Biomonitoring of pesticides by pine needles — Chemical scoring, risk of exposure, levels and...

Science of the Total Environment 476ndash477 (2014) 114ndash124

Contents lists available at ScienceDirect

Science of the Total Environment

j ourna l homepage wwwe lsev ie r com locate sc i totenv

Biomonitoring of pesticides by pine needles mdash Chemical scoring risk ofexposure levels and trends

Nuno Ratola ab Vera Homem b Joseacute Avelino Silva b Rita Arauacutejo b Joseacute Manuel Amigo cLuacutecia Santos b Arminda Alves b

a University of Murcia Physics of the Earth Campus de Espinardo 30100 Murcia Spainb LEPABEmdash Laboratory for Process Engineering Environment Biotechnology and Energy Faculty of Engineering University of Porto Rua Dr Roberto Frias 4200-465 Porto Portugalc Department of Food Science Spectroscopy and Chemometrics Faculty of Sciences University of Copenhagen Rolighedsvej 30 DK-1958 Frederiksberg C Denmark

H I G H L I G H T S

bull The concentration of 18 pesticides in pine needles reached 560 ng gminus1 (dw)bull Triazines were the predominant chemical family but molinate posed the highest riskbull Herbicides showed a much stronger presence than acaricides and insecticidesbull Rural sites showed the highest levels of total pesticidesbull Urban industrial and rural areas presented similar distribution of pesticides

Corresponding author Tel +34 868888552E-mail address nrnetoumes (N Ratola)

0048-9697$ ndash see front matter copy 2014 Elsevier BV All rihttpdxdoiorg101016jscitotenv201401003

a b s t r a c t

a r t i c l e i n f o

Article historyReceived 26 November 2013Received in revised form 2 January 2014Accepted 2 January 2014Available online xxxx

KeywordsPesticidesPine needlesBiomonitoringChemical scoringRisk of exposure

Vegetation is a useful matrix for the quantification of atmospheric pollutants such as semi-volatile organiccompounds (SVOCs) In particular pine needles stand out as effective biomonitors due to the excellent uptakeproperties of their waxy layer Having previously validated an original and reliable method to analyse pesticidesin pine needles our work team set the objective of this study to determine the levels of 18 pesticides in Pinuspinea needles collected in 12 different sampling sites in Portugal These compounds were selected among atotal of 70 pesticides by previous chemical scoring developed to assess their probability to occur in theatmosphere The risk of exposure was evaluated by the binomial chemical scorefrequency of occurrence inthe analysed samples Levels and trends of the chemical families and target of the pesticides were obtainedregarding the type of land occupation of the selected sites including the use of advanced statistics (principalcomponent analysis PCA) Finally some correlations with several characteristics of the sampling sites(population energy consumption meteorology etc) were also investigated

copy 2014 Elsevier BV All rights reserved

1 Introduction

Pesticides play an important role in modern agriculture ensuringstable and predictable food supplies and high productivity TheEuropean Union Pesticide Database lists 1297 active ingredients thatare commercialized in whole range of commercial phytosanitaryproducts (EUPD 2013) Portugal due to favourable climatic andgeographic conditions possesses a great variety of agricultural activityand therefore a relevant number of associated phytosanitary problemswhich require the use of a great diversity of pesticides to control themCurrently 907 phytopharmaceutical products comprising 248 activesubstances are authorized for use by the Portuguese National Authority

ghts reserved

for Animal Health Phytosanitation and Food (Portuguese NationalAuthority for Animal Health Phytosanitation and Food 2013) In2005 the total amount of active ingredients reported to be sold was16353 t (Vieira 2012)

Due to their inherent toxicity pesticides may affect non-targetorganisms causing undesirable side-effects on some species communi-ties or the ecosystem as a whole (van der Werf 1996) Their intensiveuse led to ubiquitous contamination of key environmental com-partments such as water soil and air (Belden et al 2012 Coscollagraveet al 2010 Koumlck-Schulmeier et al 2014 Lamprea and Ruban 2011Marcomini et al 1991 Sarigiannis et al 2013 Stangroom et al 2000Yusagrave et al 2009) Input vectors may be the application of phytosanitaryproducts onto farming or forestry areas (Asman et al 2005) volatiliza-tion from the surface of soil water and vegetation (Bidleman 1999) oreven from thermal waste treatment (incineration) of contaminatedwaste (Breivik et al 2004)

115N Ratola et al Science of the Total Environment 476ndash477 (2014) 114ndash124

Being mostly organic compounds pesticides may therefore be de-graded by biotic or abiotic processes Biotransformation of pesticides iscarried out by living organisms (such as bacteria and fungi) and involvesnumerous biochemical transformation reactions such as biodegrada-tion cometabolism and synthesis (Chaplain et al 2011) Abiotic degra-dation is due to chemical or photochemical reactions Chemicalprocesses may be hydrolysis (eventually catalysed by acids bases ormetal ions) or redox reactions Photolysis on the other handmaybedi-rect when the pesticide absorbs light and undergoes transformation orindirect when other chemical species become electronically exciteddue to photon absorption and react with the contaminant (Zeng andArnold 2013) In water and soils both degradation processes (bioticand abiotic) may occur simultaneously inmechanistically complex pro-cesses (Krieger 2001)

The effects of these and other contaminants must be continuouslymonitored and vegetation is a useful matrix for the assessment ofthese airborne pollutants As opposed to other passive samplers moni-toring using vegetation avoids previous sampling site set-up since theymay act as ldquobiological loggersrdquo of pollution Furthermore the use of veg-etation is the best tool for estimation of the atmospheric contaminationlevels at remote or poorly accessible locations In particular pineneedles stand out as effective biomonitors due to the excellent uptakeproperties of their waxy layer (Xu et al 2004) The needles surface isconstituted by a thick cuticle (di- and trihydroxy fatty acids) and a cutic-ular wax (lipids free fatty acids n-alkanes n-alkenes primary alcoholsαω-diols ketones andω-hydroxyacids) (Dolinova et al 2004 Klaacutenovaacuteet al 2009) The epicuticular wax layer acts as a barrier between theplant and its environment protecting the plant against ultraviolet radi-ation desiccation and possible attacks from pathogens Low and medi-um polar compounds have a high affinity towards this wax layer dueto their chemical composition and therefore they are effectivelyentrapped for several years Different studies prove that pine needlesare effective in the quantification of several semi-volatile organiccontaminants such as polycyclic aromatic hydrocarbons (PAHs)

Fig 1 Sampling map

(Amigo et al 2011 Piccardo et al 2005 Ratola et al 2010Tremolada et al 1996) polybrominated diphenyl ethers (PBDEs)(Ratola et al 2011) polychlorinated biphenyls (PCBs) (Grimalt andvan Drooge 2006) and organochlorine pesticides (OCPs) (Hellstroumlmet al 2004) Recently our work group developed and validated an ana-lytical method to assess pesticides of several chemical classes Thisallowed the establishment of the current study aiming to assess theirenvironment behaviour on a larger scale (Arauacutejo et al 2012)

Using appropriate correlations it is possible to estimate pollutantpesticide concentrations in several environmental matrices and esti-mate their environmental impact Although a holistic approach com-prising all pesticides would be desirable limited resources (timeanalytical capacities etc) make this approach hardly possible There-fore a judicious selection of the pesticides to be monitored should bedone in order to prioritise research efforts to the most relevant onesChemical scoring and ranking is a useful method to make this kind ofprioritisation Several models have been developed most of thembased not only on physicochemical but also toxicological properties ofthe compounds although lack of data is often a limitation to their appli-cation (Juraske et al 2007 Snyder et al 2000) In this work a simplechemical scoring methodology was developed in order to identifypotentially relevant pesticides The ranking list together with the capa-bilities shown by themethodology reported by Arauacutejo et al (2012) wasused to implement a monitorisation plan employing pine needlescollected in areas of different land occupations Data obtained frommonitorisation was also combined with the results of chemical scoringin order to assess the risk of exposure Levels trends and relationshipswith some characteristics of the pesticides and of the sampling siteswere investigated including the use of principal component analysis(PCA) to assess the environmental impact of these compounds

2 Materials and methods

21 Chemical scoring strategy

In this study a simple chemical scoringmethodologywas developedto identify a reduced number of pesticides considered ldquopotentiallyrelevantrdquo in the air compartment and therefore reduce the analyticalefforts for further monitoring

Seventy pesticides were chosen for this study on the basis of salesvolumes in the Portuguese market (Vieira 2005) Among them someof the most relevant pesticides revealed in the prioritisation were cho-sen to be monitored using pine needles as passive bio-samplers sincethis matrix reflects the levels existing in the atmosphere Unfortunatelydue to logistic and budget constraints it was not possible to include inthe study all the pesticides with the highest scores leading to the selec-tionmade in the end A database including all the data identified as nec-essary to carry out the chemical scoring processwas compiled Datawascollected from (Q)SAR modelling software EPI Suite (EPA 2011)

The ranking of the compounds was established according to theirphysicochemical properties The criteria used to rank the persistenceof pesticides in air are summarised in Fig 2 Threemain criteriawere de-fined taking into account the probability of pesticides to occur in the aircompartment air persistence volatilisation potential (waterndashair andsoilndashair) and deposition potential Scores for each criterion wereassigned between 1 (low probability of occurrence in air) and 5 (veryhigh probability) The atmospheric persistence of the compounds wasevaluated by their half-life time in air (t12) The values were estimatedbased on reactions with hydroxyl radicals and ozone According to thischemical scoring methodology compounds with t12 air in the rangeof hours are short-lived in air (low score) and those present for longerthan 40 days are considered extremely persistent (IEH 2004)

The Henrys law constant (Hcprime) defined as the ratio of a chemicalsconcentration in air to its concentration inwater at equilibrium(dimen-sionless form) was used to determine the tendency to volatilise fromthe aqueous to the gas phase As can be seen in Fig 2 compounds

Fig 2 Profile assessment criteria and scoring scheme

116 N Ratola et al Science of the Total Environment 476ndash477 (2014) 114ndash124

with Hcprime values above 10minus6 are susceptible to volatilisation while thosewith values below10minus7 are considered non-volatile (IEH 2004 VanderWerf 1996) The volatilisation potential from soil to air was evaluatedby the partitioning coefficient soilndashair (KSA) This parameter was esti-mated according to the following equation (Guth et al 2004)

KSA frac14 Csoil

Cairfrac14 Cwater

Cair

1rthorn kd

frac14 1

Hcprime

1rthorn kd

ethEqeth1THORNTHORN

where r is the weight of soilweight of water (for these calculations aratio of 6 was used corresponding to a soil moisture of approximately17) kd is the distribution coefficient characterising the partitioningof a chemical between soil and soilndashwater and Hcprime is the dimensionlessHenrys law constant The parameter kd was calculated from KOC thechemical partitioning coefficient between the organic carbon in soiland the system soilndashwater (L kgminus1) (Guth et al 2004)

kd frac14 KOC

1724 OC

100ethEqeth2THORNTHORN

where OC is the organic carbon content of the soil (it was considered20 due to the average content determined in Portuguese soils) and1724 is the conversion factor for soil organic carbon in soil organicmat-ter (Rusco et al 2001) The criteria used to classify the volatilisation

potential from soil to air of pesticides using the log KSA are describedin Fig 2 (Davie-Martin et al 2013) ie pesticides with log KSA b 6have relatively low affinities for the soil phase and therefore have agreater ability to volatilise (highest score) In contrast pesticides withlog KSA N 8 exhibited strong adsorption to soil and low volatilisation po-tential Finally the deposition potential was evaluated by the pesticidesfraction sorbed to airborne particles (Φ) This parameter was calculatedfrom the Mackay adsorption model (Harner and Bidleman 1998)

Φ frac14 kpa TSP1thorn kpa TSP

ethEqeth3THORNTHORN

where kpa is the particle-gas partition coefficient (m3 μgminus1) and TSP isthe total concentration of suspended particles (μg mminus3) In this case avalue of 80 μg mminus3 was considered The kpa was determined by the fol-lowing equation (Boethling et al 2004)

kpa frac14300 10minus6

PslethEqeth4THORNTHORN

where Psl is the saturation liquid-phase vapour pressure of the com-pound (Pa) This parameter was estimated by the EPI Suite software(EPA 2011) According to these criteria (Fig 2) if the fraction sorbedto airborne particles is more than 90 deposition is very high and

Table 1Overall rating of pesticides in the prioritisation scheme for monitoring in the air compartment

Pesticide name Function Chemical class Persistence Volatilisation potential Depositionpotential

Total score Relevance in air

Waterndashair Soilndashair

t12 air (day) Score Hc (atm m3molminus1) Hcprime Score Log Koc kd Log KSA Score Φ () Score

1 Trifluralin H Dinitroaniline 045 3 212 times 10minus4 868 times 10minus3 5 422 19032 434 5 222 5 18 Very high2 13-Dichloropropene N Fu Organochlorine 073 3 245 times 10minus2 100 times 101 5 186 084 000 5 000 5 18 Very high3 Molinate H Carbamate 035 2 678 times 10minus6 277 times 10minus4 5 226 211 391 5 003 5 17 Very high4 EPTC H Carbamate 034 2 154 times 10minus5 630 times 10minus4 5 222 190 352 5 001 5 17 Very high5 Chlorpyrifos A I Organophosphorus 012 2 252 times 10minus6 103 times 10minus4 5 386 8443 591 4 568 5 16 High6 Chlorothalonil F Aromatic 172917 5 152 times 10minus7 622 times 10minus6 3 302 1209 629 3 184 5 16 High7 Thiram F R M Carbamate 003 1 268 times 10minus5 110 times 10minus3 5 279 709 382 5 053 5 16 High8 24-D H M Phenoxy 161 3 354 times 10minus8 145 times 10minus6 3 147 034 555 4 016 5 15 High9 Pendimethalin H Dinitroaniline 035 2 145 times 10minus6 593 times 10minus5 4 375 6509 604 3 288 5 14 High10 Cypermethrin I Pyrethroid 050 3 789 times 10minus7 323 times 10minus5 4 490 92575 746 2 004 5 14 High11 α-Cypermethrin I Pyrethroid 050 3 789 times 10minus7 323 times 10minus5 4 490 92575 746 2 004 5 14 High12 Parathion-ethyl A I Organophosphorus 012 2 296 times 10minus7 121 times 10minus5 4 338 2809 637 3 2120 4 13 Moderate13 Diazinon A I R Organophosphorus 011 2 873 times 10minus8 357 times 10minus6 3 348 3520 700 3 196 5 13 Moderate14 Mancozeb F Carbamate 005 2 564 times 10minus7 231 times 10minus5 4 278 705 550 4 3080 3 13 Moderate15 Triclopyr H Pyridine 221 3 514 times 10minus9 210 times 10minus7 2 172 061 657 3 783 5 13 Moderate16 Chlorfenvinphos A I Organophosphorus 018 2 517 times 10minus8 212 times 10minus6 3 310 1467 685 3 1940 4 12 Moderate17 Methomyl A I M Carbamate 161 3 202 times 10minus9 827 times 10minus8 1 100 012 653 3 905 5 12 Moderate18 Alachlor H Chloroacetanilide 024 2 223 times 10minus8 913 times 10minus7 2 250 363 662 3 604 5 12 Moderate19 Linuron H Urea 103 3 115 times 10minus8 471 times 10minus7 2 253 394 694 3 2110 4 12 Moderate20 Terbuthylazine H Al Triazine 097 3 594 times 10minus9 243 times 10minus7 2 250 369 720 2 470 5 12 Moderate21 MCPA H M Phenoxy 085 3 133 times 10minus9 544 times 10minus8 1 147 034 697 3 339 5 12 Moderate22 Parathion-methyl I Organophosphorus 018 2 168 times 10minus7 688 times 10minus6 3 286 846 610 3 2880 4 12 Moderate23 Pirimicarb I Carbamate 007 2 262 times 10minus9 107 times 10minus7 2 175 065 688 3 526 5 12 Moderate24 Methidathion A I Organophosphorus 007 2 710 times 10minus9 291 times 10minus7 2 133 025 615 3 2800 4 11 Moderate25 Phosmet A I Organophosphorus 007 2 902 times 10minus9 369 times 10minus7 2 100 012 588 4 5570 3 11 Moderate26 Cymoxanil F Aliphatic nitrogen 178 3 331 times 10minus10 135 times 10minus8 1 116 017 739 2 679 5 11 Moderate27 Folpet F Dicarboximide 068 3 154 times 10minus9 630 times 10minus8 1 125 021 677 3 2650 4 11 Moderate28 Amitrole H Triazole 194 3 540 times 10minus10 221 times 10minus8 1 038 003 694 3 1620 4 11 Moderate29 Paraquat H Quat ammonium 050 4 322 times 10minus13 132 times 10minus11 1 351 3711 1245 1 019 5 11 Moderate30 Terbutryn H M Triazine 100 3 909 times 10minus9 372 times 10minus7 2 278 704 729 2 1500 4 11 Moderate31 Carbaryl I Carbamate 041 2 314 times 10minus9 128 times 10minus7 2 255 412 752 2 793 5 11 Moderate32 Endosulfan A I Organochlorine 130 3 903 times 10minus8 370 times 10minus6 3 383 7843 733 1 3210 3 10 Moderate

(continued on next page)

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etalScienceofthe

TotalEnvironment476

ndash477(2014)

114ndash124

Table 1 (continued)





33 Carbofuran A I N M Carbamate 041 2 163 times 10minus9 667 times 10minus8 1 198 111 728 2 017 5 10 Moderate34 Metalaxyl F Amide 040 2 805 times 10minus10 329 times 10minus8 1 159 045 727 2 677 5 10 Moderate35 Ametryn H Triazine 038 2 685 times 10minus9 280 times 10minus7 2 263 497 726 2 1350 4 10 Moderate36 Atrazine H Triazine 039 2 447 times 10minus9 183 times 10minus7 2 235 260 718 2 1760 4 10 Moderate37 Metolachlor H Chloroacetanilide 019 2 149 times 10minus9 610 times 10minus8 1 269 567 798 2 542 5 10 Moderate38 Prometryn H Triazine 028 2 909 times 10minus9 372 times 10minus7 2 282 761 732 2 1460 4 10 Moderate39 Propanil H Amide 283 3 450 times 10minus9 184 times 10minus7 2 225 204 708 2 3010 3 10 Moderate40 Propazine H Triazine 029 2 594 times 10minus9 243 times 10minus7 2 254 399 723 2 1600 4 10 Moderate41 Simazine H Triazine 097 3 337 times 10minus9 138 times 10minus7 2 217 170 713 2 4560 3 10 Moderate42 Bromoxynil H M Nitrile 5083 5 860 times 10minus10 352 times 10minus8 1 252 383 806 1 4710 3 10 Moderate43 Tetradifon A Bridged diphenyl 3025 4 232 times 10minus8 949 times 10minus7 2 423 19611 832 2 9980 1 9 Low44 Amitraz A I Formamidine 008 2 148 times 10minus8 606 times 10minus7 2 541 29819 969 1 1830 4 9 Low45 Azinphos-methyl A I Organophosphorus 007 2 286 times 10minus10 117 times 10minus8 1 172 060 782 2 2740 4 9 Low46 Dimethoate A I M Organophosphorus 014 2 211 times 10minus11 863 times 10minus10 1 111 015 856 1 492 5 9 Low47 Benalaxyl F Amide 039 2 784 times 10minus10 321 times 10minus8 1 351 3754 907 1 500 5 9 Low48 Ofurace F Amide 046 3 151 times 10minus9 618 times 10minus8 1 226 212 757 2 4360 3 9 Low49 Propamocarb F Carbamate 011 2 134 times 10minus10 548 times 10minus9 1 200 117 839 1 000 5 9 Low50 Ziram F R Carbamate 008 2 174 times 10minus9 712 times 10minus8 1 305 1293 826 1 763 5 9 Low51 Chloridazon H Pyrazole 026 2 654 times 10minus12 268 times 10minus10 1 259 450 1024 1 623 5 9 Low52 Diuron H Urea 098 3 533 times 10minus10 218 times 10minus8 1 204 127 782 2 5570 3 9 Low53 Metribuzin H Triazinone 059 3 181 times 10minus12 741 times 10minus11 1 173 062 1002 1 2930 4 9 Low54 Acephate I Organophosphorus 096 3 281 times 10minus12 115 times 10minus10 1 100 012 939 1 2010 4 9 Low55 Dicofol A Bridged diphenyl 312 3 559 times 10minus10 229 times 10minus8 1 410 14706 981 1 5770 3 8 Low56 Azinphos-ethyl A I Organophosphorus 006 2 504 times 10minus10 206 times 10minus8 1 224 200 802 1 2840 4 8 Low57 Malathion A I Organophosphorus 014 2 839 times 10minus10 343 times 10minus8 1 150 036 719 2 3470 3 8 Low58 Fenarimol F Pyrimidine 272 3 421 times 10minus13 172 times 10minus11 1 422 19342 1305 1 4900 3 8 Low59 Captan F B Dicarboximide 004 1 459 times 10minus9 188 times 10minus7 2 240 293 722 2 3810 3 8 Low60 Isoproturon H Urea 089 3 189 times 10minus9 773 times 10minus8 1 230 231 751 2 7790 2 8 Low61 Deltamethrin I M Pyrethroid 045 3 606 times 10minus8 248 times 10minus6 3 490 92575 857 1 9560 1 8 Low62 Dodine F Aliphatic nitrogen 010 2 602 times 10minus19 246 times 10minus17 1 340 2881 1807 1 4890 3 7 Low63 Chlortoluron H Urea 027 2 794 times 10minus10 325 times 10minus8 1 204 127 764 2 7520 2 7 Low64 Dinocap F A Dinitrophenol 031 2 669 times 10minus9 274 times 10minus7 2 479 71366 942 1 9060 1 6 Very low65 Carbendazim F M Carbamate 005 2 149 times 10minus12 610 times 10minus11 1 258 445 1088 1 8210 2 6 Very low66 Glyphosate H Organophosphorus 014 2 408 times 10minus19 167 times 10minus17 1 000 001 1603 1 8710 2 6 Very low67 Metamitron H Triazinone 055 3 585 times 10minus12 239 times 10minus10 1 215 165 988 1 9170 1 6 Very low68 Tebuconazole F Triazole 093 3 518 times 10minus10 212 times 10minus8 1 319 178 893 1 96 1 6 Very low69 Benomyl F Mi Benzimidazole 005 2 123 times 10minus12 503 times 10minus11 1 253 390 1091 1 9730 1 5 Very low70 Dimethomorph F Morpholine 003 1 101 times 10minus15 413 times 10minus14 1 376 66 152 1 937 1 4 Very low

A mdash acaricide Almdash algericide B mdash bactericide F mdash fungicide Fu mdash fumigant H mdash herbicide I mdash insecticide M mdash metabolite Mi mdash miticide N mdash nematicide R mdash repellent

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etalScienceofthe

TotalEnvironment476

ndash477(2014)

114ndash124


therefore the probability of the pesticide be found in air is very low Onthe other hand if theΦ is lower than 10 the pesticides have low depo-sition potential and may remain in the atmosphere

A simple additive ranking (SAR) method was used consideringequal weights for each criterion and the individual scores assignedto each criterion were added to produce an overall score (maxscore = 20) This approach was chosen due to the lack of informationregarding the relevance of each criterion of this complex system andin order to avoid ranking bias by assigning inappropriate weight factorsPesticides falling into the lsquoVery highrsquo (score 17ndash20) and lsquoHighrsquo (score14ndash17) ranges were considered to have the greatest potential to remainin the air after use Compounds with scores between 10 and 14 canmoderately remain in air while a score of 7ndash10 (lsquoLowrsquo) and 4ndash7 (lsquoVerylowrsquo) does not tend to stay in this matrix

22 Risk of exposure

The risk of exposure consists of a combination of two factors firstthe probability of the pesticide to occur in the air due to its physico-chemical properties (previously evaluated by the chemical scoring pro-cedure) and secondly by its real presence in the air (measured by thepine needlesmonitoring) In order that the risk of exposure becomes ef-fective both factors must occur simultaneously ie a pesticide mayhave a high chemical scoring and therefore a high probability to existin the air but when no samples are positively detected for this com-pound it does not constitute any potential risk

For the studied pesticides a risk of exposure chart was created plot-ting the evaluated chemical score versus the frequency of occurrenceFive exposure levels were calculated as the product between the twoformer mentioned parameters lsquoVery lowrsquo (00ndash14) lsquoLowrsquo (14ndash42)lsquoModeratersquo (42ndash82) lsquoHighrsquo (82ndash134) and lsquoVery highrsquo (134ndash200)

23 Reagents and chemicals

Standards were prepared from a Dr Ehrenstorfer (AugsburgGermany) 20 pesticides mixture (Mix-101 at 50 ng μLminus1 in acetoni-trile) which contained the target compounds for this study alachlorametryn atrazine chlorpyrifos diazinon malathion metolachlormolinate parathion-ethyl parathion-methyl pendimethalin piri-micarb prometryn propazine simazine terbuthylazine terbutryn andtrifluralin The internal standard was triphenyl phosphate (TPP 99)purchased from Sigma Aldrich (St Louis MO USA) The solvents used(acetonitrile and dichloromethane) were 998 Pestinorm grade fromProlabo (West Chester PA USA) Nitrogen (99995) for drying andHelium (999999) for GCMS operation were from Air Liquide (MaiaPortugal) The SPE cartridges were Supelclean ENVI 18 (500 mg3 mL) from Supelco (Bellefonte PA USA)

24 Sampling strategy

As shown in Fig 1 12 sites in Portugal representing different landuses were chosen to sample Pinus pinea needles in one single campaignperformed in 2006 Braga Coimbra Leiria Lisboa and Eacutevora (urban)Souselas Outatildeo and Sines (industrial) and Antuatilde Quintatildes Alcoutimand Louleacute (rural) The needles which had at least one year of exposureto the surrounding atmosphere were collected whole from the lowerbranches of the trees and immediately wrapped in aluminium foilsealed in plastic bags frozen and kept protected from light untilextraction

25 Extraction and analysis of pine needles

The analytical methodology employed in the current study has beendescribed previously (Arauacutejo et al 2012) In brief five grammes ofneedles (cut into 1 cm bits) were inserted in a glass tube spiked withthe internal standard (100 μg Lminus1) and extracted in a 360 W Selecta

ultrasonic bath (JP Selecta Barcelona Spain) for 10 min with 30 mLacetonitrile This procedure was repeated three times with fresh sol-vent The sonicated extracts were combined and then reduced toabout 05 mL in a rotary evaporator and purified following a clean-upprocedure using 500 mg of ENVI 18 cartridges conditioned with 5 mLof acetonitrile The samples were then eluted with 50 mL of acetonitrileand the extracts reduced to 05 mL and transferred to 2-mL amber glassvials using dichloromethane Finally extracts were blown-down todryness under a gentle nitrogen stream and reconstituted in 1 mL ofacetonitrile

Quantificationwas doneby gas chromatographyndashmass spectrometry(GCMS) using a Varian 3800 GC coupled with a Varian 4000 Ion TrapMass Spectrometer (Lake Forest CA USA) injecting 1 μL of sample insplitless mode under a helium (999999) flow rate of 10 mL minminus1The GC column was a FactorFour VF-5MS (30 m times 025 mm ID times025 μm film thickness) also from Varian and the temperature pro-gramme started at 60 degC (held for 1 min) then was raised at50 ordmC minminus1 to 160 degC at 25 degC minminus1 until 200 degC to 250 degC at50 degC minminus1 to 270 degC at 25 degC minminus1 and finally to 300 degC at50 degC minminus1 Acquisition was performed under in selected ion storage(SIS) mode and the temperatures of the trap transfer line andmanifoldwere 200 degC 250 degC and 50 degC respectively

26 Quality assurancequality control

The limits of detection (LODs calculated by the signal-to-noise ratio)ranged from 013 to 556 ng gminus1 (dry weight) for trifluralin andametryn respectively in linewith similar approaches applied to relatedcompounds (Martiacutenez et al 2004 Ratola et al 2006) Good repeatabil-ity was obtained and the mean values for the intermediate precisionwere 13 plusmn 10 Recoveries found were between 50 and 105 with amean value of 72 but the final results were not corrected accordinglyfollowing the commonprocedure in biomonitoring studieswhen recov-eries are not very low Procedural blanks (only solvent spiked withthe deuterated standards with no needles throughout the wholeextractionclean-upanalysis process) were done periodically butthe values for the concentration of the target compounds were belowthe LODs

3 Results and discussion

The target pesticides chosen for this study cover a range of fivechemical families (organophosphates triazines dinitroanilines chloro-acetamides carbamates) and three main functions (herbicides insecti-cides and acaricides) and were selected after a chemical scoringprocedure

31 Chemical scoring of pesticides

The objective of the chemical scoring was to classify a series ofpesticides according to their lsquorelevancersquo in the atmosphere A total of70 pesticides were included in this prioritisation scheme (about 40herbicides 35 acaricides and insecticides and 25 fungicides) andthe results can be seen in Table 1 Overall 4 pesticides were classifiedas of lsquoVery highrsquo relevance in air 7 as lsquoHighrsquo 31 as lsquoModeratersquo 21 aslsquoLowrsquo and 7 as lsquoVery lowrsquo The pesticides lacking the potential to reachtravelremain in the air do not require a thorough monitoring in thiscompartment and therefore only those classified as lsquoVery highrsquo lsquoHighrsquoor lsquoModeratersquo are of main interest for the current study However dueto the large number of compounds within this classification only 18pesticides were chosen to be monitored (cf highlighted lines inTable 1) taking also into account the analytical limitations determinedby the commercial standard mixtures available and the previousmethod validation in the research group (Arauacutejo et al 2012) This iswhy malathion although ranked of lsquoLowrsquo relevance in air was alsoincluded in the monitoring scheme

Table 2Concentrations of individual and total target pesticides for the 12 sampling sites considered (in ng gminus1 dry weight)

Braga Antuatilde Quintatildes Souselas Coimbra Leiria Lisboa Outatildeo Eacutevora Sines Alcoutim Louleacute

Molinate 488 630 1795 413 746 321 696 210 654 1213 722 633Trifluralin blod blod blod blod blod blod blod blod blod 016 blod blodSimazine blod blod blod blod blod blod blod blod blod blod blod blodAtrazine blod blod blod blod blod blod blod blod blod blod blod blodPropazine blod blod blod blod blod blod blod blod blod blod blod blodTerbutylazine blod blod blod blod blod blod blod blod blod blod blod blodDiazinon 054 082 091 blod 078 blod 041 blod 082 blod 083 blodPirimicarb 327 166 452 239 233 128 272 118 170 663 193 661Parathion-methyl blod blod blod blod blod blod blod blod blod blod blod blodAlachlor blod 048 blod blod blod blod 018 blod blod blod 028 blodAmetryn 302 342 869 361 259 460 305 360 401 749 396 1242Prometryn 860 787 1134 1560 421 895 1269 1016 813 1044 1435 2164Terbutryn blod blod blod 191 blod blod blod blod blod 155 blod blodMalathion blod blod blod 049 blod blod 042 blod blod blod blod blodMetolachlor blod blod blod blod blod blod blod blod blod blod blod blodChlorpyrifos blod blod blod 022 blod blod blod blod blod blod blod blodParathion-ethyl 477 339 526 160 139 159 414 blod 385 448 308 230Pendimethalin 076 021 221 197 blod blod 115 255 175 182 207 674TOTAL 2584 2414 5088 3190 1876 1963 3171 1958 2681 4471 3371 5605

blod mdash result is below the limit of detection


32 Levels and trends of pesticides in pine needles

The levels of individual and total pesticides for the 12 samplingpoints chosen are shown in Table 2 Results indicate values of total pes-ticides between 188 ng gminus1 (dw) in Coimbra an urban area and560 ng gminus1 (dw) in Louleacute a rural site Comparing to another typicalSVOC pollution marker PAHs these levels are an order of magnitudelower than those found for the same sites (Ratola et al 2010) Severalpesticides were not detected in any location Individually the com-pound with the highest mean concentrations was prometryn with

Fig 3 Incidence of pesticides by chemical family (top) and main function (bot

112 ng gminus1 (dw) with a sample maximum of 216 ng gminus1 (dw)detected in Louleacute Molinate ametryn and pirimicarb followed rangingfrom 30 to 71 ng gminus1 (dw)

The information available in literature about levels of these pesti-cides in pine needles is almost inexistent Still it is interesting to realisethat the detected levels of total pesticides follow the indicators of risk ofphytopharmaceuticals use in Portugal by county (INE 2009) whereareas like Louleacute or Quintatildes present a high risk and cities like BragaCoimbra and Leiria or rural areas like Antuatilde have a low use risk Whilevalidating the analytical method used in this work Arauacutejo et al

tom) for all sampling sites (left) and according to land occupation (right)

Fig 5 Scores (left) and loadings (right) plots for the principal component analysis of the families of pesticides against the types of land occupation

Fig 4 Risk of exposure chart


Table 3Pearson test (n = 12) for the correlations between chemical families and functions of pesticides and socio-economic and meteorological parameters

Parameters Total Ps Sum Carb Sum Triaz Sum CAAs Sum DNAs Sum OPPs Sum Herb Sum Acar Sum Ins

Population (hab) ndash01042 ndash00781 ndash01463 01347 ndash01974 02712 p lt 010 ndash01587 02712 ndash00836

Density (habkm2) ndash01551 ndash00822 ndash02065 01241 ndash02319 02007 p lt 005 ndash01969 02007 ndash01439

Elevation (m) ndash01740 ndash01412 ndash02694 ndash01255 ndash00853 03698 p lt 001 ndash02527 03698 ndash01271

Burnt area (ha) ndash01924 ndash01442 ndash02605 ndash01973 ndash01933 03317 ndash02915 03317 ndash00033

Total Rainfall (mm) ndash01332 01305 ndash03535 02314 ndash04920 05141 ndash02469 05141 ndash00294

Total wind speed (ms) 00339 01145 ndash01160 00660 ndash00321 03544 ndash00177 03544 ndash00202

Mean min temp (degC) 04885 02572 05219 01580 04233 01274 05274 01274 02317

Mean avg temp (degC) 06148 03725 06584 00971 05196 00257 06550 00257 04655

Mean max temp (degC) 04142 01295 05443 00772 04702 ndash01182 04761 ndash01182 02549

Percapita

Autom Fuel cons (tep) ndash00514 ndash00133 ndash00054 00503 ndash01603 ndash01370 ndash00922 ndash01370 02885

Diesel () 01158 ndash00080 01568 06513 00605 01014 01533 01014 ndash01642

Electric (domestic kWh) 04881 00649 06954 ndash03270 07781 ndash02653 05508 ndash02653 04745

Electric (non domestic kWh) 04558 04781 03317 ndash02128 01822038970182202289 07048

Electric (industry kWh) 02813 04528 01123 ndash01704 00311 00974 02199 00974 05593

Electric (agriculture kWh) 05731 06071 03780 ndash02469 03563 02908 05874 02908 03058

Total water (m3) 05528 02133 06772 ndash03318 07180 ndash01455 05753 ndash01455 06509

Urban waste (t) 06160 03524 06584 ndash05152 07353 ndash00664 06329 ndash00664 06785


(2012) analysed pine needles from four sites in the north of Portugaland concluded that the highest value of the total pesticides was foundin Leixotildees an important commercial port (4589 ng gminus1) and the low-est in Braganccedila an urban setting (1944 ng gminus1) These levels are inlinewith those from the current study Individually however the trendspresented some differences with the most detected compound beingametryn (292) followed by malathion (179) molinate (164)and prometryn (107) In the other two similar studies found Astonand Seiber (1996) reported higher concentration ranges than in thepresent work for diazinon (8ndash18 ng gminus1 dw) and for chlorpyrifos(14ndash30 ng gminus1 dw) in Pinus ponderosa needles Being these studiesdone in a different continent (North America) it is plausible that thepesticides used can vary given that also the crops can be differentThe same authors also monitored simultaneously chlorpyrifos in air andpine needles (Aston and Seiber 1997) and levels were 10ndash85 ng gminus1

(dw) in pine needles and 012ndash180 ng mminus3 in air Considerablymore in-formation on the levels of these pesticides in the nearby air would beneeded to assess the degree of the respective correlations and further en-hance the role of pine needles as biomonitors of the atmospheric loadStudies showing the incidence of the target pesticides in the atmospherealone are more frequent but still scarce For instance Yusagrave et al (2009)reviewed the levels of several pesticides in the atmosphere fromdifferentcountries and it can be seen that all of the target ones in the current studyexist in the atmosphere although not reflecting the incidence distribu-tion found in the needles The highest concentrations were found formalathion (4450 ng mminus3) followed by diazinon (612 ng mminus3) andpendimethalin (117 ng mminus3) This may again be explained by the differ-ent phytosanitary strategies followed by the countries involved On an-other study performed in agricultural land in Canada Yao et al (2008)registered levels of diazinon chlorpyrifos trifluralin metolachlor and at-razine up to 4370 38 16 121 and 10 ng mminus3 respectively and con-firmed as well the predominant presence of these pesticides in theatmospheric gas-phase In a more global approach Shunthirasinghamet al (2010) obtained a wide range of concentrations of trifluralin andpendimethalin in 29 sites worldwide and also differences even withinthe same continent The highest levels were recorded in Paris(188 ng mminus3) and Košetice Czech Republic (103 ng mminus3) respectivelyThis may be due to the high using rates and frequencies of these pesti-cides (Belden et al 2012) As can be seen the global distribution ofthese pesticides in the atmosphere is variable with the region of study

Other types of pesticides have been reported in pine needles (suchas HCB HCHs DDTs and chlordanes) their concentrations reached761 ng gminus1 for individual compounds in this case lindane (Hellstroumlm

et al 2004) yet remaining within the magnitudes shown in thepresent study

Considering the pesticides in terms of chemical families it can beseen (Fig 3 top left) that triazines predominate followed by carba-mates (both account to more than 80 of the total pesticides in eachsite) This can be the reflection of the pesticides sales in Portugal In2003 triazines and carbamates accounted for almost 60 of the totalsales (Vieira 2005) and only for OPPs is this correspondence not entire-ly verified The incidence of these pesticides in the studies sites onlyreached a maximum of 20 and their sales share in 2003 was of 37(Vieira 2005) Dinitroanilines and chloroacetamides show the lowestpresence in the pine needle samples usually fewer than 5 It is impor-tant to note that the use of such pesticides in the 90s was quite inten-sive and its use declined over the past few years due to severerestrictions imposed by the EU legislation In fact some pesticideshave been banned and therefore it is normal that the sales may havechanged accordingly

The trends by land occupation show that in average there is a pre-dominance of the pesticides load in rural areas (412 ng gminus1 dw)followed by industrial (321 ng gminus1 dw) and urban ones (246 ng gminus1dw) as indicated in Fig 3 (top right) This is again in linewith the indica-tors of risk of phytopharmaceuticals use by county in Portugal when it isclear that rural areas present the highest risks and not so much in urbanor industrial areas (INE 2009) The chemical family patterns are how-ever similar between site types with only some differences seen forthe urban sites where the OPPs have the strongest percentage anddinitroanilines the lowest (Fig 3 top right)

As to the target types of the pesticides Fig 3 (bottom left) clearlyshows the dominance of herbicides with 65 to 95 of incidence in thesampling sites This also reflects the sales trend with herbicides having83 of the combined total for herbicides acaricides and insecticides in2003 (Vieira 2005) Furthermore considering the pesticides sold byarea of agricultural land in 2007 herbicides accounted for 058 kg haminus1

and insecticides and acaricides with a total of 035 kg haminus1 (INE 2013)Considering the land occupation the scenario is similar (Fig 3 bottomright) and the differences between site types are not profound Still acar-icides have a stronger incidence in urban areas which is admissible giventhe use of these pesticides in households for pest control Herbicides onthe contrary have the lowest urban presence although the total inci-dence still shows a very high percentage (above 70) The fact that theindustrial site studies are surrounded predominantly by rural areas in-stead of large urban settings is probably reflected in the clear similitudeof industrial and rural trends in this case


33 Risk of exposure

Asmentioned before the risk of exposure depends on the product oftwo factors physicochemical properties that define the probability ofthe pesticide to occur in the air and their field presence as detected bythe pine needles monitoring As can be seen in Fig 4 maximum expo-sure risk results from the combination of a high chemical score and ahigh frequency of detectionwhileminimumrisk is associated to the op-posites low chemical score and very low or no frequency of detection

Fig 4 shows thatmolinate presents the highest risk of exposure Thisis due to a very high chemical scoring (17) combined with a high fre-quency of occurrence as it was detected in all analysed samples Infact this was the pesticide with the second highest total and averageconcentration levels (852 ng gminus1 and 71 ng gminus1 respectively) Onthe other hand trifluralin also with a very high chemical scoring (18)represents only a low risk as it was detected in only one sample andat the lowest concentration of all detected pesticides (016 ng gminus1)The lowoccurrence and concentrationmay be explained by its declininguse but also due to its extensive half-time in soil which reduces atmo-spheric emissions (Belden et al 2012) Another explanation for thismay be the fact that trifluralin undergoes direct photodegradationwhich reduces its occurrence in the atmosphere (Zeng and Arnold2013) Risk of exposure is therefore very limited A similar behaviourwas observed for chlorpyrifos and pendimethalin Both have a highchemical score but because of their different frequencies of detectiontheir risk is low and high respectively Pesticides with moderate chem-ical score (10ndash13) show quite disperse behaviour While parathion-ethyl pirimicarb prometryn and ametryn show a high risk of exposuredue to their high frequency of occurrence diazinon and alachlor presentamoderate to low risk whichmatches their occurrence in the pine nee-dle samples The same tendency is seen for their average concentra-tions The other moderate chemical score pesticides were consideredto be of a very low exposure risk as they were not detected in any sam-ple Malathion and terbutryn show identical frequencies of occurrencebut due to their different chemical scores (low and moderate respec-tively) their exposure risk is distinctive being very low for malathionand low for terbutryn The same pattern can be verified for their averageconcentrations as terbutryn shows levels nearly fourfold higher thanmalathion

The main conclusion that can be drawn is that physicochemicalproperties of pollutants even if combined through a chemical scoringmethodology are not enough to reflect thoroughly complex local phe-nomena that may occur and affect the levels of the target pollutantsTherefore after most relevant pesticides to be monitored were chosenbased on their probability of their atmospheric presence the detectedfrequencies obtained by monitoring should be correlated with their re-spective chemical score in order to asses the effective risk of exposure topopulations and environment As opposed to active or passive air sam-pling biomonitoring using pine needles exempt the need for a previoussampling site set-up andmay act a ldquobiological data loggersrdquo of pollutionas needles remain in the tree for several years

34 Principal component analysis

The use of advanced multivariate statistics like principal componentanalysis (PCA) helps the analysis and interpretation of largemultivariatedata generated from environmentalmonitoring schemes (Terrado et al2006) This technique acts as a complement of classic univariate statis-tics and allows the unveiling of concealed environmental information(Navarro et al 2006) In this case Fig 5 shows that there is some sepa-ration in the scores plots between the site types more visible in the plotof principal component (PC) 1 against PC 3 (Fig 5 bottom left) and par-ticularly between the rural and the urban areas which up to a point sug-gests different types of pesticides used in each case In the loadings plot(Fig 5 bottom right) it can be seen that chloroacetamides triazines anddinitroanilines aremore strongly linked to the rural areas whereas OPPs

have an affinity towards urban settings These findings have some corre-spondencewith the plot of PC 1 versus PC 2 (Fig 5 top right) except forOPPs which seem in this case preferentially linked to rural sites In thiscase the information provided by the dataset is not entirely conclusivewhich urges the establishment of further andmore complete field cam-paigns to monitor these and other pesticides of interest

35 Correlations with geo-energetic parameters

Trying to assess some more aspects of these pesticides behavioursome correlations with geographic meteorological and economical pa-rameters were attempted using the Pearson test with several levels ofsignificance The results are displayed in Table 3 As can be seen no sig-nificant correlationswere found for thefirst ones and only a few climat-ic trends namely the total triazines with mean minimum maximumand average temperatures and the sum of OPPs with total rainfall(p b 010) The most significant correlations were with the mean aver-age temperature just like the positive trend with the total pesticidesThis can be a reflection of a more extensive volatilisation of these com-pounds from the soils (in terms of target the herbicides are those con-tributing to this relationship) into the atmosphere favouring theirentrapment by the waxy layer of pine needles On the other hand rain-fall and temperature may have a significant importance on biotic andabiotic degradation and on the formation of non-extractable residuesthat influence persistence in soil and therefore reduces volatilisation(Loos et al 2012) For the energetic consumption more associationscan be found namely in indicators related to urban areas such asdomestic energy consumption and urban waste production Dinitro-anilines show the best correlations overall and in terms of total pesti-cides there are positive trends with agricultural energy consumptionwater use and urban waste This suggests that pesticides can be foundvirtually everywhere and their use is extended to the whole territoryIt is interesting to notice that the sum of the insecticides correlatespositively with non-domestic and industry energy consumptionwhich possibly denotes the incidence of their use in offices or industrialfacilities

4 Conclusions

The total concentration of 18 pesticides measured in pine needlesfrom 12 sampling sites in Portugal ranged between 188 and560 ng gminus1 (dw) Triazines were the predominant chemical familyfollowed by carbamates whereas in terms of their target herbicidesclearly rule with 65 to 95 of the total incidence In general the dis-tribution of the pesticide families in urban industrial and rural areasshowed similarities but there was some indication that the pesti-cides used in each of them could be different Their risk of exposurewas established based on chemical score and occurrence frequencyMolinate a widely used pesticide in Portugal offers the highest ex-posure risk while terbutryn parathion-methyl metolachlor atra-zine propazine and simazine show a very low risk due to theirabsence in the analysed samples

In terms of the relationships with geo-economical parametersdinitroanilines show the best correlations and the total pesticides hada positive link with the mean average atmospheric temperature withgeo-economical parameters suggesting stronger volatilisation fromsoils into the atmosphere in the warmer sites Overall it can be saidthat pine needles can act as reliable biomonitors of the pesticides instudy but more field campaigns are needed to be able to assess theirbehaviour more thoroughly

Disclosure of conflict of interest

Nuno Ratola Vera Homem Joseacute Avelino Silva Rita Arauacutejo JoseacuteManuel Amigo Luacutecia Santos and Arminda Alves declare no conflict ofinterests with this publication The Project PTDCAGRCFL102597


2008 and grants SFRHBPD670882009 and SFRHBPD769742011were funded by the Fundaccedilatildeo para a Ciecircncia e a Tecnologia (FCT mdash

Portugal) This work was also partially funded by the European UnionSeventh Framework Programme-Marie Curie COFUND (FP72007ndash2013) under UMU Incoming Mobility Programme ACTion (U-IMPACT)Grant Agreement 267143 This work is new and original and notunder consideration elsewhere The study does not contain any studieswith humanor animal subjects The host institutions of all authors agreeto the submission of this paper to the journal

Acknowledgements

The authors wish to thank Fundaccedilatildeo para a Ciecircncia e a Tecnologia(FCT mdash Portugal) for the Project PTDCAGR-CFL1025972008 andgrants SFRHBPD670882009 and SFRHBPD769742011 This workhas been partially funded by the European Union Seventh FrameworkProgramme-Marie Curie COFUND (FP72007ndash2013)underUMU Incom-ingMobility Programme ACTion (U-IMPACT) Grant Agreement 267143Dr Olga Ferreira is thanked for her collaboration in collecting the pineneedle samples

References

Amigo JM Ratola N Alves A Study of geographical trends of polycyclic aromatic hydro-carbons using pine needles Atmos Environ 2011455988ndash96

Arauacutejo R Ratola N Moreira JL Santos L Alves A Different extraction approaches for thebiomonitoring of pesticides in pine needles Environ Technol 2012332359ndash63

AsmanWAH Jorgensen A Bossi R Vejrup KV Bugel Mogensen B Glasius M Wet deposi-tion of pesticides and nitrophenols at two sites in Denmark measurements and con-tributions from regional sources Chemosphere 2005591023ndash31

Aston LS Seiber JN Methods for the comparative analysis of organophosphate residues infour compartments of needles of Pinus ponderosa J Agric Food Chem 1996442728ndash35

Aston LS Seiber JN Fate of summertime airborne organophosphate pesticide residues inthe Sierra Nevada mountains J Environ Qual 1997261483ndash92

Belden JB Hanson BR McMurry S Smith L Haukos DA Assessment of the effects of farm-ing and conservation programs on pesticide deposition in high plain wetlands Envi-ron Sci Tech 2012463424ndash32

Bidleman TF Atmospheric transport and air-surface exchange of pesticidesWater Air SoilPollut 1999115115ndash66

Boethling RS Howard PH Meylan WM Finding and estimating chemical property datafor environmental assessment Environ Toxicol Chem 2004232290ndash308

Breivik K Alcock R Li YF Bailey RE Fiedler H Pacyna JM Primary sources of selectedPOPs regional and global scale emission inventories Environ Pollut 20041283ndash16

Chaplain V Mamy L Vieubleacute-Gonod L Mougin C Benoit P Barriuso E et al Fate of pesti-cides in soils toward an integrated approach of influential factors pesticides in themodern worldmdash risks and benefits 2011 [httpwwwintechopencombookspesti-cides-in-the-modern-world-risks-and-benefitsfate-of-pesticides-in-soils-toward-an-integrated-approach-of-influential-factors (accessed December 2012)]

Coscollagrave C Colin P Yahyaoui A Petrique O Yusagrave V Mellouki A et al Occurrence of cur-rently used pesticides in ambient air of Centre Region (France) Atmos Environ2010443915ndash25

Davie-Martin CL Hageman KJ Chin Y-P An improved screening tool for predictingvolatilisation of pesticides applied to soils Environ Sci Tech 2013478868ndash76

Dolinova J Klaacutenovaacute J Klan P Holoubek I Photodegradation of organic pollutants on thespruce needle wax surface under laboratory conditions Chemosphere 2004571399ndash407

Environmental Protection Agency (EPA) Estimation Program Interface (EPI) Suite ver-sion 411 2011 [httpwwwepagovopptintrexposurepubsepisuitedlhtm(accessed February 2013)]

European Union Pesticide Database (EUPD)httpeceuropaeusanco_pesticidespublicindexcfmevent=activesubstanceselectionampa=1 2013 [accessed February 2013]

Grimalt J van Drooge BL Polychlorinated biphenyls in mountain pine (Pinus uncinata)needles from Central Pyrenean highmountains (Catalonia Spain) Ecotoxicol EnvironSaf 20066361ndash7

Guth JA Reischmann FJ Allen R Arnold D Hassink J Leake CR et al Volatilisation of cropprotection chemicals from crop and soil surfaces under controlled conditions mdashprediction of volatile losses from physic-chemical properties Chemosphere200457871ndash87

Harner T Bidleman TF Octanolndashair partition coefficient for describing particlegaspartitioning of aromatic compounds in urban air Environ Sci Tech 1998321494ndash502

Hellstroumlm A Kylin H Strachan WMJ Jensen S Distribution of some organochlorine com-pounds in pine needles from Central and Northern Europe Environ Pollut 200412829ndash48

INE (Instituto Nacional de Estatiacutestica mdash Statistics Portugal) Indicadores Agro-Ambientais1989ndash2007 2009 [ISBN 978-989-25-0041-6 Lisboa Portugal 175 pp]

INE (Instituto Nacional de Estatiacutestica mdash Statistics Portugal)wwwinept 2013 [assessedOctober 2013]

Institute for Environment and Health (IEH) A screeningmethod for ranking chemicals bytheir fate and behaviour in the environment and potential toxic effects in humans fol-lowing non-occupational exposure (Web Report W14) 2004 [Leicester UK httpwwwleacukieh (accessed June 2013)]

Juraske R Antoacuten A Castell F Huijbregts MAJ PestScreen a screening approach for scoringand ranking pesticides by their environmental and toxicological concern Environ Int200733886ndash93

Klaacutenovaacute J Čupr P Baraacutekovaacute D Šeda Z Anděl P Holoubek I Can pine needles indicatetrends in the air pollution levels at remote sites Environ Pollut 20091573248ndash54

Koumlck-Schulmeier M Ginebreda A Postigo C Garrido T Fraile J Alda ML et al Four-yearadvanced monitoring program of polar pesticides in groundwater of Catalonia(NE-Spain) Sci Total Environ 2014470ndash4711087ndash9

Krieger RIHandbook of pesticide toxicology vol 1 San Diego USA Academic Press 2001Lamprea K Ruban V Characterization of atmospheric deposition and runoff water in a

small suburban catchment Environ Technol 2011321141ndash9Loos M Krauss M Fenner K Pesticide nonextractable residue formation in soil Insights

from inverse modeling of degradation time series Environ Sci Tech 2012469830ndash7Marcomini A Perin G Stelluto S Da Ponte M Traverso P Selected herbicides in treated

and untreated surface water Environ Technol 1991121127ndash35Martinez E Gros M Lacorte S Barceloacute D Simplified procedures for the analysis of polycy-

clic aromatic hydrocarbons in water sediments and mussels J Chromatogr A20041047181ndash8

Navarro A Tauler R Lacorte S Barceloacute D Chemometrical investigation of the presenceand distribution of organochlorine and polyaromatic compounds in sediments ofthe Ebro River Basin Anal Bioanal Chem 20063851020ndash30

Piccardo MT PalaM Bonaccurso B Stella A Redaelli A Paola G et al Pinus nigra and Pinuspinaster needles as passive samplers of polycyclic aromatic hydrocarbons EnvironPollut 2005133293ndash301

Portuguese National Authority for Animal Health Phytosanitation and Food PAN mdashProjeto de plano de accedilatildeo nacional para o uso sustentaacutevel dos produtosfitofarmacecircuticos mdash Contexto nacional da utilizaccedilatildeo de produtos fitofarmacecircuticos2013II [httpswwwgooglepturlsa = tamprct = jampq = ampesrc = sampsource = -

webampcd = 1ampved = 0CC0QFjAAampurl = http3A2 F2Fwwwdgvmin-agriculturapt2Fxeov212Fattachfileujsp3Flook_parentBoui3D632137226att_display3Dn26att_download3Dyampe-i = euKNUoydJ6iJ7AbH2YH4DQampusg = -

AFQjCNFSqtVD-Sgx-TbgjMbj71LMxlIeIAampbvm = bv56987063bs1dYms (accessedMarch 2013)]

Ratola N Lacorte S Alves A Barceloacute D Analysis of polycyclic aromatic hydrocarbons inpine needles by gas chromatographyndashmass spectrometry comparison of differentextraction and clean-up procedures J Chromatogr A 20061114198ndash204

Ratola N Amigo JM Alves A Levels and sources of PAHs in selected sites from Portugalbiomonitoring with Pinus pinea and Pinus pinaster needles Arch Environ ContamToxicol 201058631ndash47

Ratola N Santos L Alves A Lacorte S Pine needles as passive bio-samplers to determinepolybrominated diphenyl ethers Chemosphere 201185247ndash52

Rusco E Jones R Bidoglio G Organic matter in the soils of Europe present status and fu-ture trends European Commission Directorate General JRC Joint Research Centre In-stitute for Environment and Sustainability European Soil Bureau 2001

Sarigiannis DA Kontoroupis P Solomou ES Nikolaki S Karabelas AJ Inventory of pesticideemissions into the air in Europe Atmos Environ 2013756ndash14

Shunthirasingham C Oyiliagu CE Cao X Gouin T Wania F Lee S-C et al Spatial and tem-poral pattern of pesticides in the global atmosphere J Environ Monit 2010121650ndash7

Snyder EM Snyder SA Giesy JP Blonde AA Hurlburt GK Summer CL et al SCRAM a scor-ing and ranking system for persistent bioaccumulative and toxic substances for theNorth American great lakes Environ Sci Pollut Res 20007219ndash24

Stangroom SJ Lester JN Collins CD Abiotic behaviour of organic micropollutants in soilsand the aquatic environment A review I Partitioning Environ Technol 200021845ndash63

TerradoM Barceloacute D Tauler R Identification and distribution of contamination sources inthe Ebro river basin by chemometrics modelling coupled to geographical informationsystems Talanta 200670691ndash704

Tremolada P Burnett V Calamari D Jones KC Spatial distribution of PAHs in the UK at-mosphere using pine needles Environ Sci Tech 1996303570-3477

van der Werf HMG Assessing the impact of pesticides on the environment Agric EcosystEnviron 19966081ndash96

Vieira MM Vendas de produtos fitofarmacecircuticos em Portugal em 2003 Direcccedilatildeo Geralde Protecccedilatildeo das Culturas mdash Direcccedilatildeo de Serviccedilos de Produtos Fitofarmacecircuticos2005 [PPA(AB)-0105]

Vieira MM Sales of plant protection products in Portugal 2001ndash2008 Revista de CiecircnciasAgraacuterias 20123511ndash22 (in Portuguese)

Xu D Zhong W Deng L Chai Z Mao X Regional distribution of organochlorinated pesti-cides in pine needles and its indication for socioeconomic developmentChemosphere 200454743ndash52

Yao Y Harner T Blanchard P Tuduri L Waite D Poissant L et al Pesticides in the atmo-sphere across Canadian agricultural regions Environ Sci Tech 2008425931ndash7

Yusagrave V Coscollagrave C Mellouki W Pastor A de la Guardia M Sampling and analysis of pes-ticides in ambient air J Chromatogr A 200912162972ndash83

Zeng T ArnoldWA Pesticide photolysis in prairie potholes probing photosensitized pro-cesses Environ Sci Tech 2013476735ndash45


Being mostly organic compounds pesticides may therefore be de-graded by biotic or abiotic processes Biotransformation of pesticides iscarried out by living organisms (such as bacteria and fungi) and involvesnumerous biochemical transformation reactions such as biodegrada-tion cometabolism and synthesis (Chaplain et al 2011) Abiotic degra-dation is due to chemical or photochemical reactions Chemicalprocesses may be hydrolysis (eventually catalysed by acids bases ormetal ions) or redox reactions Photolysis on the other handmaybedi-rect when the pesticide absorbs light and undergoes transformation orindirect when other chemical species become electronically exciteddue to photon absorption and react with the contaminant (Zeng andArnold 2013) In water and soils both degradation processes (bioticand abiotic) may occur simultaneously inmechanistically complex pro-cesses (Krieger 2001)

The effects of these and other contaminants must be continuouslymonitored and vegetation is a useful matrix for the assessment ofthese airborne pollutants As opposed to other passive samplers moni-toring using vegetation avoids previous sampling site set-up since theymay act as ldquobiological loggersrdquo of pollution Furthermore the use of veg-etation is the best tool for estimation of the atmospheric contaminationlevels at remote or poorly accessible locations In particular pineneedles stand out as effective biomonitors due to the excellent uptakeproperties of their waxy layer (Xu et al 2004) The needles surface isconstituted by a thick cuticle (di- and trihydroxy fatty acids) and a cutic-ular wax (lipids free fatty acids n-alkanes n-alkenes primary alcoholsαω-diols ketones andω-hydroxyacids) (Dolinova et al 2004 Klaacutenovaacuteet al 2009) The epicuticular wax layer acts as a barrier between theplant and its environment protecting the plant against ultraviolet radi-ation desiccation and possible attacks from pathogens Low and medi-um polar compounds have a high affinity towards this wax layer dueto their chemical composition and therefore they are effectivelyentrapped for several years Different studies prove that pine needlesare effective in the quantification of several semi-volatile organiccontaminants such as polycyclic aromatic hydrocarbons (PAHs)

Fig 1 Sampling map

(Amigo et al 2011 Piccardo et al 2005 Ratola et al 2010Tremolada et al 1996) polybrominated diphenyl ethers (PBDEs)(Ratola et al 2011) polychlorinated biphenyls (PCBs) (Grimalt andvan Drooge 2006) and organochlorine pesticides (OCPs) (Hellstroumlmet al 2004) Recently our work group developed and validated an ana-lytical method to assess pesticides of several chemical classes Thisallowed the establishment of the current study aiming to assess theirenvironment behaviour on a larger scale (Arauacutejo et al 2012)

Using appropriate correlations it is possible to estimate pollutantpesticide concentrations in several environmental matrices and esti-mate their environmental impact Although a holistic approach com-prising all pesticides would be desirable limited resources (timeanalytical capacities etc) make this approach hardly possible There-fore a judicious selection of the pesticides to be monitored should bedone in order to prioritise research efforts to the most relevant onesChemical scoring and ranking is a useful method to make this kind ofprioritisation Several models have been developed most of thembased not only on physicochemical but also toxicological properties ofthe compounds although lack of data is often a limitation to their appli-cation (Juraske et al 2007 Snyder et al 2000) In this work a simplechemical scoring methodology was developed in order to identifypotentially relevant pesticides The ranking list together with the capa-bilities shown by themethodology reported by Arauacutejo et al (2012) wasused to implement a monitorisation plan employing pine needlescollected in areas of different land occupations Data obtained frommonitorisation was also combined with the results of chemical scoringin order to assess the risk of exposure Levels trends and relationshipswith some characteristics of the pesticides and of the sampling siteswere investigated including the use of principal component analysis(PCA) to assess the environmental impact of these compounds

2 Materials and methods

21 Chemical scoring strategy

In this study a simple chemical scoringmethodologywas developedto identify a reduced number of pesticides considered ldquopotentiallyrelevantrdquo in the air compartment and therefore reduce the analyticalefforts for further monitoring

Seventy pesticides were chosen for this study on the basis of salesvolumes in the Portuguese market (Vieira 2005) Among them someof the most relevant pesticides revealed in the prioritisation were cho-sen to be monitored using pine needles as passive bio-samplers sincethis matrix reflects the levels existing in the atmosphere Unfortunatelydue to logistic and budget constraints it was not possible to include inthe study all the pesticides with the highest scores leading to the selec-tionmade in the end A database including all the data identified as nec-essary to carry out the chemical scoring processwas compiled Datawascollected from (Q)SAR modelling software EPI Suite (EPA 2011)

The ranking of the compounds was established according to theirphysicochemical properties The criteria used to rank the persistenceof pesticides in air are summarised in Fig 2 Threemain criteriawere de-fined taking into account the probability of pesticides to occur in the aircompartment air persistence volatilisation potential (waterndashair andsoilndashair) and deposition potential Scores for each criterion wereassigned between 1 (low probability of occurrence in air) and 5 (veryhigh probability) The atmospheric persistence of the compounds wasevaluated by their half-life time in air (t12) The values were estimatedbased on reactions with hydroxyl radicals and ozone According to thischemical scoring methodology compounds with t12 air in the rangeof hours are short-lived in air (low score) and those present for longerthan 40 days are considered extremely persistent (IEH 2004)

The Henrys law constant (Hcprime) defined as the ratio of a chemicalsconcentration in air to its concentration inwater at equilibrium(dimen-sionless form) was used to determine the tendency to volatilise fromthe aqueous to the gas phase As can be seen in Fig 2 compounds




KSA frac14 Csoil

Cairfrac14 Cwater

Cair

1rthorn kd

frac14 1

Hcprime

1rthorn kd

ethEqeth1THORNTHORN


kd frac14 KOC

1724 OC





ethEqeth3THORNTHORN












117NRatola

etalScienceofthe

TotalEnvironment476

ndash477(2014)

114ndash124

Table 1 (continued)







118NRatola

etalScienceofthe

TotalEnvironment476

ndash477(2014)

114ndash124




22 Risk of exposure










































Percapita



















33 Risk of exposure









4 Conclusions








Acknowledgements


References
























































KSA frac14 Csoil

Cairfrac14 Cwater

Cair

1rthorn kd

frac14 1

Hcprime

1rthorn kd

ethEqeth1THORNTHORN


kd frac14 KOC

1724 OC





ethEqeth3THORNTHORN












117NRatola

etalScienceofthe

TotalEnvironment476

ndash477(2014)

114ndash124

Table 1 (continued)







118NRatola

etalScienceofthe

TotalEnvironment476

ndash477(2014)

114ndash124




22 Risk of exposure










































Percapita



















33 Risk of exposure









4 Conclusions








Acknowledgements


References




























































117NRatola

etalScienceofthe

TotalEnvironment476

ndash477(2014)

114ndash124

Table 1 (continued)







118NRatola

etalScienceofthe

TotalEnvironment476

ndash477(2014)

114ndash124




22 Risk of exposure










































Percapita



















33 Risk of exposure









4 Conclusions








Acknowledgements


References





















































Table 1 (continued)







118NRatola

etalScienceofthe

TotalEnvironment476

ndash477(2014)

114ndash124




22 Risk of exposure










































Percapita



















33 Risk of exposure









4 Conclusions








Acknowledgements


References
























































22 Risk of exposure










































Percapita



















33 Risk of exposure









4 Conclusions








Acknowledgements


References














































































Percapita



















33 Risk of exposure









4 Conclusions








Acknowledgements


References






















































33 Risk of exposure









4 Conclusions








Acknowledgements


References
























































Acknowledgements


References





















































Biomonitoring of pesticides by pine needles — Chemical scoring, risk of exposure, levels and...

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Transcript of Biomonitoring of pesticides by pine needles — Chemical scoring, risk of exposure, levels and...