2) ND (<-1.35)

ND (<-5.22)

1.96 0.73 Agonist ND (<-5.00)

-4.75 ND (<-4.29)

ND (<-3.05)

ND (<-3.57)

ND (<-2.61)

Mestranol ND (<-1.03)

ND (<-4.81)

0.30 -3.21 Agonist ND (<-4.67)

ND (<-5.08)

ND (<-3.97)

ND (<-2.73)

ND (<-3.25)

ND (<-2.59)

Testosterone ND (<-1.36)

-0.77 Agonist -5.78 Agonist ND (<-5.01)

ND (<-5.15)

ND (<-4.30)

ND (<-3.06)

ND (<-3.58)

ND (<-2.62)

5α-Dihydrotestosterone (DHT)

ND (<-1.36)

0.00 Agonist -4.81 Agonist ND (<-5.00)

-5.28 ND (<-4.31)

ND (<-3.06)

ND (<-3.58)

ND (<-2.62)

17β-Trenbolone ND (<-1.39)

-0.30 Agonist -4.26 Agonist ND (<-5.04)

-2.59 ND (<-4.33)

ND (<-3.09)

-3.51 ND (<-2.65)

Levonorgestrel ND (<-1.33)

-0.56 Agonist -5.54 Agonist -4.79 -0.36 ND (<-4.26)

ND (<-3.03)

ND (<-3.55)

ND (<-2.59)

Bisphenol A (BPA) ND (<-1.47)

ND (<-5.34)

-0.67 -4.84 Agonist ND (<-5.11)

ND (<-5.51)

ND (<-4.40)

ND (<-3.17)

ND (<-3.69)

ND (<-2.72)

4-Nonylphenol (4NP) ND (<-1.48)

ND (<-5.35)

-0.53 -4.04 Agonist ND (<-5.12)

ND (<-5.53)

ND (<-4.42)

ND (<-3.18)

ND (<-3.70)

ND (<-2.74)

4-t-Octylphenol (4tOP) ND (<-1.51)

ND (<-5.38)

-0.39 -4.91 Agonist ND (<-5.15)

ND (<-5.12)

ND (<-4.45)

ND (<-3.21)

ND (<-3.73)

ND (<-2.77)

Atenolol ND (<-1.40)

ND (<-5.27)

-1.03 ND (<-7.16)

ND (<-2.06)

ND (<-4.47)

ND (<-5.47)

ND (<-4.35)

ND (<-3.10)

ND (<-3.62)

ND (<-2.66)

Caffeine ND (<-1.54)

ND (<-5.68)

ND (<-1.04)

ND (<-7.29)

ND (<-2.19)

ND (<-5.18)

ND (<-5.58)

ND (<-4.49)

ND (<-3.24)

ND (<-3.76)

ND (<-2.79)


Chemical name Liver

toxicity


HepCYP1

A2




Carbamazepine ND (<-1.45)

ND (<-5.32)

ND (<-0.95)

ND (<-7.21)

ND (<-2.17)

ND (<-5.09)

ND (<-5.50)

ND (<-4.39)

ND (<-3.15)

ND (<-3.67)

ND (<-2.71)

DEET ND (<-2.24)

ND (<-5.06)

ND (<-1.64)

ND (<-8.00)

ND (<-2.90)

ND (<-5.89)

ND (<-6.29)

ND (<-5.18)

ND (<-3.94)

ND (<-4.54)

ND (<-2.80)

Diazepam ND (<-1.67)

ND (<-5.54)

-0.74 -6.95 Agonist ND (<-5.31)

ND (<-6.70)

ND (<-4.62)

ND (<-3.37)

ND (<-3.89)

ND (<-5.63)

Diclofenac ND (<-1.32)

ND (<-5.19)

ND (<-0.82)

ND (<-7.08)

ND (<-2.34)

ND (<-4.97)

ND (<-5.37)

ND (<-4.26)

ND (<-3.02)

ND (<-3.54)

ND (<-2.58)

Gemfibrozil ND (<-1.42)

ND (<-5.81)

ND (<-0.93)

ND (<-7.18)

ND (<-2.08)

ND (<-5.07)

ND (<-5.47)

ND (<-4.36)

ND (<-3.13)

ND (<-3.65)

ND (<-2.68)

Indomethacin ND (<-1.27)

ND (<-5.42)

ND (<-0.77)

ND (<-7.03)

ND (<-1.93)

ND (<-4.91)

ND (<-5.32)

ND (<-4.21)

ND (<-2.97)

ND (<-3.49)

ND (<-2.53)

Methotrexate ND (<-0.17)

ND (<-4.31)

ND (<0.33)

-5.33 Agonist ND (<-3.81)

ND (<-4.21)

ND (<-3.10)

ND (<-1.87)

ND (<-2.39)

ND (<-2.42)

Paracetamol ND (<-1.64)

ND (<-5.79)

ND (<-1.15)

-4.51 Agonist ND (<-5.29)

ND (<-5.69)

ND (<-4.58)

ND (<-3.34)

ND (<-3.87)

ND (<-2.90)

Salicylic acid ND (<-1.68)

ND (<-5.83)

ND (<-1.18)

ND (<-7.44)

ND (<-2.34)

ND (<-5.33)

ND (<-5.73)

ND (<-4.62)

ND (<-3.38)

ND (<-3.90)

ND (<-2.94)

Sulfamethoxazole ND (<-1.42)

ND (<-5.57)

ND (<-0.56)

-7.00 Agonist ND (<-5.06)

ND (<-5.47)

ND (<-4.36)

ND (<-3.12)

ND (<-3.64)

ND (<-2.68)

Triclosan ND (<-1.36)

ND (<-5.23)

-0.11 -6.23 Agonist ND (<-5.01)

ND (<-4.51)

ND (<-4.30)

ND (<-3.06)

ND (<-3.58)

ND (<-2.62)

Atrazine ND (<-1.49)

ND (<-5.36)

ND (<-0.63)

-6.36 Agonist ND (<-5.13)

ND (<-5.88)

ND (<-4.43)

ND (<-3.19)

ND (<-3.71)

ND (<-2.75)

Chlorpyrifos ND (<-1.58)

ND (<-5.32)

-0.70 -5.31 Agonist ND (<-4.65)

ND (<-6.61)

ND (<-4.53)

ND (<-3.28)

ND (<-3.80) 0.00

Diazinon ND (<-2.04)

ND (<-6.19)

-1.53 -7.29 Agonist ND (<-5.11)

ND (<-6.09)

ND (<-4.99)

ND (<-3.74)

ND (<-4.26) -0.26

Diuron ND (<-1.46)

ND (<-5.33)

ND (<-1.43)

-6.49 ND (<-2.11)

ND (<-5.10)

ND (<-5.50)

ND (<-4.39)

ND (<-3.16)

ND (<-3.75)

ND (<-2.71)

Pentachlorophenol [ 1.37 ]*

ND (<-5.27)

ND (<-0.54)

-6.88 Agonist ND (<-5.04)

ND (<-5.45)

ND (<-4.33)

ND (<-3.10)

ND (<-3.62)

ND (<-2.66)

Simazine ND (<- ND (<- ND (<- -6.61 Agonist ND (<- ND (<- ND (<- ND (<- ND (<- ND (<-


Chemical name Liver

toxicity


HepCYP1

A2




1.22) 5.36) 0.36) 4.86) 5.27) 4.15) 2.92) 3.44) 2.78)

Trifluralin ND (<-0.90)

ND (<-5.05)

ND (<-0.04)

-6.12 Agonist ND (<-4.54)

ND (<-4.95)

ND (<-3.84)

ND (<-2.60)

ND (<-3.12)

ND (<-2.56)

Bromochloroacetic acid ND (<-2.28)

ND (<-6.15)

ND (<-1.43)

ND (<-8.04)

ND (<-2.94)

ND (<-5.93)

ND (<-6.33)

ND (<-5.23)

ND (<-3.98)

ND (<-4.50)

ND (<-2.84)

Bromodichloromethane ND (<-3.91)

ND (<-8.78)

ND (<-4.41)

ND (<-10.7)

ND (<-5.76)

ND (<-8.55)

ND (<-9.93)

ND (<-7.86)

ND (<-6.61)

-7.01 ND (<-6.16)

Bromoform ND (<-3.88)

ND (<-9.03)

-3.69 -10.1 ND (<-5.05)

ND (<-8.52)

ND (<-8.93)

ND (<-7.82)

ND (<-6.58)

-6.33 ND (<-6.14)

Chloroform ND (<-4.92)

ND (<-8.79)

ND (<-4.42)

-9.98 Agonist ND (<-8.56)

ND (<-8.97)

ND (<-7.85)

ND (<-6.62)

ND (<-7.14)

ND (<-6.17)

Dibromochloromethane ND (<-3.91)

ND (<-9.06)

ND (<-4.41)

ND (<-10.7)

-5.47 ND (<-8.55)

ND (<-8.96)

ND (<-7.85)

ND (<-6.61)

-6.48 ND (<-6.17)

NDMA ND (<-2.65)

ND (<-6.52)

ND (<-2.15)

ND (<-8.41)

ND (<-3.31)

ND (<-6.30)

ND (<-7.68)

ND (<-5.60)

ND (<-4.35)

ND (<-4.87)

ND (<-3.21)

‘BaP’ = Benzo-a-pyrene; ‘DHT’ = 5α-Dihydrotestosterone; ‘βE2’ = 17β-Estradiol; ‘Dexa’ = Dexamethasone; ‘Org 2058’ = 16α-Ethyl-21-hydroxy-19-norpregn-4-ene-3,20-dione; ‘T3’ = Triiodothyronine; ‘PMA’ = Phorbol-12-myristate-13-acetate; ‘Chlorpy’ = Chlorpyrifos; [ ]* = extrapolated, low reliability .


5.5 R es ults

5.5.1 Bioassays

Table 22 below summarises the biological activity from the full bioassay battery tested for all water samples. For non-specific and reactive endpoints (i.e. cytoxocity and genotoxicity), the results are expressed as ‘relative toxic unit’ (rTU) and ‘relative genotoxic unit’ (rGTU), respectively. A value above one indicates that the sample would have been toxic (or genotoxic) as sampled; a value below one means that the sample had to be concentrated to produce a toxic response. The lower the value, the more concentration is required to produce the effect. Extreme enrichment of water samples can result in low-level cytotoxicity, so a cytotoxic response close to the detection limit (0.05 rTU in the assays used in this project) may not necessarily indicate real toxicity but rather is an artefact of the concentration step.

For specific endpoints, the biological activity was expressed as ‘toxic equivalent’. This is the equivalent concentration of the standard compound that would have had to be in the original water sample to cause that specific response in the bioassay. Each bioassay has a different standard: benzo-a-pyrene (BaP Eq, in μg/L) for cytochrome P4501A2 induction in the HepCYP1A2 assay, dihydrotestosterone (DHT Eq, in ng/L) for androgenic activity in the AR-CALUX, flutamide (Flu Eq, in μg/L) for anti-androgenic activity in the AR-CALUX, 17β-estradiol (βE2 Eq, in ng/L) for estrogenic activity in the ERα-CALUX, tamoxifen (TMX Eq, in μg/L) for anti-estrogenic activity in the ERα-CALUX, dexamethasone (Dexa Eq, in ng/L) for glucocorticoid activity in the GR-CALUX, 16α-ethyl-21-hydroxyl-19-norpregn-4-ene-3,20-dione (Org 2058 Eq, in ng/L) for progesterone activity in the PR-CALUX, triiodothyronine (T3 Eq, in ng/L) for thyroid activity in the TRβ-CALUX, phorbol-12-myristate-13-acetate (PMA Eq, in μg/L) for cytokine induction in the THP1 assay, dexamethasone (Dexa Eq, μg/L) for inhibition of cytokine induction in the THP1 assay, and chlorpyrifos (Chlorpy Eq, in ng/L) for acetylcholinesterase inhibition in the AChE assay.

For data analysis, samples below detection limit were assigned a value of half the detection limit, a method adopted by the USEPA for dealing with censored data (Singh & Nocerino 1994; USEPA 2000b).


Table 22: Summary of bioassay results 1 of 2 – basal and reactive toxicity

Site Cytotoxicity Genotoxicity Mutagenicity


S9

Ames TA100

+S9

rTU rTU rTU rGTU rGTU rGTU rGTU rGTU

Treated sewage

WRP 1 ND (<0.5) 0.08 ± 0.01 (<0.05 – 0.11) <0.05 0.15 ± 0.02

(0.09 – 0.24) ND (<0.05) ND (<0.05) ND (<0.05) ND (<0.05)

WRP 2 ND (<0.5) <0.05 ND (<0.05) <0.05 ND (<0.05) ND (<0.05) ND (<0.05) ND (<0.05)

WRP 3 ND (<0.5) 0.11 ± 0.04 (0.07 – 0.17) ND (<0.05) 0.12 ± 0.02

(0.11 – 0.15) <0.09 ND (<0.05) ND (<0.05) ND (<0.05)

WRP 4 ND (<0.5) ND (<0.05) ND (<0.05) ND (<0.05) ND (<0.05) ND (<0.05) ND (<0.05) ND (<0.05)

WRP 5 ND (<0.5) 0.08 ± 0.04 (<0.05 – 0.31) ND (<0.05) 0.05 ± 0.01

(<0.05 – 0.09) ND (<0.05) ND (<0.05) ND (<0.05) ND (<0.05)


WRP 7 ND (<0.5) 0.05 ± 0.01 (<0.05 – 0.07) ND (<0.05) <0.05 ND (<0.05) ND (<0.05) ND (<0.05) ND (<0.05)


WRP 9 ND (<0.5) 0.10 ± 0.01 (0.08 – 0.18)

0.05 ± 0.01 (<0.05 – 0.07)

0.19 ± 0.03 (<0.15 – 0.24) ND (<0.05) ND (<0.05) ND (<0.05) ND (<0.05)

Class A recycled water

WRP 1 ND (<0.5) <0.15 <0.05 0.20 ± 0.04 (<0.15 – 0.34)

0.06 ± 0.00 (0.05 – 0.07) ND (<0.05) 0.14 ± 0.02

(0.06 – 0.24) ND (<0.05)

WRP 3 ND (<0.5) 0.08 ± 0.02 (0.06 – 0.12) ND (<0.05) 0.09 ± 0.01

(0.08 – 0.10) <0.05 ND (<0.05) ND (<0.05) ND (<0.05)

WRP 4 ND (<0.5) <0.05 <0.05 ND (<0.05) ND (<0.05) ND (<0.05) ND (<0.05) ND (<0.05)

WRP 9 ND (<0.5) ND (<0.15) <0.05 <0.15 <0.05 ND (<0.05) ND (<0.05) ND (<0.05)

RO recycled water

WRP 2 ND (<0.5) ND (<0.05) <0.05 ND (<0.05) ND (<0.05) ND (<0.05) ND (<0.05) ND (<0.05)

WRP 5 ND (<0.5) <0.05 ND (<0.05) ND (<0.05) ND (<0.05) ND (<0.05) ND (<0.05) ND (<0.05)


WRP 7 ND (<0.5) <0.05 ND (<0.05) ND (<0.05) ND (<0.05) ND (<0.05) ND (<0.05) ND (<0.05)


Site Cytotoxicity Genotoxicity Mutagenicity


S9

Ames TA100

+S9

rTU rTU rTU rGTU rGTU rGTU rGTU rGTU


Other miscellaneous

Bottled water ND (<0.5) 0.06 ND (<0.05) ND (<0.05) ND (<0.05) ND (<0.05) ND (<0.05) ND (<0.05)

Tap water ND (<0.5) <0.05 ND (<0.05) <0.05 ND (<0.05) ND (<0.05) ND (<0.05) ND (<0.05)

Rainwater ND (<0.5) 0.06 ND (<0.05) ND (<0.05) ND (<0.05) ND (<0.05) ND (<0.05) ND (<0.05)

Field blank ND (<0.5) <0.05 <0.05 ND (<0.05) ND (<0.05) ND (<0.05) ND (<0.05) ND (<0.05) Values are average ± SEM (sample size specified in Table 17). ‘rTU’ = relative toxic unit; ‘rGTU’ = relative genotoxic unit


Table 23: Summary of bioassay results 2 of 2 - specific toxicity

Site Liver

toxicity

Endocrine effect Immunotoxicity Neurotoxicity

HepCYP1

A2



BaP Eq

μg/L

DHT Eq

ng/L

Flu Eq

µg/L

βE2 Eq ng/L TMX Eq

µg/L

Dexa Eq

ng/L

Org2058 Eq

ng/L

T3 Eq

ng/L

PMA Eq

µg/L

Dexa Eq

µg/L

Chlorpy Eq

ng/L

Treated sewage

WRP 1 <19 ND (<2) ND (<25) 0.39 ± 0.21 (<0.05 – 1.51)

Agonist ND (<12) ND (<0.004) ND (<25) ND (<0.6) 0.04 ± 0.02 (< 0.02 – 0.09)

78 ± 14 (36 – 119)

WRP 2 43 ± 8.1 (<19 – 58) ND (<2) ND (<25)

0.83 ± 0.45 (<0.05 – 2.58)

Agonist 28 ± 4.8 (<24 – 40)

0.30 ± 0.29 (<0.004 – 1.66) ND (<25) ND (<0.6) 0.36 ± 0.20

(<0.02 – 1.1) 26 ± 4.6 (<15 – 37)

WRP 3 19 ± 7.4 (<19 – 30) ND (<2) ND (<25) 4.22 ± 1.15

(2.40 – 4.73) Agonist 69 ± 10 (52 – 81)

1.41 ± 0.46 (1.00 – 2.16) ND (<25) ND (<0.6)

0.44 ± 0.11 (< 0.51 – 0.65)

35 ± 2.6 (31 – 39)

WRP 4 ND (<19) ND (<2) ND (<25) ND (<0.05) ND (<2) ND (<12) ND (<0.004) ND (<25) ND (<0.6) ND (<0.02) ND (<15)

WRP 5 38 ± 10 (24 – 58) ND (<2) ND (<25) 0.80 ± 0.22

(0.27 – 1.99) Agonist 17 ± 1.9 (<12 – 21)

0.19 ± 0.13 (<0.005 – 0.85) ND (<25) ND (<0.6)

0.05 ± 0.02 (< 0.02 – 0.15)

46 ± 7.6 (27 – 92)

WRP 6 47 ND (<2) ND (<25) 0.08 ND (<2) ND (<12) ND (<0.004) ND (<25) ND (<0.6) ND (<0.02) 102

WRP 7 43 ± 3.5 (38 – 52) ND (<2) ND (<25)

0.10 ± 0.03 (<0.06 – 0.17)

ND (<2) 17 ± 3.2 (14 – 26)

0.011 ± 0.004 (0.006 – 0.020) ND (<25) ND (<0.6)

0.04 ± 0.02 (< 0.04 – 0.10)

53 ± 17 (34 – 96)

WRP 8 20 ± 6.9 (<19 – 31) ND (<2) ND (<25)

0.06 ± 0.03 (<0.05 – 0.14)

ND (<2) 18 ± 3.7 (14 – 26) ND (<0.004) ND (<25) ND (<0.6)

0.45 ± 0.18 (< 0.03 – 0.72)

20 ± 5.0 (<15 – 26)

WRP 9 <19 3.1 ± 2.3 (<2 – 18) <25 0.72 ± 0.21

(0.22 – 1.87) Agonist ND (<12) ND (<0.004) ND (<25) ND (<0.6) ND (<0.02) 21 ± 0.6 (18 – 23)


WRP 1 ND (<19) ND (<2) ND (<25) ND (<0.05) <2 ND (<12) ND (<0.004) ND (<25) ND (<0.6) 0.06 ± 0.04 (<0.02 – 0.34)

71 ± 11 (40 – 103)

WRP 3 37 ± 5.5 ND (<2) ND (<25) 1.90 ± 0.77 Agonist 62 ± 13 0.64 ± 0.38 ND (<25) ND (<0.6) 0.53 ± 0.07 30 ± 1.3


Site Liver

toxicity

Endocrine effect Immunotoxicity Neurotoxicity

HepCYP1

A2



BaP Eq

μg/L

DHT Eq

ng/L

Flu Eq

µg/L

βE2 Eq ng/L TMX Eq

µg/L

Dexa Eq

ng/L

Org2058 Eq

ng/L

T3 Eq

ng/L

PMA Eq

µg/L

Dexa Eq

µg/L

Chlorpy Eq

ng/L

(31 – 45) (1.00 – 3.10) (43 – 81) (0.30 – 1.26) (0.42 – 0.61) (28 – 31)

WRP 4 ND (<19) ND (<2) ND (<25) ND (<0.05) ND (<2) ND (<12) ND (<0.004) ND (<25) ND (<0.6) ND (<0.02) ND (<15)

WRP 9 84 ± 26 (19 – 186) ND (<2) ND (<25)

0.12 ± 0.06 (<0.04 – 0.51)

Agonist ND (<12) ND (<0.004) ND (<25) ND (<0.6) ND (<0.02) 23 ± 0.8 (20 – 26)

RO recycled water

WRP 2 ND (<19) ND (<2) ND (<25) 0.17 ± 0.15 (<0.04 – 0.87)

<2 ND (<12) ND (<0.004) ND (<25) ND (<0.6) ND (<0.02) ND (<15)

WRP 5 ND (<19) ND (<2) ND (<25) ND (<0.05) 2.3 ± 0.9 (<2 – 8) ND (<12) ND (<0.004) ND (<25) ND (<0.6) ND (<0.02) ND (<15)

WRP 6 ND (<19) ND (<2) ND (<25) ND (<0.05) 4.4 ND (<12) ND (<0.004) ND (<25) ND (<0.6) ND (<0.02) ND (<15)

WRP 7 ND (<19) ND (<2) ND (<25) 0.08 ± 0.07 <0.04 – 0.27)

ND (<2) ND (<12) ND (<0.004) ND (<25) ND (<0.6) ND (<0.02) ND (<15)

WRP 8 <19 ND (<2) ND (<25) ND (<0.05) ND (<2) <12 ND (<0.004) ND (<25) ND (<0.6) 0.16 ± 0.17 (<0.03 – 0.61)

<15

Other miscellaneous

Bottled water ND (<19) ND (<2) ND (<25) ND (<0.05) ND (<2) ND (<12) ND (<0.004) ND (<25) ND (<0.6) ND (<0.02) ND (<15)

Tap water ND (<19) ND (<2) ND (<25) ND (<0.05) ND (<2) ND (<12) ND (<0.004) ND (<25) ND (<0.6) ND (<0.02) ND (<15)

Rainwater ND (<19) ND (<2) ND (<25) ND (<0.05) ND (<2) ND (<12) ND (<0.004) ND (<25) ND (<0.6) ND (<0.02) ND (<15)

Field blank ND (<19) ND (<2) ND (<25) <0.05 <2 ND (<12) ND (<0.004) ND (<25) ND (<0.6) ND (<0.02) ND (<15) Values are average ± SEM (sample size specified in Table 17). ‘BaP’ = Benzo-a-pyrene; ‘DHT’ = 5α-Dihydrotestosterone; ‘Flu’ = Flutamide; ‘βE2’ = 17β-Estradiol; ‘TMX’ = Tamoxifen; ‘Dexa’ = Dexamethasone; ‘Org 2058’ = 16α-Ethyl-21-hydroxy-19-norpregn-4-ene-3,20-dione; ‘T3’ = Triiodothyronine; ‘PMA’ = Phorbol-12-myristate-13-acetate; ‘Chlorpy’ = Chlorpyrifos.


5.5.2 Chemical analysis

All except two of the 39 priority compounds were analysed in all samples (indomethacin and bromochloroacetic acid were not analysed due to analytical problems). Twelve compounds were not detected in any of the samples above the limit of quantification, including the hormones 17β-estradiol (<1 ng/L), 17α-estradiol (<1 ng/L), estriol (<5 ng/L), testosterone (<5 ng/L), and 5α-dihydrotestosterone (<5 ng/L); the pharmaceuticals mestranol (<5 ng/L), levonorgestrel (<5 ng/L), 17β-trenbolone (<100 ng/L) and methotrexate (<5 ng/L); the industrial compound 4-nonylphenol (<10 ng/L); and the pesticides pentacholorphenol (<5 ng/L) and trifluralin (<5 ng/L). The other compounds were detected at various concentrations in the water samples (Table 24 to Table 26).

As before, samples ‘below detection limit’ were assigned a value of half the detection limit for data analysis.


Table 24: Summary of chemical analysis results – 1 of 5 – Hormones, pharmaceuticals, industrial compounds

Site βE2 Estrone αE2 Estriol EE2 Mestr Testost DHT Trenbo Levonor BPA 4NP 4tOP

(ng/L) (ng/L) (ng/L) (ng/L) (ng/L) (ng/L) (ng/L) (ng/L) (ng/L) (ng/L) (ng/L) (ng/L) (ng/L)

Treated sewage

WRP 1 ND (<1)

1.2 ± 0.3 (<1 – 2) ND (<1) ND (<5) ND (<1) ND (<5) ND (<5) ND (<5) ND

(<100) ND (<5) ND (<70) ND (<10) ND (<10)

WRP 2 ND (<1)

4.7 ± 2.1 (<1 – 14) ND (<1) ND (<5) ND (<1) ND (<5) ND (<5) ND (<5) ND

(<100) ND (<5) ND (<70) ND (<10)

10 ± 2.0 (<10 – 15)

WRP 3 ND (<1)

20 ± 7.7 (11 – 32) ND (<1) ND (<5) ND (<1) ND (<5) ND (<5) ND (<5) ND

(<100) ND (<5) ND (<70) ND (<10)

26 ± 8.1 (14 – 37)

WRP 4 ND (<1) ND (<1) ND (<1) ND (<5) ND (<1) ND (<5) ND (<5) ND (<5) ND

(<100) ND (<5) ND (<70) ND (<10) ND (<10)

WRP 5 ND (<1)

4.0 ± 0.4 (2 – 6) ND (<1) ND (<5) ND (<1) ND (<5) ND (<5) ND (<5) ND

(<100) ND (<5) <70 ND (<10)

12 ± 2.1 (<10 – 22)

WRP 6 ND (<1) 2 ND (<1) ND (<5) ND (<1) ND (<5) ND (<5) ND (<5) ND

(<100) ND (<5) ND (<70) ND (<10) ND (<10)

WRP 7 ND (<1) <1 ND (<1) ND (<5) ND (<1) ND (<5) ND (<5) ND (<5) ND

(<100) ND (<5) 248 ± 82 (137 – 450)

ND (<10) ND (<10)

WRP 8 ND (<1) <1 ND (<1) ND (<5) ND (<1) ND (<5) ND (<5) ND (<5) ND

(<100) ND (<5) <70 ND (<10) ND (<10)

WRP 9 ND (<1)

8.0 ± 1.1 (5 – 14) ND (<1) ND (<5) ND (<1) ND (<5) ND (<5) ND (<5) ND

(<100) ND (<5) <70 ND (<10)

80 ± 16 (28 – 132)


WRP 1 ND (<1) ND (<1) ND (<1) ND (<5) <1 ND (<5) ND (<5) ND (<5) ND

(<100) ND (<5) ND (<70) ND (<10) ND (<10)

WRP 3 ND (<1)

19 ± 3.6 (15-25) ND (<1) ND (<5) ND (<1) ND (<5) ND (<5) ND (<5) ND

(<100) ND (<5) ND (<70) ND (<10)

17 ± 1.1 (15 – 18)


(<100) ND (<5) ND (<70) ND (<10) ND (<10)

WRP 9 ND (<1)

1.0 ± 0.3 (<1 – 2) ND (<1) ND (<5) ND (<1) ND (<5) ND (<5) ND (<5) ND

(<100) ND (<5) ND (<70) ND (<10)

39 ± 13 (<10 – 100)

RO recycled water


(<100) ND (<5) ND (<70) ND (<10) ND (<10)


Site βE2 Estrone αE2 Estriol EE2 Mestr Testost DHT Trenbo Levonor BPA 4NP 4tOP

(ng/L) (ng/L) (ng/L) (ng/L) (ng/L) (ng/L) (ng/L) (ng/L) (ng/L) (ng/L) (ng/L) (ng/L) (ng/L)


(<100) ND (<5) <70 ND (<10) ND (<10)


(<100) ND (<5) ND (<70) ND (<10) ND (<10)


(<100) ND (<5) ND (<70) ND (<10) ND (<10)


(<100) ND (<5) ND (<70) ND (<10) ND (<10)

Other miscellaneous

Bottled water ND (<1) ND (<1) ND (<1) ND (<5) ND (<1) ND (<5) ND (<5) ND (<5) ND

(<100) ND (<5) ND (<70) ND (<10) ND (<10)

Tap water ND (<1) ND (<1) ND (<1) ND (<5) ND (<1) ND (<5) ND (<5) ND (<5) ND

(<100) ND (<5) ND (<70) ND (<10) ND (<10)

Rainwater ND (<1) ND (<1) ND (<1) ND (<5) ND (<1) ND (<5) ND (<5) ND (<5) ND

(<100) ND (<5) ND (<70) ND (<10) ND (<10)

Field blank ND (<1) ND (<1) ND (<1) ND (<5) ND (<1) ND (<5) ND (<5) ND (<5) ND

(<100) ND (<5) ND (<70) ND (<10) ND (<10)

Values are average ± SEM (sample size specified in Table 17). ‘βE2’ = 17β-Estradiol; ‘αE2’ = 17α-Estradiol; ‘EE2’ = 17α-Ethynylestradiol; ‘Mestr’ = Mestranol; ‘Testost’ = Testosterone; ‘DHT’ = 5α-Dihydrotestosterone; ‘Trenbo’ = 17β-Trenbolone; ‘Levonor’ = Levonorgestrel; ‘BPA’ = Bisphenol A; ‘4NP’ = 4-Nonylhpenol; ‘4tOP’ = 4-t-Octylphenol.


Table 25: Summary of chemical analysis results – 2 of 5 – Pharmaceuticals and personal care products

Site Atenolol Caffeine Carbamaz DEET Diazepam Diclofenac Gemfibro Methot Paracet Salicylic Sulfameth Triclosan

(ng/L) (ng/L) (ng/L) (ng/L) (ng/L) (ng/L) (ng/L) (ng/L) (ng/L) (ng/L) (ng/L) (ng/L)

Treated sewage

WRP 1 298 ± 42 (178 – 447)

114 ± 27 (61 – 277)

550 ± 12 (507 – 602)

116 ± 13 (74 – 165)

ND (<5) 115 ± 21 (46 – 178)

139 ± 45 (26 – 297)

ND (<5)

12 ± 2.0 (7 – 19) ND (<15)

345 ± 28 (232 – 462)

34 ± 9.7 (11 – 81)

WRP 2 285 ± 77 (<5 – 513)

<20 396 ± 85 (14 - 510)

13 ± 11 (<5 - 64) <5 239 ± 38

(71 - 307) 20 ± 8.5 (5 - 57)

ND (<5)

15 ± 3.9 (<5 - 28)

16 ± 3.9 (<15 - 30)

222 ± 43 (41 - 295)

52 ± 14 (<10 - 80)

WRP 3

695 ± 170 (418 – 850)

380 ± 160 (162 – 608)

495 ± 112 (312 - 593)

116 ± 26 (75 - 143)

6.1 ± 4.4 (<5 - 13)

272 ± 52 (187 - 315)

242 ± 54 (156 - 303)

ND (<5)

9.9 ± 1.0 (8 - 11)

17 ± 11 (<15 - 36)

212 ± 47 (137 - 263)

40 ± 10 (25 - 54)

WRP 4 ND (<5) 29 ± 15 (<20 – 67) ND (<5) ND (<5) ND (<5) ND (<5) ND (<5) ND

(<5) ND (<5) <15 12 ± 4.7 (<5 - 20) ND (<10)

WRP 5 94 ± 12 (44 – 140)

22 ± 4.3 (<20 – 40)

405 ± 40 (170 - 505)

56 ± 10 (29 - 103)

ND (<5) 213 ± 19 (115 - 285)

51 ± 9.3 (18 - 100)

ND (<5)

8.2 ± 1.1 (<5 - 13)

46 ± 18 (<15 - 150)

394 ± 42 (158 - 530)

89 ± 11 (31 - 121)

WRP 6 100 ND (<20) 676 129 ND (<5) 217 63 ND (<5) ND (<5) ND (<15) 498 46

WRP 7 165 ± 13 (136 – 187)

ND (<20) 484 ± 15 (457 - 518)

102 ± 9.8 (86 - 117)

ND (<5) 237 ± 6.8 (222 - 250)

40 ± 2.5 (37 - 47)

ND (<5)

19 ± 0.9 (17 - 21) ND (<15) 228 ± 11

(203 - 247) 230 ± 219 (40 - 798)

WRP 8 206 ± 28 (170 – 277)

122 ± 96 (31 – 372)

491 ± 22 (438 – 527)

65 ± 16 (34 – 98) ND (<5)

122 ± 56 (118 – 238)

63 ± 32 (12 – 141)

ND (<5)

51 ± 44 (<5 – 165)

ND (<15) 116 ± 61 (<5 – 257)

13 ± 6.9 (<10 – 31)

WRP 9 105 ± 20 (68 - 231)

1,510 ± 227 (785 – 2,690)

288 ± 57 (187 - 658)

340 ± 104 (134 - 983)

ND (<5) 38 ± 7.8 (24 - 88)

342 ± 59 (210 - 708)

ND (<5)

102 ± 90 (11 - 693)

ND (<15) 440 ± 92 (300 – 1,030)

38 ± 21 (10 - 172)


WRP 1 191 ± 61 (<5 – 547)

99 ± 11 (59 – 134)

72 ± 47 (<5 – 378)

92 ± 15 (55 – 180)

<5 ND (<5) ND (<5) ND (<5) <5 ND (<15) <5 ND (<10)

WRP 3 802 ± 38 (745 – 852)

300 ± 38 (255 – 360)

600 ± 20 (<575 – 632)

144 ± 31 (112 - 193)

<5 218 ± 114 (33 - 323)

267 ± 13 (247 - 283)

ND (<5)

16 ± 1.9 (13 - 18) <15 239 ± 11

(222 - 253) 37 ± 4.0 (34 - 44)


Site Atenolol Caffeine Carbamaz DEET Diazepam Diclofenac Gemfibro Methot Paracet Salicylic Sulfameth Triclosan


WRP 4 ND (<5) <20 ND (<5) ND (<5) ND (<5) ND (<5) ND (<5) ND (<5) ND (<5) <15 ND (<5) ND (<10)

WRP 9 96 ± 6.8 (61 – 114)

1,240 ± 170 (715 – 1,830)

220 ± 8.7 (182 – 250)

253 ± 33 (168 – 375)

ND (<5) 6.3 ± 2.0 (<5 – 14)

236 ± 43 (118 – 355)

ND (<5)

6.5 ± 1.3 (<5 – 13)

15 ± 2.8 (<15 – 27)

166 ± 22 (103 – 252)

18 ± 5.6 (<10 – 42)

RO recycled water

WRP 2 ND (<5) ND (<20) ND (<5) ND (<5) ND (<5) ND (<5) ND (<5) ND (<5) ND (<5) ND (<15) ND (<5) ND (<10)

WRP 5 ND (<5) <20 ND (<5) ND (<5) ND (<5) ND (<5) ND (<5) ND (<5) ND (<5) <15 ND (<5) ND (<10)

WRP 6 ND (<5) ND (<20) 8 ND (<5) ND (<5) ND (<5) ND (<5) ND (<5) ND (<5) ND (<15) ND (<5) ND (<10)



Other miscellaneous

Bottled water ND (<5) ND (<20) ND (<5) ND (<5) ND (<5) ND (<5) ND (<5) ND

(<5) ND (<5) ND (<15) ND (<5) ND (<10)

Tap water ND (<5) <20 ND (<5) <5 ND (<5) ND (<5) ND (<5) ND (<5) ND (<5) ND (<15) ND (<5) ND (<10)

Rainwater ND (<5) ND (<20) ND (<5) 14 ND (<5) ND (<5) ND (<5) ND (<5) ND (<5) ND (<15) ND (<5) ND (<10)

Field blank ND (<5) ND (<20) ND (<5) ND (<5) ND (<5) ND (<5) ND (<5) ND (<5) ND (<5) ND (<15) ND (<5) ND (<10)

Values are average ± SEM (sample size specified in Table 17). ‘Carbamaz’ = Carabamazepine; ‘Gemfibro’ = Gemfibrozil; ‘Methot’ = Methotrexate; ‘Paracet’ = Paracetamol; ‘Salicylic’ = Salicylic acid; ‘Sulfameth’ = Sulfamethoxazole.


Table 26: Summary of chemical analysis results – 3 of 5 – Pesticides and disinfection by-products

Site Atrazine Chlorpy Diazinon Diuron Pentach Simazine Triflur BrDiChl Bromoform Chloroform DiBrChl NDMA


Treated sewage

WRP 1 178 ± 52 (<50 – 318)

ND (<1) 114 ± 17 (54 – 161)

638 ± 27 (520 – 722)

ND (<5) 739 ± 63 (553 – 1,083)

ND (<5) ND (<20) ND (<20) ND (<100) ND (<20) ND (<5)

WRP 2

1,650 ± 597 (<50 – 3,350)

2.7 ± 0.8 (<1 - 5)

30 ± 8.1 (<5 - 47)

96 ± 29 (<5 - 198) ND (<5)

6,790 ± 4,580 (<20 – >20,000)

ND (<5) ND (<20) ND (<20) ND (<100) ND (<20) <5

WRP 3 76 ± 63 (<50 - 178)

1.2 ± 0.5 (<1 - 2)

11 ± 5.6 (<5 - 18)

53 ± 13 (32 - 67) ND (<5)

568 ± 272 (315 – 1,010)

ND (<5) ND (<20) ND (<20) ND (<100) ND (<20) ND (<5)

WRP 4 ND (<50) ND (<1) ND (<5) ND (<5) ND (<5) ND (<20) ND (<5) ND (<20) ND (<20) ND (<100) ND (<20)

6.2 ± 0.7 (5 - 8)

WRP 5 <50 5.0 ± 1.2 (<1 - 10)

47 ± 11 (9 - 100)

107 ± 12 (52 - 163) ND (<5) 301 ± 95

(31 - 787) ND (<5) ND (<20) ND (<20) ND (<100) ND (<20) <5

WRP 6 ND (<50) 20 142 68 ND (<5) 260 ND (<5) ND (<20) ND (<20) ND (<100) ND (<20) ND (<5)

WRP 7 ND (<50) 3.5 ± 0.3 (3 - 4)

73 ± 15 (55 - 112)

54 ± 4.0 (49 - 65) ND (<5) ND (<20) ND

(<5) ND (<20) ND (<20) ND (<100) ND (<20) ND (<5)

WRP 8 ND (<50) 1.3 ± 0.6 (<1 – 3)

12 ± 8.6 (<5 – 34)

39 ± 4.3 (29 – 45) ND (<5) 35 ± 7.2

(20 – 47) ND (<5)

120 ± 96 (<20 – 364)

87 ± 89 (<20 – 319)

435 ± 444 (<100 – 1,590)

208 ± 223 (<20 – 787)

ND (<5)

WRP 9 ND (<50) ND (<1) <5 25 ± 6.1 (16 - 64) ND (<5) 175 ± 58

(54 - 525) ND (<5)

446 ± 466 (<20 – 3,500)

ND (<20) ND (<100) <20 <5


WRP 1 163 ± 59 (<50 – 453)

ND (<1) ND (<5) 446 ± 60 (283 – 786)

ND (<5) 735 ± 88 (507 – 1,169)

ND (<5)

2,230 ± 1,080 (364 – 8,500)

5,260 ± 1,980 (586 – 15,200)

1,340 ± 280 (472 – 2,950)

3,660 ± 1,610 (267 – 12,300)

ND (<5)

WRP 3 85 ± 40 (<50 - 138)

1.6 ± 0.2 (1 - 2)

12 ± 2.0 (9 - 15)

64 ± 7.4 (52 - 72) ND (<5) 622 ± 145

(385 - 745) ND (<5) ND (<20) ND (<20) ND (<100) ND (<20) ND (<5)

WRP 4 ND (<50) ND (<1) ND (<5) ND (<5) ND (<5) ND (<20) ND (<5)

660 ± 310 (218 –

986 ± 236 (492 –

231 ± 74 (112 - 388)

2,780 ± 767

ND (<5)


Site Atrazine Chlorpy Diazinon Diuron Pentach Simazine Triflur BrDiChl Bromoform Chloroform DiBrChl NDMA


1,440) 1,480) (1,630 – 4,450)

WRP 9 ND (<50) ND (<1) ND (<5) 16 ± 1.1 (13 – 21) ND (<5) 112 ± 20

(64 – 187) ND (<5)

572 ± 377 (<20 – 2,860)

ND (<20) ND (<100) 26 ± 4.0 (<20 – 37) <5

RO recycled water

WRP 2 ND (<50) ND (<1) ND (<5) ND (<5) ND (<5) 45 ± 24 (<20 - 118)

ND (<5) ND (<20) ND (<20) ND (<100) ND (<20) <5

WRP 5 ND (<50) ND (<1) ND (<5) ND (<5) ND (<5) ND (<20) ND (<5) <20 23 ± 7.4

(<20 – 57) ND (<100) 24 ± 7.2 (<20 – 53) ND (<5)

WRP 6 ND (<50) ND (<1) ND (<5) 15 ND (<5) ND (<20) ND (<5) ND (<20) ND (<20) ND (<100) ND (<20) ND (<5)


WRP 8 ND (<50) ND (<1) ND (<5) ND (<5) ND (<5) ND (<20) ND (<5)

37 ± 32 (<20 – 120)

<20 123 ± 85 (<100 – 344)

39 ± 31 (<20 – 118)

ND (<5)

Other miscellaneous

Bottled water ND (<50) ND (<1) ND (<5) ND (<5) ND (<5) ND (<20) ND (<5) ND (<20) ND (<20) ND (<100) ND (<20) ND (<5)

Tap water ND (<50) ND (<1) ND (<5) ND (<5) ND (<5) ND (<20) ND (<5)

39 ± 20 <20 - 94

42 ± 36 <20 - 170

248 ± 153 <100 - 783

166 ± 144 <20 - 674 ND (<5)

Rainwater ND (<50) ND (<1) ND (<5) ND (<5) ND (<5) ND (<20) ND (<5) ND (<20) ND (<20) ND (<100) ND (<20) ND (<5)

Field blank ND (<50) ND (<1) ND (<5) ND (<5) ND (<5) ND (<20) ND (<5) ND (<20) ND (<20) ND (<100) ND (<20) ND (<5)

Values are average ± SEM (sample size specified in Table 17). ‘Pentach’ = Pentachlorophenol; ‘Triflur’ = Trifluralin; ‘BrDiChl’ = Bromodichloromethane; ‘DiBrChl’ = Dibromochloromethane.


The chemical analysis method used in this project allowed the simultaneous detection of other chemicals not specifically targeted (Table 27). The concentrations of these compounds, which include pharmaceuticals, personal care products, industrial compounds and pesticides, are listed in Table 28 and

Table 29.

Not detected in any of the samples were the pharmaceuticals norfluoxetine (<5 ng/L; metabolite of the antidepressant fluoxetine), enalapril (<5 ng/L; used to treat hypertension), risperidone (<1 ng/L; antipsychotic), verapamil (<5 ng/L; used to treat hypertension), hydroxyzine (<5 ng/L; anti-histamine); the disinfection by-products NMEA (<10 ng/L), NDEA (<5 ng/L), NDPA (<5 ng/L), NMor (<10 ng/L), NPyr (<10 ng/L) and NPip (<10 ng/L); and the androgen hormone metabolites androstenedione (<5 ng/L), androsterone (<1 ng/L) and etiocholanolone (<5 ng/L) (data not shown).


Table 27: Additional compounds analysed.


ANZECC (ng/L)

Androstenedione 63-05-8 Hormone Metabolite of testosterone Androsterone 53-41-8 Hormone Metabolite of testosterone 14 000 Etiocholanolone 53-42-9 Hormone Metabolite of testosterone Dilantin 57-41-0 Pharmaceutical Anti-epileptic [ 120 000 ]3 Fluoxetine 54910-89-3 Pharmaceutical Anti-depressant 10 000 Norfluoxetine 126924-38-

7 Pharmaceutical Metabolite of fluoxetine, anti-depressant

Amitriptyline 50-48-6 Pharmaceutical Anti-depressant Clozapine 5786-21-0 Pharmaceutical Anti-psychotic [ 6250 ]3 Risperidone 106266-06-

2 Pharmaceutical Anti-psychotic [ 250 ]3

Meprobamate 57-53-4 Pharmaceutical Anti-anxiety p-Hydroxyatorvastatin (disodium salt) 214217-88-

6 Pharmaceutical Metabolite of atorvastatin, used to control

cholesterol

d-Hydroxyatorvastatin (dihydrate monosodium)

214217-86-4

Pharmaceutical Metabolite of atorvastatin, used to control cholesterol

Atorvastatin 134523-00-5

Pharmaceutical Used to regulate cholesterol level 5000

Simvastatin hydroxy acid (sodium salt) 12009-77-6 Pharmaceutical Metabolite of simvastatin, used to control cholesterol

Simvastatin 79902-63-9 Pharmaceutical Used to regulate cholesterol level [ 5000 ]3 Omeprazole 73590-58-6 Pharmaceutical Proton pump inhibitor, used to regulate stomach

acid [ 20 000 ]3

Primidone 125-33-7 Pharmaceutical Anti-convulsant [ 62 500 ]3 Trimethoprim 738-70-5 Pharmaceutical Antibiotic 70 000 Enalapril 75847-73-3 Pharmaceutical Used to treat hypertension [1250] for

enalaprilat

Triamterene 396-01-0 Pharmaceutical Use to treat hypertension [ 25 000 ]3 Verapamil 52-53-9 Pharmaceutical Used to treat hypertension [ 60 000 ]3



ANZECC (ng/L)

Hydroxyzine 68-88-2 Pharmaceutical Anti-histamine Metformin 657-24-9 Pharmaceutical Anti-diabetic 250 000 Ketoprofen 22071-15-4 Pharmaceutical Non-steroidal anti-inflammatory (NSAID), used

as analgesic 3500

Naproxen 22204-53-1 Pharmaceutical Non-steroidal anti-inflammatory (NSAID), used as analgesic

220 000

Ibuprofen 15687-27-1 Pharmaceutical Non-steroidal anti-inflammatory (NSAID), used as analgesic

400 000

Triclocarban 101-20-2 Personal care product

Anti-bacterial, anti-fungal

Propylparaben 94-13-3 Personal care product

Cosmetic

tris(2-Chloroethyl)phosphate (TCEP) 115-96-8 Industrial compound

Flame retardant Provisional [1000]

Linuron 330-55-2 Pesticide Herbicide [10 000]2 2-Phenylphenol 90-43-7 Pesticide Fungicide Provisional [1000] N-Nitrosomethylethylamine (NMEA) 10595-95-6 DBP Disinfection by-product N-Nitrosodiethylamine (NDEA) 62-75-9 DBP Disinfection by-product 10 N-Nitrosodipropylamine (NDPA) 621-64-7 DBP Disinfection by-product N-Nitrosomorpholine (NMor) 59-89-2 DBP Disinfection by-product 1 N-Nitrosopyrrolidine (NPyr) 930-55-2 DBP Disinfection by-product N-Nitrosopiperidine (NPip) 100-75-4 DBP Disinfection by-product 1 Australian guidelines for water recycling, Phase 2 – augmentation of drinking water supplies (EPHC/NHMRC/NRMMC); 2 General limit for all herbicides in NSW (ANZECC/ARMCANZ 2000); 3 interim guideline derived from the framework described in the Australian guidelines for water recycling (Phase 2), based on the lowest therapeutic dose (MIMS 2009).


Table 28: Summary of chemical analysis results – 4 of 5 – Additional pharmaceutical compounds

Site Dilantin Fluoxetine Amitript Clozapine Meprob pOH-ator dOH-ator Atorvast SimvaOH Simvastat Omepraz Primidone


Treated sewage

WRP 1

120 ± 6.5 (96 – 147)

ND (<5) 6.1 ± 1.4 (<5 – 11)

12 ± 2.7 (5 – 21) ND (<5) 16 ± 5.2

(<5 – 36) 7.4 ± 2.2 (<5 – 10)

5.4 ± 1.2 (<5 – 10)

1.3 ± 0.3 (<1 – 2) ND (<10) ND (<5)

205 ± 8.4 (172 – 238)

WRP 2 97 ± 21 (<5 - 125)

20 ± 4.7 (<5 - 30)

65 ± 17 (<5 - 97)

31 ± 7.2 (<5 - 42)

8.2 ± 2.2 (<5 - 14)

24 ± 15 (6 - 89)

8.6 ± 2.0 (6 - 17)

12 ± 7.1 (<5 - 42) ND (<1) ND (<10) 9.0 ± 2.0

(<5 - 16) 106 ± 22 (11 - 140)

WRP 3 92 ± 20 (60 - 114)

14 ± 3.6 (8 - 18)

21 ± 5.8 (11 - 26)

28 ± 7.5 (16 - 35) ND (<5) 189 ± 48

(112 - 235) 158 ± 36 (99 - 191)

85 ± 19 (54 - 101)

2.8 ± 0.3 (2 - 3)

31 ± 32 (<10 - 82) ND (<5) 157 ± 42

(89 - 192)


WRP 5 146 ± 14 (59 - 176)

12 ± 1.7 (<5 - 18)

43 ± 5.2 (16 - 66)

59 ± 6.1 (22 - 72) ND (<5) ND (<5) ND (<5) <5 ND (<1) ND (<10) 5.2 ± 1.2

(<5 - 9) 109 ± 10 (48 - 136)

WRP 6 114 15 37 27 ND (<5) ND (<5) ND (<5) ND (<5) ND (<1) ND (<10) ND (<5) 208

WRP 7 94 ± 3.9 (84 - 98)

18 ± 1.0 (16 - 20)

74 ± 2.4 (68 - 77)

12 ± 1.4 (10 - 16) ND (<5) ND (<5) ND (<5) ND (<5) ND (<1) ND (<10) 7.2 ± 0.9

(6 - 9) 93 ± 3.0 (86 - 97)

WRP 8 88 ± 5.9 (78 – 101)

14 ± 2.1 (11 – 19)

33 ± 14 (<5 – 63)

16 ± 7.4 (<5 – 33) ND (<5) 15 ± 14

(<5 – 51) 12 ± 12 (<5 – 42)

9 ± 7.2 (<5 – 28) ND (<1) ND (<10) <5

126 ± 8.1 (109 – 143)

WRP 9 47 ± 7.8 (22 - 94) ND (<5) 6.7 ± 1.4

(<5 - 11) <5 ND (<5) 16 ± 3.6 (9 - 38)

7.3 ± 2.1 (<5 - 20) <5 ND (<1) ND (<10) ND (<5) 54 ± 9.5

(38 - 115)


WRP 1 96 ± 18 (62 – 203)

ND (<5) ND (<5) ND (<5) ND (<5) ND (<5) ND (<5) ND (<5) ND (<1) ND (<10) ND (<5) 222 ± 25 (183 – 383)

WRP 3 138 ± 15 (114 - 155)

16 ± 1.5 (14 - 18)

21 ± 2.4 (18 - 25)

31 ± 2.7 (26 - 34) ND (<5) 165 ± 8.7

(155 - 178) 91 ± 47 (13 - 132)

75 ± 1.6 (72 - 76)

2.9 ± 1.4 (1.6 - 5.1) ND (<10) ND (<5) 196 ± 9.2

(182 - 207)

WRP 4 <5 ND (<5) ND (<5) ND (<5) ND (<5) ND (<5) ND (<5) ND (<5) ND (<1) ND (<10) ND (<5) ND (<10)

WRP 9 40 ± 2.1 (33 – 46) <5 <5 5.9 ± 0.2

(5 - 7) ND (<5) ND (<5) ND (<5) ND (<5) ND (<1) ND (<10) 10 ± 8.1 (<5 – 63)

47 ± 2.8 (37 – 54)


Site Dilantin Fluoxetine Amitript Clozapine Meprob pOH-ator dOH-ator Atorvast SimvaOH Simvastat Omepraz Primidone


RO recycled water






Other miscellaneous

Bottled water ND (<5) ND (<5) ND (<5) ND (<5) ND (<5) ND (<5) ND (<5) ND (<5) ND (<1) ND (<10) ND (<5) ND (<10)

Tap water ND (<5) ND (<5) ND (<5) ND (<5) ND (<5) ND (<5) ND (<5) ND (<5) ND (<1) ND (<10) ND (<5) ND (<10)

Rainwater ND (<5) ND (<5) ND (<5) ND (<5) ND (<5) ND (<5) ND (<5) ND (<5) ND (<1) ND (<10) ND (<5) ND (<10)

Field blank ND (<5) ND (<5) ND (<5) ND (<5) ND (<5) ND (<5) ND (<5) ND (<5) ND (<1) ND (<10) ND (<5) ND (<10) Values are average ± SEM (sample size specified in Table 17). ‘Amitript’ = amitriptyline; ‘Meprob’ = meprobamate; ‘pOH-ator’ = p-hydroxyatorvastatin; ‘dOH-ator’ = d-hydroxyatorvastatin; ‘Atorvast’ = atorvastatin; ‘SimvaOH’ = simvastatin hydroxyl acid; ‘Simvastat’ = simvastatin; ‘Omepraz’ = omeprazole.


Table 29: Summary of chemical analysis results – 5 of 5 – Additional pharmaceutical, personal care, industrial and pesticide compounds

Site Trimetho Triamter Metform Ketoprof Naproxen Ibuprofen Triclocarb Propylpara TCEP Linuron Phenylph

(ng/L) (ng/L) (ng/L) (ng/L) (ng/L) (ng/L) (ng/L) (ng/L) (ng/L) (ng/L) (ng/L)

Treated sewage

WRP 1 278 ± 80 (66 - 485)

20 ± 0.5 (18 - 23)

2,240 ± 50 (2,050 – 2,520)

20 ± 4.6 (<5 - 38)

93 ± 21 (33 - 158)

20 ± 1.9 (13 - 26) ND (<5) 11 ± 6.1

(<10 - 51)

222 ± 13 (183 - 285)

5.2 ± 1.1 (<5 - 9)

11 ± 3.1 (<5 - 23)

WRP 2 50 ± 38 (<5 - 222)

51 ± 15 (<5 - 98)

1,140 ± 230 (383 – 1,930)

21 ± 12 (<5 - 74)

36 ± 17 (<5 - 109)

29 ± 6.2 (<5 - 43)

10 ± 2.9 (<5 - 17) ND (<10)

249 ± 54 (33 - 383)

<5 19 ± 5.1 (<5 - 38)

WRP 3 313 ± 74 (192 - 382)

29 ± 6.9 (18 - 37)

3,710 ± 1,380 (1,570 – 5,400)

104 ± 25 (63 - 129)

341 ± 78 (213 - 408)

268 ± 50 (187 - 312)

24 ± 14 (7 - 46) ND (<10)

158 ± 57 (67 - 222)

ND (<5) 53 ± 18 (36 - 83)

WRP 4 ND (<5) ND (<5) 978 ± 45 (884 – 1,070) ND (<5) ND (<5) ND (<5) ND (<5) ND (<10) ND (<10) ND (<5) ND (<5)

WRP 5 208 ± 44 (98 - 396)

19 ± 1.9 (8 - 25)

486 ± 85 (66 - 709)

51 ± 9.8 (<5 - 91)

232 ± 29 (81 - 343)

30 ± 4.8 (<5 - 47)

20 ± 3.1 (8 - 34) ND (<10)

238 ± 42 (84 - 397)

ND (<5) 34 ± 9.1 (7 - 67)

WRP 6 54 18 1,320 34 109 69 14 ND (<10) 382 ND (<5) 38

WRP 7 122 ± 7.2 (103 - 130)

29 ± 6.3 (19 - 44)

890 ± 101 (653 – 1,050)

21 ± 0.6 (20 - 22)

62 ± 3.7 (54 - 68)

30 ± 0.7 (29 - 32)

10 ± 0.7 (9 - 11) ND (<10)

296 ± 15 (263 - 322)

ND (<5) 31 ± 9.2 (14 - 52)

WRP 8 45 ± 28 (<5 – 113)

14 ± 3.1 (6 – 19)

987 ± 444 (545 – 2,133)

39 ± 15 (14 – 73)

117 ± 70 (14 – 292)

75 ± 47 (23 – 193)

10 ± 1.1 (9 – 13) ND (<5)

183 ± 5.6 (170 – 193)

ND (<5) 5.9 ± 2.6 (<5 – 12)

WRP 9 133 ± 31 (85 - 333)

5.1 ± 1.8 (<5 - 16)

2,160 ± 547 (933 – 5,460) <5

483 ± 123 (245 – 1,260)

457 ± 94 (126 - 858) <5 ND (<10)

262 ± 57 (164 - 617)

ND (<5) 51 ± 14 (14 - 116)


WRP 1 <5 ND (<5) 770 ± 457 (<10 – 3,600) ND (<5) ND (<5) ND (<5) ND (<5) ND (<10)

303 ± 37 (215 – 467)

<5 ND (<5)

WRP 3 343 ± 7.4 (335 - 355)

35 ± 2.8 (30 - 37)

4,100 ± 77 (4,000 – 4,220)

108 ± 12 (89 - 124)

366 ± 24 (335 - 403)

293 ± 42 (258 - 362)

18 ± 2.2 (16 - 22) ND (<10)

209 ± 38 (173 - 270)

ND (<5) 72 ± 19 (42 - 94)

WRP 4 ND (<5) ND (<5) 268 ± 132 (116 - 605) ND (<5) ND (<5) ND (<5) ND (<5) ND (<10) ND (<10) ND (<5) ND (<5)

WRP 9 119 ± 6.6 (104 – 7.9 ± 1.8 3,200 ± 400 ND (<5) 338 ± 19 333 ± 58 ND (<5) ND (<10) 265 ± 8.2

(242 – ND (<5) 11 ± 2.9


Site Trimetho Triamter Metform Ketoprof Naproxen Ibuprofen Triclocarb Propylpara TCEP Linuron Phenylph

(ng/L) (ng/L) (ng/L) (ng/L) (ng/L) (ng/L) (ng/L) (ng/L) (ng/L) (ng/L) (ng/L)

150) (<5 – 19) (2,200 – 5,100) (283 – 412) (123 – 518) 300) (<5 – 25)

RO recycled water



WRP 6 ND (<5) ND (<5) 12 ND (<5) ND (<5) ND (<5) 5 ND (<10) ND (<10) ND (<5) ND (<5)



Other miscellaneous

Bottled water ND (<5) ND (<5) ND (<10) ND (<5) ND (<5) ND (<5) ND (<5) ND (<10) ND (<10) ND (<5) ND (<5)

Tap water ND (<5) ND (<5) ND (<10) ND (<5) ND (<5) ND (<5) ND (<5) ND (<10) ND (<10) ND (<5) ND (<5)

Rainwater ND (<5) ND (<5) ND (<10) ND (<5) ND (<5) ND (<5) ND (<5) ND (<10) ND (<10) ND (<5) ND (<5)

Field blank ND (<5) ND (<5) ND (<10) ND (<5) ND (<5) ND (<5) ND (<5) ND (<10) ND (<10) ND (<5) ND (<5) Values are average ± SEM (sample size specified in Table 17). ‘Trimetho’ = trimethoprim; ‘Triamter’ = triamterene; ‘Metform’ = metformin; ‘Ketoprof’ = ketoprofen; ‘Triclocarb’ = triclocarban; ‘Propylpara’ = propylparaben; ‘TCEP’ = tris(2-chloroethyl)phosphate; ‘Phenylph’ = 2-phenylphenol.


5.6 Dis c us s ion

5.6.1 Water quality

‘Blank’ water samples (bottled, tap, rain water and field blanks)

With the exception of low-level cytotoxicity in the WIL2NS TOX assay, there was no detectable biological activity in any of the ‘blank’ water samples (i.e. bottled, tap and rain water, and the field blanks) (Table 22 and

Table 23).

B ioas s ays

The bottled water and rainwater samples exhibited cytotoxicity just above the detection limit (0.06 vs 0.05 rTU). This ‘low level toxicity’ is not uncommon in water samples that have been highly concentrated by solid-phase extraction (SPE), and has been reported previously (Escher et al. 2008; Reitsema et al. 2010). This residual toxicity is thought to be an artefact of the concentration step and is of no concern if it is not significantly above the detection limit and no other specific and/or reactive toxicity are associated with the sample, as was the case here.

C hemic al analys is

Most compounds monitored were below detection limit in all samples with the exception of low part-per-trillion (ng/L) concentrations of the insect-repellent DEET in the rainwater sample and trihalomethane (THM) disinfection by-products (bromodichloromethane, bromoform, chloroform and dibromochloromethane) in two of the five tap water samples (Table 24 to

Table 29). It should be noted that the concentration of these four compounds in the tap water samples was several orders of magnitude below the drinking water guideline of 250 000 ng/L for total THM (NHMRC 2004).

C ombined analys is

Chemical and bioassay analysis were in good agreement, with little to no detections in the ‘blank’ samples. DEET was not biologically-active in any of the bioassays, and while the THMs were active in several bioassays, their relative potency was low (Table 20 and Table 21) and ng/L concentrations are unlikely to be detectable by bioassays.

Treated sewage

Treated sewage samples contained a variety of biologically-active compounds, with positive results in almost all bioassays except the Ames test for mutagenicity, the TRβ-CALUX for thyroid activity and the THP1+ CPA for immunostimulation (Table 22 and

Table 23).

B ioas s ays

Low-level cytotoxicity (measured by Caco2-NRU, WIL2NS TOX and HepaTOX assays) was detectable in most of the treated sewage samples, consistently (but only slightly) above the detection limit. Importantly, none of the samples exceeded 1 rTU (the highest was 0.31 rTU in treated sewage at WRP 5), meaning that none of the samples would have been cytotoxic without sample


concentration. These results are comparable to previous studies in WA (Reitsema et al. 2010) and QLD (Muller et al. 2007), which reported the equivalent of 0.03–0.12 rTU in treated sewage effluent from urban wastewater plants with the MicroLumo/Microtox assay for bacterial cytotoxicity.

A few treated sewage samples exhibited low-level genotoxicity (measured by the WIL2NS FCMN), although again none of the samples exceeded 1 rGTU, meaning that none of those samples would have been genotoxic without sample concentration. None of the treated sewage samples exhibited quantifiable mutagenicity (measured in the combined Ames test). These results are comparable to previous studies in WA (Reitsema et al. 2010) and QLD (Macova et al. 2010), which reported the equivalent of <0.1 rGTU in treated sewage effluent with the umuC assay for bacterial genotoxicity.

Most treated sewage samples exhibited significant and consistent induction of liver enzyme activity (measured by the HepCYP1A2 assay), with the highest activity in samples from WRP 2, WRP 5, WRP 6 and WRP 7. Samples from the other sites were close to or below the detection limit of the assay. To our knowledge there are no other reports using CYP1A2 activity to assess the bioactivity of waste waters in Australia. A number of other reports have used chemical methods to analyse for polyaromatic hydrocarbons in waste water bioactivity in this CYP1A2 assay.

Except for WRP 4 (which had no detectable activity in any of the bioassays), all treated sewage samples contained some level of endocrine activity. More specifically, the samples contained significant estrogenic (up to 4.73 ng/L βE2 Eq), glucocorticoid (up to 81 ng/L Dexa Eq) and progesterone-like activity (up to 2.16 ng/L Org2058 Eq). Anti-estrogenic activity could generally not be determined because of the estrogenic activity in the samples. Androgenic activity was only detected at one of the plants (WRP 9) slightly above the detection limit of 2 ng/L DHT Eq, and was below detection limit at all other sites. Anti-androgenic and thyroid activities were not detected in any of the samples. There is an extensive dataset on estrogenicity in treated sewage in Australia (reviewed in Allinson et al. 2011), and the low ng/L estrogenic activity in treated sewage reported here is comparable to previously reported Australian values. Less data exists for androgenic activity, but a previous study in WA (Reitsema et al. 2010) reported <2.5 ng/L DHT Eq in treated sewage and a Dutch study (Van der Linden et al. 2008) reported 0.73–0.83 ng/L DHT Eq in treated sewage, both determined by AR-CALUX assay. To our knowledge, the other endocrine endpoints have never been monitored in Australia, but the concentrations reported here are in the same range as those reported in a Dutch study (Van der Linden et al. 2008), which reported 0.78–0.86 ng/L Org2058 Eq and 11–38 ng/L Dexa Eq in treated sewage using the PR and GR-CALUX assays, respectively.

None of the treated sewage samples caused any immunostimulatory effects (measured by the THP1 assay in agonist mode; ‘THP1+’), but a few samples caused a significant immunosuppressive effect (measured by the THP1 assay in antagonist mode; ‘THP1-‘), particularly WRP 2 (up to 1.1 µg/L Dexa Eq), WRP 3 (up to 0.65 µg/L Dexa Eq) and WRP 8 (up to 0.72 µg/L Dexa Eq). The other samples were either slightly above the detection limit (WRP 1, WRP 5 and WRP 7) or below the detection limit (WRP 4, WRP 6 and WRP 9). This is the first time this assay has been used to quantify water quality, and there are no previously-reported results for water samples.

Finally, except for WRP 4 (which, as previously stated, had no detectable activity in any of the bioassays), all treated sewage samples caused inhibition of acetylcholinesterase activity (measured by the AChE assay), up to 102 ng/L Chlorpy Eq at WRP 6. Bioactivity in secondary-treated waste water causing inhibition of acetylcholine esterase activity has been reported previously (Macova et al. 2010).


None of the natural hormones were detected above quantification limit in any of the treated sewage samples with the exception of estrone, which was commonly detected in the treated sewage samples (up to 32 ng/L at WRP 3).


The industrial compound 4-t-octylphenol was detected in the low ng/L range in half of the treated sewage samples (up to 132 ng/L going into WRP 9), while bisphenol A was only detected in samples from WRP 7 (a municipal plant with high industrial input) up to 450 ng/L. The flame retardant TCEP was also detected in all treated sewage samples (except at WRP 4) in the low to mid ng/L range.

Most of the pharmaceuticals and personal care products in the priority list were detected from low parts-per-trillion (ng/L) to low parts-per-million (µg/L) concentrations in treated effluent, except methotrexate, which was not detected in any of the samples (<5 ng/L). Caffeine was detected at the highest concentration (2690 ng/L), in treated sewage going into WRP 9. Additional pharmaceutical and personal care products (i.e. in addition to the 39 priority compounds) were also detected in most treated sewage samples, usually in the low to mid ng/L range, with the exception of metformin (an anti-diabetic drug), which was detected in the low µg/L range at most plants (up to 5460 ng/L going into WRP 9). Caffeine and metformin were two of the only-three compounds detected in sewage going into WRP 4 (with an average of 29 and 978 ng/L, respectively).

Most pesticides on the priority list (except pentachlorophenol and trifluralin) were detected in many of the treated sewage samples (except at WRP 4, where none of the monitored pesticides were detected). Very high concentrations of the herbicide atrazine and simazine were detected in treated sewage going into WRP 2 (up to 3350 and 20 000 ng/L, respectively). The insecticides chlorpyrifos and diazinon were detected at low to mid-ng/L in most treated sewage samples. The concentration of pesticides in treated sewage going to WRP 9 was usually low (below detection limit for most except diuron, simazine and phenylphenol), consistent with mostly domestic sewage.

Finally, disinfection by-products were only detectable in treated sewage from a few locations. All four monitored THMs were detectable in treated sewage going into WRP 8 (where trials of different disinfection regimes were underway during our sampling), bromodichloromethane was detected on one occasion in sewage going to WRP 9, and NDMA was detected consistently in treated sewage going to WRP 4 (one of the few compounds detected in that treated sewage).

C ombined analys is

The chemical and bioassay analysis were in good agreement, and both report significant concentrations of trace organic contaminants in the treated sewage samples. Compounds that were identified as having a cytotoxic fingerprint (Table 20) were present in the samples at concentrations that could explain the low cytotoxicity measured by bioassay.

No androgen hormones were detected, and no androgenic activity was detected (except in treated sewage to WRP 9, were the detected androgenic activity in the AR-CALUX assay was below the detection limit of the chemical analysis of 5 ng/L for most androgens).

The presence of estrone in sewage samples suggests that other related (and more potent) estrogen hormones such as 17β-estradiol and estriol are also likely present, although they are below the detection limit of the chemical analysis method. Those additional compounds could, however, significantly contribute to the estrogenic activity measured by the bioassay (which has a much lower detection limit) even if they were below detection limit of the chemical analysis (<1 ng/L).

It is not yet known what compounds cause glucocorticoid activity (measured by the GR-CALUX). In a Dutch study (Van der Linden et al. 2008), industrial and hospital wastewater contained the highest levels of GR activity (243 and 96 ng/L Dexa Eq, respectively). Corticosteroids are used in several pharmaceutical applications (such as asthma medication, cream for skin irritation) and are likely contributing to the GR activity. Other drugs involved in glucose regulation (such as metformin, which was found at high concentration in most sewage samples) may also have an effect on GR activity. It is unknown what causes GR-like activity in industrial wastewater.


Likewise, it is unclear what compounds cause progesterone activity (measured by the PR-CALUX). A recent study (Van der Linden et al. 2008) suggests that natural and synthetic hormones and their metabolites could be contributing to a significant portion of that activity.

For thyroid and immunostimulatory activity, the chemical fingerprints and bioassay results were in agreement: none of the 39 compounds tested had any thyroid (TRβ-CALUX) or immunostimulatory (THP1+) fingerprint (

Table 23), and neither of these biological activities were detected in any of the samples.

The highest immunosuppressive activity (measured by the THP1- assay) was detected in treated sewage going to WRP 2, WRP 3 and WRP 8. In the chemical fingerprint, estrone was the most active immunosuppressive compound tested (although it should be noted that its potency was still almost 1000× less than the standard used for that assay, dexamethasone). Some of the THMs were also very lightly immunosuppressive (although 1 000 000× less so than dexamethasone). Treated sewage into WRP 2 and 3 had the highest estrogenic activity, and sewage into WRP 8 had high levels of all four THMs. Those concentrations of estrone and THMs in the treated sewage going into the plants are not sufficient to explain the immunosuppressive activity detected in sewage into WRP 2, 3 and 8, but suggests that estrogens and DBPs as a group are causing most of that activity.

Finally the acetylcholinesterase activity (measured by AChE assay) correlated extremely well with chlorpyrifos and diazinon concentrations, which explained 40–97 per cent of the activity at most sites (except in wastewater to WRP 9, where neither compound was detected but 20 ng/L Chlorpy Eq was reported by the bioassay). In all cases, diazinon was responsible for most of the calculated activity, from 73–99 per cent, although it is slightly less potent that chlorpyrifos. It is unclear what compound is causing the AChE activity at WRP 9, although considering the specificity of the assay it is likely to be a related organophosphate or carbamate pesticide.


Three treatment types were used to produce class A recycled water from highly treated sewage: dissolved air flotation/filtration and chlorination (at WRP 1), ultrafiltration and chlorination/chloramination (at WRP 3 and 9) and UV/chlorine disinfection (at WRP 4). The latter two (ultrafiltration and UV) were used primarily for additional pathogen removal and only had a minor effect on removal of organic contaminants.

B ioas s ays

Although it was generally lower than in the source water (treated sewage), the water from WRP 1, 3, and 9 still contained biological activity in many bioassays. Samples from WRP 4, which were already below detection limit in all bioassays in the source water, were also all below detection limit in the class A recycled water.

It is unclear if treatment had much impact on basal cytotoxicity (measured by Caco2-NRU, WIL2NS TOX and HepaTOX assays) at WRP 1 and 9 (even though no cytotoxicity was detected in those samples) because the detection limit for those samples in the WIL2NS TOX assay was slightly higher than for the other samples (0.15 rTU, vs 0.05 rTU for most other samples). Cytotoxicity at WRP 3 was in the same range as in the source water, slightly above the detection limit.

Genotoxicity (measured by WIL2NS FCMN) was also in the same range in the class A recycled water and source water (treated sewage) at WRP 1, 3 and 9. Mutagenicity (measured by Ames test) was detected in class A water from WRP 1 in the test configuration without S9 fraction. Addition of S9 fraction, which contains metabolising enzymes from the cytosolic and microsomal fraction of liver homogenates, reduced mutagenicity back to below detection limits (<0.05 rGTU). This indicates that either the mutagenic compounds were effectively detoxified by liver enzymes or the mutagenic


compounds were bound to components of the S9 fraction (such as lipids) and no longer bioavailable. Mutagenicity was not detected in any other class A samples (i.e. from WRP 3, 4 and 9).

Induction of liver enzyme activity (measured by the HepCYP1A2 assay) was below detection limit in class A water from WRP 1 and 4 (as it was in the source water for those plants), but increased by 2× and at least 4× at WRP 3 and 9, respectively, compared to the levels in the source water (treated sewage). The highest activity was found in class A samples from WRP 9 (up to 186 µg/L BaP Eq).

Ultrafiltration and chlorination/chloramination (at WRP 3 and 9) reduced the endocrine activity of the sewage samples by 10–83 per cent, but despite this reduction, the endocrine activity present in the source water was still detectable in class A recycled water samples. These results are consistent with expected removal efficacies of endocrine disrupting compounds by ultrafiltration and chlorination/chloramination (Snyder et al. 2007). Samples from WRP 4 did not have detectable endocrine activity, although no activity was detected in the source water to WRP 4, so it is unclear if UV and chlorine disinfection had any additional impact on removing the endocrine-active compounds. WRP 1 likewise had no detectable endocrine activity, even though up to 1.51 ng/L βE2 Eq had been detected in source water. This indicates that the dissolved air floatation/filtration and chlorination treatment were effective in remove the estrogenic compounds from the water. Steroid hormones are highly lipophilic and likely to be removed by adsorbtion mechanisms, such as those used in dissolved air floatation/filtration treatment. No thyroid activity (measured by TRβ-CALUX) was detected in any of the samples.

None of the class A samples caused any immunostimulatory effects (measured by the THP1+ assay), but a few of the class A samples (WRP 1 and 3) contained significant immunosuppressive activity (measured by THP1- assay) in a similar range to what was detected in the source water (treated sewage). The other two sites (WRP 4 and 9) did not contain any immunosuppressive activity in either the source water or the class A recycled water.

Finally acetylcholinesterase inhibition (measured by AChE assay) was not reduced by dissolved air flotation/filtration, ultrafiltration or chlorination/chloramination, with concentrations in source water and class A recycled water at WRP 1, 3 and 9 unchanged by treatment. Other work has shown that the inhibitory effects on the acetyl chlorine esterase activity were reduced following progressive treatment in a sewage treatment plant (Escher et al 2008). It should be noted however that the chlorpyrifos equivalent concentrations detected are well below the health guideline level of 10 000 ng/L in the Australian guidelines for water recycling (EPHC/NHMRC/NRMMC 2008).


Chemical analysis shows that class A recycled water still contained detectable concentrations of several of the priority compounds, with varying removal efficacies depending on the treatment type.

Similar to what was found in source water (treated sewage), the class A recycled water contained no detectable traces of hormones except for low ng/L of estrone at WRP 3 and 9, which relied on ultrafiltration and chlorination/chloramination. The very low concentration of estrone in source water at WRP 1 was removed below detection limit by dissolved air flotation/filtration.

Traces of the industrial compounds found in source waters (4-t-octylphenol and TCEP) were also found in class A recycled water at concentrations slightly lower but comparable to those in source waters.

Likewise pharmaceuticals were detected at similar concentrations in class A recycled water as in source waters at the plants using ultrafiltration and chlorination/chloramination (WRP 3 and 9). The poor removal efficacy of ultrafiltration and chlorination/chloramination for small organic compounds such as pharmaceuticals has been demonstrated before (Snyder et al. 2007). Lipophilic pharmaceuticals (such as diclofenac and gemfibrozil, with log Kow > 4) were effectively removed by


dissolved air floatation/filtration and chlorination (at WRP 1), while more water soluble pharmaceuticals (such as atenolol, caffeine and primidone, with log Kow < 1) were poorly removed and still present at concentrations comparable to the source water in class A water.

The same trends were also visible with pesticides, with poor removal by ultrafiltration when the compounds were present in the source water, and efficient removal of lipophilic compounds by dissolved air floatation/filtration with variable (but generally less efficient) removal of the more water-soluble pesticides (like the herbicides).

Finally, disinfection by-products (and particularly THMs) were very high in a few of the class A water samples. The THMs were found in chlorinated samples (WRP 1, 4 and 9), up to µg/L concentrations at all three sites. Chloramination was used instead of chlorination at WRP 3, and no THMs were detected there. Average NDMA concentration was below detection limit at all four class A production sites.

C ombined analys is

The trends in chemical concentrations determined by chemical analysis were in agreement with those observed for biological activity in the bioassay analysis.

Ultrafiltration and chloramination (WRP 3) was ineffective for removing organic compounds, with concentrations in class A recycled water on average eight per cent higher than in treated sewage at those plants (based on 34 compounds detected in both source and final water). Ultrafiltration and chlorination (WRP 9) was slightly more effective, with an average removal of 28 per cent (based on 23 compounds detected in both source and final water). Free chlorine has previously been found to oxidise trace organic compounds to some extent (Snyder et al. 2007), and chlorination could be responsible for the slightly better removal compared with ultrafiltration and chloramination (at WRP 3).

Dissolved air flotation/filtration and chlorination was effective at removing lipophilic compounds, but more variable with water-soluble compounds (log Kow < 3; Error! Reference source not found.).

Figure 15. Relationship between Kow and removal efficacy during dissolved air floatation/filtration and chlorination (WRP 1), for compounds with a concentration of at least 30 ng/L in the source water

Ultrafiltration and chloramination (WRP 3) slightly reduced chlorpyrifos and diazinon, and acetylcholinesterase activity (measured by AChE) likewise was slightly reduced (from 35 to 30 ng/L


Chlorpy Eq). The two compounds account for 27 per cent of the biological activity. Ultrafiltration followed by chlorination had no effect on the AChE activity at WRP 9, and it is still unclear what compound(s) is causing the activity, although based on the specificity of the endpoint, it is likely to be a related organophosphate or carbamate pesticide. Diazinon was effectively removed by dissolved air floatation/filtration and chlorination, but surprisingly AChE activity was still comparable to that in the source water (71 vs 78 ng/L Chlorpy Eq). The reason for this discrepancy is unclear. It is possible that the high level of chlorination at WRP 1 (as indicated by the high concentrations of THMs) oxidises diazinon to a slightly different chemical structure (which would not be detectable as diazinon in the chemical analysis), which retains most of its activity in the AChE assay. This is currently unknown, and more experiments are needed to test this hypothesis.

Chlorine disinfection resulted in a significant increase in disinfection by-products (such as THMs), which are likely the cause of the slight mutagenic and immunosuppressive activity at high levels of DBPs (WRP 1) but not at intermediate levels (WRP 4 and 9). Chloramination on the other hand did not create THM disinfection by-products (at WRP 3) or a detectable increase in genotoxicity or mutagenicity.

Reverse osmosis (RO)

Most activity and compounds were effectively removed by reverse osmosis treatment, as described below.

B ioas s ay analys is

Low-level cytotoxicity (measured by WIL2NS TOX assay) comparable to that measured in the blank samples was detected at one of the RO sites (WRP 8). As previously discussed, this low-level activity is most likely an artefact of SPE concentration than a measure of real toxicity. Previous studies in WA (Reitsema et al. 2010) and QLD (Muller et al. 2007) likewise reported very low cytotoxicity (equivalent of 0.03–0.05 rTU) in highly treated water (including RO) using the MicroLumo/Microtox assay for bacterial cytotoxicity, and equally concluded that it was likely an artefact of SPE concentration than a real measure of toxicity.

There was no detectable genotoxicity (measured by WIL2NS FCMN assay) or mutagenicity (measured by Ames test) in any of the RO samples. This is comparable to previous Australian studies (Reitsema et al. 2010, Macova et al. 2010), which reported the equivalent of < 0.01 rGTU.

Likewise, there was no detectable induction of liver enzymes (measured by HepCYP1A2 assay) in any of the RO samples, even though CYP1A2 activity was significantly induced by the source water (treated sewage for WRP 2, 5, 6, 7 and 8). RO treatment was thus an effective barrier to compounds that can induce CYP1A2.

For endocrine activity, some of the RO samples at two of the plants (WRP 2 and 7) had low sub-ng/L estrogenic activity. A previous WA study (Reitsema et al. 2010) reported < 1 ng/L βE2 Eq in RO-treated water. The detection limit in the current project was lower (0.02 vs 1 ng/L βE2 Eq), so the detection here may be a reflection of the improved detection limit. There are no bioassay-based guidelines for water recycling, but the Australian guidelines for water recycling, Phase 2 –augmentation of drinking water supplies (EPHC/NHMRC/NRMMC 2008) set guidelines for estrogens ranging from 1.5 ng/L for the potent synthetic estrogen ethynylestradiol to 175 ng/L for the natural hormone 17β-estradiol. The levels reported here are much lower than these levels, and unlikely to be of health significance to humans exposed through drinking. Anti-estrogenic activity was also detected just above the detection limit at two of the plants, WRP 5 and 6. Little is known about anti-estrogenicity, and to our knowledge this is the first study to quantify it in RO-treated water. No other endocrine (androgenic, antiandrogenic, glucocorticoid, progresterone and thyroid) activity was detected in any of the RO samples, indicating that RO treatment effectively removed all PR and GR-like activity that was detected in the treated sewage going into plants WRP 2, 5, 7 and 8.


The RO samples did not display immunostimulatory activity (measured by THP1+ assay) or, with the exception of WRP 8, immunosuppressive activity (measured by THP1- assay). One of the 4 RO samples from WRP 8 exhibited low immunosuppressive activity (0.61 µg/L Dexa Eq). To our knowledge this is the first time this assay is used for water quality monitoring, and no other data is available for comparison.

Acetylcholinesterase inhibition (measured by AChE assay) was not detected in any of the RO-treated samples.


Very few compounds were detected in RO-treated water, suggesting that RO is an effective barrier against trace organic compounds.

No hormones or industrial compounds were detected in any of the RO-treated samples.

Likewise pharmaceuticals, which were sometimes present at µg/L concentrations in source water (treated sewage), were not detected in RO-treated water from WRP 2, 5, 7 and 8. Two pharmaceuticals were detected close to the detection limit in RO water from WRP 6. These were carbamazepine at 8 ng/L and metformin at 12 ng/L, reduced by 99 per cent from the source water concentrations (676 and 1,320 ng/L, respectively). The recycled water guidelines for carbamazepine and metformin are 100 000 and 250 000 ng/L, respectively (EPHC/NHMRC/NRMMC 2008), and the concentrations here are several orders of magnitude lower than these health guideline levels.

No personal care products were detected in any of the RO-treated samples from WRP 2, 5, 7 and 8. The anti-bacterial compound triclocarban, used in disinfectants and soaps, was found at the limit of detection (5 ng/L) in RO-treated water from WRP 6. No guideline could be found for triclocarban, but a conservative interim guideline of 350 ng/L can be derived using the framework in the Australian guidelines for water recycling (EPHC/NHMRC/NRMMC 2008). The concentration of triclocarban detected here is significantly lower than this interim guideline level.

None of the monitored pesticides were detected in any of the RO-treated samples from WRP 5, 7 and 8, but two of the monitored herbicides (diuron and simazine) were detected at ng/L concentrations in RO-treated water from WRP 2 and WRP 6. At WRP 2, very high simazine concentrations were detected in the source waters (treated sewage) on two of the sampling days (>20 000 ng/L, above the reliable quantification range). On those same two days, simazine was detected in the RO-treated water, at 112 and 118 ng/L. These concentrations are more than 99.5 per cent lower than in the source water, and several orders of magnitude lower than the recycled water guidelines of 20 000 ng/L (EPHC/NHMRC/NRMMC 2008), but clearly highlight that RO membranes are a very efficient but not complete barrier to trace organic contaminants. Diuron was also detected at 15 ng/L in RO-treated water from WRP 6. While detectable, this concentration is also several orders of magnitude below the recycled water guideline of 30 000 ng/L (EPHC/NHMRC/NRMMC 2008).

Finally, THMs were detected in samples from WRP 5 and 8. THMS are frequently detected in chlorinated waters, and the concentrations of THMs detected here (<20–344 ng/L) were within the same range as THMs in tap water samples. Both were orders of magnitude below the levels of 6000–200 000 ng/L for each individual compound in the recycled water guidelines (EPHC/NHMRC/NRMMC 2008) and 250,000 ng/L for total THM in the Australian drinking water guidelines (NHMRC 2004). NDMA was not detected in any of the RO-treated water samples.

C ombined analys is

Both bioassay and chemical analysis show that reverse osmosis is an effective but not absolute barrier to trace organic contaminants. On a quantitative basis however, the bioassays and chemical analysis did not agree as well as with the other sample types. The detection limit of the bioassays


were, in some instances, lower than the chemical method detection limits and it is possible that low concentrations of compounds (below chemical detection limits) may still be detectable by bioassay analysis.

No compounds were detected in RO samples that could explain the low estrogenic and anti-estrogenic activities reported by the ERα-CALUX bioassay. Many compounds are lightly estrogenic (Table 21), and it is probable that the activity is the result of the very low activity of several compounds present but below the chemical detection limit. A recent paper predicted concentrations of roughly 0.5 ng/L βE2 Eq in RO-recycled water based on the highest reported concentrations of 13 estrogenic compounds (Leusch et al. 2009), and the concentrations reported here are slightly lower than that prediction (0.17 and 0.08 ng/L βE2 Eq at WRP 2 and 7, respectively). Assuming water consumption of 2 L per day, the daily estrogenic intake from recycled water at these concentrations was less than 0.1 per cent of the total daily intake, with dietary source responsible for most of the daily estrogenic intake. Little information is available about anti-estrogens, and most currently-identified anti-estrogens are synthetic drugs (Van der Burg et al. 2010). Plasticisers and phthalates are known weak estrogen mimics (Leusch et al. 2009) and may also be weak anti-estrogens. It is possible (although this remains to be proven) that low concentrations of plastics and phthalates from membrane material could be causing this activity.

The low immunosuppressive activity detected on one occasion in RO-treated water from WRP 8 may be attributable to unknown disinfection by-products. Trials of different disinfection regimes were underway at WRP 8 during our sampling effort, and THMs were detected in that same sample. While some THMs were active in this assay, they were not present at sufficiently high concentration to explain the detected biological activity. However it is likely that other DBPs were also present in the sample and those unknown DBPs may be the cause of that activity. More research is needed to understand what compounds cause immunosuppressive activity in the THP1- assay.

5.6.2 Performance of the bioassay toolbox

Relating to conventional chemical analysis

Overall, there was good agreement between the results of the bioassay toolbox and conventional chemical analysis. With ‘effect fingerprints’ for only 39 compounds, it is not yet possible to link individual compounds to a bioassay response, but it was possible to link classes of compounds to specific bioassay responses and use bioassays as indicators of classes of pollutants in the water sample (Table 30).


Table 30. Bioassays as indicators of classes of pollutants.

Bioassay Human domestic wastewater (hormones, pharmaceuticals)

Disinfection by-products

Pesticides Other compounds

Cytotoxicity

Caco2-NRU Estrogen hormones, industrial compounds, pharmaceuticals

Possible Yes ?

WIL2NS TOX Estrogen hormones, industrial compounds, pharmaceuticals

Possible Yes ?

HepaTOX Estrogen hormones, industrial compounds, pharmaceuticals

Possible Yes ?

Genotoxicity

WIL2NS FCMN Androgen hormones, synthetic estrogens, pharmaceuticals

Possible No ?

Mutagenicity

Ames test No Yes (but not THMs)

No ?

Induction of liver enzyme activity

HepCYP1A2 Industrial compounds, some pharmaceuticals

Unlikely Yes ?

Endocrine effect

AR-CALUX agonist (+) Androgen hormones, pharmaceuticals

No No ?

AR-CALUX antagonist (-)

Estrogen hormones, industrial compounds, pharmaceuticals

Unlikely Yes Likely

ERα-CALUX agonist (+)

Estrogen hormones, and to a lesser extent androgen and pharmaceuticals

Possible, but potency so low that unlikely

Yes Likely

ERα-CALUX antagonist (-)

Likely Possible, but potency so low that unlikely

? Likely

GR-CALUX Human hormones, pharmaceuticals

No No Probable

PR-CALUX Human hormones, pharmaceuticals

No No ?

TRβ-CALUX No No No ?

Immunostimulation

THP1 cytokine inducer (+)

No No No ?

Immunosuppression

THP1 cytokine inhibitor (-)

Estrogen hormones Yes (but probably other than THMs)

No ?

Acetylcholinesterase inhibition

AChE assay No No Yes ?


C ytotoxic ity

Of the cytotoxicity assays, the WIL2NS TOX appeared to be the most sensitive, providing a few detections, particularly with treated sewage samples. The HepaTOX assay was much less sensitive, with quantifiable activity in sewage samples from one site only. No cytotoxicity was detected in any of the samples with the Caco2-NRU test, although its detection limit was 10× higher than the other two assays (0.5 vs 0.05 rTUs) because of the way the assay was performed. A slight modification in operating protocol (increase of the sample loading from 0.1 to 1 per cent) would result in detection limits comparable to the other assays with no negative impact on the assay system.

G enotoxic ity

Hormones, pharmaceuticals and to a lesser extent some disinfection by-products caused genotoxicity in the WIL2NS FCMN assay. This was noticeable in the water samples as well, with detectable genotoxicity in samples that contained domestic wastewater or disinfection by-products.

Mutagenic ity

Mutagenicity has previously been associated with disinfection by-products (NRC 1998). In this project, the THMs tested did not induce mutagenicity in the Ames test. However the samples with the highest THM concentrations also were the only ones to cause detectable mutagenicity. This suggests that mutagenicity is associated with DBPs, but not those monitored in this project.

E ndoc rine ac tivity

Several endocrine endpoints were included in the bioassay battery deployed in this project. Many compounds were active in the ERα-CALUX assay, although with very low potencies compared with the hormones (often <1 000 000× or less potent than 17β-estradiol). Generally however, ERα-CALUX activity indicates the presence of estrogen hormones and plasticisers. Only a few samples induced anti-estrogenic activity and the causative chemicals are unclear, but plasticisers are likely candidates. Only androgen hormones and pharmaceuticals were biologically active in the AR-CALUX assay, while many estrogenic compounds proved to be anti-androgens. No androgen hormones were detected in any of the samples, and only one sample was active in the AR-CALUX. None of the compounds tested were highly active in the GR-CALUX assay, however some activity was detected in treated sewage samples and one class A recycled water sample. Other research has suggested that corticosteroids and other pharmaceuticals may explain some of the GR-CALUX activity (van der Linden et al. 2008). Levonorgestrel, an active ingredient in contraceptive pills, had significant activity in the PR-CALUX. Other researchers have suggested that natural hormones (such as progesterone) and other pharmaceuticals may also be responsible for some of the PR-like activity detected in environmental water samples (van der Linden et al. 2008). In this project, a positive PR and GR-CALUX result was well correlated with treated domestic wastewater and pharmaceuticals. None of the compounds tested resulted in significant TRβ-CALUX activity, and neither did any of the samples.

Immunos timulation

None of the compounds or samples tested caused any detectable activity in the THP1+ assay for immunostimulation.

Immunos uppres s ion

Estrogens (particularly estrone) and to a lesser extent THMs caused immunosuppression in the THP1- assay. A positive THP1- result always corresponded with the presence of estrone or THMs. The concentration of THMs was however not sufficient to explain the THP1- response, but it is likely that other DBPs also present in the water caused the slight activity in some of the chlorinated water samples.


A c etylc holines teras e inhibition

Of the tested compounds, chlorpyrifos and diazinon were potent AChE inhibitors. In most samples, the concentrations of those two compounds explained most of the AChE inhibition measured by the AChE assay. In a few samples, however, this was not the case. Considering the specificity of the endpoint measured by this assay, it is likely that other related organophosphate or carbamate pesticides may have been present and responsible for the bioassay activity.

Relating to toxicity testing

It is difficult to say how protective of human health the bioassay battery is without whole animal (in vivo) or epidemiological data,. Some of the endpoints used have been shown to be well correlated with in vivo measurements. For example, toxicity in the Caco2-NRU test was well correlated with acute toxicity in vivo (Konsoula & Barile 2005) and estrogenicity and androgenicity in the ERα- and AR-CALUX assays, respectively, were well correlated with in vivo estrogenicity and androgenicity (Sonneveld et al. 2006).

The battery was developed to provide a measure of potential toxicity for human health endpoints that were highly relevant to exposure to organic contaminants from drinking water (Chapter 3). A few relevant endpoints were however not included in the battery: developmental and reproductive toxicity. Development and reproduction are meta-cellular events and it is currently not possible to adequately predict toxicity to these events in humans using in vitro models. Future developments in biotechnology may make these possible, and the bioassays to be included in the toolbox should be constantly re-evaluated.

5.7 C onc lus ions The main conclusions for this chapter are:

• bioassay and chemical analysis were in agreement and complementary

• classes of chemicals can be identified by their ‘effects fingerprint’ in the bioassay battery, but significantly more chemicals need to be tested in the battery to develop individual chemical fingerprints

• the bioassays were able to detect activity at concentrations below current chemical method detection limits and to provide a sum measure of all biologically-active compounds for that bioassay, thus providing an additional degree of confidence in water quality

• reverse osmosis was a significant but not absolute barrier to trace organic contaminants. However, all detected organic contaminants in RO-treated samples were several orders of magnitude below guideline levels.


6 Enhancing risk communication from science to policy and regulation and implementation of recycled water in Australia

6.1 Introduc tion One of the major impediments to water recycling in Australia has been the lack of public acceptance and clear regulatory guidance relating to the chemicals that remain in the treated water. Given that public perceptions of risk are a key factor in the rejection of water recycling schemes (e.g. Hurlimann & Dolnicar 2010; Po et al. 2003; Ross 2009), a major challenge in gaining public acceptance will be improving the way estimated risk is communicated. Risk communication has been defined as:

‘An interactive process involving the exchange among individuals, groups and institutions of information and expert opinion about the nature, severity and acceptability of risks and the decisions taken to combat them’ (enHealth 2004).

As public perceptions of risk have been shown to be influenced by perceptions of the credibility of the responsible authority (e.g. policy maker/regulator or water manager), it is vital that policy makers, regulators and water managers provide assurance to the community that they are making decisions informed by the best-available science. To achieve this it will be necessary to establish and maintain stronger links and more comprehensive communication between scientists, policy makers and managers of recycled water.

6.2 R is k perc eptions and the rejec tion of water rec yc ling s c hemes

Research to date shows that high levels of public support for water recycling are usually only seen in non-potable contexts (e.g. Hurlimann & McKay 2004; Marks 2004). There have been noteworthy examples in Australia and the US where the public has rejected proposed indirect potable reuse projects because of perceived health risks. In Australia, proposals for indirect potable reuse (IPR) projects by the Maroochy, Caloundra and Toowoomba councils were strongly opposed by a subset of their communities and the schemes were ultimately rejected. Public opposition to the projects was fuelled by campaigns from opposition groups such as Citizens Against Drinking Sewage (CADS) who warned of alleged health risks associated with drinking recycled water For example, claims of the presence of ‘gender-bending’ hormones, infectious hospital, abattoir and industrial waste in the recycled water (Po et al. 2003).

More recently, in south-east Queensland, the Western Corridor Recycled Water Project was put on hold, despite the completion of the extensive infrastructure necessary for the project. Extensive media speculation (e.g. Roberts 2008) regarding possible health risks impacted significantly on community


confidence.1

As demonstrated by the cases of unsuccessful IPR projects discussed above, a central factor influencing public acceptance of recycled water is the perceived public health risk. The rejection of these projects clearly demonstrates that the health risks associated with recycled water are not well understood by the public and are perceived as unacceptable. To increase public understanding of IPR projects and enhance public confidence in the system of risk management, it is vital to improve the way estimated risks associated with recycled water projects are communicated within the industry and to consumers.

As a result of erosion of community confidence in the project (as well as unexpected rainfall which restored dam levels) the government changed its policy to only introduce recycled water to drinking water supplies in an emergency – which would be triggered by a drop in combined SEQ dam levels to 40 per cent (Queensland Water Commission 2008).

It has been demonstrated that the way the public perceive risk is different and more complex than the way technical experts view the risk. However, public confidence can be seriously weakened if the authority is perceived to be incompetent, biased or compromised, or appear to arrive at decisions without considering the public (Baggett et al. 2006). When criticism and misunderstanding exist between scientists, policy makers and the public, the result is a loss of the public’s faith in the ability of science to solve its problems and in political leaders to act in the public’s interest (Garvin 2001). It is crucial to ensure that research scientists, policy makers and managers of recycled water understand each other before attempting to communicate risks to the public. Stronger links and improved communication between these groups will also ensure the public that policy decisions are informed by best-available science. The risk communication process should enable all stakeholders to make informed decisions about risks and their management (enHealth 2004).

6.3 S c ienc e, polic y and prac tic e Within the ‘science to policy’ literature, attention has been drawn to the gap that seems to exist between the science and policy making communities (Haas 2004; Lomas 2000; Owens et al. 2006; Pohl 2008; Tsui 2006; WHO 2009). The importance of bridging this gap has been highlighted across a range of contexts, including the area of water supply and reuse (see CCME 2002; Matthews 2008; Quevauviller 2009). Much of the literature emphasises the importance of establishing and maintaining links and more comprehensive communication between researchers, water policy and program managers (CCME 2002; Lomas 2000; Quevauviller 2010).

To achieve the goal of strengthening links between research, policy and practice it is important to first understand why members of these groups often talk past and through one another (Garvin 2001). It has been suggested that gaps between science, policy and practice are due to different values, goals and priorities (Francis et al. 2005; Tsui 2006). For example, Bosch et al. (2003) asserted that researchers and policy makers often work in independent cycles – researchers acquire knowledge, while policy makers and managers try to apply knowledge without any feedback loop having been set up between the two groups. Baggett et al. (2006) also suggested that one of the most difficult aspects of translating science into policy is scientific uncertainty. While scientists are familiar with uncertainty and complexity, policy makers, on the other hand, often require certainty to guide their decisions. To ensure a better understanding of each other’s worlds, more consideration of each other’s values and objectives is required (Lomas 2000; Tsui 2006).

1 Market research conducted by the government showed support for the project dropped from 75 per cent in early 2007 to only 55 per cent in late 2008 (Queensland Water Commission 2008).


6.4 A theoretic al approac h Garvin (2001) used a critical theoretical approach to explore how evidence is recognised and validated, and how limits are placed on knowledge by research scientists, policy makers and the public. She provided a discussion on how evidence and knowledge is used in relation to risk by synthesising theoretical developments across fields such as the sociology of science, public understandings of science, risk analysis and communications, and policy development and analysis. Garvin suggests that research scientists, policy makers and the public each employ their own rationality when evaluating evidence, an approach that can produce considerable criticism and misunderstandings between each group. For example, Garvin argues that scientists often have difficulty understanding the political nature of the policy process, while policy makers often see research scientists as methodologically rigid, limited in scope, and non-committal in conclusions. However, she did not provide any empirical evidence to support these claims. In addition, although this appears to be a global discussion, Garvin was not specific about in/for which contexts or demographics these theoretical approaches were developed, making it difficult to apply this approach generally.

6.5 E mpiric al res earc h

6.5.1 The science policy gap

With the aim to provide some of the first empirical data addressing questions about how science informs policy decision-making, Francis et al. (2005) conducted interviews with local policy planners within the context of the land-use policy process in Washington State. The research specifically investigated how policy planners define, collect and interpret the best-available science.

Results showed that peer-reviewed literature and government agency publications were the most commonly used types of scientific information. Other types of information used were expert opinion and non-peer reviewed information such as monitoring data, inventories and jurisdiction research. The findings also revealed that the incorporation of best-available science into local policies varied across jurisdictions, and that in many cases, political or legal influences had a greater influence than science on decisions about natural resources.

Variation was also found with how jurisdictions made decisions about conflicting scientific information. Some respondents reported using the most reliable and rigorous science available, while others reported using state government sources or considering political or legal influences. According to Francis et al. (2005), scientists sometimes present conflicting information to policy makers without providing adequate tools for handling scientific uncertainty and disagreement. They suggest that research scientists and policy makers have not yet determined how to make the relationship between best-available science and environmental policy work.

Francis et al. (2005) concluded that the different values and objectives inherent in science and policy prevent successful partnerships. Although the research by Francis and colleagues provides insight into the science policy gap in the land use context, it should be noted that the sample (specifically chosen to include areas in Washington State required to update their planning processes) is unlikely to generalise to wider populations.

6.5.2 Differences in stakeholders’ risk perceptions

In the context of participatory planning of water reuse projects, Baggett et al. (2006) investigated the levels of knowledge and understanding between and within stakeholder groups. They investigated the views of stakeholders from four groups (regulators, researchers, managers and domestic consumers) regarding their perceptions of the most important risks of a water reuse project. They also asked each


of the groups how similar or different they thought they were to each other in terms of perceived risks. The coded and grouped responses identified and classified the differences between the stakeholder groups.

The results suggested that levels of expected agreement regarding risk varied between different stakeholder groups and also showed a marked difference in expectations of the most likely potential risks. Baggett et al. (2006) propose that these findings could help to describe the requirements of each group and guide efforts to improve social learning between the stakeholder groups. The authors concluded that early assessment of risk perception and the impact it might have on other stakeholders could have long term benefits for reuse projects. As with Garvin’s paper, no information was provided about the area or demographics from where the data were collected.

6.5.3 Options for improving communication

The Canadian Council of Ministers of the Environment (CCME 2002) conducted a Water Reuse and Recycling workshop as part of a series of workshops on linking water science to policy. The aim was to communicate the results of new research and management practices to senior decision makers and policy makers as a means for scientists and policy makers to provide expert input into water programs.

Workshop participants were enthusiastic about the need to maintain and improve communication between researchers and water policy and program managers. In the workshop report a range of options for maintaining and expanding on the dialogue were recommended. These were:

• to create a committee/task force of academic, industry and government experts to develop a Canadian context for recycling

• identify short- and long-term implementation opportunities

• refine research needs

• convene periodic follow-up workshops for both the science and policy communities

• the use of electronic networking as a means of ensuring information flow.

Given the need to improve understanding between scientists, policy makers and managers of recycled water, these recommendations would appear a practical starting place for addressing communication issues between these groups. The workshop report stated that ultimately, the logic for bringing researchers and public policy managers together is to make better public policy decisions.

6.5.4 What is working, what is not, and why

Of all the recent research on the science to policy link, perhaps the most informative results were provided by Holmes and Clark (2008) who reported on research conducted in the UK by the Environment Research Funder’s Forum (ERFF) which investigated what is working, what is not, and why, in relation to the link between science and environmental policy making and regulation. The studies were conducted in response to the UK government’s strong promotion of the more effective use of science to inform policy and regulation.

The research was conducted through semi-structured interviews with participants selected to represent different roles relating to the science-policy interface. A scoping study was initially conducted to identify key issues needing to be addressed to enhance the use of science in policy and regulation. This was followed by a more in-depth study which built on the findings from the scoping study. The second study addressed four key issues including establishing research questions and agendas; accessing information and expertise; the role of interpreters; and transparency and evaluation.


In terms of the first key issue – establishing research questions and agendas – participants identified a number of difficulties. These were associated with scientific evidence not being used enough in establishing policy, the framing of policy questions and lack of time devoted to the anticipation of issues requiring research, the need for a better understanding of public concerns from researchers and policy makers, and research often not providing sufficiently coherent outputs to inform policy making.

With regard to the second key issue – accessing information and expertise – participants expressed concerns that policy makers made relatively little use of papers published in peer-reviewed journals. However it was also acknowledged that most scientific papers tend to be too technical and detailed for policy makers and that policy makers do not have time to read enough papers to develop an overall understanding of an issue. Assessing the reliability and quality of information was also a concern for policy makers, as was knowing how to contact experts about particular issues. Participants highlighted the need for a searchable database or register of experts as well as the need for more opportunities for researchers and policy makers to meet and interact.

Respondents also acknowledged that barriers to communication between researchers and policy makers often led to a science policy gap and the need was highlighted for the role of interpreters to facilitate interactions between researchers and policy makers. Participants suggested that this role should involve describing the policy implications of research findings to policy makers, facilitating the development of research questions to meet policy needs and communicating these to researchers, and providing a balanced overview and synthesis on scientific knowledge relating to policy issues. In terms of transparency, the importance of the science to policy process in engendering trust was emphasised. The need was stressed for the establishment of clearer ‘audit trails’ to evaluate how science is used in policy making.

In presenting the results of these studies, Holmes and Clark provided an informative and up to date evidence base on what is working and what is not regarding science and environmental policy-making and regulation in the UK. As they argue, this provides a basis for new initiatives, as well as ways to address some of the problems identified. Holmes and Clark assert that the results demonstrate a desire to strengthen the use of science and evidence in policy making and regulatory decision making, but caution that the actions of researchers and policy makers are as yet not appropriately aligned.

6.5.5 Summary

Overall the results of the literature reviewed point to a general need to establish and maintain stronger links and improved communication between research scientists, policy and program managers to reduce the science-policy gap, and to facilitate better public policy decisions. Public confidence can be seriously diminished if authorities (e.g. policy makers and water managers) are perceived as incompetent or biased. Given the history of public rejection of indirect potable reuse schemes in Australia, this will be crucial to acceptance of future schemes.

6.6 T his res earc h This study aimed to understand risk communication between scientists, policy makers and managers of recycled water. The results of this research will provide a baseline measure of current practices in science, policy and regulation, and practice with regard to water recycling in Australia. As reuse is currently ‘not a policy option’ as a potable water supply option in most states in Australia, the context of the research will be recycled water for a range of uses (including potable reuse). For the purpose of this research, recycled water will include both effluent reuse and stormwater reuse. Given that little is known about risk communication between these groups and that to the author’s knowledge, there has been no research conducted in this specific context in Australia, the study will provide a starting point for understanding the role of communication between science, policy and practice in the context of


water recycling in Australia. It is intended that this research will identify key issues, and provide a basis for future research and industry practice.

6.6.1 Research questions

Based on the literature reviewed and the identified need to establish stronger links and better communication between scientists, policy makers and managers of recycled water in Australia, the following research questions were formulated:

• What are the perceived obstacles to improving links between science, policy and regulation, and practice with regard to water recycling in Australia?

• What are the best sources of technical information to guide decision making about recycled water and how are these accessed?

• Are difficulties experienced in accessing and interpreting scientific information to inform policy, regulation and practice?

• Are the science, policy and implementation processes consistent across Australian states and territories?

6.7 Method

6.7.1 Participants, procedure and measures

Semi-structured, individual, face-to-face interviews were conducted with water industry representatives from Queensland, New South Wales, Australian Capital Territory, Victoria, South Australia and Western Australia. Potential interviewees were recommended initially by senior water industry representatives as having significant knowledge and experience in the industry. Interviewees were purposefully selected from this group by the researchers to provide a broad range of perspectives on science, policy and practice in water in Australia. They were contacted by email and invited to take part in the research. An attempt was made to have representatives from each of the research, policy and regulation, and industry sectors for each participating state, although this was not possible in every case. Several interviewees were also national representatives.

Twenty interviewees participated. The sample consisted of 11 females and nine males, with experience in the water industry ranging from three to thirty years. The numbers were equally divided among the stakeholder groups. The average water industry experience across the sample was 14.7 years. Information regarding the purpose and scope of the research, including assurance of confidentiality and anonymity, were provided to participants in advance. Ethical clearance was provided by the Griffith University Human Research Ethics Committee. The interviews were recorded and took between 30 and 60 minutes to complete. They were all conducted by the same researcher to ensure consistency and reliability across interviews.

The semi-structured questionnaire was designed to gain perspectives on the way water science is transferred through to policy and regulation, and then to the implementation of the policy in Australia. It also aimed to identify any key issues and investigate whether issues experienced overseas (e.g. difficulties accessing and keeping up with scientific information, communication issues) were also a concern in Australia. The semi-structured format was chosen because it provided enough consistency across interviews for points of comparison, while still being open ended to elicit in-depth responses and flexible enough to be tailored to different perspectives of the participants.

To provide some background information, participants were initially asked to briefly describe their professional background and current role, as well as the length of time working in the water industry. The first set of questions related to the water industry in general (including recycled water). The


questions asked participants to describe the ways that they knew that water science was translated into policy and whether they perceived obstacles to stronger links between science and policy/regulation, and from policy/regulation to practice.

The next set of questions was specific to recycled water (including potable reuse). Participants were asked whether they thought it was important for scientists, policy makers and regulators, and water managers to be on ‘the same page’ in terms of the safety of recycled water. They were also asked what they thought were the most important sources of technical information to guide decision making with regard to recycled water and whether they felt policy makers and regulators experienced difficulties in locating this information.

There were also questions about whether participants thought federal and state policies and regulations governing recycled water were consistent, and whether they thought there were differences in how scientists, the public, policy makers and regulators, and water managers perceived possible risks associated with recycled water. Participants were also asked about the importance of language and terminology in communicating about recycled water and what they thought were the most important issues for gaining public confidence in the risk management of these projects. The questionnaire concluded with an open-ended question which asked interviewees if they would like to make any other comments regarding communication around recycled water in Australia. This provided an opportunity to gain any additional information that may not have been captured through the interview.

The digitally recorded interviews were converted into verbatim written transcripts using a professional transcription service. All transcripts were checked for accuracy and spelling against the original recordings before being imported into the qualitative data analysis program ‘NVivo’. The data were analysed using thematic analysis – a method where the researcher plays an active role in identifying relevant patterns or themes in the data (Braun & Clarke 2006).

Given that previous research has not been conducted specifically in this area, an inductive or ‘data-driven’ approach was used, where the themes identified were linked to the content of the data rather than to a pre-determined coding frame. This approach is useful when investigating an under-researched area, or when working with participants whose views on the topic are not known (Braun & Clarke 2006).

The data were coded systematically into themes and subthemes that captured important and relevant information. A number of key themes were identified and appeared to fit clearly under the general topics of research issues, science-to-policy translation, implementation issues, consistency of guidelines and regulations, political nervousness around recycled water, whether science, policy and regulation and industry were ‘on the same page’ in terms of the safety of recycled water, risk perceptions, and the use of language and terminology regarding recycled water.

6.7.2 Issues identified around recycled water research

Although the original research questions related specifically to communication from science to policy and to practice, some significant issues were also identified within the water research sector itself. These related to difficulties with the current research funding models in Australia, a need for a more strategic approach and better priority setting in research, and the need for a structure to support research dialogue.

Research priority setting

Interviewees were generally aware that water shortages were always likely to be an issue in Australia due to factors such as climate change and population growth regardless of whether their area was suffering water shortages or had received rainfall. For example, “we haven’t seen the last of drought and climate change and water shortages and I think in a few years this will be alive again”. A number


of participants emphasised the importance of looking ahead and setting research priorities for the future. As one put it “research should be commissioned so it is there at the fall of the ball”. It was claimed that research priority setting for recycled water in Australia is currently a very random process set by individual researchers.

A number of interviewees spoke positively about research that was or will be a collaboration between government, industry and academics – and mentioned the Urban Water Security Research Alliance (UWRSA) and the Australian Centre of Excellence for Water Recycling (AWRCoE) as examples. However it was pointed out that there was still room for improvement in terms of better engagement between government departments, academics and researchers to aid priority setting.

Funding

Some interviewees identified the difficulties faced by researchers in terms of funding pressures. It was claimed that the funding models in Australia lend themselves to people competing for funds. Several participants also pointed out that researchers need to be seen to be different to receive funding grants – which creates disagreement among scientists. As one interviewee stated “if they’re contentious – they’re more likely to get money because if they’re going out for grants, they’ve got to establish a need to be funded for the funding to occur. If you go out and say everything is wonderful, everything is perfectly safe, that’s not going to attract a whole lot of money – so that can be an issue”. Another issue that was raised in relation to research funding was that funds are available for the development of, but not for the application of technologies.

Some participants also felt that scientists should take more responsibility in the context and outcomes of their research (i.e. in terms of how their research would be used and applied in policy and regulation). As one interviewee articulated, “a lot of researchers are happy to take the money and go off and do the research – but it can be too difficult to implement the outcomes – so they go off and do more research”. It was suggested that good scientists need to be good communicators and would deliver their message better if they could discuss their research and relevance of outcomes in layman’s terms.

A need for a more strategic approach

A significant theme identified in the data was that participants from every sector felt there was a strong need for a more strategic and national approach to water research in Australia. It was generally thought that the present approach to research was not coordinated and that there should be more cohesion within the research community. Others claimed that Australia is not big enough to have a number of different units working on the same areas at the same time. Participants also identified a need for better strategic alignment between research, government and industry representatives. It was suggested that researchers and regulators should line up their needs before applying for project funding.

Structures to support dialogue

Another issue raised was the need for a support structure to provide individual researchers with a dialogue. However, there were differing opinions on how this should work depending on interviewees’ perspectives. A number of interviewees suggested that researchers should have their own forum, just as the regulators have the National Recycled Water Regulators’ Forum. According to one industry member, an Australian peer network of scientists “would be more effective than having to rely on one or two scientific experts. That way they can work through the issues and get a consolidated view rather than one person having a specific view”. The recent recycled water guidelines were cited as a good example of successfully getting a group together to achieve something. However, concerns were also expressed about the possibility of politics and vested interests involved with establishing such a support structure. It was claimed that this had occurred in the past when establishing panels –


“only certain people are on the board and they get what they want”. An example was given where a scientific panel was established but had been perceived as being used by the government “for their own purposes”.

6.7.3 Issues with science to policy translation

In terms of science-to-policy translation, a number of interviewees felt that effective communication between scientists and the policy formulators and regulators was generally lacking. Several participants felt that there will always be some obstacles to science-to-policy communication because science and policy and regulation have different purposes and different agendas. It was also thought that a national process is needed for translating science into water policy and that professional networks played an important role in the translation of science into policy. It was reported that policy makers and regulators often had difficulties accessing and keeping up with scientific information and a need for a national database was identified.

Different perspectives, agendas and timeframes

Many participants described scientists and policy makers as being on different wavelengths. One suggested that scientists tend to be more interested in technical details and policy makers more interested in looking at the big picture. Another interviewee described the differences between scientists and regulators this way, “science is about new ideas. That’s the nature of science whereas regulation wants to see the application of that and that’s not always exciting for scientists as looking at new stuff on the horizon”. It was generally thought that there was a need for scientists, regulators and policy makers to talk to each other more so they can better understand each other’s roles.

The issue of the different timeframes between science and policy was described by one interviewee this way – “in policy they may have to respond to the minister’s request on the day, very reactionary, where research is a very long-term, forward thinking environment – the time frames don’t mix. Industry also has more of a short-term focus. Policy involves very short-term thinking and time frames to do things in”. The same interviewee also spoke of the difficulties involved with decision making in short time frames, saying that policy makers and regulators “don’t always get things right… It’s hard to simplify things. Good scientists and good policy people are the ones who can condense a complex issue into something that is digestible and makes sense to people without being too incorrect”.

Accessing and keeping up with scientific information

A number of interviewees from across all sectors felt that health departments are under-resourced, and that policy makers and regulators struggle to keep up with new information constantly becoming available. As one participant said “even for a technical person it is really difficult to keep across all the technical papers – it would be impossible for a policy person”. In contrast, there were several people who felt that policy makers did not try hard enough and that they should find a way to keep up to date with the science.

Another barrier to accessing scientific information was the pressure on scientists to publish their research in high ranking academic journals, which are not readily understood by non-scientists. As one researcher said “scientists tend to publish in papers that policy makers don’t read because they’re too technical and there’s way too much irrelevant information for them to sift through to find the relevant information. This requires some degree of interest and perhaps goodwill on the part of the scientists in order to make an effort to make findings and outcomes more easily digestible and available to policymakers”. Several scientists mentioned that they were having some success in getting around this problem by going out and giving presentations to regulators and by writing up lay-persons’ summaries of their research.


Some policy makers and regulators reported that they had scientists working in their departments who were able to act as interpreters. However, it was still thought that there was a need for more scientists/interpreters to be working in government departments, and for proper communication frameworks and strategies to be put in place. According to one participant, “the further up you go in government and policy the less (scientifically) literate they become because their backgrounds aren’t in science… you wouldn’t expect a minister or a director to read and understand a scientific paper because it’s all jargon”.

Other interviewees from policy and regulation who reported they did not have any difficulties with accessing and keeping up with information said that this was because they had well established professional networks and emphasised the importance of this. One participant said that although policy and regulation was a rapidly growing and changing field “the situation is improving; regulators have a better awareness of how they should be going about their job and where they might find people who might be able to answer some of their questions”.

Processes for water science into policy

A senior water industry professional pointed out that Australia does not currently have a system for translating science into water policy. He stated that “the science effort and water policy and water management are disconnected and the science effort is fragmented … there is very little process and both sides are to blame for that. Science providers, science users and science managers should all take their share of responsibility for this. Just as scientists are working hard to communicate their findings, there is a reciprocal obligation on policy makers and regulators to invest the time and intellectual effort into understanding the science”.

A number of participants felt that policy makers and regulators should be more proactive in approaching scientists, while several regulators felt that scientists should approach them to ask about the gaps in science that need to be addressed. Overall, it was generally agreed that more collaboration between researchers, regulators and policy makers would be beneficial. It was also noted that this type of coordination will not happen automatically and would require resources and incentives.

Several interviewees mentioned that there have been times where policy-relevant research has been conducted but policy makers and regulators only became aware of it after it was published in a journal or printed in a newspaper. One participant said “when you have individual researchers doing their own thing and not linked into a network where the results can be disseminated out to those who need them it can be a problem”. The need was identified to set up formal processes to bring scientists, policy makers and regulators together so that they could keep each other ‘in the loop’. It was also considered important that this process occurred early so that there was a connection with researchers before developing policies. A number of participants mentioned the importance of research organisations such as WQRA in providing research, although it was noted that very few regulators are able to afford the membership fees to join large research centres.

Participants cautioned that science-to-policy translation was more complex than simply asking the researchers “to give us the answers we want”. It was generally felt that stakeholders (i.e. researchers, industry, regulators and policy formulators) should get together as a group to work on issues. From one industry professional’s perspective this process was already occurring – “once we would have said ‘we need to get those researchers to give us the answers that we want’, now it’s more like ‘let’s sit down with everybody who’s a stakeholder in this and say, what do we need to do to remove the knowledge gaps that stop us from moving on with another source of water, and whatever it is put that together in a way that we can then as a group, researchers, industry, regulators, take the issue somewhere – get some advancement’”.


Relationships and networks

As mentioned earlier, strong professional networks were noted as being fundamental to bridging the science–policy gap. Many interviewees stressed the importance of developing and maintaining good relationships between scientists and regulators and that these relationships assist in accessibility to research information. However, a number of people thought that policy makers don’t necessarily have a network with the research industry and as a result experienced difficulties in locating scientific knowledge. As one regulator mentioned “where they do have connections there’s a lack of understanding of what each other is talking about – the policy maker’s language is often different to scientists language and sometimes it gets interpreted in any which way they choose”. To overcome this, participants felt there should be more formal networking opportunities and more formal consultation with the research industry.

A need for a national database

Another issue raised was that it would be very useful if there was a mechanism to make data from studies more widely available. As one interviewee said “utilities tend to keep their information for themselves and to show their regulators. There’s no real onus on those people to necessarily share that information. Although some is presented at conferences etc, it’s not done in any systematic way. There’s no real repository for this sort of information”. It was therefore suggested that a national database or a systematic way of collecting and sharing information from individual projects was needed.

6.7.4 Implementation issues

Policy to implementation was generally seen as the biggest obstacles in terms of the science, policy and practice continuum. One interviewee summed it up this way “I think the biggest … probably the biggest problem is implementation (in terms of science to policy to practice) and it's because nobody likes doing it. It's the hardest part, nobody likes doing it, a lot of people are happy to turn their backs on it and walk away”. Difficulties in complying with strict regulations, as well as time and costs involved with putting the required resources in place were seen as major obstacles to implementation. In addition, political nervousness around the use of recycled water and a lack of national consistency in the implementation of guidelines were also thought to be issues.

Regulation and compliance

Regulation and compliance were seen as the main barrier to implementation of recycled water schemes. As one interviewee put it “regulation and compliance cost money … anything you try and advance – the more regulations and economic impact it potentially has, the less likely it is to be implemented, even though the benefits might be very, very high. It will require more approval processes and cost more money”. Policy makers were described as ‘risk averse’ and some regulators were seen as too rigid in their approach. It was also claimed that policy and regulation people “don’t understand the practicalities of trying to replicate what was done in a laboratory in a university to conditions in the field”. As a result, participants said that some projects become impossible to implement due to time and cost, and they ‘fall over’. However, there were also examples given of where these barriers were being overcome. For example, one industry professional explained that to overcome barriers to implementation, their organisation made an effort to “bring our regulators along with what we want to implement, to ensure there is policy there that they can develop as we go, so there’s quite a close link then between the policy and being able to implement it”.

Practicalities of implementation

Many participants thought that the resources and effort required for implementation of projects were generally underestimated. For example, one interviewee said “getting from policy to actual


implementation is hindered by the resources attached to it … there will be policy decisions such as we will allow or we will encourage effluent recycling for country towns but to enable the country towns to actually do effluent recycling requires educational support … and technical support for them to do it safely … Is the appropriate regulatory structure in place to actually do that? There isn’t really a good analysis of the whole impact of what they’re intending to do with the policy. If that was to be done they would see, we actually need to put more resources in at this point rather than putting it in place and seeing there’s a problem. It certainly requires a lot of thinking by a lot of different agencies”.

The same interviewee also provided this valuable insight into the difficulties of implementing projects. “When you get right to the bottom of any program you probably need to put a lot more work into thinking about what you’re doing. That’s the boring bit. Who’s going to check the auditing? Who’s checking the compliance? Who’s following up with these things? What do we do when they fail? That gets ignored or lost in the initial let’s get this moving and let’s get it going”. The implementation of the federal government’s home insulation scheme was given as an analogy to implementing recycled water projects, “they’d made a decision at policy level that we want to spend this much money and then we’ll do it these ways and suddenly put a huge amount of money into it without the initial planning and without thinking how are we going to actually manage that program. That’s where it fell to pieces”.

A need was identified for more industry consultation and discussion with operational people about what would be needed ‘people-wise and resource-wise’. One interviewee made the point that a lot could be learnt from private contractors (e.g. Veolia, GHD, Black and Veitch) due to the experience and expertise they bring from implementing recycled water projects overseas.

6.7.5 Consistency of guidelines and regulations

Most participants spoke favourably about the national recycled water guidelines as a framework for the implementation of recycled water schemes. They were described as “world class” and “one of the best sources” of technical information on recycled water. However there were concerns that the implementation of the guidelines is occurring differently across jurisdictions. As one industry professional noted “while it is great to have guidelines rather than regulations, because it allows for innovation and flexibility, it does also create uncertainty”.

Views were divided as to the importance of consistency of regulations across states. A senior water industry professional felt that it was important to be consistent across states “because they can be reassured that if there’s litigation or a political challenge it puts them in a firmer situation. We’ve adopted the national standards … why don’t we adopt a harmonised regulatory routine – in a uniform way across Australia? Why should developers have to get expensive approvals over and over again - if the system works in Brisbane it will work in Sydney?”

Others felt that implementation was not one solution fits all, for example, “it’s going to be regionally dependent because each city and each area is different and has different resources available to it. There needs to be a realisation across Australia that we’ve got different resources and we’re working in different paradigms and what we need to do is what is environmentally sustainable and suitable for where we happen to live”. It was noted that building a national validation framework for recycled water would be addressed through one of the goals of the Australian Centre of Excellence for Water Recycling.

6.7.6 Political nervousness about recycled water

A number of participants noted that Australia does not have a ‘real’ national policy on water recycling. As one interviewee stated “there is a lot of nervousness when it comes to high end, high quality potable reuse … could have a stronger policy message coming out of Canberra … it is a bit of a toe in the water for the federal government”. Some interviewees felt that decisions about recycled water made by politicians and their advisors are influenced by vested interests and the media. Another


suggested that political decisions about potable reuse are also emotive and “even though we could probably sit with them and work out how to do it without any public health risk it’s not on the agenda ... it’s a very difficult thing to do in an election cycle”. It was also noted that one reason that desalination takes off so well is that “it’s quick to build, it’s a continuous supply and it’s something people can see – it’s easier to get through”.

One industry professional made the point that “politics can get in the way of good policy” and used the Western Corridor Recycled Water project being put on hold as a good example of this. “In the circumstances of the main dams being down to 18 per cent … it was good policy to secure a safe alternative water supply in purified recycled water, but it didn’t turn out to be good politics (in terms of public unpopularity)”.

It was generally thought that potable reuse should be ‘on the table’ as one of a range of water supply options. One interviewee summed it up this way “we’ve got increased population, climate change, and a whole lot of other things happening that means we’ll have to adapt how we live in this country and how we manage our water resources. Part of that is going to be increased use of recycled water. We need to get our politicians confident about what we’re doing … we have the technology to be able to produce safe recycled water and the real critical issue I see is to get enough political support and confidence to go ahead with it and then to bring the public with us”.

6.7.7 Is everyone on the same page?

Participants were vocal but polarised in their views about whether it was important for science, policy and regulation and industry to be on the same page in terms of the safety of recycled water. On one side participants reported that it was valid to have scientific disagreements and that healthy debate about the risks was important. As one person suggested “from a scientific viewpoint the only way to analyse or assess any situation is to look at it from different perspectives … If we expect everyone to be on the same page it is not science and it is not democracy”.

In contrast, others felt it was vital to all be on the same page regarding the safety of the quality of recycled water. One industry professional said “if you can’t at least get a consensus from experts that something is safe then even the scientists have got to worry – do we really know what we’re doing? ... If experts can’t legitimately agree then maybe we should take a more conservative approach”.

Other interviewees believed that differences in opinion on the safety of recycled water creates public uncertainty and undermines public trust. Some participants stressed that when communicating with the public and the media it was important to have a consistent message and a united front. “You don’t want an ugly conflict in public with industry saying this is safe and the health regulator saying it’s not”. A number of interviewees mentioned the damage done to public confidence in potable reuse when “the occasional renegade scientist” has made controversial comments about possible health risks, which were seen as motivated by a desire to attract publicity. The negative publicity about the Western Corridor Recycled Water Scheme (as mentioned in the introduction) was cited as an example.

Some participants were optimistic that regulators, science, industry are generally on the same page. It was also mentioned that WSAA and AWA do a good job in speaking with authority about water quality management, for example “this is the way the water industry in Australia manages this to best practice”.

6.7.8 Risk perceptions

Interviewees felt that peoples’ attitudes towards risk are generally influenced by their backgrounds, describing a continuum with scientists and engineers being the most comfortable with risk, and the general public the least comfortable. It was noted by a number of participants that generally people are comfortable with non-potable recycling and only perceive potable reuse as high risk. For example


“for indirect potable reuse the perception is always bad, as in that it’s high risk. I don’t think the public sees any real risk with a recycled water program that's anything other than indirect potable. I really don’t think they see any significant public health risk for it”.

Policy makers were described as having similar risk perceptions to the general public, for example “I think that a lot of people in policy don't necessarily have a technical background; they've got a law background. They don't have any more understanding of how safe it is or not. I think some of the policies have been written from that perspective. They think it's a really high risk activity that we're doing”. Several interviewees described policy makers and regulators as ‘risk averse’ – as one put it, “the regulator’s reaction to the fact that the public is so concerned is (that) they are overly conservative … especially health departments”. Others pointed out that policy makers and regulators have a responsibility to protect public health and therefore have to be conservative in their decision-making.

Some interviewees also felt that there were differences among regulators in terms of levels of understanding of risk and how to interpret it. One participant put it this way,“I think some of the regulators have different concerns to other regulators. I think the approaches to assessing risk are the same but I think the situation in which they’re interpreting the risk might be different ... to some extent … the health regulators also have that level of variability. There are some people within health departments that will be more averse to risk than others ... not just regulators but people in regulatory and policymaking positions in terms of recycled water and what’s a safe way to manage recycled water”.

The majority of comments on risk perceptions were related to the public and its ability to understand and assess risk, for example “I have to say to get the average member of the public with no background in science or probability statistics and so on to (have) a level of understanding where they can be comfortable in judging the different relative risks … maybe it’s unachievable. There’s a lot less general knowledge about recycled water in the general population than what there is with the industry professionals and the regulators. It’s not your regular person’s job to be able to assess risk. I mean they do it subconsciously in that they decide whether to step out in front of a bus on the road or not”.

A number of participants made the point that some of the language used to communicate risk can be alarming for the community, and that the public generally claim they would prefer no risk, even though that is not possible in everyday life. One participant summed it up this way, “another thing is to change the way that we talk about risk. Again, the general public don’t understand risk … It’s a very loaded term because risk (for) the general public seems bad and they prefer a scenario where there’s no risk. I think the public has a very low appetite for risk”.

Some interviewees were also critical of the media in misleading the public and distorting risk perceptions around potable reuse schemes. For example, “the media don’t necessarily understand either. You can tell by some of the things you see written … The media drive the education I guess, of the public and it’s often wrong”. It was also thought that the public were confused by ‘mixed messages’ they received and could be quite easily misled by the media.

6.7.9 Language/terminology

Among interviewees it was generally thought that confusing and inconsistent use of language was a barrier to communicating about recycled water; both within the industry and when communicating with the public. A number of interviewees mentioned that there was confusion about terminology within the water industry and that there was a need for a consistent and common language. This was felt to be particularly important when industry professionals are working together to discuss policy and regulation. As one participant said “I think this is a real problem because people use the same terms and mean different things. It can be quite difficult, I think, to get conformity across the industry. So you think you’re talking about the same thing, when in actual fact you’re not. So I think there’s a lot of difficulty in getting a coherent policy platform out there”.


Another interviewee provided this insight into difficulties around terminology “it’s so ingrained actually in our disciplines … people who work in science, it’s so ingrained in us to define uncertainty around whatever it is that we’re investigating. With engineers it’s so ingrained in them to be precise; you’re building a bridge of this dimension and that’s exactly how you build it. So I think there’s a difference, even among the professionals within the water industry, as to how we like to convey information and express it. That also creates uncertainty with the public because they go, well this guy said it was all right and this one says it’s plus or minus. So that is a problem that we need to address within the industry, to try and create a greater level of confidence in the community”.

It was noted that there is still much confusion among non-technical people about basic recycled water terminology. An example was given of a city councillor who spoke to the media about the council’s achievement in introducing ‘grey water’ to irrigate parklands, when in fact the water was treated sewage. A number of interviewees felt that confusing terms and inconsistent use of language created uncertainty and fear in the public. It was generally thought that when communicating with the public it was very important to provide clear and consistent messages using ‘plain English’.

There were some differences of opinion in how terminology should be used when communicating with the public and the media. For example, one interviewee pointed out that negative terminology around recycled water had developed because wastewater was traditionally viewed as a liability rather than a resource. He and a number of other participants felt that there was a need to develop a more positive terminology. In contrast, another interviewee felt strongly that “you get better respect if you call a spade a spade ... I think by using terms like purified recycled water and insisting on them, you can use that term if you want but if somebody else says this is treated sewage … and you say no it’s not, it’s purified recycled water… you’re trying to hide the reality … I think that’s counterproductive”. Several others concurred, cautioning that it was important not to “whitewash” and that public trust can be lost through trying to “paint too rosy a picture”.

Some participants mentioned that attempts have been made to improve recycled water terminology. For example, it was noted that an effort was being made through the National Recycled Water Guidelines to standardise the language, such as the terms ‘drinkable and non-drinkable’ being used rather than ‘potable and non-potable’. However, it was generally thought that there was much work needed to improve current terminology.

6.8 Dis c us s ion The current research aimed to provide a clear picture of current issues and practices in risk communication from science-to-policy and implementation with regard to water recycling in Australia. It is envisioned that the results presented from this research will provide a starting point for a better understanding of these issues and thus provide a foundation for improving the science, policy, practice, and process within the water industry.

6.8.1 Perceived obstacles

In addressing the first research question, a number of obstacles to improving links between science policy and practice were identified. One of the initial themes identified related to issues around the water research sector. To facilitate improved communication from science to policy to practice it will firstly be important for the research sector to address these issues. The results clearly identify a need to put in place formal structures and processes to facilitate research priority setting, a more strategic approach to water research in Australia, and provide a dialogue for water researchers. The results also suggest current funding models for water research should be reviewed to ensure that water research funding in Australia encourages relevant and constructive research that will fit within well-defined research priorities.


In terms of science-to-policy translation, it was clear that there was a need for more effective interaction between industry members, scientists, and policy formulators and regulators so that they can better understand each other’s roles. A need was also identified for formal processes to bring all stakeholders together to aid the transfer of water science into policy and regulation.

Implementation of water recycling projects was clearly perceived as the most difficult part of the science, policy, and practice process. The results suggest that more consultation is needed between stakeholders to assist with more accurate estimation of the time, costs and resources required to implement projects. Although some industry professionals reported good relationships and networks with their peers, it is apparent that more effort is required to facilitate and maintain strong professional networks between the research, government and industry sectors.

In line with Baggett et al’s (2006) research, interviewees identified differing perceptions of risk between industry, government, scientists and the public as a barrier to risk communication. The role of the media in influencing the public’s (and possibly politicians’) perceptions of potable reuse is an area that would be worthwhile investigating. It was also thought that a lack of a strong federal government policy on potable reuse was an obstacle to implementation of projects.

The confusing and inconsistent use of language and terminology was also identified as a barrier to communication about recycled water, both with the industry and when communicating with the public. It is clear that the development and use of a clear and consistent terminology around recycled water would provide clarity and greatly enhance communication processes.

6.8.2 Accessing and keeping up with information

Consistent with Holmes and Clark’s (2008) research, Australian water policy makers and regulators were described as under-resourced and reported difficulties accessing and keeping up with scientific information. Strong professional networks and the employment of more scientists in government departments to act as interpreters were seen as the most effective ways of overcoming these barriers.

6.8.3 Water recycling guidelines and consistency of implementation across states

The Australian guidelines for water recycling were identified as a significant document and the best source of technical information for decision-making regarding recycled water. However, the research identified concerns with the fact that the implementation of the guidelines was not occurring consistently across jurisdictions in Australia. This lack of consistency between states was identified as an issue of concern.

6.8.4 Summary

The findings from this research have identified some significant issues that could be addressed to improve the way science is currently translated into policy and the implementation of recycled water projects in Australia. It is important to gain public acceptance of water supply solutions and this is enhanced if there is clear regulatory guidance and a clear process for communication to the public. It is also essential that those engaged in the provision of water and those that regulate it also have confidence that the decisions they make are based on sound science. Potential future directions are discussed in Chapter 7.


7 Conclusions and recommendations This project used a holistic approach to human health risk assessment, communication and management of chemicals in water from recycled water projects. A recent impediment to the uptake of recycled water schemes has been a lack of acceptance based on uncertainty regarding the safety of the water product for long-term use. A chemical-by-chemical approach to management of chemicals in water is no longer enough to answer the ‘what if’ questions that can arise from changing the traditional way we provide water to a more sustainable approach where water is reclaimed and reused for a range of beneficial purposes. The work reported here is based on the use of biological testing methods in addition to traditional chemical measurements to directly measure the biological activity of water samples and provide more complete information on potential adverse health effects than chemical data alone. Some of the bioassays can be used to monitor discharged wastewater and/or treatment efficacy prior to discharge, thus preserving the quality of Australia’s environment and the health of the human population. A significant strength of this approach is that the information can be applied to potential ecosystem and human health effects because we are dealing with effects at a sub-organism level. The broad aim of this study was to adopt and validate tools and methods for assessing human health impacts from drinking water that contains a significant proportion of recycled water and to communicate the outcome to a range of stakeholders. The project is of particular relevance to Clause 90 (outcomes i, iii and v) and 92 (outcome i) of the National Water Initiative.

7.1 R is k as s es s ment (C hapter 2) The way chemicals have been regulated in Australia by the federal government was reviewed. Although there is rigour in the methods used by the various agencies in Australia, the information usually provided as national guidelines does not directly contribute knowledge to recycled water schemes. For example water providers have not previously been represented on water quality committees. The chemical risk assessments are specific to particular uses for which the chemicals are imported or manufactured in Australia, and they generally do not directly assess exposure from recycled water. Another constraint is the range of chemicals that could be present in the source waters to advanced recycled water treatment plants, which is greater than what is usually present in traditional drinking water sources. Some of the additional chemicals that may be present do not have guidelines or are unregulated, although there has been rapid progress towards this recently, for example in Queensland (QG 2005).

The Australian water recycling guidelines, Phase 2 – augmentation of surface waters (2008) outlines an approach to deriving a guideline when there is not one present for a particular chemical. There are a number of sources of information as to how this can be done but no guidance on who is responsible for conducting the risk assessments in the absence of an available guideline. A question then arises as to whether there is an efficient feed-back loop from water providers to commonwealth regulators. In the absence of robust toxicological data, an interim method using thresholds of toxicological concern (TTC) can be used to provide an INTERIM value. It is important however that this not be taken as a default guideline. Rather this should be a trigger for full toxicological investigation.

Neither guideline values nor TTCs take into account the potential toxicity associated with multiple chemicals present in a water sample at the same time (‘mixture toxicity’), each of which may well be present below either the analytical detection limit or the available guideline values. A response to this challenge is the use of biological methods, or bioassays, in combination with other methods to assess hazards to human health and treatment efficacy. It also assists understanding how effects manifest from the molecular to whole animal level based on biological endpoints that are more sensitive (lower detection limits) compared to chemical analysis. To date in vitro methods have by and large been used as screening tools in risk management or hazard identification tools in risk assessment.


Considerable work still needs to be done to enable the use of in vitro bioassays for more comprehensive risk assessment. This would include the predictive capacity of the in vitro methods to assess probable in vivo effects

In the meantime the use of in vitro bioassays has the potential to make a considerable contribution to water recycling scheme monitoring programs, including investigative programs, validation and verification of efficacy and compliance with water quality criteria within the approach set out in the National Water Quality Framework. To date, the methods have not been taken up in Australia to any significant extent outside of research programs, possibly due to the perceived esoteric nature of the newly applied methods and lack of understanding of the strengths and the limitations of the methods. The information gained from the inclusion of these methods in monitoring programs can be a powerful tool in prioritisation of unregulated chemicals that required further investigation or guideline values developed by the appropriate agencies. This has the benefit of making recycling scheme validation more affordable, particularly smaller schemes in fringe urban or regional areas.

7.2 R es ults of the monitoring program (C hapter 5)

7.2.1 Bioassay fingerprint

Several of the priority compounds induced biological activity in one or several of the in vitro bioassays.

For non-specific and reactive toxicity:

• cytotoxicity (Caco2NRU, WIL2NS TOX and HepaTOX) was caused by some hormones, pharmaceuticals, pesticides and disinfection by-products

• genotoxicity (WIL2NS FCMN) was induced by some hormones, pharmaceuticals and disinfection by-products

• mutagenicity (Ames test) was induced by a few disinfection by-products.

For specific toxicity:

• liver enzymes (HepCYP1A2) were induced by one of the pesticides (pentachlorophenol), although it is likely that it would be induced by many more compounds (such as pharmaceuticals and pesticides) if those were tested at higher concentrations

• androgenic activity (AR-CALUX + mode) was induced by androgen hormones; while anti-androgenic activity (AR-CALUX – mode) was induced by estrogens, plasticisers and industrial surfactants, some pharmaceuticals and pesticides

• estrogenic activity (ERα-CALUX + mode) was mostly induced by estrogens, but most other compounds were slightly estrogenic; while slight anti-estrogenic activity (ERα-CALUX – mode) was detected only with a disinfection by-product, although determination of anti-estrogenic activity of most compounds was rendered difficult due to their agonistic activity

• glucocorticoid activity (GR-CALUX) was only induced by the pharmaceutical levonorgestrel, but is likely to be also induced by other pharmaceuticals (in particular corticosteroids)

• progesterone activity (PR-CALUX) was induced by hormones and pharmaceuticals, and is of course also induced by the hormone progesterone, its natural ligand

• thyroid activity (TRβ-CALUX) was not induced by any of the priority compounds, and no thyroid activity was detected in any of the water samples

• immunostimulation (THP1 + mode) was not induced by any of the priority compounds, and no immunostimulatory activity was detected in any of the water samples


• immunosuppression (THP1 – mode) was induced by the estrogen hormone estrone, the growth hormone 17β-trenbolone and, to a lesser extent, by disinfection by-products

• acetylcholinesterase inhibition (AChE assay), a measure of neurotoxicity, was caused by the insecticides chlorpyrifos and diazinon.

7.2.2 Bioassay results

The bioassay battery showed detectable levels of activity in treated sewage, particularly for specific toxicity endpoints (such as endocrine activity, liver enzyme induction, immunosuppression and acetylcholinesterase activity).

Reverse osmosis efficiently removed most of the biological activity, however slight estrogenic and anti-estrogenic activity was detectable in RO-treated water (likely associated with plasticisers used in membranes) and slight immunosuppressive activity (likely associated with disinfection by-products). There are no bioassay-based guidelines for drinking or recycled water, but based on extrapolation of chemical guidelines for estrogen hormones (ranging from 1.5 to 175 ng/L), xeno-estrogenic plasticisers (ranging from 50 000 to 200 000 ng/L) and disinfection by-products (ranging from 6 to 100 µg/L), this low-level activity (<0.87 ng/L 17β-estradiol equivalent and <0.61 µg/L dexamethasone equivalent for estrogenic and immunosuppressive activity, respectively) is unlikely to be of human health concern.

Simpler water reclamation technologies such as ultrafiltration and dissolved air flotation/filtration were only marginally effective and biological activity remained in class A recycled water at levels comparable to the source water (treated sewage). In some instances, low-level genotoxicity and mutagenicity were detectable in these samples, particularly associated with chlorine disinfection.

7.2.3 Chemical analysis

The chemical analysis results agreed with the bioassay results, with treated sewage showing ng/L to low µg/L concentrations for several of the compounds monitored in this study in treated sewage.

Reverse osmosis efficiently removed those trace organic compounds to close to or below detection limit, and chemical concentrations in all RO-treated samples were several orders of magnitude below the Australian guidelines for water recycling (EPHC/NHMRC/NRMMC 2008). The herbicide simazine was detected in RO-treated samples on two occasions, when the concentration in treated sewage was extreme (>20 000 ng/L). The concentrations in RO-treated waters were several orders of magnitude below guideline level, but this incident highlights that reverse osmosis provides a very efficient but not absolute barrier to trace organic contaminants, and that source control should remain an important initial barrier in recycled water schemes.

Simpler water reclamation technologies such as ultrafiltration and dissolved air flotation/filtration were less effective at removing the monitored compounds. It should be noted however that most compounds were still below Australian guidelines for water recycling. The two exceptions were caffeine (found at concentrations up to 1830 ng/L in class A recycled water), which has an extremely conservative interim guideline of 350 ng/L (by comparison the average coffee contains 400 mg/L of caffeine, i.e. a million times more than the guideline value); and dibromochloromethane (found at concentrations up to 8500 ng/L in class A recycled water), a by-product of chlorine disinfection with a guideline value of 6000 ng/L. Note that class A recycled water is not intended for human consumption, and hence should not really be compared to the Australian guidelines for water recycling (which are drinking water guidelines protective of human health), and was only done here to highlight that even relatively basic treatment meets most of those very stringent guidelines. For the few compounds where an ANZECC guideline does exist for irrigation water (ANZECC/ARMCANZ 2000), all class A recycled water samples were compliant with those guidelines.


7.2.4 Combining bioassay and chemical results

Bioassay and chemical results were in agreement, and while it was usually not possible to identify individual compounds from the measured biological activity, it was possible to identify classes of compounds (e.g. ‘estrogen hormones’, or ‘disinfection by-products’) to target using subsequent chemical analysis. Some of the assays also had higher sensitivity than the chemical methods, and were able to detect low-level activity in some samples where none of the selected chemicals was detected. These two factors (i.e. good agreement with chemical methods and broader detection abilities) support the use of in vitro bioassays in a tiered screening process, with a positive response to be followed by more comprehensive chemical analysis and toxicology to determine the causative compound and its toxicity in vivo.

7.3 R ole of bioas s ays in ris k as s es s ment The inclusion of a suite of in vitro assays into a water quality risk assessment process could be beneficial in several ways. The following headings are the major steps in the risk assessment framework shown in Chapter 2 (Source: enHealth 2004).

Issue identification

A suite of bioassays could be employed as a broad screen where water quality is simply not known or contamination is suspected. The techniques offer clear advantages for monitoring as they measure biological activity of mixtures in addition to individual chemicals. They could assist in problem or issue identification and act to direct the remaining steps in the risk assessment process, or assist in the decision as to whether a full risk assessment is warranted.

Hazard assessment

In vitro bioassays can be used to identify if particular chemicals or groups of chemicals in a mixture are biologically-active and if new and emerging chemicals are present in a particular water source. The bioassays can also detect whether treatment produces biologically-active transformation products. A response in an in vitro assay (‘primary effect’) does not always result in harm to a whole organism (‘secondary effect’) but can provide valuable information as to whether a secondary effect is likely. Combined with chemical data, this combined hazard assessment is more robust than relying on chemical data only. This would also be a trigger for a full toxicological investigation if the activity cannot be controlled either at the source or in the treated water product.

Exposure identification

The methods can be used in exposure assessment to assess the presence of chemicals in the source water, and the subsequent removal by the various stages of a treatment train. The assays can also indicate a likely chemical group based on the effects fingerprint, and therefore become a prioritisation tool for further investigation.

Risk characterisation

Further development is needed on the development of models using in vitro data to predict in vivo (whole animal) effects. Part of this work is being progressed by programs such as the US EPA’s Tox21 Initiative, in a collaboration between agencies in the United States to identify mechanisms of chemically induced biological activity, prioritise chemicals for more extensive toxicological evaluation, and develop more predictive models of in vivo biological response <http://alttox.org>. Eventually this information will be able to be incorporated more fully into risk assessment processes.


Risk management

An important component of risk management involves management of not only the quantitative risks but also perceived risk by social groups, politicians and water industry practitioners. The bioassay methods are able to address, to a certain extent, the ‘what if’ questions on effects of chemical interactions and the presence of a chemical not suspected to be present and therefore not looked for. As success of water recycling schemes is largely societal and based on trust in the water provider, it is critical to have tools with which to communicate and bioassays can provide a powerful education tool to demonstrate water safety.

7.4 Importanc e of c ommunic ation and management of knowledge (C hapter 6)

In considering the safety of recycled water it is important to also consider how knowledge is shared and used. A lot of new knowledge is generated on an ongoing basis and published in scientific journals, but this is not always accessible to the decision makers or available in a form that can be easily used. This is widely recognised by the water industry looking for rapid solutions to issues, and who for example cannot wait several years to assess the safety of the scheme based on traditional risk characterisation methods. Some of the scientists employed in government roles report being ‘over stretched’ in their present roles and complain about the lack of inter-agency communication. This means that while they are aware of the issues, they are not always able to respond. Chapter 6 discusses these issues in some detail based on anonymous feedback from a range of stakeholders from industry, government and academia. As confidence in recycled water generally relies on good communication and management of knowledge, this is now recognised as being of high priority. The role of science interpretation is also now well recognised. There seems to be an increasing requirement for training and education, and the creation of multiple forums within which knowledge sharing and uptake into practice can occur.

7.5 R ec ommendations 1. Multi-barrier approach to water recycling

Reverse osmosis is a very significant, but not absolute, barrier to trace organic contaminants. Additional barriers and/or source control also need to be maintained to ensure recycled water is safe for humans.

2. Correlation between in vitro responses and in vivo outcomes

In vitro bioassays are very sensitive and can measure very low concentrations of biologically-active material. This can lead to a premature conclusion of harm. The dose/response in a bioassay needs to be correlated with dose/response in vivo to use the in vitro data to reliably predict likely health outcomes. With sufficient information, a value could be determined for an in vitro response as a trigger for further chemical investigation (i.e. a bioassay-based interim guideline).

3. Further development of the effects fingerprint

The biological activity of more compounds should be tested to improve the ability of ‘bioassay fingerprinting’ to direct chemical analysis.

4. Expansion of the range of endpoints

While exhaustive, the bioassay toolbox presented here did not measure potential effects on reproductive and developmental toxicity. These endpoints are currently impractical to measure in vitro, but may become feasible in the future. The measures of immunotoxicity and neurotoxicity were


of limited breadth, and further development in their respective fields may provide new bioassay tools that could replace the currently-used assays. The bioassays toolbox is a ‘work in progress’ and which bioassays to include should be continually revisited and improved based on developing knowledge.

5. Investigate the source of the low-level (anti)estrogenic activity in RO samples

The cause of the low-level (anti)estrogenic activity found in some of the RO-treated water samples should be investigated. If plasticisers from the RO membranes are indeed contributing to this activity (as hypothesised in this report), the age of the membranes may play a role in leaching potential. Identification of the causative compounds would also perhaps provide the means to eliminate their occurrence.

6. Investigate the source of pesticides in treated sewage

Treated sewage samples occasionally contained high concentrations of some pesticides, in particular the herbicides atrazine, diuron and simazine. While these compounds were removed below guideline levels after additional treatment, some only had a 10× margin of safety compared with the guideline levels. The source of these compounds (e.g. stormwater ingress or inappropriate domestic drain disposal) should be identified to reduce their occurrence in treated sewage and increase the margin of safety.

7. Risk communication

Chapter 6 summarised a number of issues regarding communication between and within different stakeholder groups, but as yet has not proposed solutions. It should be noted that this research was designed as a starting point to identify key issues with communication within the industry. The development of a national strategy to facilitate and increase the benefits of communication between diverse groups of stakeholders would be helpful. However, support for a national strategy may have to develop from success at the state and local level.


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9 Appendices 9.1 A ppendix I: Detailed res ults from s olid phas e

extrac tion experiments

ATENOLOL by LC-MS/MS (ESI positive) Vials Elute

(ml)

1 HLB

2 C18

3 Char

4 HLB-C18-Charcoal

5 C18-HLB-Charcoal

6 HLB-Charcoal

Accu. HLB C18 Char C18 HLB Char HLB Char 1 MeOH 20 20 2710 454 0 2700 0 0 411 0 0 2740 0 2 MeOH 9 29 251 245 33 0 298 0 37 3 MeOH 9 38 23.8 416 17.5 0 345 0 21.6 4 MeOH 9 47 8.79 425 5.52 0 369 0 6.09 5 MeOH 9 56 3.77 442 2.87 0 406 0 3.16 6 MeOH 9 65 2.8 442 2.47 0 409 0 2.5 7 MeOH 9 74 2.19 289 1.61 0 317 0 2 8 MeOH 9 83 1.71 224 1.35 0 225 0 1.49 9 MeOH 9 92 2.11 156 1.22 0 148 0 1.41 10 MeOH 9 101 0.85 119 0.94 0 98.7 0 0.88 1 MTBE 9 110 0.66 66.9 0 0.38 0 0 57 0 0 0.6 0 2 MTBE 9 119 0.63 29.7 0.38 0 24.6 0 0.36 3 MTBE 9 128 0 2.67 0 0 1.56 0 0 4 MTBE 9 137 0 0 0 0 0 0 0 5 MTBE 9 146 0 0 0 0 0 0 0 6 MTBE 9 155 0 0 0 0 0 0 0 7 MTBE 9 164 0 0 0 0 0 0 0 8 MTBE 9 173 0 0 0 0 0 0 0 9 MTBE 9 182 0 0 0 0 0 0 0 10 MTBE 9 191 0 0 0 0 0 0 0 1 DCM 5 0 0 0 0 Total 3008 3311 0 2767 0 3110 0 0 2817 0 Recovery MeOH

75 80 0 69 0 0 76 0 0 70 0

Recovery total

75 83 69 78 70


PARACETAMOL by LC-MS/MS (ESI positive) Vials Elute

(ml)

1 HLB

2 C18

3 Char

4 HLB-C18-Charcoal

5 C18-HLB-Charcoal

6 HLB-Charcoal

Accu. HLB C18 Char C18 HLB Char HLB Char 1 MeOH 20 20 2950 235 0 2970 0 0 196 2520 0 3030 0 2 MeOH 9 29 30.5 52.2 16.6 0 47.3 422 35 3 MeOH 9 38 3.63 17 3.98 0 19.7 4.56 4.51 4 MeOH 9 47 0.887 0.27 1.02 0 10.8 1.11 0.899 5 MeOH 9 56 0.565 0.177 0.519 0 0.16 0.701 0.547 6 MeOH 9 65 0.37 0.139 0.419 0 0.15 0.646 0.385 7 MeOH 9 74 0.267 0.07 0.3 0 0.08 0.331 0.317 8 MeOH 9 83 0.212 0.054 0.247 0 0.05 10.1 0.156 9 MeOH 9 92 0.445 0.05 0.55 0 0.04 0.681 0.495 10 MeOH 9 101 0.128 0 0.1 0 0 0.148 0.128 1 MTBE 9 110 0 0 0.144 0 0 0 0.136 0 0.122 0 2 MTBE 9 119 0.034 0.129 0 0 0.181 0.112 3 MTBE 9 128 0.054 0.329 0 0.05 0.313 0.185 4 MTBE 9 137 0.096 0.04 0 0.04 0.441 0.137 5 MTBE 9 146 0.03 0.02 0 0.02 0.146 0.08 6 MTBE 9 155 0.04 0.083 0 0.03 0.122 0.05 7 MTBE 9 164 0.04 0.31 0 0 0.575 0.115 8 MTBE 9 173 0 0.06 0 0.03 1.2 0.09 9 MTBE 9 182 0.04 0.06 0 0.02 0.119 0.136 10 MTBE 9 191 0 0.116 0 0 0.124 0.149 1 DCM 5 0 0 0 0 Total 2987 305 0 2995 0 274 2964 0 3074 0 Recovery MeOH

75 8 0 75 0 0 7 74 0 77 0

Recovery sub-total

75 8 75 7 74 77


SULFAMETHOXAZOLE by LC-MS/MS (ESI positive) Vials Elute

(ml)

1 HLB

2 C18

3 Char

4 HLB-C18-Charcoal

5 C18-HLB-Charcoal

6 HLB-Charcoal

Accu. HLB C18 Char C18 HLB Char HLB Char 1 MeOH 20 20 1720 1360 0 1090 0 0 1110 304 0 1140 0 2 MeOH 9 29 1290 309 1590 0 312 1010 1710 3 MeOH 9 38 62.9 121 85.9 0 137 157 143 4 MeOH 9 47 15.3 0.298 29.2 0 74 16.6 28.3 5 MeOH 9 56 5.75 0.504 8.21 0 0.28 4.98 9.06 6 MeOH 9 65 3.63 0.1 4.19 0 0.41 2.61 5.04 7 MeOH 9 74 3.28 0 3.5 0 0 1.49 4.24 8 MeOH 9 83 1.75 0 1.64 0 0 2.49 2.03 9 MeOH 9 92 1.56 0 2.19 0 0 0.964 2.07 10 MeOH 9 101 0.986 0 0.703 0 0 0.102 0.895 1 MTBE 9 110 0.86 0 0 0.937 0 0 0 0.343 0 1.19 0 2 MTBE 9 119 0.962 0 1.1 0 0 0.237 1.17 3 MTBE 9 128 0.16 0 0.732 0 0 0.156 0.164 4 MTBE 9 137 0.01 0 0.04 0 0 0 0 5 MTBE 9 146 0.041 0 0.155 0 0 0 0.123 6 MTBE 9 155 0.115 0 0.085 0 0 0 0 7 MTBE 9 164 0.007 0 0.364 0 0 0 0.005 8 MTBE 9 173 0.11 0 0.09 0 0 0.03 0.116 9 MTBE 9 182 0.468 0 0.297 0 0 0 0.156 10 MTBE 9 191 0.428 0 0.185 0 0 0 0.24 1 DCM 5 0 0 0 0 Total 3108 1791 0 2820 0 1634 1501 0 3048 0 Recovery MeOH

78 45 0 70 0 0 41 38 0 76 0

Recovery total

78 45 70 41 38 76


CAFFEINE by LC-MS/MS (ESI positive) Vials Elute

(ml)

1 HLB

2 C18

3 Char

4 HLB-C18-Charcoal

5 C18-HLB-Charcoal

6 HLB-Charcoal

Accu. HLB C18 Char C18 HLB

Char

HLB Char

1 MeOH 20 20 773 2150 0 306 0 0 2030 0 0 391 0 2 MeOH 9 29 1830 444 1790 0 369 0 1700 3 MeOH 9 38 738 93.1 715 0 129 0 1020 4 MeOH 9 47 77.6 2.16 92.4 0 18.2 0 100 5 MeOH 9 56 20.1 0.75

7 24.7 0 1 0 27.5

6 MeOH 9 65 8.18 0 9.13 0 0 0 9.51 7 MeOH 9 74 5.2 0 5.44 0 0 0 5.7 8 MeOH 9 83 2.6 0 3.35 0 0 0 3.53 9 MeOH 9 92 2.1 0 2.13 0 0 0 2.6 10 MeOH 9 101 1.73 0 1.58 0 0 0 1.71 1 MTBE 9 110 0.748 0 0 0.738 0 0 0 0 0 0.933 0 2 MTBE 9 119 0.533 0 0.792 0 0 0 0.879 3 MTBE 9 128 0.204 0 0.41 0 0 0 0.41 4 MTBE 9 137 0.033 0 0.169 0 0 0 0.029 5 MTBE 9 146 0.285 0 0.09 0 0 0 0.117 6 MTBE 9 155 0.095 0 0 0 0 0 0 7 MTBE 9 164 0 0 0.127 0 0 0 0 8 MTBE 9 173 0 0 0.018 0 0 0 0 9 MTBE 9 182 0.239 0 0 0 0 0 0 10 MTBE 9 191 0 0 0 0 0 0 0 1 DCM 5 0 0 0 0 Total 3461 2690 0 2952 0 2547 0 0 3264 0 Recovery MeOH

86 67 0 74 0 0 64 0 0 82 0

Recov-ery sub-total

87 67 74 64 0 82


CARBAMAZEPINE by LC-MS/MS (ESI positive) Vials Elute

(ml)

1 HLB

2 C18

3 Char

4 HLB-C18-Charcoal

5 C18-HLB-Charcoal

6 HLB-Charcoal

Accu. HLB C18 Char C18 HLB Char HLB Char 1 MeOH 20 20 1010 845 0 900 0.868 0 383 0.636 886 0 2 MeOH 9 29 734 437 789 0.126 271 0.355 833 3 MeOH 9 38 66.8 161 90.8 0 75.5 0 126 4 MeOH 9 47 17.4 1.61 34 0 19.3 0 29.8 5 MeOH 9 56 6.05 0.759 8.81 0 1.35 0 8.43 6 MeOH 9 65 3.8 0.646 5 0 0.53 0 5.3 7 MeOH 9 74 2.71 0.328 3.54 0 0.37 0 3.71 8 MeOH 9 83 1.89 0.495 2.35 0 0.26 0 1.95 9 MeOH 9 92 1.77 0.124 2.46 0 0.16 0 2.41 10 MeOH 9 101 0.985 0.208 1.24 0 0.14 0 1.25 1 MTBE 9 110 0.888 0.276 0 0.82 0 0 0 0 0 0.896 0 2 MTBE 9 119 0.926 0.099 1.11 0 0.1 0 0.89 3 MTBE 9 128 1.16 0.338 1.03 0 0.69 0 1.58 4 MTBE 9 137 1.18 0.239 0.83 0 0.67 0 0 5 MTBE 9 146 1.04 0.318 1.18 0 0.76 0 1.33 6 MTBE 9 155 0.618 0.312 1.01 0 0.23 0 0.654 7 MTBE 9 164 0.48 0.133 1.27 0 0.24 0 0.493 8 MTBE 9 173 0.346 0.119 0.477 0 0.02 0 0.437 9 MTBE 9 182 0.265 0.085 0.23 0 0.22 0 0.322 10 MTBE 9 191 0.249 0.07 0.293 0 0.12 0 0.261 1 DCM 5 0 0 0 0 Total 1853 1449 0 1845 1 755 1 0 1905 0 Recovery MeOH

46 36 0 46 0 0 19 0 0 47 0

Recovery total

46 36 46 19 0 48


ATRAZINE by LC-MS/MS (ESI positive) Vials Elute

(ml)

1 HLB

2 C18

3 Char

4 HLB-C18-Charcoal

5 C18-HLB-Charcoal

6 HLB-Charcoal

Accu. HLB C18 Char C18 HLB Char HLB Char 1 MeOH 20 20 1800 1800 0 1430 0 0 2010 0 0 1460 0 2 MeOH 9 29 1150 459 1270 0 230 0 1400 3 MeOH 9 38 57.8 145 88.3 0 67.2 0 115 4 MeOH 9 47 16.2 1.73 31.9 0 16.5 0 26.1 5 MeOH 9 56 4.48 0.883 7.03 0 1.24 0 6.23 6 MeOH 9 65 2.45 0.77 3.86 0 0.51 0 3.72 7 MeOH 9 74 1.59 0.346 2.42 0 0.27 0 2.21 8 MeOH 9 83 0.885 0.467 1.14 0 0.22 0 1.04 9 MeOH 9 92 0.846 0.09 1.57 0 0.13 0 1.35 10 MeOH 9 101 0.105 0.083 0.241 0 0 0 0.405 1 MTBE 9 110 0 0 0 0 0 0 0 0 0 2 MTBE 9 119 0 0 0 0 0 3 MTBE 9 128 0 0 0 0 0 4 MTBE 9 137 0 0 0 0 0 5 MTBE 9 146 0 0 0 0 0 6 MTBE 9 155 0 0 0 0 0 7 MTBE 9 164 0 0 0 0 0 8 MTBE 9 173 0 0 0 0 0 9 MTBE 9 182 0 0 0 0 0 10 MTBE 9 191 0 0 0 0 0 1 DCM 5 0 0 0 0 Total 3034 2408 0 2836 0 2326 0 0 3016 0 Recovery MeOH

76 60 0 71 0 0 58 0 0 75 0

Recovery sub-total

76 60 71 58 0 75


DEET by LC-MS/MS (ESI positive) Vials Elute

(ml)

1 HLB

2 C18

3 Char

4 HLB-C18-Charcoal

5 C18-HLB-Charcoal

6 HLB-Charcoal

Accu. HLB C18 Char C18 HLB Char HLB Char 1 MeOH 20 20 286 301 0 266 0.998 0 251 1.16 0 264 0 2 MeOH 9 29 236 280 236 0 210 0.43 307 3 MeOH 9 38 26.9 134 44.7 0 66.7 0 51.1 4 MeOH 9 47 7.42 2.11 15.4 0 18 0 13.1 5 MeOH 9 56 2.71 1.3 4.11 0 1.53 0 4.37 6 MeOH 9 65 1.74 0.978 3 0 0.54 0 3.12 7 MeOH 9 74 1.08 0.501 1.85 0 0.38 0 1.94 8 MeOH 9 83 0.83 0.686 1.12 0 0.24 0 0.946 9 MeOH 9 92 0.901 0.177 1.44 0 0.07 0 1.3 10 MeOH 9 101 0.276 0.178 0.369 0 0 0 0.286 1 MTBE 9 110 2.07 0.138 0 3.76 0 0 0 1.94 0 3.33 0 2 MTBE 9 119 4.32 0.077 6.12 0 0.02 5.5 9.35 3 MTBE 9 128 0.805 0 2.46 0 0 0 1.21 4 MTBE 9 137 0.433 0 0.711 0 0 0 0.827 5 MTBE 9 146 0.24 0 0.167 0 0 0 0.357 6 MTBE 9 155 0.08 0 0.238 0 0 0 0 7 MTBE 9 164 0 0 0 0 0 0 0 8 MTBE 9 173 0 0 0 0 0 0 0 9 MTBE 9 182 0 0 0 0 0 0 0 10 MTBE 9 191 0 0 0 0 0 0 0 1 DCM 5 322 0.266 0.212 0.08 Total 572 721 322 587 1 548 9 0 662 0 Recovery MeOH

14 18 0 14 0 0 14 0 0 16 0

Recovery total

14 18 8 15 0 0 14 0 0 17 0


DIAZEPAM by LC-MS/MS (ESI positive) Vials Elute

(ml)

1 HLB

2 C18

3 Char

4 HLB-C18-Charcoal

5 C18-HLB-Charcoal

6 HLB-Charcoal

Accu. HLB C18 Char C18 HLB Char HLB Char 1 MeOH 20 20 170 2300 0 12.8 0.578 0 2480 0 0 4.24 0 2 MeOH 9 29 556 495 83 0.145 184 0 69.1 3 MeOH 9 38 1070 96.7 513 0 53.1 0.18 558 4 MeOH 9 47 911 18.1 1190 0 1.97 0.249 1100 5 MeOH 9 56 555 1.27 953 0 3.25 0 943 6 MeOH 9 65 246 0.993 425 0 1.74 0 421 7 MeOH 9 74 96.9 0.894 145 0 1.44 0 160 8 MeOH 9 83 41.5 0.85 62.8 0 1.25 0 58.1 9 MeOH 9 92 20.5 0.515 31.2 0 1.09 0 31.4 10 MeOH 9 101 14.5 0.485 20 0 0.96 0 20.6 1 MTBE 9 110 9.1 0.482 0 13.4 0 0 0.75 0 0 13.9 0 2 MTBE 9 119 9 0.74 12.3 0 3.08 0 13.3 3 MTBE 9 128 11.7 2.7 14.7 0 4.78 0 15 4 MTBE 9 137 8.03 0.92 9.74 0 1.96 0 9.75 5 MTBE 9 146 7.16 0.24 6.84 0 0.81 0 7.96 6 MTBE 9 155 4.3 0.193 4.57 0 0.27 0 4.53 7 MTBE 9 164 3 0.844 3.2 0 0.29 0 3.36 8 MTBE 9 173 2.18 0.05 2.47 0 0.05 0 2.57 9 MTBE 9 182 1.69 0.02 1.57 0 0.23 0 2.06 10 MTBE 9 191 1.53 0 1.72 0 0.13 0 1.72 1 DCM 5 12.6 0 0 0 0 Total 3691 2921 13 3436 1 2741 0 0 3365 0 Recovery MeOH

92 73 0 86 0 0 68 0 0 84 0

Recovery sub-total

92 73 0 86 69 0 84


BISPHENOL A by LC-MS/MS (ESI negative) Vials Elute

(ml)

1 HLB

2 C18

3 Char

4 HLB-C18-Charcoal

5 C18-HLB-Charcoal

6 HLB-Charcoal

Accu. HLB C18 Char C18 HLB Char HLB Char 1 MeOH 20 20 400 2240 0 70.2 1.28 0 2380 1.09 0.31 64.4 0 2 MeOH 9 29 1770 593 1020 0 281 1.41 919 3 MeOH 9 38 1100 218 1700 0 84.3 0 1710 4 MeOH 9 47 230 0 376 0 20.6 0 427 5 MeOH 9 56 33.2 0 52.9 0 0 0 55.8 6 MeOH 9 65 9.74 0 16.5 0 0 0 16 7 MeOH 9 74 3.87 0 6.3 0 0 0 5.86 8 MeOH 9 83 0.936 0 2.25 0 0 0 2.1 9 MeOH 9 92 1.59 0 1.37 0 0 0 1.16 10 MeOH 9 101 0.129 0 0.0117 0 0 0 0.392 1 MTBE 9 110 0 0 1.61 0 0 0 0 0 0 0 2 MTBE 9 119 0 0 0 0 0 0 0.421 3 MTBE 9 128 0.135 0 0.511 0 0 0 0.077 4 MTBE 9 137 0.407 0 0 0 0 0 0 5 MTBE 9 146 0 0 0 0 0 0 0 6 MTBE 9 155 0 0 0 0 0 0 0 7 MTBE 9 164 0 0 0 0 0 0 0 8 MTBE 9 173 0 0 0 0 0 0 0 9 MTBE 9 182 0 0 0 0 0 0 0 10 MTBE 9 191 0 0 0 0 0 0 0 1 DCM 5 5.54 0 0 0 Total 3550 3051 2 3246 1 2766 3 0 3202 0 Recovery MeOH

89 76 0 81 0 0 69 0 0 80 0

Recovery total

89 76 81 69 80


GEMFIBROZIL by LC-MS/MS (ESI negative)

Vials Elute

(ml)

1 HLB

2 C18

3 Char

4 HLB-C18-Charcoal

5 C18-HLB-Charcoal

6 HLB-Charcoal

Accu. HLB C18 Char C18 HLB Char HLB Char 1 MeOH 20 20 427 1380 0 79.8 21.6 0 1420 0 0 72.7 0 2 MeOH 9 29 1310 469 873 5.37 192 23.2 848 3 MeOH 9 38 936 134 1270 0.74 67.5 29.2 1230 4 MeOH 9 47 278 3.46 481 0 23.8 8.71 476 5 MeOH 9 56 56.8 1.72 93.9 0 4.25 1.39 103 6 MeOH 9 65 20.4 1.24 34.4 0 1.26 0.132 34.7 7 MeOH 9 74 10.3 0.663 14.9 0 0.95 0 14.5 8 MeOH 9 83 5.79 0.661 7.96 0 0 0 7.23 9 MeOH 9 92 3.9 0.175 5.26 0 0.41 0 4.84 10 MeOH 9 101 3.66 0.225 4.03 0 0.18 0 3.42 1 MTBE 9 110 1.77 0.089 0 2.34 0 0 0 0 0 2.47 0 2 MTBE 9 119 2.19 0.406 2.68 0 1.22 0 2.98 3 MTBE 9 128 3.9 1.19 4.81 0 2.59 0 4.97 4 MTBE 9 137 4.51 1.36 4.12 0 3.49 0 5.37 5 MTBE 9 146 2.97 0 2.16 0 1.67 0 0 6 MTBE 9 155 0 0.432 1.05 0 0.64 0 1.05 7 MTBE 9 164 0.744 0.081 0.678 0 0.61 0 0 8 MTBE 9 173 0.458 0 0.509 0 0.13 0 0.558 9 MTBE 9 182 0 0 0.345 0 0.41 0 0.445 10 MTBE 9 191 0 0.26 1.03 0 0.23 0 0.484 1 DCM 5 0 0 0 0 Total 3068 1995 0 2884 28 1721 63 0 2813 0 Recovery MeOH

76 50 0 72 1 0 43 2 0 70 0

Recovery sub-total

77 50 72 43 2 70


TRICLOSAN by LC-MS/MS (ESI negative) Vials Elute

(ml)

1 HLB

2 C18

3 Char

4 HLB-C18-Charcoal

5 C18-HLB-Charcoal

6 HLB-Charcoal

Accu. HLB C18 Char C18 HLB Char HLB Char 1 MeOH 20 20 58.8 739 0 2.86 4.62 0 752 0.06 0 0.438 0 2 MeOH 9 29 107 376 7.74 0.99 204 0.177 2.03 3 MeOH 9 38 124 141 11.3 0.18 71.1 0.22 7.9 4 MeOH 9 47 180 16.3 24 0.12 36.7 0 27.3 5 MeOH 9 56 261 14 63.7 0 17.9 0.44 74.9 6 MeOH 9 65 328 13.1 178 0.05 14.3 0.888 205 7 MeOH 9 74 348 11 364 0 13 2.2 395 8 MeOH 9 83 329 10 457 0 12.5 2.34 443 9 MeOH 9 92 294 8.44 443 0 11.7 2.44 418 10 MeOH 9 101 247 10.1 370 0 11.4 1.6 345 1 MTBE 9 110 142 7.22 0 211 0 0 6.93 0.913 0 213 0 2 MTBE 9 119 109 22.3 135 0.06 30.4 0.64 148 3 MTBE 9 128 99.3 76.5 113 0.2 76.5 0.51 117 4 MTBE 9 137 39.2 94.9 40.9 0.26 98.9 0.17 41.4 5 MTBE 9 146 33.1 25.7 27.3 0.07 38.8 0.04 33.2 6 MTBE 9 155 66.8 17.9 48.4 0.02 17 0.09 48.2 7 MTBE 9 164 82 9.57 59.7 0.178 11.5 0.205 64.4 8 MTBE 9 173 67.8 5.2 51.1 0 6.07 0.1 51 9 MTBE 9 182 48.2 3.71 31.2 0.08 4.58 0.03 35.5 10 MTBE 9 191 35.8 281 29.1 0.04 3.41 0 26.3 1 DCM 5 0.643 0 0 0 Total 3000 1883 0 2668 7 1439 13 0 2697 0 Recovery MeOH

57 33 0 48 0 0 29 0 0 48 0

Recovery total

75 47 67 36 0 67


DICLOFENAC by LC-MS/MS (ESI negative) Vials Elute

(ml)

1 HLB

2 C18

3 Char

4 HLB-C18-Charcoal

5 C18-HLB-Charcoal

6 HLB-Charcoal

Accu. HLB C18 Char C18 HLB Char HLB Char 1 MeOH 20 20 267 616 0 18.4 2.69 0 620 0 0 7.44 0 2 MeOH 9 29 582 392 198 0.213 276 0.767 195 3 MeOH 9 38 589 185 586 0.62 126 2.91 562 4 MeOH 9 47 354 3.18 618 0 32.2 2.7 583 5 MeOH 9 56 98.9 1.15 328 0 3.74 0.34 355 6 MeOH 9 65 34.3 0.765 97.3 0 0.99 0 104 7 MeOH 9 74 16.7 0.155 33.4 0 0.65 0 36.5 8 MeOH 9 83 8.89 0.236 17.3 0 0.33 0 15.8 9 MeOH 9 92 5.35 0 10.3 0 0.16 0 9.04 10 MeOH 9 101 4.27 0 6.55 0 0.03 0 5.95 1 MTBE 9 110 2.47 0 0 4.45 0 0 0 0 0 4.81 0 2 MTBE 9 119 3.52 0 5.37 0 0.01 0 5.52 3 MTBE 9 128 0.675 0.437 2.07 0 0.56 0 1.49 4 MTBE 9 137 0.6 0 1.03 0 0.64 0 1 5 MTBE 9 146 0.746 0 1.26 0 0.31 0 1.4 6 MTBE 9 155 0.807 0 1.18 0 0 0 1.06 7 MTBE 9 164 0.856 0 1.33 0 0 0 1.2 8 MTBE 9 173 0.92 0 1.19 0 0 0 1.08 9 MTBE 9 182 0.67 0 0.83 0 0 0 1.04 10 MTBE 9 191 1.11 0 1.4 0 0 0 1.04 1 DCM 5 0 0 0 0 Total 1973 1199 0 1933 4 1062 7 0 1893 0 Recovery MeOH

49 30 0 48 0 0 27 0 0 47 0

Recovery sub-total

49 30 48 27 0 47


T-OCTYLPHENOL by LC-MS/MS (ESI negative) Vials Elute

(ml)

1 HLB

2 C18

3 Char

4 HLB-C18-Charcoal

5 C18-HLB-Charcoal

6 HLB-Charcoal

Accu. HLB C18 Char C18 HLB Char HLB Char 1 MeOH 20 20 710 2890 0 240 0 0 2680 0 0 296 0 2 MeOH 9 29 1540 463 1630 0 150 0 1580 3 MeOH 9 38 245 97 292 0 59.5 0 457 4 MeOH 9 47 34.8 1.31 51.3 0 26 0 55.7 5 MeOH 9 56 11.1 0.92 18.9 0 2.39 0 18.3 6 MeOH 9 65 4.92 1.52 6.85 0 0 0 7.7 7 MeOH 9 74 3.3 0.39 4.38 0 0.83 0 4.12 8 MeOH 9 83 1.45 1.54 1.35 0 0.75 0 1.82 9 MeOH 9 92 0.827 0 0.85 0 0 0 1.62 10 MeOH 9 101 0 0 0 0 0 0 0 1 MTBE 9 110 0 0 202 2 0 0 0 0 0 0.212 0 2 MTBE 9 119 12.4 3.19 1.29 0.18 5.06 0 1.52 3 MTBE 9 128 14.7 7.03 11 1.26 9.69 0 0 4 MTBE 9 137 11.6 0 8.24 0 0 0 6.85 5 MTBE 9 146 3.32 0 2.57 0 0 0 4.78 6 MTBE 9 155 1.09 0 0.809 0 0 0 0.628 7 MTBE 9 164 0.498 0 0 0 0 0 0.179 8 MTBE 9 173 0 0 0 0 0 0 0 9 MTBE 9 182 0 0 0 0 0 0 0 10 MTBE 9 191 0 0 0 0 0 0 0 1 DCM 5 99.6 0 0 0 Total 2595 3466 202 2272 1 2934 0 0 2436 0 Recovery MeOH

64 86 0 56 0 0 73 0 0 61 0

Recovery total

65 87 57 73 0 61


NONYLPHENOL by LC-MS/MS (ESI negative) Vials Elute

(ml) 1 HLB

2 C18

3 Char

4 HLB-C18-Charcoal

5 C18-HLB-Charcoal

6 HLB-Charcoal

Accu. HLB C18 Char C18 HLB Char HLB Char 1 MeOH 20 20 18 1080 0 1.9 2.35 0 1210 0.01 0 0.633 0 2 MeOH 9 29 43.3 457 5.5 0.58 156 0.05 3.1 3 MeOH 9 38 77.1 206 15.1 0 139 0.09 20.6 4 MeOH 9 47 136 92.8 73.3 0 107 0.11 89.1 5 MeOH 9 56 173 68.5 234 0 75 0.247 262 6 MeOH 9 65 277 58.4 462 0 86.1 0.5 458 7 MeOH 9 74 333 52.5 480 0 64.1 0.72 433 8 MeOH 9 83 251 48.6 267 0 55.6 0.43 249 9 MeOH 9 92 181 40.7 151 0 49.7 0.3 150 10 MeOH 9 101 144 45 130 0 50.4 0.283 120 1 MTBE 9 110 84.1 37.2 0 71.2 0.17 0 36.8 0.1 0 78.9 0 2 MTBE 9 119 106 117 74.5 0.35 154 0.15 97.7 3 MTBE 9 128 291 361 237 0.65 360 0.29 273 4 MTBE 9 137 484 323 394 0.6 333 0.4 393 5 MTBE 9 146 307 65.2 247 0.179 92.6 0.2 278 6 MTBE 9 155 160 30.5 127 0.16 30.3 0.1 109 7 MTBE 9 164 97.4 15.5 94.6 0.426 16.7 0.08 79.7 8 MTBE 9 173 49.6 7.35 45.2 0.176 9.26 0.04 44.9 9 MTBE 9 182 26.7 5.17 21.9 0.19 6.55 0 25.2 10 MTBE 9 191 18.5 4.02 17.4 0.173 5.74 0 16.4 1 DCM 5 3.25 0 0 0 Total 3258 3115 0 3150 6 3038 4 0 3181 0 Recovery MeOH

41 54 0 45 0 0 50 0 0 45 0

Recovery sub-total

81 78 79 76 0 80


9.2 A ppendix II: C hemic al s elec tion matrices

Literature and guideline documents

Chemical analysis possible

Expected biological activity

Health: science and perception

Occurrence

Selected? Overal Score CASRN Chemical name Selection

comment Class / type Description / mode of action Score Score Score Score Score

1 9.1 80-05-7 Bisphenol A (BPA) Industrial Estrogenic / anti-estrogenic 3.5 1 1.6 2 1

1 8.7 3380-34-5 Triclosan Pharmaceutical Antibacterial 3.5 1 2.2 1 1

1 8.5 104-40-5 4-Nonylphenol Industrial Estrogenic / anti-estrogenic 3.5 0.5 1 2.5 1

1 8.35 1912-24-9 Atrazine Herbicide Neuroendocrine 2.5 1 2.6 1.75 0.5

1 7.9 50-28-2 17b-Estradiol (E2) Hormone Estrogenic / anti-estrogenic 3.5 1 1.4 1 1

1 7.75

140-66-9 (1806-26-4 is general Octylphenol)

4-tert-octylphenol Industrial Estrogenic / anti-estrogenic 3.5 0.5 1 1.75 1

1 6.8 67-66-3 Chloroform DBP THM 1.5 0.5 2.3 1.5 1

1 6.6 87-86-5 Pentachlorophenol Pesticide 1.5 0.25 3.1 0.75 1

1 6.5 723-46-6 Sulfamethoxazole (Cotrim®) Pharmaceutical

Antibacterial (inhibits bacterial dihydrofolate synthetase, causing interference in the conversion of p-aminobenzoic acid (PABA) into folic acid, an essential component of bacterial development)

2.5 1 2 0 1

1 6.4 122-34-9 Simazine Pesticide Herbicide 1.5 0.25 2.4 1.75 0.5

1 5.9 57-63-6 17a-Ethynylestradiol (EE2) Pharmaceutical 2.5 1 1.4 1 0

1 5.9 53-16-7 Estrone (E1) Hormone Estrogenic / anti-estrogenic 2.5 1 1.4 1 0

1 5.8 62-75-9 NDMA (N-Nitrosodimethylamine) DBP Nitrosamine 1.5 0.5 1.8 1 1






Occurrence



1 5.65 59-05-2 Methotrexate Pharmaceutical

Chemotherapy agent (inhibits folic acid reductase, leading to inhibition of DNA synthesis and inhibition of cellular replication)

3.5 0.25 1.4 0 0.5

1 5.6 1582-09-8 Trifluralin Pesticide Herbicide 1.5 0.25 2.6 0.75 0.5

1 5.4 439-14-5 Diazepam (Valium®) Pharmaceutical

Benzodiazapine antianxiety (bind to benzodiazepine receptors which mediate sleep, affects muscle relaxation, anticonvulsant activity, motor coordination, and memory)

2.5 1 0.9 0 1

1 5.4 124-48-1 Dibromochloromethane (Chlorodibromomethane) DBP THM 1.5 0.5 1.4 1 1

1 5.2 75-27-4 Bromodichloromethane DBP THM 1.5 0.5 1.2 1 1

1 5.2 330-54-1 Diuron Pesticide Herbicide 1.5 0.25 2.2 0.75 0.5

1 5.1 134-62-3 DEET (Diethyltoluamide) PCP 1.5 1 0.6 1 1

1 5 10161-33-8 17b-Trenbolone Vet Drug Androgen, synthetic 1 1 2 1 0

1 5 15307-86-5 Diclofenac (Cataflam®, Voltaren®) Pharmaceutical

NSAID (inhibition of leukocyte migration and the enzymes COX-1 and COX-2, leading to the peripheral inhibition of prostaglandin synthesis)

2.5 1 0.5 0 1

1 4.9 298-46-4 Carbamazepine Pharmaceutical

Anticonvulsant and mood stabiliser (inhibits sustained repetitive firing by blocking use-dependent sodium channels)

2.5 1 0.4 0 1

1 4.9 58-22-0 Testosterone Hormone Androgen 1.5 1 1.4 1 0






Occurrence



1 4.8 25812-30-0 Gemfibrozil (Lopid®) Pharmaceutical

Antilipidemic (the activity of extrahepatic lipoprotein lipase, thereby increasing lipoprotein triglyceride lipolysis. Chylomicrons are degraded, VLDLs are converted to LDLs, and LDLs are converted to HDL)

2.5 1 0.8 0 0.5

1 4.6 75-25-2 Bromoform DBP THM 1.5 0.5 1.1 1 0.5

1 4.5 57-91-0 17a-Estradiol (aE2) Hormone Estrogen 1.5 1 1 1 0

1 4.5 50-27-1 Estriol (E3) Hormone Estrogen 1.5 1 1 1 0

1 4.5 103-90-2 Paracetamol Pharmaceutical 1.5 1 1 0 1

1 4.2 69-72-7 Salicylic acid Pharmaceutical 1.5 0.5 0.7 0.5 1

1 4.15 53-86-1 Indomethacin Pharmaceutical Anti-inflammatory 2.5 0.25 0.9 0 0.5

1 4.1 58-08-2 Caffeine Pharmaceutical 1.5 1 0.6 0 1

1 4 521-18-6 5a-Dihydro-testosterone (5a-DHT, stanolone) Hormone Androgen 1 1 1 1 0

1 4 5589-96-8 Bromochloroacetic acid DBP HAA 1 0.5 1 1 0.5

1 3.9 29122-68-7 Atenolol (Tenormin®) Pharmaceutical

Beta-blocker (competes with sympathomimetic neurotransmitters for binding at beta(1)-adrenergic receptors in the heart and vascular smooth muscle, inhibiting sympathetic stimulation)

2 1 0.4 0 0.5

1 3.65 333-41-5 Diazinon Pesticide Organophosphate 1.5 0.25 1.4 0 0.5

1 3.45 2921-88-2 Chlorpyrifos Pesticide Organophosphate 0.5 0 2.2 0.75 0

1 3.4 72-33-3 Mestranol Pharmaceutical Estrogen, synthetic 1.5 0.5 1.4 0 0






Occurrence



1 3 797-63-7 Levonorgestrel Pharmaceutical Progestin 1 0 2 0 0

0 7

25154-52-3 (other are for specific isomers, such as 104-40-5 / 84852-15-3)

Nonylphenol

See 4-n-nonylphenol (104-40-5) entry

1.5 1 1 3 0.5

0 6.15 117-81-7 Bis(2-ethylhexyl) phthalate (DEHP)

Ubiquitous, nightmare to measure

Industrial

Estrogenic / anti-estrogenic, Androgenic / anti-androgenic

2 0 1.9 2.25 0

0 5.8 94-75-7 2,4-D (2,4-Dichlorophenoxyacetic acid)

Chemical analysis method unavailable

Pesticide Herbicide 1.5 0 2.8 1 0.5

0 5 738-70-5 Trimethoprim (Cotrim®) Low biological activity Pharmaceutical

Antiinfective (binds to bacterial dihydrofolate reductase, subsequently interfering with the uptake of p-aminobenzoic acid (PABA) into folic acid, an essential component of bacterial development)

2.5 1 1 0 0.5

0 4.6 85-68-7 Benzyl butyl phthalate (BBP) Ubiquitous, nightmare to measure

Industrial Estrogenic / anti-estrogenic 2 0 1.6 1 0

0 4 68-23-5 Norethynodrel (NE)

No chem analysis & unlikely to occur (?)

Progestin 1 0 3 0 0

0 4 7440-43-9 Cadmium

No chem analysis, low biological activity

Inorganic 1.5 0 0.5 1 1






Occurrence



0 3.5 57-41-0 Phenytoin (Dilantin®) Very specific biological activity

Drug

Anticonvulsant (acts on sodium channels on the neuronal cell membrane, limiting the spread of seizure activity and reducing seizure propagation)

2 1 0 0 0.5

0 3.45 123-91-1 1,4-dioxane

No chem analysis, unclear biological activity

Industrial Solvent 1 0.25 1.2 1 0

0 3.25 73334-07-3 Iopamidol/iopromide (Isovue®)

No chem analysis, unclear biological activity

Pharmaceutical

X-ray contrast media (blocks xrays as they pass through the body, thereby allowing body structures containing iodine to be delineated in contrast)

2.5 0.25 0 0 0.5

0 3.25 6190-65-4 Desethyl atrazine Metabolite of atrazine 1 0.25 0 1 1

0 3.25 80214-83-1 Roxithromycin Very specific biological activity 1.5 0.25 1 0 0.5

0 3 79-94-7 Tetrabromobisphenol A (TBBPA)

No chem analysis & unlikely to occur (?)

Industrial 1 0 2 0 0

0 3 7429-90-5 Aluminium

No chem analysis, low biological activity

Inorganic 1 0 0 1 1

0 3 16320-04-0 Gestrinone

Hormone, unlikely to occur (?), no chem analysis

Androgen, synthetic 1 0 2 0 0

0 3 71-58-9 Medroxyprogesterone acetate (MPA)


Progestin 1 0 2 0 0






Occurrence



0 3 965-93-5 Methyltrienolone (R1881, Metribolone)



0 3 3704-09-4 Mibolerone



0 3 68-22-4 Norethisterone (NET, Norethindrone)


Progestin 1 0 2 0 0

0 3 14797-73-0 Perchlorate

No chem analysis, unknown occurrence

Industrial 1 0 1 1 0

0 3 10540-29-1 Tamoxifen

No chem analysis, unknown occurrence

Drug

Chemotherapy agent (binds to estrogen receptors, inducing a conformational change in the receptor, blocking or changing the expression of estrogen dependent genes)

2 0 1 0 0

0 3 3764-87-2 Trestolone (MENT)



0 3 115-96-8 Tri(2-chloroethyl) phosphate (TCEP)

Low bio activity, no perceived health concern 1.5 1 0 0 0.5

0 2.9 79-43-6 Dichloroacetic Acid

No chem analysis, other related chems already covered

DBP HAA 1.5 0 0.4 0 1

0 2.75 60-00-4 EDTA (ethylenediaminetetraacetic acid)

Low bio activity, no perceived health concern 1.5 0.25 0 0 1

0 2.75 114-07-8 Erythromycin No available chem analysis 1.5 0.25 0.5 0 0.5






Occurrence



0 2.75 54-31-9 Frusemide (Furosemide, Lasix®)

Specific biological activity, not considered a health risk

Drug

Loop diuretic (inhibits the reabsorption of sodium and chloride in the ascending limb of the loop of Henle, increases the urinary excretion of sodium, chloride, and water)

2 0.25 0 0 0.5

0 2.5 378-44-9 Bethamethasone

No chem analysis, specific biological activity

Pharmaceutical Glucocorticoid 1 0 1.5 0 0

0 2.5 1222-05-5 HHCB (Galaxolide®)

No chem analysis, unknown toxicity, unknown occurrence

PCP Musk 1.5 0 0 1 0

0 2.5 57-88-5 Cholesterol Not considered a health hazard Hormone Steroid precursor 1 0.5 0 0 1

0 2.5 106-44-5 4-Methylphenol 1.5 0.5 0 0 0.5

0 2.5 7440-42-8 Boron Inorganic 1.5 0 0 0 1

0 2.5 14866-68-3 Chlorate 1 0 0 1 0.5

0 2.5 7440-50-8 Copper Inorganic 1.5 0 0 0 1

0 2.5 84-74-2 Di-n-butyl phthalate Industrial chemical


1.5 0 0 1 0

0 2.5 16984-48-8 Fluoride 1.5 0 0 0 1

0 2.5 54910-89-3 Fluoxetine 1.5 1 0 0 0

0 2.5 58-89-9 Lindane (BHC-gamma) Pesticide


1.5 0 0 1 0

0 2.5 7439-96-5 Manganese Inorganic 1.5 0 0 0 1

0 2.5 94-74-6 MCPA (2-methyl-4-chlorophenoxyacetic acid) 1 1 0 0 0.5






Occurrence



0 2.5 22204-53-1 Naproxen (Aleve®) Drug

NSAID (inhibits cyclooxygenase activity; inhibition of COX-1 is thought to be associated with gastrointestinal and renal toxicity while inhibition of COX-2 provides anti-inflammatory activity)

1.5 1 0 0 0

0 2.5 57-83-0 Progesterone Progestogen 1.5 0 1 0 0

0 2.25 59729-33-8 Citalopram Very specific biological activity

Pharmaceutical 1 0.25 0 0 1

0 2.25 75-99-0 Dalapon

Low biological activity (?), unknown toxicity

Pesticide Herbicide 1 0.25 0 0 1

0 2.25 76-57-3 / 6059-47-8 Codeine 1.5 0.25 0 0 0.5

0 2.25 144010-85-5 Desmethylcitalopram 1 0.25 0 0 1

0 2.25 93106-60-6 Enrofloxacin 1.5 0.25 0 0 0.5

0 2.25 51218-45-2 Metolachlor 1.5 0.25 0 0 0.5

0 2.25 37350-58-6 / 51384-51-1 Metoprolol 1.5 0.25 0 0 0.5

0 2.25 70458-96-7 Norfloxacin 1.5 0.25 0 0 0.5

0 2.25 525-66-6 Propranolol 1.5 0.25 0 0 0.5

0 2.25 599-79-1 Sulfasalazine 1.5 0.25 0 0 0.5

0 2.25 846-50-4 Temazepam 1.5 0.25 0 0 0.5

0 2.25 55335-06-3 Triclopyr 1 0.25 0 0 1

0 2 87-65-0 2,6-Dichlorophenol 0.5 0 0 1.5 0

0 2 120-83-2 24 DP (2,4-Dicholorophenol) 1.5 0 0 0 0.5

0 2 6893-02-3 3,5,3-Triiodothyronine (T3) Thyroid hormone 1 0 1 0 0

0 2 34256-82-1 Acetochlor Industrial 1 0 1 0 0






Occurrence



0 2 50-76-0 Actinomycin D Antibiotics 1 0 1 0 0

0 2 15541-45-4 Bromate 1 0 0 0 1

0 2 50-22-6 Corticosterone Corticosteroid 1 0 1 0 0

0 2 50-23-7 Cortisol (Hydroxycortisone) Corticosteroid 1 0 1 0 0

0 2 50-18-0 Cyclophosphamide Alkylating agents 1.5 0 0.5 0 0

0 2 427-51-0 Cyproterone acetate (CA) Progestin 1 0 1 0 0

0 2 1088-11-5 Desmethyldiazepam (Nordazepam) 1 0.5 0 0 0.5

0 2 50-02-2 Dexamethasone Glucocorticoid 1 0 1 0 0

0 2 75-09-2 Dichloromethane (Methylene chloride) 0.5 0 0 1.5 0

0 2 56-53-1 Diethylstilbestrol (DES) Estrogen, synthetic 1 0 1 0 0

0 2 75847-73-3 Enalapril (Enalaprit®) Drug

ACE inhibitor (competes with angiotensin I for binding at the angiotensin-converting enzyme, blocking the conversion of angiotensin I to angiotensin II, a vasoconstrictor, leading to decreases in blood pressure and stimulation of baroreceptor reflex mechanisms)

1 1 0 0 0

0 2 446-72-0 Genistein Phytoestrogen 1 0 1 0 0

0 2 7439-89-6 Iron 1 0 0 0 1

0 2 330-55-2 Linuron Herbicide Androgenic / anti-androgenic 1 1 0 0 0

0 2 ? NH-3 ? 1 0 1 0 0

0 2 35189-28-7 Norgestimate Progestin 1 0 1 0 0






Occurrence



0 2 24320-06-7 ORG2058 (16a-ethyl-21-hydroxy-19-norpregn-4-ene-3,20-dione) Progestin 1 0 1 0 0

0 2 50-24-8 Prednisolone Glucocorticoid 1 0 1 0 0

0 2 53-03-2 Prednisone

Anti-inflammatory agents 1 0 1 0 0

0 2 106266-06-2 Risperidone (Risperidal®) Drug

Antipsychotic (blocks dopaminergic D2 receptors in the limbic system alleviating symptoms of schizophrenia, and blocks serotonergic 5-HT2 receptors in the mesocortical tract, causing an excess of dopamine and an increase in dopamine transmission)

1 1 0 0 0

0 2 79902-63-9 Simvastatin (Zocor®) Drug

Antilipidemic (inhibits the hepatic enzyme HMG-CoA reductase, reducing conversion of HMG-CoA to mevalonate, a precursor of cholesterol)

1 1 0 0 0

0 2 14808-79-8 Sulphate 1 0 0 0 1

0 1.75 1007-28-9 Desoisopropyl atrazine 1 0.25 0 0 0.5

0 1.75 1918-00-9 Dicamba 1 0.25 0 0 0.5

0 1.75 469-21-6 Doxylamine 1 0.25 0 0 0.5

0 1.75 69377-81-7 Fluroxypyr 1 0.25 0 0 0.5

0 1.75 60142-96-3 Gabapentin 1 0.25 0 0 0.5

0 1.75 1071-83-6 Glyphosate 1 0.25 0 0 0.5

0 1.75 58-93-5 Hydrochlorthiazide 1 0.25 0 0 0.5

0 1.75 GROUP Linear alkylbenzene sulphonates (LAS) 1 0.25 0 0 0.5






Occurrence



0 1.75 93-65-2 Mecoprop (MCPP) 1 0.25 0 0 0.5

0 1.75 604-75-1 Oxazepam 1 0.25 0 0 0.5

0 1.75 76-42-6 Oxycodone 1 0.25 0 0 0.5

0 1.75 375-73-5 / 59933-66-3

Perfluorobutanesulfonate (PFBS) 1 0.25 0 0 0.5

0 1.75 335-76-2 Perfluorodecanoic acid (PFDA) 1 0.25 0 0 0.5

0 1.75 307-55-1 Perfluorododecanoic acid (PFDoDA) 1 0.25 0 0 0.5

0 1.75 375-85-9 Perfluoroheptanoic acid (PFHpA) 1 0.25 0 0 0.5

0 1.75 432-50-7 Perfluorohexanesulfonate (PFHxS) 1 0.25 0 0 0.5

0 1.75 307-24-4 Perfluorohexanoic acid (PFHxA) 1 0.25 0 0 0.5

0 1.75 375-95-1 Perfluorononanoic acid (PFNA) 1 0.25 0 0 0.5

0 1.75 335-67-1 Perfluoroocatnoate (PFOA) 1 0.25 0 0 0.5

0 1.75 754-91-6 Perfluorooctanesulfonamide (PFOSA) 1 0.25 0 0 0.5

0 1.75 1763-23-1 Perfluorooctanesulfonate (PFOS) 1 0.25 0 0 0.5

0 1.75 4234-23-5 Perfluoroundecanoic acid (PFUnDA) 1 0.25 0 0 0.5

0 1.75 55268-74-1 Praziquantel 1 0.25 0 0 0.5

0 1.75 114-26-1 Propoxur 1 0.25 0 0 0.5

0 1.75 107534-96-3 Tebuconazole 1 0.25 0 0 0.5

0 1.75 886-50-0 Terbutryn 1 0.25 0 0 0.5

0 1.75 27203-92-5 Tramadol 1 0.25 0 0 0.5

0 1.5 101-20-2 Triclocarban

Unknown toxicity, unknown occurrence

Pharmaceutical PPCP (antibacterial / antifungal) 1 0.5 0 0 0

0 1.5 95-57-8 2-Chlorophenol 0.5 0 0 1 0

0 1.5 95-76-1 3,4 Dichloroaniline 1 0 0 0 0.5

0 1.5 106-48-9 4-Chlorophenol 0.5 0 0 1 0






Occurrence



0 1.5 23214-92-8 / 29042-30-6 Adriablastin (Doxorubicin) Antibiotics 1 0 0.5 0 0

0 1.5 GROUP Alkane ethoxy sulfonates (AES) 1 0 0 0 0.5

0 1.5 834-12-8 Ametryn 1 0 0 0 0.5

0 1.5 1066-51-9 AMPA (1-Aminomethylphosphonic acid) 1 0 0 0 0.5

0 1.5 77521-29-0 AMPA (alpha-Amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid) 1 0 0 0 0.5

0 1.5 53-41-8 Androsterone 0.5 1 0 0 0

0 1.5 134523-00-5 Atorvastatin 0.5 1 0 0 0

0 1.5 86-50-0 Azinphos-methyl Pesticide Organophosphate 0.5 0 1 0 0

0 1.5 50-32-8 Benzo(a)pyrene 0.5 0 0 1 0

0 1.5 GROUP BHA (butylated hydroxy …) 1 0.5 0 0 0

0 1.5 11056-06-7 Bleomycin Antibiotics 1 0 0.5 0 0

0 1.5 79-08-3 Bromoacetic acid DBP HAA 0.5 0 0 1 0

0 1.5 74-97-5 Bromochloromethane 0.5 0 0 1 0

0 1.5 10049-04-4 Chlorine dioxide 1 0 0 0 0.5

0 1.5 14998-27-7 / 1318-59-8 Chlorite 1 0 0 0 0.5

0 1.5 62-73-7 Dichlorvos Pesticide Organophosphate 0.5 0 1 0 0

0 1.5 564-25-0 Doxycycline Drug

Tetracycline antibiotic (reversibly binds to ribosomal subunits, blocking the binding of aminoacyl tRNA to mRNA and inhibiting bacterial protein synthesis)

1.5 0 0 0 0

0 1.5 15687-27-1 Ibuprofen 0.5 1 0 0 0






Occurrence



0 1.5 3778-73-2 Isophosphamide (Ifosfamide) Drug

Chemotherapy agent (active metabolites alkylate or bind with many intracellular molecular structures, including nucleic acids; cytotoxic action is primarily due to crosslinking of strands of DNA and RNA, as well as inhibition of protein synthesis)

1.5 0 0 0 0

0 1.5 22071-15-4 Ketoprofen 0.5 1 0 0 0

0 1.5 121-75-5 Malathion Pesticide Organophosphate 0.5 0 1 0 0

0 1.5 57-53-4 Meprobamate (Equanil®, Miltown®) Drug

Antianxiety agent (mechanism of action not known; affects multiple sites in the central nervous system, including the thalamus and limbic system; binds to GABAA receptors which interrupt neuronal communication in the reticular formation and spinal cord)

1 0.5 0 0 0

0 1.5 18378-89-7 Mithramycin Antibiotics 1 0 0.5 0 0

0 1.5 50-07-7 Mitomycin C Antibiotics 1 0 0.5 0 0

0 1.5 56-38-2 Parathion (ethyl parathion) Pesticide Organophosphate 0.5 0 1 0 0

0 1.5 298-00-0 Parathion-methyl (Methyl parathion) Pesticide Organophosphate 0.5 0 1 0 0

0 1.5 GROUP PCBs (total) 0.5 0 0 1 0

0 1.5 52-67-5 Penicillamine

Anti-inflammatory agents 1 0 0.5 0 0

0 1.5 GROUP Secondary alkanesulphonates 1 0 0 0 0.5

0 1.5 56573-85-4 Tributyltin 0.5 0 0 1 0






Occurrence



0 1.5 76-03-9 Trichloroacetic acid 0.5 0 0 1 0

0 1.25 120-12-7 Anthracene 0.5 0 0 0.75 0

0 1.25 7439-92-1 Lead Inorganic 0.5 0 0 0.75 0

0 1.25 91-20-3 Naphthalene 0.5 0 0 0.75 0

0 1.25 4151-50-2 N-ethyl perfluorooctane sulfonamide (N-EtFOSA; Sulfuramid) 1 0.25 0 0 0

0 1.25 ? N-ethyl perfluorooctane sulfonamidoacetate (N-EtFOSAA) 1 0.25 0 0 0

0 1.25 93413-69-5 Venlafaxine 1 0.25 0 0 0

0 1 882-09-7 2-(p-Chlorophenoxy)-2-methylpropionic acid (Clofibric acid) 0.5 0.5 0 0 0

0 1 90-43-7 2-Phenylphenol 0.5 0.5 0 0 0

0 1 599-64-4 4-Cumylphenol 0.5 0.5 0 0 0

0 1 15972-60-8 Alachlor 0.5 0 0 0.5 0

0 1 66376-36-1 Alendronate (Fosamex®) Drug

Bisphonate inhibitor of bone resorption (reduces elevated rate of bone turnover)

1 0 0 0 0

0 1 134523-00-5 Atorvastatin (Lipitor®) Drug

Antilipidemic (inhibits the hepatic enzyme HMG-CoA reductase, reducing conversion of HMG-CoA to mevalonate, a precursor of cholesterol)

1 0 0 0 0

0 1 25013-16-5 Butylated hydroxyanisole (3-tert-butyl-4-hydroxy anisole) 0.5 0.5 0 0 0

0 1 128-37-0 Butylated hydroxytoluene (2,6-Di-tert-Butyl-p-Cresol) 0.5 0.5 0 0 0






Occurrence



0 1 100643-71-8 Desloratadine (Clarinex®) Drug

Antihistamine (competes with free histamine for binding at H1-receptors, blocking the action of endogenous histamine and leading to relief of negative symptoms brought on by histamine)

1 0 0 0 0

0 1 86386-73-4 Fluconazole (Diflucan®) Drug

Antifungal (interacts with 14-α demethylase, a cytochrome P-450 enzyme necessary to convert lanosterol to ergosterol, an essential component of the fungal cell membrane)

1 0 0 0 0

0 1 206-44-0 Fluoranthene 0.5 0 0 0.5 0

0 1 54910-89-3 Fluoxetine (Prozac®) Drug

SSRI anti-depressant (blocks the reuptake of serotonin at the serotonin reuptake pump of the neuronal membrane, enhancing the actions of serotonin on 5HT1A autoreceptors)

1 0 0 0 0

0 1 118-74-1 Hexachlorobenzene 0 0 0 1 0

0 1 87-68-3 Hexachlorobutadiene 0 0 0 1 0

0 1 608-73-1 Hexachlorocyclohexane 0 0 0 1 0

0 1 LIST Hospital waste list? 1 0 0 0 0






Occurrence



0 1 103577-45-3 Lansoprazole (Prevacid®) Drug

Antacid/ proton pump inhibitor (suppresses gastric acid secretion by specific inhibition of the (H+,K+)-ATPase enzyme system at the secretory surface of the gastric parietal cell, blocking the final step of acid production)

1 0 0 0 0

0 1 7439-97-6 Mercury 0 0 0 1 0

0 1 72-43-5 Methoxychlor Pesticide


1 0 0 0 0

0 1 61337-67-5 Mirtazapine (Remeron®) Drug

Tetracyclic antidepressant (antagonist at central presynaptic alpha(2)-receptors, inhibiting negative feedback to the presynaptic nerve and causing an increase in NE release)

1 0 0 0 0

0 1 81-14-1 Musk ketone 0.5 0.5 0 0 0

0 1 7440-02-0 Nickel Inorganic 0.5 0 0 0.5 0

0 1 55-18-5 N-Nitrosodiethylamine DBP Nitrosamine 0.5 0.5 0 0 0

0 1 608-93-5 Pentachorobenzene 0 0 0 1 0

0 1 50471-44-8 Vinclozolin Fungicide Androgenic / anti-androgenic 1 0 0 0 0

0 0.75 120-82-1 1,2,4-Trichlorobenzene 0 0 0 0.75 0

0 0.75 34123-59-6 Isoproturon 0 0 0 0.75 0

0 0.5 4408-81-5 (Propylenedinitrilo) tetraacetic acid (PDTA) 0.5 0 0 0 0

0 0.5 75-35-4 1,1-Dichloroethene (11DCE; 1,1-Dichloroethylene) 0.5 0 0 0 0






Occurrence



0 0.5 107-06-2 1,2-Dichloroethane 0 0 0 0.5 0

0 0.5 611-59-6 1,7-Dimethylxanthine (Paraxanthine) 0.5 0 0 0 0

0 0.5 38380-08-4 2,3,3',4,4',5-Hexachlorobiphenyl (PCB156) 0.5 0 0 0 0

0 0.5 32598-14-4 2,3,3',4,4'-pentachlorobiphenyl (PCB105) 0.5 0 0 0 0

0 0.5 31508-00-6 2,3',4,4',5-Pentachlorobiphenyl (PCB118) 0.5 0 0 0 0

0 0.5 52663-72-6 2,4,5,3',4',5'-Hexachlorobiphenyl (PCB167) 0.5 0 0 0 0

0 0.5 88-06-2 2,4,6-Trichlorophenol (2,4,6-T) 0.5 0 0 0 0

0 0.5 81-15-2 2,4,6-Trinitro-1,3-dimethyl-5-tert-butylbenzene (musk xylene) 0.5 0 0 0 0

0 0.5 490-79-9 2,5-Dihydroxybenzoic acid 0.5 0 0 0 0

0 0.5 719-22-2

2,6-di-tert-butyl-1,4-benzoquinone (2,6-bis(1,1-dimethylethyl)-2,5-Cyclohexadiene-1,4-dione)

0.5 0 0 0 0

0 0.5 128-39-2 2,6-di-tert-butylphenol (2,6-bis(1,1-dimethylethyl)phenol) 0.5 0 0 0 0

0 0.5 32774-16-6 3,4,5,3’,4’,5’-Hexachlorobiphenyl (PCB169) 0.5 0 0 0 0

0 0.5 72-55-9 4,4’-DDE 0.5 0 0 0 0

0 0.5 50-29-3 4,4’-DDT 0.5 0 0 0 0

0 0.5 13171-00-1 4-Acetyl-6-t-butyl-1,1-dimethylindan 0.5 0 0 0 0

0 0.5 100-02-7 4-Nitrophenol 0.5 0 0 0 0

0 0.5 136-85-6 5-methyl-1H-benzotriazole 0.5 0 0 0 0

0 0.5 1506-02-1 6-Acetyl-1,1,2,4,4,7-hexamethyltetraline 0.5 0 0 0 0

0 0.5 98-86-2 Acetophenone 0.5 0 0 0 0

0 0.5 12587-46-1 Alpha particles 0.5 0 0 0 0

0 0.5 28981-97-7 Alprazolam 0.5 0 0 0 0






Occurrence



0 0.5 26787-78-0 Amoxycillin 0.5 0 0 0 0

0 0.5 23893-13-2 Anhydroerythromycin A 0.5 0 0 0 0

0 0.5 7440-36-0 Antimony Inorganic 0.5 0 0 0 0

0 0.5 60-80-0 Antipyrine 0.5 0 0 0 0

0 0.5 7440-38-2 Arsenic Inorganic 0.5 0 0 0 0

0 0.5 83905-01-5 Azithromycin 0.5 0 0 0 0

0 0.5 7440-39-3 Barium Inorganic 0.5 0 0 0 0

0 0.5 71-43-2 Benzene 0 0 0 0.5 0

0 0.5 100-44-7 Benzyl chloride 0.5 0 0 0 0

0 0.5 12587-47-2 Beta particles & photon emitters 0.5 0 0 0 0

0 0.5 63659-18-7 Betaxolol 0.5 0 0 0 0

0 0.5 41859-67-0 Bezafibrate (Benzafibrate) 0.5 0 0 0 0

0 0.5 66722-44-9 Bisoprolol 0.5 0 0 0 0

0 0.5 24959-67-9 Bromide 0.5 0 0 0 0

0 0.5 7726-95-6 Bromine 0.5 0 0 0 0

0 0.5 83463-62-1 Bromochloroacetonitrile 0.5 0 0 0 0

0 0.5 4824-78-6 Bromophos-ethyl 0.5 0 0 0 0

0 0.5 57775-29-8 Carazolol 0.5 0 0 0 0

0 0.5 10605-21-7 Carbendazim 0.5 0 0 0 0

0 0.5 70356-03-5 Cefaclor 0.5 0 0 0 0

0 0.5 15686-71-2 Cephalexin 0.5 0 0 0 0

0 0.5 56-75-7 Chloramphenicol 0.5 0 0 0 0

0 0.5 57-47-9 Chlordane 0.5 0 0 0 0

0 0.5 470-90-6 Chlorfenvinphos 0 0 0 0.5 0

0 0.5 7782-50-5 Chlorine 0.5 0 0 0 0






Occurrence



0 0.5 120-32-1 Chlorophene 0.5 0 0 0 0

0 0.5 5598-13-0 Chlorpyrifos-methyl 0.5 0 0 0 0

0 0.5 57-62-5 Chlortetracycline 0.5 0 0 0 0

0 0.5 7440-47-3 Chromium Inorganic 0.5 0 0 0 0

0 0.5 51481-61-9 Cimetidine 0.5 0 0 0 0

0 0.5 85721-33-1 Ciprofloxacin 0.5 0 0 0 0

0 0.5 81103-11-9 Clarithromycin 0.5 0 0 0 0

0 0.5 37148-27-9 Clenbuterol 0.5 0 0 0 0

0 0.5 18323-44-9 Clindamycin 0.5 0 0 0 0

0 0.5 360-68-9 Coprostanol (5beta-Cholestan-3beta-ol) 0.5 0 0 0 0

0 0.5 486-56-6 Cotinine ((S)-1-methyl-5-(3-pyridinyl)-2-Pyrrolidinone) 0.5 0 0 0 0

0 0.5 91-64-5 Coumarin 0.5 0 0 0 0

0 0.5 52315-07-8 Cypermethrin 0.5 0 0 0 0

0 0.5 67035-22-7 Dehydronifedipine 0.5 0 0 0 0

0 0.5 127-33-3 Demeclocycline 0.5 0 0 0 0

0 0.5 126-75-0 Demeton-S 0.5 0 0 0 0

0 0.5 737-31-5 Diatrizoate sodium 0.5 0 0 0 0

0 0.5 117-96-4 Diatrizoic acid 0.5 0 0 0 0

0 0.5 1002-53-5 Dibutyltin 0.5 0 0 0 0

0 0.5 3018-12-0 Dichloroacetonitrile 0.5 0 0 0 0

0 0.5 42399-41-7 Diltiazem 0.5 0 0 0 0

0 0.5 60-51-5 Dimethoate 0.5 0 0 0 0

0 0.5 GROUP Dioxin like compounds (Total) 0.5 0 0 0 0

0 0.5 76420-72-9 Enalaprilat 0.5 0 0 0 0






Occurrence



0 0.5 1031-07-8 Endosulfan sulfate 0.5 0 0 0 0

0 0.5 517-09-9 Equilenin 0.5 0 0 0 0

0 0.5 474-86-2 Equilin 0.5 0 0 0 0

0 0.5 563-12-2 Ethion 0.5 0 0 0 0

0 0.5 13194-48-4 Ethoprophos (Mocap) 0.5 0 0 0 0

0 0.5 31879-05-7 Fenoprofen 0.5 0 0 0 0

0 0.5 55-38-9 Fenthion (fenthion-methyl) 0.5 0 0 0 0

0 0.5 13674-87-8 Fyrol FR 2 (tri(dichlorisopropyl) phosphate) 0.5 0 0 0 0

0 0.5 7553-56-2 Iodine 0.5 0 0 0 0

0 0.5 66108-95-0 Iohexol 0.5 0 0 0 0

0 0.5 60166-93-0 Iopamidol 0.5 0 0 0 0

0 0.5 154-21-2 Lincomycin 0.5 0 0 0 0

0 0.5 657-24-9 Metformin (1,1-dimethylbiguanide) 0.5 0 0 0 0

0 0.5 7439-98-7 Molybdenum Inorganic 0.5 0 0 0 0

0 0.5 17090-79-8 Monensin 0.5 0 0 0 0

0 0.5 78763-54-9 Monobutyltin (MBT) 0.5 0 0 0 0

0 0.5 145-39-1 Musk tibetene 0.5 0 0 0 0

0 0.5 42200-33-9 Nadolol 0.5 0 0 0 0

0 0.5 389-08-2 Nalidixic acid (Negram, Naladixic acid) 0.5 0 0 0 0

0 0.5 7697-37-2 Nitrate (NO3-) 0.5 0 0 0 0

0 0.5 139-13-9 Nitrilotriacetic acid (NTA) 0.5 0 0 0 0

0 0.5 14797-65-0 Nitrite (NO2) 0.5 0 0 0 0

0 0.5 59-89-2 N-nitrosomorpholine 0.5 0 0 0 0

0 0.5 68-22-4 Norethindrone 0.5 0 0 0 0






Occurrence



0 0.5 3268-87-9 Octachlorodibenzo-p-dioxin 0.5 0 0 0 0

0 0.5 79-57-2 Oxytetracycline 0.5 0 0 0 0

0 0.5 61-33-6 Penicillin G 0.5 0 0 0 0

0 0.5 87-08-1 Penicillin V 0.5 0 0 0 0

0 0.5 116-66-5 Pentamethyl-4,6-dinitroindane (Musk moskene) 0.5 0 0 0 0

0 0.5 67-43-6 Pentetic acid 0.5 0 0 0 0

0 0.5 85-01-8 Phenanthrene 0.5 0 0 0 0

0 0.5 108-95-2 Phenol 0.5 0 0 0 0

0 0.5 85-44-9 Phthalic anhydride 0.5 0 0 0 0

0 0.5 129-00-0 Pyrene 0.5 0 0 0 0

0 0.5 18559-94-9 Salbutamol 0.5 0 0 0 0

0 0.5 7782-49-2 Selenium 0.5 0 0 0 0

0 0.5 7440-22-4 Silver Inorganic 0.5 0 0 0 0

0 0.5 19466-47-8 Stigmastanol 0.5 0 0 0 0

0 0.5 122-11-2 Sulfadimethoxine (SDMX) 0.5 0 0 0 0

0 0.5 57-68-1 Sulfamethazine (SMTZ) 0.5 0 0 0 0

0 0.5 144-82-1 Sulfamethizole 0.5 0 0 0 0

0 0.5 72-14-0 Sulfathiazole 0.5 0 0 0 0

0 0.5 23031-25-6 Terbutaline 0.5 0 0 0 0

0 0.5 60-54-8 Tetracycline 0.5 0 0 0 0

0 0.5 23564-06-9 Thiophanate 0.5 0 0 0 0

0 0.5 26839-75-8 Timolol 0.5 0 0 0 0

0 0.5 13710-19-5 Tolfenamic acid 0.5 0 0 0 0

0 0.5 78-51-3 Tri(butyl cellosolve) phosphate (ethanol,2-butoxy-phosphate) 0.5 0 0 0 0






Occurrence



0 0.5 126-73-8 Tributyl phosphate 0.5 0 0 0 0

0 0.5 115-86-6 Triphenyl Phosphate 0.5 0 0 0 0

0 0.5 1401-69-0 Tylosin 0.5 0 0 0 0

0 0.5 7440-62-2 Vanadium Inorganic 0.5 0 0 0 0

0 0.5 319-84-6 α-BHC (alpha-BHC; alpha-lindane) 0.5 0 0 0 0

0 0.5 319-85-7 β-BHC (beta-BHC; beta-lindane) 0.5 0 0 0 0


9.3 A ppendix III – A bridged S OP for c ollec tion and extrac tion of water s amples (full S OP available upon reques t)

Overview

Samples will be taken on Monday, Wednesday, Friday and Sunday at approximately 10 am in the morning.

One sample consists of two x 1 L water extracted with two x HLB/Charcoal tandem cartridges. After elution the extracts will be combined to produce a final sample to be divided and distributed for bioassay and chemical analysis.

Two samples will be taken each sampling day;

1. Source water – whatever the source water is to the recycled water plant.

2. Recycled water – at the end of the process.

In addition one process blank consisting of two x 1 L of MQ water will be extracted once per sampling period.

After extraction all cartridges will be sent to UNSW for elution.

Equipment

• Funnel

• Four 1 L schott bottles (with 2 additional if field blank is required), prewashed with detergent and rinsed 3x milliQ and 3xMethanol. Rinses should be enough to easily coat the inside of the bottle surface, approx 5mL

• VisiPrep SPE extraction manifold

• Coupling adapter to join HLB cartridge to charcoal cartridge (Supelco 57020-U)

• Tubing and adapters to fit 6cc HLB cartridges (NB: Make sure you rinse the tubing prior to use with MeOH – simple to attach to the top of an empty cartridge and suck a few mL of MeOH through the tubing).

• Esky with ice

• Labels/pens

• Aluminium foil

• Clipseal bags

• Gloves

• Pipette (1 mL + size)

• Vacuum pump (if required)

• Vacuum trap and tubing

• PPE – (check with plant for specifics such as helmets, hi vis vests), safety glasses, steel capped boots, gloves, lab coat

• Antibacterial handwash


• Sampling and analysis plan, chain of custody forms, field observation forms

• Spray bottle of MeOH

• Camera

Reagents

• Methanol

• MilliQ/distilled water

Consumables

• pH 0-14 indicator strips or pH meter

• Oasis HLB SPE 6cc cartridges (Waters Oasis HLB 0.5g/6cc, Waters 186000115)

• Charcoal cartridges (Supelclean Coconut charcoal SPE, 2g/6mL, Supelco 57144-U

Extraction protocol

Collection and pre-treatment of samples

• Identify, photograph and describe the collection point of the source water and the final water. Ensure you have been fully inducted and briefed by the operator before entering the site.

• Collect two one-litre source water samples and two one-litre final water samples. Use-pre rinsed Schott bottles (cross reference) and use the side markings for measurement. It is crucial to measure the 1 L as accurately as possible.

• Immediately place on ice in an esky, and extract as soon as possible. Keep out of light as much as possible.

Preconditioning of SPE cartridges

• Add 5 mL methanol and allow to run slowly through the cartridge. Do not apply vacuum, gravity alone should be enough. When the methanol is level with the top of the sorbent bed add 5 mL MilliQ water. Again let it pass by gravity. Once the MilliQ water is level with the top of the sorbent bed, refill the cartridge with MilliQ water and shut the vacuum valve to prevent further loss of water. (NB: Precondition the Oasis and charcoal cartridges separately, not stacked).

• Attach the 6cc HLB oasis cartridge above the 6cc charcoal cartridge (Supelco 57144-U, 2 g/6 mL) using the adapter (Supelco 57020-U).

• Place the tandem cartridge onto the manifold ensuring that the valve is closed open (see photo/diagram).

*NB once the condition process has started the cartridge bed cannot be allowed to dry out.

Extraction

• Ensure all valves under cartridges are turned off.

• Connect the SPE manifold to the vacuum trap and the vacuum trap to the vacuum inlet (or pump).

• Connect the tube adapter into the top of the HLB cartridge and place the other end of the tube into the sample bottle.

• Label the SPE cartridge clearly using either a sticker or indelible pen


• Turn the vacuum on and slowly open the vacuum port under the cartridge. Do one cartridge at a time and adjust the flow to a slow drip. Each drip should be clearly separated from the previous drip (as a rule of thumb, aim for approximately 40 drops in 15 seconds or approximately 5 ml/min).

**do not let the level of the water in the cartridge reservoir fall below the top of the sorbent bed. If necessary stop the vacuum to that cartridge and top it up with distilled water. Then reconnect the sample tube and re-apply vacuum. Ensure there is a good seal between the adaptor and the top of the SPE cartridge (that is the most likely cause of the problem).

• When all the sample has run through the cartridge, rinse the sample bottle with distilled water (a few mL) and run the rinse through the cartridge also. Finally rinse the walls of the cartridge with water and let it run through also.

• Separate the cartridges and leave the cartridge on the vacuum for aprox. 30 minutes to dry out.

• Wrap with aluminium foil and place in a labelled clip seal bag. Store in the Fridge until the end of the sampling event then send to UNSW laboratory.

Elution

Elution will take place at UNSW

Key points:

• Dry the HLB cartridges thoroughly with nitrogen. Make sure all water droplets are removed from the inside of the cartridge.

• Cartridges will be separated and eluted separately into one collection vessel).

• HLB cartridges eluted with methanol (2 x 5 mL). Charcoal eluted with methanol (2 x 5 mL).

• Suck final solvent from the cartridge under slight vacuum.

• The eluants will be centrifugally evaporated under vacuum at 350C using a Turbovap LV concentrator and reconstituted to 1 mL with MeOH.

• Label and split into three aliquots, one for chemistry, and two for bioassays. (GC vials to be used and volumes for bioassays: 400 µL to be sent to AWQC (SA Water) for bioassay analysis; 300 µL to be sent to SWRC (Griffith University) for bioassay analysis; 300 µL kept at UNSW for chemical analysis.


9.4 A ppendix IV – P riority c hemic als fac ts heets

9.4.1 17β-Estradiol (βE2)

G eneral information CASRN: 50-28-2 Chemical name: 17b-Estradiol Other name(s): Estradiol; beta-Estradiol; Oestradiol; Estra-1,3,5(10)-triene-3,17-diol,(17.beta.)- Related chemicals: 17a-estradiol, 17a-ethinylestradiol SMILES: C1[C@H]2[C@H]3[C@@H](c4c(cc(O)cc4)CC3)CC[C@@]2([C@H](O)C1)C MW: 272.39 G uidelines AU AGWR: 0.175 ug/L AU ADWG: Not available WHO DWG: Not available EU DWG: Not available NZ DWG: Not available CA DWG: Not available US NPDWS: Not available Qld PHR: 0.175 ug/L ANZECC-Env: Not available ANZECC-PI: Not available ANZECC-HH: Not available Other: None found Registration information PUBCRIS: Yes ARTG: Yes AICS: Yes HVICL: No Listed in NPI: No

Risk assessment NICNAS: Not available APVMA: Not available UN: Not available Other: None found Epidemiology: No information found Considerations: Carcinogenicity: IARC - Group 1, carcinogenic to humans USEPA - no assessment available Endocrine disruption: EU Priority List - not assessed Naturally occuring steroidal hormone; major estrogen occuring in humans. Sources and treatment Sources: Naturally present in human excreta. Source control: Wastewater treatment DWT efficacy: Alum, FeCl3, Softening - Low; PAC - Medium; Cl2, NH2Cl - High; O3, O3+H2O2, UV+H2O2 - High; UV, UF, NF, MIEX - Low WWT efficacy: 30.52% (EPIWin estimate) Fate: No information found. Uses: Reproductive hormone. Ingredient in prescription medicines.


Fate and modelling Cramer class: Intermediate (II) Log Kow: 4.01 (exper) Solubility: 3.9 mg/L (exper) VP: 1.99E-009 mm Hg (Modified Grain method) HLC: 3.64E-011 atm-m3/mole (Bond Method); 1.41E-012 atm-m3/mole (Group Method) BP: 395.47 deg C (Adapted Stein & Brown method)) MP: 221.5 deg C (exper) Log BCF: 2.388 Log Koc: 4.186 (MCI method); 2.899 (Kow Method) HL Biota: No information found HL Sediment: No information found HL Water: No information found pKa: 0.71 Toxicity Type Description Value In vitro – endocrine ERb-CALUX logEC50 = -9.63M; RTA = 100% In vitro – endocrine ERa-CALUX logEC50 = -10.80M; RTA = 100% In vitro – endocrine AR-CALUX logEC50 = -6.28M; RTA = 32% In vitro – endocrine PR-CALUX logEC50 = >-5M (ND) In vitro – endocrine GR-CALUX logEC50 = >-6M (ND) In vivo – acute toxicity Freshwater amphibians

(Rana spp.), 14-d Mortality LC50 = 2 - 6 uM

Occurrences Type Country Value Surface waters United States LOD 0.005 - 0.093 ug/L Unknown Australia 0.0006 - 0.018 ug/L Untreated wastewater Canada <0.005 - 0.015 ug/L WWTP effluent Australia 0.027 ug/L WWTP effluent Australia <0.05 ug/L WWTP effluent Canada <0.005 ug/L

9.4.2 Estrone (E1)

G eneral information CASRN: 53-16-7 Chemical name: Estrone Other name(s): E1; Estra-1,3,5(10)-trien-17-one, 3-hydroxy- Related chemical: Estriol, 17b-Estradiol, 17a-ethinylestradiol, 17a-estradiol SMILES: C1[C@H]2[C@H]3[C@@H](c4c(cc(O)cc4)CC3)CC[C@@]2(C(=O)C1)C MW: 270.37 G uidelines AU AGWR: 0.03 ug/L AU ADWG: Not available WHO DWG: Not available EU DWG: Not available NZ DWG: Not available CA DWG: Not available US NPDWS: Not available Qld PHR: 0.03 ug/L


ANZECC-Env: Not available ANZECC-PI: Not available ANZECC-HH: Not available Other: None found Registration information PUBCRIS: No ARTG: Yes AICS: Yes HVICL: No Listed in NPI: No

Risk assessment NICNAS: Not available APVMA: Not available UN: Not available Other: None found Epidemiology: Increased risk of endometrial cancer in postmenopausal women placed on unopposed

systemic estrogen replacement therapy for prolonged periods Considerations: Carcinogenicity: IARC classification - Group 1 (carcinogenic to humans) USEPA - no assessment available Endocrine disruption: EU Priority List - not assessed. Naturally occurring steroidal hormone. Sources and treatment Sources: WWTP effluent (human and animal excreta) Source control: Wastewater treatment methods DWT efficacy: Pre-Cl2 - 35%; Coagulation/sedimentation - 30%; Rapid filtration - 98%; Post-Cl2 - 25%;

Whole process - 90% WWT efficacy: 32.92% (modelled by EPIWin); Field: highly variable, -743 - 95.1% (Activated sludge);

82% (Biological nutrient removal) Fate: Biotransformation, biodegradation and adsorption Uses: Natural steroidal hormone Fate and modelling Cramer class: Intermediate (II) Log Kow: 3.13 (exper) Solubility: 30 mg/L (exper) VP: 0.00509 mm Hg (Modified Grain method) HLC: 3.80E-010 atm-m3/mole (Bond Method) BP: 154 deg C (exper) MP: 260.2 deg C (exper) Log BCF: 1.732 (regression-based method); 1.117 (Arnot-Gobas method) Log Koc: 4.375 (MCI method); 3.019 (Kow method) HL Biota: No information found HL Sediment: No information found HL Water: No information found pKa: 10.25 Toxicity Type Description Value In vivo – develomental Freshwater fish, Javanese ricefish (Oryzias

javanicus) 239 days post-hatch, Imposex/intersex conditions

LOEC = > 3.701 ug/L

In vivo – develomental Freshwater fish, Javanese ricefish (Oryzias javanicus) 239 days post-hatch, Organ weight to body weight ratio increased

LOEC = 3.701 ug/L

In vivo – develomental Freshwater fish, Medaka (Oryzias latipes) 85-110 days, Imposex/intersex conditions

LOEC = 0.008 ug/L

In vivo – endocrine Freshwater crustacean (Neomysis integer) 4-day Vitellogenesis

LOEC = 1 ug/L

In vivo – endocrine Freshwater fish, Zebrafish (Danio rerio) 18-day Vitellogenesis

EC50 = 0.078 ug/L

In vivo – endocrine Freshwater fish, Zebrafish (Danio rerio) 18-day Vitellogenesis

LOEC = 0.014 ug/L

In vivo – endocrine Freshwater fish, Rainbow trout (O. mykiss) 14-day Vitellogenesis

LOEC = 0.0033 ug/L

In vivo – endocrine Freshwater fish, Javanese ricefish (Oryzias javanicus) 239 days post-hatch Vitellogenesis

LOEC = 0.484 ug/L

In vivo – long-term Freshwater fish, Zebrafish (Danio rerio) 40-day, Mortality/Survival

NOEC = 0.0977 ug/L

In vivo – long-term Freshwater fish, Zebrafish (Danio rerio) 40-day, Sex ratio

NOEC = 0.0355 ug/L

In vivo – reproductive Freshwater fish, Javanese ricefish (Oryzias javanicus) 239 days post-hatch, Fertility

LOEC = 1.188 ug/L


In vivo – short term Freshwater fish, Javanese ricefish (Oryzias javanicus) 17-day Mortality/hatching

LOEC = 0.484 ug/L

In vivo – short term Saltwater crustacean (Acartia tonsa) 5-day Development

EC50 = 210 - 810 ug/L (410 ug/L)

Occurrences Type Country Value Reclaimed water Australia <0.25 ug/L Reclaimed water Australia <0.005 ug/L Surface waters United States <0.005 - 0.112 ug/L Unknown Australia <0.0001 - 0.0025 ug/L Unknown Australia 0.007 ug/L WWTP effluent Australia <0.25 ug/L WWTP effluent Unknown <0.006 - 0.109 ug/L

9.4.3 17α-Estradiol (αE2)

G eneral information CASRN: 57-91-0 Chemical name: 17a-estradiol Other name(s): 17-alpha-Estradiol; 17-alpha-Oestradiol; 1,3,5-Estratriene-3,17-alpha-diol Related chemical: 17b-Estradiol, 17a-ethinylestradiol, Mestranol, Estrone, 16alpha-hydroxyestrone SMILES: C1[C@H]2[C@@H]3[C@@H](c4c(cc(O)cc4)CC3)CC[C@@]2([C@@H](O)C1)C MW: 272.386 G uidelines AU AGWR: 0.175 ug/L AU ADWG: Not available WHO DWG: Not available EU DWG: Not available NZ DWG: Not available CA DWG: Not available US NPDWS: Not available Qld PHR: 0.175 ug/L ANZECC-Env: Not available ANZECC-PI: Not available ANZECC-HH: Not available Other: None found Registration information PUBCRIS: No ARTG: No AICS: No HVICL: No Listed in NPI: No

Risk assessment NICNAS: Not available APVMA: Not available UN: Not available Other: None found Epidemiology: None found


Considerations: Carcinogenicity: IARC - Group 1, carcinogenic to humans (1987) USEPA - no assessment available. Endocrine disruption: EU Priority List - not assessed Other: Naturally occurring steroidal hormone. Sources and treatment Sources: Steroidal hormone present naturally in animal and human excreta. Source control: Wastewater treatment. DWT efficacy: No information found. WWT efficacy: 30.52% (EPIWin estimate) Fate: No information found. Uses: Natural steroidal hormone. No direct uses. Fate and modelling Cramer class: Intermediate (II) Log Kow: 4.01 (exper) Solubility: 3.6 mg/L (27 deg C); 3.9 mg/L (25 deg C) (exper) VP: 1.99E-009 mm Hg (Modified Grain method) HLC: 3.64E-011 atm-m3/mole (Bond method); 1.41E-012 atm-m3/mole (Group method) BP: 395.47 deg C (Adapted Stein & Brown method) MP: 221.5 deg C (exper) Log BCF: 2.313 (regression-based method); 1.545 (Arnot-Gobas method) Log Koc: 4.186 (MCI method); 2.899 (Kow method) HL Biota: No information found HL Sediment: No information found HL Water: No information found pKa: 10.4 Toxicity No information found. Occurrences Type Country Value Purified recycled water Australia <0.005 ug/L Purified recycled water Australia <0.05 ug/L Surface waters United States <0.005 - 0.074 ug/L WWTP effluent Australia <0.005 ug/L WWTP effluent Australia <0.05 ug/L

9.4.4 Estriol (E3)

G eneral information CASRN: 50-27-1 Chemical name: Estriol Other name(s): Oestriol; Estra-1,3,5(10)-triene-3,16,17-triol, (16alpha,17beta)- Related chemical: 17b-Estradiol, 17a-ethinylestradiol, 17a-estradiol, Mestranol, Progesterone SMILES: C1[C@H]2[C@H]3[C@@H](c4c(cc(O)cc4)CC3)CC[C@@]2([C@H](O)[C@@H]1O)C MW: 288.3846 G uidelines AU AGWR: 0.05 ug/L AU ADWG: Not available WHO DWG: Not available


EU DWG: Not available NZ DWG: Not available CA DWG: Not available US NPDWS: Not available Qld PHR: 0.05 ug/L ANZECC-Env: Not available ANZECC-PI: Not available ANZECC-HH: Not available Other: None found Registration information PUBCRIS: Yes ARTG: Yes AICS: Yes HVICL: No Listed in NPI: No

Risk assessment NICNAS: Not available APVMA: Not available UN: Not available Other: None found Epidemiology: No information found Considerations: Carcinogenicity: IARC - Group 1, carcinogenic to humans (1979) USEPA - no assessment available. Endocrine disruption: EU Priority List - not assessed (2006); natural estrogen. Sources and treatment Sources: Wastewater effluent - steroidal hormone present naturally in human and animal excreta. Source control: Wastewater treatment. DWT efficacy: No information found WWT efficacy: No information found Fate: No information found Uses: Used in hormone replacement therapy in humans and animals. Fate and modelling Cramer class: Intermediate (II) Log Kow: 2.45 (exper) Solubility: 440.8 mg/L (estimate from log Kow) VP: 9.37E-012 mm Hg (Modified Grain method) HLC: 8.066E-015 atm-m3/mole (VP/WSol estimate) BP: 431.81 deg C (Adapted Stein & Brown method) MP: 290 deg C (exper) Log BCF: 1.284 (regression-based method); 0.754 (Arnot-Gobas method) Log Koc: 3.082 (MCI method); 1.624 (Kow method) HL Biota: No information found HL Sediment: No information found HL Water: No information found pKa: 10.38 Toxicity

Type Description Value In vitro – endocrine Freshwater fish, Zebrafish (Danio rerio), 18-day, Vitellogenesis LOEC = 0.6 ug/L In vivo – endocrine Freshwater fish, Zebrafish (Danio rerio), 18-day, Vitellogenesis NOEC = 0.30 ug/L In vivo – long-term Freshwater fish, Zebrafish (Danio rerio), 40-day,

Mortality/Survival NOEC = 21.7 ug/L

In vivo – long-term Freshwater fish, Zebrafish (Danio rerio), 40-day, Sex ratio NOEC = 6.7 ug/L In vivo – long-term Freshwater fish, Medaka (Oryzias latipes), 85 - 110 day,

Imposex/Intersex conditions NOEC = 0.075 ug/L

In vivo – long-term Freshwater fish, Zebrafish (Danio rerio), 40-day, Sex ratio LOEC = 21.7 ug/L In vivo – long-term Freshwater fish, Medaka (Oryzias latipes), 85 - 110 day,

Imposex/Intersex conditions LOEC = 0.75 ug/L

Occurrences

Type Country Value Surface waters United States <0.005 - 0.051 ug/L


9.4.5 17α-Ethynylestradiol (EE2)

G eneral information CASRN: 57-63-6 Chemical name: 17a-ethinylestradiol Other name(s): 17a-ethinyl estradiol; 19-Norpregna-1,3,5(10)-trien-20-yne-3,17-diol, (17.alpha.)-

ethinyloestradiol; ethynylestradiol Related chemical: 17β-Estradiol, 17α-Estradiol Link: http://toxnet.nlm.nih.gov/cgi-bin/sis/search/r?dbs+hsdb:@term+@na+ethinylestradiol SMILES: C1[C@@H]2[C@@]([C@@](C#C)(O)C1)(CC[C@H]1[C@H]2CCc2c1ccc(c2)O)C MW: 296.408 G uidelines AU AGWR: 0.0015 ug/L AU ADWG: Not available WHO DWG: Not available EU DWG: Not available NZ DWG: Not available CA DWG: Not available US NPDWS: Not available Qld PHR: 0.0015 ug/L ANZECC-Env: Not available ANZECC-PI: Not available ANZECC-HH: Not available Other: None found Registration information PUBCRIS: Yes ARTG: Yes AICS: Yes HVICL: No Listed in NPI: No Comment: ARTG as ethinylestradiol; PUBCRIS as ethinyloestradiol Risk assessment NICNAS: Not available APVMA: Not available UN: IPCS PIM 221 (1997) Other: None found Epidemiology: No information found Considerations: Carcinogenicity: IARC - Group 1, carcinogenic to humans (1987)

Endocrine disruption: EU Priority List - not assessed Synthetic derivative of the natural estrogen estradiol.

Sources and treatment Sources: Wastewater effluent following absorption/excretion by humans consuming oral

contraceptives. Source control: Wastewater treatment. DWT efficacy: Alum, FeCl3, Softening - low; PAC - med-high; Cl2, NH2Cl - high; UV - low; O3,

O3/H2O2, UV/H2O2 - high; UF - low; NF - med-high; MIEX - low-med WWT efficacy: 17.51% (EPIWin estimate) Fate: No information found Uses: A semisynthetic alkylated estradiol with a 17-alpha-ethinyl substitution. It has high

estrogenic potency when administered orally, and is often used as the estrogenic component in oral contraceptives.

Fate and modelling


Cramer class: Intermediate (II) Log Kow: 3.67 (exper) Solubility: 11.3 mg/L (27 deg C) VP: 1.95E-009 mm Hg (Modified Grain method) HLC: 7.94E-012 atm-m3/mole (Bond Method) BP: 411.21 deg C (Adapted Stein & Brown method) MP: 183 deg C (exper) Log BCF: 2.088 (regression-based method); 1.145 (Arnot-Gobas method) Log Koc: 4.650 (MCI method); 2.710 (Kow method) HL Biota: No information found HL Sediment: No information found HL Water: No information found pKa: No information found Toxicity Type Description Value In vitro – endocrine AR-CALUX logEC50 = >-5M (ND) In vitro – endocrine PR-CALUX logEC50 = -6.94M; RTA = 57% In vitro – endocrine ERa-CALUX logEC50 = -11.07M; RTA = 92% In vitro – endocrine ERb-CALUX logEC50 = -9.10M; RTA = 104% In vitro – endocrine GR-CALUX logEC50 = >-5M (ND) Occurrences Type Country Value Purified recycled water Australia <0.05 ug/L Surface waters United States LOD 0.005 - 0.831 ug/L Unknown Australia <0.001 ug/L Untreated wastewater Australia <0.01 ug/L WWTP effluent Australia <0.05 ug/L WWTP effluent Germany LOD - 0.062 ug/L WWTP effluent Australia <0.005 ug/L

9.4.6 Mestranol

G eneral information CASRN: 72-33-3 Chemical name: Mestranol Other name(s): 17-alpha-19-Norpregna-1,3,5(10)-trien-20-yn-17-ol, 3- methoxy-; 17.alpha.-

Ethynylestradiol, 3-methyl ether Related chemical: 17b-Estradiol, 17a-ethinylestradiol, 17a-estradiol, Estrone, 16alpha-hydroxyestrone,

Progesterone Chemical class: OC - Organic chemical Link: http://toxnet.nlm.nih.gov/cgi-bin/sis/search/r?dbs+hsdb:@term+@na+mestranol SMILES: C1[C@H]2[C@H]3[C@@H](c4c(cc(OC)cc4)CC3)CC[C@@]2([C@@](C#C)(O)C1)C MW: 310.434 G uidelines AU AGWR: 0.0025 ng/L AU ADWG: Not available WHO DWG: Not available EU DWG: Not available NZ DWG: Not available CA DWG: Not available


US NPDWS: Not available Qld PHR: 0.0025 ug/L ANZECC-Env: Not available ANZECC-PI: Not available ANZECC-HH: Not available Other: None found Registration information PUBCRIS: No ARTG: Yes AICS: Yes HVICL: No Listed in NPI: No

Risk assessment NICNAS: Not available APVMA: Not available UN: Not available Other: None found Epidemiology: No information found Considerations: Carcinogenicity: IARC - Group 1, carcinogenic to humans (1987) Endocrine disruption: EU Priority List - not assessed Synthetic derivative of the natural estrogen estradiol. Sources and treatment Sources: Wastewater effluent following absorption/excretion by humans consuming menestrol as

an ingredient in oral contraceptives. Source control: Wastewater treatment. DWT efficacy: No information found. WWT efficacy: 80.44% (EPIWin/Biowin estimate) Fate: No information found. Uses: Oral contraceptive - ovulation inhibitor. Fate and modelling Cramer class: Intermediate (II) Log Kow: 4.68 (KOWWIN v1.67 estimate) Solubility: 3.498 mg/L (estimate from Kow) VP: 9.75E-009 mm Hg (Modified Grain method) HLC: 1.139E-009 atm-m3/mole (VP/WSol estimate) BP: 400.03 deg C (Adapted Stein & Brown method) MP: 150.5 deg C (exper) Log BCF: 2.753 (regression-based method); 2.046 (Arnot-Gobas method) Log Koc: 4.348 (MCI method); 3.158 (Kow method) HL Biota: No information found HL Sediment: No information found HL Water: No information found pKa: No information found Toxicity No information found. Occurrences Type Country Value Surface waters United States <0.005 - 0.407 ug/L


9.4.7 Testosterone

G eneral information CASRN: 58-22-0 Chemical name: Testosterone Other name(s): Androst-4-en-3-one, 17-hydroxy-, (17beta)- Related chemical: Androsterone, Norethindrone, Etiocholanolone, Trenbolone Chemical class: OC - Organic chemical SMILES: [C@@H]12[C@@H]([C@@]3(C(=CC(=O)CC3)CC2)C)CC[C@]2([C@H]

1CC[C@@H]2O)C MW: 288.4282 G uidelines AU AGWR: 7 ug/L AU ADWG: Not available WHO DWG: Not available EU DWG: Not available NZ DWG: Not available CA DWG: Not available US NPDWS: Not available Qld PHR: 7 ug/L ANZECC-Env: Not available ANZECC-PI: Not available ANZECC-HH: Not available Registration information PUBCRIS: Yes ARTG: Yes AICS: Yes HVICL: No Listed in NPI: No

Risk assessment NICNAS: Not available APVMA: Not available UN: IPCS PIM 519 (1998) Other: None found Epidemiology: No information found Considerations: Carcinogenicity: IARC - no assessment available

USEPA - no assessment available Endocrine disruption: EU Priority List - not assessed Natural androgen. Sources and treatment Sources: Wastewater effluent - natural reproductive hormone, also used in human and veterinary

pharmaceutical products. Source control: Wastewater treatment. DWT efficacy: No information found. WWT efficacy: 37.84% (EPIWin estimate) Fate: No information found. Uses: Used in human pharmaceutical and veterinary agents. Natural reproductive hormone. Fate and modelling Cramer class: High (III) Log Kow: 3.32 (exper) Solubility: 23.4 mg/L (exper)


VP: 1.71E-008 mm Hg (Modified Grain method) HLC: 9.577E-011 atm-m3/mole (VP/WSol estimate) BP: 390.02 deg C (Adapted Stein & Brown method) MP: 144.56 deg C (Mean or Weighted MP) Log BCF: 1.858 (regression-based method); 2.215 (Arnot-Gobas method) Log Koc: 3.339 (MCI method); 2.546 (Kow method) HL Biota: No information found HL Sediment: No information found HL Water: No information found pKa: No information found Toxicity Type Description Value In vitro – endocrine AR-CALUX logEC50 = 9.06M; RTA = 103% In vitro – endocrine PR-CALUX logEC50 = >5M (ND) In vitro – endocrine Era-CALUX logEC50 = 5.34M; RTA = 127% In vitro – endocrine ERb-CALUX logEC50 = 5.56M; RTA = 96% In vitro – endocrine GR-CALUX logEC50 = >5M (ND) In vivo – acute toxicity

Saltwater crustacean (Acartia tonsa), 2 day, Mortality

LC50 = 5600 (4700 - 6600) ug/L

In vivo – acute toxicity

Saltwater crustacean (Neomysis integer), 4 day, Mortality

LC50 = 1950 (550 - 9080) ug/L

In vivo – develomental

Saltwater crustacean (Acartia tonsa), 5 day egg exposure, slowed/retarded/delayed or non-development

EC50 = 1500 (1400 - 1800) ug/L

In vivo – reproductive Freshwater crustacean (Ceriodaphnia dubia), 6-7 day, Progeny count

IC25 = 2755 ug/L

In vivo – reproductive Freshwater crustacean (Ceriodaphnia dubia), 6-7 day, Progeny count

NOEC = 2500 ug/L

Occurrences Type Country Value Surface waters United States <0.005 - 0.214 ug/L

9.4.8 5α-Dihydrotestosterone (DHT)

General information CASRN: 521-18-6 Chemical name: Stanolone (DHT) Other name(s): Androstanolone; 5-alpha-Dihydrotestosterone; 5a-DHT; DHT; 17beta-Hydroxy-5alpha-

androstan-3-one Related chemical: Testosterone SMILES: C1[C@@H]2[C@@H]([C@@]3([C@@H](CC(=O)CC3)C1)C)C

C[C@@]1([C@H]2CC[C@@H]1O)C MW: 290.444 G uidelines AU AGWR: Not available AU ADWG: Not available WHO DWG: Not available EU DWG: Not available NZ DWG: Not available CA DWG: Not available


US NPDWS: Not available Qld PHR: Not available ANZECC-Env: Not available ANZECC-PI: Not available ANZECC-HH: Not available Other: None found Registration information PUBCRIS: No ARTG: No AICS: No HVICL: No Listed in NPI: No Risk assessment NICNAS: Not available APVMA: Not available UN: IPCS PIM 917 (1994); IPCS PIM G007 (Anabolic steriods) (1994). Other: None found Epidemiology: No information found Considerations: Carcinogenicity: IARC - no assessment available USEPA - no assessment available Endocrine disruption: EU Priority List - not assessed Other: Naturally-occuring anabolic steroid. Sources and treatment Sources: Wastewater treatment plant effluent. Source control: Wastewater treatment. DWT efficacy: No information found. WWT efficacy: 14.22% (EPIWin estimate) Fate: Sludge adsorption (EPIWin) Uses: A potent androgenic metabolite of testosterone. Dihydrotestosterone (DHT) is generated

by a 5-alpha reduction of testosterone. Unlike testosterone, DHT cannot be aromatized to estradiol, therefore DHT is considered a pure androgenic steroid.

Fate and modelling Cramer class: High (class III) Log Kow: 3.55 (exper) Solubility: 5.25e+005 mg/L (exper) VP: 1.12E-008 mm Hg (Modified Grain method) HLC: 6.37E-009 atm-m3/mole (Bond Method); 9.96E-011 atm-m3/mole (Group Method); 1.020E-010 atm-m3/mole (VP/WSol estimate) BP: 386.13 deg C (Adapted Stein & Brown method) MP: 146.10 deg C (Mean or Weighted MP) Log BCF: 2.033 (regression-based method) Log Koc: 3.147 HL Biota: No information found HL Sediment: No information found HL Water: No information found pKa: No information found Toxicity Type Description Value In vivo – endocrine Saltwater fish, Threespine Stickleback (Gasterosteus

aculeatus), 21-d Spiggin protein induction LOEC = 0.003 ug/L

In vivo – endocrine Saltwater fish, Threespine Stickleback (Gasterosteus aculeatus), 21-d Spiggin protein induction

NOEC = 0.002 ug/L


LOEC = 0.002 ug/L


NOEC = 0.001 ug/L

Occurrences No information found.


9.4.9 17β-Trenbolone

G eneral information CASRN: 10161-33-8 Chemical name: Trenbolone Other name(s): 17b-trenbolone; 17beta-Hydroxyestr-4,9,11-trien-3-one Related chemical: Androsterone, Testosterone, Norethindrone Chemical class: OC - Organic chemical SMILES: C1[C@H]2[C@H]3C(=C4C(=CC(=O)CC4)CC3)C=C[C@@]2([C@H](O)C1)C MW: 270.37 G uidelines AU AGWR: Not available AU ADWG: Not available WHO DWG: Not available EU DWG: Not available NZ DWG: Not available CA DWG: Not available US NPDWS: Not available Qld PHR: Not available ANZECC-Env: Not available ANZECC-PI: Not available ANZECC-HH: Not available Other: None found Registration information PUBCRIS: Yes ARTG: No AICS: No HVICL: No Listed in NPI: No

Risk assessment NICNAS: Not available APVMA: Not available UN: IPCS PIM G007 Other: None found Epidemiology: No information found Considerations: Carcinogenicity: IARC - no assessment available USEPA - no assessment available Endocrine disruption: EU Priority List - not assessed Sources and treatment Sources: Agricultural runoff/effluent following absorption/excretion by animals treated with

trenbolone as a growth promotant. Source control: No information found DWT efficacy: No information found WWT efficacy: 3.60% (EPIWIN estimate) Fate: No information found Uses: An anabolic steroid used mainly as a growth substance in animals. Fate and modelling Cramer class: High (class III) Log Kow: 2.65 (KOWWIN estimate) Solubility: 324.1 mg/L (Estimate from Log Kow) VP: 1.86E-008 mmHg (Modified Grain method)


HLC: 1.90E-009 atm-m3/mole (Bond Method) BP: 392.32 deg C (Adapted Stein & Brown method) MP: 145.61 deg C (Mean or Weighted MP) Log BCF: 1.337 (regression-based method) Log Koc: 2.957 HL Biota: No information found HL Sediment: No information found HL Water: No information found pKa: No information found Toxicity Type Description Value In vitro – endocrine AR-CALUX logEC50 = -9.83M; RTA = 104% In vitro – endocrine PR-CALUX logEC50 = -8.61M; RTA = 96% In vitro – endocrine ERa-CALUX logEC50 = -6.16M; RTA = 105% In vitro – endocrine ERb-CALUX logEC50 = -5.50M; RTA = 109% In vitro – endocrine GR-CALUX logEC50 = >-5M (ND) In vivo – endocrine Freshwater fish, Zebrafish (Danio rerio), 21-

day, Vitellogenesis LOEC = 0.351 ug/L

In vivo – endocrine Freshwater fish, Medaka (Oryzias latipes), 21-day, Vitellogenesis

LOEC = 0.0397 ug/L

In vivo – endocrine Freshwater fish, Medaka (Oryzias latipes), 14-day, Vitellogenesis

LOEC = 0.365 ug/L

In vivo – endocrine Freshwater fish, Fathead minnow (Pimephales promelas), 21-day, Vitellogenesis

LOEC = 4.06 ug/L

In vivo – endocrine Freshwater fish, Fathead minnow (Pimephales promelas), 21-day, Vitellogenesis

LOEC = 0.026 ug/L

Occurrences No information found.

9.4.10 Levonorgestrel

G eneral information CASRN: 797-63-7 Chemical name: Levonorgestrel Other name(s): D-Norgestrel; 18,19-Dinor-17-alpha-pregn-4-en-20-yn-3-one, 13-ethyl-17- hydroxy- Related chemical: 17b-Estradiol, 17a-ethinylestradiol, 17a-estradiol, Estriol, Mestranol, Progesterone Chemical class: OC - Organic chemical SMILES: [C@@]12([C@H]([C@H]3[C@H](CC2)[C@@H]2C(=CC(=O)CC2)

CC3)CC[C@]1(C#C)O)CC MW: 312.4502 G uidelines AU AGWR: Not available AU ADWG: Not available WHO DWG: Not available EU DWG: Not available NZ DWG: Not available CA DWG: Not available US NPDWS: Not available


Qld PHR: Not available ANZECC-Env: Not available ANZECC-PI: Not available ANZECC-HH: Not available Other: None found Registration information PUBCRIS: No ARTG: Yes AICS: Yes HVICL: No Listed in NPI: No Comment: AICS listing under D-Norgestrel Risk assessment NICNAS: Not available APVMA: Not available UN: Not available Other: None found Epidemiology: No information found Considerations: Carcinogenicity: IARC - Group 2B, possibly carcinogenic to humans (progestins) (1987)

USEPA - no assessment available Endocrine disruption: EU Priority List - not assessed Synthetic steriodal hormone (progestogen). Sources and treatment Sources: Wastewater effluent following absorption/excretion by humans consuming hormone

contraception pills containing levonorgestrel. Source control: Wastewater treatment. DWT efficacy: No information found WWT efficacy: 24.52% (EPIWin/Biowin estimate) Fate: No information found Uses: Ingredient in some oral contraceptive pills. Fate and modelling Cramer class: High (III) Log Kow: 3.48 (KOWWIN v1.67 estimate) Solubility: 1.73 mg/L (exper) VP: 1E-009 mm Hg (Modified Grain method) HLC: 1.147E-011 atm-m3/mole (VP/WSol estimate) BP: 411.88 deg C (Adapted Stein & Brown method) MP: 206 deg C (exper) Log BCF: 1.963 (regression-based method); 2.261 (Arnot-Gobas method) Log Koc: 3.910 (MCI method); 2.634 (Kow method) HL Biota: No information found HL Sediment: No information found HL Water: No information found pKa: No information found Toxicity No information found. Occurrences No information found.


9.4.11 Bisphenol A

G eneral information CASRN: 80-05-7 Chemical name: Bisphenol A Other name(s): Phenol, 4,4 -(1-methylethylidene)bis-; BPA Related chemical: Tetrabromobisphenol A (TBBPA), Tetrachlorobisphenol A (TCBPA) SMILES: Oc(ccc(c1)C(c(ccc(O)c2)c2)(C)C)c1 MW: 228.29 G uidelines AU AGWR: 200 ug/L AU ADWG: Not available WHO DWG: Not available EU DWG: Not available NZ DWG: Not available CA DWG: Not available US NPDWS: Not available Qld PHR: 200 ug/L ANZECC-Env: Not available ANZECC-PI: Not available ANZECC-HH: Not available Other: None found Registration information PUBCRIS: No ARTG: No AICS: Yes HVICL: Yes Listed in NPI: No Comment: AICS name: Phenol, 4,4'-(1-methyethylidene)bis - --- HVICL Category: 1,000-9,999 tonnes Risk assessment NICNAS: Not available APVMA: Not available UN: IPCS ICSC 0634 (1994); INFOSAN Information Note No. 5/2009 - Bisphenol A (2009) Other: EC RAR (2008); Health Canada (2008); US NTP (2008); IRIS Oral RfD Assessment (1993) Epidemiology: No information found Considerations: Carcinogenicity: IARC classification - no assessment available USEPA - no assessment available Endocrine disruption: EU Priority List - not assessed Other: Endocrine disrupting compound, human reproductive and developmental effects (controversial) Sources and treatment Sources: Industrial effluents or wastewater treatment plant effluents. Source control: Wastewater treatment. DWT efficacy: 76 % removal measured (clarification, disinfection, filtration). WWT efficacy: up to 99 % removal in secondary WWTPs Fate: 0.02% to water; 0.98% to sludge; 99% transformed Uses: Monomer used in the production of polycarbonate plastic and epoxy resins; plasticiser

added to PVC products. Fate and modelling


Cramer class: High (III) Log Kow: 3.32 (exper) Solubility: 120 mg/L (exper) VP: 2.27E-007 (Modified Grain method) HLC: 9.16E-012 atm-m3/mole (Bond method) BP: 220 deg C (at 4 mm Hg) (exper) MP: 153 deg C (exper) Log BCF: 1.858 (regression-based method), 2.237 (Arnot-Gobas method) Log Koc: 4.576 (MCI Method); 3.095 (Kow Method) HL Biota: No information found HL Sediment: No information found HL Water: No information found pKa: 9.73 Toxicity Type Description Value In vivo – acute toxicity Guinea pig, Oral, Mortality LD50 = 4000 mg/kgbw In vivo – acute toxicity Mouse, Oral, Mortality LD50 = 2400 mg/kgbw In vivo – acute toxicity Rabbit, Oral, Mortality LD50 = 2230 mg/kgbw In vivo – acute toxicity Rat, Oral, Mortality LD50 = 3250 mg/kgbw In vivo – acute toxicity Green alga (Pseudokichneriella

subcapitata), 4-d, Growth EC50 = 2700 ug/L

In vivo – acute toxicity Green alga (Pseudokichneriella subcapitata), 4-d, Biomass

EC50 = 3100 ug/L

In vivo – acute toxicity Diatom (Skeletonema costatum), 4-d, Chlorophyll

EC50 = 1800 ug/L

In vivo – acute toxicity Diatom (Skeletonema costatum), 4-d, Growth

EC50 = 1000 ug/L

In vivo – acute toxicity Amphibian (Rana temporaria), 4-d Mortality LD50 = 1000 ug/L In vivo – endocrine Amphibian, Black Spotted Frog (Rana

nigromaculata), 15-60 days, Hormonal changes

NOEC = 200 ug/L

In vivo – long-term Amphibian, African clawed frog (Xenopus laevis), 120-d, Sex ratio changes

LOEC = 1E-7 M; NOEC = 1E-8 M

Occurrences Type Country Value Purified recycled water Australia LOD 0.0005 - 0.03 ug/L Surface waters United States LOD 0.09 - 12 ug/L Surface waters United States 1.9 ug/L (9.5% detected) WWTP effluent Australia LOD 0.0005 - 0.032 ug/L

9.4.12 4-Nonylphenol

General Information CASRN: 25154-52-3 Chemical name: 4-Nonylphenol Other name(s): Related chemical: 2-Nonylphenol, Nonylphenol polyethoxylate (NPEOs) SMILES: c1(ccc(O)cc1)CCCCCCCCC MW: 220.36 G uidelines


AU AGWR: 500 ug/L (Table A2) AU ADWG: Not available WHO DWG: Not available EU DWG: Not available NZ DWG: Not available CA DWG: Not available US NPDWS: Not available Qld PHR: 500 ug/L ANZECC-Env: Not available (see note on endocrine disruptors, p. 8.3-294 of vol 2) ANZECC-PI: Not available ANZECC-HH: Not available Other: None found Registration information PUBCRIS: No ARTG: No AICS: Yes HVICL: Yes Listed in NPI: No Comment: AICS name: Phenol, nonyl- --- HVICL Category: 1,000-9,999; Classified as a hazardous substance according to the NOHSC Risk assessment NICNAS: Not available APVMA: Not available UN: IPCS ICSC 0309 (2005) Other: EU RAR (2002); Environment Canada (2001) Epidemiology: No information found Considerations: Carcinogenicity: IARC - no assessment available USEPA - no assessment available

Endocrine disruption: EU Priority List (nonylphenol ethoxylate) - Category 1, evidence of endocrine disrupting activity in at least one species using intact animals.

Sources and treatment Sources: Incomplete degradation nonylphenol ethoxylates in wastewater treatment plants Source control: Both formed and degraded in wastewater treatment processes. DWT efficacy: Ozonation, activated carbon filtration and chlorination: 95% removal. WWT efficacy: 91% (modelled) Fate: Biotransformed or sorbed to sludge. Uses: Detergents, emulsifiers, wetting agents and dispersing agents, as well as the production

of resins, plastics, stabilizers. Used in production of nonylphenol ethoxylate (surfactant). Fate and modelling Cramer class: Intermediate (II) Log Kow: 5.76 (exper) Solubility: 7 mg/L (exper) VP: 2.36E-05 mm Hg (exper) HLC: 3.40E-05 atm-m3/mole (exper) BP: 293-297 deg C (exper) MP: 42 deg C (exper) Log BCF: 2.093 (regression-based method), 2.344 (Arnot-Gobas method) Log Koc: 4.583 (MCI method); 4.278 (Kow method) HL Biota: 4 days (in salmon before excretion) HL Sediment: No information found HL Water: No information found pKa: Not ionic Toxicity Type Description Value In vivo – acute toxicity Mouse, Oral, Mortality LD50 = 1 231 mg/kgbw In vivo – acute toxicity Rat, Oral, Mortality LD50 = 580 mg/kgbw Occurrences Type Country Value Purified recycled water Australia <15 ng/L Purified recycled water Australia 0.033 - 0.171 ug/L Unknown Australia <0.6 ug/L Untreated wastewater Australia 18 ug/L WWTP effluent Australia <15 ng/L WWTP effluent Australia 0.014 - 0.185 ug/L WWTP effluent Australia <1.0 ug/L


9.4.13 4-t-Octylphenol

G eneral information CASRN: 140-66-9 Chemical name: 4-tert-octylphenol Other name(s): Phenol, 4-(1,1,3,3-tetramethylbutyl)-; p-Octylphenol; p-tert-Octylphenol SMILES: Oc(ccc(c1)C(CC(C)(C)C)(C)C)c1 MW: 206.33 G uidelines AU AGWR: 50 ug/L AU ADWG: Not available WHO DWG: Not available EU DWG: Not available NZ DWG: Not available CA DWG: Not available US NPDWS: Not available Qld PHR: 50 ug/L ANZECC-Env: Not available ANZECC-PI: Not available ANZECC-HH: Not available Other: None found Registration information PUBCRIS: No ARTG: No AICS: Yes HVICL: No Listed in NPI: No Comment: AICS listing under Phenol, 4-(1,1,3,3-tetramethylbutyl)- Risk assessment NICNAS: Not available APVMA: Not available UN: IPCS SIDS (1995); IPCS ICSC 1345 (1999) Other: UKEA Environmental Risk Evaluation Report: 4-tert-octylphenol (2005) Epidemiology: No information found Considerations: Carcinogenicity: IARC - no assessment available USEPA - no assessment available

Endocrine disruption - Category 1, evidence of endocrine disrupting activity in at least one species using intact animals

Sources and treatment Sources: Industrial effluents or wastewater treatment plant effluents Source control: Wastewater treatment DWT efficacy: 100 % removal at one DWT plant (aluminium sulphate flocculation/sand

filtration/ozonation/GAC filtration/chlorination). OP partitioned to sludge WWT efficacy: Secondary (activated sludge) WWT 69 % (one study) Fate: Partitions to sludge Uses: Most used as an intermediate for the production of resins, non-ionic surfactants and

rubber additives. Also used for the manufacturing of antioxidants, fuel oil stabilizers, adhesives, dyestuffs, fungicides, bactericides, and for vulcanizing of synthetic rubber.

Fate and modelling Cramer class: Low (I) Log Kow: 5.28 (KOWWIN v1.67 estimate) Solubility: 4.821 mg/L (estimate from Log Kow)


VP: 4.78E-04 mm Hg (exper) HLC: 4.50E-006 atm-m3/mole (Bond method); 6.89E-006 atm-m3/mole (Group method); 2.917E-005 atm-m3/mole (VP/WSol estimate) BP: 158 deg C (at 15 mm Hg) (exper) MP: 84.5 deg C (exper) Log BCF: 3.148 (regression-based method); 2.563 (Arnot-Gobas method) Log Koc: 3.999 (MCI method); 4.012 (Kow method) HL Biota: No information found HL Sediment: No information found HL Water: No information found pKa: 10.33 Toxicity Type Description Value In vivo – acute toxicity Freshwater fish, Fathead minnow (Pimephales

promelas), 24-96 h Mortality LC50 = 0.25 - 0.29 mg/L

In vivo – acute toxicity Freshwater fish, Fathead minnow (Pimephales promelas), 96-h Flow-through

NOEC = 0.077 mg/L

In vivo – acute toxicity Freshwater crustacean (Daphnia magna), 24-48 h, Mortality

LC50 = 0.26 - 0.27 mg/L

In vivo – acute toxicity Freshwater crustacean (Daphnia magna), 48-h NOEC = 0.11 mg/L In vivo – acute toxicity Freshwater alga (Selenastrum capricornutrum), 96-h EC50 = 1.9 mg/L In vivo – acute toxicity Freshwater alga (Selenastrum capricornutrum), 96-h NOEC = <1.0 mg/L In vivo – acute toxicity Activated sludge bacteria, 3-h Inactivation EC50 = 10 mg/L In vivo – acute toxicity Rat (Sprague-Dawley), Oral, Mortality LD50 = >2000 mg/kgbw In vivo – acute toxicity Mouse, Oral, Mortality LD50 = 3210 mg/kgbw In vivo – acute toxicity Freshwater fish (Leuciscus idus), 48-96 h Mortality,

Static/Semi-static system LC50 = 0.26 - 0.6 mg/L

In vivo – long-term Freshwater crustacean (Daphnia magna), 21-d Immobilisation

EC50 = 0.34 mg/L

In vivo – long-term Freshwater crustacean (Daphnia magna), 21-d flow through

EC50 = 0.34 mg/L (MATC = 0.037 < x < 0.062 mg/L)

In vivo – short term Rat (Sprague-Dawley), 28-d Repeated oral dose, Liver and kidney changes

LOAEL = 150 mg/kgbw/day

In vivo – short term Rat (Sprague-Dawley & Crj:CD), 28-d Repeated oral dose

NOAEL = 15 mg/kgbw/day

In vivo – short term Freshwater fish, Rainbow trout (Salmo gairdneri), 60-d flow through

MATC = 0.0061 < x < 0.011

In vivo – short term Freshwater fish, Rainbow trout (Salmo gairdneri), 6-d, Mortality

LC50 = 0.17 mg/L

In vivo – short term Freshwater fish, Rainbow trout (Salmo gairdneri), 14-d, Mortality

LC50 = 0.12 mg/L

In vivo – short term Freshwater fish, Rainbow trout (Salmo gairdneri), 14-60 day Flow through

NOEC = 0.0061 - 0.084 mg/L

Occurrences Type Country Value Unknown Australia <0.1 ug/L Untreated wastewater Australia <10 ug/L WWTP effluent Australia <1.0 ug/L WWTP effluent Australia <0.0005 - 0.014 ug/L WWTP effluent Australia <10 ng/L

9.4.14 Atenolol


G eneral information CASRN: 29122-68-7 Chemical name: Atenolol Other name(s): Benzeneacetamide, 4-[2-hydroxy-3-[(1-methylethyl)amino]propoxy]- Related chemical: Timolol, Metoprolol, Nadolol, Propranolol, Celiprolol, Carazolol, Betaxolol, Bisoprolol SMILES: c1(ccc(CC(N)=O)cc1)OC[C@@H](CNC(C)C)O MW: 266.339 G uidelines AU AGWR: [25 ug/L] (CoTC) AU ADWG: Not available WHO DWG: Not available EU DWG: Not available NZ DWG: Not available CA DWG: Not available US NPDWS: Not available Qld PHR: 25 ug/L ANZECC-Env: Not available ANZECC-PI: Not available ANZECC-HH: Not available Other: CoTC: LTD of 50 mg/d, P = 1, SF = 1000 Registration information PUBCRIS: No ARTG: Yes AICS: Yes HVICL: No Listed in NPI: No Comment: AICS listing under Benzeneacetamide, 4-[2-hydroxy-3-[(1-methylethyl)amino]propoxy]- Risk assessment NICNAS: Not available APVMA: Not available UN: Not available Other: None found Epidemiology: No information found Considerations: Carcinogenicity: IARC - no assessment available; USEPA - no assessment available. Endocrine disruption: EU Priority List - not assessed Other: Beta-blocker. Sources and treatment Sources: Wastewater effluent following absorption/excretion by humans consuming atenolol, for

example as a treatment for hypertension (beta-blocker). Source control: Wastewater treatment DWT efficacy: No information found. WWT efficacy: 1.85% (modelled) Fate: Adsorption to sludge (modelled) Uses: Prescription drug (a cardioselective beta-adrenergic blocker), used in the treatment of

hypertension, angina pectoris and acts as an anti-arrhythmic to regulate the heartbeat in the prevention of myocardial infarctions.

Fate and modelling Cramer class: High (III) Log Kow: 0.16 (exper) Solubility: 1.33e+004 mg/L (exper) VP: 7.69E-010 mm Hg (Modified Grain method) HLC: 1.37E-018 atm-m3/mole (Bond Method) BP: 438.63 deg C (Adapted Stein & Brown method) MP: 147 deg C (exper) Log BCF: 0.500 (regression-based method); -0.023 (Arnot-Gobas method) Log Koc: 1.825 (MCI method); 0.611 (Kow method) HL Biota: No information found HL Sediment: No information found HL Water: No information found pKa: 9.5 Toxicity Type Description Value In vivo – acute toxicity Mouse, Oral, Mortality LD50 = 2000 mg/kgbw In vivo – acute toxicity Rat, Oral, Mortality LD50 = >2000 mg/kgbw In vivo – acute toxicity Human, Oral, Lowest reported toxic dose TDLo = 10 - 129 mg/kgbw


Occurrences Type Country Value Purified recycled water Australia 0.07 – 0.28 ug/L

9.4.15 Caffeine

G eneral information CASRN: 58-08-2 Chemical name: Caffeine Other name(s): 1,3,7-Trimethylxanthine; 3,7-Dihydro-1,3,7-trimethyl-1H-purine-2,6-dione;

Methyltheobromine; Methyltheophylline SMILES: CN1C(=O)N(C)c2ncn(C)c2C1(=O) MW: 194.19 G uidelines AU AGWR: 0.35 ug/L AU ADWG: Not available WHO DWG: Not available EU DWG: Not available NZ DWG: Not available CA DWG: Not available US NPDWS: Not available Qld PHR: 0.35 ug/L ANZECC-Env: Not available ANZECC-PI: Not available ANZECC-HH: Not available Other: None found Registration information PUBCRIS: Yes ARTG: Yes AICS: Yes HVICL: No Listed in NPI: No Comment: Classified as a hazardous substance by the NOHSC. Risk assessment NICNAS: Not available APVMA: Not available UN: IPCS ICSC 0405 (2005); OECD SIDS (2004) Other: ANZFA (2000) Epidemiology: Two of five epidemiological studies have revealed an inverse relationship between

caffeine intake (coffee drinking) and blood pressure, two revealed no relationship and one yielded a positive relationship.

Considerations: Carcinogenicity: IARC - Group 3, not classifiable as to carcinogenicity in humans (1991) USEPA - no assessment available.

Endocrine disruption: EU Priority List - not assesed Sources and treatment Sources: Human consumption of coffee beans, tea leaves, cocoa beans, and guarana paste

(~10% excreted unchanged); disposal directly to household and commercial drains. Source control: Wastewater treatment; drinking water treatment. DWT efficacy: Alum, FeCl3, Softening - Low; PAC - med; Cl2 - low-med (pH dependent); NH2Cl - low;

O3, O3/H2O2 - high; UV, UV/H2O2, UF, MIEX - low; NF - low-med


WWT efficacy: 81 – 99.9 % Fate: No information found Uses: Found naturally in certain foods/drinks (eg. coffee, tea); ingredient in various pharmaceutical products. Fate and modelling Cramer class: High (III) Log Kow: -0.091 (at 23 deg C) (exper) Solubility: 2.16E+004 mg/L (exper) VP: 0.0000047 Pa (calculated) HLC: 3.58E-011 atm-m3/mole (Bond method) BP: Sublimation at 178 deg C MP: 238 deg C (exper) Log BCF: 0.5 (regression-based method), -0.029 (Arnot-Gobas method) Log Koc: 1.0 (MCI method), 0.980 (Kow method) HL Biota: No information found HL Sediment: No information found HL Water: No information found pKa: 0.6 Toxicity Type Description Value In vivo – acute toxicity Cat, Oral, Lowest recorded lethal dose LDLo = 100 mg/kgbw In vivo – acute toxicity Human (Child), Oral, Lowest recorded lethal dose LDLo = 320 mg/kgbw In vivo – acute toxicity Dog, Oral, Mortality LD50 = 140 mg/kgbw In vivo – acute toxicity Guinea Pig, Oral, Mortality LD50 = 230 mg/kgbw In vivo – acute toxicity Hamster, Oral, Mortality LD50 = 230 mg/kgbw In vivo – acute toxicity Human, Oral, Lowest recorded lethal dose LDLo = 192 mg/kgbw In vivo – acute toxicity Human (Infant), Oral, Lowest recorded toxic dose TDLo = 14.7 - 140 mg/kgbw In vivo – acute toxicity Human (M), Oral, Lowest recorded toxic dose TDLo = 51 mg/kgbw In vivo – acute toxicity Mouse, Oral, Mortality LD50 = 127 mg/kgbw In vivo – acute toxicity Rabbit, Oral, Mortality LD50 = 224 mg/kgbw In vivo – acute toxicity Human (F), Oral, Lowest recorded lethal dose LDLo = 400 - 1000 mg/kgbw In vivo – acute toxicity Human (F), Oral, Lowest recorded toxic dose TDLo = 96 mg/kgbw In vivo – acute toxicity African Clawed Frog (Xenopus laevis), 4-d Growth

abnormalities EC50 = 0.068 - 0.218 ug/L

In vivo – acute toxicity African Clawed Frog (Xenopus laevis), 5-d Growth abnormalities

EC50 = 130 000 ug/L

In vivo – acute toxicity African Clawed Frog (Xenopus laevis), 4-d Mortality LC50 = 0.22 - 0.37 ug/L In vivo – acute toxicity African Clawed Frog (Xenopus laevis), 5-d Mortality LC50 = 190 000 ug/L In vivo – acute toxicity African Clawed Frog (Xenopus laevis), 4-d Growth

abnormalities LOEC = 0.05 - 0.1 ug/L

In vivo – acute toxicity African Clawed Frog (Xenopus laevis), 4-d Growth (general)

LOEC = 0.05 - 0.125 ug/L

In vivo – acute toxicity In vivo – acute toxicityAfrican Clawed Frog (Xenopus laevis), 5-d Growth (general)

LOEC = 80 000 ug/L

In vivo – acute toxicity Freshwater crustacean, Waterflea (D. magna), 24-h Immobilisation

EC50 = 0.8 - 3.5 mM

In vivo – acute toxicity Saltwater crustacean (Artemia salina), 24-h Mortality

LC50 = 17 800 - 52 730 ug/L

In vivo – acute toxicity Freshwater crustacean, Fairy shrimp (Streptocephalus proboscideus), 24-h Mortality

LC50 = 2110 ug/L

In vivo – acute toxicity Freshwater fish (Leuciscus idus), 96-h Mortality LC50 = 87 mg/L In vivo – acute toxicity Freshwater crustacean (Daphnia magna), 48-h

Immobilisation EC50 = 182 mg/L

In vivo – acute toxicity Rat, Oral, Mortality LD50 = 192 - 400 mg/kgbw In vivo – acute toxicity Mouse, Oral, Mortality LD50 = 185 mg/kgbw In vivo – acute toxicity Freshwater invertebrate (Brachionus calyciflorus),

24-h Mortality LC50 = 24 000 ug/L

In vivo – long-term Rat (F), 90-d drinking water NOAEL = 174 mg/kgbw/d In vivo – long-term Rat (M), 90-d drinking water NOAEL = 151 mg/kgbw/d In vivo – long-term Mouse (F), 90-d drinking water NOAEL = 179 mg/kgbw/d In vivo – long-term Mouse (M), 90-d drinking water NOAEL = 167 mg/kgbw/d In vivo – long-term Insect (Tribolium castaneum) 20-d Adult mortality LD50 = 288 mg/kgbw In vivo – long-term Insect (Tribolium castaneum) 20-d Mortality larvae LD50 = 251 mg/kgbw In vivo – short-term Green alga (Scenedesmus subspicatus), 72-h EC50 = >100 mg/L In vivo – short-term Freshwater fish, Fathead minnow (Pimephales

promelas), 5-d Growth abnormalities EC50 = 70 000 (40 000 - 110 000) ug/L

In vivo – short-term Freshwater fish, Fathead minnow (Pimephales promelas), 5-d Mortality

LC50 = 720 000 ug/L

In vivo – short-term Freshwater fish, Fathead minnow (Pimephales promelas), 5-d Growth (general)

LOEC = 20 000 ug/L


Occurrences Type Country Value Surface waters United States LOD 0.014 - 6.0 µg/L

Untreated wastewater Spain 52 - 192 ug/L WWTP effluent Spain 1.4 - 44 ug/L

9.4.16 Carbamazepine

G eneral information CASRN: 298-46-4 Chemical name: Carbamazepine Other name(s): 5H-Dibenz[b,f]azepine-5-carboxamide; Tegretol SMILES: N1(c2c(cccc2)C=Cc2c1cccc2)C(N)=O MW: 236.273 G uidelines AU AGWR: 100 ug/L AU ADWG: Not available WHO DWG: Not available EU DWG: Not available NZ DWG: Not available CA DWG: Not available US NPDWS: Not available Qld PHR: 100 ug/L ANZECC-Env: Not available ANZECC-PI: Not available ANZECC-HH: Not available Other: None found Registration information PUBCRIS: No ARTG: Yes AICS: Yes HVICL: No Listed in NPI: No Comment: Listed on ARTG as Tegretol Risk assessment NICNAS: Not available APVMA: Not available UN: IPCS PIM 100 (1999) Other: None found Epidemiology: No information found Considerations: Carcinogenicity: IARC - no assessment available USEPA - no assessment available. Endocrine disruption: EU Priority List - not assessed (Ref. 96, 2006). Sources and treatment Sources: Wastewater treatment plant effluent following human excretion of the drug. Source control: Wastewater treatment. DWT efficacy: Alum, FeCl3, Softening, Cl2, NH2Cl, UV, UF, MIEX - low; NF - medium; PAC - med-high;

O3, O3/H2O2 - high


WWT efficacy: 9 % Fate: No information found Uses: Pharmaceutical. Anticonvulsant used in the treatment of seizures, nerve pain and bipolar disorder. Fate and modelling Cramer class: High (III) Log Kow: 2.45 (exper) Solubility: 112 mg/L (exper) VP: 8.8E-008 (Modified Grain method) HLC: 1.08E-010 atm-m3/mole (Bond method) BP: 410.02 deg C (Adapted Stein & Brown method) MP: 190.2 deg C (exper) Log BCF: 1.284 (regression-based method); 1.286 (Arnot-Gobas method) Log Koc: 3.123 (MCI method); 2.227 (Kow method) HL Biota: No information found HL Sediment: No information found HL Water: No information found PEC: No information found pKa: 13.4 Toxicity Type Description Value In vivo – acute toxicity Mouse, Oral, Mortality LD50 = 3750 mg/kg In vivo – acute toxicity Rats, Oral, Mortality LD50 = 4025 mg/kg In vivo – acute toxicity Algae (Chl. vulgaris) - 24h growth EC50 = 469.5 uM In vivo – acute toxicity Algae (Chl. vulgaris) - 48h growth EC50 = 155 uM In vivo – acute toxicity Cladoceran (D. magna) - 24h immobilisation EC50 = 475 uM In vivo – acute toxicity Cladoceran (D. magna) - 48h immobilisation EC50 = 414 uM Occurrences Type Country Value Reclaimed water Australia 15.9 - 25.7 ug/L Reclaimed water Australia <0.1 ug/L Surface waters Germany 1.1 ug/L Surface waters Canada 0.1 - 2.3 ug/L Surface waters United States 0.19 ug/L (21.6% detected) WWTP effluent Germany 6.3 ug/L WWTP effluent Spain 0.11 - 0.23 ug/L WWTP effluent Australia 0.51 - 0.78 ug/L WWTP effluent Australia 19.8 - 27.3 ug/L

9.4.17 Diethyltoluamide (DEET)

G eneral information CASRN: 134-62-3 Chemical name: N,N-diethyltoluamide (DEET) Other name(s): N,N-diethyl-3-methylbenzamide Chemical class: OC - Organic chemical Link: http://toxnet.nlm.nih.gov/cgi-bin/sis/search/r?dbs+hsdb:@term+@na+deet SMILES: O=C(c1cc(ccc1)C)N(CC)CC MW: 191.28


G uidelines AU AGWR: 2500 ug/L AU ADWG: Not available WHO DWG: Not available EU DWG: 0.5 ug/L (total pesticides) NZ DWG: Not available CA DWG: Not available US NPDWS: Not available Qld PHR: 2500 ug/L ANZECC-Env: Not available ANZECC-PI: Not available ANZECC-HH: Not available Other: None found Registration information PUBCRIS: Yes ARTG: No AICS: Yes HVICL: No Listed in NPI: No Comment: AICS listing under Benzamide, N,N-diethyl-3-methyl-; Classified as a hazardous

substance by the NOHSC. Risk assessment NICNAS: Not available APVMA: Not available UN: WHO PDS 80 (undated); IPCS PIM 170 (1990) Other: USEPA RED (1998); CEPA Risk Characterisation Document (2000). Epidemiology: Low risk of serious neurological effects associated with use of DEET in insect repellents. Considerations: Carcinogenicity: IARC - no assessment available

USEPA - no assessment available. Endocrine disruption: EU Priority List - not assessed (Ref. 96, 2006). Sources and treatment Sources: Sewage - following rinsing from the skin and absorption/excretion by humans. Source control: Wastewater treatment. DWT efficacy: Alum, FeCl3, Softening - Low; PAC - med; Cl2, NH2Cl - low; O3, O3/H2O2 - high; UV,

UV/H2O2 - low; UF, MIEX - low; NF - med; WWT efficacy: Secondary (activated sludge) wastewater treatment - 56 %; tertiary treatment (sand

filtration and ozonation) - 69 %. Fate: No information found Uses: Insect repellent. Fate and modelling Cramer class: High (III) Log Kow: 2.18 (exper) Solubility: 666 mg/L (estimate) VP: 2.00E-03 mm Hg (exper) HLC: 2.08E-008 atm-m3/mole (Bond method) BP: 290 deg C (exper) MP: -45 deg C (exper) Log BCF: 1.105 (regression-based method), 1.124 (Arnot-Gobas method) Log Koc: 2.055 (MCI method); 1.858 (Kow method) HL Biota: No information found HL Sediment: No information found HL Water: No information found pKa: No information found Toxicity Type Description Value In vivo – acute toxicity Freshwater fish, Rainbow trout (O. mykiss), 96-h

Mortality LC50 = 71 250 ug/L

In vivo – acute toxicity Child, Oral, Lowest recorded toxic dose TDLo = 4 750 mg/kgbw In vivo – acute toxicity Human, Oral, Lowest recorded lethal dose LDLo = 679 mg/kgbw In vivo – acute toxicity Mouse, Oral, Mortality LD50 = 1 170 mg/kgbw In vivo – acute toxicity Rat, Oral, Mortality LD50 = 1 950 mg/kgbw In vivo – acute toxicity Human (F), Oral, Lowest recorded toxic dose

(coma) TDLo = 950 mg/kgbw

Occurrences Type Country Value Drinking water United States 0.07 ug/L (25% detected, n=4) Groundwater United States 13.00 ug/L (26% detected, n=47)


Groundwater United States 13.00 ug/L (80% detected, n=5) Groundwater Spain 0.03 ug/L Open water World 1.09 ug/L (North Sea, 86% detected, n=14) Septic waste United States 1.0 ug/L (40% detection, n=5) Surface waters Australia 0.49 ug/L (97% detection, n=35) Surface waters Germany 0.03 ug/L (100% detection, n=8) Surface waters Netherlands 0.04 ug/L (63% detection, n=8) Surface waters United States 0.19 ug/L (80% detection, n=5) Surface waters United States 1.13 ug/L (74% detection, n=55) Surface waters United States 0.64 ug/L (66% detected, n=29) Surface waters United States 0.13 ug/L (4% detected, n=75) Surface waters United States LOD 0.04 - 1.1 ug/L Untreated wastewater Australia 1.5 ug/L (100% detection, n=2) Untreated wastewater Germany 3.00 ug/L (98% detected, n=41) WWTP effluent Australia 0.14 ug/L (100% detected, n=2) WWTP effluent Germany 1.50 ug/L (100% detected, n=41) WWTP effluent United States 2.1 ug/L (82% detected, n=11) WWTP effluent Australia LOD 0.01 - 0.78 ug/L

9.4.18 Diazepam

G eneral information CASRN: 439-14-5 Chemical name: Diazepam Other name(s): Valium; 2H-1,4-Benzodiazepin-2-one, 7-chloro-1,3-dihydro-1-methyl-5-phenyl-; 7-Chloro-

1,3-dihydro-1-methyl-5-phenyl-2H-1,4-benzodiazepin-2-one Related chemical: Desmethyldiazepam, Oxazepam, Temazepam SMILES: C1(=NCC(=O)N(c2c1cc(cc2)Cl)C)c1ccccc1 MW: 284.7447 G uidelines AU AGWR: 2.5 ug/L AU ADWG: Not available WHO DWG: Not available EU DWG: Not available NZ DWG: Not available CA DWG: Not available US NPDWS: Not available Qld PHR: 2.5 ug/L ANZECC-Env: Not available ANZECC-PI: Not available ANZECC-HH: Not available Other: None found Registration information PUBCRIS: Yes ARTG: Yes AICS: Yes HVICL: No Listed in NPI: No


Risk assessment NICNAS: Not available APVMA: Not available UN: IPCS PIM 181 (1998) Other: None found Epidemiology: No information found Considerations: Carcinogenicity: IARC - Group 3, not classifiable as to carcinogenicity in humans (1986) USEPA - no assessment available Endocrine disruption: EU Priority List - not assessed Sources and treatment Sources: Wastewater effluent following absorption/excretion by humans consuming the drug

diazapam (Valium). Source control: Wastewater treatment. DWT efficacy: No information found WWT efficacy: 27.70% (Biowin/EPIWin estimate) Fate: Biodegradation (Biowin/EPIWin estimate) Uses: Benzodiazepine anxiolytic and sedative-hypnotic. Used for treating anxiety, insomnia and seizures. Fate and modelling Cramer class: High (III) Log Kow: 2.82 (exper) Solubility: 50 mg/L (exper) VP: 1.02E-007 mm Hg (Modified Grain method) HLC: 6.502E-010 atm-m3/mole (VP/WSol estimate) BP: 434.32 deg C (Adapted Stein & Brown method) MP: 132 deg C (exper) Log BCF: 1.528 (regression-based method); 1.761 (Arnot-Gobas method) Log Koc: 3.875 (MCI method); 2.438 (Kow method) HL Biota: No information found HL Sediment: No information found HL Water: No information found pKa: No information found Toxicity Type Description Value In vivo – acute toxicity Saltwater crustacean (Artemia salina), 24-h

Mortality LC50 = 230 - >351 uM

In vivo – acute toxicity Freshwater crustacean (Streptocephalus proboscideus), 24-h Mortality

LC50 = 131 - 362 uM

In vivo – acute toxicity Eastern mosquitofish (Gambusia holbrooki), 96-h Mortality

LC50 = 12700 (12570 - 12850) ug/L

In vivo – acute toxicity Freshwater crustacean, Water flea (Daphnia magna), 24-h Mortality

LC50 = 32.3 - 49.4 uM

In vivo – acute toxicity Freshwater invertebrates, Rotifers (Brachionus spp.), 24-h Mortality

LC50 = 166 - >35 100 uM

In vivo – acute toxicity Daphnia immobilisation EC50 = 0.015 - 0.049 mM In vivo – reproductive Marine alga (Tetraselmis chuii), 4-day, Population

growth IC50 = 16 500 ug/L

Occurrences Type Country Value Groundwater United States 10 - 40 ug/L Reclaimed Water Australia <0.1 ug/L Reclaimed Water Australia 0.9 - 2.92 ug/L WWTP effluent Germany 0.04 ug/L WWTP effluent Australia <0.1 ug/L


9.4.19 Diclofenac

G eneral information CASRN: 15307-86-5 Chemical name: Diclofenac Other name(s): Benzeneacetic acid, 2-((2,6-dichlorophenyl)amino)- (9CI); Acetic acid, (o-(2,6-

dichloroanilino)phenyl)- SMILES: OC(=O)CC1=CC=CC=C1NC1=C(Cl)C=CC=C1Cl MW: 296.152 G uidelines AU AGWR: 1.8 ug/L AU ADWG: Not available WHO DWG: Not available EU DWG: Not available NZ DWG: Not available CA DWG: Not available US NPDWS: Not available Qld PHR: 1.8 ug/L ANZECC-Env: Not available ANZECC-PI: Not available ANZECC-HH: Not available Other: None found Registration information PUBCRIS: No ARTG: Yes AICS: Yes HVICL: No Listed in NPI: No Comment: AICS listing under Benzeneacetic acid, 2-[(2,6-dichlorophenyl)amino]- Risk assessment NICNAS: Not available APVMA: Not available UN: Not available Other: None found Epidemiology: No information found Considerations: Carcinogenicity: IARC - no assessment available

USEPA - no assessment available. Endocrine disruption: EU Priority List - not assessed (Ref. 96). Sources and treatment Sources: Wastewater effluent following absorption/excretion by humans consuming the drug diclofenac. Source control: Wastewater treatment. DWT efficacy: Ozone (0.5mg/L): >90%; GAC: >90% (throughput up to 70 m3/kg) WWT efficacy: Total: 56.55% (EPIWin estimate); Conventional Activated Sludge: 8-69%, Membrane

ultrafiltration: 39-51%, O3: 97%, PAC: 39%, Cl2: 90-95% Fate: Sludge adsorption (EPIWin) Uses: A non-steroidal anti-inflammatory agent (NSAID) with antipyretic and analgesic actions. It

is primarily available as a sodium salt. Fate and modelling Cramer class: High (class III) Log Kow: 4.51 (exper)


Solubility: 2.37 mg/L (exper) VP: 6.14E-008 mmHg (Modified Grain method) HLC: 5.296E-009 atm-m3/mole (VP/WSol estimate); 4.73E-012 atm-m3/mole (Bond Method) BP: 423.77 deg C (Adapted Stein & Brown method) MP: 174.60 deg C (Mean or Weighted MP) Log BCF: 0.500 (regression based method) Log Koc: 2.921 (estimate) HL Biota: No information found HL Sediment: No information found HL Water: No information found pKa: 4.15 Toxicity No information found. Occurrences Type Country Value Surface waters Germany 0.15 ug/L (1.2 ug/L max) Surface waters Switzerland n.d. - 0.15 ug/L Surface waters Brazil n.d. - 0.06 ug/L WWTP effluent Germany 0.81 ug/L (median) WWTP effluent Australia 0.17 - 0.36 ug/L WWTP effluent Switzerland 0.1 - 0.7 ug/L WWTP effluent Canada 0.36 ug/L (28.4 ug/L max)

9.4.20 Gemfibrozil

G eneral information CASRN: 25812-30-0 Chemical name: Gemfibrozil Other name(s): Pentanoic acid, 5-(2,5-dimethylphenoxy)-2,2-dimethyl- SMILES: c1(c(ccc(c1)C)C)OCCCC(C(O)=O)(C)C MW: 250.336 G uidelines AU AGWR: 600 ug/L AU ADWG: Not available WHO DWG: Not available EU DWG: Not available NZ DWG: Not available CA DWG: Not available US NPDWS: Not available Qld PHR: 600 ug/L ANZECC-Env: Not available ANZECC-PI: Not available ANZECC-HH: Not available Other: None found Registration information PUBCRIS: No ARTG: Yes AICS: No HVICL: No


Listed in NPI: No Risk assessment NICNAS: Not available APVMA: Not available UN: Not available Other: None found Epidemiology: No information found Considerations: Carcinogenicity: IARC - Group 3, not classifiable as to carcinogenicity in humans (1999) USEPA - no assessment available. Endocrine disruption: EU Priority List - not assessed (Ref. 96). Sources and treatment Sources: Wastewater effluent after absorption/excretion by humans consuming the drug

gemfibrozil. Source control: Wastewater treatment. DWT efficacy: PAC: 37%; Cl2: 100%; O3: 98% WWT efficacy: 69.10% (EPIWin estimate) Fate: Sludge adsorption (EPIWin) Uses: A lipid-regulating agent that lowers elevated serum lipids primarily by decreasing serum

triglycerides with a variable reduction in total cholesterol. These decreases occur primarily in the VLDL fraction and less frequently in the LDL fraction. Gemfibrozil increases HDL subfractions HDL2 and HDL3 as well as apolipoproteins A-I and A-II. Its mechanism of action has not been definitely established.

Fate and modelling Cramer class: Intermediate (II) Log Kow: 4.77 (KOWWIN v1.67 estimate) Solubility: 4.964 mg/L (estimate from log Kow) VP: 3.05E-005 mmHg (Modified Grain method) HLC: 1.19E-008 atm-m3/mole (Bond Method); 2.024E-006 atm-m3/mole (VP/WSol estimate) BP: 158.5 deg C @ 0.02 mm Hg (exper) MP: 62 deg C (exper) Log BCF: 0.500 (regression based method) Log Koc: 2.656 (estimate) HL Biota: No information found HL Sediment: No information found HL Water: No information found pKa: 4.7 Toxicity Type Description Value In vivo – acute toxicity Green alga (Chlorella vulgaris), 3-day, Population

growth NOEC = 50 uM

In vivo – acute toxicity Green alga (Chlorella vulgaris), 1-3 day, Population growth

EC50 = 600 - 778 uM

In vivo – acute toxicity Water flea (Daphnia magna), 3-day Mortality NOEC = 30 uM In vivo – acute toxicity Water flea (Daphnia magna), 1-3 day

Immobilisation EC50 = 120 - 228 uM

Occurrences Type Country Value Reclaimed water Australia <0.1 ug/L Surface waters Germany 0.51 ug/L Surface waters United States LOD 0.015 - 0.79 ug/L Untreated wastewater Australia 3 ug/L WWTP effluent Australia 0.16 - 0.42 ug/L WWTP effluent Canada 0.043 - 2.174 ug/L WWTP effluent Germany 1.5 ug/L


9.4.21 Indomethacin

G eneral information CASRN: 53-86-1 Chemical name: Indomethacin(e) Other name(s): 1H-Indole-3-acetic acid, 1-(4-chlorobenzoyl)-5-methoxy-2-methyl- ; 1-(4-Chlorobenzoyl)-

5-methoxy-2-methylindol-3-ylacetic acid; Indomethazine SMILES: c12c(c(CC(O)=O)c(n1C(c1ccc(Cl)cc1)=O)C)cc(OC)cc2 MW: 357.791 G uidelines AU AGWR: 25 ug/L AU ADWG: Not available WHO DWG: Not available EU DWG: Not available NZ DWG: Not available CA DWG: Not available US NPDWS: Not available Qld PHR: 25 ug/L ANZECC-Env: Not available ANZECC-PI: Not available ANZECC-HH: Not available Other: None found Registration information PUBCRIS: Yes ARTG: Yes AICS: Yes HVICL: No Listed in NPI: No Comment: AICS listing under: 1H-Indole-3-acetic acid, 1-(4-chlorobenzoyl)-5-methoxy-2-methyl- Risk assessment NICNAS: Not available APVMA: Not available UN: Not available Other: None found Epidemiology: No information found Considerations: Carcinogenicity: IARC - no assessment available

USEPA - no assessment available Endocrine disruption: EU Priority List - not assessed (Ref. 96; 2006). Sources and treatment Sources: Wastewater effluent following absorption/excretion by human consumption of the drug

indomethacin. Source control: Wastewater treatment. DWT efficacy: Cl2: 100% degradation (30 min) WWT efficacy: 78.45% (BioWin/EPIWin estimate) Fate: Biodegradation and adsorption to sludge (BioWin/EPIWin estimate) Uses: A non-steroidal anti-inflammatory agent (NSAID) that inhibits the enzyme

cyclooxygenase necessary for the formation of prostaglandins and other autacoids. It also inhibits the motility of polymorphonuclear leukocytes.

Fate and modelling Cramer class: High (III) Log Kow: 4.27 (exper)


Solubility: 0.937 mg/L (exper) VP: 5.12E-010 mm Hg (Modified Grain method) HLC: 3.13E-014 atm-m3/mole (Bond Method) BP: 514.50 deg C (Adapted Stein & Brown method) MP: 158 deg C (exper) Log BCF: 0.500 (regression-based method); 2.939 (Arnot-Gobas method) Log Koc: 2.904 (MCI method); 2.344 (Kow method) HL Biota: No information found HL Sediment: No information found HL Water: No information found pKa: 4.5 Toxicity Type Description Value In vivo – acute toxicity Cat, Oral, Mortality LD50 = 320 mg/kgbw In vivo – acute toxicity Dog, Oral, Mortality LD50 = 160 mg/kgbw In vivo – acute toxicity Guinea Pig, Oral, Mortality LD50 = 100 mg/kgbw In vivo – acute toxicity Hamster, Oral, Mortality LD50 = 81 mg/kgbw In vivo – acute toxicity Mouse, Oral, Mortality LD50 = 11.841 mg/kgbw In vivo – acute toxicity Rabbit, Oral, Mortality LD50 = 135 mg/kgbw In vivo – acute toxicity Rat, Oral, Mortality LD50 = 2.42 mg/kgbw Occurrences Type Country Value Purified recycled water Australia <0.1 ug/L Surface waters Germany 0.2 ug/L Untreated wastewater Brazil 0.95 ug/L WWTP effluent Germany 0.60 ug/L WWTP effluent Australia LOD 0.1 - 0.19 ug/L

9.4.22 Methotrexate

G eneral information CASRN: 59-05-2 Chemical name: Methotrexate Other name(s): Glutamic acid, N-(p-(((2,4-diamino-6- pteridinyl)methyl)methylamino)benzoyl)-, L- Link: http://toxnet.nlm.nih.gov/cgi-bin/sis/search/r?dbs+hsdb:@term+@na+methotrexate SMILES: c12c(ncc(n1)CN(c1ccc(C(N[C@H](CCC(O)=O)C(O)=O)=O)cc1)C)nc(N)nc2N MW: 454.445 G uidelines AU AGWR: Not available AU ADWG: Not available WHO DWG: Not available EU DWG: Not available NZ DWG: Not available CA DWG: Not available US NPDWS: Not available Qld PHR: 0.005 ug/L ANZECC-Env: Not available ANZECC-PI: Not available ANZECC-HH: Not available Other: None found


Registration information PUBCRIS: No ARTG: Yes AICS: Yes HVICL: No Listed in NPI: No Risk assessment NICNAS: Not available APVMA: Not available UN: Not available Other: None found Epidemiology: No information found Considerations: Carcinogenicity: IARC - Group 3, not classifiable as to carcinogenicity in humans (1987)

USEPA - no assessment available. Endocrine disruption: EU Priority List - not assessed Sources and treatment Sources: Hospital wastewater and wastewater treatment plant effluent following

absorption/excretion by humans consuming the drug methotrexate as a chemotherapeutic cytotoxic agent.

Source control: Incineration of hospital waste or chemical degradation/inactivation using sodium hypochlorite, hydrogen peroxide and/or Fenton reagent. Alternatively, via electrolysis of sodium chloride solution, which generates sodium hypochlorite.

DWT efficacy: Ozone: 97% (2 min@10mg/L O3) WWT efficacy: 21.97% (Biowin/EPIWin estimate) Fate: Biodegradation (Biowin/EPIWin estimate) Uses: Methotrexate is a chemotherapeutic and cytotoxic agent used as an active ingredient in

prescription medicines used to treat cancer and various rheumatic conditions. It is ‘an antineoplastic antimetabolite with immunosuppressant properties. It is an inhibitor of tetrahydrofolate dehydrogenase and prevents the formation of tetrahydrofolate, necessary for synthesis of thymidylate, an essential component of DNA’.

Fate and modelling Cramer class: High (III) Log Kow: -1.85 (exper) Solubility: 2600 mg/L (Estimate from Log Kow) VP: 1.51E-017 mm Hg (Modified Grain method) HLC: 3.473E-021 atm-m3/mole (VP/WSol estimate) BP: 783.46 deg C (Adapted Stein & Brown method) MP: 195 deg C Log BCF: 0.500 (regression based method); -0.049 (Arnot-Gobas method) Log Koc: 3.167 (MCI method); -0.387 (Kow method) HL Biota: No information found HL Sediment: No information found HL Water: No information found pKa: 4.8 and 5.5 Toxicity No information found. Occurrences Type Country Value WWTP effluent Australia 1 ug/L


9.4.23 Paracetamol (Acetaminophen)

G eneral information CASRN: 103-90-2 Chemical name: Acetaminophen (Paracetamol) Other name(s): Acetamide, N-(4-hydroxyphenyl)- ; 4'-Hydroxyacetanilide; N-Acetyl-p-aminophenol SMILES: c1(ccc(O)cc1)NC(C)=O MW: 151.17 G uidelines AU AGWR: 175 ug/L AU ADWG: Not available WHO DWG: Not available EU DWG: Not available NZ DWG: Not available CA DWG: Not available US NPDWS: Not available Qld PHR: 175 ug/L ANZECC-Env: Not available ANZECC-PI: Not available ANZECC-HH: Not available Other: None found Registration information PUBCRIS: No ARTG: Yes AICS: Yes HVICL: No Listed in NPI: No Risk assessment NICNAS: Not available APVMA: Not available UN: IPCS PIM 396 (1996); IPCS ICSC 1330 (2008) Other: None found Epidemiology: No information found Considerations: Carcinogenicity: IARC - no assessment available

USEPA - no assessment available. Endocrine disruption: EU Priority List - not assessed Sources and treatment Sources: Wastewater effluent following absorption/excretion by humans consuming the drug

paracetamol. Source control: Wastewater treatment DWT efficacy: Coagulation, Softening - low; PAC - high; Cl2, NH2Cl - high; O3, O3/H2O2 - high; UV -

low; UV/H2O2 - high; UF - low-med; NF, MIEX - low WWT efficacy: 74.46% (Biowin/EPIWin) Fate: Biodegradation (Biowin/EPIWin) Uses: Over-the-counter analgesic and antipyretic. It is commonly used for the relief of fever,

headaches, and other minor aches and pains, and is a major ingredient in numerous cold and flu remedies.

Fate and modelling Cramer class: High (III) Log Kow: 0.46 (exper) Solubility: 1.4E+004 mg/L (exper)


VP: 1.94E-006 mm Hg (Modified Grain method) HLC: 6.42E-013 atm-m3/mole (Bond method) BP: 340.65 deg C (Adapted Stein & Brown method) MP: 170 deg C (exper) Log BCF: 0.5 (regression-based method), -0.007 (Arnot-Gobas method) Log Koc: 1.654 (MCI method); 1.321 (Kow method) HL Biota: No information found HL Sediment: No information found HL Water: No information found pKa: 9.5 Toxicity Type Description Value In vivo – acute toxicity Water flea (Daphnia sp.), Immobilisation EC50 = 0.27 - 0.90 mM In vivo – acute toxicity Rat, Oral LD50 = 1944 mg/kgbw In vivo – acute toxicity Human (Child), 2-7 day, Oral, Lowest reported

lethal dose LDLo = 50 - 360 mg/kgbw

In vivo – acute toxicity Dog, Oral, Lowest reported lethal dose LDLo = 2000 mg/kgbw In vivo – acute toxicity Guinea pig, Oral LD50 = 2620 mg/kgbw In vivo – acute toxicity Human (M), Oral, Lowest reported lethal dose LDLo = 143 - 714 mg/kgbw In vivo – acute toxicity Human (M), Oral, Lowest reported toxic dose TDLo = 9.3 mg/kgbw In vivo – acute toxicity Mouse, Oral LD50 = 338 mg/kgbw In vivo – acute toxicity Human (F), Oral, Lowest reported lethal dose LDLo = 260 - 650 mg/kgbw In vivo – acute toxicity Human (F), Oral, Lowest reported toxic dose TDLo = 5 - 490 mg/kgbw In vivo – acute toxicity Freshwater crustacean, Water flea (Daphnia

magna), 24-h Immobilisation EC50 = 0.4 - 0.9 mM

In vivo – acute toxicity Saltwater crustacean (Artemia salina), 24-h Mortality

LC50 = 3.8 mM

In vivo – acute toxicity Freshwater crustacean, Fairy shrimp (Streptocephalus proboscideus), 24-h Mortality

LC50 = 169 uM

In vivo – acute toxicity Freshwater fish, Fathead minnow (Pimephales promelas), 4-d Mortality

LC50 = 814 mg/L

In vivo – acute toxicity Freshwater invertebrate (Brachionus calyciflorus), 24-h Mortality

LC50 = 35.1 mM

In vivo – acute toxicity Saltwater invertebrate (Brachionus plicatilis), 24-h Mortality

LC50 = 24.8 mM

Occurrences Type Country Value WWTP effluent Germany 6.0 ng/L WWTP effluent Spain LOD - 4.3 ug/L

9.4.24 Salicylic acid

G eneral information CASRN: 69-72-7 Chemical name: Salicylic acid Other name(s): Benzoic acid, 2-hydroxy- Related chemical: Aspirin (Acetylsalicylic acid) SMILES: O=C(O)c(c(O)ccc1)c1 MW: 138.12 G uidelines


AU AGWR: 105 ug/L AU ADWG: Not available WHO DWG: Not available EU DWG: Not available NZ DWG: Not available CA DWG: Not available US NPDWS: Not available Qld PHR: 105 ug/L ANZECC-Env: Not available ANZECC-PI: Not available ANZECC-HH: Not available Other: None found Registration information PUBCRIS: Yes ARTG: Yes AICS: Yes HVICL: Yes Listed in NPI: No

Risk assessment NICNAS: Not available APVMA: Not available UN: IPCS PIM 642 (1998); IPCS ICSC 0563 (1997) Other: Not available Epidemiology: No information found Considerations: Carcinogenicity: IARC - no assessment available

USEPA - no assessment available. Endocrine disruption: EU Priority List - not assessed (Ref. 96; 2006). Sources and treatment Sources: Wastewater treatment plant effluent following human excretion of acetylsalicylic acid

(aspirin) metabolite, as well as direct release to sewers via personal care products (facewash etc).

Source control: Wastewater treatment. DWT efficacy: Coagulation, flocculation, sedimentation, dual-media gravity filtration: 39-56% (+ GAC -

31 -39%). WWT efficacy: up to 99 % Fate: No information found Uses: Primary hydrolytic metabolite of acetylsalicylic acid (aspirin). Also used in pharmaceutical

and cosmetic industries as keratolytic and dermatice, as well as a preservative of food. Fate and modelling Cramer class: Low (I) Log Kow: 2.26 (exper) Solubility: 2240 mg/L (exper) VP: 8.20E-05 mm Hg (exper) HLC: 7.34E-09 atm-m3/mole (exper) BP: 211 deg C MP: 158 deg C Log BCF: 0.5 (regression-based method), 1.078 (Arnot-Gobas method) Log Koc: 1.336 (MCI method); 1.573 (Kow method) HL Biota: No information found HL Sediment: No information found HL Water: No information found pKa: 3.009; 3.52; 3.29; 2.96; 2.97 Toxicity Type Description Value In vivo – acute toxicity Rat, Oral, Mortality LD50 = 891 mg/kgbw In vivo – acute toxicity Mouse, Oral, Mortality LD50 = 480 mg/kgbw In vivo – acute toxicity Cat, Oral, Mortality LD50 = 400 mg/kgbw In vivo – acute toxicity Rabbit, Oral, Mortality LD50 = 1 300 mg/kgbw In vivo – Acute toxicity Freshwater crustacean (Daphnia magna), 24-48 h

Immobilisation EC50 = 870 000 – 1 060 000 ug/L

In vivo – acute toxicity Freahwater fish, Ide (Leuciscus idus), Mortality LC50 = 90 000 ug/L In vivo – short-term Freshwater plants, Duckweed (Lemna minor), 7-d

Chlorophyll and biomass LOEC = 60 000 ug/L

In vivo – short-term Freshwater plants, Duckweed (Lemna minor), 7-d Chlorophyll and biomass

NOEC = 30 000 ug/L

Occurrences Type Country Value Surface waters Germany 4.1 ug/L


Untreated wastewater Germany 55 ug/L WWTP effluent Canada 3.6 - 59.6 ug/L WWTP effluent Germany 0.14 - 54 ug/L

9.4.25 Sulfamethoxazole

G eneral information CASRN: 723-46-6 Chemical name: Sulfamethoxazole (SMXZ) Other name(s): Sulphamethoxazole; Benzenesulfonamide, 4-amino-N-(5-methyl-3-isoxazolyl)- Related chemical: Sulfamonomethoxine (SMMX), Sulfamerazine (SMRZ), Sulfamethizole, Sulfamethazine

(SMTZ), Sulfamethoxine SMILES: c1(S(Nc2cc(C)on2)(=O)=O)ccc(N)cc1 MW: 253.281 G uidelines AU AGWR: 35 ug/L AU ADWG: Not available WHO DWG: Not available EU DWG: Not available NZ DWG: Not available CA DWG: Not available US NPDWS: Not available Qld PHR: 35 ug/L ANZECC-Env: Not available ANZECC-PI: Not available ANZECC-HH: Not available Other: None found Registration information PUBCRIS: No ARTG: Yes AICS: Yes HVICL: No Listed in NPI: No Comment: AICS listing under Benzenesulfonamide, 4-amino-N-(5-methyl-3-isoxazolyl)- Risk assessment NICNAS: Not available APVMA: Not available UN: Not available Other: Not available Epidemiology: No information found Considerations: Carcinogenicity: IARC - Group 3, not classifiable as to carcinogenicity in humans (2001) Endocrine disruption: EU Priority List - not assessed (2006). Sources and treatment Sources: Wastewater effluent following absorption/excretion by humans consuming

sulfamethoxazole as an antibiotic. Source control: Wastewater treatment. DWT efficacy: Alum, FeCl3, Softening - low; PAC - med; Cl2 - high; NH2Cl - low; O3, O3/H2O2 - high;

UV - med; UV/H2O2 - high; UF, MIEX - low; NF - med


WWT efficacy: 22.05% (BioWin/EPIWin estimate) Fate: Biodegradation (BioWin/EPIWin estimate) Uses: Prescription medicine - a broad-spectrum bacteriostatic antibacterial agent that interferes

with folic acid synthesis in susceptible bacteria. Fate and modelling Cramer class: High (III) Log Kow: 0.89 (exper) Solubility: 610 mg/L (37 deg C) (exper) VP: 1.3E-007 mm Hg (Modified Grain method) HLC: 9.56E-013 atm-m3/mole (Bond Method) BP: 414.01 deg C (Adapted Stein & Brown method) MP: 167 deg C (exper) Log BCF: 0.500 (regression-based method); 0.168 (Arnot-Gobas method) Log Koc: 2.412 (MCI method); 1.536 (Kow method) HL Biota: No information found HL Sediment: No information found HL Water: No information found pKa: 5.7 Toxicity Type Description Value In vivo – acute toxicity Mouse, Oral LD50 = 2300 mg/kgbw In vivo – acute toxicity Rat, Oral LD50 = 6200 mg/kgbw In vivo – short-term Green alga (Pseudokirchneriella subcapitata), 3-

day Biomass decrease IC50 = 1900 ug/L

In vivo – short-term Green alga (Pseudokirchneriella subcapitata), 3-day Biomass decrease

LOEC = 800 ug/L

In vivo – short-term Green alga (Pseudokirchneriella subcapitata), 3-day Biomass decrease

NOEC = < 500 ug/L

Occurrences Type Country Value Purified recycled water Australia 0.094 ug/L Purified recycled water Australia <0.05 ug/L Surface waters United States <0.023 - 1.9 ug/L WWTP effluent Australia 0.38 - 0.52 ug/L WWTP effluent Australia 0.014 ug/L WWTP effluent Australia 0.16 ug/L WWTP effluent Australia 0.40 ug/L

9.4.26 Triclosan

G eneral information CASRN: 3380-34-5 Chemical name: Triclosan Other name(s): Phenol, 5-chloro-2-(2,4-dichlorophenoxy)-; 2,4,4'-trichloro-2'-hydroxy-diphenyl ether;

Microban SMILES: c1(Oc2c(cc(Cl)cc2)Cl)c(cc(Cl)cc1)O MW: 289.544 G uidelines AU AGWR: 0.35 ug/L AU ADWG: Not available WHO DWG: Not available


EU DWG: Not available NZ DWG: Not available CA DWG: Not available US NPDWS: Not available Qld PHR: 0.35 ug/L ANZECC-Env: Not available ANZECC-PI: Not available ANZECC-HH: Not available Other: None found Registration information PUBCRIS: Yes ARTG: Yes AICS: Yes HVICL: No Listed in NPI: No Comment: Classified as a hazardous substance by the NOHSC. AICS listing under Phenol, 5-

chloro-2-(2,4-dichlorophenoxy)- Risk assessment NICNAS: Priority Existing Chemical Assesment Report No. 30 - Triclosan APVMA: Not available UN: Not available Other: USEPA RED (2008) Epidemiology: No information found Considerations: Carcinogenicity: IARC - no assessment available

USEPA - no assessment available. Endocrine disruption: EU Priority List - not assessed (Ref. 96, 2006). Other: Potential to cause antibiotic resistance; formation of pesistant, bioaccumulative and toxic

chlorinated derivatives (Ref. 113) Sources and treatment Sources: Wastewater effluent following human use as bactericide in various consumer products. Source control: Wastewater treatment. DWT efficacy: Alum, FeCl3, Softening - low; PAC, Cl2, NH2Cl, O3, O3/H2O2, UV/H2O2 - high; UV -

med; UF, NF, MIEX - high WWT efficacy: Secondary (activated sludge) wastewater treatment - 55-99 %; tertiary treatment - 87-99

%. Fate: Biological degradation & adsorption to sludge. Uses: A broad-spectrum bactericide used in pharmaceutical and personal care products, e.g,

toothpaste, mouthwash, medical skin creams, hand-disinfecting soaps, deodorant, household cleaners and textiles (sportswear, bed clothes, shoes and carpets).

Fate and modelling Cramer class: High (III) Log Kow: 4.76 (exper) Solubility: 10 mg/L (20 deg C) (exper) VP: 4.65E-006 mm Hg (Modified Grain method) HLC: 4.99E-009 atm-m3/mole (Bond Method); 2.13E-008 atm-m3/mole (Group Method) BP: 373.62 deg C (Adapted Stein & Brown method) MP: 54-57.3 deg C (exper) Log BCF: 2.808 (regression-based method); 3.039 (Arnot-Gobas method) Log Koc: 4.369 (MCI method); 3.925 (Kow method) HL Biota: No information HL Sediment: No information HL Water: No information pKa: 7.9 Toxicity Type Description Value In vivo – acute toxicity Mouse, Oral, Mortality LD50 = 4 530 000 ug/kgbw In vivo – acute toxicity Rat, Oral, Mortality LD50 = 3 700 000 ug/kgbw In vivo – acute toxicity Freshwater algae (4 spp.), 96-h Biomass

growth EC25 = 0.67 - >66.0 ug/L

In vivo – acute toxicity Freshwater algae (4 spp.), 96-h Biomass growth

EC50 = 0.97 - >66.0 ug/L

In vivo – reproductive Green algae (Closterium ahrenbergii), 120-h Reproduction

EC50 = 620 ug/L

Occurrences Type Country Value Surface waters Germany 0.05 - 0.15 ug/L Surface waters United States LOD 0.05 - 2.3 ug/L WWTP effluent Spain 0.08 - 0.40 ug/L


9.4.27 Atrazine

G eneral information CASRN: 1912-24-9 Chemical name: Atrazine Other name(s): 6-chloro-N-ethyl-N’-isopropyl-1,3,5-triazine-2,4-diamine Related chemical: Desisopropylatrazine, Desethylatrazine SMILES: c1(nc(nc(n1)Cl)NCC)NC(C)C MW: 215.7 G uidelines AU AGWR: 40 ug/L AU ADWG: 0.1 ug/L (guideline); 40 ug/L (health) WHO DWG: 2 ug/L EU DWG: (0.5 ug/L total pesticides) NZ DWG: 2 ug/L CA DWG: 5 ug/L (incl metabolites) US NPDWS: 3 ug/L Qld PHR: 40 ug/L (incl metabolites) ANZECC-Env: Trigger Values for Aquatic Ecosystems (ug/L for 99, 95, 90, 80% species protection):

Freshwater - 0.7, 13, 45, 150; Marine - 13 (low reliability) ANZECC-PI: Not available ANZECC-HH: Not available Other: California OEHHA: 0.15 ug/L Registration information PUBCRIS: Yes ARTG: No AICS: Yes HVICL: No Listed in NPI: No Comment: Classified as a hazardous substance by the NOHSC. Risk assessment NICNAS: Not available APVMA: Final review of Atrazine (2008) UN: IPCS PDS 82 (1996); IPCS HSG 47 (1990) Other: ATSDR Tox Profile 153 (2003); IRIS (On-line) Oral RfD Assessment (1993) Epidemiology: Increased risk of small-for-gestational-age babies where drinking water concentrations >

0.1 µg/L Considerations: Carcinogenicity: IARC - Group 3, not classifiable as to carcinogenicity in humans (1999)

USEPA - no assessment available. Endocrine disruption: EU Priority List - not assessed (2006). Sources and treatment Sources: Agricultural runoff following application of atrazine as a herbicide Source control: APVMA pesticide registration restrictions for application DWT efficacy: (1) O3, O3+Alum - 50%; Cl2, Cl2+Alum - 20%; AC - 55%; O3+AC - 80%; Alum - 10-25%

(dose dependent); Cl2+AC+Alum - 65%; O3+AC+Alum - 90%; (2) NH2Cl, UV, UF, MIEX - low; O3+H2O2 - med-high; UV+H2O2, NF - med

WWT efficacy: 3 % (modelled); 23 % (experimental) Fate: Adsorption to sludge Uses: Herbicide widely used against grasses and broad-leaved weeds in a variety of vines,

orchards, plantations and crops, particularly maize and sorghum. Atrazine is one of the main herbicides used in Australia.


Fate and modelling Cramer class: High (III) Log Kow: 2.61 (exper) Solubility: 34.7 mg/L (26 deg C) (exper) VP: 2.89E-07 mm Hg (exper) HLC: 2.63E-09 atm-m3/mole (exper) BP: 313.03 (Adapted Stein & Brown method) MP: 173 deg C (exper) Log BCF: 0.872 (regression-based method), 1.276 (Arnot-Gobas method) Log Koc: 2.24 (exper) HL Biota: 0.03 h in algae; 1.52 d in catfish; 9.5 h in daphnids; biochemical t½ = 64 d from screening

model calculations; t½ = 25.6 d in bean, t½ = 24.3 d in turnips and t½ = 14.6 d in oats at 20 ± 1°C from plant surfaces

HL Sediment: 145 d in a Wisconsin Lake sediment; ~ 30 d for Chesapeake Bay sediment; 7–28 d for 0.1 µg mL–1 to rapidly degrade in both aerobic and low oxygen systems in estuarine sediment/water at 12–35°C; > 35 d for 0.1–1.0 μg mL–1 to slowly biodegrade in sediment/water at 25°C; 60–120 d in surface sediment; 60–223 d in subsurface sediment; biodegradation t½ = 47–128 d in the surface and t½ = 70–770 in subsurface sediment

HL Water: Surface water: estimated t½ ~ 3.21 d in aqueous solution from river die-away tests; 1–4 wk in estuarine systems; 21.3 and 7.3 h in distilled and river water, respectively (laboratory study); 235 d at 6°C; 164 d at 22°C in darkness; 59 d under sunlight conditions for river water at pH 17.3; 130 d at 22°C in darkness for filtered river water at pH 7.3; 200 d at 22°C in darkness; 169 d under sunlight conditions for seawater, pH 8.1

Grooundwater: 6–15 months for 0.72–10 μg mL–1 to biodegrade slowly at 25°C; reported half-lives or persistence, t½ = 60–150, 71, 74, and 130 d

pKa: 1.68 Toxicity Type Description Value In vivo – acute toxicity Freshwater algae (2 spp.), 48-96h growth EC50 = 21 - 377 ug/L In vivo – acute toxicity Freshwater algae (Scenedesmus subspicatus)

Growth EC50 = 21 ug/L

In vivo – acute toxicity Freshwater algae (Scenedesmus subspicatus) Growth

EC50 = 110 ug/L

In vivo – acute toxicity Freshwater fish (14 spp.) 96-h Mortality LC50 = 500 - 71 000 ug/L In vivo – acute toxicity Freshwater fish, Harlequin (Rasbora

heteromorpha) 48-h Mortality LC50 = 500 ug/L

In vivo – acute toxicity Freshwater fish, Guppy (Poecilia reticulata), 96-h Mortality

LC50 = 71 000 ug/L

In vivo – acute toxicity Freshwater crustaceans (5 spp.), 48-96 h Immobilisation

EC50 = 5 700 - 54 000 ug/L

In vivo – acute toxicity Freshwater fish, Mosquitofish (Gambusia holbrooki),96-h Mortality

LC50 = 18 900 ug/L

In vivo – acute toxicity Freshwater fish, Firetail gudgeon (Hypseleotris gallii), 96-h Mortality

LC50 = 258 000 ug/L

In vivo – acute toxicity Freshwater crustacean (C. dubia), 48-h Mortality LC50 = 18 300 ug/L In vivo – acute toxicity Freshwater algae (Selenastrum capricornutum) 72-

h EC50 = 359 ug/L

In vivo – acute toxicity Freshwater fish, Rainbow trout (O. mykiss), 96-h Mortality

LC50 = 4 500 - 11 000 ug/L

In vivo – acute toxicity Freshwater fish, Carp (C. carpio), 96-h Mortality LC50 = 76 000 - >100 000 ug/L

In vivo – acute toxicity Freshwater fish, Bluegill (Lepomis macrochirus), 96-h Mortality

LC50 = 16 000 ug/L

In vivo – acute toxicity Freshwater fish, Coho salmon (Oncorhynchus kisutch) 96-h Mortality

LC50 = 15 000 ug/L

In vivo – acute toxicity Freshwater fish, Catfish, 96-h Mortality LC50 = 7 600 ug/L In vivo – acute toxicity Freshwater fish, Guppy (Poecilia reticulata), 96-h


In vivo – acute toxicity Freshwater crustaceans (Daphnia magna), 48-h Immobilisation

EC50 = 6 100 ug/L

In vivo – acute toxicity 14-d repeated dosing (Earthworm) LC50 = 78 000 ug/kg of soil In vivo – long-term Freshwater crustacean (Daphnia magna), 21-d

Immobilisation EC50 = >120 ug/L

In vivo – long-term Freshwater fish, Zebrafish (Brachydanio rerio), 35-d Mortality

NOEC = 300 ug/L

In vivo – long-term Freshwater fish, Rainbow trout (O. mykiss) 21-d Mortality

NOEC = 60 ug/L

In vivo – long-term Freshwater fish, Fathead minnow (Pimephales promelas), 274-d Growth

NOEC = 250 ug/L

In vivo – reproductive Freshwater fish, Fathead minnow (Pimephales promelas), 274-d reproduction

NOEC = 2000 ug/L (highest concentration)


Occurrences Type Country Value Purified recycled water United States <0.1 µg/L Purified recycled water Australia <0.1 ug/L Unknown Australia <0.01 ug/L Untreated wastewater Australia <0.1 ug/L WWTP effluent Australia <0.5 ug/L WWTP effluent Australia <0.1 ug/L WWTP effluent Australia <0.1 ug/L WWTP effluent Australia <0.1 ug/L WWTP effluent Australia <0.1 ug/L WWTP effluent Australia <0.5 ug/L WWTP effluent Australia 0.21 - 0.88 ug/L

9.4.28 Chlorpyrifos

G eneral information CASRN: 2921-88-2 Chemical name: Chlorpyrifos Other name(s): Phosphorothioic acid, O,O-diethyl O-(3,5,6-trichloro-2-pyridinyl) ester O,O-diethyl O-

3,5,6-trichloro-2-pyridyl phosphorothioate (IUPAC) Related chemical: Chlorpyrifos-methyl, Chlorpyrifos oxon SMILES: CCOP(=S)(OCC)Oc1nc(Cl)c(Cl)cc1Cl MW: 350.59 G uidelines AU AGWR: 10 ug/L AU ADWG: 10 ug/L WHO DWG: 30 ug/L EU DWG: 0.5 ug/L (total pesticides) NZ DWG: Not available CA DWG: 90 ug/L US NPDWS: Not available Qld PHR: 10 ug/L ANZECC-Env: Trigger Values for Aquatic Ecosystems (ug/L for 99, 95, 90, 80% species protection):

Freshwater - 0.00004, 0.01, 0.11, 1.2; Marine - 0.0005, 0.009, 0.04, 0.3 ANZECC-PI: <0.001 µg/L (aquaculture, FW) ANZECC-HH: 2 µg/L Other: USEPA Health Advisories 2006 - DWEL = 10 ug/L Registration information PUBCRIS: Yes ARTG: No AICS: Yes HVICL: No Listed in NPI: No

Risk assessment NICNAS: Not available


APVMA: NRA Review of Chlorpyrifos - Interim Report (2000); Chlorpyrifos Preliminary Review Findings (2009) UN: IPCS ICSC 0851 (2005); IPCS PDS 18 (1975); JPMR M&E 959 (1999) Other: ATSDR Tox Profile (1997); USEPA RAR (2006); IRIS Oral RfD Assessment (1988) Epidemiology: Transient neurotoxicity. Considerations: Carcinogenicity: IARC - no assessment available

USEPA - no assessment available. Endocrine disruption: EU Priority List - Category 3, no evidence of endocrine disrupting

activity or no data available Sources and treatment Sources: Agricultural runoff or spray drift following application as a pesticide. Source control: APVMA pesticide registration restrictions for application methods. DWT efficacy: Ozone preoxidation: 80 %; chlorine preoxidation: 100 %; activated carbon adsorption 90

%; aluminium sulphate precipitation: 40 % WWT efficacy: No information found Fate: No information found Uses: Broad-spectrum organophosphorus (OP) insecticide Fate and modelling Cramer class: High (class III) Log Kow: 4.66 KOWWIN v1.67 estimate; 4.96 Exper. database match Solubility: 0.357 mg/L at 25 deg C (Estimate from Log Kow) 1.12 mg/L at 24 deg C (Exper.

database match) VP: 2.03E-05 mm Hg (2.71E-003 Pa) at 25 deg C (exp database) HLC: 2.52E-006 atm-m3/mole (2.56E-001 Pa-m3/mole) (Bond method) 2.93E-06 atm-m3/mole

(2.97E-001 Pa-m3/mole) (exp database) BP: 377.43 deg C (Adapted Stein & Brown method) MP: 82.93 deg C (Mean or Weighted MP) 42 deg C (exp database) Log BCF: 2.940 (from regression-based method) Log Koc: 1.73 (neutral); 3.52 (anionic) HL Biota: 335 h clearance from fish; biochemical t½ = 63 d from screening model calculations;

elimination t½ ~ 3.3 d in channel catfish HL Sediment: 24 d in 10 g untreated sediment/100 mL of a pesticide-seawater solution; > 28 d in 10 g

sterile sediment/100 mL of a pesticide-seawater solution; 150–200 d in anaerobic pond sediments; First-order degradation k = 0.034 d–1 with t½ = 20.3 d under aerobic conditions, k = 0.003 d–1 with t½ = 223 d under anaerobic conditions in sediment from San Diego Creek, Orange County, CA; first-order degradation k = 0.029 d–1 with t½ = 23.7 d under aerobic conditions, k = 0.012 d–1 with t½ = 57.6 d under anaerobic conditions in sediment from Bonita Creek, Orange County, CA

HL Water: Surface water: based on Henry’s law constant, volatilization t½ ~ 9.0 d for a model river 1-meter deep, flowing 1 m/s with a wind velocity of 3 m/s; < 2.0 d, indoor at 25°C with 12-h photo-period white fluorescent light; 4.6 d, outdoor-light (stoppered, Pyrex flasks exposed to ambient sunlight with temperature 22–45°C); 7.1 d, outdoor-dark (foil-covered flasks) and t½ = 24 d in an estuary; 120 d in water at pH 6.1, 20°C

pKa: 4.55 Toxicity Type Description Value In vivo – acute toxicity Rat (F), Oral, Mortality LD50 = 96 mg/kgbw In vivo – acute toxicity Mouse (M), Oral, Mortality LD50 = 102 mg/kgbw In vivo – long-term Mouse, Oral, 78-wk, plasma ChE inhibition LOAEL = 0.7 mg/kgbw/d In vivo – long-term Mouse, Oral, 78-wk, RBC and brain ChE

inhibition NOAEL = 0.7 mg/kgbw/d

In vivo – long-term Mouse, Oral, 78-wk, RBC and brain ChE inhibition

LOAEL = 6.1 mg/kgbw/d

In vivo – long-term Rat, Oral, 2 year, Plasma and RBC ChE inhibition

NOAEL = 0.1 mg/kgbw/d

In vivo – long-term Rat, Oral, 2 year, Plasma and RBC ChE inhibition


In vivo – long-term Rat, Oral, 2 year, Brain ChE inhibition NOAEL = 1.0 mg/kgbw/d In vivo – long-term Rat, Oral, 2 year, Brain ChE inhibition LOAEL = 3.0 mg/kgbw/d In vivo – long-term Rat, Oral, 2 year, Plasma ChE inhibition NOAEL = 0.012 mg/kgbw/d In vivo – long-term Rat, Oral, 2 year, Plasma ChE inhibition LOAEL = 0.3 mg/kgbw/d In vivo – long-term Rat, Oral, 2 year, Brain ChE inhibition NOAEL = 0.3 mg/kgbw/d In vivo – long-term Rat, Oral, 2 year, Brain ChE inhibition LOAEL = 6 mg/kgbw/d In vivo – long-term Rat, Oral, 2 year, Plasma ChE inhibition NOAEL = 0.1 mg/kgbw/d In vivo – long-term Rat, Oral, 2 year, Plasma ChE inhibition LOAEL = 1.0 mg/kgbw/d In vivo – long-term Rat, Oral, 2 year, Brain and RBC ChE inhibition NOAEL = 1.0 mg/kgbw/d In vivo – long-term Rat, Oral, 2 year, Brain and RBC ChE inhibition LOAEL = 10 mg/kgbw/d In vivo – long-term Dog, Oral, 2 year, Plasma ChE inhibition NOAEL = 0.01 mg/kgbw/d In vivo – long-term Dog, Oral, 2 year, Plasma ChE inhibition LOAEL = 0.03 mg/kgbw/d In vivo – long-term Dog, Oral, 2 year, RBC ChE inhibition NOAEL = 0.03 mg/kgbw/d In vivo – long-term Dog, Oral, 2 year, RBC ChE inhibition LOAEL = 0.1 mg/kgbw/d


In vivo – long-term Dog, Oral, 2 year, Brain ChE inhibition NOAEL = 1.0 mg/kgbw/d In vivo – long-term Dog, Oral, 2 year, Brain ChE inhibition LOAEL = 3.0 mg/kgbw/d Occurrences Type Country Value Purified recycled water United States <0.1 µg/L Reclaimed water Australia <0.1 ug/L Reclaimed water Australia <0.1 ug/L Untreated wastewater Australia <0.1 ug/L WWTP effluent Australia <0.01 ug/L WWTP effluent Australia <0.7 ug/L WWTP effluent Australia <0.1 ug/L WWTP effluent Australia <0.1 ug/L WWTP effluent Australia <0.7 ug/L WWTP effluent Australia LOD 0.5 - 0.7 ug/L WWTP effluent Australia <0.7 ug/L WWTP effluent Australia <0.05 ug/L WWTP effluent Australia <0.05 ug/L WWTP effluent Australia <0.1 ug/L WWTP effluent Australia <0.01 ug/L

9.4.29 Diazinon

G eneral information CASRN: 333-41-5 Chemical name: Diazinon Other name(s): Phosphorothioic acid, O,O-diethyl O-[6-methyl-2-(1-methylethyl)-4-pyrimidinyl] ester Link: http://toxnet.nlm.nih.gov/cgi-bin/sis/search/r?dbs+hsdb:@term+@na+diazinon SMILES: O(c1nc(nc(c1)C)C(C)C)P(OCC)(OCC)=S MW: 304.3489 G uidelines AU AGWR: 3 ug/L AU ADWG: 1 ug/L (guideline), 3 ug/L (health) WHO DWG: Not available (‘unlikely to occur in drinking-water’) EU DWG: 0.5 ug/L (total pesticides) NZ DWG: 10 ug/L CA DWG: 20 ug/L US NPDWS: Not available Qld PHR: 3 ug/L ANZECC-Env: Trigger Values for Aquatic Ecosystems (ug/L for 99, 90, 95, 80% species protection):

Freshwater - 0.00003, 0.01, 0.2, 2; Marine - 0.01 (low reliability) ANZECC-PI: Not available ANZECC-HH: 10 ug/L Other: USEPA Health Advisories 2006: DWEL = 70 ug/L Registration information PUBCRIS: Yes ARTG: No AICS: Yes HVICL: No Listed in NPI: No


Comment: Classified as a hazardous substance according to NOHSC. AICS listing under Phosphorothioic acid, O,O-diethyl O-[6-methyl-2-(1-methylethyl)-4-pyrimidinyl] ester

Risk assessment NICNAS: Not available APVMA: Review in progress UN: IPCS ICSC 0137 (2004); IPCS EHC 198 (1998); JPMR M&E 982 (2001); WHO PDS 45

(1979) Other: ATSDR Tox Profile 86 (2008); USEPA RED (2006) Epidemiology: Weak associations reported between exposure to diazinon and lung cancer. Possible

links between diazinon exposure and non-Hodgkin’s lymphoma, multiple myeloma, and childhood brain cancer (these studies involved exposured to multiple pesticides).

Considerations: Carcinogenicity: IARC - no assessment available; USEPA - no assessment available. Endocrine disruption: EU Priority List - Category 2, at least some in vitro evidence of

biological activity related to endocrine disruption Sources and treatment Sources: Agricultural runoff or spray drift following application as an insecticide. Source control: APVMA pesticide registration restrictions for application methods. DWT efficacy: No information found WWT efficacy: 57.35% (BioWin/EPIWin estimate) Fate: Biodegradation and adsorption to sludge (BioWin/EPIWin estimate) Uses: Insecticide used for controlling sucking and chewing insects and mites on a wide variety

of crops, for fruit flies on harvested fruit as well as flies, cockroaches and other household pests. In Australia, diazinon has almost 450 registered uses including over 50 food crops such as fruit, root and leaf vegetables, mushrooms, rice, nuts, cereal, and non-food crops such as cotton, turf, trees and nursery plants. It is commonly used on farm and pet animals against ectoparasites. It is also used for pest control in domestic, industrial and agricultural buildings, boats, trains and other vehicles, food processing areas, food stored animal hides, on garbage tips and on ponds against mosquitoes.

Fate and modelling Cramer class: High (III) Log Kow: 3.81 (exper) Solubility: 40 mg/L (exper) VP: 9.01E-05 mm Hg (exper) HLC: 1.13E-07 atm-m3/mole (exper) BP: 125 deg C @ 1 mm Hg (exper) MP: < 25 deg C (exper) Log BCF: 2.181 (regression-based method); 2.455 (Arnot-Gobas method) Log Koc: 2.75 (exper) HL Biota: biochemical t½ = 32 d from screening model calculations; excretion t½ = 9.9 h by willow

shiner; t½ = 25 h in eel’s liver and t½ = 26 h in eel’s muscle HL Sediment: first-order degradation k = 0.048 d–1 with t½ = 14.4 d under aerobic conditions, k = 0.022

d–1 with t½ = 31.7 d under anaerobic conditions in sediment from San Diego Creek, Orange County, CA; first-order degradation k = 0.033 d–1 with t½ = 21.1 d under aerobic conditions, k = 0.029 d–1 with t½ = 23.7 d under anaerobic conditions in sediment from Bonita Creek, Orange County, CA

HL Water: Surface water: photolysis t½ = 41 d without humic substances; t½ = 13 d and 5 d with concn of humic acid 20 mg/L and 50 mg/L, respectively, under light intensity λ ≥ 290 nm; t½ = 144 d at 6°C, t½ = 69 d at 22°C in darkness for Milli-Q water; t½ = 181 d at 6°C, t½ = 80 d at 22°C in darkness, t½ = 43 d under sunlight conditions for river water at pH 7.3; t½ = 132 d at 6°C, t½ = 52 d at 22°C in darkness for filtered river water at pH 7.3; t½ = 125 d at 6°C, t½ = 50 d at 22°C in darkness, t½ = 47 d under sunlight conditions for seawater, pH 8.1

pKa: 2.6 Toxicity Type Description Value In vivo – acute toxicity Human (F), Oral, Lowest recoded lethal dose LDLo = 293 mg/kgbw In vivo – acute toxicity Rat, Oral, Mortality LD50 = 76 - 466 mg/kgbw In vivo – acute toxicity Human, Oral, Tachypnea, Cyanosis/Bradycardia,

Tachycardia/Metabolic acidosis/Stupor, Coma LOAEL = 240 - 509 mg/kgbw/d

In vivo – acute toxicity Human, Oral, Haematological changes NOAEL = 240 - 509 mg/kgbw/d

In vivo – acute toxicity Human (F), Oral, Heavily congested lungs/bleeding in GIT/brain bleeding

LOAEL = 293 mg/kgbw/d

In vivo – acute toxicity Rat, Oral, Decreased body weight gain LOAEL = 300 mg/kgbw/day In vivo – acute toxicity Rat, Oral, Decreased body weight gain NOAEL = 150 mg/kgbw/day In vivo – acute toxicity Rat (M), Oral, Haematological changes LOAEL = 4.4 mg/kgbw/d In vivo – acute toxicity Rat (F), 14-d Oral, Haematological changes LOAEL = 52 mg/kgbw/day In vivo – acute toxicity Rat, Oral, Liver enzyme changes LOAEL = 300 mg/kgbw/day In vivo – develomental Rat, Oral, 133-d, Decreased F1 pup survival LOAEL = 7.63 mg/kgbw/d


In vivo – developmental Rat, Oral, 133-d, Developmental NOAEL = 0.77 mg/kgbw/d In vivo – developmental Mouse, Oral, Gestation day 1-18, Reduced early

weight gain/Increased mortality LOAEL = 9 mg/kgbw/d

In vivo – developmental Mouse, Oral, Gestation day 1-18, Reduced early weight gain/Increased mortality


In vivo – developmental Mouse, Oral, Gestation day 1-18, Developmental defects


In vivo – long-term Beagle dog, Oral, 52 weeks, Depressed body weight gain


In vivo – long-term Beagle dog, Oral, 52 weeks, Depressed body weight gain


In vivo – long-term Beagle dog, Oral, 52 weeks, AChE inhibition LOAEL = 4.6 mg/kgbw/d In vivo – long-term Beagle dog, Oral, 52 weeks, AChE inhibition NOAEL = 0.017 mg/kgbw/day In vivo – long-term Rat, Oral, 52-98 weeks, AChE inhibition LOAEL = 5.5 mg/kgbw/day In vivo – long-term Rat, Oral, 52-98 weeks, AChE inhibition NOAEL = 0.065 mg/kgbw/d In vivo – long-term Rat, Oral, 52-98 weeks, Systemic and reproductive NOAEL = 11 mg/kgbw/d In vivo – reproductive Rat, Oral, 65-d Increased sperm

abnormalities/Decreased fertility LOAEL = 1.5 mg/kgbw/d

In vivo – reproductive Rat, Oral, 4 - 13 weeks, Reproductive problems NOAEL = 0.05 - 212 mg/kgbw/d

In vivo – reproductive Beagle dog, Oral, 13-wk Reproductive problems NOAEL = 11.6 mg/kgbw/d In vivo – reproductive Beagle dog, Oral, 8 months, Testicular atrophy LOAEL = 10 mg/kgbw/d In vivo – reproductive Beagle dog, Oral, 8 months, Testicular atrophy NOAEL = 5 mg/kgbw/d In vivo – short-term Human, 28-31 d Haematological changes NOAEL = 0.03 mg/kgbw/d In vivo – short-term Rat, Oral, 1-28 wk Reduction in body weight gain LOAEL = 0.5 - 213 mg/kgbw In vivo – short-term Rat, Oral, 30-90 day Reduction in body weight gain NOAEL = 2.86 - 23 mg/kgbw/d In vivo – short-term Rat, Oral, 4 wk Liver indicator changes LOAEL = 30 mg/kgbw/d In vivo – short-term Rat, Oral, Hepatic enzyme changes/Significant

haematological changes LOAEL = 10 mg/kgbw/d

In vivo – short-term Rat, Oral, 6 months, Haematological changes/Hepatic changes/BW gain reduction


In vivo – short-term Rat (F), Oral, 13 wk Haematological changes LOAEL = 212 mg/kgbw/d In vivo – short-term Beagle dog, Oral, 8 months

Haematological/Hepatic/Renal/Endocrine changes LOAEL = 10 mg/kgbw/d

In vivo – short-term Human, Oral, 28-31 d AChE Inhibition NOAEL = 0.03 mg/kgbw/d In vivo – short-term Rat, Oral, 28-92 d, AChE Inhibition LOAEL = 0.27 - 2.86

mg/kgbw/d In vivo – short-term Rat, Oral, 28-92 d AChE Inhibition NOAEL = 0.018 - 0.4

mg/kgbw/d In vivo – short-term Rat (M), Oral, 42-d, AChE Inhibition (29 - >59%) LOAEL = 1.68 - 8.6

mg/kgbw/day In vivo – short-term Rat (M), Oral, 42-d, AChE Inhibition (29 - >59%) NOAEL = 0.17 mg/kgbw/d In vivo – short-term Rat (F), Oral, 42-d, AChE Inhibition (16 - >59%) LOAEL = 1.82 - 9.27

mg/kgbw/d In vivo – short-term Rat (F), Oral, 42-d, AChE Inhibition (16 - >59%) NOAEL = 0.19 mg/kgbw/d In vivo – short-term Rat (M), Oral, 6-wk, AChE Inhibition (21 - 58%) LOAEL = 8.4 - 150.8

mg/kgbw/d In vivo – short-term Rat (M), Oral, 6-wk, AChE Inhibition (21 - 58%) NOAEL = 0.2 mg/kgbw/d In vivo – short-term Rat (F), Oral, 6-wk, AChE Inhibition (21 - 61%) LOAEL = 9.4 - 182.9

mg/kgbw/d Occurrences Type Country Value Purified recycled water Australia <0.1 ug/L Purified recycled water United States <0.1 µg/L Purified recycled water Australia <0.1 ug/L Reclaimed water Australia <0.5 ug/L Surface waters United States LOD 0.03 - 0.35 ug/L Untreated wastewater Australia <0.1 ug/L WWTP effluent Australia <0.1 ug/L WWTP effluent Australia <0.1 ug/L WWTP effluent Australia <3.2 ug/L WWTP effluent Australia LOD 0.01 - 0.12 ug/L WWTP effluent Australia <0.1 ug/L WWTP effluent Australia <0.1 ug/L WWTP effluent Australia <3.2 ug/L WWTP effluent Australia <3.2 ug/L WWTP effluent Australia <3.2 ug/L WWTP effluent Australia <0.1 ug/L WWTP effluent Australia <0.1 ug/L


9.4.30 Diuron

G eneral information CASRN: 330-54-1 Chemical name: Diuron Other name(s): Urea, N'-(3,4-dichlorophenyl)-N,N-dimethyl- Related chemical: Linuron, Propanil, 3,4-Dichloroaniline (3,4-DCA) Link: http://toxnet.nlm.nih.gov/cgi-bin/sis/search/r?dbs+hsdb:@term+@na+diuron SMILES: c1(c(ccc(c1)NC(N(C)C)=O)Cl)Cl MW: 233.10 G uidelines AU AGWR: 30 ug/L AU ADWG: 30 ug/L (health value) WHO DWG: Not available EU DWG: 0.5 ug/L (total pesticides) NZ DWG: 20 ug/L CA DWG: 150 ug/L US NPDWS: Not available Qld PHR: 30 ug/L ANZECC-Env: Trigger Values for Aquatic Ecosystems (ug/L): Freshwater - 0.2 (low reliability, AF = 200);

Marine - 1.8 (low reliability, AF = 1000) ANZECC-PI: Irrigation Water Residue Trigger Value: 2 ug/L ANZECC-HH: 40 ug/L Other: USEPA Health Advisories 2006: DWEL = 100 ug/L Registration information PUBCRIS: Yes ARTG: No AICS: Yes HVICL: No Listed in NPI: No Risk assessment NICNAS: Not available APVMA: Diuron Preliminary Review (2005) UN: Not available Other: Oral RfD Assessment (1988); USEPA RED (2003) Epidemiology: No information found Considerations: Carcinogenicity: IARC - no assessment available

USEPA – ‘known/likely’ human carcinogen (2003). Endocrine disruption: EU Priority List - Category 2, at least some in vitro evidence of

biological activity related to endocrine disruption (2005) Other: Primary diuron target sites are blood, bladder, and kidney (2003). Sources and treatment Sources: Agricultural runoff or spray drift following application as a herbicide. Source control: APVMA pesticide registration restrictions for application methods. DWT efficacy: O3, O3+Alum - 75%; Cl2 - 100%; AC - 65%; AC+O3 - 95%; Alum - 30%; O3+AC+Alum -

100% Cl2+AC+Alum - 100% WWT efficacy: 3.73% (lowest modelled value) Fate: No information found Uses: Herbicide with a wide variety of uses including total weed control in commercial areas,

roads and railways and rights-of-way and selective control of grasses and broadleaf weeds in crops. It has around 2200 registered uses in Australia, including around 25


crops, cereals, vegetables, orchards and plantations, and flower nurseries as well as commercial areas, weed control in flood mitigation channels and as a boat antifoulant.

Fate and modelling Cramer class: High (III) Log Kow: 2.68 (exper) Solubility: 42 mg/L (exper) VP: 6.90E-08 mm Hg (exper) HLC: 5.04E-10 atm-m3/mole (exper) BP: 353.86 deg C (Adapted Stein & Brown method) MP: 158 deg C (exper) Log BCF: 1.435 (regression-based method), 1.041 (Arnot-Gobas method) Log Koc: 2.4 (exper) HL Biota: biochemical t½ = 328 d from screening model calculations HL Sediment: t½ = 3–10 d for 40 μg/mL to biodegrade in pond sediment of anaerobic media at 30°C;

t½ < 17 d for 40 μg/mL to biodegrade in pond sediment at 30°C; t½ ~ 5 d for 0.22 μg/mL to biodegrade in pond sediment of anaerobic media

HL Water: Surface water: should be photolyzed within a few days; Groundwater: reported half-lives or persistence, t½ = 20–70, 90–180, 200, and 328 d

pKa: 13.55 Toxicity Type Description Value In vivo – acute toxicity Oral - unspecified mammalian species LD50 >5 000 000 ug/kgbw In vivo – acute toxicity Freshwater fish (15 spp.), 48-96 h Mortality LC50 = 500 - 63 000 ug/L In vivo – acute toxicity Freshwater fish, Harlequin (Rasbora

heteromorpha), 48-h Mortality LC50 = 190 000 ug/L

In vivo – acute toxicity Freshwater crustaceans (6 spp.), 48-96 h Mortality LC50 = 160 - 15 500 ug/L In vivo – acute toxicity Freshwater insects (2 spp.), 48-96 h Mortality LC50 = 1 200 - 3 600 ug/L In vivo – acute toxicity Marine fish (1 sp.), 48-h Mortality LC50 = 6 300 ug/L In vivo – acute toxicity Marine mollusc (1 sp.), 96-h Growth EC50 = 1 800 ug/L In vivo – acute toxicity Rat, Oral, Mortality LD50 = 1 017 000 ug/kgbw In vivo – long-term Freshwater fish, Fathead minnow (P. promelas),

64-d Mortality NOEC = 33.4 ug/L

Occurrences Type Country Value Purified recycled water Australia <20 ug/L WWTP effluent Australia 0.26 - 0.29 ug/L WWTP effluent Australia <20 ug/L

9.4.31 Pentachlorophenol

G eneral information CASRN: 87-86-5 Chemical name: Pentachlorophenol (PCP) Related chemical: 4-Chlorophenol, 2,4-Dichlorophenol, 2,3,4,6-Tetrachlorophenol, 2,6-dichlorophenol, 2,4,6-Trichlorophenol, 2,3,6-trichlorophenol, 2-Chlorophenol, 2,4,5-trichlorophenol SMILES: c1(c(c(c(Cl)c(c1Cl)Cl)Cl)Cl)O MW: 266.34 G uidelines AU AGWR: 10 ug/L


AU ADWG: 0.01 ug/L (guideline), 50 ug/L (health) WHO DWG: 9 ug/L (provisional) EU DWG: 0.5 ug/L (total pesticides) NZ DWG: 9 ug/L CA DWG: 60 ug/L US NPDWS: 1 ug/L Qld PHR: 10 ug/L ANZECC-Env: Trigger Values for Aquatic Ecosystems (ug/L for 99, 95, 90, 80% species protection):

Freshwater - 3.6, 10, 17, 27; Marine - 11, 22, 33, 55 ANZECC-PI: GLs for prevention of tainting of fish flesh: 30 ug/L ANZECC-HH: 10 ug/L Other: California OEHHA: 0.4 ug/L Registration information PUBCRIS: No ARTG: No AICS: Yes HVICL: No Listed in NPI: No Comment: PIC Chemical, Rotterdam Convention; Classified as a hazardous substance according to the NOHSC Risk assessment NICNAS: Not available APVMA: Not available UN: IPCS EHC 71 (1987); IPCS PIM 405 (1989); IPCS ICSC 0069 (2003); IPCS HSG 19 (1989) Other: ATSDR Tox Profile 51 (2001); IRIS Tox Profile (2010); IRIS Chronic Oral RfD

Assessment (2010) and Carcinogenicity Assessment (2010) Epidemiology: No information found Considerations: Carcinogenicity: IARC- Group 2B, possibly carcinogenic to humans (Ref. 80; 1981)

USEPA - Likely to be carcinogenic to humans (Ref. 86, 2010) Endocrine disruption: EU Priority List - not assessed (Ref. 96). Sources and treatment Sources: May be present in the environment from widespread historical use of pentachlorophenol

as a herbicide to protect softwood timber from decay. Source control: No longer registered for use as a pesticide. DWT efficacy: No information found WWT efficacy: 81.16% (EpiWin estimate) Fate: Adsorption to sludge (EpiWin estimate) Uses: An insecticide and herbicide that was used as a wood preservative. Also used as an

intermediate for pesticides and fungicides; cooling towers additive of electric plants; as additives to adhesives; additive in shingles, brick walls, concrete blocks, insulation, pipe sealant compounds, photographic solutions, and textiles and in drilling mud in the petroleum industry.

Fate and modelling Cramer class: High (III) Log Kow: 5.12 (exper) Solubility: 14 mg/L (exper) VP: 1.10E-04 mm Hg (exper) HLC: 2.45E-08 atm-m3/mole (exper) BP: 309.5 deg C (exper) MP: 174 deg C (exper) Log BCF: 3.045 (regression-based method), 2.405 (Arnot-Gobas method) Log Koc: 3.7 (exper) HL Biota: biological t½ = 30 d in guppy (Lebistes reticulatus); elimination t½ = 23, 9.3, 6.9, and 6.2

h for fat, liver muscle, and blood, respectively; estimated t½ = 7.0 d in trout; clearance from flagfish: t½ = 0.68 d from whole fish and t½ = 0.68 d from fish lipid

HL Sediment: first order microbial degradation rate constant k = 7.4 × 10–4 h–1 in sediment and water HL Water: Surface water: calculated photolysis t½ = 4.75 h; photolysis t½ = 1.5 d was estimated

from photolytic destruction by sunlight in an aqueous solution; t½ = 1.5 to 3.0 d for direct photo-transformation from outdoor ponds; t½ = 1 h (summer), t½ = 2 h (winter) for distilled water; t½ = 2 h (summer), t½ = 3 h (winter) for estuarine water; t½ = 2 h (summer), t½ = 3 h (winter) for poisoned estuarine water, based on photo-transformation rate constants (Hwang et al. 1986); t½ = 6 d (summer), t½ = 14 d (winter) for distilled water; t½ = 3 d (summer), t½ = 7 d (winter) for estuarine water; t½ = 6 d (summer), t½ = 10 d (winter) for poisoned estuarine water, based on photo-mineralization rate constants; t½ = 0.75 h and 0.96 h, based on photochemical transformation; 1–110 h, based on aqueous photolysis half-life; photodegradation t½ = 1.0 h (summer), t½ = 2.0 h (winter) in distilled water and t½ = 2.0 h (summer), t½ = 3.0 h (winter) in estuarine water under irradiation by natural sunlight; t½(aerobic) = 23 d, t½(anaerobic) = 42 d in natural waters; Groundwater: t½ = 1104–36480 h, based on estimated unacclimated aqueous aerobic sediment grab sample data


pKa: 4.6 – 5.25 Toxicity Type Description Value In vivo – acute toxicity Human (Oral) - lowest recorder lethal dose LDlo = 29 mg/kgbw In vivo – acute toxicity Rat (Oral) LD50 = 50 mg/kgbw In vivo – acute toxicity Freshwater fish, Rainbow trout (O. mykiss), lowest

acute mortality value LC50 = 18 ug/L

In vivo – acute toxicity Freshwater fish, Rainbow trout (O. mykiss), mean acute mortality value

LC50 = 285 ug/L

In vivo – acute toxicity Freshwater fish, Roach (Leuciscus rutilus), mortality

LC50 = 38 ug/L

In vivo – acute toxicity Freshwater fish, Muksun (Coregonus muksun), mortality

LC50 = 40 ug/L

In vivo – acute toxicity Freshwater fish, Northern pike (Esox lucius), mortality

LC50 = 45 ug/L

In vivo – acute toxicity Freshwater fish (Melanotaenia duboulayi), 96-h Mortality

LC50 = 1 500±300 ug/L

In vivo – acute toxicity Freshwater fish, Mosquitofish (Gambusia holbrooki), 96-h mortality

LC50 = 1 060±300 ug/L

In vivo – acute toxicity Freshwater fish, Zebrafish (Brachydanio rerio), 96-h mortality

LC50 = 950±60 ug/L

In vivo – acute toxicity Freshwater fish, Zebrafish (Brachydanio rerio), 96-h mortality

LC50 = 1 230 ug/L

In vivo – acute toxicity Freshwater fish, Rainbow trout (O. mykiss), 48-h mortality, NZ data

LC50 = 90 - 1 945 ug/L (depending on conditions)

In vivo – acute toxicity Amphibian (C. cornuta) 48-h EC50 = 70 ug/L In vivo – acute toxicity Freshwater crustacean (C. dubia), 48-h EC50 = 150 - 300 ug/L In vivo – acute toxicity Echinoderm, 48-h Growth EC50 = 710 - 870 ug/L In vivo – acute toxicity Freshwater insect (Delatidium sp.), 96-h Mortality LC50 = 219 ug/L In vivo – acute toxicity Freshwater crustacean (D. magna), 48-h EC50 = 187 ug/L In vivo – developmental Freshwater fish, Fathead minnow (P. promelas),

28-d hatching NOEC = 128 ug/L

In vivo – long-term Freshwater fish, Largemouth bass (Micropterus salmoides), 45-d mortality

NOEC = 41 ug/L

In vivo – long-term Freshwater fish, Medaka (Oryzias latipes), 21-d mortality

NOEC = 271 ug/L

In vivo – long-term Freshwater crustacean (Simocephalus vetulus & D. magna), 14-day

NOEC = 50 ug/L

In vivo – long-term 4 cladoceran spp., 13-21 d reproduction and mortality

NOEC = 50-320 ug/L

In vivo – long-term Freshwater fish, Fathead minnow (P. promelas), 28-d growth

NOEC = 45 ug/L

In vivo – long-term Freshwater fish, Fathead minnow (P. promelas), 28-d mortality

NOEC = 73 ug/L

In vivo – reproductive Freshwater crustacean (C. dubia), 14-d reproduction

NOEC = 100 ug/L

In vivo – short term Crayfish, 8-day Mortality LC50 = 3000 - 5500 ug/L In vivo – short-term Freshwater alga (Pseudokirchnierella subcapitata),

96-h Growth EC50 = 580 - 890 ug/L

In vivo – short-term Oyster, 12-d Mortality LC50 = 71 ug/L Occurrences Type Country Value Purified recycled water United States <1.0 ug/L Purified recycled water Australia <2 ug/L Purified recycled water Australia <50 ng/L Reclaimed water Australia <4 ug/L Unknown Australia <0.2 ug/L Unknown Australia <0.002 ug/L WWTP effluent Australia LOD 0.05 - 0.179 ug/L WWTP effluent Australia <2 ug/L WWTP effluent Australia <0.1 ug/L WWTP effluent Australia <0.1 ug/L WWTP effluent Australia <2 ug/L


9.4.32 Simazine

G eneral information CASRN: 122-34-9 Chemical name: Simazine Other name(s): 1,3,5-Triazine-2,4-diamine, 6-chloro-N,N'-diethyl-; 2-Chloro-4,6-bis(ethylamino)-5-triazine Related chemical: Atrazine SMILES: c1(nc(nc(n1)Cl)NCC)NCC MW: 201.66 G uidelines AU AGWR: 20 ug/L AU ADWG: 20 ug/L WHO DWG: 2 ug/L EU DWG: 0.5 ug/L (total pesticides) NZ DWG: 2 ug/L CA DWG: 10 ug/L US NPDWS: 4 ug/L Qld PHR: 20 ug/L ANZECC-Env: Trigger Values for Aquatic Ecosystems (ug/L for 99, 95, 90, 80% species protection):

Freshwater - 0.2, 3.2, 11, 35; Marine - 3.2 (low reliability) ANZECC-PI: Not available ANZECC-HH: Not available Other: California OEHHA: 4 ug/L Registration information PUBCRIS: Yes ARTG: No AICS: Yes HVICL: No Listed in NPI: No Comment: AICS listing under 1,3,5-Triazine-2,4-diamine, 6-chloro-N,N'-diethyl-; Classified as a

hazardous substance by the NOHSC. Risk assessment NICNAS: Not available APVMA: nominated for review UN: IPCS ICSC 0699 (2005) Other: IRIS Oral RfD Assessment (1994) Epidemiology: No information found Considerations: Carcinogenicity: IARC - Group 3, not classifiable as to carcinogenicity in humans (Ref.

80; 1999); USEPA - no assessment available. Endocrine disruption: EU Priority List - Category 2, at least some in vitro evidence of

biological activity related to endocrine disruption (Ref. 96; 2006). Sources and treatment Sources: Agricultural runoff or spray drift following application as a herbicide. Source control: APVMA pesticide registration restrictions for application methods. DWT efficacy: O3, O3/Alum - 65%; Cl2, Cl2/Alum - 50%; AC - 55%; O3/AC - 80%; Alum - 20%;

Cl2/AC/Alum - 65%; O3/AC/Alum - 90% WWT efficacy: 2.45% (modelled) Fate: No information found Uses: Herbicide used for control of a wide variety of grasses and broad-leaved weeds in fruit,

vines, nuts, pineapples, vegetables, flowers, sugarcane, coffee, tea, turf and in forestry. Fate and modelling Cramer class: High (III)


Log Kow: 2.18 (exper) Solubility: 6.2 mg/L (20 deg C) (exper) VP: 2.21E-08 mm Hg (exper) HLC: 9.42E-10 atm-m3/mole (exper) BP: 307.45 deg C (Adapted Stein & Brown method) MP: 226 deg C (exper) Log BCF: 0.588 (regression-based method), 1.055 (Arnot-Gobas method) Log Koc: 2.1 (exper) HL Biota: 75 days (from screening model calculations) HL Sediment: 8 - 27 days (for 3 ug/mL to biodegrade) HL Water: Surface water: >32 days (for 3 ug/mL); Estuarine systems: 1 - 4 weeks; Ponds: 30 days;

Groundwater: 15 - 174 days pKa: 1.65; 1.60; 1.70 (@21 degC); 2.00; 12.35; 12.3 Toxicity Type Description Value In vivo – acute toxicity Freshwater fish (7 spp.), 24-96 h Mortality LC50 = 90 - 6 600 ug/L In vivo – acute toxicity Freshwater crustaceans (3 spp.) 48-h

Immobilisation EC50 = 1 000 - 3 700 ug/L

In vivo – acute toxicity Freshwater insects (2 spp.), 48-96 h Mortality LC50 = 1 900 - 3 580 ug/L In vivo – acute toxicity Freshwater algae (2 spp.), 48-96 h Growth and

population growth EC50 = 160 - 320 ug/L

In vivo – acute toxicity Freshwater algae (2 spp.), 48-96 h Photosynthesis EC50 = 2.24 ug/L In vivo – acute toxicity Chicken (also Rabbit & Pidgeon), Oral, Mortality LD50 = >5000 mg/kgbw In vivo – acute toxicity Mammal (unspecified), Oral, Mortality LD50 = 2014 mg/kgbw In vivo – acute toxicity Rat, Oral, Mortality LD50 = 971 mg/kgbw In vivo – long-term Rat , Oral (feeding), 2-year, Reduction in weight

gains; hematological changes in females LOAEL = 5.3 mg/kg-day

In vivo – long-term Rat , Oral (feeding), 2-year, Reduction in weight gains; hematological changes in females

NOAEL = 0.52 mg/kg-day

Occurrences Type Country Value Purified recycled water United States <0.07 µg/L Purified recycled water Australia <0.1 ug/L Reclaimed water Australia <0.5 - 0.7 ug/L WWTP effluent Australia <0.1 ug/L WWTP effluent Australia <0.1 ug/L WWTP effluent Australia 0.9 - 1.04 ug/L

9.4.33 Trifluralin

G eneral information CASRN: 1582-09-8 Chemical name: Trifluralin Other name(s): Benzenamine, 2,6-dinitro-N,N-dipropyl-4-(trifluoromethyl)-; 2,6-Dinitro-N,N-dipropyl-4-

trifluoromethylaniline SMILES: c1(c(cc(C(F)(F)F)cc1[N+](=O)[O-])[N+](=O)[O-])N(CCC)CCC MW: 335.29


G uidelines AU AGWR: 50 ug/L AU ADWG: 50 ug/L WHO DWG: 20 ug/L EU DWG: 0.5 ug/L (total pesticides) NZ DWG: 30 ug/L CA DWG: 45 ug/L US NPDWS: Not available Qld PHR: 50 ug/L ANZECC-Env: Trigger Values for Aquatic Ecosystems (ug/L for 99, 90, 95, 80% species protection):

Freshwater - 2.6, 4.4, 6, 9; Marine - 2.6 (low reliability, no data) ANZECC-PI: Not available ANZECC-HH: 500 ug/L Other: None found Registration information PUBCRIS: Yes ARTG: No AICS: Yes HVICL: No Listed in NPI: No

Risk assessment NICNAS: Not available APVMA: Not available UN: IPCS ICSC 0205 (2005) Other: IRIS Oral RfD Assesment (1989) and Carcinogenicity Assessment (1993) Epidemiology: No information found Considerations: Carcinogenicity: IARC - Group 3, not classifiable as to carcinogenicity in humans (Ref.

80; 1991); USEPA - class C, possible human carcinogen (Ref. 86; 1993) Endocrine disruption: EU Priority List - not assessed (Ref. 96). Fate and modelling Cramer class: High (III) Log Kow: 5.34 (exper) Solubility: 0.184 mg/L (exper) VP: 4.58E-05 mm Hg (exper) HLC: 1.03E-04 atm-m3/mole (exper) BP: 139-140 deg C (@ 4.2 mm Hg) (exper) MP: 49 deg C (exper) Log BCF: 3.190 (regression-based method), 3.176 (Arnot-Gobas method) Log Koc: 4.2 (exper) HL Biota: t½ = 22–31 d in river saugers, t½ = 17–57 d in river shorthead redhorse, t½ = 23 d in

river golden redhorse, t½ = 3 d in lab. fathead minnow; biochemical t½ = 132 d HL Sediment: degradation t½ = 9 d in estuarine sediment (18o/∞) system; t½ = 18.5 d in flooded

sediment HL Water: Surface water: calculated t½ = 21 min from midday direct sunlight photolysis rate

constant of 2.0 h–1; calculated t½ = 0.94 h for disappearance via direct sunlight photolysis in aqueous media; t½ < 20 d for 2.5–5 cm water over flooded soils, t½ ~ 20 h in water above sediment in estuarine sedimentwater microcosm; t½ < 9 h in buffered aqueous solution of pH 7 under Xenon lamp; t½ = 1–400 min in reaction mixture of 0.5 mM and 100 mg/L goethite solution for pHs from 6.5 to 7.73; Groundwater: reported t½ = 4–67, 57–126, 70, 83, and 105–132 d

pKa: No information found Toxicity Type Description Value In vivo – acute toxicity 12 freshwater fish spp., 48-96 h, Mortality LC50 = 8.4 - 2200 ug/L In vivo – acute toxicity 1 freshwater amphibian sp., 48-96 h, Mortality LC50 = 100 - 170 ug/L In vivo – acute toxicity 12 freshwater crustacean spp., 48-96 h

Immobilisation EC50/LC50 = 37 - 2200 ug/L

In vivo – acute toxicity 2 freshwater insect spp., 48-96 h Mortality LC50 = 1000 - 4200 ug/L In vivo – acute toxicity 1 freshwater mollusc sp., 48-h Mortality LC50 = 8000 ug/L In vivo – acute toxicity 1 marine crustacean, 96-h mortality LC50 = 300 - 330 ug/L In vivo – acute toxicity Freshwater fish, Eastern mosquitofish (Gambusia

holbrooki), Mortality LC50 = 1100 ug/L

In vivo – acute toxicity Freshwater fish, Firetail gudgeon (Hypseleotris gallii), Mortality

LC50 = 270 ug/L

In vivo – acute toxicity Mouse, Oral, Mortality LD50 = 3197 mg/kgbw In vivo – acute toxicity Rat, OralL, Mortality LD50 = 1930 mg/kgbw In vivo – long-term Dog, Oral 1-year feeding study, Mild hepatic effects NOAEL = 0.75 mg/kgbw

Occurrences


Type Country Value Purified recycled water Australia <0.02 ug/L Purified recycled water Australia <0.1 ug/L Reclaimed water Australia <0.1 ug/L Unknown Cyprus 0.6 ug/L WWTP effluent Australia <0.1 ug/L WWTP effluent Australia <0.1 ug/L WWTP effluent Australia <0.02 - 0.15 ug/L

9.4.34 Bromochloroacetic acid

G eneral information CASRN: 5589-96-8 Chemical name: Bromochloroacetic acid Other name(s): BCA; BCAA Related chemical: Dibromochloroacetic acid, Dibromoacetic acid, Dichlorobromoacetic acid, Tribromacetic

acid (TBA), Trichloroacetic acid, Bromoacetic acid, Chloroacetic acid, Dichloroacetic Acid SMILES: C([C@@H](Cl)Br)(=O)O MW: 173.39 G uidelines AU AGWR: [0.014 ug/L] (CoTC) AU ADWG: Not available WHO DWG: Not available EU DWG: Not available NZ DWG: Not available CA DWG: (80 ug/L for total HAAs) US NPDWS: (60 ug/L for total HAA5 - does not include bromochloroacetic acid) Qld PHR: 0.014 ug/L ANZECC-Env: Not available ANZECC-PI: Not available ANZECC-HH: Not available Other: CoTC based on TTC, genotoxic, P=0.2 Registration information PUBCRIS: No ARTG: No AICS: No HVICL: No Listed in NPI: No Risk assessment NICNAS: Not available APVMA: Not available UN: IPCS EHC 216 (DBPs) (2000) Other: NTP TR 579 (Bromochloroacetic acid) (2009) Epidemiology: Reproductive and carcinogenesis effects have been associated with disinfection

byproducts, no specific epidemiology studies available for this chemical Considerations: Carcinogenicity: IARC classification - no assessment available

USEPA - no assessment available. Endocrine disruption: EU Priority List - not assessed (Ref. 96; 2006).


Other: Carcinogen and repoductive toxin (Ref. 154; 2009). Sources and treatment Sources: Chlorination and chloramination disinfection by-product Source control: Alteration of drinking water treatment processes. For example, coagulation to remove

precursors (bromide, organics) prior to disinfection. DWT efficacy: No information found WWT efficacy: 1.86% (modelled) Fate: No information found Uses: Not intentionally produced or used in Australia Fate and modelling Cramer class: High (III) Log Kow: 0.61 (KOWWIN estimate) Solubility: 4.427E+004 (est. from Log Kow) VP: 0.137 mm Hg (Modified Grain method) HLC: 2.22E-008 atm-m3/mole (Bond method) BP: 215 deg C (exper) MP: 31.5 deg C (exper) Log BCF: 0.5 (regression-based method), 0.095 (Arnot-Gobas method) Log Koc: 0.353 (MCI method); 0.493 (Kow method) HL Biota: No information found HL Sediment: No information found HL Water: No information found pKa: 1.39 Toxicity No information found. Occurrences Type Country Value Purified recycled water Australia <0.01 ug/L WWTP effluent Australia <0.01 ug/L

9.4.35 Bromodichloromethane

G eneral information CASRN: 75-27-4 Chemical name: Bromodichloromethane Other name(s): Dichlorobromomethane; BDCM Related chemical: Dibromochloromethane, Bromochloroiodomethane, Bromodiiodomethane,

Dibromoiodomethane, Dichloroiodomethane, Chlorotribromomethane (Tribromochloromethane), Chlorodiiodomethane, Bromomethane (methyl bromide), Chloromethane, Methyl iodide (Iodomethane), Dibromomethane, Bromochloromethane, Dichloromethane, Dichloromonofluoromethane, Trichlorofluoromethane (Freon 11), Dichlorodifluoromethane, Chloroform, Bromoform, Iodoform

SMILES: C(Cl)(Cl)Br MW: 163.83 G uidelines AU AGWR: 6 ug/L AU ADWG: 250 ug/L (combined total for all THMs) WHO DWG: 60 ug/L EU DWG: 100 ug/L (for total THMs)


NZ DWG: 60 ug/L CA DWG: 16 ug/L (for BDCM), 100 ug/L (for total THMs) US NPDWS: 80 ug/L Qld PHR: 6 ug/L ANZECC-Env: Not available ANZECC-PI: Not available ANZECC-HH: Not available Other: None found Registration information PUBCRIS: No ARTG: No AICS: No HVICL: No Listed in NPI: No Risk assessment NICNAS: Not available APVMA: Not available UN: EHC 216 (DBP) (2000); IPCS ICSC 0393 (2005) Other: ATDSR Tox Profile 129 (1989); IRIS Oral RfD Assessment (1991) and Carcinogenicity

Assessment (1993) Epidemiology: Studies are available linking ingestion of chlorinated drinking water with increased cancer

rates, but these do not distinguish effects of BDCM from other disinfection byproducts. Considerations: Carcinogenicity: IARC - Group 2B, possibly carcinogenic to humans (1991); USEPA -

Group B2, probable human carcinogen (1993). Endocrine disruption: EU Priority List - not assessed (2006). Sources and treatment Sources: Byproduct of drinking water disinfection. Source control: Alteration of drinking water treatment processes. DWT efficacy: No information found WWT efficacy: 46.99% (modelled) Fate: No information found Uses: Only minimal quantities intentionally produced. Fate and modelling Cramer class: High (III) Log Kow: 2.00 (exper) Solubility: 3030 mg/L (30 deg C) (exper) VP: 57.4 mm Hg (mean of Antoine & Grain methods) HLC: 2.12E-03 atm-m3/mole (exper) BP: 90 deg C (exper) MP: -57 deg C (exper) Log BCF: 0.987 Log Koc: 1.78 (exper) HL Biota: No information found HL Sediment: No information found HL Water: hydrolysis rate constant k = 1.6 × 10–10 s–1 with t½ = 137 yr at pH 7 and 25°C pKa: 12.9 Toxicity Type Description Value In vivo – acute toxicity Mouse, Oral, Mortality LD50 = 450 mg/kgbw In vivo – acute toxicity Rat, Oral, Mortality LD50 = 430 mg/kgbw

Occurrences Type Country Value Purified recycled water Australia <2 ug/L Reclaimed water Australia 5 - 118 ug/L WWTP effluent Australia <2 ug/L WWTP effluent Australia <5 - 22 ug/L


9.4.36 Bromoform

G eneral information CASRN: 75-25-2 Chemical name: Bromoform Other name(s): Methane, tribromo- Related chemical: Dibromochloromethane, Bromochloroiodomethane, Bromodiiodomethane,

Dibromoiodomethane, Dichloroiodomethane, Chlorotribromomethane (Tribromochloromethane), Chlorodiiodomethane, Bromomethane (methyl bromide), Chloromethane, Methyl iodide (Iodomethane), Dibromomethane, Bromochloromethane, Dichloromethane, Bromodichloromethane, Dichloromonofluoromethane, Trichlorofluoromethane (Freon 11), Dichlorodifluoromethane, Chloroform, Iodoform

SMILES: C(Br)(Br)Br MW: 252.73 G uidelines AU AGWR: 100 ug/L AU ADWG: (250 ug/L combined total for THMs) WHO DWG: 100 ug/L EU DWG: (100 ug/L for total THMs) NZ DWG: 100 ug/L CA DWG: (100 ug/L for total THMs) US NPDWS: (80 ug/L for total THMs) Qld PHR: 100 ug/L ANZECC-Env: Not available ANZECC-PI: Not available ANZECC-HH: Not available Other: None found Registration information PUBCRIS: No ARTG: No AICS: Yes HVICL: No Listed in NPI: No Comment: Classified by NOHSC as a hazardous substance. Risk assessment NICNAS: Not available APVMA: Not available UN: IPCS EHC 216 (DBPs) (2000); IPCS ICSC 0108 (2004) Other: IRIS (On-line) Oral RfD Assessment (1991) & Carcinogenicity Assessment (1991);

ATSDR Tox Profile 130 (2005) Epidemiology: Studies are available linking ingestion of chlorinated drinking water with increased cancer

rates, but these do not distinguish effects of bromoform from other disinfection byproducts.

Considerations: Carcinogenicity: IARC - Group 3, not classifiable as to carcinogenicity in humans (1991); USEPA - Group B2, probable human carcinogen (1991).

Endocrine disruption: EU Priority List - not assessed (2006). Sources and treatment Sources: Primarily present in drinking water as a byproduct of disinfection. Source control: Alteration of drinking water treatment processes. For example, removal of precursors

from raw water sources prior to oxidative disinfection. DWT efficacy: May be produced by conventional DWT processes. WWT efficacy: 21.25% (modelled) Fate: No information found


Uses: Industrial uses include mineral ore separations, electronics and rubber manufacturing, as a solvent, or an intermediate in chemical syntheses.

Fate and modelling Cramer class: High (III) Log Kow: 2.40 (exper) Solubility: 3100 mg/L (exper) VP: 5.40 mm Hg (exper) HLC: 5.35E-04 atm-m3/mole (exper) BP: 149.1 deg C (exper) MP: 8.0 deg C (exper) Log BCF: 1.251 (regression-based method), 1.290 (Arnot-Gobas method) Log Koc: 2.06 (exper) HL Biota: No information found HL Sediment: No information found HL Water: No information found pKa: 11.8 Toxicity Type Description Value In vivo – acute toxicity Human, Oral, Lowest recorded lethal dose LDLo = 143 mg/kgbw In vivo – acute toxicity Mouse, Oral, Mortality LD50 = 1072 mg/kgbw In vivo – acute toxicity Rat, Oral, Mortality LD50 = 933 mg/kgbw Occurrences Type Country Value Purified recycled water Australia 0.01 - 0.02 ug/L Reclaimed water Australia LOD 1 - 5 ug/L WWTP effluent Australia <1 ug/L WWTP effluent Australia <1 ug/L WWTP effluent Australia 0.04 - 0.16 ug/L WWTP effluent Australia LOD 5 - 81 ug/L

9.4.37 Chloroform

General information CASRN: 67-66-3 Chemical name: Chloroform Other name(s): Trichloromethane Related chemical: Dibromochloromethane, Bromochloroiodomethane, Bromodiiodomethane,

Dibromoiodomethane, Dichloroiodomethane, Chlorotribromomethane (Tribromochloromethane), Chlorodiiodomethane, Bromomethane (methyl bromide), Chloromethane, Methyl iodide (Iodomethane), Dibromomethane, Bromochloromethane, Dichloromethane, Bromodichloromethane, Dichloromonofluoromethane, Trichlorofluoromethane (Freon 11), Dichlorodifluoromethane, Bromoform, Iodoform

SMILES: C(Cl)(Cl)Cl MW: 119.38 G uidelines AU AGWR: 200 ug/L AU ADWG: 250 ug/L (combined total for THMs) WHO DWG: 200 ug/L EU DWG: 100 ug/L (for total THMs)


NZ DWG: 200 ug/L CA DWG: 100 ug/L (for total THMs) US NPDWS: 80 ug/L (total THMs) Qld PHR: 200 ug/L ANZECC-Env: Trigger Values for Aquatic Ecosystems (ug/L for 99, 95, 90, 80% species protection):

Freshwater - 370 (low reliability); Marine - 370 (low reliability) ANZECC-PI: Not available ANZECC-HH: Not available Other: None found Registration information PUBCRIS: No ARTG: Yes AICS: Yes HVICL: No Listed in NPI: Yes Comment: Classified as a hazardous substance by the NOHSC. Risk assessment NICNAS: Not available APVMA: Not available UN: IPCS HSG 87 (1004); IPCS EHC 163 (1994); IPCS CICAD 58 (2004); IPCS PIM 121

(1993); IPCS ICSC 0027 (2004); JECFA FAS 14 Other: ATSDR Tox Profile 6 (1997); IRIS Tox Review 25 (2001); IRIS Oral RfD Assessment

(2001) and Carcinogenicity Assessment (2001); EC Draft RAR (2007) Epidemiology: Association between chronic exposure to chlorinated drinking water and increased

incidences of colon cancer, pancreatic cancer and bladder cancer. However, numerous other potential toxicants known to exist in chlorinated drinking water may easily account for these effects.

Considerations: Carcinogenicity: IARC - Group 2B, possibly carcinogenic to humans (1999) USEPA - Group B2, probable human carcinogen (2001).

Endocrine disruption: EU Priority List - not assesed (2006). Sources and treatment Sources: Primarily present in drinking water as byproduct of disinfection. Domestic and industrial

wastewaters. Hazardous waste sites, landfills and spills. Natural sources include marine macroalgae, soil biota under anaerobic conditions.

Source control: Process controls in drinking water treatment. For example, removal of precursors from raw water using activated carbon, coagulation followed by filtration, or by oxidation with ozone or potassium permanganate, or use of alternative disinfectants.

DWT efficacy: No information found. WWT efficacy: 59.81% (modelled) Fate: No information found. Uses: Industrial solvent and ingredient in various pharmaceutical and cosmetic preparations Fate and modelling Cramer class: High (III) Log Kow: 1.97 (exper) Solubility: 7950 mg/L (exper) VP: 1.97E+02 mm Hg (exper) HLC: 3.67E-03 atm-m3/mole (exper) BP: 61.1 deg C (exper) MP: -63.6 deg C (exper) Log BCF: 0.967 (regression-based method), 0.940 (Arnot-Gobas method) Log Koc: 1.6 (exper) HL Biota: t½ < 1 d in tissues of bluegill sunfish; t½ = 10–50 d, subject plant uptake via volatilization HL Sediment: No information found HL Water: Surface water: not important for aqueous phase; t½ = 1.0–31 d in various location in the

Netherlands in case of a first order reduction process; t½ = 672–4320 h, based on estimated aqueous aerobic biodegradation half-life; Groundwater: t½ = 1344–43200 h, based on unacclimated aqueous aerobic biodegradation and grab sample data of aerobic soil from a groundwater aquifer.

pKa: 24.1 Toxicity Type Description Value In vivo – acute toxicity Rat, Oral, Mortality LD50 = 450 - 2000 mg/kg bw In vivo – long-term Crustacean (D. magna), 16-d growth NOEC = 15 mg/L In vivo – long-term Freshwater algae, 8-d growth NOEC = 93 - 550 mg/L In vivo – long-term Marine diatoms, 5-d growth/biomass NOEC = 41 - 216 mg/L In vivo – reproductive Crustacean (C. dubia), 7-d Reproduction NOEC = 200 ug/L In vivo – reproductive Crustacean (D. magna), 21-d reproductive

impairment NOEC = 6.3 mg/L

In vivo – short-term Mouse (M), Oral, 15-d Mortality LD50 = 36 - 460 mg/kg bw


In vivo – short-term Mouse (F), Oral, 15-d Mortality LD50 = 353 - 1 366 mg/kg bw In vivo – short-term Crustacean (C. dubia), 7-d Mortality NOEC = 2.4 mg/L Occurrences Type Country Value Purified recycled water Australia 0.16 - 0.20 ug/L Reclaimed water Australia LOD 5 - 107 ug/L WWTP effluent Australia <1 ug/L WWTP effluent Australia <1 ug/L WWTP effluent Australia 0.13 - 0.37 ug/L

9.4.38 Dibromochloromethane

General information CASRN: 124-48-1 Chemical name: Dibromochloromethane Other name(s): Chlorodibromomethane; DBCM Related chemical: Bromochloroiodomethane, Bromodiiodomethane, Dibromoiodomethane,

Dichloroiodomethane, Chlorotribromomethane (Tribromochloromethane), Chlorodiiodomethane, Bromomethane (methyl bromide), Chloromethane, Methyl iodide (Iodomethane), Dibromomethane, Bromochloromethane, Dichloromethane, Bromodichloromethane, Dichloromonofluoromethane, Trichlorofluoromethane (Freon 11), Dichlorodifluoromethane, Chloroform, Bromoform, Iodoform

SMILES: C(Cl)(Br)Br MW: 208.28 G uidelines AU AGWR: 100 ug/L AU ADWG: 250 ug/L (combined total for THMs) WHO DWG: 100 ug/L EU DWG: 100 ug/L (for total THMs) NZ DWG: 150 ug/L CA DWG: 100 ug/L (for total THMs) US NPDWS: 80 ug/L Qld PHR: 100 ug/L ANZECC-Env: Not available ANZECC-PI: Not available ANZECC-HH: Not available Other: None found Registration information PUBCRIS: No ARTG: No AICS: No HVICL: No Listed in NPI: No Risk assessment NICNAS: Not available APVMA: Not available UN: Not available


Other: ATSDR Tox Profile 130 (2005); IRIS Oral RfD Assessment (1991) and Carcinogenicity Assessment (1992) Epidemiology: Studies are available linking ingestion of chlorinated drinking water with increased cancer

rates, but these do not distinguish effects of DBCM from other disinfection byproducts. Considerations: Carcinogenicity: IARC - Group 3, not classifiable as to carcinogenicity in humans (1999)

USEPA - class C, possible human carcinogen (1992) Endocrine disruption: EU Priority List - not assessed (2006). Other: Liver appears to be the most sensitive target organ (2005). Sources and treatment Sources: Byproduct of drinking water disinfection. Source control: Alteration of drinking water treatment processes. DWT efficacy: No information found WWT efficacy: 26.73% (modelled) Fate: No information found Uses: No current commercial use. Fate and modelling Cramer class: High (III) Log Kow: 2.16 (exper) Solubility: 2700 mg/L (20 deg C) (exper) VP: 15.6 mm Hg (mean VP of Antoine & Grain methods) HLC: 7.83E-04 atm-m3/mole (exper) BP: 120 deg C (exper) MP: -20 deg C (exper) Log BCF: 1.092 (regression-based method); 1.107 (Arnot-Gobas method) Log Koc: 1.92 (exper) HL Biota: No information found HL Sediment: No information found HL Water: Surface water: t½ = 274 yr at pH 7 and 25°C based on hydrolysis rate constant; t½ =

672–4320 h, based on aerobic screening test data; Groundwater: t½ = 336–4320 h, based on estimated aqueous aerobic and anaerobic biodegradation half-life

pKa: No information found Toxicity Type Description Value In vivo – acute toxicity Mouse, Oral, Mortality LD50 = 800 mg/kgbw In vivo – acute toxicity Rat, Oral, Mortality LD50 = 370 mg/kgbw In vivo – acute toxicity Freshwater fish, Carp (Cyprinus carpio), 3-5 day,


In vivo – acute toxicity Freshwater invertebrate, Ciliate (Tetrahymena pyriformis), 24-h Population growth

EC50 = 65 000 ug/L

In vivo – long-term Rats, Subchronic Gavage Bioassay - Hepatic lesions (Dose adjusted for gavage schedule (5\days/week))

NOAEL = 21.4 mg/kgbw/day

In vivo – long-term Rats, Subchronic Gavage Bioassay - Hepatic lesions (Dose adjusted for gavage schedule (5\days/week))

LOAEL = 42.9 mg/kgbw/day

Occurrences Type Country Value Reclaimed water Australia LOD 5 - 22 ug/L WWTP effluent Australia <1 ug/L WWTP effluent Australia <1 ug/L WWTP effluent Australia 0.04 - 0.12 ug/L


9.4.39 N-Nitrosodimethylamine (NDMA)

G eneral information CASRN: 62-75-9 Chemical name: N-Nitrosodimethylamine (NDMA) Other name(s): Methanamine, N-methyl-N-nitroso-; N,N-dimethylnitrous amide Related chemical: N-Nitrosodiethylamine, N-Nitrosodi-n-propylamine (N-nitrosodipropylamine; NDPA), N-

Nitrosodiphenylamine, N-nitrosodi-n-butylamine (NDBA), Dicyclohexylnitrosamine (N-nitroso-dicyclohexylamine, NDcHxA)

SMILES: N(N=O)(C)C MW: 74.08 G uidelines AU AGWR: 0.01 ug/L AU ADWG: Not available WHO DWG: 0.1 ug/L EU DWG: Not available NZ DWG: Not available CA DWG: Not available US NPDWS: Not available Qld PHR: 0.01 ug/L ANZECC-Env: Not available ANZECC-PI: Not available ANZECC-HH: Not available Other: California OEHHA: 0.003 ug/L Registration information PUBCRIS: No ARTG: No AICS: Yes HVICL: No Listed in NPI: No Risk assessment NICNAS: Not available APVMA: Not available UN: IPCS CICAD 38 (2002); IPCS ICSC 0525 (2005) Other: ATSDR Tox Profile 141 (1989); IRIS Carcinogenicity Assessment (1993) Epidemiology: No information found Considerations: Carcinogenicity: IARC - Group 2A, probably carcinogenic to humans (1978); USEPA -

class B2, probable human carcinogen (1993). Endocrine disruption: EU Priority List - not assessed (2006). Other: Hepatoxicity (liver) (1989). Sources and treatment Sources: Byproduct of drinking water disinfection (primarily chloramination). Source control: Alteration of drinking water treatment processes. DWT efficacy: Difficult to remove WWT efficacy: 1.95% (modelled) Fate: No information found Uses: Not used directly - an industrial by-product or waste product of several industrial

processes. Fate and modelling Cramer class: High (III) Log Kow: -0.57 (exper) Solubility: 1E+006 mg/L (24 deg C) (exper)


VP: 2.70 mm Hg (20 deg C) (exper) HLC: 1.82E-06 atm-m3/mole (exper) BP: 154 deg C (exper) MP: <25 deg C (exper) Log BCF: 0.5 (regression-based method), -0.044 (Arnot-Gobas method) Log Koc: 1.358 (MCI method); 0.566 (Kow method) HL Biota: No information found HL Sediment: No information found HL Water: Surface water: t½ = 0.5–1.0 h, based on measured rate of photolysis in the vapor phase

under sunlight; Groundwater: t½ = 1008–8640 h, based on estimated unacclimated aqueous aerobic biodegradation half-life

pKa: 9.3, <1.0 Toxicity Type Description Value In vivo – acute toxicity Freshwater fish, Fathead minnow (Pimephales

promelas), 96-h Mortality LC50 = 940 mg/L

In vivo – acute toxicity Freshwater invertebrates (Dugesia dorotocephala), 96-h Mortality

LC50 = 1365 mg/L

In vivo – acute toxicity Salwater crustacean (Gammarus limnaeus), 96-h Mortality

LC50 = 280 - 445 mg/L

In vivo – acute toxicity Freshwater fish, Mummichog (Fundulus heteroclitus), 24-120 h, Mortality

LC50 = 2700 - 8300 mg/L

In vivo – acute toxicity Rat, Oral, Mortality LD50 = 23 - 40 mg/kgbw In vivo – long-term Freshwater alga (Selenastrum capricornutum), 13-

d growth EC50 = 4 mg/L

In vivo – long-term Freshwater alga (Anabaena flos-aquae), 13-d growth

EC50 = 5.1 mg/L

Occurrences Type Country Value Purified recycled water United States 551 ng/L Purified recycled water Australia 10 ng/L Surface waters Japan 1.0 - 9 300 ng/L WWTP effluent Japan 11 - 33 000 ng/L WWTP effluent Australia 4 - 21 ng/L

A national approach to health risk assessment, risk communication and management of chemical hazards...

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