HEALTH IMPACTS OF SOME PERSISTENT ORGANIC ...

HEALTH IMPACTS OF SOME PERSISTENT

ORGANIC POLLUTANTS IN EXPECTING

MOTHERS FROM PUNJAB

By

Anber Naqvi

under the Supervision of

Dr. Abdul Qadir

M.Sc. (UAF), Ph.D. (QAU)

Dr. Adeel Mahmood M.Sc. (QAU), Ph.D. (QAU)

A thesis submitted to University of the Punjab in partial fulfillment of

requirements for the degree of Ph.D in Environmental Sciences

COLLEGE OF EARTH AND ENVIRONMENTAL SCIENCES

UNIVERSITY OF THE PUNJAB, LAHORE, PAKISTAN

SESSION: 2009-2014

I dedicate this thesis to my late father, to my children and

husband

CERTIFICATE OF APPROVAL

We hereby certify that this research work is based on the results of experimental work

carried out by Ms. Anber Naqvi under my supervision. We have personally gone through

all data / results / materials reported in the manuscript and certify their correctness/

authenticity. We further certify that the materials included in this thesis have not been

used in part or full in the manuscript already submitted or in the process of submission in

partial / complete fulfillment for the award of any other degree from any other institution.

Ms. Anber Naqvi has fulfilled all conditions established by the University of the Punjab

for the submission of PhD thesis through the official procedure of the University.

(Dr. Abdul Qadir)

Assistant Professor / Supervisor

College of Earth and Environmental Sciences

University of the Punjab

Lahore-Pakistan

(Dr. Adeel Mahmood)

Assistant Professor

Department of Environmental Sciences

Govt. College Women University,

Sialkot-Pakistan

DECLARATION CERTIFICATE

The thesis being submitted for the degree of PhD in the University of the Punjab does not

contain any material which has been submitted for the award of PhD degree in any other

University and to the best of my knowledge and belief, neither does this thesis contain

any material published or written previously by another person, except when due

reference is made to the source in the text of the thesis.

Anber Naqvi

PhD Scholar


University of the Punjab, Lahore

AUTHOR’S DECLARATION

I, Anber Naqvi hereby state that my PhD Thesis titled ―Health Impacts of some

persistent Organic Pollutants in Expecting Mothers from Punjab, Pakistan” is my own

work and has not been submitted previously by me for taking any degree from the

University of the Punjab, Lahore, Pakistan or anywhere else in the country / world. At

any time if my statement is found to be incorrect even after my graduation the University

has the right to withdraw my PhD degree.

Anber Naqvi

PhD Scholar



PLAGIARISM UNDERTAKING

I, solemnly declare that research work presented in the thesis titled ―Health Impacts of

some persistent Organic Pollutants in Expecting Mothers from Punjab, Pakistan” is

solely my research work with no significant contribution from any other person. Small

contribution / help wherever taken has been duly acknowledged and that complete thesis

has been written by me.

I understand the zero tolerance policy of the HEC and University of the Punjab, Lahore,

Pakistan towards plagiarism. Therefore I as an Author of the above titled thesis declare

that no portion of my thesis has been plagiarized and any material used as reference is

properly referred / cited.

I undertake that if I am found guilty of any formal plagiarism in the above titled thesis

even after award of PhD degree, the University reserves the rights to withdraw / revoke

my PhD degree and that HEC and the University has the right to publish my name on the

HEC / University Website on which names of students are placed who submitted

plagiarized thesis.

Anber Naqvi

PhD Scholar



i

ABSTRACT

Polychlorinated Biphenyls (PCBs) are persistent, toxic and bio-accumulative organic

pollutants present everywhere in the biosphere. PCBs enter the environment mainly as a

result of anthropogenic activities and accumulate in mothers through food and respiration.

These chemicals transfer from mother body to the fetus inside the womb. Keeping this in

view, the present study was conducted to monitor the PCBs and their impacts on

expecting mothers from the Punjab Province, Pakistan. In this regard, blood serum, milk

and placenta samples were collected from 45 women with the cesarean section from five

districts viz; Lahore, Sialkot, Khanewal, Okara and Chakwal. All samples were collected

and screened for the quantification of 34 PCB congeners including eight dioxin like PCBs

(dl-PCBs) viz: ∑PCB70, 126, 169, 105, 114, 118, 156 and 189) and six indicator PCBs

(∑PCB52, 101, 118, 138, 153 and 180 using Gas Chromatograph Mass Spectrometer

(GC/MS). The mean concentrations of ∑34 PCB in blood serum (232.33 ± 110.62 ng/g

lip), milk (44.07 ± 27.27 ng/g lip) and placenta (80.26 ± 19.87 ng/g lip) were recorded.

The mean concentration of indicator PCBs in blood serum, milk and the placentawas

49.55 ± 42.31 ng/g lip, 10.31 ± 11.70 ng/g lip and 17.69 ± 12.40 ng/g lip respectively.

Whereas, the mean concentration (ng/g lw) of dl-PCBs in blood serum, milk and placenta

of each study subject were 33.39 ± 40.18, 6.15 ± 8.72 and 9.48 ± 10.92, respectively. The

concentration of ∑34PCBs in blood serum on the basis of spatial distribution was recorded

highest in women belonging to Chakwal (310.59 ± 119.47 ng/g lip), whereas, the

minimum concentration at Okara (251.28 ± 152.61ng/g lip). A similar trend was observed

in the case of milk with the highest concentration of ∑34PCBs was recorded from

Chakwal (68.33 ± 20.11ng/g lip) and lowest from Okara (30.46 ± 31.07 ng/g lip). In the

case of the placenta, the highest concentration was recorded from Chakwal (93.47 ± 29.41

ng/g lip), and lowest from Khanewal (70.46 ± 31.20 ng/g lip). The spatial distribution

ii

pattern of PCBs homolog concentrations was represented in the maps developed using

Arc GIS.The mean Toxic Equivalent Concentration (TEQ) for ∑8dl-PCBs in blood serum

was calculated as 0.24 ng TEQ/g lipids. The mean TEQ concentration in human milk and

the placenta was calculated as 0.06 ng TEQ/g lipids and 0.08 ng TEQ/g lipids,

respectively. The potential risk of dl-PCBs to the health of subject mothers was calculated

by Estimated Daily Intake (EDI). The calculated EDI (pg WHO-Teq/ kg body weight) in

blood serum (1.94 x 10-3

), milk (5.10 x 10-4

) and placenta (6.56 x 10-4

) were found below

the WHO Tolerable Daily Intake (TDI). Among the socio-demographic parameters, dairy

products, maternal weight and height showed a positive significant relationship to

∑34PCBs in blood serum and placenta respectively. The concentrations of ∑34PCBs in

human milk showed a negative correlation with age of the mothers, parity and lactation.

The calculated EDI for dl-PCBs in nursing infants ranged from 400 to 26624.1 pg TEQ

kg-1

bw day-1

(mean: 8862.6 pg TEQ kg-1

bw day-1

) were found to be significantly higher

than the tolerable daily intake limits provided by WHO (1–4 pg TEQ kg-1

bw day-1

).A

negative association was observed in levels of PCBs in human milk and infant

anthropometric measures viz; birth weight, head circumference, mid arm circumference

and chest circumference and concentrations in human placenta showed a negative

relationship with infant‘s birth weight and crown to heel length. These results highlighted

that prenatal exposure to PCBs may affect fetal anthropometric development and impair

the health of infants. Similarly, the hazard quotient values for ∑8dl-PCBs (range: 40.42 to

2662.41) were far above the benchmark value of 1 at all the sampling sites, indicating the

high levels of adverse health risks to infants in the region through breast milk

consumption. These results highlighted the need for comprehensive monitoring of PCBs

in human and food products to assess and manage the continuous releases of PCB into the

environment to prevent future human exposure, particularly of fetuses and infants.

iii

ACKNOWLEDGMENTS

First and the foremost, all praise to Almighty ALLAH who granted me strength

for the completion of this research work. I express my sincere gratitude to Prof. Dr.

Sajid Rashid Ahmad, Procipal, College of Earth and Environmental Sciences,

University of the Punjab, Lahore for providing me the existing research facilities to

complete this research work. Special thanks are due to my supervisors, Dr. Abdul Qadir,

Assistant Professor, College of Earth and Environmental Sciences, University of the

Punjab, Lahoreand Dr. Adeel Mahmood, Assistant Professor, Department of

Environmental Sciences, Government College Women University, Sialkot for being

helpful and guiding me throughout this research work. It is due to their scientific spirit,

insightful and constructive criticism; I have been able to make this thesis in its present

form.

I am grateful to State Key Laboratory of Organic Geochemistry, Guangzhou

Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou, China for

providing a platform to perform PCB analysis. A special thanks to Food and

Biotechnology Research Center (FBRC), Pakistan Council of Scientific and Industrial

Research (PCSIR) for facilitating the milk fat analysis. I express my utmost gratitude to

all those volunteers who donated their body matrices for this study. Many regard to the

paramedical staff for their assistance during sample collection.My heartiest gratitude is

due to Iqra Aslam for helping me in PCB analysis. I am extremely thankful to Hira

Iqbal, Mujtaba Baqir, Mehvish Mumtaz, Dr. Ammara Mumtaz, Dr. Rana Farhan

Sajid, Nomita Wajid, Abida Ayuband all the laboratory attendants for helping me at

various stages of this research work.

In the last, the morale support and sustained encouragement by my family,

parents, in-laws and husband cannot be overlooked. I am deeply indebted to all of them

for being my support during this research work.

ANBER NAQVI

iv

LIST OF TABLES

Sr. Number Title Page No

Chapter Four Results

Table 4.1 Descriptive Statistics of PCB Homolog in blood serum

collected from five districts of the Punjab, Pakistan

48

Table 4.2 Descriptive Statistics of PCB homolog in human milk

samples collected from five districts of the Punjab,

Pakistan

51

Table 4.3 Descriptive Statistics of PCB homolog in human placenta

samples collected from five districts of the Punjab,

Pakistan

54

Table 4.4 Toxic Equivalent (TEQs) calculated for blood serum

samples using WHO‘s 2005 Toxicity Equivalency Factors

(TEFs)

71

Table 4.5 Toxic Equivalent (TEQs) calculated for human milk


(TEFs)

73

Table 4.6 Toxic Equivalent (TEQs) calculated for human placenta


(TEFs)

75

Table 4.7 Socio-demographic characteristics and their relationship

with PCB concentration in blood serum

77

Table 4.8 Relationship of maternal characteristics and infant‘s

anthropometric measures with ∑34PCBs in human milk

80

Table 4.9 Relationship of maternal characteristics and infant‘s

anthropometric measures with ∑34PCBs in human

placenta

82

Table 4.10 Estimated Daily Intake EDI (ng kg-1

body weight day-1

)

and Hazard Quotient (HQ) in infants

92

v

Chapter Five Discussion

Table 5.1 Comparison of concentrations (ng/g lip) of PCB in blood

serum with other studies from developed and developing

countries

96

Table 5.2 Comparison of concentrations (ng/g lip) of PCB in human

milk with other studies

99

Table 5.3 Comparison of concentrations (ng/g lip) of PCB in

placenta with other studies

102

vi

LIST OF FIGURES

Sr. Number Title Page No.

Chapter One Introduction

Figure 1.1. Exposure pathways of PCBs in human and non-human

biota

6

Chapter

Three

Materials and Methods

Figure 3.1 Map of study area showing selected sampling districts of

the Punjab Pakistan

31

Figure 3.2 Agilent GC-MS used for PCB analysis during present

study

38


Figure 4.1 Concentration of dioxin like and indicator PCB detected in

blood serum

47

Figure 4.2 Concentration of dioxin like and indicator PCBs detected

in human milk

50

Figure 4.3 Concentration of dioxin like and indicator PCBs detected

in human placenta

53

Figure 4.4 Distribution of PCB Homolog in human blood serum, milk

and placenta collected from five districts of Punjab

Pakistan

55

Figure 4.5 The comparison of dioxin like and indicator PCBs in blood

serum, milk and placenta

56

Figure 4.6 Spatial distribution patterns of PCB homolog in blood

serum from five districts of Punjab Pakistan

58

Figure 4.7 Spatial distribution patterns of PCB homolog in human

milk from five district of Punjab, Pakistan

59

Figure 4.8 Spatial Distribution patterns of PCB homolog in human

placenta from five district of Punjab, Pakistan

61

Figure 4.9 Spatial distribution patterns of blood, milk and placenta


63

Figure 4.10 Spatial trends of PCB homolog in blood, milk and placenta 66

vii

samples collected from urban and rural areas of Lahore

Figure 4.11 Spatial trends of PCB homolog in blood, milk and placenta

samples collected from urban and rural areas of Khanewal

66


samples collected from urban and rural areas of Okara

67


samples collected from urban and rural areas of Sialkot

68


samples collected from urban and rural areas of Chakwal

69

Figure 4.15 Eating habits of mothers selected from five districts of

Punjab Pakistan

78

Figure 4.16 Relationship between ∑34PCBsin maternal serum and; a)

infant‘s height (cm); b) head circumference (cm) ; c) mid

arm circumference (cm) and d) chest circumference (cm)

e) Combined Anthropometric Measures (cm)

84

Figure 4.17 Relationship between ∑34PCBsin human milk and; a)

infant‘s body weight (kg) ; b) head circumference cm) ; c)

mid arm circumference (cm) and d) chest circumference

(cm) e) Combined Anthropometric Measures (cm)

86

Figure 4.18 Relationship between ∑34PCBsin human placenta and; a)

Maternal Height (meter) ; b) Maternal Body weight (kg) ;

c) Infant‘s Height (cm) and d) Infant‘s Body weight (kg)

e) Combined Anthropometric Measures (cm)

89

viii

LIST OF ABBREVIATIONS

Abbreviations Full Form

P Significance level

R2 Coefficient of determination

R Pearson‘s correlation Coefficient

SD Standard Deviation

Ah Aryl Hydrocarbon

APGAR Activity, Pulse, Grimace, Appearance, and Respiration

BMI Body Mass Index

CHOL Total Cholesterol

DCM Dichloromethane

DDE Dichlorodiphenyldichloroethylene

DDT Dichlorodiphenyltrichloroethane

ECNI Electron Capture Negative Ionization

EDI Estimated Daily Intake

EMV Electron Multiplier Voltage

EWRS E-Waste Dumps/Recycling Sites

EI Electron Ionization

GCMS Gas Chromatograph Mass Spectrometer

HCB Hexachlorobenzene

HCH Hexachlorocyclohexane

IUPAC International Union of Pure and Applied Chemistry

MEAs Multilateral Environmental Agreements

OCP Organochlorine Pesticide

OC Organochlorine

PAH Polyaromatic Hydrocarbon

PCB Polychlorinated Biphenyl

PBDE Polybrominated Diphenyl Ether

PBT Persistent, Bioaccumulative And Toxic

PCDD Polychlorinated Dibenzodioxins

PCDF Polychlorinated dibenzofurans

POPs Persistent Organic Pollutant

SPE Solid Phase Extraction

TDI Tolerable Daily Intake

TEF Toxicity Equivalent Factor

TEQ Toxic Equivalent

TG Triglycerides

UNEP United Nation‘s Environment Program

ix

LIST OF APPENDICES

Page No.

Appendix-1 Spatial details of women selected from five districts of

Punjab, Pakistan

154

Appendix-2 Consent Letter 156

Appendix-3 Socio-Demographic and Health Survey 157

Appendix-4 Descriptive Statistics of PCB Congeners in Blood Serum


(Concentration in ng/g lip)

160

Appendix-5 Descriptive Statistics of PCB Congeners in human milk



162

Appendix-6 Descriptive Statistics of PCB Congeners in human

placenta collected from five districts of the Punjab,

Pakistan (Concentration in ng/g lip)

164

Table of Contents Page No.

Abstract ............................................................................................................................ i

Acknowledgement ........................................................................................................ iii

List of Tables ................................................................................................................. iv

List of Figure.................................................................................................................. vi

List of Abbreviations .................................................................................................. viii

List of Appendices ......................................................................................................... ix

CHAPTER ONE: INTRODUCTION

1.1 Classes of POPs and their Implications ................................................................... 3

1.2 PCBs and their Legal Framework ............................................................................ 3

1.3 Global Distribution pattern of PCBs in the Environment ......................................... 5

1.4 Exposure Pathways of PCBs in Human ................................................................... 5

1.5 Problem Statement ................................................................................................... 8

1.6 Objectives .............................................................................................................. 10

CHAPTER TWO: LITERATURE REVIEW

2.1 Polychlorinated Biphenyls (PCBs) ........................................................................ 13

2.1.1 PCB congeners and homolog ....................................................................... 13

2.1.2 Dioxins like PCBs ........................................................................................ 14

2.2 Sources of PCBs .................................................................................................... 15

2.3 Global Production of PCBs .................................................................................... 17

2.4 Historical Events of PCBs Poisoning .................................................................... 17

2.5 Wildlife and PCBs ................................................................................................. 17

2.6 PCBs Exposure in Man .......................................................................................... 18

2.7 Metabolism of PCBs in Human ............................................................................. 19

2.8 Effects of PCBs on Human .................................................................................... 20

2.8.1 PCBs toxicity in women .............................................................................. 20

2.8.2 Women exposure to PCBs in Asia ............................................................... 22

2.8.3 Fetal exposure .............................................................................................. 23

2.8.4. Women exposure to PCBs in South Asia ................................................... 25

2.9 PCBs Scenario in Pakistan ..................................................................................... 26

CHAPTER THREE: MATERIALS AND METHODS

3.1 Study Area ............................................................................................................. 29

3.1.1 Lahore . ......................................................................................................... 30

3.1.2 Khanewal ..................................................................................................... 32

3.1.3 Okara ............................................................................................................ 33

3.1.4 Sialkot .......................................................................................................... 33

3.1.5 Chakwal ....................................................................................................... 33

3.2 Sampling Strategy .................................................................................................. 34

3.3 Sample Collection and Transportation ................................................................... 35

3.3.1. Blood Serum ............................................................................................... 35

3.3.2. Human Milk ................................................................................................ 35

3.3.3. Human Placenta .......................................................................................... 35

3.3.4 Transportation and storage .......................................................................... 35

3.4 Sample preparation ................................................................................................ 36

3.4.1 Extraction of PCBs ...................................................................................... 36

3.4.2 Sample cleanup ............................................................................................ 37

3.5 Instrumental Analysis ............................................................................................ 37

3.6 Chromatographical Analysis .................................................................................. 39

3.7. Lipid Determination .............................................................................................. 39

3.7.1 Determination of blood serum lipids ........................................................... 39

3.7.2 Determination of Milk Fat ........................................................................... 40

3.7.3 Determination of Placental Lipids ............................................................... 40

3.8 Quality Control and Assurance .............................................................................. 42

3.9 Socio-demographic Characteristics ....................................................................... 42

3.10 Risk Assessment .................................................................................................. 42

3.10.1 Toxicity Equivalent Factor (TEF) ............................................................. 42

3.10.2 Risk assessment of mothers and infants .................................................... 43

3.10.3. Estimated Daily Intake of dioxin like PCBs in women ............................ 43

3.10.4. Estimated Daily Intake of PCBs in Infants ............................................... 43

3.11 Statistical Analysis ............................................................................................... 44

CHAPTER FOUR: RESULTS

4.1 PCB profile and congener specific analysis in blood serum ................................... 46

4.2 PCB profile and congener specific analysis in human milk ................................... 49

4.3 PCB profile and congener specific analysis in human placenta ............................. 52

4.4 Comparison of PCBs homolog in human blood serum, milk and placenta ............ 55

4.5 Spatial Distribution Patterns of PCB Homolog ...................................................... 57

4.5.1 Spatial Distribution patterns of PCB Homolog in blood serum ................... 57

4.5.2 Spatial Distribution Patterns of PCB Homolog in human milk .................... 57

4.5.4 Comparison of spatial patterns of PCB hornolog in blood serum, milk and

placenta .................................................................................................................. 62

4.6 PCBs Trends in Urban and Rural areas ................................................................. 64

4.7 Toxicity Equivalent Fluxes of Dioxin like PCBs ................................................... 69

4.7.1 TEQ and estimated daily intake of dl-PCBs in blood serum ........................ 69

4.7.2 TEQ and estimated daily intake of dl-PCBs in human milk ........................ 72

4.7.3 T TEQ and estimated daily intake of dl-PCBs in human placenta ............... 74

4.7.4 Relative Comparison of TEQ ....................................................................... 76

4.8 Association of PCBs in blood, milk and placenta with maternal social and

demographic characteristics .......................................................................................... 76

4.9 Association of PCBs in blood, milk and placenta with infants anthropometric

characteristics ................................................................................................................ 83

4.10 Exposure and Risk assessment to nursing Infants (milk) ..................................... 92

CHAPTER FIVE: DISCUSSION

5.1 Comparative Analysis of PCBs profile ................................................................... 94

5.1.1 PCBs profile in blood serum ......................................................................... 94

5.1.2 PCB profile in human milk ........................................................................... 98

5.1.3 PCB profile in placenta ............................................................................... 101

5.1.4 Overall PCBs trend in multiple body matrices ........................................... 103

5.2 Spatial trends of PCB Homolog with potential sources ....................................... 104

5.3 PCB Trends in Urban and Rural areas .................................................................. 107

5.4 Risk Assessment of Maternal Health .................................................................... 109

5.4.1 Exposure to dl-PCBs and TEQ fluxes ........................................................ 109

5.4.2. Risk assessment of dioxin like PCBs......................................................... 109

5.4.3. Risk assessment of Physiological Characteristics ..................................... 110

5.5 Relationship of PCBs with neonatal anthropometric measures ............................ 113

CHAPTER SIX: CONCLUSION

Conclusion .................................................................................................................. 116

Recommendations and future perspectives ................................................................ 117

References ................................................................................................................... 119

Appendices .................................................................................................................. 154

Publications ................................................................................................................. 166

CHAPTER ONE

INTRODUCTION


2

Chapter One

INTRODUCTION

Industrial, urban and agricultural development has provided countless benefit to

mankind such as the provision of food, shelter, health, communication and transportation

facilities (McMichael, 2011). For the continuous supply of all these facilities, natural

resources are indiscriminately used and this process is going on (Nchuchuwe &

Adejuwon, 2012). In the course of modernization, thousands of new chemicals have been

introduced into the environment, which are putting the integrity of ecosystems at the risk

(Kodavanti et al., 2008). Organic compounds are one of the major groups of xenobiotic

chemicals. The synthesis of these organic chemicals is increasing tremendously which are

ultimately discharged into the environment. Even now these chemicals are found

everywhere on the planet from ocean sediments to the alpine glaciers. The chemicals

resulted as the product of anthropogenic activities have been reported from Arctic and

Antarctic Regions (AMAP, 1998; Wei et al., 2007). These are the Persistent Organic

Pollutants (POPs) stay in the environment for longer times, bio-accumulate and disrupt

the metabolic integrity within living organisms (Dórea, 2008). POPs exhibit properties

such as less soluble in water and are semi-volatile and bioaccumulative (Fiedler et al.,

2002; UNEP, 2011). POPs present in the environment as chemical mixtures; enter in the

body of organisms through multiple routes (Lohmann et al., 2007) viz; food, air and

working environment and due to lipophilic properties are capable to accumulate in lipid

rich tissues of living organisms (Lee et al., 2007). The origin of POPs is either

intentionally synthesized or accidentally formed as the by-product during different

chemical processes (Lignell, 2013).


3

1.1. Classes of POPs and their Implications

The most classical POPs are categorized as Organochlorine Pesticides (OCPs),

Industrial chemicals i.e. Polychlorinated Biphenyls (PCBs), Hexa Chlorobenzenes (HCB)

and their byproducts, which are unintentionally formed e.g. furans and dioxins (Zhang et

al., 2005). These cumulatively are called as the Dirty Dozen, whereas; example of

emerging POPs included polychlorinated dibenzo-p-dioxins (PCDD), dibenzofurans (Fs)

and Polycyclic Aromatic Hydrocarbons (PAHs; Lohmann et al., 2007). The Dirty Dozen

POPs have half-lives of 5 to 15 years (Ritter et al., 2011). Due to toxicological

implications of POPs in organisms,these have attained the attention of the global and

regional scientific community at the end of the 20th century (Breivik et al., 2004).

Eco-toxicological implications of POPs were recognized in the 1960s, starting

with the side effect of Dichlorodiphenyltrichloroethane (DDT) the first synthetic OCP on

the bird population. Among different POPs; PCBs are of growing concern as these are

endocrine disrupters and present in the environment, consumer goods and food, enter the

human body and interfere with reproduction (Schlumpf et al., 2011). Several authors have

been reported POPs to impart deleterious impacts on wildlife, human and environment

after the work of Rachel Carson in 1962 (Cui & Forssberg, 2003; Zhang et al., 2005).

1.2. PCBs and their Legal Framework

Polychlorinated Biphenyls (PCBs) are legacy POPs and ubiquitous in biological

and environmental media (Kodavanti et al., 2017). Generally, PCBs generated in one

place can be displaced to other countries through air circulations, water and sediment

movement, putting the wildlife and human health at risk (Jones & De Voogt, 1999). The

major reasons behind growing concerns about regulation of the PCBs are their hormone

disrupting capabilities in wildlife and human (Mehmood et al., 2017). According to the


4

concept of ―human-wildlife connection‖, detection of harmful effects in wildlife may act

as an early warning for mankind (Ross &Birnbaum, 2003). Birth defects and behavioral

abnormalities detected in birds, fish and mammals species around the Great Lake, Ontario

Canada (Gilbertson et al., 1991); reproductive disorders observed in Baltic Ringed Seals

(Helle et al., 1976) and thinning of egg shells of fish (contaminated by DDT) eating birds

(Jensen et al., 1969) instigated the scientist to investigate human population in relation to

PCBs exposure. The Great Lake was highly polluted due to discharges of heavy metals,

various POPs viz; PCBs and OCPs and other industrial and agricultural pollutants which

warranted the concerns about wildlife and human health.

The toxicological effects of PCBs attracted the attention of scientific community

all over the world. In connection to this, various measures had been taken by

governments, organizations and scientific communities around the globe to keep the

human and environment safe (Godduhn & Duffy, 2003). Several Multilateral

Environmental Agreements (MEAs) have been signed by nations all over the world to

overcome pollution caused by trans-boundary movement of these contaminants (Elabbas

et al., 2013). Thus nations across the world joined to sign the United Nation‘s

groundbreaking treaty named as the Stockholm Convention on POPs, adopted in 2001

(US EPA, 2002). The scope of PCBs under the Stockholm convention is to eliminate their

production, restricting their use or minimizing their unintentional production (Nost,

2014). The Stockholm Convention banned worldwide usage of twelve POPs and out of

these 12 POPs nine were ranked as highly hazardous pollutants (Gilden et al., 2010).

Other legal frameworks regulating the PCBs include; Rotterdam Convention, Basel

Convention, Aarhus Protocol and Helsinki Convention (Boalt et al., 2013).


5

1.3. Global Distribution Patterns of PCBs in the Environment

The distribution of PCBs is cosmopolitan ranging from the equator to poles and

from deep oceans to lofty mountains. Due to persistent nature, these chemicals could

reach to the longer distances governed by their physical and chemical properties (Wania

2003). PCBs enter into the food chain, accumulate in the organism and reach into those

areas, where they have not ever been produced (Aguilar et al., 2002). According to a

global fractionation theory proposed by Wania and Mackay (1993), atmospheric currents

circulate and condense the POPs (including PCBs) that are released at low latitude

towards the high latitudes e.g. Polar Regions. This dispersal mechanism of atmospheric

transport has been significant for legacy POPs such as PCBs whereas, emerging

pollutants tend to transport/disperse through oceanic currents (Lohmann et al., 2007).

Later on, several authors confirmed that least disturbed areas of the world like Antarctica

and the Arctic zone have been found contaminated with PCBs due to their wide range

atmospheric transport by different environmental matrices (Meijer et al., 2013). A study

on fish species from Elephant Island, Antarctica to monitor the levels of PCBs revealed

their detection in variable concentration in different fish species.

1.4. Exposure Pathways of PCBs in Human

Regardless their extraordinary roles in the development of modern society, PCBs

are recognized for many negative impacts on human and environmental health (Tan,

2009). Human exposure to PCBs include intake of food and water contaminated with

PCBs, inhalation of PCB contaminated air-borne particles and ambient air and dermal

absorption (Duarte & Jones, 1994). However, consumer products (Vestergren & Cousins

2009) and indoor environments such as building materials and air (Shoeib et al., 2011)

are additional exposure pathways of PCBs in human (Figure 1.1). PCBs may enter the


6

food chain from various industrial sources and once released into the environment, PCBs

persist for years, widely distribute in the environment by air, water and soil, enter the

food chain and bioaccumulate in living organisms (in their fatty tissues) up to seventy

thousand times higher than their initial levels (UNEP, 2009). Non-human biota is affected

through inhalation of PCBs contaminated air, water and eating plants and agricultural

commodities polluted with PCBs. Whereas, human are exposed to PCBs from consumer

products at home, workplace products, releases from agricultural lands, uptake of

contaminated air, water and fauna; and dietary intake of commodities contaminated with

PCBs (Brennan & Withgott, 2005).

Figure 1.1. Exposure pathways of PCBs in human and non-human biota (modified from

Brennan & Withgott, 2005).

Due to the dispersion and movement within atmosphere and hyderosphere, PCBs

accumulate in organisms and magnify at higher trophic levels. That is why, PCBs

contamination concerns are pronounced at the top predator species including human

(Jones &De Voogt, 1999). Their exposure in human particularly in women is of major

concern as most of the PCBs being lipophilic tend to move along lipid rich body matrices

Industry & Manufacturing

Air

Water Non-human

Biota

Soil

Agriculture Human

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of women into the fetuses and infants. Therefore, the toxicological implications of

perinatal exposure to PCBs have been extensively reviewed across the globe as they have

also been found to cause infertility in human (Spano et al., 2005). Due to their stability,

persistence in the environment and wide ranged toxic effects; PCBs are considered a

threat to the health of the environment and human which highlights a need for their

detailed monitoring in environmental and epidemiological studies (Porpora et al., 2013).

Studies have highlighted the transplacental transference of PCBs from mothers to

developing fetuses during pregnancy and transmammary into the infants during the

course of lactation (Hassine et al., 2012). In fetuses and infants, PCBs may induce

adverse health effects such as growth retardation reduced birth weight and behavioral

disorders.

Assessment of human exposure to PCBs has extensively relied upon blood serum

as a biomarker and most of the studies estimating the human body burden were focused

on PCBs in blood and serum (Song & Li, 2014). Govart et al., (2010) highlighted the

association between human PCB concentrations in blood serum and dietary intake.

Human milk has extensively been used to examine the amount and types of both desirable

and undesirable compounds in nursing mothers and their exposure to the breastfed

offspring. It is a primary food of developing infants and plays a pivotal role in their

development due to its high nutritional and lipids contents. Human milk; during first five

days is characterized as ‗colostrum‘ and is rich in protein and lower in lipids than mature

milk. Human milk provides all the nutrition requirement of nursing infants during the first

six months of their lives and protects infection (WHO, 2007). Researchers have reported

PCB levels in human milk, exposure to infants and their associated health risks to infants.

In the recent years, the human placenta has been identified as a biomarker for the

monitoring of PCBs (Leino et al., 2013). Several studies across the globe have reported


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the PCB residues in the environmental media and human body matrices. But,

unfortunately, few studies are available in Pakistan regarding presence of PCBs in the

multiple human body matrices to make a cross comparison. With the rapid

industrialization, an ongoing and continuous use of PCBs has been reported in the

country. Hence, there is a dire need for monitoring the PCBs in human in order to assess

the risks to human and future generations.

1.5. Problem Statement

Pakistan is among those developing countries which are in the transitional phase

of their industrial and agricultural development. During the last three decades, rapid boost

in industrial development and urban sprawl has raised several environmental issues

pertaining to PCBs pollution and put the ecological integrities and human health at risk

(Ahmed, 2003). There are some reports that PCBs are also present in the glaciers in

Northern Areas of Pakistan, where industrial activities exist. A major share of PCBs

enters into the human body from food, whereas, the small share is contributed from direct

environmental exposure such as air, water and soil (Patandin, 1999). In Pakistan, PCBs

have been banned after Stockholm Convention, but these are still detected in various

matrices (Ullah et al., 2016). In this regard, several researchers put their efforts in tracing

PCBs in Pakistan, but most of the work was in the different environmental media such as

water, sediments and soil etc. But unfortunately, PCBs were not well characterized in

human studies in Punjab, Pakistan due to the paucity of data and lack of interest on

institutional level.


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Punjab is a densely populated Province of Pakistan with an emerging area based

mainly on agriculture and industrial growth. Urban expansion in the Province and an

increased demand for consumer goods has put natural resources at stress. Five districts

(Lahore, Khanewal, Okara, Sialkot and Chakwal) were selected in different geographical

locations within Punjab. These districts were selected because these are in the grip of

pollution related issues mainly due to industrial and agrochemical activities. Lahore and

Sialkot are districts with major industrial clusters of the country. Steel manufacturing

units, furnaces and paint industries and electronic waste dumping sites in/near Lahore,

leather and tanning industry and steel and surgical tools manufacturing units in Sialkot

are potential sources of PCBs contamination in these areas (Syed et al., 2013; Mahmood

et al., 2014). Whereas, District Chakwal with distinct topography is experiencing the

PCBs pollution mainly from cement industries located therein. Khanewal and Okara are

mainly agricultural areas with extensive application history of POPs. These areas may

experience the PCBs contamination due to dispersion from urban areas, use of PCBs

based pesticides and the general practice of wood and solid waste burning (Ali et al.,

2013). The populations living in rural areas prefer to eat dairy products viz; milk and

butter which are dietary sources of PCBs exposure in human.

The indiscriminate use of consumer goods, industrial growth and use of agro

chemical has led the PCBs contamination in environmental media of the Punjab, Province

and highlighted a need for their monitoring in human being. The biomonitoring of PCBs

in human body matrices may represent significant information about their level, body

burden and potential risks to the health of the person exposed (Song & Li, 2014).

Expecting women particularly are more vulnerable and at greater risk of exposure to

PCBs due to heightened sensitivity and up bearer of future human generations. Most of

the previous studies on the human in Pakistan were focused on OCPs with a few on


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concentration of PCBs in human blood and milk, whereas, none of the past studies from

Pakistan have reported the concentration of PCBs in the placenta. Due to lack of

information of PCBs in women from Pakistan, this study was designed to explore the

PCBs body burden in women using three matrices such as blood, milk and placenta from

different areas of Punjab Pakistan. Human blood, milk, and placenta are the best matrices

to measure PCBs concentrations in pregnant and nursing mothers. Monitoring of PCBs in

blood, milk and placenta not only depicts body burden in women but may also be helpful

in assessing the risks to the fetuses and breastfeeding infants (Toms et al.,

2007).Therefore, expecting mothers from five districts of Punjab were selected to monitor

the mother-infant PCBs contamination. Determination of PCBs in these multiple body

matrices can provide more reliable information which can be helpful in the formation of

strategies coping with PCBs pollution and reducing risks to human health and

environment. The aforesaid observations and information triggered to design and start the

present study.

1.6. Objectives

Monitoring of PCBs in expecting women is of immense importance because fetuses

and infants are dependent upon their mothers during development with in womb and

mother milk during the early stages of their life. Studies have affirmed that PCBs not only

contribute to adverse health effects in women but also may transfer from mother to

fetuses and infants leading to reproductive and behavioral disorders (Vafeiadi et al.,

2014). The current study was aimed to monitor the exposure to PCBs and associated

health risks in expecting women from the Punjab Province, Pakistan. The study achieved

the following objectives:

i. to assess the concentration of PCBs in blood serum, milk and placenta

of women from some districts of the Punjab Pakistan.


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ii. to monitor the spatial and urban rural distribution patterns of PCBs

among the women living in selected districts of the Punjab, Pakistan.

iii. To evaluate the association of PCBs with socio-demographic

characteristic to highlight the health impacts on mothers and infants

CHAPTER TWO

LITERATURE REVIEW

Chapter Two Literature Review

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Chapter Two

LITERATURE REVIEW

2.1. Polychlorinated Biphenyls (PCBs)

PCBs are environmental pollutants having the basic structural unit with biphenyl

ring and are classified into Persistent, Bioaccumulative and Toxic (PBT) substances

(Schaum et al., 2003). These noxious chemicals are generally inert, thermally stable and

resist to acids and alkalies that is why useful for different applications e.g. di-electric

fluids in transformers and capacitors, lubricants, pesticides extenders, fluids for heat

transfer, cars, vacuums pump and household electrical appliances (Brievik et al., 2004).

Due to these insulating and inflammable properties, the use of PCBs was expanded in the

past for about 50 years i.e. 1929-1977 (Erickson & Kaley, 2011). PCBs are usually

described as PCB congeners i.e. individual compounds of PCBs; PCB homolog

categorization of PCBs on the basis of similar chlorine substitutes and PCB aroclors

combination or the mixture of various PCB congeners (NAVFAC, 2012).

2.1.1. PCB congeners and homolog

There are 209 PCB congeners and the International Union of Pure and Applied

Chemistry (IUPAC) has sequentially numbered all of these congeners from PCB-1 to

PCB-209 according to their increasing number of chlorine atoms. These congeners have

different positions and numbers of chlorine atoms which determine the physico-chemical

properties of the molecule and also affect toxicity and its types (Harremoes, 2001). PCBs

are also classified into PCB homolog on the basis of the location of their chlorine

substitutes in biphenyl rings. There are ten PCB homologviz; Mono- chlorobiphenyl, Di-

chlorobiphenyl, Tri- chlorobiphenyl, Tetra- chlorobiphenyl, Penta- chlorobiphenyl, Hexa-


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chlorobiphenyl, Hepta- chlorobiphenyl, Octa- chlorobiphenyl, Nona- chlorobiphenyl and

Deca-chlorobiphenyl. PCB congeners have been used in the past as mixture to obtain the

desired industrial materials under various trademarks and formulation namely i.e. Aroclor

by Monsanto (USA), Prodolec (France), Phenoclor (Japan) and Chlopen (Germany)

(Rushneck et al., 2004). Among these mixtures, aroclors were widely used in the USA

with distinctive congeners and homolog fingerprints.

2.1.2. Dioxins like PCBs

PCB congeners may be grouped as co-planar PCBs and other non-dioxin like

PCBs. The chemical subclasses of PCBs that exhibit similar toxic properties as of PCDDs

and Polychlorinated dibenzofurans (PCDFs) by exerting their effects through Aryl

Hydrocarbon (AhR) receptor are called dioxin-like PCBs and categorized as non-

orthosubstituded PCBs (PCB-77, 81, 126 and 169) and mono-orthosubstituded PCBs

(PCB-105, 114, 118, 123, 156, 157, 167 and 189; (Silverstone, 2009; Srogi, 2008).

The sources of dioxins in the environment include thermal processes, incomplete

combustion of municipal, medical and hazardous wastes and numerous chemical

formulation and industrial processes viz. copper smelting, paper and pulp chlorine

bleaching processes (Wittsiepe et al., 2015). US EPA has identified the sources of

exposure to dioxin like compound in an adult as under; food (96%), inhalation (2%), soil

(1%) and water (0.001%). Dioxin like PCBs are biologically persistent and ubiquitous in

the environment with diverse adverse effects viz. cancer, non-cancer effects such as

immunosuppression, changed hormonal and growth factors, diabetes, wasting syndrome,

chloracne and hyperplasia etc. (Ott et al., 2006).


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2.2. Sources ofPCBs

Environmental releases of PCBs can be attributed to volatilization, accidental release

and leaks from facilities containing PCBs including disposal activities, open burning of

waste materials, incomplete burning of chlorine containing sewage sludge, landfills and

waste incinerations facilities (Kim & Yoon, 2014). Other sources of PCBs include

possible direct and indirect release into the environment during their applications. During

the recent years, substantial municipal waste open dumping in suburban areas around the

cities of developing countries in Asia has garnered ample attention as probable sources of

POPs (Someya et al., 2010). Low-cost repairing, auctions, and transportation of old

transformers to industries causing leakage of oil, burning of municipal and electronic

waste, rubber and plastic material release dioxin like PCBs which cumulatively leads to

PCBs contamination into the environment (Jain & Sareen, 2006). Atmospheric air

circulation plays a basic role in contaminating the environment with PCBs which can

dissipate from a specific point source towards distant areas. Apart from the air, urban

runoff, discharge of wastewaters and municipal sewage sludge into the rivers may also

lead to PCBs contamination in the marine environment. Once released into the

environment, PCBs bind firmly to sediments, soils and particulate matter (Eqani et al.,

2012). In soils, PCBs leach down slowly and translocate to plants insignificantly. Their

rate of breakdown may last for months to years; in soils and sediments, it decreases with

increasing degree of chlorination (ASTDR, 2000, 2014).

2.3. Global Production of PCBs

The use of PCBs was started on large scale since 1929 in the USA for commercial

purposes as lubricating oils, inks, a plastic, carbonless copy paper, impregnating, paints,

sealing liquids, adhesives and additives, waxes, immersion oils, fire retardants, plaster,


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casting and dedusting agents in different industries (Wittsiepe et al., 2015). Several

decades of the 20th

century are recognized for wide use of PCBs (Nost, 2014) for

industrial applications and electrical companies as largest consumer worldwide (Erickson

& Kaley, 2011). Worldwide, about 1.5 million tons PCBs were manufactured and used in

industrial and agricultural sector (Iszatt, 2015) in the 1970s. Since their production in the

late 1920s, the amount of commercially produced PCBs is around 2×109 kg out of which

2×108 kg remains in environmental mobile reservoirs (WHO, 2003). Although their

detrimental effects became evident since 1937, the first warning of PCBs was reported by

Jensen et al., (1969) during their work, they found a remarkably high concentration of

PCBs in Baltic Sea Eagles. The major share of PCBs production is contributed by United

States of America(USA) i.e. the production of about 650,000 metric tons (half of the

world‘s) PCBs production and importer of 50% of the rest produced by other countries.

Therefore, manufacturing, distribution and use of PCBs got restricted by the United States

Environmental Protection Agency (US EPA) during 1979 (Schecter, 2013; UNEP, 1999).

In Japan, the historical origin of PCBs have been found in agricultural chemicals with

abrupt increase starting from late 1950s, peaked in 1967 to 1972 and declined

continuously from 1972-1981 till leveled off (Yao et al., 2002). An estimated ~80 metric

ton of PCBs have been utilized in oil of electric transformers as reported by the

Government of Bangladesh‘s Department of Environment (Mamun, 2007).

PCBs production on the industrial scale got banned in developed countries owing

to their toxicity, but till then about two million tons of PCBs were discharged in the

environment (Roszko et al., 2014). In Korea, PCBs were enormously used (about 4292

tons) until their ban in 1996. These were banned in China during 1974 but, some

electrical equipment with PCBs are still in use and a serious threat to the environment

(Shao, 2001). The International Agency for Research on Cancer (IARC) announced that


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some PCBs have potential as carcinogens (Arrebola et al., 2012). Despiteworldwide ban

on PCBs commercial production, significant amounts of PCBs are purchased by industry

for commercial purposes in some countries (Ludewig, 2008). They still are detected in the

atmosphere, soil, sediments, rivers, lakes, fish, animals, wildlife and even in various

human tissues (Wu et al., 2011).

2.4. Historical Events of PCBs Poisoning

A number of events have been occurred in the past putting human individuals at

higher exposure levels of PCBs. The first historic and highly documented event of PCBs

poisoning of human was ―oil disease‖ in Japan in 1968. This mass exposure occurred

accidentally by ingestion of PCBs contaminated rice oil and dioxin like compound

causing poisoning to about 1800 persons and 300 of them were deceased (Yu, 2005). The

symptoms of this incident were characterized by imparting acne, skin manifestations and

eye discharge (Onozuka et al., 2009; Yoshimura, 2003). Another incident similar to oil

disease was also reported from central Taiwan between years 1978 to 1979, which was

later named as Yucheng (oil disease in Chinese). This human food poising was caused by

ingestion of contaminated rice-bran cooking oil with PCBs and 2060 human individuals

were reported as victims (Hsu et al., 2005). Later on, studies confirmed the presence of

PCBs in blood and adipose tissues of the Yusho / Yucheng victims which led to abnormal

pregnancy in women, reproductive and fetal developmental disorders and suggestive

carcinogenesis of lungs (Mitoma et al., 2015).

2.5. Wildlife and PCBs

Exposure to PCBs results in multiple adverse health outcomes in human as well as

in wildlife (Faroon & Ruiz, 2015). PCBs are endocrine disruptors and may be responsible

for various dysfunctions in the reproductive system of human and wildlife, change in


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hormonal balance, pathological and physiological disorder (Timbrell, 2001). Wildlife is

exposed to PCBs due to their transfer up in food chains. The first warning about PCBs

came from Jensen et al., (1969) who reported the high concentration of PCBs in Baltic

Sea Eagles. The lipid rich tissues of aquatic organism living in the marine ecosystem have

more potential to accumulate the PCBs (Bordajandi et al., 2003). Bioaccumulation levels

of PCBs within species of a particular ecosystem depend upon the structure of food chain

however different metabolic features of different species also direct the bioaccumulation

potential of POPs (Vallack et al., 1998).

A study on PCBs residues in common cormorant (a bird species) revealed the

impaired gross size and growth rate of this bird specie (Murata et al., 2013) and further

studies explored many fish species to be endangered due to PCBs including eel

population (Blanchet- Letrouve et al., 2014). Marine mammals and fish contain PCBs

thousands of times higher than their level in the water (ASTDR, 2000) and contamination

of marine organisms depends upon the congener‘s chemical properties. An association

has also been identified between carcinogens and mutation in livers and lungs of rats in

experimental studies (Maddox et al., 2008).

2.6. PCBs Exposure in Man

Man is exposed to PCBs through water, air and food intake (WHO, 2003). The

primary exposure route of PCBs in human is dietary intake accounting for 90%, i.e.

consumption of contaminated food and 75% of it is mainly through animal origin food

such as milk, butter, fish, meat and poultry (Darnerud et al., 2006). This dietary exposure

could be helpful to predict the concentration of PCBs in the human body (Caspersen et

al., 2013). Other sources of PCBs include dust ingestion and inhalation of indoor air. The

higher amount of PCBs is present in indoor air as compare to the surrounding outdoor


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ambient air (Macleod, 1981). Significant levels of PCBs have been detected in public

buildings having PCBs as a sealant (Kang et al., 2008). Man is the top of the food chain

with possible higher concentrations of PCBs in liver, brain, adipose tissue and skin,

blood, milk, placenta and cord blood (Esteban & Castano, 2009).

2.7. Metabolism of PCBs in Human

Once PCBs enter into the human body, these are partly metabolized and excreted

through urine and feces, whereas, most of the PCBs are resistant to metabolic

degradations, lipid soluble and sequester in fat/ lipid rich tissue (Hites, 2004). The half-

lives of PCBs depend upon the degree of chlorination and these may last from a few

months up to numerous decades (Lignell, 2013). The metabolism of PCBs in human takes

place through hepatic microsomal oxidases into hydroxylated metabolites (OH-PCBs)

that excretes through urine and excreta (Letcher et al., 2000). The metabolism rate and

excretion of PCBs and their metabolites from human body inversely depends upon the

degree and location of chlorine atom of biphenyl molecule, which also governs the rate of

elimination from body, vizmono and di-chlorinated biphenyls excrete via urinary tract

and high chlorinated biphenlys excrete via feces (Yu, 2005). Some of PCBs metabolites

bind to body matrices of human and wildlife and cause toxic effects. Hence, toxic effects

identified in an individual may be combined effects of concentration of PCBs and their

metabolites present in the body (Gomara et al., 2012).

Several techniques are being adapted to break down PCBs into less harmful

products in the environment, giving an insight to various fields of science. These include

physical process such as photolysis using ultraviolet radiations for dechlorination of

PCBs, use of magnetic nanoparticles and biological degradation in lakes, rivers, soils and

sediments e.g. microbial degradation, phytoremediation (Fang et al., 2013).


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2.8. Effects of PCBs on Human

Nevertheless, PCBs withdrawals from use, these are still detected in soils,

sediments, water and living organisms due to their environmental persistence. Several

casting criticisms and environmental ills are endorsed to PCBs, which are also known as

immunosuppressive (Shaw & Chadwick, 1998). Monitoring of environmental

concentration is of major concern while assessing the potential harm that a particular

environmental pollutant may pose to the environment and organisms. Identification of

exposure to chemicals, responses to that specific chemical and its susceptible toxic effects

are fundamentals of toxicology. Biomarkers are tools that play pivotal role in this regard

and can be divided into three types, i.e. biomarkers of exposure, response and

susceptibility (Timbrell, 2001). Biomarkers of exposure have been used as a monitoring

tool for more than hundred years in occupational studies and have more recent

applications in environmental studies (Ott et al., 2006). Examples of human biomarker

are blood, milk, placenta and adipose tissues etc.

Recent epidemiological and experimental studies have highlighted that PCBs

exposure in humans may cause detrimental toxic effects viz; immunotoxicity,

developmental, physiological and structural disorders, endocrinal disruption, reproduction

abnormalities such as low birth weight, cancer, neurological effects, impairment of vision

and hearing disorder, depressive responsiveness and decreased Intelligence Quotient (IQ)

level (Kodavanti, 2014).

2.8.1. PCBs Toxicity in Women

Women are more vulnerable to PCBs exposure as compared to the men. They

have a relatively higher proportion of fatter than men and PCBs being lipophilic tend to

accumulate in fatty tissues. Consumption of these contaminants not only causes adverse


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health effects to exposed women but pre and post natal exposure causes developmental,

reproductive, behavioral disorders and suboptimal neurological effects in the fetus

(Daglioglu et al., 2010). PCBs may enter in women through food, inhalation of

contaminated air, whereas, pregnancy and menstruation are major excretion routes for

PCBs in addition to urine and feces (Noren & Meironyte, 2000). Another important route

of their excretion from women is lactation/ breastfeeding the nursing infants (Rawn et al.,

2017). PCBs may also be released from the woman's body through the placenta, crossing

the placental barrier and may enter to the developing fetuses (Iyengar & Rapp, 2001).

Women blood serum, cord serum, placenta and milk have been used as indicators

of PCBs exposure in different studies. A research on Taiwanese mothers was conducted

on these four body matrices and showed highest concentration ratio of human milk

relative to blood serum levels as compared to the placenta and cord serum (Wang et al.,

2004). Maternal blood has been used as an important biomarker of exposure to PCBs in

Mexican women, participants, where significant PCB residues were detected than other

POPs in the study population (Orta-Garcia et al., 2014). Wittsiepe et al., (2015) provided

a detailed analysis of the impacts that PCBs have been causing on the individuals working

on the Electronic Waste Recycling Sites (EWRS) of Africa, located in Ghana and from

the suburb without direct exposure to electronic waste management activities. The study

established a direct as well as an indirect pathway of PCBs exposure among those

participating in the study.

PCBs presence in human milk is an alarming threat and it is in conjunction with

air and food has been selected by United Nation‘s Environment Program (UNEP) for the

POPs global monitoring (Sharma et al., 2014). Korrick & Altshul (1998) have identified a

correlation between PCB concentrations in human milk and occupational PCBs exposure.

According to Someya et al., (2010), the highest PCB levels in breast milk were consistent


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with occupational PCBs exposure. They also focused on the feeding habits of the

community and established that the higher PCBs concentration in breast milk was due to

consumption of fish rather than occupational exposure. Postnatal exposure to PCBs was

also studied by Park et al., (2011). They evaluated PCB congeners and found their

concentrations in breastfeeding mothers in California, USA.

The placenta is one of the best biomarkers that could potentially establish a

relationship between POPs exposure and body burden is the placenta. The significance of

healthy placental life is crucial in determining the health of fetus in addition to different

pathways that may interfere with the normal placental functioning. The process of POPs

transference during pregnancy into the fetus is still not well recognized (Vizcaino et al.,

2014). In human, placenta performs dual transportation functions; at one end it facilitates

the bio-substances and nutrients passage from mother to the developing fetus and on the

other side for some materials, it acts as a barrier. Besides the passage of nutrients, some

harmful substances may also intrude and cross the placenta barrier and damage embryo

(Iyengar & Rapp, 2001). Myllynena et al., (2004) studied the toxicological impacts of

environmental exposures, including pesticides and dioxins in placenta samples. This

study provided a basis for further investigations, however, gave a set of historical data

highlighted the placental transfer of PCBs and emphasized on the further studies to

validate the role of PCBs in placental toxicology.

2.8.2. Women exposure to PCBs in Asia

Wang et al., (2004) studied the residues of PCBs and Dioxins in blood serum,

umbilical cord serum, milk and placenta in mothers of Central Taiwan. Dioxin like PCBs

was reported highest in human milk followed by venous serum, placenta and were least in

cord serum. Kang et al., (2008) highlight the OCPs and PCBs in human population,


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including both male and female in urban areas of Korea. The results showed frequent

detection of HCB, HCH and trans-nonachlor and Dichlorodiphenyldichloroethylene

(DDE) as the most abundant OCP. Lipid adjusted serum levels of 23 PCB congeners were

also determined and showed PCB-153 as the dominant congener and concentrations of

PCB and OCP were positively correlated with each other. In Shanxi, China, Ma et al.,

(2012) detected PCBs and its metabolites in 130 placental tissues of women from rural

area (80 samples were from neonates with neural tube defects and the rest were from

healthy newborns). Level of PCBs was detected slightly higher in the samples from

defects neural tube than control samples, butit was concluded that no risk factor of neural

tube defects associated with the level of PCBs and PBDE.

2.8.3. Fetal exposure to PCBs

The chemicals which are present in food and environment of expecting mothers

could be traced in tissues and blood streams of their fetuses (Lopez-Espinosa et al.,

2007). In addition to pregnant women, fetuses, neonates, and children also are susceptible

to the harmful effects of these contaminants due to heightened sensitivity as their organs

are comparatively immature (Barr et al., 2007). There are several studies that highlighted

maternal levels of PCBs in relation to the developing fetuses and infants through the

placenta and breast milk (Doucet, 2009). The developmental processes in fetuses and

infants are more susceptible to PCBs exposure even at the low level (Mostafalou &

Abdollahi, 2013). This early development exposure may lead to health risks in fetuses

and infants, who are more susceptible to the impacts of these environmental toxins as

their immune system, organs, and detoxification mechanism, are partially developed than

adults (Wu et al., 2011). A common reason of exposure to these contaminants may

include maternal lifestyle habits, medication or environmental and occupational exposure.


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Human placenta has been identified as a biomarker for the monitoring of particular

environmental contaminant transfers from mother to fetus (Myllynen et al., 2005).

Human milk plays a vital role in the growth and development of infants due to its

high contents of lipids and nutrients. Environmental exposure of PCBs to women results

in their accumulation in human milk which is carried over from mother to infants through

lactation (Dirtu et al., 2009). However, transmammary exposure has been reported to lead

to higher levels of POPs in neonates than in nursing mothers (Gascon et al., 2013; Verner

et al., 2013). Trans-placental exposure has been attributed to a similar or lesser level of

PCBs in infant‘s blood streams as compared to mothers (Mori et al., 2014).

The fetal organs in which these pollutants accumulate are blood, bone-marrow,

brain, spleen and liver (Leino et al., 2013). Preterm birth, reduced birth weight and

intrauterine growth retardation are resulted due to prenatal exposure to PCBs (Covaci et

al., 2002). Latent effects may include disturbed levels of thyroid hormone, reproductive

impairments, cognitive deficits, altered perceptual and motor development and adverse

neurobehavioral impacts (Kodavanti et al.,2017). Leino et al., (2013) reported the

association between pollutants fate in mothers and fetal exposure through the placenta.

The findings of the study reflected that fetal exposures to PCBs were dependent upon the

mother‘s demographic parameters such as age and parity. Lipophilicity and molecular

size are among the various PCBs and factors governing the exact dispersal of pollutants in

the body organs (Giaginis et al., 2009). Placental transfer of PCBs was also characterized

by Porpora et al., (2013) in maternal serum and cord blood collected from Rome and a

linear correlation was identified between concentrations in serum and cord blood.

However, maternal serum and maternal cord blood did not show association with infant‘s

birth weight and gestational age. The study also observed significant relationship between


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increasing PCB concentrations in cord serum and decreasing Apgar score in infants was

also found in that study.

Similarly, a correlation of umbilical cord levels of PCBs in Chinese mothers with

premature birth, low birth weight, stillbirth and lowered Apgar score in infants was also

reported. A study on PCBs in seven birth cohorts from Europe was conducted by Iszatt

(2015) to report prenatal exposure in fetuses through cord serum and postnatal exposure

in infants through breastfeeding. The study findings revealed that maternal serum levels

of PCB-153 (a proxy biomarker for most of the PCB congeners) were not significantly

correlated with infant growth, but, postnatal exposure of PCB-153 in the infants (through

lactation) was associated with reduced infant growth.

2.8.4. Women exposure to PCBs in South Asia

PCB residues in human body tissues have also been reported in numerous studies

from India. High concentrations of PCBs in milk of women living in surroundings of e-

waste dumping site near Kolkata were detected and compared with a referenced site near

it. The findings of this study revealed that levels of dual- PCBs were significantly higher

in comparison to the referenced site. It was found that PCBs concentration is affected by

fish intake and the number of years of residence near the dumping site (Someya et

al.,2010). In contrast tothe above study, the PCBs level in human milk from Mumbai,

Delhi and Kolkata showed no significant relationship was studied among PCBs

concentrations and age and parity of mothers (Devanathan et al., 2009). Dewan et al.,

(2013) reported a detailed study on OCPs in blood, milk, and placenta of women from

New Delhi India had reported detectable levels. The results showed that trans-placental

transfer of all the OCPs was significant, whereas, insignificant results were observed for

trans-mammary transfer of most of the OCPs. Mamun (2007) highlighted the levels of


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DDT, HCH, PCBs and HCB in human blood from Bangladesh and reported significant

levels of DDT with a low concentration of PCBs.

2.9. PCBs Scenario in Pakistan

Pakistan is a State Party and signatory of the Stockholm Convention, Basel

Convention, and Rotterdam Convention. Despite environmental legislations in the

country a gap exists in between the legislation and its implementation (Jaspal & Haider,

2014). Industry and agriculture are the major sectors of PCBs consumption in Pakistan;

their fresh inputs and continuous use have been reported in recent studies conducted

across the country (Ullah et al., 2016). A few studies were conducted in the country on

PCBs in relation to their occurrence and accumulation in freshwater resources, sediments

and freshwater fish of River Chenab, (Eqani et al.,2013; 2012), and Indus Basin soils

(Usman et al., 2015). Moreover, PCBs in the air, soil and indoor dust have also been

reported in the past (Ali et al., 2014; Syed et al., 2014). A detailed study on PCBs

exposure through contaminated food stuff (cereal crops) was reported in 2014 followed

by another study in 2016 (Mahmood et al., 2014; Mumtaz et al., 2016). Levels of

organochlorides (OC) have also been reported in the blood serum from different

Provinces of the country (Naqvi & Jahan, 1999; Parveen, 2001). In addition to the above

cited study, Yasmeen et al., (2016) and Khawaja et al., (2010) reported organochlorine

pesticides (OCPs) in the milk of breastfeeding mothers from Karachi and southern

Punjab, Pakistan. Several studies have been conducted to establish the transplacental

transfer of PCBs from mother to infants (Gascon et al., 2012; Porpara et al., 2013). But

data from Pakistan is scarce; so far no study on the placental transfer of PCBs in the

country has been conducted to trace the PCBs contamination according to the extent of

our knowledge.


27

The study was conducted to report the current status of PCBs in the blood, milk

and placenta of mothers from different districts of Punjab, Pakistan. According to our

known information, the present study is the pioneer in explaining and highlighting the

effects of PCBs on mothers and infants.

CHAPTER THREE

MATERIALS AND METHODS

Chapter Three Materials and Methods

29

Chapter Three


3.1. Study Area

The Punjab province is a land of five rivers originating from Western Himalayas

and is one of the most fertile alluvial plains in South Asia. This province is historically

and culturally rich and experiences different regimes from the Indus valley civilization

the British Empire. With an area of 205,344 km2 and the population of 100 million

(population density = 353 persons per square kilometer), the Punjab is the most populous

province life-line of Pakistan. Most of the province population belongs to rural areas and

is dependent upon agriculture activities as it comprised of mostly intensified cultivated

areas of South Asia, where wheat, cotton, rice, maize and sugarcane is primarily grown

along with certain areas enriched with fruit and vegetable farming. Whereas, the

remaining population residing in cities is mainly engaged in urban and industrial

activities. Punjab experiences four distinct seasons viz; summer (including monsoon),

autumn, winter and short spring seasons, whereas, the temperature in the province ranges

between -2o to 40

oC. The rate of precipitation is higher in its north as compared to south

western parts.

The study area encompasses five districts of Province, Punjab, Pakistan viz;

Lahore, Khanewal, Okara, Sialkot and Chakwal (Figure 3.1). Rapid urbanization and

industrialization have caused an increase in environmental pollution in these major cities

of the province due to vehicular emission, chemical emissions and effluent discharge

from industries. The people purchase power in the area increases the demand of used or

end-of-life products that soon ends in the form of waste. The situation further worsens

due to the burning of tons of filth and solid waste by metropolitan corporations in most of

the parts of cities. There are electronic waste dumping sites near urban areas viz; Lahore,


30

which arepotential sources of PCBs contamination in these areas. The manufacturing and

distribution of PCBs and its use are not lawfully regulated in the country which is a major

reason of their ongoing detection in different environmental media and in the human.

3.1.1. Lahore

Lahore is an eminent city with a population around 10 million (increasing at a rate

of 4% per year) is considered as the second largest metropolitan of Pakistan and is located

in an arid region between 31°35′ North latitude and 74°21′ East longitude and 217m

above sea level (Jalees & Asim, 2016). Lahore is known as a business hub with most of

the population engaged in industry, trade and commerce and other services (Mirza, 2013).

The city is characterized as heavily industrialized and endured with 271 industrial units

mainly comprising of textile, chemical, paints and plastic, foundries and steel re-rolling

mills, dairy and food processing units, pulp and paper units, leather, tanneries, pesticides,

pharmaceuticals and poultry (Hamid et al., 2013). Rapid urbanization has resulted in an

increase of the municipal area of Lahore from 332 to 1000 km2 (Ali & Azhar, 2010). In

the race of massive industrialization and urbanization, Lahore is experiencing pollution

related issues due to industrial emissions, along with the lack of efficient pollution control

measures, solid waste management and mass transportation (Biswas et al., 2008).


31

Figure 3.1: Map of the study area showing five sampling districts of the Punjab Pakistan


32

3.1.2. Khanewal

Khanewal is a district of Punjab, located between 30°18'0 N 71°56'0 E and 125 m

above sea level with an arid, subtropical continental climate (Tauseef et al., 2012). The

district has an area of about 1,059,630 acres. Khanewal is dominating with agriculture

land with major crops (wheat, sugarcane, and cotton), fruit orchards of mangoes, guava

and citrus, and a range of vegetable farmlands. The main industries of the district

Khanewal include textile spinning units, flour mills, weaving mills, vegetable ghee/oil

mills, sugar mill, a unit for solvent oil extraction, beverage unit, a number of oil expelling

and cotton ginning units and numerous engineering goods manufacturing industries

(GOP, 2016). There are uncontrolled discharges from industrial sources and the extensive

spraying of pesticides/ insecticides on the agricultural fields mainly on cotton crops

causing POPs pollution in the agricultural soils, contamination of water resources in the

area and ultimately affecting the human health (Azizullah et al., 2011).

3.1.3. Okara

Okara is a located between 30.801380°N and 73.448334°E with a total area of

4,377 km2. The district is comprised of three tehsils namely Okara, Depalpur and Renala

Khurd. The district Okara is renowned for its fertile agricultural lands supporting crop

and vegetable fields and fruit orchards. Major industries of the district Okara are dairy

products, poultry feed, cotton ginning and pressing, textile composite and textile weaving,

flour mills, deep freezers, food industry, oil mills, rice mills, sugar, seed processing, and

vegetable ghee/cooking oil (GoP, 2016). Application of agrochemicals in the agricultural

fields of the area to meet the food demand, improper disposal and burning of municipal

solid waste are causing the environmental contamination. In addition, there is a general

practice of burning of biomass as a household energy source in the rural areas of Pakistan.


33

This is a leading cause of indoor air pollution in most of the rural areas of the Pakistan

(Nasir et al., 2014).

3.1.4 Sialkot

Sialkot district is located between 32°29′33″N and 74°31′52″E about 244m above

the sea level in province Punjab Pakistan. It is considered as the 12th

most populous city

in the country with a population more than 3 million and population density 903 per km2

and annual growth rate of 1.99 % (GoP, 2016; Malik et al., 2010). It is an export oriented

and highly industrialized city with more than 300,000 population directly or indirectly

engaged in industry. The city is acknowledged for numerous medium and small sized

enterprises producing commodities, mainly leather products, sports goods (Qadir et al.,

2008). During the last two decades, this city has witnessed rapid urbanization,

industrialization along with agricultural development, making the city susceptible to

persistent effects of environmental pollution (Malik et al., 2010).

3.1.5. Chakwal

Chakwal is a district with a population of 0.10 million individuals and located

between 32°55′49″N and 72°51′20″E and 498m above sea level. It is a rain fed district

with hilly and plateau terrain that lies in the Potohar Plateau and the Salt Range. The

climatic conditions are sub-humid with scrubby vegetation and most of the population

living in rural areas (Ghazanfar et al., 2011). The district consists of four subdivisions i.e.

Chakwal, Talagang, Choasedan Shah and Kallar Kahar. Chakwal has some cement plants

and textile spinning units. Dust and gas emissions from these cement plants are

deteriorating the ambient air quality. Improper solid waste disposal is contaminating the

ground water in the area. Moreover, the city suffers from a load of thousands of


34

vehiclespassing through it every day. All these conditions are leading to environmental

pollution in the area.

3.2. Sampling Strategy

Five districts of the Province, Punjabwere selected as sampling sties viz; one

district of Potohar region (Chakwal), southern region (Khanewal), the central region

(Okara), eastern region (Lahore) and northeast (Sialkot). Site selection criterion was

based on the industrial and agricultural activities and spatial diversity and variation in

these districts. The areas within each selected five districts were further subdivided into

two zones viz; urban and rural to make the cross comparison between them. The rural

women were those who purely belonged from areas with agricultural activities. The urban

women were those who belong to areas with urban/ industrial localities and

anthropogenic activities leading to PCBs exposure in the study area (Appendix-1). A

group of gravid women (n=45) with cesarean sections at public sector hospitals located in

the study area was selected for the collection of blood, milk and placenta samples. Among

these women, 19 were from rural areas and 26 were from urban areas. The inclusion

criteria of the subject women included those who were born and currently residing in

Punjab province, age ranging between 18 years up to 45 years with no previous history of

cardiac disease (Thomas et al., 2006). Medical Officers were requested to help in the

collection of blood, milk and placenta from the women volunteers who have shown their

consent after they were briefed about the purpose of the research.

The placenta and blood serum samples were collected within a few hours of baby

birth, whereas, milk sample was taken within three days of the baby birth. Prior to the

sampling, the ethical and scientific approval of the present study was obtained from

Advanced Studies and Research Board (ASRB) at the University of the Punjab, Lahore,


35

Pakistan. All ethical responsibilities were strictly observed. A specimen of patient consent

used during this study is placed as Appendix-2.

3.3. Sample Collection and Transportation

3.3.1. Blood serum

About five to 8.0 mL of blood of each volunteer from five districts (Lahore,

Khanewal, Okara, Sialkot and Chakwal) was collected through venipuncture in red top

BD glass vacationers with the help of paramedical staff (Thomas et al.,2006). After

collection, the serum was separated by centrifugation 3000 revolutions per minutes (rpm)

to obtain approximately 3.0 mL of blood serum (Inoue et al., 2006).

3.3.2. Human milk

The volunteers were selected for collection of milk samples. About 20 ml of milk

sample was obtained from each volunteer through breast pumps with the help of

paramedical staff within three to four days after delivery. The collected milk samples

were transferred to designated glass bottles.

3.3.3. Human placenta

The women with caesarean sections in hospitals of the study area were selected as

subjects. Placental tissues (20g) were collected during the course of caesarean section in a

sterilized glass container. Placenta was cut in triangular portion, taken from fetal and

maternal disc side, including peripheral and central parts of the human placental disc.

3.3.4. Transportation and storage

After collection blood serum, milk and placenta samples were immediately kept

on dry ice in the ice box and transferred to the Ecotoxicological Laboratory at the College

of Earth and Environmental Sciences, University of the Punjab, Lahore, Pakistan,


36

whereall the collected samples were stored at -20oC in a freezer until further analysis

(Nanes et al., 2014).

3.4. Sample Preparation and Analysis

Assessment of PCBs residues needs their extraction from the sample using

organic solvents, clean up for lipid removal to avoid interference in analysis, gas

chromatography for PCBs separation from other compound present in the extract and its

confirmation by a mass spectrometer (MS). These are described below in detail.

3.4.1. Extraction of PCBs

The methods as described by Tyagi et al., (2015) and Covaci and Voorspoels

(2005) were used for sample extraction and cleanup with slight modifications i.e. using

ultrasonic treatment instead of mechanical shaker. The blood serum, placenta and milk

samples were thawed and homogenized at room temperature. Placenta samples were

weighed one gram (1g) in a weighing balance, shredded into small pieces and transferred

to glass vial. Maternal serum and milk (1 mL each) and 1g placenta tissue (finely

chopped) were taken and spiked with surrogate standards (5ppb) of PCB-209 and 2,4,5,6-

tetrachloro-m-xylene (TCmX), sonicated for 20 minutes and kept overnight at 40C. The

PCBs were extracted next day by adding n-hexane (6mL) and acetone (3mL) into the

spiked samples. The selection of these solvents was based on the fact that they facilitate

the PCBs transference from the sample into polar solvents, thus enhancing the extraction

efficiency of PCBs (Tan, 2009). All the samples were equilibrated by ultrasonic treatment

for one hour at 30C, followed by their centrifugation for ten minutes at 2000RPM (Dewan

et al., 2015). The clear upper layer of n-hexane, containing PCBs was then separated into

another glass vial and the remaining part was extracted twice by repeating the procedure

and addition of clear layers of n-hexane to the previous fractions.


37

3.4.2. Sample cleanup

The extracts were then cleaned up in an Agilent‘s solid phase extraction assembly

using Agilent Bond Elut polypropylene cartridges, prepared by filling the cartridges from

bottom to top, with acidic silica (2g), the 200mg silica (activated at 1200C for 2 hours)

and 500 mg anhydrous sodium sulphate, respectively. Acidified silica was prepared

according to the method described by Covaci and Schepens (2001). Briefly, it was

prepared by drop wise adding 27 mL of sulphuric acid into the 50 g silica gel. The

mixture was stirred for 30 minutes for good homogeneity. Prior to cartridges preparation,

the empty cartridges were cleaned with 5 mL DCM, activated through MeOH (5 mL) and

dionized-distilled water (5 mL). Moreover, the prepared clean-up cartridges were eluted

with DCM (5 mL) before sample loading. The cleanup process involved loading of PCBs

extract on the prepared column, elution of analytes with n-hexane (4 mL) and

dichloromethane (DCM 2 mL) (Jaraczewska et al., 2006). Each cleaned sample was then

concentrated through gentle nitrogen streaming by an injection volume viz. 0.5 mL and

equilibrated with 50µl of iso-octane (Vizcaino et al., 2014). About 10 µL of the internal

standard of 13

C-PCB-141 was added in the mixture followed by vortex and transferred to

the 1.5 mL septa vials. The prepared samples were then transferred to the State Key

Laboratory of Organic Geochemistry, Guangzhou Institute of Geochemistry, Chinese

Academy of Sciences, Guangzhou, China for further analysis on Gas Chromatography-

Mass Spectrometry (GC MS).

3.5. Instrumental Analysis

Gas Chromatograph (GC-7890A) connected to an Agilent 7000A Triple Quad

Mass Spectrometer (MS) and Autosampler (Agilent 7693) was used for the quantification

of PCBs (Figure 3.2). The operational mode of GCMS was electron-capture negative

ionization (ECNI). For PCBs analysis, a CP-8 capillary column (from Netherland) was


38

used. A carrier gas (helium) was used with a constant flow rate of 1.2ml/min with initial

pressure of 20.9 psi. The operational mode of mass spectrometer was electron ionization

(EI) with electron multiplier voltage (EMV) of 2947 V. The ion source temperature was

set at 230°C. Employing the pulsed splitless mode, one μl of the final extract was

injected with initial temperature at 100°C, pulse pressure of 25 psi kept for 0.5min. The

total splitless time was 1.5 min. The initial temperature of the CP-8 column was

increased from 100°C to 160°C at the rate of 20°C/min (0 min hold time), 4°C/min to

240°C (0 min hold time) and finally to 296°C at a rate of 8°C /min with holdup time of

5.5 min. The length of total rum per sample was 40ms. The identification of analytes was

based upon their respective retentions time and ion chromatographs against the standards.

Quantification was done by creating (multi-level) calibration curves and for all the

concentration ranges detected in the samples, a good linearity of r2> 0.99 was achieved.

Figure 3.2: Agilent GC-MS used for PCBs analysis during the present study


39

3.6. Chromatographical Analysis

The concentrations of 34 PCB congeners including eight dioxin like PCBs (PCB-

70, 126, 169, 105, 114, 118, 156 and 189) and six indicator PCBs (PCB-52, 101, 118,

138, 153 and 180) were determined. These 34 congener were grouped as follows: PCB

homolog: Tri-CBs (∑PCB-30 and37), Tetra-CBs (∑PCB-44,49,52,54,60,66,70,74 and

77),Penta-CBs (∑PCB-82,87,99,101,105,114,118 and 126), Hexa-CBs (∑PCB-128, 138,

153, 156, 158, 166 and 169) Hepta-CBs ∑PCB-170, 179, 180, 183, 187 and 189) Octa-

CB (PCB-198) and Deca-CB (PCB-209). The selection of these PCB congeners was

based upon the optimization of the instrumentation for these congeners, their relative

abundance, persistence and toxicity concerns in the environment (particularly of dioxin

like PCBs).

3.7. Lipid Determination

3.7.1. Determination of blood serum lipids

The total lipids in the serum samples were determined as demonstrated by Covaci

et al., (2005). Total Cholesterol (CHOL) and Triglycerides (TG) were analyzed using

enzymes in already separated aliquots/ subsets of the serum samples from a government

hospital‘s laboratory. The total lipids were determined by putting the CHOL and TG

values in the following formula i.e.

TL (g/l) = 1.12×CHOL+ 1.33×TG + 1.48 (Covaci & Voorspoels, 2005)

Final concentrations of PCBs in each sample were expressed as ng/g lipid weight

(ng/g lip).


40

3.7.2. Determination of milk fat

Fat content in human milk samples was determined in Food and Biotechnology

Research Center (FBRC), Pakistan Council of Scientific and Industrial Research

(PCSIR). Fat contents (%) of each milk sample were determined using separate aliquots

of samples by a method/ protocol of Association of Official Analytical Chemists (AOAC,

International) 2000.18. The method employs the use of sulfuric acid (10mL), taken in a

butyrometer along with milk sample (10.75 ml) pipetted out by the sides of butyrometer

and 1 ml of ISO- amyl alcohol was poured on it with tilt measure. The butyrometer was

stoppered and shaken well, kept in a hot water bath at 15-210C and then placed in a

Gerber centrifuge for four minutes at 1100 RPM. The butyrometer was then taken out and

tempered by placing again in water bath at 650C for five minutes. The fat content

appeared at the top was measured as percentage fat content, i.e. grams per 100 mL.

3.7.3. Determination of placental lipids

Placental lipids were measured gravimetrically through a method described earlier

by Vizcaino et al., (2014). The process involved taking placenta tissue (1g),

homogenizing it in a 5mL solution of chloroform: methanol: hydrochloric acid (20:10:0.1

i.e. v/v/v), repeating the previous process twice, followed by the addition of 0.1N HCl

(10mL) and centrifugation for ten minutes at 3000RPM. The organic phase containing

lipids was collected and the process was repeated to obtain maximum lipids. The extract

was then concentrated under a nitrogen stream and dry lipids were measured

gravimetrically.

3.8. Quality Control and Assurance

The surrogate standards of 2,4,5,6-tetrachloro-m-xylene (TCmX) and PCB-209

were purchased from CPA Chem Ltd. Stara Zagora, Bulgaria. Internal standard of 13

C


41

PCB-141 was purchased from Cambridge Isotope Laboratories, Inc. Highwood drive

Tewksbury, MS, USA. A blank of n-hexane was run prior to every batch of five samples.

All the solvent and procedural blanks were as run as the original samples to monitor the

influence of any possible contamination in the course of processing samples and the

instrumental analysis. Six calibration standards of PCBs (prepared in association) were

used to evaluate the method‘s and instrument‘s accuracy and precision. These were the

serial dilutions of standards ranging from 5 ppb to 200 ppb. These standards were

purchased from Aqua Standard Company, USA. The average surrogate standard

recovery in blood serum samples was 69 ± 6% for TCmX and 76 ±8 % for PCB-209 and

in human milk sample recovery rate was 72 ± 7 % for TCmX and 78 ± 9 % for PCB-209.

Whereas, the surrogate standard recovery in human placenta samples was 79 ± 8% for

TCmX and 82 ±9 % for PCB209.A follow up standard of 50 ppb was run daily to

calibrate the instrument. A deviation of the ion intensity ratios was considered as

acceptable when it was within 20% of the mean values of calibration standards. The

Calibration was made when the mean value exceeded 20%. Data processing was done

using Agilent Mass Hunter workstation software. Agilent Mass Hunter workstation

software was used for integrating peaks and data processing.

Organic solvents i.e. dichloromethane (DCM), n-hexane, methanol (MeOH),

acetone, and iso-octane were of HPLC grade purchased from Merck KGaA (Germany).

Concentrated sulphuric acid (H2SO4), anhydrous sodium sulphate (Na2SO4) and silica gel

were also purchased from Merck and Co., Inc. All the glassware used in the present study

were washed with DCM and later on with double-distilled water for 30 minutes, wrapped

in aluminum foil and oven dried at 115 oC for one day. These were baked next day at 450

oC in a muffle furnace for four hours.


42

3.9. Socio-demographic Characteristics

In order to depict the socio-demographic conditions of the sampling group, a

questionnaire was used that was filled by getting information from the each volunteer

woman during sampling. It included an array of question regarding the age, location,

marital status, parity, Body Mass Index (BMI), lifestyle and eating habits, fish and milk

intake, personal disease history (if any), number of children carried and breastfed and

child anthropometric measurements given below. Questions regarding demographic

characteristics and feeding habits were modified from US EPA studies on food and total

diet (USFDA, 2004). The concentrations of PCBs in these women were also evaluated on

the basis of their spatial distribution and for this purpose were categorized as rural and

urban.

3.10. Risk Assessment

3.10.1. Toxicity Equivalent Factor (TEQ)

There are some coplanar PCBs that exhibit similar properties and toxicity, as of

TCDD and are referred as dioxin like compounds (Chovancovaet al., 2012). In the

present study, eight dioxin like PCB congeners, i.e. three non-ortho and five mono-ortho

were studied. Individual congener‘s concentration was multiplied by its TEF to determine

TCDD equivalent, represented as WHO2005-TEQs (Van den Berg et al.,2006).

3.10.2. Risk assessment of mothers and infants

In order to assess the association between levels of PCBs in mothers and their

relationship with maternal health; their personal attributes viz; body weight, height, BMI,

age, parity, number of children carried and breast fed, location and eating habits were

compared with PCB residues in the body. Also the health risks to infants were assessed

using their anthropometric characteristics including head and chest circumference, mid


43

arm circumference, crown to heel length and birth weight was measured. The detailed

questionnaire used during sampling is attached as Appendix-3.

3.10.3. Estimated Daily Intake of dioxin like PCBs in women

Dioxin like PCBs are highly toxic, therefore, a small but long term

exposure to these chemical could be great public and scientific concern (Fromme et al.,

2009). Theses chemicals enterintothe human body through food.Dioxin like PCBs and

risks to the health of selected women in this study was evaluated by estimating their daily

intake of dl-PCBs using the following equation.

Where f is the absorption factor assumed to be 50% for absorption from food for

human, *ln2 = 0.693 and assumed half-life of 7.5 years. Moreover, a Tolerable Daily

Intake (TDI) of 1–4 pg TEQ/kg for human had also been recommended by a WHO

consultation. The risks to the health of the women using the aforesaid formula was

calculated and expressed as an Estimated Daily Intake (EDI).

3.10.4. Estimated daily intake of PCBs in infants

Several studies reported that PCBs could easily be transferred from mother to

infant through breast milk (Needham, 2011). The potential health risks of PCBs exposure

to infants through breast milk intake was assessed by determining hazard quotient (HQ)

for dl-PCBs using equation given bellow:

Hazard quotient is a ratio between exposure (i.e., the estimated daily intake (EDI)

and provisional tolerable daily intake (PTDI). The PTDI is the maximum daily amount of


44

a chemical that is considered safe during the entire human lifetime (FAO/WHO, 1995).

The calculated HQ value less than 1 indicates no adverse health effect is expected,

whereas an HQ greater than 1 indicates the possibility of adverse health effects.

Where EDI is the estimated daily intake (ng kg-1

body weight day-1

), CPCB is the

concentration of PCB. To calculate EDI, the concentration of PCBs analyzed in milk

samples of lactating mothers was multiplied with daily milk intake of infants, which was

700 ml and divided by individual infant weight (FAO/WHO,1 995; Klincic et al., 2016).

PTDI value used for PCBs was 10 pg kg-1

body weight day-1

(van Leeuwen et al., 2000).

Above this PTDI value, PCBs exposure is considered not safe for humans during their

entire life (FAO/WHO, 1995).

3.11. Statistical Analysis

The quantitative analysis of PCBs concentrations was carried out using statistical

software SPSS version 16.0 and Microsoft Excel, (2010) to represent basic descriptive

statistics. A Pearson‘s correlation analysis was performed to assess the relationship

between PCB concentrations in women body matrices and data on physiological

parameters of mothers and infants. Linear regression curves were plotted using Microsoft

Excel, 2010 for a graphical representation of the influence of prenatal exposure on the

mother and infant characteristics. The concentrations of PCBs (ng/g lipid weight) were

represented as descriptive data using mean, standard deviation and range. The Originpro,

2017 software, Arc GIS version 10.2.2 and soft stat. software 12.5 were employed to

exhibit spatial patterns of PCBs.

45

CHAPTER FOUR

RESULTS


46

Chapter Four

RESULTS

4.1. PCBs Profile and Congener Specific Analysis in Blood Serum

The blood serum concentrations of PCBs were determined on the basis of lipid

concentration. The total lipids in blood serum samples were ranged between 4.34 to 5.97

g/L with the mean value of 4.98 ± 0.40 g/L. The concentrations of ∑34 PCBs in maternal

serum samples ranged between 47.49 to 522.06 ng/g lip (mean: 232.33 ± 110.62 ng/g lip).

Among the PCB congeners, the highest mean concentration was determined for PCB-70,

(18.72 ng/g lip) ranging between 0.56 to 44.25 ng/g lip, whereas, the lowest was observed

for PCB-166 (0.44 ng/g lip; range 0.00-2.38 ng/g lip). The other dominant congeners of

PCB were PCB-52 (17.46 ng/g lip, range: 3.67-51.98 ng/g lip), PCB-37 (17.20 ng/g lip,

range: 3.95-45.57 ng/g lip), PCB-60 (16.82 ng/g lip, range: 1.93-39.27 ng/g lip) and PCB-

66 (16.77 ng/g lip, range:0.00-37.31 ng/g lip). The descriptive statistics of each PCB

congener is presented in Appendix-4.

Six indicator PCBs were also determined in blood serum samples viz; PCB-52

(17.46 ng/g lip), PCB-101 (12.10 ng/g lip), PCB-118 (5.99 ng/g lip), PCB-138 (4.87 ng/g

lip), PCB-153 (6.64 ng/g lip) and PCB-180 (2.46 ng/g lip). The mean concentration of

indicator PCBs in blood serum was (49.55 ± 42.31 ng/g lip). These are the conveners

often found in human and in the environment. Among these congeners, PCB-153, PCB-

138 and PCB-180 are also known as the persistent congeners. Moreover, the mean

concentration of eight dioxins like PCBs (33.39 ± 40.18 ng/g lip) was recorded. The

concentrations of each PCBs congener are given in Figure 4.1. Based on the (mean)

concentrations, the PCBs homolog were in the descending order as; Tetra-CBs (mean:

128.05 ng/g lip) > Penta-CBs (54.38 ng/g lip) > Hexa-CBs (22.17 ng/g lip) > Tri-CBs


47

(19.18 ng/g lip) > Hepta-CBs (8.21 ng/g lip) > Deca-CB (1.85 ng/g lip) > Octa-CB (1.35

ng/g lip). The results of PCB homolog are presented in Table 4.1.

Figure 4.1: Concentration of dl-PCBs and indicator PCBs detected in the blood serum

0

5

10

15

20

25

30

35

PC

B7

7

PC

B1

26

PC

B1

69

PC

B1

05

PC

B1

14

PC

B1

18

PC

B1

56

PC

B1

89

PC

B5

2

PC

B1

01

PC

B1

18

PC

B1

38

PC

B1

53

PC

B1

80

dl-PCBs Indicator PCBs

PC

B c

on

cen

trat

ion

(n

g/g

lip.


48

Table 4.1: Descriptive Statistics of PCB Homolog in blood serum collected from five districts of the Punjab, Pakistan (Concentration in

ng/g lip)

District Lahore Khanewal Okara Sialkot Chakwal

PCB

Homolog

Mean ± SD Range Mean ± SD Range Mean ± SD Range Mean ± SD Range Mean ± SD Range

Tri-CBs1 24.81 ± 10.87 10.55-50.41 16.09 ± 5.76 9.32-28.78 13.70±4.75 8.12-23.37 20.94±10.83 5.16-37.02 18.29±4.80 13.71-25.33

Tetra-CBs2 168.83 ± 48.62 92.57-249.14 103.32 ± 37.59 45.70-161.61 80.87±36.32 40.40-152.52 142.43±82.31 16.28- 97.17 139.33±42.69 98.75-202.74

Penta-CBs3 66.19 ± 29.20 27.88-128.58 48.28 ± 22.55 22.33-88.55 28.62±26.67 10.63- 91.81 56.90±34.34 12.44-19.16 76.00±27.41 53.77-112.56

Hexa-CBs4 20.88± 9.46 2.38-35.45 14.59 ± 10.22 6.20-33.52 13.22±16.94 3.38-57.71 21.21±14.10 5.15-51.97 55.94±30.83 25.24-88.70

Hepta-CBs5 10.27± 8.15 2.08-30.01 4.18 ± 2.09 2.31-9.14 7.94±5.39 1.33-16.15 6.44± 7.03 2.00-25.34 16.80±9.29 6.03-26.30

Octa-CB6 2.15± 2.91 0.09-9.55 1.12 ± 0.97 0.02-2.88 0.61 ± 0.66 0.02-2.04 0.71±0.47 0.02-1.41 2.59±3.58 0.38-8.87

Deca-CB6 1.25 ± 1.49 0.07-4.38 2.55 ± 2.43 0.30-8.88 0.78±1.60 0.05-5.05 2.65- 3.52 0.09- 12.00 1.65±0.88 0.35-2.75

∑34PCBs 294.37 ± 110.7 135.62-507.53 190.13 ± 81.62 86.17-333.36 145.74±92.32 63.92-348.64 251.28±152.61 41.14-44.06 310.59±119.47 198.23-467.23

1 Tri-CBs = ∑ PCB-30, 37

2 Tetra-PCB-s = ∑ PCB-44, 49, 52, 54, 60, 66, 70, 74, 77

3 Penta-CBs ∑PCB-82, 87, 99, 101, 105, 114, 118, 126

4 Hexa-CBs ∑PCB-128, 138, 153,156, 158, 166, 169

5 Hepta-CBs ∑PCB-170, 179, 180, 183, 187,189

6 Octa-CB PCB-198 and

7 Deca-CB PCB-209


49

4.2. PCBs profile and Congener Specific Analysis in Human Milk

The detailed results of PCB congeners in milk are given in Table 4.2. The range of

∑34PCBs concentrations in human milk from 0.8 % to 6.6 % (mean: 3.03%). The mean

∑34PCBs concentration in human milk was calculated as 44.07±27.27 ng/g lip and ranged

between 8.93 ng/g lip to 116.17 ng/g lip. In the PCB congeners specific analysis, the

highest concentration of PCB-60 (mean: 3.88 ng/g lip, range 0.46-11.39 ng/g lip)was

recorded, followed by PCB-66 (mean: 3.26 ng/g lip, range: 0.39-9.92 ng/g lip) and PCB-

70 (mean: 3.14 ng/g lip, range: 0.07-10.05 ng/g lip), whereas PCB-166 has shown the

lowest concentrations (mean: 0.11 ng/g lip, range: 0.00-0.65 ng/g lip). Descriptive

statistics of each PCB congener in the milk sample from five districts of Punjab are

presented in Appendix-5.

The mean concentration of six indicators PCBs in milk samples was 10.31 ± 11.70

ng/g lip. The trend of each congener from highest to lowest was recorded as follows:

PCB-52 (2.94 ng/g lip), PCB-101 (2.94 ng/g lip), PCB-118 (1.96 ng/g lip) and the

persistent PCB congeners with the following concentrations viz; PCB-153 (1.89 ng/g lip),

PCB-138 (1.41 ng/g lip) and PCB-180 (0.75 ng/g lip) with a 12 % contribution of the

total PCBs concentrations. Moreover, eight dl-PCBs were also detected with mean

concentrations of 6.15 ± 8.72 ng/g lip (Figure 4.2).

The PCB homolog profile according to prevalence and abundance basis was in

following order: Tetra-CBs (mean: 22.79 ng/g lip) > Penta-CBs (9.29 ng/g lip) > Hexa-

CBs (5.93 ng/g lip) > Tri-CBs (3.10 ng/g lip) > Hepta-CBs (2.26 ng/g lip) > Octa-CB

(0.35 ng/g lip) > Deca-CB (0.31 ng/g lip).


50

Figure 4.2: Concentration of dl PCBs and indicator PCBs detected in human milk

0.00

1.00

2.00

3.00

4.00

5.00

6.00

7.00

PC

B7

7

PC

B1

26

PC

B1

69

PC

B1

05

PC

B1

14

PC

B1

18

PC

B1

56

PC

B1

89

PC

B5

2

PC

B1

01

PC

B1

18

PC

B1

38

PC

B1

53

PC

B1

80


PC

B c

on

cen

trat

ion

(n

g/g

lip)


51

Table 4.2: Descriptive Statistics of PCB homolog in human milk samples collected from five districts of the Punjab, Pakistan



PCB

Homolog


Tri-CBs1 2.56± 2.29 0.96-8.81 2.92±1.43 0.96-5.56 2.99±1.81 0.93-7.29 3.52±1.50 1.73-5.64 4.21±1.53 2.66-6.37

Tetra-CBs2 18.37±15.73 7.67-61.80 23.16±15.37 10.97-58.28 16.27± 7.98 9.52-34.73 32.77±20.00 3.83-60.97 30.04±6.14 23.55-38.86

Penta-CBs3 6.48±5.27 2.19-20.29 10.92±8.76 2.07-24.12 5.07±2.80 1.64-11.33 12.18±7.25 1.60-24.80 16.42±2.87 12.88-19.05

Hexa-CBs4 5.80 ±4.42 2.25-17.68 4.37±3.33 0.75-10.80 3.85±2.64 1.16-9.41 6.24±5.55 0.43-5.48 12.75±6.51 4.90-1.24

Hepta-CBs5 2.15±2.20 0.31-7.46 1.72±1.55 0.72-5.69 1.86±1.25 0.23-4.54 2.52±2.27 0.96-7.42 3.94±2.00 1.17-6.62

Octa-CB6 0.43±0.44 0.02-1.25 0.17±0.18 0.03-0.50 0.44±0.57 0.03-1.86 0.14±0.14 0.01-0.42 0.65±0.90 0.09-2.23

Deca-CB6 0.23±0.24 0.00-0.70 0.40±0.44 0.00-1.10 0.38±0.54 0.00-1.80 0.24±0.30 0.00-0.80 0.32±0.15 0.10-0.50

∑34PCBs 36.03±30.60 13.4-118.0 43.67±31.07 15.49-106.05 30.86±17.60 13.50-70.96 57.62±37.03 8.57- 15.52 68.33±20.11 45.34-94.87


52

4.3. PCBs Profile and Congener Specific Analysis in Human Placenta

The basic descriptive statistics of PCB homolog in human placenta are presented

in Table 4.3. The range of concentrations of PCBs was varied from 0.7 % to 3.0 % (mean:

1.8%). The descriptive statistics of PCB congeners in human placenta are presented in

provided as Appendix-6. The mean concentration of ∑34PCBs in placentas was recorded

as 80.26±19.87 ng/g lip with the range from 20.20 ng/g lip to 115.98 ng/g lip. In congener

specific analysis, PCB-70 (6.63 ng/g lip) was recorded as a dominant congener (range

2.21 ng/g lip -14.00 ng/g lip), whereas, PCB-166 (0.21 ng/g lip) exhibited the least

concentration ranging from 0.004 ng/g lip -1.308 ng/g lip.

The overall dominance profile of PCB congeners the placenta tissues was as

follow: PCB-70 (mean: 6.79 ng/g lip) > PCB-66 (6.41 ng/g lip) > PCB-52 (5.92 ng/g lip)

> PCB-49 (5.88 ng/g lip) > PCB-37 (5.58 ng/g lip) > PCB-60 (5.19 ng/g lip) > PCB-74

(4.67 ng/g lip). Alike blood serum and human milk samples, the presence of non-

persistent PCBs, including PCB-52 (mean: 6.001 ng/g lip), PCB-66 (6.553 ng/g lip),

PCB-74 (4.662 ng/g lip), PCB-101(4.179 ng/g lip), PCB-105(1.652 ng/g lip) and PCB-

128 (0.84 ng/g lip) was also detected in the placenta samples indicating the sampling

population‘s recent and continuous exposure to these congeners and thus ongoing usage

of PCBs in the study area.

In addition to this, six indicator PCBs and eight dl-PCBs were also measured in

placenta tissues with the mean concentration of 17.69 ± 12.40 ng/g lip and 9.48 ± 10.92

ng/g lip, respectively. The concentration of each congener from these two groups is

presented in Figure 4.3.


53

Figure 4.3: Concentration of dl PCBs and indicator PCBs detected in human placenta

0.00

1.00

2.00

3.00

4.00

5.00

6.00

7.00

8.00

PC

B7

7

PC

B1

26

PC

B1

69

PC

B1

05

PC

B1

14

PC

B1

18

PC

B1

56

PC

B1

89

PC

B5

2

PC

B1

01

PC

B1

18

PC

B1

38

PC

B1

53

PC

B1

80


PC

B c

on

cen

trat

ion

(n

g/g

lip


54

Table 4.3: Descriptive Statistics of PCB homolog in human placenta samples collected from five districts of the Punjab, Pakistan



PCB

Homolog


Tri-CBs1 7.80±2.34 4.38-12.92 4.69±2.01 1.26-8.97 4.02±1.44 2.04-6.62 8.82±2.08 6.10-11.72 5.48±1.56 4.04-8.11

Tetra-CBs2 48.12±10.16 32.59-60.56 42.18±17.20 12.65-66.05 34.35±10.08 22.20-52.17 50.14±7.72 36.11- 61.54 45.34±10.12 37.57-61.46

Penta-CBs3 17.19± 5.96 8.90-24.91 12.89±7.56 3.11-23.74 17.75±6.25 10.85-30.57 14.10±5.32 6.16-23.16 20.66±7.36 13.27-30.56

Hexa-CBs4 7.96±4.75 3.10-18.71 5.33±2.69 1.20-9.22 12.32±5.36 5.97-22.54 7.67±3.22 3.04-13.15 15.28±7.15 6.20-23.49

Hepta-CBs5 4.46±2.43 1.67-8.48 1.53±0.84 0.77-3.64 3.30±2.07 0.49-7.40 2.66±1.05 0.55-3.74 6.25±2.54 4.30-10.43

Octa-CB6 1.14±1.37 0.08-4.20 0.45±0.33 0.01-0.92 2.68±7.66 0.13-24.48 0.57±0.39 0.03-1.06 0.20±0.24 0.03-0.60

Deca-CB6 1.14±1.46 0.03-4.89 0.40±0.56 0.07-1.46 0.24±0.18 0.02-0.55 0.58±0.74 0.02-2.44 0.26± 0.44 0.02-1.04

∑34PCBs 87.80±28.47 50.76-134.68 67.46±31.20 19.08-113.99 74.67±33.04 41.71-144.32 84.52±20.51 52.00-116.81 93.47±29.41 65.42-135.70


55

4.4. Comparison of PCBs Homolog in Human Blood Serum, Milk and

Placenta

The highest concentration of PCB homolog was recorded in blood serum,

followed by placenta and milk matrices, respectively (Figure 4.4).

Figure 4.4: Distribution of PCB Homolog in human blood serum, milk and placenta

collected from five districts of Punjab Pakistan

The comparative analysis explained the abundance profile of homolog in

descending order of magnitude as follow; Tri-CBs: blood serum (67%) > human placenta

(22%) > human milk (11%) , Tetra-CBs: blood serum (66%) > human placenta (21.5%) >

human milk (12.5%), Penta-CBs: blood serum (68%) > human placenta (20.5%) > human

18.93 126.60 53.44

21.94 8.28

1.32

1.81

3.11 22.79 9.29

5.93 2.27

0.35

0.32

6.24 43.88 16.06 9.09 3.35

1.10

0.55

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

TriPCBs TetraPCBs PentaPCBs HexaPCBs HeptaPCBs OctaPCB DecaPCB

Blood Milk Placenta


56

milk (11.5%), Octa-CB: blood serum (48%) > human placenta (39%) > human milk

(13%), Deca-CB: blood serum (68%) > human placenta (21%) > human milk (11%).

While the Hexa-CBs and Hepta-CBs have shown identical pattern as follow: blood serum

(59%) > human placenta (24%) > human milk (16%).

Comparison of dl- PCBs and indicator PCBs in blood, milk and the placenta is

given in (Figure 4.5). The highest mean concentration of dl-PCBs in blood serum was

recorded as (49.55 ng/g lip), followed by 17.69 ng/g lip and 17.69 ng/g lip in placenta and

milk samples, respectively. The highest concentration of indicator PCBs was also

recorded in blood serum (33.39 ng/g lip) followed by the placenta (9.54 ng/g lip) and

milk samples (6.15 ng/g lip).

Figure 4.5: The comparison of dl PCBs and indicator PCBs concentrations in blood

serum, milk and placenta

0.00

10.00

20.00

30.00

40.00

50.00

60.00

Blood Milk Placenta

Co

nce

ntr

atio

n (

ng/

g lip

Women Body Matrices

Indicator PCBs Dl-PCBs


57

4.5. Spatial Distribution Patterns of PCB Homolog

4.5.1 Spatial Distribution patterns of PCB homolog in blood serum

The spatial trend of PCB homolog in maternal serum from five sampling districts

is presented in Figure 4.6. The overall spatial distribution trend of PCB homolog in blood

serum on the basis of mean ∑34PCBs was followed as: Chakwal (310.59 ng/g lip) >

Lahore (294.37 ng/g lip) > Sialkot (251.28 ng/g lip) > Khanewal (190.13 ng/g lip) >

Okara (145.74 ng/g lip.). An overall prevalence of Tetra-CBs was observed highest in all

the sampling districts followed by Penta-CBs, and Hexa-CBs.

Level of lower chlorinated PCB homolog i.e. Tri-CBs and Tetra-CBs were highest

with the mean concentration of 24.81 ng/g lip and 168.83 ng/g lip in blood serum of

mothers from district Lahore in comparison to those from other districts. Whereas, higher

chlorinated PCB homolog in blood serum were prevalent in mothers from Chakwal with a

mean concentration of Penta-CBs (mean: 76.00 ng/g lip), Hexa-CBs (55.94 ng/g lip),

Hepta-CBs (16.80 ng/g lip), Octa-CB (2.59 ng/g lip) and Deca-CB (1.65 ng/g lip).

4.5.2. Spatial distribution patterns of PCB homolog in human milk

Spatial distribution of PCB homolog in human milk from five districts is

presented in Figure 4.7. The overall spatial distribution trend of PCB homolog in milk

samples from selected sampling districts was as follows: Chakwal (mean: 68.33 ng/g lip)

> Sialkot (57.61 ng/g lip) > Khanewal (43.67 ng/g lip) > Lahore (36.03 ng/g lip) > Okara

(30.86 ng/g lip). The concentration of PCB homolog on the basis of abundance was;

TetraCBs (22.79 ng/g lip) >PentaCBs (9.29 ng/g lip) > HexaCBs (5.93ng/g lip) > TriCBs

(3.11 ng/g lip) > HeptaCBs (2.27 ng/g lip) > OctaCB (0.35 ng/g lip) > DecaCB (0.32 ng/g

lip).


58

Figure 4.6: Spatial distribution of PCB homolog in blood serum from five districts of

Punjab Pakistan

Among all the PCB homolog, Tetra-CBs were predominant in all the sampling

districts with the highest concentration in Sialkot city (i.e. 32.77 ng/g lip). While, the

highest mean levels of Tri-CBs (4.21 ng/g lip), Penta-CBs (16.42 ng/g lip), Hexa-CBs

(12.75 ng/g lip), Hepta-CBs (3.94 ng/g lip) and Octa-CB (0.65 ng/g lip) were observed in

Chakwal.


59

Figure 4.7: Spatial distribution patterns of PCB homolog in human milk from five

district of Punjab, Pakistan

4.5.3. Spatial distribution pattern of PCB homolog in human placenta

The spatial distribution patterns of PCBs in placenta of sampling districts are

presented in Figure 4.8. The overall spatial distribution of PCB homolog in placental

samples on the basis of mean ∑34PCBs was followed the order as: Chakwal (93.45 ng/g

lip) > Lahore (87.80 ng/g lip) > Sialkot (84.52 ng/g lip) > Okara (74.66 ng/g lip) >

Khanewal (67.45 ng/g lip). The PCB homolog profile on abundance basis was in order


60

follow: Tetra-CBs (43.87 ng/g lip) > Penta-CBs (16.05 ng/g lip) > Hexa-CBs (9.09 ng/g

lip) > Tri-CBs (6.23 ng/g lip) > Hepta-CBs (3.35 ng/g lip) > Octa-CB (1.09 ng/g lip) >

Deca-CB (0.55 ng/g lip).

The highest Tetra-CBs concentration was observed in Sialkot (50.14 ng/g lip)

followed by Lahore (48.12 ng/g lip) and Chakwal (45.34 ng/g lip). Similarly, the mean

concentration of Tri-CBs was highest in Sialkot (8.82 ng/g lip) and Lahore (7.80 ng/g

lip). Whereas, higher chlorinated PCBs i.e. Penta-CBs, Hexa-CBs and Hepta-CBs were

dominant in Chakwal with mean values 20.65 ng/g lip, 15.27 ng/g lip and 6.24 ng/g lip,

respectively.


61

Figure 4.8: Spatial patterns of PCB homolog in human placenta from five district of

Punjab, Pakistan


62

4.5.4. Comparison of spatial patterns of PCB homolog in blood serum, milk and

placenta

A comparison was made among concentrations of PCBs in blood serum, milk and

placentas collected from five districts of Punjab Pakistan and results are presented in

Figure 4.9. The concentrations of all the PCB homolog in blood serum were highest at all

the sampling districts followed by the placenta and milk. The spatial trend of Tri-CBs

showed the highest concentration in blood serum from district Lahore (24.81 ng/g lip) and

lower concentration in placentas (8.82 ng/g lip) of mothers from Sialkot and in human

milk (4.21 ng/g lip) from Chakwal. The concentration of Tetra-CBs (168.83 ng/g lip) was

also higher in blood serum from district Lahore in comparison to the placenta (50.14 ng/g

lip) and milk samples (32.77 ng/g lip) from district Sialkot. Moreover, the concentration

of Penta-CBs was highest in the blood (76.00 ng/g lip), placenta (20.66 ng/g lip) and milk

(16.46 ng/g lip) from district Chakwal.

Similarly, the concentration of higher chlorinated PCB homolog i.e. Hexa-CBs

and Hepta-CBs were also highest in the blood, placenta, and milk of mothers from district

Chakwal. The concentration trend of PCB homolog in District Chakwal was as follows:

Hexa-CBs in blood serum (55.94 ng/g lip)> human placenta (12.75ng/g lip) > human

milk (15.28 ng/g lip) and Hepta-CBs in blood serum (16.80 ng/g lip)> human placenta

(3.94 ng/g lip) > human milk (6.25 ng/g lip). The concentration of Octa-CB was also

highest in the blood serum (2.59 ng/g lip) and milk (0.65 ng/g lip) from Chakwal.

However, the placental concentration of Octa-CB was observed as highest in mothers

from Okara. The concentration of Deca-CB was highest in blood serum (2.65 ng/g lip) of

mothers from Sialkot and in placentas of mothers from Lahore (1.14 ng/g lip) and human

milk from Khanewal (0.40 ng/g lip).


63

Figure 4.9: Spatial distribution patterns of blood, milk and placenta collected from five

districts of the Punjab, Pakistan (Concentration in ng/g lip)


64

4.6. PCBs Trends in Urban and Rural areas

The overall results of PCB homolog showed higher concentrations in blood, milk

and placenta of mothers from urban areas than those from rural areas. Spatial distribution

of PCB homolog in blood serum reflected their abundance in urban population

(235.66±118.93 ng/g lip) in comparison to rural population (231.09±98.93 ng/g lip). The

concentrations of ∑34PCBs in milk samples from urban areas (49.32±26.46 ng/g lip)

were higher than rural areas (36.66±27.44 ng/g lip). Levels of ∑34PCBs in human

placenta from urban mothers (84.62±16.58 ng/g lip) were higher than rural areas

(74.30±22.76 ng/g lip).

A district-wise comparison on concentration of PCB homolog in urban and rural

areas was performed and results depicted that the concentrations of Tri-CBs were

relatively higher in blood serum (24.82 ng/g lip), placenta (8.20 ng/g lip) and milk

samples (2.91 ng/g lip) of mothers from urban areas of Lahore than those found in blood

serum (24.74 ng/g lip), placenta (6.18 ng/g lip) and milk (1.14 ng/g lip) of mothers from

rural areas (Figure 4.10). Tetra-CBs were also prominent in blood serum 177.60 ng/g lip),

placenta (48.70 ng/g lip) and milk (20.59 ng/g lip) samples from urban areas of Lahore as

compared to rural areas. The profile of Penta-CBs was as followed: Blood (urban= 70.09

ng/g lip, rural = 50.58 ng/g lip) > Placenta (urban = 17.26 ng/g lip, rural = 16.91 ng/g lip)

> human milk (urban = 7.42 ng/g lip, rural = 2.71 ng/g lip). The concentration of Hepta-

CBs was higher in blood serum of rural mothers (12.80 ng/g lip) than urban mothers (9.64

ng/g lip).

The concentration of Tri-CBs was higher in the blood (17.10 ng/g lip) and milk

(3.30 ng/g lip) of urban mothers than those in the blood (15.07 ng/g lip) and milk (2.62

ng/g lip) of rural mothers. Whereas, Tri-CBs in the placenta of urban and rural mothers

showed less difference (urban = 4.45 ng/g lip, rural = 4.92 ng/g lip). Tetra-CBs, Penta-


65

CBs and Hepta-CBs were prevalent in the body matrices of urban mothers than rural in

District Khanewal. These were in the following order: Penta-CBs in blood (urban = 51.31

ng/g lip, rural = 45.26 ng/g lip) > placenta (urban = 16.90 ng/g lip, rural = 8.87 ng/g lip) >

milk (urban = 15.21 ng/g lip, rural = 7.49 ng/g lip). Octa-CB was higher in the blood

(1.13 ng/g lip) and placenta (0.55 ng/g lip) samples of rural mothers than blood (0.93 ng/g

lip) and placenta (0.36 ng/g lip) of urban mothers (Figure 4.11).

In rural mothers from district Okara, Tri-CBs, Tetra-CBs and Hexa-CBs were

higher in blood and placenta and lower in milk samples as compared to urban mothers.

The concentrations of Tetra-CBs in blood serum of rural women were higher 89.75 ng/g

lip than urban women (69.77 ng/g lip) whereas; in placenta the concentration in urban and

rural mothers were 36.33 ng/g lip and 36.33 ng/g lip respectively. The overall trend

showed the concentration of most of the PCB homolog in blood and placenta was higher

in rural mothers than urban in district Okara. However, concentrations of all the PCB

homolog in milk samples of urban mothers were higher than rural mothers (Figure 4.12).


66

Figure 4.10: Spatial trends of PCB homolog in blood, milk and placenta samples

collected from urban and rural areas of Lahore


collected from urban and rural areas of Khanewal

0.00

50.00

100.00

150.00

200.00

250.00

300.00

Urban Rural Urban Rural Urban Rural Urban Rural Urban Rural Urban Rural Urban Rural

TriCBs TetraCBs PentaCBs HexaCBs HeptaCBs OctaCB DecaCB

Co

nce

ntr

atio

n n

g/g

lip

Lahore

Blood Milk

0.0020.0040.0060.0080.00

100.00120.00140.00160.00180.00200.00



Co

nce

ntr

atio

n n

g/g

lw

Khanewal

Blood Milk Placenta


67


collected from urban and rural areas of Okara

The concentration of all the PCB homolog in the milk of mothers from Sialkot

was higher in urban mothers except for Hepta-CBs which was higher in rural (4.26 ng/g

lip) mothers than urban ( 1.83 ng/g lip) Figure 4.13. Similarly, concentrations of all the

PCB homolog in the placenta were higher in urban mothers than rural except for Tetra-

CBs and Octa-CBs. The concentration in placenta samples was as follow: Tri-CBs (urban

= 9.01 ng/g lip, rural =8.53 ng/g lip), Penta-CBs (urban= 14.97 ng/g lip , rural = 12.79

ng/g lip), Hexa-CBs (urban = 8.27 ng/g lip, rural =6.76 ng/g lip), Hepta-CBs (urban =3.22

ng/g lip , rural = 1.82 ng/g lip) and Deca-CB (urban = 0.72 ng/g lip rural =0.38 ng/g lip).

The PCB homolog showed varying concentration in case of blood samples wherein, low

chlorinated PCBs were prevailing in rural mothers than urban.

0.00

20.00

40.00

60.00

80.00

100.00

120.00

140.00

160.00



Co

nce

ntr

atio

n n

g/g

lip

Okara

Blood Milk Placenta


68


collected from urban and rural areas of Sialkot

The concentrations of Tri-CBs were higher in blood and placenta of mothers from

urban setting of Chakwal (Figure 4.14). Similarly, Tetra-CBs were higher in blood

(140.95 ng/g lip) and placenta (49.73 ng/g lip) in urban mothers than blood (136.88 ng/g

lip) and placenta (38.76 ng/g lip) of rural mothers. However, this was not the same in case

of milk samples where the rural population showed higher levels of Tri-CBs and Tetra-

CBs. The concentrations of Penta-CBs were higher in blood (u=88.58 ng/g lip, r= 57.12

ng/g lip) and milk (u= 16.81ng/g lip, r= 15.83 ng/g lip) of urban women and these were

higher in placenta of mothers from rural areas (19.46 ng/g lip) than those from urban

(22.44 ng/g lip).

0.00

50.00

100.00

150.00

200.00

250.00

300.00



Co

nce

ntr

atio

n n

g/g

lip

Sialkot

Blood Milk Placenta


69


collected from urban and rural areas of Chakwal

4.7. Toxicity Equivalent Fluxes of Dioxin like PCBs

4.7.1. TEQ and Estimated Daily Intake of dl-PCBs in blood serum

Dioxins like PCBs (dl-PCBs) including three non-ortho (PCB-77, PCB-126, PCB-

169), five mono-ortho (PCB-105, PCB-114, PCB-118, PCB-156, PCB-189) were

monitored in the present study. The concentration of these ∑8dl-PCBs were ranged

between 1.29 ng/g lip and 12.49 ng/g lip; with a mean value of 33.39±3.80 ng/g lip.

Whereas, mean levels of individual congeners was as: PCB-77 (12.495 ng/g lip), PCB-

126 (2.02 ng/g lip), PCB-169 (1.38 ng/g lip), PCB-105(4.87 ng/g lip), PCB-114 (3.93

ng/g lip), PCB-118 (5.99 ng/g lip), PCB-156 (1.40 ng/g lip), PCB-189 ( 1.29 ng/g lip).

Toxicity equivalents (TEQ) were calculated for dl-PCBs based on

WHO2005TEF(Van den Berg et al.,2006) and are presented in Table 4.4. The mean TEQ

of these dl-PCBs was recorded as 0.24 ng TEQ/g lipid and ranged between 3.8 x 10-5

ng

TEQ/g lipids - 0.20 ng TEQ/g lipids).

0.00

50.00

100.00

150.00

200.00

250.00



Co

nce

ntr

atio

n n

g/g

lip

Chakwal

Blood Milk Placenta


70

Based upon the equation of WHO consultation in 1998 EDI of these dl-PCBs in

all the selected women were calculated and results are presented in Table 4.4. The EDI in

∑8dl-PCBs in blood serum was 1.94 x 10-3

with the lowest concentration in PCB-189

(3.07 x 10-7

) and the highest in PCB-126 (1.59 x 10-3

).


71

Table 4.4: Toxic Equivalent (ng TEQ/g lip) calculated for blood serum samples using WHO’s 2005 Toxicity Equivalency Factors (TEFs)

PCB Congeners Mean ± SD WHO 2005 TEF TEQ EDI (pg WHO-TEq/kg bw)

Non-ortho substituted PCB

PCB-77 12.492±16.43 0.0001 1.2 x 10-3

9.88 x 10-6

PCB-126 2.02±1.77 0.1 0.202 1.59 x 10-3

PCB-169 1.38±1.63 0.03 0.042 3.28 x 10-4

Mono-ortho substituted PCB

PCB-105 4.87±4.10 0.00003 1.46 x 10-4

1.15 x 10-6

PCB-114 3.93±4.66 0.00003 1.17 x 10-4

9.31 x 10-7

PCB-118 5.99±7.67 0.00003 1.79 x 10-4

1.42 x 10-6

PCB-156 1.40±1.50 0.00003 4.22 x 10-5

3.33 x 10-7

PCB-189 1.29±2.41 0.00003 3.88 x 10-5

3.07 x 10-7

∑8dl-PCBs 33.39±40.18 - 0.245 1.94 x 10-3


72

4.7.2. Toxicity Equivalent Fluxes of Dioxin like PCBs in human milk

The concentrations of dl-PCBs in human milk samples were also determined. The

∑8dl-PCBs concentrations were ranged from 0.29 ng/g lip to 1.35 ng/g lip, with mean

value of 6.15 ± 8.72 ng/g lip Whereas, mean levels of individual dl-PCB congeners were

in order as: PCB-77 (1.33 ng/g lip) , PCB-126 (0.54 ng/g lip) , PCB-169 (0.31 ng/g lip),

PCB-105 (1.02 ng/g lip), PCB-114 (0.89 ng/g lip), PCB-118 (1.35 ng/g lip), PCB-156

(0.44 ng/g lip), PCB-189 ( 0.29 ng/g lip). The toxicity equivalent fluxes of dioxin like

PCBs were also determined in human milk samples (Table 4.5). The TEQ values for dl-

PCBs were ranged from 8.5 x 10-6 ng TEQ/g lipid to 0.05 ng TEQ/g lipid with a mean

concentration calculated as 0.063 ng TEQ/g lipid.

The EDI of dl-PCBs in human milk were also calculated and results are presented

in Table 4.5. The EDI in ∑8dl-PCBs in human milk was 5.10 x 10-4

(pg WHO-TEq/kg

bw) and lowest in PCB-189 (6.96 x 10-8

pg WHO-TEq/kg bw) and highest in PCB-126

(4.35 x 10-4

pg WHO-TEq/kg bw).


73

Table 4.5: Toxic Equivalent (ng TEQ/g lip) calculated for maternal human milk samples using WHO’s 2005 Toxicity Equivalency

Factors (TEFs)

PCB Congeners Mean ± SD WHO 2005 TEF TEQ EDI (pg WHO-TEq/kg bw)

Non-ortho substituted PCB

PCB-77 1.32±2.01 0.0001 1.32 x 10-4

1.11 x 10-6

PCB-126 0.54±0.41 0.1 0.05 4.35 x 10-4

PCB-169 0.31±0.34 0.03 0.01 7.34 x 10-5

Mono-ortho substituted PCB

PCB-105 1.02±0.87 0.00003 3.05 x 10-5

2.55 x 10-7

PCB-114 0.89±1.68 0.00003 2.68 x 10-5

2.00 x 10-7

PCB-118 1.35±2.22 0.00003 4.04 x 10-5

3.20 x 10-7

PCB-156 0.44±0.82 0.00003 1.31 x 10-5

1.07 x 10-7

PCB-189 0.29±0.38 0.00003 8.58 x 10-6

6.96 x 10-8

∑8dl-PCBs 6.15±8.72 - 0.063 5.10 x 10-4


74

4.7.3. Toxicity Equivalent Fluxes of Dioxin like PCBs in human placenta

Similar to blood serum and human milk matrices, the dl-PCBs were also

determined in human placenta samples that contributed 14% of the total PCBs. The levels

of dl-PCBs in human placenta ranged from 0.39 ng/g lip to 3.42 ng/g lip; with mean

concentration of 9.48 ± 10.92 ng/g lip The mean levels of individual congeners was as:

PCB-77 (3.42 ng/g lip), PCB-126 (0.67 ng/g lip) , PCB-169 (0.54 ng/g lip), PCB-105

(1.65 ng/g lip), PCB-114 (0.88 ng/g lip), PCB-118 (1.49 ng/g lip), PCB-156 (0.39 ng/g

lip), PCB-189 ( 0.44 ng/g lip).

The TEQ values calculated using (WHO-TEF)2005for these dl-PCBs in human

placenta are presented in Table 4.6. The TEQ values in human placenta tissue were

ranged from 1.15 x 10-5

ng/g lip to 6.7 x 10-2

ng/g lip with ∑8 dl-PCBs calculated as 0.08

ng/g lip. These concentrations were higher than those reported in human placenta from

Taiwan (0.00291ng/g lip) and Japan (0.0012 ng/g lip) (Suzuki et al., 2005, Wang et al.,

2004).

The EDI of ∑8dl-PCBs in human placenta were calculated and presented in Table

4.6. The EDI of these dioxin like congeners was found to be 6.56 x 10-4

(pg WHO-TEq/kg

bw) and the lowest and the highest concentrations were observed in PCB-156 (9.27 x 10-

8)and PCB-126 (5.25 x 10

-4) respectively.


75

Table 4.6: Toxic Equivalent (ng TEQ/g lip) calculated for human placenta samples using WHO‘s 2005 Toxicity Equivalency Factors (TEFs)

PCB Congeners Mean ± SD WHO 2005 TEF TEQs EDI (pg WHO-TEq/kg bw)

Non-ortho substituted

PCB-77 3.42±4.24 0.0001 3.43 x 10-4

2.69 x 10-6

PCB-126 0.67±0.65 0.1 6.70 x 10-2

5.25 x 10-4

PCB-169 0.54±0.70 0.03 1.62 x 10-2

1.27 x 10-4

Mono-ortho substituted

PCB-105 1.65±1.57 0.00003 4.82 x 10-5

3.90 x 10-7

PCB-114 0.88±1.10 0.00003 2.65 x 10-5

2.07 x 10-7

PCB-118 1.49±1.64 0.00003 4.74 x 10-5

3.51 x 10-7

PCB-156 0.39±0.43 0.00003 1.15 x 10-5

9.27 x 10-8

PCB-189 0.44±0.57 0.00003 1.32 x 10-5

1.04 x 10-7

∑8PCBs 9.48±10.92 - 0.0837 6.56 x 10-4

*WHO 2005 –TEFs (Van den Berg et al., 2006).


76

4.7.4. Relative Comparison of TEQ

The TEQ concentrations of dl-PCBs in blood serum were higher in comparison to

the human milk and placenta tissues. The TEQ values for dl-PCBs was highest in blood

serum with value of 0.24 ng TEQ/g lip followed by placenta with 0.08 ng TEQ/g lip and

milk samples with 0.06 ng TEQ/g lip. In all the matrices PCB-126 was more predominant

with a highest TEQ value than other congeners, i.e. 0.20 ng TEQ/g lip in blood, 0.07 ng

TEQ/g lip in placenta and 0.05 ng TEQ/g lip in milk samples. Whereas the PCB-189 was

the congener with lowest TEQ values (ng TEQ/g lip) in the blood (3.88 x 10-5

) and milk

(8.58 x 10-6

) samples, while in placenta samples lowest TEQ was observed for PCB-156

(1.15 x 10-5

). Similarly the highest EDI of dl-PCBs were observed in blood serum (1.94 x

10-3

pg WHO-TEq/kg bw) followed by placenta (6.56 x 10-4

pg WHO-TEq/kg bw) and

milk samples (5.10 x 10-4

pg WHO-TEq/kg bw).

4.8. Association of PCBs in Blood, Milk and Placenta with Maternal

Social and Demographic Characteristics

The socio-demographic characteristics of mothers were evaluated in the present

study against the concentration of PCBs in body matrices to evaluate the association

between the two and to ascertain the potential impacts on the maternal health. The social

and demographic characteristics of mothers are presented in Table 4.7. The age of the

volunteersranged between 19 to 45 years and their average BMI was calculated as 25.77

kg/m2. Most of the women population was comprised of multiparae with the baby birth

weight of 2.21 kg, belonged to rural areas (61 %) and preferred to eat dairy products (34

%) and take milk daily (68 %) (Figure 4.15). The results of one way ANOVA for sum

PCBs, performed for the socio-demographic parameters viz; locality, age, BMI, the

number of children, eating habits and fish intake are given in Table 3.3. PCBs showed a


77

significant relationship (p < 0.05) for milk/ dairy products intake, indicating the affinities

of PCBs with lipophilic substances.

Table 4.7: Socio-demographic characteristics and their relationship with PCBs

concentration in blood serum (n=44, Concentration in ng/g lip)

Subject Characteristics Mean± SD F value p Value

Age (years) 29.32 ± 5.77 0.563 0.901

Body Mass Index (kg/m2) 25.81 ± 5.4 0.594 0.848

Infant Birth weight (kg) 2.21 ±1.02 0.563 0.864

Total Lipids g/L 4.4±1.18

Parity 0.565 0.456

Primiparae 15 (34%)

Multiparae 29 (65%)

Location 1.257 0.269

Urban 27 (39%)

Rural 17 (61%)

Fish Intake 0.547 0.702

Never 9 (20%)

Once in a week 4 (9%)

Once in a month 14 (32%)

Quarterly 17 (39%)

Milk Intake 3.218 0.050*

Never 3 (7%)

Daily 30 (68%)

Weekly 11 (25%)

Eating Habits 0.176 0.982

Meat 2 (4%)

Vegetable 13 (30%)

Dairy 15 (34%)

All of them 3 (7%)

Vegetable and Dairy 9 (21%)

Meat and Dairy 1 (2%)

Meat and Vegetable 1 (2%)

* Correlation is significant at the 0.05 level (one way ANOVA).


78

Figure 4.15: Eating habits of mothers selected from five districts of Punjab Pakistan

The detail of socio-demographic characteristics of sampling women and their

relationship with ∑34 PCBs concentration in human milk and the socio-demographic

characteristics was evaluated using Pearson‘s Correlation Coefficients and presented in

Table 4.8. The age of sampling mothers showed a negative relationship with the

concentration of PCBs (r = -0.310, p = 0.024). In the present study, for milk analysis,

primiparae and multiparae constituted 29% and 71% of the sampling population (n=41)

respectively. The ∑34 PCB levels were found higher in human milk of primiparae and

lower in multiparae and showed a significantly strong negative relationship between

parity and ∑34PCBs (r = -0.849, p =0.01).

Food is considered as a major source of PCBs in human. The sampling population

was evaluated on the basis eating habits such as meat, dairy, vegetable and a combination

of all of these. These eating habits were positively associated with ∑34 PCBs (r=0.353,

p=0.012).

Meat 5%

Vegetable 30%

Dairy 34%

All of them 7%

Vegetable and Dairy 20%

Meat and Dairy 2%

Meat and Vegetable

2%


79

A moderate negative correlation was found between ∑34 PCBs in human milk and

lactation (r= -0.569, p=0.001) indicating that women who breastfed one child have a

higher level of PCBs than those who have breastfed more children. The fat content (%) in

human milk ranged between 0.8% and 6.6 % (mean 3.03 ± 1.20). A positive association

of lipids was observed with maternal age, parity and number of children carried (Table

4.8).


80

Table 4.8: Relationship of maternal characteristics and infant‘s anthropometric

measures with ∑34PCBs in Human Milk (n=41, Concentration in ng/g lip)

Subject Characteristics Mean ± SD Correlation

r value p Value

Maternal Characteristics

Age (years) 29.56± 5.87 -0.310 0.024*

Body Mass Index (kg/m2) 25.81± 5.4 0.025 0.438

Parity

Primiparae 12

(29%)

Multiparae 29

(71%)

-0.849 0.001*

Number of children 2.46 ± 1.09 -0.782 0.001*

Lactation

36 (88%)

-0.569 0.001*

Lactating

Non-lactating 5 (12%)

Location

24 (60%)

0.231 0.073

Urban

Rural 17 (40%)

Eating Habits 0.353 0.012*

Meat 2 (5%)

Vegetable 11 (27%)

Dairy 14 (34%)

Vegetable and dairy 9 (22%)


81

Subject Characteristics Mean ± SD Correlation

r value p value

Meat and dairy 1(2.5%)

Meat and vegetable 1 (2.5%)

All of them 3 (7%)

Education 13 (32%) 0.145 0.184

Literate

Illiterate 28 (68%)

Infants anthropometric

measurements

Infant‘s birth weight (kg) 2.08 ± 0.91 -0.732 0.01*

Crown to heel length 42.47 ± 4.27 -0.204 0.100

Head circumference (cm) 33.10 ± 2.16 -0.586 0.001*

Mid arm circumference 9.90 ± 1.51 -0.303 0.027**

Chest circumference 31.26 ± 1.97 -0.293 0.032**

*correlation is significant at p = 0.01, ** correlation is significant at p = 0.05


82

An association between the concentrations of ∑34PCBs in the placenta tissue with

maternal physiological features was evaluated using Pearson‘s Correlation Coefficient.

PCB concentrations were compared with maternal height, body weight, body mass index

(BMI) and age. Results are illustrated in Table 4.9. Conversely, in the present study no

significant relationship could be found in levels of PCBs and maternal age. However, a

positive significant correlation of levels of PCBs in placenta with maternal weight and

height was observed. A negative correlation was found between PCBs and spatial

distribution of the study population.

Table 4.9: Correlation of PCBs with maternal characteristics and neonatal

anthropometric measures (Concentration in ng/g lip)

Characteristics r value p value

Maternal Characteristics

Age (years) 0.088 0.282

Height (meters) 0.323 0.015*

Body weight (Kg) 0.277 0.033*

BMI 0.058 0.353

Spatial Distribution -0.213 0.080

Neonatal physiological

index

Birth weight (ponds) -0.250 0.049*

Crown to heel length (cm) -0.261 0.042*

Head circumference -0.185 0.112

Chest Circumference -0.274 0.034*

Mid arm circumference -0.098 0.261


83

4.9. Association of PCBs in Blood, Milk and Placenta with Infants

Anthropometric Characteristics

A Pearson‘s correlation was applied between the concentration of PCBs in blood

serum with infant‘s anthropometric parameters viz; height, head circumference, chest

circumference and mid arm circumference. The results showed a significant negative

correlation between PCB levels in blood and infant‘s head circumference (r= -0.446,

p=0.001) and a negative but non-significant relationship with infant‘s height (r= -0.081,

p=0.301) chest circumference (r= -0.059, p=0.352) and mid arm circumference (r= -

0.083, p=0.296). Regression plots were drawn to show the relationship between these

variable and blood serum levels (Figure 4.16).


84

a)

b)

y = -0.0033x + 42.968 R² = 0.0066

25

30

35

40

45

50

55

0.0 100.0 200.0 300.0 400.0 500.0 600.0

He

igh

t (c

m)

PCB Concentration (ng/g lip)

y = -0.0085x + 34.859 R² = 0.1987

27

29

31

33

35

37

39

0.0 100.0 200.0 300.0 400.0 500.0 600.0

He

ad c

ircu

mfe

ren

ce (

cm)

PCB concentration (ng/g lip)


85

d)

e)

Figure 4.16: Relationship between ∑34PCBsin blood serum (ng/g lip) and; a) infant‘s

height (cm); b) head circumference (cm); c) mid arm circumference (cm) and d) chest

circumference (cm) e) Combined Anthropomentic Measures (cm)

y = -0.0013x + 31.884 R² = 0.0035

25

27

29

31

33

35

37

0.0 100.0 200.0 300.0 400.0 500.0 600.0

Ch

est

Cir

cum

fere

nce

(cm

)


y = -0.0618x + 47.364 R² = 0.068 (Crown to Heel Length)

y = -0.0219x + 35.026 R² = 0.0342 (Head Circumference)

y = -0.0012x + 31.853 R² = 0.0001 (Chest Circumference)

y = -0.0011x + 10.127 R² = 0.0069 Mid Arm Circumference

0.00

10.00

20.00

30.00

40.00

50.00

60.00

0.00 100.00 200.00 300.00 400.00 500.00 600.00

Infa

nt

an

thro

po

me

tric

me

asu

res

(cm

)

PCB Concentration (ng/g lip. wt.)


86

Different growth parameters of infants (birth weight, head circumference, chest

circumference and mid arm circumference) were correlated with the ∑34 PCBs in human

milk. A significant negative relationship was observed between the infant birth weight

and level of PCBs (r= -0.732, p=0.01) reflecting that with an increase in PCBs

concentration in human milk, the birth weight of the infants decreases. Similarly, a

significant negative correlation was found between ∑34 PCBs and other neonatal growth

parameters viz: head circumference (r= -0.586n p= 0.001), mid arm circumference (r= -

0.303, 0.027), chest circumference (r= -0.293, p=0.032). However, a negative but non-

significant relationship was also observed between the PCBs concentration and infant‘s

crown to heel length (r= -0.204, p=0.100). Linear regression plots were drawn to show the

relationship between these parameters and concentration of human milk samples (Figure

4.17).

a)

y = -0.0246x + 3.166 R² = 0.5357

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

0.00 20.00 40.00 60.00 80.00 100.00 120.00 140.00

Bo

dy

We

igh

t (k

g)



87

b)

c)

y = -0.0465x + 35.158 R² = 0.3439

25

27

29

31

33

35

37

39

0.00 20.00 40.00 60.00 80.00 100.00 120.00 140.00

He

ad C

ircu

mfe

ren

ce (

cm)


y = -0.0168x + 10.644 R² = 0.0916

6

7

8

9

10

11

12

13

14

0.00 20.00 40.00 60.00 80.00 100.00 120.00 140.00

Mid

arm

Cir

cum

fere

nce

(cm

)



88

d)

e)

Figure 4.17: Relationship between ∑34PCBsin human milk (ng/g lip) and; a) infant‘s

body weight (kg); b) head circumference cm); c) mid arm circumference (cm) and d)

chest circumference (cm); e) Combined Anthropometric Measures (cm)

y = -0.0212x + 32.203 R² = 0.0858

25

27

29

31

33

35

37

0.00 20.00 40.00 60.00 80.00 100.00 120.00 140.00

Ch

est

Cir

cum

fere

nce

(cm

)


y = -0.0618x + 47.364 R² = 0.068 (Crown to Heel Legth))



y = -0.0011x + 10.127 R² = 0.0069 (Mid Arm Circumference

0.00

10.00

20.00

30.00

40.00

50.00

60.00

0.00 100.00 200.00 300.00 400.00 500.00 600.00

Infa

nt

an

thro

po

me

tric

me

asu

res

(cm

)

PCB Concentration Milk (ng/g lip. wt.)


89

In order to ascertain the trans-placental transfer of PCBs, a maternal- fetal

correlation (Pearson‘s) was performed comparing levels of PCBs in placental tissues

against various fetal growth parameters (Table 4.9). Concentrations of PCBs in placenta

showed a negative significant correlation with infant birth weight (r= -0.250, p= 0.049),

crown to heel length (r= -0.261, p= 0.042) and chest circumference (r= -0.274, p= 0.034).

Similarly, a negative but non-significant correlation was found between concentrations of

PCBs in placenta tissues and infant‘s head circumference (r= -0.185, p= 0.112) and mid

arm circumference (r= -0.098, p=0.261). The results are graphically presented in Figure

4.18.

a)

y = 0.0008x + 1.52 R² = 0.0277

1.1

1.2

1.3

1.4

1.5

1.6

1.7

1.8

1.9

0.00 20.00 40.00 60.00 80.00 100.00 120.00 140.00

Mat

ern

al H

eig

ht

(me

ter)



90

b)

c)

y = -0.147x + 76.225 R² = 0.0509

20

30

40

50

60

70

80

90

100

110

120

0.00 20.00 40.00 60.00 80.00 100.00 120.00 140.00

Mat

ern

al b

od

y W

eig

ht

(kg)


y = -0.0228x + 44.223 R² = 0.0092

30

35

40

45

50

55

0.00 20.00 40.00 60.00 80.00 100.00 120.00 140.00

Infa

nt'

s H

eig

ht

(cm

)

PCBConcentration (ng/g lip)


91

d)

e)

Figure 4.18: Relationship between ∑34PCBsin human placenta (ng/g lip) and; a) Maternal

Height (meter); b) Maternal Body Weight (kg) ; c) Infant‘s Height (cm) and d) Infant‘s

Body Weight (kg); e) Combined Infants Anthropometric Measures (cm)

y = -0.0193x + 5.7818 R² = 0.0472

0

1

2

3

4

5

6

7

8

9

0.00 20.00 40.00 60.00 80.00 100.00 120.00 140.00

Infa

nt'

s B

od

y W

eig

ht

(kg)


y = -0.0618x + 47.364 R² = 0.068 (Crown to Heel Length)



y = -0.0073x + 10.467 R² = 0.0096 (Mid Arm Circumference

0.00

10.00

20.00

30.00

40.00

50.00

60.00

0.00 20.00 40.00 60.00 80.00 100.00 120.00 140.00

Infa

nt

an

thro

po

me

tric

me

asu

res

(cm

)



92

4.7. Exposure and Risk Assessment to Nursing Infants (milk)

The Estimated Daily Intake of dl-PCBs through lactation in all the nursing infants

was calculated. The calculated EDI forDL-PCBs ranged from 400 to 26624.1pg TEQ kg-1

bwday-1

(mean: 8862.6pg TEQkg-1

bw day-1

) were found to be significantly higher than the

tolerable daily intake limits provided by WHO (1–4 pg TEQ kg-1

bw day-1

) (Table 4.10).

Highest EDI was observed in Sialkot, with EDI = 13002.55 pg TEQ kg-1

bw day-1

and

lowest EDI was found in samples from Lahore, i.e. EDI = 7500.91pg TEQ kg-1

bw day-1

.

Moreover, in this study, the Hazard Quotient for ∑DL-PCBsranged from 40.42 to2662.41

(mean: 886.26) which is far above the benchmark value of 1 at all the sampling sites.

Table 4.10: Estimated Daily Intake EDI (ng kg-1

body weight day-1

) and Hazard

Quotient (HQ) in infants

Location EDI HQ

Punjab Province 8.86 (0.4-26.62) 886.26 (40.42-2662.41)

Lahore 7.51 (0.85-20.65) 750.09 (85.3-2064.52)

Khanewal 7.71 (4.6-12.04) 769.78 (460.32-1203.90)

Okara 7.76 (0.4-16.05) 775.76 (40.42-1604.89)

Sialkot 13 (1.78-26.62) 1300.25 (177.75-2662.41)

Chakwal 10.1 (1.34-25.06) 1009.68 (133.67-2506.23)

93

CHAPTER FIVE

DISCUSSION


94

Chapter Five

DISCUSSION

Monitoring of environmental pollutants in women, particularly; PCBs are of

immense importance because women play a crucial role in the survival of a society

through rearing and upbringing of future generations. Their exposure to environmental

pollutants ultimately lead to adverse health effects on their body as well as in fetuses and

infants. Thus, monitoring of PCBs in women is crucial as exposure to PCBs has been

considered to decrease their fecundity (Buck et al., 2000). In addition to pregnant women,

fetuses, neonates and children also are susceptible to the harmful effects of these

contaminants due to heightened sensitivity as their organs are comparatively immature

(Faroon and Ruiz 2015). Therefore, the present study was intended to monitor the levels

of PCBs in the multiple body matrices of women from different areas of Punjab, Pakistan

as a first step for assessing the risks to the health of women and their infants.

5.1. Comparative Analysis of PCBs Profile

5.1.1. PCBs profile in blood serum

Congener specific analysis of PCBs in blood serum reflected the occurrence of

some of the most persistent congeners with the mean values of PCB-153 (6.64 ng/g lip),

PCB-138 (4.9 ng/g lip) and PCB-180 (2.5 ng/g lip). The concentrations of these persistent

congeners were found higher than those reported in a previous study from Pakistan

highlighting the PCBs levels in blood serum of urban and rural mothers from Islamabad

and Gujrat, Pakistan respectively. The mean concentrations of PCBs were PCB-153 (2.63

ng/g lip), PCB-138 (2.04 ng/g lip) and PCB-180 (1.3 ng/g lip) (Ali et al., 2013). The

concentrations of these persistent PCB congeners were also higher than Mexican

population reporting mean values of 1.90 ng/g lip, 1.80 ng/g lip and 1.00 ng/g lip for


95

PCB-153, PCB-138 and PCB-180, respectively (Garcia et al., 2014). Whereas, the

findings of persistent congeners from the current study were lower than those reported

from Bolivia, viz. PCB-153 (59 ng/g lip), PCB-138 (1.80 ng/g lip) and PCB-180 (1.00

ng/g lip) (Arrebola et al., 2012).

These persistent congeners belong to high Hexa-CBs and Hepta-CBs group and

their specific sources may include steel manufacturing units and coal burning in iron ore

sintering (Biterna & Voutsa, 2005). Moreover, non-persistent PCB congeners (PCB-52,

PCB-66, PCB-74, PCB-101, PCB-105, PCB-128 and PCB-149) have been detected in the

blood serum samples, which are indicating their continuous and recent exposure to the

sampling population from commercial use with multiple sources (Covaci et al., 2002). A

comparison of the present study with other studies from developed and developing

countries on PCBs residues in blood serum is given in Table 5.1. The mean concentration

of ∑34PCBs in the blood serum of the present study was 232.3 ng/g lip. The

concentration in the present study was comparable to the study reported from Japan (292

ng/g lip), Korea (127.0 ng/g lip) and Chinese electronic dismantling workers. In

comparison to other developing countries, the mean serum concentrations of PCBs in the

current study were lower than those reported in Belgium (595ng/g lip) and Romania (680

ng/g lip) (Table 5.1). This might be due to the industrialized and developed countries

have an extensive application history of PCBs used in the past than developing countries.

The aforesaid study had also mentioned the indiscriminate use of PCBs by these two

European countries in the past and the possibility of an ongoing use as a reason for their

higher levels of detection in general population.


96

Table 5.1: Comparison of concentrations (ng/g lip) of PCBs in blood serum with

other studies from developed and developing countries

Country

Name

Sample

Size

PCB congeners studied PCB

Concentrations

References

Median Mean

Pakistan 44 30, 37, 44, 49, 52, 54, 60, 66, 70,

74, 77, 82, 87, 99, 101, 105 114,

118,126, 128,138, 153, 156, 158,

166, 169, 170, 179, 180, 183 187,

189, 198, and 209

- 232.3 Present study

Sweden 201 105, 118, 138, 153, 156, 167, and

180

552 Glynn et al.,

2003

Japan 89 74, 99, 118, 138, 146,153, 156,

163/164, 170, 180, 182/187,

194,199, 206, and 209

38 232

Inoue et al.,

2006

Romania 53 101, 99, 118, 146, 153, 105, 138,

187, 184, 156, and180

- 680 Dirtu et al.,

2009

Belgium 20 101, 99, 118, 146, 153, 105, 138,

187, 184, 156, and 180

- 595 Dirtu et al.,

2009

Poland 22 28, 52, 74, 99,101, 105, 118, 138,

153, 156, 170, 180, 183, 187, 194,

196/203 and 199

79.4 83.0 Jaraczewska

et al., 2006


97

Pakistan 34 118, 153, 180 and 170 13 34 Ali et al.,

2013

Bangladesh 24 138, 146, 153 and180 26 - Mamun et al.

2007

India - - - 0.187* Eguchi et al.,

2012

China 47 - 115 134 Bi et al., 2007

Taiwan 20 28, 52, 101, 138, 153, 180, 77, 81,

126, 169, 105, 114, 118, 123, 156,

157, 167, 189

36.41 28.2

Wang et al.,

2004

Korea 40 18, 20, 28, 52, 66, 70, 74, 99, 101,

105, 111, 118, 138, 146, 153, 156,

164, 167, 170, 178, 180, 183 and

187

127 Kang et al.,

2008

*Concentration in wet weight


98

5.1.2. PCBs profile in human milk

Persistent PCB congeners were also detected in human milk samples with

following concentrations; PCB-153 (1.89 ng/g lip), PCB-138 (1.41 ng/g lip) and PCB-180

(0.75 ng/g lip). These concentrations were lower but comparable to those found in the

milk of Turkish mother viz: PCB-153 (3.36 ng/g lip), PCB-138 (1.63 ng/g lip) and PCB-

180 (1.61 ng/g lip) (Cok et al., 2012). The concentrations were found lower than previous

studies from Pakistan, which reported the concentrations of PCB-153, PCB-138 and

PCB-180 as 6.00 ng/g lip, 3.00 ng/g lip and 204.00 ng/g lip respectively. (Khawaja et al.,

2010). Moreover, the concentration of these congeners in the present study was much

lower than those reported from Eastern Siberia, Russia (Mamontova et al., 2017).

The concentration of ∑34PCBs in human milk was also compared with the values

observed in other countries (Table 5.2). The results of the PCBs in human milk from the

present study were comparable to those reported from Indian cities viz; New Delhi (23

ng/g lip), Mumbai (30 ng/g lip), Kolkata (40.0 ng/g lip) and Chennai (34 ng/g lip)

(Devanathan et al., 2009). The concentration of PCBs in human milk in the present study

was found higher than those reported from Turkey (9.91 mg/g lip). However, the ∑PCBs

levels in human milk were lower than previous studies from Pakistan (390 ng/g lip),

Canada (50 ng/g lip) Russia (240 ng/g lip), and Eastern Siberia, Russia (394.58 ng/g lip)

and Zagreb, Croatia (79 ng/g lip) (Table 5.2).


99

Table 5.2: Comparison of concentrations (ng/g lip) of PCBs in human milk with

other studies

Country Name

Sample

Size

PCB congeners studied PCBs

Conc.

References

Punjab, Pakistan 41 30, 37, 44, 49, 52, 54, 60, 66,

70, 74, 77, 82, 87, 99, 101,

105, 114, 118, 126, 128, 138,

153, 156, 158, 166, 169, 170,

179, 180, 183, 187, 189, 198

and 209

44.06 Present Study

Lahore 36.03

Khanewal 43.67

Okara 30.86

Sialkot 57.61

Chakwal 68.33

Pakistan 23 28, 52, 101, 138, 153, 180

and 209

390 (Khawaja et

al., 2010)

India

New Delhi

Mumbai

Kolkata

Chennai

64

23

30

40

34

(Deanathan et

al., 2009)

Russia 35 - 240 (Tsydenova et

al., 2007)

India 46 Chennai (34) 32.5 (Subramanian

et al., 2017)

Eastern Siberia,

Russia

155 28, 52, 49, 44, 74, 70, 95, 66,

101, 90, 99, 97, 87, 115, 110,

118, 153, 132, 105, 138, 158,

187 183, 180, 190 and 170

394.58

(Mamontova et

al., 2017)

Northern Tanzania 150 28, 52, 101, 118, 138, 153

and 180

157 (Muller et al.,

2017)

Canada

298 PCB 105, 114, 118, 123,

156, 157, 167, 189, 77, 81,

126, 169,18, 47, 49, 66, 74,

99, 110, 128, 141, 170, 178,

50

(Rawn et al.,

2017)


100

183, 187, 194, 195, 201, 203,

206, 209, 28, 52, 101, 138,

153 and 180

Zagreb, Croatia

33 28, 52, 101, 153, 138, 180,

77, 126, 169, 105, 114, 118,

123, 156, 157, 167, 189,

60,74 and 170

79

(Klincic et al.,

2016)

Turkey

47 28, 52, 101, 138, 153, 180,

77, 81, 126, 169, 105, 114,

118, 123, 156, 157, 167 and

189

9.91 (Cok et al.,

2012)


101

5.1.3. PCBs profile in placenta

The mean concentration of persistent congeners was determined in placenta

tissuesof sampling population. The concentration of these congeners was as follow; PCB-

153 (mean: 1.89 ng/g lip), PCB-138 (1.41 ng/g lip) and PCB-180 (0.75 ng/g lip). These

concentrations were lower than those reported in placentas of Italian mothers with median

concentrations of PCB-PCB-138 (18 ng/g lip), PCB-153 (26 ng/g lip), and PCB-180 (18

ng/g lip) (Bergonzi et al., 2009). Patandin (1999) reported the mean concentration of

PCBs in maternal blood plasma PCB-138 (0.6 µg/L), PCB-153 (0.91 µg/L), and PCB-180

(0.54 µg/L) from Netherland and these results are comparable to the present study. The

concentrations of these congeners were also lower than those reported from, USA with

mean values of PCB-153 (19.44 ng/g lip), PCB-138 (14.29 ng/g lip) and PCB-180 (7.12

ng/g lip ). The mean concentration of these persistent congeners was 4.07 ng/g lip which

was again found to be lower than those detected in Taiwanese mothers, i.e. 28.83 ng/g lip

by Wang et al., (2004). Several studies have highlighted the effects of PCBs on the

development and growth of the fetuses leading to intrauterine retardation, later possible

effect leading infants and childhood disease, poor cognitive development ectodermal

defects and behavioral disorder such as hyperactivity (Patandin et al., 1999).

A comparative analysis of ∑34PCBs in placenta tissues of mothers in the present

study with other countries was made that reflected comparable findings from the present

study than those in Japan (98.7 mg/g lip), Taiwan (37.74 ng/g lip) and USA (40.5 ng/g

lip) However, the current levels were lower than those reported in Spanish mothers (303

ng/g lip) and Italian mothers (98.7 ng/g lip) (Table 5.3).. This may be due to differences

in sample size, number and type of congeners studied, difference in geographic location,

changed eating habits and levels of exposure. The concentrations of PCBs in human

placenta in the present study were found higher than those detected in the placentas of


102

Chinese mothers (9.8 ng/g lip). A comparison of the results of the present study with

previously published studies from other countries is presented in Table 5.3.

Table 5.3: Comparison of concentrations (ng/g lip) of PCBs in placenta with other

studies

Country Sample

Size

PCB congeners studied PCBs

Conc.

Reference

Pakistan 45 PCB-30, 37, 44, 49, 52, 54, 60, 66, 70, 74, 77, 82,

87, 99, 101, 105, 114, 118, 126, 128, 138, 153, 156,

158, 166, 169, 170, 179, 180, 183, 187, 189, 198

and 209

80.26 Present Study

China 130 PCB-105, 118, 156, 157, 189, 206 and 209 9.8 (Ma et al.,

2012)

Taiwan 20 PCB-126, 169, 105, 114, 118, 123, 156, 157, 167,

189, 138, 153 and 180

37.75 (Wang et al.,

2004)

Japan - 10 groups of PCB homolog (Tri-CBs to Deca-CB) 70 (Fukata, 2005)

Italy 70 28, 31, 52, 74, 99, 101, 105, 114, 118, 123, 128,

138, 146, 153, 156, 157, 167, 170, 172, 177, 180,

183, 187, 189, 194, 196, 201, 203, 206 and 209

98.7* (Bergonzi et

al., 2009)

USA 42 28, 52, 101, 118, 138, 153,180, 77, 105, 114, 118,

126, 156, 169 and 189

40.5* (Nanes et al.,

2014)

Spain 17 28, 52, 101, 118, 146, 153, 105, 138, 187, 183, 128,

156, 180, 170 and 189

303* (Gomara et al.,

2012)

* Values calculated after conversions as described by Nanes et al., 2014


103

5.1.4. Overall PCBs trend in multiple body matrices

Biomonitoring of pollutant concentration in the human tissues such as the

placenta, breast milk, urine, blood and hair serum, umbilical cord other tissues depict a

varying degree of body burden. Quantification of PCBs in the present study showed that

the lipid adjusted concentration of PCBs varied among all three examined body matrices

and were found notably higher in blood serum than another two biomarkers examined i.e.

placenta and milk. The percentage contributions of blood serum in each class/ homolog of

PCBs were predominant in comparison to milk and placenta. These findings were

comparable to the Japanese mothers where lipid adjusted maternal serum levels were

higher than those found in cord serum and umbilical cord (Fukata, 2005). In contradiction

to this; a study from Taiwan on maternal venous serum, breast milk, cord serum and

placenta tissue showed the highest concentration of PCBs in human milk samples than

other body matrices (Wang et al., 2004).

The possible reason for higher levels in blood serum was better explained by their

pharmacokinetics in the blood as described Noren et al., (1999) that different chemicals in

blood tend to bind to albumin and lipoproteins instead of getting dissolved in the lipids

and their distribution in blood plasma varies according to the types of chemicals.

Therefore, there is a possibility that free and non-binding part of the chemicalsare evenly

distributed, whereas, distribution and transportation process of protein bound form is

somehow complicated, which might be the reason for the high concentration of PCBs in

the blood than milk and placenta (Fukata, 2005). The blood absorbs the chemicals

directly from the intestine and lungs, then liver and kidneys metabolize and excrete the

POPs with limited capacity. However, the excretion rate is the slow process and PCBs

tend to accumulate in adipose tissue and stay for longer time. From the blood, these

pollutants can cross the placenta due to lipophilic nature. In case of milk, mammary gland


104

cells synthesize the milk and some amount of PCBs may transfer from blood to the milk

(Verner et al., 2008).

5.2. Spatial Trends of PCB Homolog with Potential Sources

In blood serum, the level of PCBs was highest from Chakwal followed by

industrial cities of Lahore and Sialkot, while lowest levels of PCBs were also detected in

agricultural dominant areas i.e. Khanewal and Okara. Among all the PCB homolog in

maternal serum samples, an overall prevalence of Tetra-CBs was observed at all the

sampling sites followed by Penta-CBs and Hexa-CBs. This trend was different from the

previous studies from Pakistan, Bangladesh and China on human serum, where higher

chlorinated PCBs were predominant. Although Tetra-CBs were prevailing at all the

sampling sites, their levels were higher in blood serum from Lahore.

The distribution trend of PCB homolog in human milk samples from selected

sampling sites was as follows: Chakwal > Sialkot > Khanewal > Lahore > Okara. Among

all the PCB homolog, Tetra-CBs were predominant in all the sampling sites with the

highest concentration (32.77 ng/g lip) in Sialkot city. The concentration profile of mean

Σ34PCBs in placenta samples on spatial basis was in order: Chakwal > Lahore > Sialkot>

Okara > Khanewal. The highest level of PCBs was detected in placenta samples of

mothers from Chakwal with mean Σ34PCBs value of 93.46 ng/g lip. The concentration of

PCBs in urban and industrial cities i.e. Lahore (87.80 ng/g lip) and Sialkot (84.52 ng/g

lip) were higher than Okara (74.67 ng/g lip) and Khanewal (67.46 ng/g lip). In placenta

samples, Tetra-CBs were the most dominant PCB homolog with a mean value of 43.87

ng/g lip.

Volatilization from PCBs containing building materials, incineration emissions,

vehicular emissions, combustion of PCBs contaminated waste materials and oil spills


105

from industrial sites, metal recovery from e-waste burning, (Farooq et al., 2011); old

equipment, including transformers dismantling steel industry and coal combustion and

industrial waste are potential sources of PCBs in the environment (Chi et al., 2007).

Sources of Tri-CBs includes electrical appliances and particularly paints and

sealants in building material (Robson et al., 2010), whereas those of Tetra-CBs and

Penta-CBs includes combustion during the iron ore sintering process and steel

manufacturing units. Tetra-CBs were found the dominant homolog group at all the

sampling sites and in all the examined body matrices of selected mothers. Previous

regional studies have shown Tetra-CBs as dominating the configuration characteristics of

PCBs in the atmosphere of the Asia (Jaward et al., 2005; Li et al., 2012). Nevertheless,

findings of earlier studies from Pakistan on monitoring of levels of PCBs in food

commodities were also consistent with the findings of this study and reflected that Tetra-

CBs were prevalent in rice and grain cereal crops. However, some previous studied in

serum from Bangladesh and China (Mamun et al., 2007; Bi et al., 2007) showed a

different trend where higher chlorinated PCBs were predominant and identified fish as a

major source of exposure to these PCBs. In Punjab, fish is not a major and a regular diet

source in the present study population, which also can be an explanation of the reason

why higher chlorinated PCBs were not prevalent in comparison to lower chlorinated

PCBs in the study population. According to Liem et al., (2000) 95% of the human

exposure to PCBs are through consumption of fatty food. A major source of high

chlorinated PCBs is food, i.e. fish, meat, poultry and dairy product (Xing et al., 2009).

Exhaust from cement industries have been identified as pollution sources of the higher

chlorinated biphenyls (Rodriguez, 2016).

In Pakistan, PCBs have mainly been used as a technical mixture of Tetra CBs,

Penta CBs and Tri CBs (Syed et al., 2014). Therefore, higher levels of these three


106

homolog in the study population have validated the ongoing usage and its subsequent

human exposure. Among the entire sampling sites, significant levels of PCBs have been

detected in sampling population from Chakwal. Levels of higher chlorinated PCBs were

also predominant in District Chakwal. A possible explanation that Chakwal is situated in

a geographic territory with major cement plants fulfilling a major portion of the country‘s

demand for cement. People living in areas with cement plants and incinerators are

categorized by ASTDR as ―special population‖ which may be highly exposed to PCBs as

compared to the general population (ASTDR, 2014). Cement industries in Pakistan have

been using tire derived fuel and refused derived fuel to overcome energy crisis and meet

the energy crisis. These releases substantial amount of dioxins and dioxin like PCBs

(Cheema & Badshah, 2013). Cement industry exhaust is one of the key exposure routes to

high chlorinated biphenyls that could lead to the PCBs contamination of food/ diet,

particularly fish, meat, oil, dairy products and water. In addition to this, studies have

highlighted Chakwal with heavy traffic load and thousands of diesel trucks passing

through it which may lead to vehicular emissions of PCBs (Aziz & Bajwa, 2008).

Therefore, higher concentrations in Chakwal may be linked to multiple sources via; food,

contaminated air coming from cement plants and vehicular load, burning of solid waste

and indoor PCBs exposure.

Lahore is a city with the increasing trend of urbanization and the hub of many

industrial activities including chemical manufacturing, paints, and dyes manufacturing

units so exposure of lower chlorinated biphenyls was higher in mothers from Lahore. In

Sialkot district, burning of plastic and leather waste, steel and surgical tool manufacturing

industries, chemical industry, transformer repairing units and many other similar

resources in urban and peri-urban could be the source of PCBs contamination (Mahmood

et al., 2014).


107

Lower chlorinated PCBs are volatile in nature which might be the reason of their

abundance and long range transport in the air (Ali et al., 2015). This phenomenon,

perhaps is the possible source of PCBs exposure in Khanewal and Okara. Furthermore,

there exists a common practice of backyard burning of solid waste in general population

living in suburbs and remote areas. The incomplete waste combustion has been

recognized as an important route of exposure to PCBs (Someya et al., 2010). Punjab is

located in a geographic territory where it shares a border with India. PCBs generated are

capable of trans-boundary movement; hence PCBs may enter into the environment of the

Punjab, Pakistan through air circulations. The Province has been experiencing episodes of

smog since last two years, mainly due to transboundary movement/ advection of

pollutants from India which may also contain PCBs particles.

5.3. PCBs Trend in Urban and Rural Areas

The concentration of ∑34PCBs in blood serum was slightly higher in urban

mothers (235.66 ng/g lip) than rural mothers (231.09 ng/g lip). Whereas, in both

environmental setting levels of lower chlorinated PCBs (particularly Tetra-CBs) were

predominant among all the homolog. Similarly, the ∑34PCBs concentrations were also

higher in milk (49.31 ng/g lip) and placenta samples of urban mothers (84.62 ng/g lip) as

compared to those found in milk (36.66 ng/g lip) and placenta of rural mothers (74.30

ng/g lip), respectively. A similar trend was observed by Turci et al., (2006) in women

populations living in different environmental settings in Italy. The current findings were

also inconsistent with the results of the previous study from the neighboring country

(India), where the highest level of PCBs was observed in the milk samples of women

living in cities (Table 5.2).


108

In industrial districts, significant sources of PCBs exposure in rural population

are associated with the burning of agricultural waste, cow dung, and wood fuel,

particularly in an indoor environment. In Sialkot district, industries are scattered and there

is a general practice of open burning of solid waste which might be a reason of higher

concentrations of some PCB homolog in the rural population of the area. Whereas, in

urban environment, municipal solid waste (MSW), power producing plants, transformers,

and emissions from dumping sites may attribute to high exposures to PCBs in urban areas

(FAO/WHO, 2005). The lower chlorinated biphenyls are unintentionally formed as by

products in paints and pigments industry. The disposal and decomposition of PCB

containing materials are one of their sources and it enters the human body through

inhalation (Rodriguez, 2016). These semi volatile lower chlorinated biphenyls have been

detected in substantial quantities in outdoor and indoor air in cities, and older buildings

having sealants containing PCBs (Herrick et al., 2004).

Higher levels of PCBs in mothers from industrial and urban cities reflect their

continuing exposure to PCBs sources in the urban environment (Diamond et al., 2010)

Sialkot and Lahore is an urban and industrial city having steel and surgical tools

manufacturing units, transformer repairing and pigment, industrial units and the PCBs

release from these units and incomplete burning of plastic openly may be likely sources

of PCBs in the urban environment of the region. There are waste disposal stations

(including those of electronic waste) near the urban and industrial areas which might be a

key cause of higher levels of PCBs therein. Past studies from other environmental

matrixes of the Punjab Province have also emphasized on higher environmental PCBs

levels in urban and industrial areas than rural (Syed et al., 2014).

However, still, a relative significant concentration of PCBs in rural areas of

Khanewal and Okara are apparently may be due to their volatilization, diffusive gaseous


109

transport and tend to travel long distances in the environment which might lead to their

abundant occurrence in some of the rural areas (Gasic et al., 2010).

5.4. Risk Assessment of Maternal Health

5.4.1. Exposure to dl-PCBs and TEQ fluxes

Dioxin-like PCBs exhibits toxic effects in human and 90% of their exposure are

due to dietary intake, particularly animal origin food as the prime source. While other

sources of exposure i.e. uptake from various non-food and ambient sources (inhalation of

dust or air, water ingestion, and soil ingestion) only account up to 10% of the total mean

exposure (Malisch & Kotz, 2014). Mono-ortho PCBs and non-ortho PCBs have been

analyzed in various studies conducted on maternal serum, milk and placenta tissues.

Monitoring of these toxic PCB congeners is of key importance due to their dietary uptake

of adults and toxic potential of trans mammary transfer into breast feeding infants (WHO,

2003). The mean concentration of sum dioxin-like PCBs studied in maternal serum, milk

and placenta tissues were 0.24 ng/g lip, 0.06 ng/g lip and 0.08 ng/g lip, respectively. The

TEQ comparison with previous studies revealed that results for maternal serum were

higher than those reported from Taiwan (0.0045 ng/g lip) (Wang et al., 2004). For human

milk samples, the TEQ values were found to be higher than those in milk samples of

Canadian mothers (1.5 pg g-1 lip) and much lower than those from Chinese mothers (3.68

ng/g lip) (Lu et al., 2015; Rawn et al., 2017). Whereas, the TEQ concentrations in

placental tissues were higher than those reported from Taiwan (0.00291ng/g lip) and

Japan (0.0012 ng/g lip) (Suzuki et al., 2005; Wang et al., 2004).

5.4.2. Risk Assessment of dioxin like PCBs

As described earlier that PCBs are lipophilic and tend to bio-accumulate in the

food chain and exposure in human to dioxin like PCBs is of public and scientific concern


110

due to their high toxicity. Exposure risks to subject women through the dietary intake in

the present study were evaluated by using WHO TEF and calculating the Estimated Daily

Intake of dl-PCBs. The EDI in all the women were found below the Tolerable Daily

Intake (TDI) of 1-4 pg TEq/ kg bw recommended by WHO. The EDI levels in blood,

milk and the placenta was 1.94 x 10-3

, 5.10 x 10-4

, 6.56 x 10-4

, respectively. These

indicate that at present, exposure to dl-PCBs in women through dietary exposure is quite

low in Punjab Pakistan. But their detection in the lower amount in body matrices

indicates their presence and a continuous exposure of sampling population. Therefore, if

the long term exposure continues and intake increases with industrialization and

urbanization, the study population may be at risk in future.

A study was conducted by Fromme et al., (2009) on the German population to

monitor the body burden of dl-PCBs and daily intake through food. The study findings

reflected an EDI of 0.19 pg WHO-TEQ/ kg bw indicating that the risk of exposure

through food intake was quite low in participants of that study. A similar study was

conducted in the UK by Food Standard Agency (FSA) in 2003, which revealed higher

levels (0.4 pg WHO-TEQ/ kg bw) in comparison to our study but lower than the TDI

given by WHO.

5.4.3. Risk Assessment through Physiological Characteristics

There are several factors having an influential role in the levels of PCBs

contamination in a study population, such as maternal age, body mass index, parity,

lactation and dietary intake, particularly dietary habits which vary across different areas.

In addition to these, exposure through inhalation and ingestion of dust/ dermal contact,

lifestyle and exposure time may also influence the levels of PCB in human body matrices

(Myllynen et al., 2005). Levels of PCBs were evaluated in all the three body matrices of


111

the women with their demographic and physiological parameters in order to ascertain any

adverse health effect.

Through many previous studies on human serum or plasma, the personal

attributes of mothers, i.e. age, body mass index, lactation, parity, eating habits and

location have been associated with levels of PCBs in the body (Glynn et al., 2003).

However, the maternal serum samples in the present study showed no association with

maternal age, height and body mass index. These findings were consistent with a study

from China (Bi et al., 2007) where no association between serum levels of PCBs and age

was observed. However, in case of human milk samples, age of sampling mothers showed

a negative relationship with the concentration of PCBs in this study (r = -0.310, p =

0.024) which was inconsistent to a recent study on POPs levels in milk from Pakistan

(Yasmeen et al., 2017). The variations in the association between maternal age and

various body matrices could be explained by the reason that the concentration of POPs in

women body increases as age progresses because of storage within the fat, on the other

hand, women excrete POPs during mensuration, pregnancy and lactation, thereby

lowering their body burden, though no statistically significant differences can be found

(Thomsen et al., 2010).

Lactation or breastfeeding has been recognized to mobilize the PCBs from the

body, thus reducing the body burden in breastfeeding mothers (Glynn et al., 2003). A

moderate negative correlation was observed between levels of PCBs in milk and lactation

(r= -0.569, p=0.001) indicating that women who breastfed one child have relatively

higher levels of PCBs than those who have breastfed more children. These results were

similar to previous studies in Indonesia (Sudaryanto et al., 2006). Most of the sampling

women in the present study were lactating and preferred to breastfeed their infants rather


112

in earlier stages of their development. Hence, this might be a reason of a significant

negative correlation between PCBs level in milk and lactation.

Food is considered as a major source of exposure to PCBs (90%) in human. The

sampling population was evaluated on the basis of eating habits such as meat, dairy,

vegetable and a combination of all of these. These eating habits were positively correlated

with levels of PCB in milk (r=0.353, p=0.012). Most of the women in the present study,

preferred to eat dairy products (34%) and vegetables (30 %) (Figure 4.11). Human milk

is rich in milk fat and PCBs being lipophilic tend to accumulate in lipid rich body part of

humans. Perhaps, this a reason why eating habits showed a significant positive correlation

with level of PCBs in human milk.

Furthermore, meat, fish, poultry and eggs are considered as a prime source of

higher chlorinated PCBs contamination in human. In the present study no significant

association could be found between body burden of PCBs in all the three body matrices

and fish consumption. These results were in contrast to previous studies conducted in

Swedish women where fish consumption was significantly correlated with serum

concentration of PCBs (Glynn et al., 2003). In the current study, fish consumption was

not a major dietary source most of the mothers were eating fish on quarterly basis in a

year. Perhaps this is a reason of difference in results when compared to Swedish and

Bangladeshi population. Also, the possible explanation for this may be regional variations

in food consumption patterns and difference in distribution of PCBs in food items of

geographically distinct areas.

The kinetics of PCBs are somehow complex in human and several factors are

attributed for reducing the body levels of PCBs in women one of these is parity i.e.

number of births. In the present study, primiparae (women with one birth) and multiparae

(women with two or more births) constituted 29% and 71% of the sampling


113

population,respectively. The levels of PCBs were found higher in human milk of

primiparae and lower in multiparae and showed significant strong negative relationship

between parity and level of PCBs (r = -0.849, p =0.01). These results were similar to the

findings of an Indonesian study, which observed that primiparous women tend to have

higher level of PCBs than do multiparae women (Sudaryanto et al., 2006). However, no

association could be observed in the serum and placental concentration of PCBs and

parity.

Age and body mass index are factors that may affect the levels of PCBs and their

circulations in the human body (Muller et al., 2017). In the present study, none of the

body matrices showed any association with BMI. A study from China showed a positive

association between the serum levels of PCBs and body mass index (Lee et al., 2007).

Another study on serum of Swedish women showed positive association with age, body

mass index, change in body weight and their geographic location (Glynn et al., 2003).

This may be due to change in geographic location, genetic differences, eating habits, time

and levels of exposure. Nonetheless, placental concentration of PCBs showed a

significant positive correlation with maternal height (r= 0.323, p=0.015) and body weight

(r= 0.277, p= 0.033). This shows that women with greater height and more body weight

tend to have more PCBs in the body matrices.

5.5. Relationship of PCBs with Neonatal Anthropometric Measures

PCBs are grouped among the notorious endocrine disrupting chemicals (Shaw,

2009) and pose the serious threat to nervous system, hence; therefore, it is essential to

assess their impacts on infants. PCBs toxicity has been evaluated in developing fetus,

infants, and children due to accidental exposure episodes in Japan in 1968 and in Taiwan

in 1979 (Korrick & Altshul, 1998). Increased infant mortality and a congenital syndrome


114

of low birth weight, ectodermal defects (deformed nails, natal teeth, and

hyperpigmentation), and delayed growth, including cognitive decrement and

psychological problems have been observed in children born to mothers with direct or

indirect exposure to PCB. According to Berg (2016) global monitoring study by WHO

indicated that recent human exposure to PCBs is still above the toxicologically safe level

for the fetus and breastfed infants as compared to DDT.

The potential risks to the health of infants through the trans-mammary transfer of

human milk were assessed in the present study. For this purpose, their Estimated Daily

Intake of dl-PCBs through lactation and hazard quotient was determined by using the

equation given by FAO/WHO (1995). The EDI in infants from the present study area was

lower than the values from Croatia (EDI = 50,000-700,000 pg TEQ kg-1

bw day-1

)

(Klincicet al., 2016), and Turkey (EDI = 13,000-2,010,000pg TEQ kg-1

bw day-1

) (Coket

al., 2012), but still slightly comparable and higher than the findings from Tanzanian

mothers (EDI = 200-114,000pg TEQ kg-1

bw day-1

) and Chinese mothers (mean EDI =

32.4 pg TEQ kg-1

bw day-1

), respectively. Moreover, in this study, the Hazard Quotient

was found far above the benchmark value of 1 at all the sampling sites which is

inagreement to the findings from Tanzania and China, in which HQ was above one in

most of the infants (Lu et al., 2015; Mülleret al., 2017). The hazard quotient higher than

one indicates high levels of adverse health risks to infants through breast milk

consumption from exposed mothers, and if the PCBsexposure prevails; the infants‘ health

would be susceptible to high levels of risk. However, the breast milk is a vital component

in infants‘ development so the PCBs exposure needs to be controlled at source. Also,

some evident significant effects can be assessed through correlation between the

concentration of PCBs in mother body matrices and physiological parameters as

discussed below.


115

The anthropometric measures of infants were also compared with levels of PCBs

in human milk in order to evaluate any association between these two and to ascertain the

trans-mammary transfer of PCBs into infants. A significant negative relationship was

observed between the infant birth weight and level of PCBs (r= -0.732, p=0.01) reflecting

that with an increase in PCBs concentration, the birth weight of the infants decreases.

These results were similar to the studies conducted in Spain and USA (Ribas et al., 2002;

Sagiv et al., 2007). The infant birth weight was assessed for an association with maternal

serum level, but no significant relationship could be found between the two.

The placental concentration showed negative significant relationship with

neonatal birth weight and crown to heel length. These results were similar to previous

studies conducted in the USA (Wolff, 2010) and another study on European Cohort

(Govarts et al., 2012). These decrements found in neonatal growth in the present study

were also similar to previous studies conducted on other POPs in China (Wu et al., 2011),

India (Dewan et al., 2013), Saudi Arabia (Al-Saleh et al., 2012) and Singapore (Tan et

al., 2009).These findings suggested that maternal levels of PCBs negatively impact the

health of fetuses and infants.

Being lipophilic, PCBs accumulate in the food chain from the atmosphere and

enter into the human body. The results of this study indicated spatially varied maternal

exposure to PCBs and resulting transfer in infants and from the mother into the

developing fetuses and infants.Detection of PCBs in the human blood, milk and placenta

might indicate residues of previous releases into the environment or perhaps ongoing

uses.

Conclusion

116

CONCLUSION

This study was the first detailed report about the concentration of PCBs in blood

serum, milk and placentas from Punjab Province of Pakistan. The concentration of

∑34PCBs was highest in human blood serum followed by the placenta and milk,

includingeight dioxin- like and six indicator PCBs. Among different homolog of PCBs,

Tetra-CBs were higher in all the studied body matrices of mothers from selected five

districts. A significant correlation between milk and placental levels of PCBs and

physiological characteristics of mothers was observed in this study. Nevertheless, the

study also found a significant negative correlation between PCBs concentration in body

matrices of mothers and fetal growth parameters indicating the impacts of PCBs on

women health and infants.

The presence of PCBs in placenta and milk indicated the burden on women as

well as infants. These PCBs can transfer from mother to fetus through the placenta during

development and after birth through milk. The dioxin like and indicator PCBs could lead

to impaired fetal development leading to fetal abnormalities, growth retardation,

behavioral and physiological changes at later stages. An associationof anthropometric

parameters with PCBs concentration is indicating the impacts on the healthy young ones.

Though, at present, the maternal exposure to PCBs in Pakistan exerts lower risks to

neonatal health. But expansion in the industrial sector of the country which in

combination with the urban sprawl and recycling of obsolete electric equipment and/or e-

waste could be the potential source adding to the PCBs pollution in the country, hence

levels of PCBs could be higher in near future. Therefore, PCBs monitoring in human

body matrices should be a part of regular monitoring programs to assess the potential

health risks to the general public and future generations at earlier stages of their lives. The

results of this study provide the database from the country for futurtowe studies and

Conclusion

117

highlight the need for large scale assessment of PCB residues in mothers from Pakistan

and their carryover to the fetus.

Recommendations and Future Perspectives

In the light of the results of the present study, the recommendation made are hereunder:

1. The present study opened a new chapter to start the detailed study in different

human populations to know about the existing status of PCBs and POPs,

therefore, POPs monitoring in human population should be continued.

2. Long term studies should be conducted to focus on specific congeners of PCBs

and their metabolism and mechanism of action in mixture form in the human

population to make their risk assessment more reliable.

3. A regular monitoring of meat and dairy products should be conducted by the

concerned authorities to evaluate the potential PCBs contamination of food

items.

4. There is a need to identify the major sources of POPs in Pakistan to develop a

comprehensive plan for source reduction.

5. There should be proper planning for the comprehensive study of PCBs and other

POPs at the national level to make emission inventories that may be accessible

by multiple users to avoid redundancy and confusion in research.

6. Regular studies on PCBs in multiple human body matrices, wildlife, and

environmental media should be made in future for better understanding

7. There must be a comprehensive plan to raise awareness among the masses and

stakeholders to reduce the contamination of PCBs in the environment and

accumulation in the food chain.

Conclusion

118

8. A regular monitoring could help us to assess the historical trends of PCBs in the

environment, particularly, in Pakistan to evaluate the trend after Stockholm

Convention.

9. There should be more research to search the alternatives of PCBs for industrial

use and technological modifications are required to ensure the prevention of

release of complete combustion of PCBs and its safe disposal in the

environment.

10. Every industrial unit must follow the existing laws related to control and

eradication of POPs and public department and local authorities should take

effective measures for the implementation of legislative bindings on industries

and other consumer supplies to minimize the exposure to PCBs in the future.

11. There should be proper monitoring of POPs, PCBs and other contaminants in the

case of still births, aborted and abnormal babies, especially in those areas, where

POPs and other organic pollutants are high in concentration in the environment.

12. There must be awareness programs for the community on media, school,

colleges and non-govt. organizations to highlight the PCBs impacts on women

and infants

13. There should be a regulatory mechanism to phase out PCBs fromdifferent

geographical areas. Pakistan is a signatory to the Stockholm Convention and

Government of Pakistan must enforce the legislation and effective policy

making for safe disposal of persistent organic pollutants.

References

119

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APPENDICES

Appendix-1

Appendix-1: Spatial details of women selected from five districts of Punjab, Pakistan

Name of Locality District/ City Urban/Rural Coordinates

East North

Samanabad Lahore Urban 31.59516412 74.30718175

Islampura Lahore Rural 31.61728688 74.28223978

Muridkey Sheikhupura Urban 31.81136363 74.25078735

Shahdara Lahore Urban 31.68427166 74.30341701

Ichra Lahore Rural 31.57338467 74.33876024

Basra Muridkey Urban 31.78033985 74.25194844

Narang mandi Sheikhupura Urban 31.83625518 74.42777647

Imamia Colony Lahore Urban 31.69928038 74.25621919

Kahna Nau Lahore Urban 31.45677858 74.37343883

Sabzi mandi Lahore Urban 31.4984622 74.2670834

Mehar Shah Khanewal Rural 30.23779505 71.88338121

Jangal Mariyala Khanewal Rural 30.18412722 71.86741816

Parvezwala Khanewal Urban 30.1264282 71.8464785

Basti Sirajia Khanewal Urban 30.0735978 71.83928017

Kabirwala Khanewal Urban 30.2805961 72.0297794

Mehar Shah Khanewal Rural 30.39527083 71.89996139

Chak no.112 Khanewal Rural 30.24333408 71.86500089

Gulberg town Khanewal Urban 30.03061171 71.80539364

Tariqabad Khanewal Urban 30.3073971 71.9476209

Renala Okara Urban 30.8800345 73.6003308

Baseerpur Okara Rural 30.5779666 73.8360251

Depalpur Okara Urban 30.66058179 73.6595075

Havelilakha Okara Rural 30.4479619 73.6948234

Doctors Colony Okara Urban 30.81477603 73.43635208

Makkah Madina

town

Okara Urban 30.8003044 73.46588259

Basti hazur sahib Okara Rural 30.87214996 73.59797509

Shergarh Okara Rural 30.8312494 73.7389596

33/4-L Okara Okara Rural 30.62910476 73.38456197

Appendices

155

Havelilakha Okara Rural 30.46694155 73.66915037

Wazirabad Road Sialkot Urban 32.46531923 74.2482762

Sambrial Sialkot Urban 32.47060449 74.30837782

MalkaKalan Sialkot Sialkot Urban 32.49304144 74.4987617

Model town Ugoke Sialkot Rural 32.43626092 74.44557118

Sambrial Sialkot Urban 32.47175144 74.34933682

Muradpur Sialkot Sialkot Rural 32.55582297 74.52200298

Raipur Sialkot Rural 32.54486448 74.43875172

Sialkot Sialkot Urban 32.50657775 74.55333423

Sahowala Sialkot Rural 32.44572784 74.38674757

Bhagwal Chakwal Rural 33.07535174 72.59537403

Ropwal Chakwal Rural 33.0464493 72.52895293

Choa Saidaen Shah Chakwal Urban 32.72168991 72.94027169

Pichnand Chakwal Urban 32.8894416 71.9913849

Choa Saidaen Shah Chakwal Urban 32.80185735 72.96626737

Appendices

156

Appendix-2

Appendix-2: Consent Letter

I________________________________hereby declare that I am willingly giving blood

serum, milk and placenta samples for the research project titled ―Health Impacts of some

Persistent Organic Pollutants in expecting mothers from Punjab, Pakistan‖. I further declare

that I agree to participate in this research project as a volunteer subject to conditions that my

name and other personal details will be kept confidential.

Name: ________________________________

Date: ________________________________

Appendices

157

Appendix-3

Appendix-3: Socio-Demographic and Health Survey

Health Impacts of Some Persistent Organic Pollutants in Expecting Mothers from

Punjab, Pakistan

QUESTIONNAIRE FOR DATA COLLECTION

We are carrying out a research work to assess the possible impacts of persistent organic

pollutant (POPs) on the health of women from different environmental settings of Punjab-

Pakistan.

Name of Interviewer

Date of Interview

DEMO-GRAPHICAL SURVEY

1. Name of Interviewee

2. Locality Rural Urban

3. Age (Years)

4. Marital Status Single Married

5. Number of Children

6. Education a. Literate b. Illiterate c. Any other

7. Occupation

8. Are you a working woman? (Number of dependents in case of working woman)

a. Yes

b. No `

9. Number of years since working?

10. Previous jobs

Appendices

158

SOCIO-DEMOGRAPHIC AND HEALTH SURVEY

11. Weight (Kg)

12. Height (Metres)

13. Infant birth size a. Birth weight

b. Crown heel length

14. Are you suffering from any health problem/ disease?

a. No b. If yes then, Weakness/ tiredness c. Skin problem d. Eyes Irritation e. Stomach Problem f. Asthma g. Any other

15. How long are you suffering from a health problem/disease?

a. 1-12 months b. 2-3 years c. 3-5 years d. More than five years

16. Are symptoms/ Health Problems are worse during working hours or at home?

a. Worse at work b. Worse at home c. Remarks

17. Are you aware off effects of pesticides on health?

b.

a. Yes b. No c. Remarks

12. Do you try to protect yourself from pesticides exposure?

a. Yes b. No c. Remarks

13. Do you spray pesticides/ insecticides at home?

a. Yes b. No

14. Are you a feeding mother? a. Yes b. No

15. Any congenital problems in previous births or in family (close relative) births

a. Yes

b. No

16. What do you prefer to eat?

a. Meat Vegetable Dairy

17. How often you eat fish? a. Once in a week

b. Once in a month

c. Quarterly

Appendices

159

d. Never

18. How often you take milk? a. Daily

b. Weekly

c. Never

19. Specify sources of milk intake a. Fresh milk

b. Tetra-pack

20. Which one of the following do you use for cooking?

a. Oil b. Ghee

21. Infant characteristics Birth Weight (kg) Head circumference (cm) Chest circumference (cm) Mid arm circumference (cm)

Appendices

160

Appendix-4

Appendix-4: Descriptive Statistics of PCB Congeners in Blood Serum collected from five districts of the Punjab, Pakistan


Lahore Khanewal Okara Sialkot Chakwal

PCB

Congener

Mean SD Min Max Mean SD Min Max Mean SD Min Max Mean SD Min Max Mean SD Min Max

PCB030 2.55 2.63 0.24 8.58 1.26 0.73 0.44 2.54 1.39 0.94 0.09 2.34 1.88 1.95 0.17 6.93 1.33 0.98 0.21 2.66

PCB037 22.26 10.67 6.79 45.57 14.83 5.52 8.03 26.51 12.31 4.84 5.88 21.02 19.06 9.94 3.95 31.23 16.95 4.77 11.04 23.35

PCB44 0.99 0.73 0.00 1.99 0.72 1.03 0.00 2.84 1.17 0.76 0.00 2.27 0.84 0.71 0.00 2.02 0.96 0.64 0.00 1.57

PCB49 21.99 11.99 9.50 49.63 13.97 6.38 5.59 23.45 12.69 5.48 5.38 20.14 22.82 16.4 3.67 51.98 13.29 4.66 7.69 17.86

PCB52 21.79 8.47 9.66 36.40 15.84 6.61 6.58 25.44 10.11 6.97 0.07 20.30 17.49 9.33 2.20 34.22 14.21 3.92 10.58 20.25

PCB54 21.28 8.20 9.65 38.50 12.35 8.32 1.40 23.95 9.62 6.15 0.41 19.74 18.72 9.18 0.80 33.54 16.90 2.96 13.19 21.26

PCB60 13.81 5.52 7.22 23.69 7.17 5.94 0.13 16.23 9.56 10.23 0.46 35.38 11.00 6.52 0.56 24.12 19.26 6.29 13.76 27.65

PCB66 22.00 7.85 9.04 32.45 16.62 9.62 4.55 30.41 13.06 11.66 3.18 37.73 18.60 13.1 0.56 44.25 26.80 9.66 17.97 38.36

PCB70 16.35 8.98 0.00 33.31 17.74 8.92 4.64 29.98 10.88 6.73 0.63 20.35 17.51 9.91 1.74 37.31 24.77 7.67 16.61 34.44

PCB74 22.19 6.46 8.68 30.86 15.55 9.73 3.91 29.30 10.84 7.62 1.93 24.87 17.27 10.20 5.54 39.27 18.44 11.9 6.59 36.74

PCB77 28.44 16.25 1.73 51.98 3.37 2.51 0.00 8.87 2.94 2.64 0.45 9.11 18.17 21.81 1.21 63.55 4.68 1.76 2.60 6.97

PCB82 15.19 5.20 9.23 23.84 10.71 5.61 5.27 18.60 6.59 4.77 1.05 16.49 12.33 8.98 2.59 31.51 18.24 10.7 3.32 32.85

PCB87 15.58 5.70 9.31 23.96 11.34 5.48 5.03 18.75 4.94 5.06 0.25 14.88 12.46 7.89 2.75 31.47 15.01 8.51 3.74 25.83

PCB99 8.00 3.40 1.98 14.41 7.13 5.47 2.46 19.49 3.11 2.16 0.49 6.58 12.87 8.16 2.54 24.94 8.24 3.15 3.11 11.25

PCB101 7.85 10.04 0.10 32.99 3.40 2.43 0.95 9.52 3.33 5.97 0.28 19.03 5.36 5.27 0.07 13.82 3.98 5.91 0.07 14.29

PCB105 6.33 7.05 0.04 17.59 4.89 4.89 0.10 13.76 3.11 4.47 0.21 14.45 4.95 6.12 0.02 17.36 14.77 14.8 0.52 37.29

PCB114 6.02 5.01 0.25 15.83 4.73 4.50 0.10 12.56 2.59 4.31 0.14 13.62 1.75 2.48 0.02 8.26 4.92 7.12 0.65 17.56

PCB118 5.30 4.00 1.06 11.49 4.34 2.66 0.30 9.49 2.68 3.36 0.03 10.61 5.65 4.89 1.37 16.87 7.46 5.56 1.64 15.15

PCB126 1.90 1.63 0.13 4.74 1.75 1.98 0.10 6.03 2.26 2.31 0.32 6.80 1.51 0.96 0.07 3.38 3.39 1.71 1.11 5.48

PCB128 7.11 4.83 0.93 15.24 3.67 3.32 0.08 8.68 2.92 4.60 0.12 12.88 5.52 4.93 0.42 15.68 20.62 10.4 11.89 37.47

PCB138 4.37 3.19 0.14 9.40 3.31 2.69 0.46 7.88 2.47 4.42 0.14 13.78 5.12 3.91 0.88 13.82 12.89 7.74 4.80 23.12

PCB153 5.27 5.39 0.18 13.35 4.26 4.29 0.19 11.26 3.79 6.24 0.34 19.85 7.17 5.76 0.29 19.53 16.41 13.0

7

2.18 32.74

PCB156 0.30 0.21 0.05 0.72 0.76 0.83 0.04 2.38 0.32 0.24 0.00 0.71 0.38 0.29 0.02 0.84 0.38 0.19 0.12 0.66

PCB158 0.58 0.78 0.02 2.65 0.89 0.95 0.06 2.88 0.69 0.71 0.06 2.28 0.74 0.75 0.07 2.52 1.00 0.85 0.29 2.33

PCB166 2.14 1.75 0.10 4.91 0.60 0.69 0.11 2.01 1.41 1.67 0.08 4.44 1.27 1.03 0.07 3.28 1.83 2.27 0.30 5.78

PCB169 1.11 1.46 0.02 3.83 1.11 1.28 0.06 4.43 1.62 1.99 0.26 5.87 1.02 1.23 0.14 3.71 2.80 2.33 0.30 5.43

PCB170 1.20 1.25 0.13 4.07 0.41 0.31 0.07 1.02 1.09 1.05 0.10 3.44 0.62 0.69 0.09 2.33 1.28 0.60 0.57 2.03

PCB179 1.49 2.08 0.05 6.68 0.31 0.23 0.10 0.86 0.91 1.24 0.04 4.13 1.00 1.48 0.10 5.04 2.58 2.94 0.26 6.81

Appendices

161

PCB180 2.06 2.13 0.10 6.76 0.96 1.13 0.33 3.92 1.30 1.35 0.24 4.09 0.76 0.59 0.08 1.78 2.90 2.31 0.37 5.32

PCB183 1.65 1.19 0.38 3.33 0.95 0.74 0.24 2.12 1.59 1.40 0.32 3.96 2.93 5.01 0.08 16.26 7.78 5.03 2.08 13.84

PCB187 1.52 1.74 0.06 4.82 0.61 0.58 0.02 1.83 1.45 1.44 0.09 4.90 0.67 0.74 0.04 2.54 1.31 1.33 0.37 3.62

PCB189 2.36 4.36 0.03 14.13 0.95 0.95 0.05 2.66 1.60 2.38 0.04 7.72 0.47 0.57 0.02 1.93 0.95 0.65 0.02 1.63

PCB198 2.15 2.91 0.09 9.55 1.12 0.97 0.02 2.88 0.61 0.66 0.02 2.04 0.71 0.47 0.02 1.41 2.59 3.58 0.38 8.87

PCB209 1.25 1.49 0.07 4.38 2.55 2.43 0.30 8.88 0.78 1.60 0.05 5.05 2.65 3.52 0.09 12.00 1.65 0.88 0.35 2.75

∑34PCBs 294.37 164.53 86.97 601.63 190.13 119.73 51.68 391.45 145.74 127.43 23.15 410.86 251.28 184.83 32.24 618.68 310.59 166.86 138.72 543.19

Appendices

162

Appendix-5

Appendix-5: Descriptive Statistics of PCB Congeners in human milk collected from five districts of the Punjab, Pakistan (Concentration

in ng/g lip)


PCB

Congener

Mean SD Min Max Mean SD Min Max Mean SD Min Max Mean SD Min Max Mean SD Min Max

PCB030 0.22 0.22 0.02 0.60 0.46 0.35 0.03 1.22 0.45 0.48 0.06 1.49 0.18 0.14 0.05 0.42 0.38 0.19 0.14 0.53

PCB037 2.34 2.15 0.93 8.21 2.46 1.16 0.93 4.34 2.54 1.76 0.70 6.94 3.34 1.45 1.61 5.21 3.83 1.50 2.14 5.88

PCB44 1.75 1.77 0.39 6.47 2.74 1.98 0.33 6.41 2.45 1.27 0.89 5.45 3.45 2.51 0.65 7.52 3.55 0.46 3.12 4.28

PCB49 1.30 0.72 0.06 2.72 3.66 2.88 0.49 9.65 1.98 1.63 0.33 5.83 4.95 4.43 0.77 14.38 3.10 0.34 2.73 3.57

PCB52 2.97 4.46 0.45 15.45 3.62 2.54 1.72 9.64 1.53 1.05 0.33 3.69 4.03 4.40 0.21 13.38 2.98 0.80 1.94 4.15

PCB54 0.28 0.22 0.00 0.82 0.33 0.69 0.00 2.13 0.29 0.27 0.00 0.79 0.28 0.18 0.00 0.50 0.25 0.18 0.00 0.44

PCB60 2.14 1.56 0.67 5.42 4.31 3.25 0.46 9.84 3.57 3.17 1.23 10.93 6.12 4.80 0.53 11.39 4.07 1.95 1.28 5.72

PCB66 3.23 2.42 1.68 9.92 2.61 2.06 1.12 7.72 2.38 1.14 0.98 5.08 4.11 2.38 0.39 8.10 5.10 1.43 4.16 7.49

PCB70 3.11 2.78 0.07 10.05 2.61 2.48 0.41 8.43 1.99 1.17 0.90 4.50 3.62 2.68 0.38 8.14 5.86 1.77 4.18 8.66

PCB74 2.23 2.01 0.00 6.96 2.36 1.90 0.48 6.20 1.60 0.80 0.73 3.00 2.92 1.99 0.35 5.69 4.26 0.61 3.29 4.86

PCB77 1.36 2.10 0.26 7.18 0.92 0.70 0.15 1.97 0.50 0.47 0.11 1.60 3.30 3.61 0.00 9.59 0.88 0.46 0.50 1.67

PCB82 0.40 0.44 0.06 1.28 0.90 1.38 0.04 4.24 0.23 0.15 0.06 0.47 0.35 0.29 0.01 0.84 0.87 1.11 0.02 2.77

PCB87 0.71 0.56 0.01 2.01 1.40 1.36 0.01 3.71 0.45 0.52 0.01 1.81 1.23 0.79 0.54 2.41 2.15 1.27 0.60 3.91

PCB99 1.13 0.83 0.20 2.57 2.64 2.93 0.18 8.89 1.01 0.59 0.24 2.31 2.44 1.95 0.23 6.08 3.64 1.67 1.67 6.18

PCB101 1.44 1.38 0.18 4.83 2.56 2.94 0.24 8.74 1.01 0.54 0.31 2.31 1.72 1.03 0.23 3.03 4.14 2.34 0.79 7.22

PCB105 0.72 0.88 0.15 3.02 1.14 0.88 0.27 2.64 0.61 0.38 0.19 1.39 1.24 0.88 0.26 2.91 1.87 1.03 0.32 2.69

PCB114 0.17 0.18 0.02 0.59 0.88 1.39 0.03 4.41 0.47 0.31 0.04 0.94 2.40 3.32 0.00 9.21 1.11 1.36 0.16 3.41

PCB118 1.46 2.26 0.06 7.08 0.93 1.51 0.00 4.75 0.80 1.48 0.03 4.84 2.01 3.56 0.02 9.83 2.04 2.59 0.01 5.81

PCB126 0.45 0.43 0.01 1.45 0.46 0.25 0.05 0.76 0.49 0.38 0.02 1.12 0.79 0.49 0.09 1.48 0.60 0.54 0.08 1.38

PCB128 0.27 0.18 0.10 0.53 0.16 0.26 0.01 0.82 0.14 0.23 0.00 0.76 0.22 0.14 0.07 0.42 0.20 0.21 0.06 0.56

PCB138 1.48 1.06 0.42 3.42 1.14 1.31 0.02 3.64 0.63 0.44 0.04 1.28 1.55 1.59 0.02 4.39 3.15 1.59 0.93 4.59

PCB153 1.76 2.23 0.47 7.99 1.59 1.43 0.06 3.92 1.18 0.98 0.00 3.58 1.63 1.53 0.03 4.64 4.49 2.59 2.31 8.97

PCB156 0.30 0.39 0.02 1.30 0.35 0.41 0.01 1.22 0.64 1.46 0.05 4.79 0.46 0.82 0.01 2.30 0.43 0.31 0.13 0.90

PCB158 1.60 1.31 0.16 4.42 0.76 1.41 0.03 4.36 0.82 0.68 0.11 2.08 1.87 2.46 0.02 6.30 4.01 2.88 0.55 6.60

PCB166 0.11 0.12 0.00 0.36 0.08 0.08 0.01 0.26 0.21 0.21 0.01 0.65 0.09 0.10 0.01 0.30 0.07 0.05 0.02 0.16

PCB169 0.28 0.19 0.02 0.54 0.30 0.33 0.02 1.04 0.22 0.23 0.02 0.63 0.43 0.48 0.02 1.36 0.41 0.56 0.03 1.37

PCB170 0.21 0.31 0.01 1.03 0.29 0.26 0.03 0.84 0.29 0.20 0.01 0.63 0.41 0.42 0.09 1.13 0.29 0.35 0.07 0.91

PCB179 0.20 0.16 0.07 0.54 0.13 0.10 0.01 0.27 0.11 0.08 0.01 0.27 0.11 0.06 0.01 0.17 0.30 0.12 0.11 0.44

PCB180 0.80 1.08 0.03 3.30 0.19 0.30 0.01 0.99 0.52 0.65 0.01 2.10 0.91 1.69 0.12 4.70 1.90 1.13 0.40 3.48

Appendices

163

PCB183 0.39 0.35 0.04 1.08 0.51 0.58 0.07 2.00 0.50 0.65 0.02 2.23 0.23 0.40 0.01 1.12 0.67 0.57 0.07 1.34

PCB187 0.26 0.32 0.01 0.88 0.43 0.62 0.04 2.00 0.26 0.19 0.02 0.54 0.26 0.37 0.02 1.08 0.55 0.66 0.07 1.63

PCB189 0.29 0.34 0.02 0.94 0.17 0.14 0.01 0.42 0.19 0.27 0.01 0.86 0.60 0.69 0.04 1.99 0.24 0.10 0.14 0.38

PCB198 0.43 0.44 0.02 1.25 0.17 0.18 0.03 0.50 0.44 0.57 0.03 1.86 0.14 0.14 0.01 0.42 0.65 0.90 0.09 2.23

PCB209 0.23 0.24 0.00 0.70 0.40 0.44 0.00 1.10 0.38 0.54 0.00 1.80 0.24 0.30 0.00 0.80 0.32 0.15 0.10 0.50

∑34PCBs 36.03 36.07 6.60 124.91 43.67 40.47 7.31 129.08 30.86 24.93 7.54 88.53 57.62 52.06 6.81 151.24 68.33 33.76 32.23 114.68

Appendices

164

Appendix-6

Appendix-6: Descriptive Statistics of PCB Congeners in human placenta collected from five districts of the Punjab, Pakistan



PCB

Congener

Mean SD Min Max Mea

n

SD Min Max Mea

n

SD Min Max Mean SD Min Max Mean SD Min Max

PCB030 1.68 2.56 0.26 8.79 0.29 0.25 0.01 0.71 0.30 0.21 0.01 0.76 0.50 0.31 0.13 1.03 0.38 0.40 0.07 1.04

PCB037 6.12 1.58 3.27 8.08 4.39 2.08 0.59 8.49 3.72 1.46 1.80 6.29 8.31 2.08 5.07 10.93 5.10 1.64 3.97 8.01

PCB44 5.09 2.89 0.45 9.30 4.75 2.43 1.41 9.75 4.52 1.55 1.77 6.44 6.16 2.34 1.16 9.81 0.25 0.16 0.00 0.38

PCB49 6.74 3.79 2.33 14.32 4.41 2.90 0.38 10.47 3.65 1.55 0.71 6.28 8.71 3.31 3.92 14.60 5.10 1.10 4.36 7.03

PCB52 6.88 3.48 1.59 14.22 5.86 4.13 0.62 13.15 4.12 1.49 2.30 6.85 7.22 3.25 3.30 13.96 5.86 2.91 1.36 9.15

PCB54 0.54 0.38 0.00 1.12 0.30 0.18 0.14 0.74 0.24 0.17 0.00 0.57 0.25 0.21 0.00 0.52 6.55 0.93 5.58 7.73

PCB60 5.88 4.31 0.03 12.91 4.51 1.84 1.64 7.38 5.22 2.62 3.06 11.57 5.17 2.96 0.50 9.92 5.23 1.22 3.59 6.64

PCB66 7.01 2.77 1.59 10.97 5.16 2.22 1.04 8.59 5.91 3.22 2.57 13.70 7.09 2.97 1.82 9.65 8.66 3.41 4.74 13.97

PCB70 6.93 1.85 4.00 10.37 5.62 2.03 2.21 9.18 5.77 3.52 2.47 12.99 7.88 2.20 4.31 11.36 7.34 3.87 4.56 14.00

PCB74 6.09 2.60 0.84 10.15 3.66 1.61 1.63 6.40 4.19 1.81 2.47 6.99 4.46 3.56 0.73 9.70 5.18 1.46 3.71 7.45

PCB77 2.96 1.67 1.18 5.93 7.91 7.09 0.19 22.95 0.74 0.58 0.12 1.81 3.20 1.03 1.17 4.49 1.17 0.43 0.83 1.92

PCB82 1.37 2.12 0.20 7.27 1.16 1.37 0.03 4.76 0.42 0.23 0.07 0.83 1.06 1.38 0.01 4.76 5.03 2.40 1.31 7.22

PCB87 2.01 1.04 0.65 4.06 1.48 0.99 0.41 3.30 2.16 0.99 0.69 3.59 1.75 0.78 0.06 2.96 4.17 1.82 2.46 6.77

PCB99 3.70 2.32 0.50 6.62 4.09 2.41 0.93 8.89 3.94 3.32 0.60 11.61 3.35 1.21 1.17 5.48 2.50 1.67 0.63 4.53

PCB101 4.67 1.95 1.17 6.61 3.20 2.26 0.18 6.55 6.59 2.06 4.45 9.99 3.52 1.70 1.29 6.01 1.65 0.96 0.25 2.93

PCB105 1.70 1.66 0.07 5.84 0.76 0.56 0.11 1.88 2.10 1.17 0.05 3.86 1.37 1.20 0.03 3.42 3.01 3.00 0.13 6.46

PCB114 1.48 1.39 0.15 4.72 0.56 0.70 0.05 2.29 0.30 0.33 0.01 1.04 1.13 1.38 0.02 3.56 0.99 1.14 0.09 2.94

PCB118 1.32 1.40 0.06 4.59 1.25 0.94 0.33 2.65 1.82 2.57 0.03 7.89 1.04 1.24 0.04 3.27 2.53 1.59 0.69 4.49

PCB126 0.94 0.68 0.12 2.17 0.39 0.20 0.12 0.77 0.42 0.66 0.00 1.95 0.88 0.77 0.11 2.70 0.77 0.74 0.03 1.63

PCB128 0.42 0.34 0.01 1.09 0.14 0.12 0.01 0.36 0.29 0.28 0.01 0.73 0.55 0.51 0.03 1.41 4.75 2.51 1.81 8.00

PCB138 1.55 1.36 0.02 4.08 1.11 0.75 0.10 1.99 3.30 1.25 1.12 5.02 2.08 1.11 0.33 3.39 4.32 1.72 1.59 5.54

PCB153 2.50 1.73 0.16 5.87 1.71 1.62 0.02 5.14 5.19 2.57 2.16 9.10 1.64 1.29 0.18 3.42 4.56 3.45 0.15 8.03

PCB156 0.49 0.50 0.00 1.44 0.37 0.34 0.01 0.95 0.21 0.19 0.02 0.67 0.58 0.59 0.05 1.98 0.24 0.39 0.02 0.92

PCB158 1.77 2.07 0.04 5.86 1.46 1.15 0.04 2.89 3.04 2.36 0.20 7.27 1.94 1.59 0.03 4.86 0.51 0.61 0.06 1.50

PCB166 0.39 0.44 0.04 1.31 0.09 0.09 0.01 0.27 0.09 0.07 0.00 0.23 0.31 0.32 0.01 0.89 0.14 0.11 0.02 0.31

PCB169 0.84 1.14 0.05 3.74 0.44 0.28 0.04 0.91 0.21 0.13 0.05 0.43 0.56 0.49 0.05 1.51 0.75 1.10 0.02 2.71

PCB170 0.84 1.41 0.02 4.22 0.50 0.70 0.01 2.33 0.60 0.69 0.01 2.43 0.81 1.06 0.06 3.23 0.77 0.62 0.13 1.53

PCB179 0.40 0.29 0.03 0.89 0.19 0.18 0.07 0.67 0.30 0.38 0.02 1.09 0.15 0.11 0.03 0.39 1.67 1.25 0.34 3.55

PCB180 1.66 1.57 0.12 3.67 0.32 0.25 0.10 0.91 0.54 0.43 0.11 1.40 0.32 0.35 0.04 1.11 1.48 1.31 0.32 3.58

Appendices

165

PCB183 0.46 0.24 0.07 0.91 0.17 0.17 0.01 0.46 0.83 0.86 0.07 2.75 0.45 0.43 0.03 1.30 1.53 0.82 0.36 2.33

PCB187 0.38 0.27 0.04 0.85 0.14 0.14 0.01 0.45 0.77 0.58 0.05 1.62 0.38 0.39 0.05 1.04 0.33 0.23 0.05 0.67

PCB189 0.72 0.78 0.02 2.21 0.21 0.20 0.02 0.59 0.27 0.40 0.01 1.29 0.54 0.68 0.02 2.03 0.47 0.53 0.02 1.28

PCB198 1.14 1.37 0.08 4.20 0.45 0.33 0.01 0.92 2.68 7.66 0.13 24.48 0.57 0.39 0.03 1.06 0.20 0.24 0.03 0.60

PCB209 1.14 1.46 0.03 4.89 0.40 0.56 0.07 1.46 0.24 0.18 0.02 0.55 0.58 0.74 0.02 2.44 0.26 0.44 0.02 1.04

∑34PCBs 87.80 55.39 19.18 193.25 67.46 43.06 12.54 149.18 74.67 47.55 27.16 174.04 84.52 43.92 25.79 158.22 93.47 46.17 43.29 155.90

Publications

166

PUBLICATIONS

1. Quantification of polychlorinated biphenyl contamination using human placenta as

biomarker from Punjab Province, Pakistan

2. Assessment of Polychlorinated Biphenyls (PCBs) in maternal blood from selected

districts of Punjab, Pakistan, accepted for publication in Punjab University Journal of

Zoology.

RESEARCH ARTICLE

Quantification of polychlorinated biphenyl contamination using humanplacenta as biomarker from Punjab Province, Pakistan

Anber Naqvi1 & Abdul Qadir1 & Adeel Mahmood2& Mujtaba Baqar3 & Iqra Aslam1

& Farhan Sajid4& Mehvish Mumtaz5 &

Jun Li6 & Gan Zhang6

Received: 30 October 2017 /Accepted: 13 February 2018 /Published online: 11 March 2018# Springer-Verlag GmbH Germany, part of Springer Nature 2018

AbstractThe present study biomonitored the placental polychlorinated biphenyl (PCB) concentrations in women from PunjabProvince, Pakistan, that provides the pioneer data for occurrence and body burden of PCBs in placental tissues fromSouth Asian women. The ∑34PCB concentrations in placental tissues were ranged from 20.2 to 115.98 ng/g lipid weight(lip. wt.), with predominance of tetra-PCB (54.67%). The levels of ∑8DL-PCBs and ∑6-indicator PCBs were rangedfrom 2.03 to 27.64 ng/g (lip. wt.) and 1.71 to 30.8 ng/g (lip. wt.), respectively. The WHO-TEQ2005 values for DL-PCBswere ranged from 1.18 × 10−5 to 0.067 ng/g (lip. wt.), with highest value evaluated for CB-126. The estimated dailyintake (EDI) for DL-PCBs was ranged from 9.27 × 10−8 to 5.25 × 10−4 pg WHO-TEQ/kg body weight (bw), which waswithin the tolerable daily intake (TDI) values established by international organizations. The spatial distribution patternsof Σ34PCB concentrations from study area have shown relative higher concentrations in samples from urban andindustrial cities than rural areas, and industrial and urban releases along with e-waste handling were recognized as vitalPCB sources in the environment. In order to ascertain the transplacental transfer of PCBs, the fetal growth parameterswere correlated with the ∑34PCB concentrations in placental tissues. The relationship between ∑34PCB concentrations inplacental tissues and infant’s anthropometric measures through multiple linear regression showed a negative correlationof infant’s body weight (R2 = 0.0728), crown to heel length (R2 = 0.068), head circumference (R2 = 0.0342), chestcircumference (R2 = 0.0001), and mid arm circumference (R2 = 0.0096) that noticeably highlights the inhibited fetalanthropometric development associated with maternal PCB bioaccumulation. Hence, an immediate elimination of on-going PCB addition in the studied area has been emphasized and further investigations are suggested to appropriatelymanage the public and neonatal health risks in the region.

Keywords PCBs . Dioxin-like PCBs . Placental transfer . Neonatal health

Responsible editor: Hongwen Sun

Electronic supplementary material The online version of this article(https://doi.org/10.1007/s11356-018-1535-z) contains supplementarymaterial, which is available to authorized users.

* Abdul [email protected]

* Mujtaba [email protected]

1 College of Earth and Environmental Sciences, University of thePunjab, Lahore 54590, Pakistan

2 Department of Environmental Sciences, Government CollegeWomen University, Sialkot 51310, Pakistan

3 Sustainable Development Study Centre, Government CollegeUniversity, Lahore 54000, Pakistan

4 District Headquarters Hospital Khanewal, Department of Primary andSecondary Health Care, Government of the Punjab, Lahore 58270,Pakistan

5 School of Environment, Tsinghua University, Beijing 100084, China6 State Key Laboratory of Organic Geochemistry, Guangzhou Institute

ofGeochemistry, Chinese Academy of Sciences, Guangzhou 510640,China

Environmental Science and Pollution Research (2018) 25:14551–14562https://doi.org/10.1007/s11356-018-1535-z

http://crossmark.crossref.org/dialog/?doi=10.1007/s11356-018-1535-z&domain=pdf

https://doi.org/10.1007/s11356-018-1535-z

mailto:[email protected]

mailto:[email protected]

Introduction

Polychlorinated biphenyls (PCBs) are industrial chemicalsthat are of great global concern, owing to their persistent,lipophilic, bioaccumulative, and toxicity in various biologicaland environmental media (Kodavanti et al. 2017).On the basisof in vivo and in vitro studies, the International Agency forResearch on Cancer (IARC) has placed PCBs into group 1compounds, i.e., carcinogenic to humans (IARC 2016).Besides their carcinogenic characteristics, the PCB exposurehas been established to cause reproductive, endocrinal, neuro-logical, and immunological disorders (Letcher et al. 2010).

In recognition to the PCBs’ adverse environmental effects,its production and usage was prohibited globally under theStockholmConvention on Persistent Organic Pollutants, 2001(UNEP 2001). However, the easier availability, adaptability,and low-cost of PCBs sustain its usage in developing countries,including Pakistan (Baqar et al. 2017a). Subsequently, theyhavebeenreleasedfrommanufacturingandrepairingofelectricwares, industrial wastewater discharges, and unsafe waste dis-posal techniques (Eqani et al. 2013; Kim andYoon 2014; Syedet al. 2014). Besides, a vast human population in Pakistan haslimited earnings to purchase advanced electronic devices,which broadens the consumer market for reconditioned andused outdated devices, predominantly imported from devel-oped countries and possibly contains PCBs. These end-of-lifeproducts produce e-waste that undergone open burning andother hazardous recycling techniques to recover metals (Iqbalet al. 2015; Baqar et al. 2017b) and subsequently increases theatmospheric PCB concentrations in Asian countries (Li et al.2007; Eqani et al. 2012b; Ali et al. 2013).

In humans, principal exposure route to PCBs is the dietaryintake that accounts 90% of the total exposure (Chovancováet al. 2012); other sources include dermal exposure, ingestion,and inhalation from ambient environment (Ullah et al.2016).Once entered the human body, they persist andbioaccumulate in the body lipids (Hites 2004), and can betraced out in the blood streams and tissues (Lopez-Espinosaet al. 2007). Nevertheless, studies have suggested that thePCBs exposure originates in the uterine stage of life throughplacental transfer (Vizcaino et al. 2014; Nanes et al. 2014).The placenta is considered to be the lifeline to fetus in utero,supplying oxygen and nutrients, and waste removal from fetalenvironment (Leino et al. 2013; Nanes et al. 2014). However,the persistent and bioaccumulative compounds, includingPCBs have been reported to reach the fetus by crossing theplacental barrier through blood circulation (Covaci et al. 2002;Wang et al. 2006). The prenatal exposure to PCBs may lead topreterm birth, reduced birth weight, and intrauterine growthretardation; along with some latent effects, such as disturbedlevels of thyroid hormone, reproductive impairments, cogni-tive deficits, altered perceptual development, and adverse neu-robehavioral impacts (Covaci et al. 2002; Bergonzi et al.

2009; Kodavanti et al. 2017). The PCBs’ exposure at earlydevelopmental stages may lead to health risks in fetuses andinfants, who are more susceptible to the impacts of these en-vironmental toxins as their detoxification mechanisms are par-tially developed than adults (Vizcaino et al. 2014).

In recent years, studies had effectively biomonitored thePCB levels in human placental tissues worldwide (Myllynenet al. 2005; Gómara et al. 2012; Leino et al. 2013; Tsukimoriet al. 2013; Nanes et al. 2014; Vizcaino et al. 2014). In con-trast, though high PCB concentrations have been reportedfrom various environmental compartments in Pakistan(Eqani et al. 2012a,b, 2013; Syed et al. 2013, 2014;Mahmood et al. 2014b; Mumtaz et al. 2016; Yasmeen et al.2017); to date, no study has undertaken to assess placentaltransfer of PCBs. The present study was conducted for quan-titative screening of PCB levels in the placental tissue samplesfrom Punjab Province, Pakistan and its correlation with ma-ternal characteristics and neonatal anthropometric measures.The objectives of the study include occurrence, spatial distri-bution and source apportionment, comparative analysis, tox-icity equivalency (TEQ), and estimated daily intake (EDI)assessment for dioxin-like PCBs (DL-PCBs) that providesbaseline data on the maternal and prenatal exposure levels ofPCBs from Pakistan as well as South Asia that could be sig-nificantly valuable in future epidemiological and humanhealth risk studies.

Materials and methods

Study area and sampling strategy

The study area encompasses five districts of Punjab province(Pakistan) viz. Lahore, Sialkot, Khanewal, Okara, andChakwal (Fig. 1). The Punjab is the most populated provinceof Pakistan that is considered to be the life-line for the country;as it contains the most intensively cultivated areas, i.e.,Rechna Doab, Jech Doab, and Bari Doab, and most numberof industrial units. Urban expansion and industrialization inthe province have caused an increase in environmental pollu-tion due to vehicular emission, coal combustion, industrialemissions and effluents discharges, and municipal solid wasteburning in open dumps. Besides, the e-waste burning to re-cover valuables is also a common practice observed in mostparts of the province. However, despite of the fact thatPakistan ratifies the Stockholm Convention, 2001, yet, thecountry is experiencing PCB environmental release (Eqaniet al. 2012a; Syed et al. 2014) that led to PCB contaminationin various environmental media of the Punjab province.

Site selection criterion was based on the industrial and ag-ricultural activities and spatial diversity within these districts.The study area was further subdivided into two zones, viz.,urban and rural to make the cross-comparison between them.

14552 Environ Sci Pollut Res (2018) 25:14551–14562

The rural women were those who purely belonged from areaswith agricultural activities. Women (n = 43) (rural = 18 andurban = 25) with a cesarean section at public sector hospitals,located in the study area were selected as subjects (Table S1).The inclusion criteria included those who were born and cur-rently residing in Punjab province, age ranging between 18and 45 years, with no previous history of cardiac disease(Thomas et al. 2006) and maternal or fetal anomalies (Naneset al. 2014). Prior to sampling, the approval of the presentstudy was obtained from Advanced Studies and ResearchBoard (ASRB), University of the Punjab, Pakistan, and writ-ten consent was obtained from the subjects, after they werebriefed about the purpose of the research.

Sample and data collection

Placental tissues (~ 20 g each)were collectedwith the assistanceof paramedical staff from the villous parenchyma, excluding thedecidua basalis and chorionic plate, within 10–15 min after thedelivery, insterilizedglass jars,pre-rinsedwithdichloromethane.The collected sampleswere sealed and labeled immediately afterthecollectionandkept inanice-boxwithdry ice,andfollowedbytheir transfer toEcotoxicologyLaboratoryat theCollegeofEarthandEnvironmental Sciences,University of thePunjab, Pakistan;where the samples were stored till further analysis (Dewan et al.2013; Nanes et al. 2014). In order to obtain information aboutmaternal and neonatal anthropometric characteristics, lifestyle

and socio-demographic conditions of the subjects, a self-administered questionnaire was used. The descriptive statisticalresults of the questionnaire are summarized in Table S2.

Sample preparation

Each frozen placenta sample was thawed, finely chopped,weighed to 1 g, transferred to 30 mL glass vial, and spikedwith the surrogate standards, i.e., 2,4,5,6-tetrachloro-m-xy-lene (TCmX) and Decachlorobiphenyl (CB-209). The spikedsample was then vortexed, stirred, and sonicated for 20 minand kept overnight at 4 °C (Covaci and Voorspoels 2005;Vizcaino et al. 2014). PCB residues were then extracted usingpreviously described method by Bergonzi et al. (2009) andDewan et al. (2013). Briefly, sonicated-spiked samples wereextracted with n-hexane (6 mL) and acetone (3 mL), andequilibrated by ultrasonic treatment for 1 h at 3 °C, followedby their centrifugation for 10 min at 2000 revolution per min-ute (rpm). The clear upper layer of n-hexane, containing PCBswas then separated into another glass vial and repeating theprocess twice to obtain maximum extract, with no PCB resid-ual left. The extract was then cleaned and purified through 12-mm diameter silica-alumina packed column, containing sodi-um sulphate, anhydrous(1 cm), sulfuric acid-silica, 50%(4 cm), neutral silica, 3% deactivated (4 cm), and neutral alu-mina, 3% deactivated (4 cm), and eluted with n-hexane(10 mL) and dichloromethane (5 mL). The purified extract

ATTOCK

KHUSHAB

JHELUM

RAWALPINDI

SARGODHA

MIANWALI

KOHAT

LHR - ISB M2

Mianwali - Chakwal Rd

Mianwali Rd

P.D.Khan RoadKhushab Rd

Fateh Jang Rd

Sarg

odha

Roa

d

G.T.Road (N

5)

LHR - ISB M2

N-5

G. T. R

oad (N5)

Kohat Road

Indus Highway N

-55

Hota Road

Muz

affa

rgar

h-M

ianw

ali R

oad

Sahiwal To Kasur

Bahawalpur To Sahiwal

Lodh

ran

Rd

Jaranwala Rd

Mianwali Rd

Rahim Yar Khan Rd.

Mianwali - Chakwal Rd

Gojra - Samundri Rd.

Muzaff

argarh

To Jh

ang R

oad

Bhakkar-Jhang Road

Jhan

g-Kh

usha

b R

oad

Hasilpur - Vehari R

d.

Chiniot-Phalia Road

Nar

owal

Rd

P.D.Khan RoadKhushab Rd

Road

Hasilpur - Bahawalpur Rd.

Jhang-Chiniot-Pindi Bhattian Road

Indu

s H

ighw

ay

Fateh Jang Rd

Muzaffargarh To Layyah R

oad

Daska Rd

Sahiwal - Noor Shah - H

alla Rd.

Jhumra Road

Pattoki - Raiwind RoadNankan Sahib Rd.

Toba Road

N-5

Chakwal

Sialkot

Lahore

Okara

Khanewal

GUJRAT

NAROWALGUJRANWALA

MR Link Canal

BRBD Link Canal

M.L

.L(U

.C.C

)

Nurpur Disty

Nokhar Branch

Main Li

ne U

pper

(UCC)

Kam

oke

Dis

ty

Sadhoke Disty Kotli Disty

Jamke Disty

Nal

a

Naddha Disty

Nar

owal

Rd

Eman

abad

Rd

G. T. R

oad (N5)

Daska Rd

N-5

Shakar Garh Rd

Pasrur Rd

Bhimber - Kotla R

d

BRBD

Lin

k C

anal

Muridke Disty

Shahd

ara D

isty

QB

Link

can

al

M.L

.L(U

.C.C

)

Sikhanwala Disty

M.B.L of BRBD Link.

Bucharkhana disty of BRBD Link.

Raiwind ditsy of MBL.

Niazbe

g disty

of La

hore

Br:

Udehra

Dist

y

Nou

sher

a D

isty H

arpoki Disty

Lahore Branch of BRBD Link.

Mangta

nwala

Feed

er

Sohal Disty

Akba

r Dis

ty

Bucherkana Disty Reverse

N-5

LHR - ISB M2

G.T.Road (N

5)

Sargodha Rd

N-5

SHEIKHUPURA

KASUR

GUJRANWALA

B.S.

Link

-I

Gugera Branch

B.S.

Lin

k-II

Lower Bari Doab Canal

Pakpattan Canal Upper

Depalpur Disty

2 L Disty

1 L D

isty

Nehranwala Disty

Attari Disty

4 L Disty

Lower Sohag Branch

NalaChunian Disty

Pakhoke Disty

Joya

Dist

y

Noor pur Disty

1-R Disty

Escape Vahn

Chunian disty

Jandraka Disty

Kanganpur Disty

Rakh disty of MBL

Bunga Hayat Disty

Chorkot Disty

Khadir Branch1-AR Disty

Haveli diksty

Lallu

ggud

ar D

isty

Vahn disty of MBL

5L G

ambe

r Dist

y

Kul Disty

4R Jhilwala Disty

Gulshah Disty

Bejanpur Disty2 R A Disty

Chhina disty of MBL

1AL F

eede

r Dist

y

Gaja Disty

Upper sohag Branch

Chuc

hack

Dist

y

Sem Nala

Gillanwala Disty

5AR Disty

Sahiwal - Noor Shah - H

alla Rd.

Depalpur Rd

Piplipahar Rd

Sherga

rh Rd

KASUR

PAKPATTAN

SAHIWAL

FAISALABAD

SAHIWAL BHAWALNAGAR

SHEIKHUPURA

Tail Disty

10R Branch& Disty

8L D

isty

Sidhnai Canal

14L

Dist

y

Shujabad Branch

15L Dulwan Disty

3R Disty

11L Disty7R Disty Lower

8R Disty

4R-Disty Kabirwala Disty

Rangp

ur ca

nal

12L Disty

Miani D

isty

Haji Upper Disty

8R Tulamba Disty

4L-D

isty

Matital

Dist

y

Harri Disty

Multan Branch

Lower Bari Doab Canal

S.M

.B.L

(Sid

hnai

Mai

lsi B

ahaw

al L

ink)

Can

al

Forest Disty

Faizpur Disty

Abdul

Hakim

Disty

Tarp

ai D

isty

9L Disty Lower

Pakpattan Canal Upper 4R Disty

Koranga Disty

Khad

il D

isty

Sher Singh Disty

Allahabad Disty9R Disty

5R Disty

3L D

isty

Fazal Shah Disty

15AL Disty

Madina Disty 3R/15L Minor

Chaupar Hatta Disty

Mungawala Disty

Dhallu Disty

Hav

eli M

ain

Line

Ghulam Wah Disty

Darkhana Disty

Sidhnai Feeder

7ER Disty

7L D

isty

Inayat Pur Disty

10AL

Dist

y

1L/15L Minor 12

L Dist

y

G.T.Road (N5)

Lodh

ran

Rd

Mailsi - Multan Rd.Dunyapur Rd

VEHARI

MULTAN

JHANGTOBA TEK SINGHLAYYAH

SAHIWAL

LODHRAN

MUZAFARGARH

BAHAWALPUR

Chakwal

Sialkot

Lahore

Khanewal

Okara

Legend

River / Canals

Punjab Provincial Boundary

Locations

Punjab District Boundary

0 250 500125

Kilometers

Sindh

Balochistan

Punjab

FATA

ICT

KPKDisputedArea

GB

AJK

PAKISTAN

PUNJAB

SUTLEJ RIVER

RAVI RIVER

CHENAB RIVER

JHELUM RIVER

INDU

S RI

VER

RoadsPakistan Railway

Fig. 1 Map of the study area showing sampling sites within five districts of the Punjab Province, Pakistan

Environ Sci Pollut Res (2018) 25:14551–14562 14553

was then concentrated through gentle nitrogen streaming, until0.2 mL of the extract obtained, followed by addition of iso-octane (50 μl) and 13C–PCB 141 as solvent keeper and inter-nal standard, respectively (Vizcaino et al. 2014).

Chromatographic analysis

In total 34 PCB congeners (IUPACNo. 30, 37, 44, 49, 52, 54,60, 66, 70, 74, 77, 82, 87, 99, 101, 105, 114, 118, 126, 128,138, 153, 156, 158, 166, 169, 170, 179, 180, 183, 187, 189,198, and 209) were analyzed at the State Key Laboratory ofOrganic Geochemistry, Guangzhou Institute of Geochemistry,Chinese Academy of Sciences, China, using a TripleQuadrupole GC/MS (Agilent 7000A) connected with GasChromatograph (Agilent 7890A) and autosampler (Agilent7693) having CP-8 capillary column (CP7481, CP-Sil 8,50 m × 0.25 mm× 0.12 μm from Netherlands). The tempera-ture of the injector was adjusted to 280 °C and temperature ofthe oven was set initially at 100 °C (for 3 min), which waslater increased to 160 °C at the rate of 20.0 °C per min andultimately to 296 °C at 8 °C per min, with holding time of5.5 min. The electron impact spectrometry per three fragmentions in selected-ion monitoring mode was employed to iden-tify the PCB congeners. The mass selective detector (MSD)source and quadruple temperatures were set at 230 and150 °C, respectively. The identification of analyte was basedupon their respective retentions time and ion chromatographsagainst the standards. Quantification was done by creating(multi-level) calibration curves and for all the concentrationranges detected in the samples, a good linearity of R2 > 0.99was achieved.

Lipid determination

Placental lipids were measured gravimetrically by taking pla-centa tissue (1 g), and thrice homogenizing it in 5-mL solutionof chloroform: methanol: hydrochloric acid (20:10:0.1 v/v/v),followed by the addition of 0.1 N HCl (10 mL) and centrifu-gation for 10 min at 3000 rpm. Organic phase containinglipids was then collected and the process was repeated to ob-tain maximum lipids. The extract was then concentrated undernitrogen stream and dry lipids were measured gravimetrically(Vizcaino et al. 2014).

Quality control and quality assurance

Quality assurance and controls were firmly followed for theentire analysis. The glassware was thoroughly rinsed withdouble-distilled water and later baked in muffle furnace for4 h at 450 °C before their usage, to prevent any contaminationto the samples. All the chemicals/reagents consumed werepurchased from Merck KGaA (Germany) and were ofHPLC grade. The internal and surrogate standards were

purchased from Dr. Ehrenstorfer GmbH (Germany) andCPA chem Ltd. (Bulgaria), respectively. For instrumental cal-ibration, standards were run every day and solvent blankswere analyzed after every 15 samples (a batch) to avoid anyrepeatability and cross-contamination in chemical analysis.For PCB peak integration, the Agilent MassHunterWorkstation Software Quantitative Analysis was used. Theprescribed procedure by Syed et al. (2014) was used to calcu-late method detection limit (MDL) and instrumental detectionlimit (IDL). The determination of congeners below the IDLwas referred as not detected (ND). The MDLs were calculatedas thrice to the standard deviation (SD) of the blank(Mahmood et al. 2014a). The calibration standards were usedon daily basis to compute the calibration curves. Mean recov-ery values were79 ± 8% for TCmX and 82 ± 9% for CB-209,respectively and all the results were adjusted to the blanks andstandards recovery ratios; for determination of CB-209 wasonly used as surrogate standard (Baqar et al. 2017b).

Statistical analysis

The Statistical Package for the Social Sciences (SPSS) version16.0 was used to test basic descriptive statistic and multipleregression analysis of PCBs with maternal and neonatal phys-iological data. The Arc GIS version 10.2.2 was employed todisplay PCB spatial distribution patterns in the study area.

Results and discussion

PCB congeners and homologs profile

Descriptive statistics of 34PCBcongeners and7PCBhomologsinplacental tissuesfromPakistanaresummarizedinTableS3andTable 1, respectively. The placental lipids values were rangedfrom 0.7% to 3.0% (mean: 1.8%). The ∑34PCB concentrationsinplacental tissuesweremeasuredbetween20.2and115.98ng/glipid weight (lip. wt.) with mean concentration of 80.26 ±19.87 ng/g (lip.wt.).All the studiedPCBcongenerswere detect-ed from placental samples. The PCB congener-specific profilehighlighted the CB-70 (mean: 6.63 ng/g (lip. wt.) as most dom-inant PCB congener, followed by CB-66 and CB-52, and leastconcentrationwasdepictedbyCB-166(mean0.21ng/g(lip.wt.).ThePCBhomologsprofile revealed thedominanceof tetra-PCBwith 54.67% contribution of the total PCBs. The overall PCBhomologs distribution trend in placental tissues from Pakistanwere in magnitude as; tetra-CB > penta-CB > hexa-CB > tri-CB > hepta-CB > octa-CB > deca-CB. In Pakistan, PCBs areprimarily used as a technical mixture of tetra-CB, penta-CB andtri-CB (Syed et al. 2014; Baqar et al. 2017b) which has beenvalidated by the prevalence of tetra-CB and penta-CB in placen-tal tissuesamplesfromthestudyarea.Thedominanceof tetra-CBand penta-CB in placental tissues were in consistent to previous


findings of Nanes et al. (2014) from the USA. Moreover, thedegree of chlorination has also been highlighted to affect theplacental PCB concentrations (Vizcaino et al. 2014) that is dom-inated by higher chlorinated congeners due to their longer half-lives and greater bioaccumulation capacity in humans (Leinoet al. 2013). However, the predominance of less chlorinatedPCBs in placental tissues was reported in some previous studies(Fernandez et al. 2012;Ma et al. 2012; Needham et al. 2011).

Among the 34 PCB congeners, six indicator (or marker)PCB congeners (i.e., CB-52, CB-101, CB-118, CB-138, CB-153, andCB-180)were also evaluated. The∑6PCB (sumof sixindicator PCBs) mean concentrations was measured as17.69 ng/g (lip. wt.). The CB-52 has shown the highest meanconcentration (6.01 ng/g (lip. wt.)) among indicator PCB con-geners, followed by CB-101 and CB-153. Previously, Naneset al. (2014) andGómara et al. (2012) highlighted that the dom-inance of CB-52, followed by the CB-101 in placental tissuesamples fromUnitedStates andSpanishmothers. Some studieshave shown abundance of CB-153 in placental tissue samples(Porpora et al. 2013; Vizcaino et al. 2014). Though CB-52 andCB-101 are rapidly metabolized in living organisms, so theirhigh levels are occasional in humans; indicating recent humanexposure to low chlorinated PCB congeners (Gómara et al.2012). Similarly, the occurrence of non-persistent congeners(i.e., CB-52, CB-66, CB-74, CB-101, CB-105, and CB-128)athighconcentrations indicated the recentand localexposureofthe subjects to PCBs (Covaci et al. 2001).

Despite of the fact that few studies have attempted to quan-tify the PCB accumulation levels in placental tissues; a com-parative analysis of ∑PCB concentrations in placental tissuesfrom current study with other studies across the world wasperformed (Table 2). The comparison revealed that ∑PCBlevels in placental tissues from present study were found tobe slightly higher or comparable to those reported fromFinnish (median: 48.5 ng/g lip. wt.) (Leino et al. 2013),Italian (92.5 ng/g lip. wt.) (Bergonzi et al. 2009), andSpanish mothers (40 ng/g lip. wt.) Vizcaino et al. 2014).However, the current placental PCB levels were much higherthan the findings from China (Ma et al., 2012), the USA(Nanes et al. 2014), and Spain (Gómara et al. 2012). In past,the placental PCB levels in Asian mothers were reported to belower than those from North American and European mothers(Nanes et al. 2014), reflecting the fact that 80% of the globalPCB production occurred in these industrialized regions(Breivik et al. 2002). Nevertheless, soon after the global pro-hibition on the production and use of PCB under theStockholmConvention (2001), the PCB production and usagewas strategically eliminated in developed countries (Fiedleret al. 2013). However, the ongoing usage of PCBs is beingreported from Pakistan along with its release from uncon-trolled e-waste recycling (Mahmood et al. 2014a; Iqbal et al.2015) which is also validated by PCB concentrations in pla-cental tissue samples from Pakistan.Ta

ble1

Descriptiv

estatisticsof

PCBsin

placentaltissues

(ng/g(lip.w

t.))samples

from

five

districtsof

thePu

njab

Province,P

akistan

Hom

ologs

Lahore

Khanewal

Okara

Sialkot

Chakw

al

Mean±SD

Median

Range

Mean±SD

Median

Range

Mean±SD

Median

Range

Mean±SD

Median

Range

Mean±SD

Median

Range

tri-PC

Ba

7.8±2.34

7.81

4.38–12.92

4.69

±2.01

4.23

1.26–8.97

4.02

±1.44

3.95

2.04–6.62

8.82

±2.08

8.91

6.1–11.72

5.48

±1.56

4.97

4.03–8.11

tetra-PC

Bb

48.12±10.16

52.24

32.59–60.56

42.18±17.2

42.31

12.65–66.05

34.35±10.08

32.97

22.2–52.17

50.14±7.72

51.05

36.11–61.54

45.34±10.12

39.94

37.57–61.45

penta-PC

Bc

17.19±5.96

18.25

8.9–24.91

12.89±7.56

12.32

3.11–23.74

17.75±6.25

16.16

10.85–30.57

14.1±5.32

14.15

6.16–23.16

20.65±7.36

19.24

13.26–30.56

hexa-PCBd

7.96

±4.75

6.84

3.1–18.71

5.33

±2.69

6.15

1.2–9.22

12.32±5.36

12.44

5.97–22.54

7.67

±3.22

7.89

3.04–13.15

15.27±7.14

15.69

6.19–23.48

hepta-PC

Be

4.46

±2.43

4.11

1.67–8.48

1.53

±0.84

1.35

0.77–3.64

3.3±2.07

3.02

0.49–7.4

2.66

±1.05

2.88

0.55–3.74

6.24

±2.54

4.84

4.3–10.43

octa-PCBf

1.14

±1.37

0.60

0.08–4.2

0.45

±0.33

0.36

0.01–0.92

2.68

±7.66

0.20

0.13–24.48

0.58

±0.39

0.46

0.03–1.06

0.19

±0.23

0.11

0.02–0.6

deca-PCBg

1.14

±1.46

0.77

0.03–4.89

0.4±0.56

0.11

0.07–1.46

0.24

±0.18

0.19

0.02–0.55

0.57

±0.74

0.31

0.02–2.44

0.26

±0.44

0.07

0.01–1.04

∑34PCBs

87.80±55.39

6.84

19.18–193.25

67.47±43.06

66.83

12.54–149.18

74.66±47.55

68.94

27.16–174.04

84.54±43.92

85.65

25.79–158.22

93.43±46.17

84.82

43.29–155.90

aSu

mof

CB30

and37

bSu

mof

CB44,49,52,54,60,66.70,74,and77

cSu

mof

CB82,87,99,101,105,114,118,and

126

dSu

mof

CB128,138,153,156,158,166,and169

eSu

mof

CB170,179,180,183,187,and189

fCB198only

gCB209only


Spatial distribution and source apportionmentof PCBs

The PCBs’ spatial distribution in placental tissues from thePunjab, Pakistanis is illustrated in Fig. 2. The mean Σ34PCBconcentration-based spatial profile followed the order as:Chakwal > Lahore > Sialkot > Okara > Khanewal. Relativelyhigher Σ34PCB concentrations were detected in placentalsamples from urban and industrial cities, i.e., Chakwal(93.43 ± 46.17 ng/g (lip. wt.)), Lahore (87.80 ± 55.39 ng/g(lip. wt.)), and Sialkot (84.54 ± 43.92 ng/g (lip. wt.)) thanthose from the rural towns of Okara (74.66 ± 47.55 ng/g (lip.wt.)), and Khanewal (67.47 ± 43.06 ng/g (lip. wt.)). The rela-tive higher spatial levels of PCBs associated with urban andindustrial areas have also been established in previous studiesfrom the Punjab province (Ali et al. 2014; Mumtaz et al.2016), and other parts of the world (Toan and Quy 2015).High levels of PCBs in placental tissues from industrial andurban cities reflect their continual exposure to PCB sources inthe urban environment (Diamond et al. 2010). Among thePCB homologs in placental tissues, the tetra-PCB was themost dominated homolog, with highest mean concentrationobserved in samples from Sialkot (50.14 ± 7.72 ng/g (lip.wt.)), followed by Lahore (48.12 ± 10.16 ng/g (lip. wt.)), andChakwal (45.34 ± 10.12 ng/g (lip. wt.)). Similar distributionpattern was observed for tri-PCB, where Sialkot (8.82 ±2.08 ng/g (lip. wt.)) and Lahore (7.8 ± 2.34 ng/g (lip. wt.))have exhibited the highest levels of contamination.However, in the case of penta-PCB, hepta-PCB, and hexa-PCB concentrations, the Chakwal has shown the utmost levelsof contamination (Fig. 2).

In the study area, the Sialkot and Lahore are urban as well asindustrial cities having steel production and recycling, surgicaltool manufacturing, transformer repairing, old equipment dis-mantling and maintenance, and metal recovery from e-waste,pigment, and PVC (polyvinyl chloride) industries (Farooq et al.2011;Eqanietal.2012a;Syedetal.2014;Mahmoodetal.2014b;Iqbal et al. 2015), thesearepossible sourcesofPCBenvironmen-tal release. Besides that, coal combustion, oil spills from indus-trial sites,volatilizationfromPCB-containingbuildingmaterials,incineration emissions, and open burning ofmunicipal solid andindustrial waste (Chi et al. 2007; Syed et al. 2013; Chakrabortyet al. 2016) are also possible sources of PCB human exposure,leading to placental accumulation in mothers from Sialkot andLahore (Mahmood et al. 2014b).

Despite the fact that Chakwal is not an urban city, yet thedistrict hosts large number of cement factories, supplying asubstantial portion of the regional cement requirement and thewidespread energy crisis in the country encouraged the cementindustries to use the tire-derived fuels (TDF) and refused-derived fuels (RDFs) (Cheema and Badshah 2013), possiblyemitting the PCBs in the local environment in the absence ofany legislative mechanism for PCB emissions control. PeopleTa

ble2

Com

parisonof

PCBsconcentrations

(ng/g(lip.w

t.))in

placentaltissues

with

previous

studiesfrom

othercountries

Country

Num

berof

samples

Num

berof

congeners

PCBcongenersstudied

Concentratio

nReference

Mean

Median

Range

Pakistan

4334

CB-30,37,44,49,52,54,60,66,70,74,77,82,87,99,101,105,114,118,126,

128,138,153,156,158,166,169,170,179,180,183,187,189,198,and209

80.26

67.08

20.2–115.98

Presentstudy

China

130

08CB-105,118,156,157,167,189,206,and

209

–0.89

ND-9.8

Maetal.(2012)

Italy

7030

CB-28,31,52,74,99,101,105,114,118,123,128,138,146,153,156,157,167,

170,172,177,180,183,187,189,194,196,201,203,206,and209

–92.5

–Bergonzietal.(2009)

Finland

130

37CB-18,28,33,47,49,51,52,60,66,74,77,81,99,101,105,110,114,118,122,

123,126,128,138,141,153,156,157,167,169,170,180,183,187,189,194,

206,and209

–48.5

–Leino

etal.(2013)

USA

4232

CB-8,28,37,44,49,52,60,66,70,74,77,82,87,99,101,105,114,118,126,128,

138,153,156,158,166,169,170,179,180,183,187,and189

0.371

–0.076–0.856

Nanes

etal.(2014)

Spain

1715

CB-28,52,101,118,146,153,105,138,187,183,128,156,180,170,and

189

2.546

–0.943–4.331

Gom

araetal.(2012)

Spain

497

CB-28,52,101,118,153,138,and

180

–40

10–230

Vizcainoetal.(2014)


dwelling adjacent to the cement plants and incinerators are cat-egorizedbyAgency forToxicSubstances andDiseaseRegistry(ASTDR) as Bspecial population^which are vulnerable to highlevel exposure to PCBs as compared to general population(ASTDR 2014). Moreover, in the recent times, co-processingof solid waste and sewage sludge, fly ash and tires in cementindustries have been recognized as key sources of PCB envi-ronmental emissions (Jin et al. 2017; Richards andAgranovski2017).At the same time, the areahas alsobeenhighlightedwithtraffic congestion by thousands of diesel-operated trucks pass-ing through theChakwal district eachday (Aziz et al. 2014) thatmay lead to PCB emissions associated with heavy duty vehic-ular emissions (Laroo et al. 2012). All of these sources exertsynergisticeffect to localPCBexposure that isalsoevident fromthe highest concentrations of certain PCB homologs in placen-tal samples from Chakwal. Whereas, the presence of PCBs in

placental samples from Khanewal and Okara (rural areas) areapparently attributed to semi-volatile nature of PCBs, diffusivegaseous transport and their tendency to travel long distances inthe environmentwhichmight lead to their abrupt occurrence inrural areas from study area (Gasic et al. 2010; Mahmood et al.2014b; Syed et al. 2013).

Dioxin-like PCBs

Profile distribution of dioxin-like PCBs

In total, eight DL-PCBs, including three non-ortho (CB-77, 126, and 169) and five mono-ortho (CB-105, 114,118, 156, and 189) were determined in this study(Table 3). The DL-PCBs exhibit various similar toxicolog-ical characteristics as of polychlorinated dibenzo-p-dioxins

Fig. 2 Spatial patterns of PCB homologs in placental tissues from five districts of Punjab Province, Pakistan


(PCDDs) and polychlorinated dibenzofurans (PCDFs) dueto their structural similarities. The levels of ∑8DL-PCBs inplacenta tissues ranged from 2.03 to 27.64 ng/g (lip. wt.),with mean concentration of 9.48 ± 10.9 ng/g (lip. wt.);exhibiting a considerable 11.82% contribution of the totalPCBs in placenta tissues. The DL-PCB distribution profilewas dominated by CB-77, followed in abundance by CB-105 and CB-118. The non-ortho DL-PCBs in placenta con-tributed prominently in total DL-PCBs with 45.91% of the∑8DL-PCBs. The noticeable occurrence of non-ortho DL-PCB congeners in placental tissues from the region isalarming due to their carcinogenicity; as they possess sim-ilar characteristics to the tetrachlorodienzo-p-dioxin(TCDD) (Mahmood et al. 2014a).Similarly, the occurrenceof CB-105 and CB-156 indicates the ongoing environmen-tal release of commercial PCBs as both of these congenersare vital ingredients of technical Aroclor mixtures/products(Kim et al. 2009; Malik et al. 2014).

Toxicity equivalency (TEQ) of dioxin-like PCBs

The toxicological similarities of the DL-PCBs to PCDDs wereevaluated through assessment of toxicity equivalence (TEQs)for dioxins using Eq. (1), where BC^ is the DL-PCB conge-ner’s concentration and BTEF^ is the toxicity equivalence fac-tor established by the World Health Organization,International Programme on Chemical Safety (WHO-IPCS)in 2005 (Van den Berg et al. 2006).

TEQ ¼ C� TEF ð1Þ

The calculated WHO-TEQs values of three non-ortho andfive mono-ortho PCBs are summarized in Table 4. TheWHO-TEQ values for DL-PCBs were ranged from 1.18 × 10−5 to0.067 ng/g (lip. wt.), with the highest value evaluated for

CB-126 and least for CB-156 in maternal placenta tissues.The WHO-TEQ value for ∑8PCBs was calculated as0.083 ng/g (lip. wt.). The WHO-TEQ values in placental tis-sues from present study were found to be higher than thosefrommaternal placental samples in Taiwan (0.00291 ng/g (lip.wt.)) and Japan (0.0012 ng/g (lip. wt.)) (Suzukiet al. 2005;Wang et al. 2004).

Estimation of human daily intake of dioxin-like PCBs

The magnitude of the maternal health risk associated to bodyburden of the DL-PCBs exposure was evaluated as estimateddaily intake (EDI) using the Eq. (2), established by WHO(1998) and later followed Japanese Environmental HealthCommittee of the Central Environment Council (EHCCEC1999) and Finish Department of Environmental Health

Table 3 Mean concentrations ofeight dioxin-like and six indicatorPCBs in human placental samples(ng/g (lip. wt.)) from five districtsof the Punjab Province, Pakistan

PCB congener/class Lahore Khanewal Okara Sialkot Chakwal

CB-77 2.96 7.91 0.74 3.20 1.17CB-126 0.94 0.39 0.42 0.88 0.77CB-169 0.84 0.44 0.21 0.56 0.75∑non-ortho 4.75 8.73 1.37 4.64 2.69CB-105 1.70 0.76 2.10 1.37 3.01CB-114 1.48 0.56 0.30 1.13 0.99CB-118 1.32 1.25 1.82 1.04 2.53CB-156 0.49 0.37 0.21 0.58 0.24CB-189 0.72 0.21 0.27 0.54 0.47∑mono-ortho 5.71 3.15 4.70 4.67 7.24∑8DL-PCBs 10.45 11.88 6.07 9.31 9.93CB-52 6.88 5.86 4.12 7.22 5.86CB-101 4.67 3.20 6.59 3.52 1.65CB-118 1.32 1.25 1.82 1.04 2.53CB-138 1.55 1.11 3.30 2.08 4.32CB-153 2.50 1.71 5.19 1.64 4.56CB-180 1.66 0.32 0.54 0.32 1.48∑6-indicator PCBs 18.59 13.45 21.55 15.82 20.40

Table 4 Toxicity equivalency (TEQ) and estimated daily intake (EDI)of dioxin-like PCBs in maternal placental samples

Compound WHO 2005–TEF

WHO-TEQs(ng/g)

EDI (pg WHO-TEQ/kg bw)

Non-ortho substituted

CB-77 0.0001 3.43 × 10−4 2.69 × 10−6

CB-126 0.1 0.067 5.25 × 10−4

CB-169 0.03 0.0162 1.27 × 10−4

Mono-ortho substituted

CB-105 0.00003 4.96 × 10−5 3.90 × 10−7

CB-114 0.00003 2.64 × 10−5 2.07 × 10−7

CB-118 0.00003 4.47 × 10−5 3.51 × 10−7

CB-156 0.00003 1.18 × 10−5 9.27 × 10−8

CB-189 0.00003 1.32 × 10−5 1.04 × 10−7

∑8PCBs – 0.083 6.56 × 10−4

*WHO 2005–TEFs (Van den Berg et al. 2006)


(Kiviranta 2005).

EDI ng=kg=dayð Þ ¼ Body Burden ng=kgð Þ x ln 2ð Þhalf−lifeð Þ x f

ð2Þ

where f is the absorption factor (assumed to be 50%), ln(2) =0.693,andhalf-life isassumed tobe7.5years (EHCCEC1999).

The calculated EDI for DL-PCBs was ranged from 9.27 ×10−8 to 5.25 × 10−4 pg WHO-TEQ/kg bw (body weight); withhighest and lowest EDI computed for CB-126 and CB-156, re-spectively. ThemeanEDI of∑8dl-PCBswas found to be 6.56 ×10−4 pgWHO-TEQ/kg bw. The current EDI results were foundto be much lower than the tolerable daily intake (TDI) valuesestablished by WHO (1–4 pg TEQ/kg bw), The EuropeanUnion Scientific Committee on Food (EU SCF) (2 pg TEQ/kgbw), Joint FAO/WHO Expert Committee on Food Additives(JECFA) (2.3 pg TEQ/kg bw), United States EnvironmentalProtection Agency (USEPA) (0.001–0.01 pg TEQ/ kg bw), andtheUKCommitteeonToxicityofChemicals inFood,Consumer

Products and Environment (COT) (2 pg TEQ/kg bw) (VanLeeuwen et al. 2000; European Commission 2001; WHO/FAO, 2001; USEPA 2000).

Correlation between placental PCB levelsand neonatal anthropometric measures

A prenatal POPs exposure had been reported to inhibit fetalgrowth, resulting in reduced birth weight, crown to heellength, head, mid-arm, and chest circumferences (Dewanet al. 2013; Vafeiadi et al. 2014). The relationship between∑34PCB concentrations in placental tissues and infant’s an-thropometric measures through multiple linear regression(Table S4) have shown a negative correlation of infant’s bodyweight (R2 = 0.0728) (Fig. 3), crown to heel length (R2 =0.068), head circumference (R2 = 0.0342), chest circumfer-ence (R2 = 0.0001), and mid arm circumference (R2 =0.0096) (Fig. 4). These negative correlation findings havereflected neonatal and fetal health effects associated with ma-ternal PCB exposure and its subsequent bioaccumulation. Thecurrent decrements in fetal and neonatal growth associatedwith maternal or/and neonatal PCB concentrations were inconsistent to the findings of the previous studies conductedin the USA (Sagivet al. 2007), Europe (Govarts et al. 2012),China (Wu et al. 2011), India (Dewan et al. 2013), SaudiArabia (Al-Saleh et al. 2012), and Singapore (Tan et al. 2009).

Conclusions

The occurrence, congeners profile, spatial variations, sourceapportionment, toxicity equivalence (WHO-TEQ), and esti-mated daily intake (EDI) associated to maternal exposure ofPCBs from five districts of Punjab Province, Pakistan, were

y = -0.0168x + 5.8716

R² = 0.0728

0

1

2

3

4

5

6

7

8

0.00 20.00 40.00 60.00 80.00 100.00 120.00 140.00

Infa

nt's

Body

Wei

ght (

Poun

ds)

PCB Concentration (ng/g lip. wt)

Fig. 3 Simple linear regression showing relationship between PCBs inmaternal placenta and infants body weight

y = -0.0618x + 47.364

R² = 0.068

y = -0.0219x + 35.026

R² = 0.0342

y = -0.0012x + 31.853

R² = 0.0001

y = -0.0073x + 10.467

R² = 0.0096

0.00

10.00

20.00

30.00

40.00

50.00

60.00

0.00 20.00 40.00 60.00 80.00 100.00 120.00 140.00

Infa

nt a

nthr

opom

etri

c mea

sure

s (cm

)


Crown to Heel Length

Head Circumference

Chest Circumference

Mid Arm Circumference

Linear (Crown to Heel Length)

Linear (Head Circumference)

Linear (Chest Circumference)

Linear (Mid Arm Circumference)

Fig. 4 Multiple linear regressionsshowing relationship betweenPCBs in maternal placenta andinfants anthropometric measures


assessed that they provide a pioneer data for PCBs in placentaltissues from South Asian women. Among the PCB congeners,the CB-70 was the most dominant PCB congener, followed inabundance by CB-66 and CB-52. The overall PCB homologdistribution trend in placental tissues from Pakistan were indescending order as tetra-PCB > penta-PCB > hexa-PCB >tri-PCB > hepta-PCB > octa-PCB > deca-PCB. The placentalPCB levels from study area were found to be comparable orhigher than those assessed in similar studies. The meanΣ34PCB concentration-based spatial profile followed the or-der as Chakwal > Lahore > Sialkot > Okara > Khanewal,representing relatively higher levels of contamination in urbanand industrial cities than the rural towns. The multi-regressionanalysis has shown reduction in neonatal anthropometric mea-surements associated with maternal PCB body burden. Thus,at present, the maternal exposure to PCBs in Pakistan exertsrisks to neonatal health and detailed human biomonitoring andrisk assessment studies are needed to be conducted in theregion. The present study also suggests the best pollution con-trol practices and rigid legislative actions to be opted on emer-gency grounds to prevent PCB future environmental releasesto safeguard human health risks.

Acknowledgements We express our sincere gratitude to the State KeyLaboratory of Organic Geochemistry, Guangzhou Institute ofGeochemistry, Chinese Academy of Sciences, Guangzhou, China forproviding support for laboratory analysis.

Compliance with ethical standards

The approval of the present study was obtained from Advanced Studiesand Research Board (ASRB), University of the Punjab, Pakistan, andwritten consent was obtained from the subjects, after they were briefedabout the purpose of the research.

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http://www.pops.int/default.htm

Punjab Univ. J. Zool., Vol. 32 (2), pp. 251-264, 2017 ISSN 1016-1597(Print) ISSN2313-8556 (online)

189-PUJZ-71022150/17/0251-0264 Copyright 2017, Dept. Zool., P.U., Lahore, Pakistan #Part of thesis *Corresponding author: [email protected]

Original Article Assessment of polychlorinated biphenyls (PCBs) in maternal blood serum from selected districts of Punjab, Pakistan Anber Naqvi1*#, Abdul Qadir1, Adeel Mahmood2, Mehvish Mumtaz3, Iqra Aslam1, Gan Zhang4 1College of Earth and Environmental Sciences, University of the Punjab, Lahore, Pakistan 2Department of Environmental Sciences, Government College Women University, Sialkot, Pakistan 3School of Environment, Tsinghua University, Beijing, P.R China 4State Key Laboratory of Organic Geochemistry, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou 510640, China Article history Received: May 12, 2016 Revised: August 07, 2017 Accepted: November 28, 2017 Authors’ Contribution AN, AQ, AM: conceived and planned the present research work. GZ: provided the facilities for samples analysis and supervised the analytical process. Key words Dioxin like PCBs Maternal blood serum Congener specific analysis, Spatial distribution

Abstract The current study was conducted to screen the PCB residues in blood serum of mothers residing in five districts of Punjab Province, Pakistan. The mean concentration of ∑34 PCB congeners was recorded 232.3 ng/g l.w. PCB homologs profile showed highest levels of Tetra-CBs (53%) followed by Penta-CBs (23%) and Hexa-CBs(11%), respectively. Spatial distribution of PCB homolog reflected the higher prevalence of lower chlorinated PCBs in urban population descending towards rural population. The mean TEQ concentration of eight dioxins like PCBs was recorded as 0.03 ng/g l.w. Analysis of ∑34PCBs and socio-demographic parameters reflected significant results for milk intake. The study findings suggested a need for regulating the continuous intentional or unintentional releases of PCB into the environment to prevent human exposure.

To cite this article: NAQVI, A., QADIR, A., MAHMOOD, A., MUMTAZ, M., ASLAM, I. AND ZHANG, G., 2017. Assessment of polychlorinated biphenyls in maternal blood serum from selected districts of Punjab, Pakistan. Punjab Univ. J. Zool., 32(2): 251-264.

INTRODUCTION

olychlorinated Biphenyls (PCBs) are the toxic, bio-accumulative substances that can persist in the environment for a long

time and lipophilic in nature having more tendency of bioaccumulation and bio-magnification in organisms (Howard and Muir, 2010; Antonio et al., 2008; Fiedler et al., 2002). PCBs have toxicological implications, deleterious impacts on the environment, wildlife and human and have attained the attention of global and regional scientific community during the last decades (Mahmood et al., 2014; Breivik et al., 2004; Cui and Forssberg 2003; Zhang et

al., 2005). PCBs are consistent and stable in the environment; therefore, the United States Environmental Protection Agency has declared them as probable human carcinogens (Judd et al., 2003). The use of PCBs has been started on the large scale since the 1930s for commercial purposes such as plastic, lubricating oils, inks, carbonless copy paper, impregnating, paints, sealing liquids, adhesives, additives, waxes, immersion oils, fire retardants, plaster and casting in different industries (Wittsiepe et al., 2015; Afghan and Chau 1989). There are several other sources of PCBs pollution from waste material such as incineration of municipal waste, volatilization from the contaminated water bodies and landfill sites having transformers and

P

A. NAQVI ET AL. 252

coolants as waste components (Mahmood et al., 2014; Wittsiepe et al., 2015; Mumtaz et al., 2016). Up till now, 209 congeners of PCB has been identified in the environment resulted from different anthropogenic activities (WHO, 2003).

Despite from a worldwide ban on PCBs production, some countries, particularly developing countries from Asia, Africa, Europe and Latin America continued their use for industrial and commercial purposes (Mamun et al., 2007). Therefore, PCBs are still detected in the environment and pose great threats to the health of the environment and human (Hernik et al., 2016; Thomas et al., 2006). The human population is generally exposed to PCBs through the air, water, and food (WHO 2003; Jensen et al., 1987; Sharma et al., 2014). Dietary intake of contaminated fish, meat, and dairy products is considered the primary exposure route of PCBs to human which pose Neuro-developmental risks (Eguchi et al., 2012; Ginsberg et al., 2015; Hernik et al., 2016; Sharma et al., 2014). Certain biomarkers have been recommended by the World Health Organization (WHO) for the assessment of pollution caused by PCBs (Osman et al.,, 2017). A positive association between PCBs levels in human serum and consumption of meat and dairy fat has also been reported in previous studies (Govart et al.,2010). The predominant target organs/tissues in human with possible higher concentrations of PCBs are liver, brain, adipose tissues and skin, blood, milk, placenta and cord blood (WHO 2003; Esteban and Castano 2009; Dirtu et al., 2009). PCBs can cause adverse health impacts to human health e.g. immunotoxicity, developmental, physiological and structural disorders and even it also causes reproductive and genetic disorders such as low birth weight, vision and hearing disorder etc. (Kodavanti et al., 2008; Tyagi et al., 2015; Darnerud, 2003; Larsen, 2006).

Limited information regarding human exposure to POPs is available in developing Asian countries (Tanabe and Kunisue, 2007). Screening of PCBs levels in the human body is the least addressed research area in Pakistan and a few studies on PCBs assessment in male human serum (Ali et al., 2014) and in maternal blood serum (Ali et al., 2013) are available from Pakistan. The negative effects of rapid urbanization and industrialization have been observed in environmental matrices of Punjab Pakistan (Azmat et al., 2016). Women are more vulnerable to PCB exposure as they have a

relatively higher proportion of fat and PCBs being lipophilic tend to accumulate in their fatty tissues. Consumption of these contaminants not only causes adverse health effects to exposed women, but pre and post natal exposure causes developmental, reproductive, behavioral disorders and neurological effects in the fetus (Daglioglu et al., 2010; De Rosa and Hicks 2001; Rice, 2012). The present study was designed to focus on the evaluation of PCBs in maternal blood serum from different environmental settings of Punjab Province, Pakistan.


Study area and sampling strategy

This study was conducted in urban and rural areas of Lahore, Sialkot, Chakwal, Okara and Khanewal district of Punjab province, Pakistan. Punjab is the most populous province with an area of 205,344 km2 and the population of 100 million (population density = 353 persons/ km2). A higher proportion of the population of the province is living in rural areas and involved in agricultural activities, whereas, an urban population of the province is normally engaged in industrial and other service activities. These areas were selected by considering the fact that both the rural and urban environments are exposed to PCBs pollution. A map of study area representing the location of selected sampling sites is given in Figure1. The female volunteers with age between18 years to 45 years were selected from each sampling site. The maternal blood samples (n=44) were collected from women with the help of paramedical staff by visiting the hospitals (Thomas et al., 2006). After collection, the serum was separated by centrifugation and placed in the refrigerator (-20ºC) until final analysis (Inoue et al., 2006; Covaci and Schepens, 2001). Sample preparation

The blood serum samples were thawed and homogenized at room temperature. One milliliter (1 ml) of maternal blood serum was taken, spiked with the surrogate standards (5ppb) of PCB 209 and 2,4,5,6-tetrachloro-m-xylene TcmX and kept overnight at 40C (Covaci and Schepens 2001; Covaci and Voorspoels 2005). Extraction of PCBs was done by adding n-hexane (6 ml) and acetone (3 ml) into serum samples followed by homogenization through

ASSESSMENT OF POLYCHLORINATED BIPHENYLS IN MATERNAL BLOOD SERUM 253

ultrasonic treatment for one hour at 3ºC. The clear supernatant was separated into glass tubes by centrifugation of samples at 2000 revolution per minute (RPM) (Dewan et al., 2013). The same procedure was repeated twice and the supernatant was collected (Tyagi et al., 2014). The empty Agilent Bond Elut polypropylene cartridge was pre-washed with DCM (5 mL), activated through MeOH (5mL), water (5 mL) and finally dried by centrifugation for 15 minutes (Covaci and Schepens, 2001; Covaci and Schepens 2001). A column for sample cleanup was prepared by the method (Jaraczewska et al., 2006). The sample was loaded on the cartridge and analytes were

eluted with hexane (4 mL) and Dichloromethane (2 mL; Jaraczewska et al., 2006). Samples were concentrated through nitrogen streaming (Inoue et al., 2006) and equilibrated with fifty microliters (50 µl) of iso-octane (Covaci and Schepens 2001). Internal standards of 13C-PCB 141 (10µL) were added to the mixture followed by vortexing and transferred to the septa vials (1.5 mL). The samples were transferred to the State Key Laboratory of Organic Geochemistry, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou, China for further analysis on GC/MS (Gas Chromatograph Mass Spectrometer).

Figure 1: Map of study area of the showing different sampling districts in Punjab, Pakistan Instrumental Analysis An Agilent (7890A) gas chromatograph (GC) coupled with an Agilent (7000A) triple quad mass spectrometer (MS) detector and Agilent 7693 auto-sampler was used for the quantification of PCBs. The operational mode of GCMS was electron-capture negative ionization (ECNI). For PCBs analysis, a CP- Sil 8 capillary column from Netherland was used. The helium as a carrier gas was applied with a fixed flow rate of 1.2ml/min with an initial pressure of 20.9 psi. The operational mode of mass spectrometer was electron ionization (EI) with Electron Multiplier Voltage (EVM) of 2947 V and 230°C was set at the ion source temperature. Employing the pulsed splitless mode, one microliter (1μl) of the final extract was injected with the initial temperature at 100°C, pulse pressure of 25 psi kept for 0.5min. The length of

total rum per sample was 40 ms and 1.5 min was total splitless time. Concentrations of 34 PCB congeners including eight (8) dioxin like PCBs (PCB70, PCB126, PCB169, PCB105, PCB114, PCB118, PCB156 and PCB189) and six indicator PCBs (PCB52, PCB101, PCB118, PCB138, PCB153 and PCB180) were determined. The total lipid in the serum samples was determined as demonstrated by Covaci et al., 2005). Total cholesterol (CHOL) and triglycerides (TG) were measured enzymatically in separate subsets of the serum samples. The total lipids were determined by putting the CHOL and TG values in the formula as follows: TL (g/l) = 1.12×CHOL+ 1.33×TG + 1.48 (Covaci et al., 2006). Final concentrations of PCBs in each sample were expressed in ng/g lipid weight.

A. NAQVI ET AL. 254

Demographic Status In order to collect the demographic

information of the sampling group, a questionnaire was developed and filled by getting information from the each volunteer woman during the blood sampling regarding the age, location, parity, body mass index (BMI), eating habits, fish and milk intake and infant’s birth weight. Quality control and assurance The surrogate standards; PCB 209 and TCmX were obtained from CPA Chem. Ltd. Stara Zagora, Bulgaria, whereas, the internal standard (13C PCB 141) was obtained from Cambridge Isotope Laboratories, Inc., USA. DCM, methanol, n-hexane (Hex), acetone, iso-octane, concentrated H2SO4, Anhydrous sodium sulphate, and silica gel of analytical grade was purchased from Merck. WHO guidelines were followed during sample collection, transportation, and storage. Glassware was washed with distilled water, oven dried at 115 oC and baked for six hours (at 450 oC). A blank was run prior to each sample set during analysis on GCMS. The average surrogate standards recovery was 69 ± 6% for TCmX and 76 ± % for PCB 209. PCB calibration standards of 2 ppb, 10 ppb, 20 ppb, 50 ppb, 100 ppb and 200 ppb were used for evaluation of the instrument and method accuracy. Toxicity Equivalent Factor (TEQ)

The TEQ of Some dioxin like PCBs exhibiting similar properties and toxicity as of tetrachlorodibenzo-p-dioxin (TCDD) was calculated (Chovancovaet al., 2012). Occurrence of dioxin like PCB congeners (non-ortho PCB 77, -126, -169 and mono-ortho PCB 105, -114,-118, - 156, -189) were also detected in the sampling population (Ahlborg et al., 1994; De Vito and Birnbaum 1995; Van den Berg et al.,, 2006). Statistical Analysis

PCBs concentrations in maternal serum were analyzed statistically using Microsoft excel and statistical software SPSS (version 16). The concentration of PCBs in serum samples was represented as mean, standard deviation and range. Maps were prepared using Arc GIS 10.0. Box whisker plots were prepared using Statistica 10.0 to represent spatial distribution patterns of PCBs.

RESULTS AND DISCUSSION

PCB profile and congener specific analysis The maternal blood serum samples were analyzed to determine the concentration of 34 PCB congeners. The concentrations of 34 PCB congeners were grouped on the basis of different classes/ PCB homolog and their results in the form of mean, standard deviation and range are summarized in Table-I. The mean values of total lipids in serum samples were 4.4 g/L and ranged between 4.3 to 5.2 g/L. The mean value of all PCBs in maternal serum was 232.30 ng/g (l.w.). The concentration of PCBs in sampling population ranged from 47.5 ng/g l.w.to 522.1 ng/g l.w. The congener specific analysis showed that the priority PCB congeners were PCB 70, PCB 52, PCB 37, PCB 60 and PCB 66 in a decreasing order. However, the highest concentration of PCB 70 (mean; 18.7 ng/g l.w.) and ranged between 0.6 ng/g l.w. to 44.3 ng/g l.w., whereas, the lowest level of PCB 166 (mean value 0.4 ng/g l.w., range 0.00 ng/g l.w. to 2.4 ng/g l.w.) were recorded from study area. Six indicator PCBs were also detected in the present study and their mean concentrations were PCB 52 (17.46 ng/g l.w.), PCB 101 (12.10 ng/g l.w.), PCB 118 (5.99 ng/g l.w.), PCB 138 (4.87 ng/g l.w.), PCB 153 (6.64 ng/g l.w.) and PCB 180 (2.46 ng/g l.w.). The occurrence of persistent congeners was observed in the PCB profile with mean values as PCB 153 (6.64 ng/g l.w.), PCB 138 (4.9 ng/g l.w.) and PCB 180 (2.5 ng/g l.w.). The concentrations of these congeners were found lower than Bolivia, where the concentrations of PCBs were recorded as PCB 153 (59 ng/g l.w.), PCB 138 (1.80 ng/g l.w.) and PCB 180 (1.00 ng/g l.w.; Arrebola et al., 2012). Ali et al., (2013) reported the mean values of PCBs from Pakistan as PCB 153 (2.63 ng/g l.w.), PCB 138 (2.04 ng/g l.w.) and PCB 180 (1.3 ng/g l.w.) which were lower than the reported in the present study. Garcia et al., (2014) reported the PCB congeners in the blood of the Mexican population as PCB 153 (1.90 ng/g l.w.), PCB 138 (1.80 ng/g l.w.) and PCB 180 (1.00 ng/g l.w.) which was higher than the reported in the present study. Moreover, non-persistent PCB congeners (PCB 52, PCB 66, PCB 74, PCB 101, PCB 105, PCB 128 and PCB 149) were detected in the blood serum samples, indicating that there is continuous and recent exposure to these PCBs from multiple sources (Covaci et al., 2001).


The PCBs congeners were grouped as PCB homologs and their basic descriptive statistics was summarized in Table I. The percentage of PCB homologs on the basis of

abundance was recorded as Tetra-CBs (53%) ≥ Penta-CBs (23%) ≥ Hexa-CBs (11%) ≥ Tri-CBs (8%) ≥ Hepta CBs (4%) ≥ Deca-CB (0.7%) ≥ Octa-CB (0.5%).

Table-I: Descriptive Statistics of PCB Congeners in maternal serum samples District Lahore Khanewal Okara Sialkot Chakwal PCBs Homolog

Mean ± SD

Range Mean ± SD

Range Mean ± SD

Range Mean ± SD

Range Mean ± SD

Range

TriCBs 25 ±11

7-54 16 ± 6

8.4-29 14 ± 5

6-23 21 ± 11

4 -38 18 ± 5

11-26

Tetra CBs 169 ± 48

55 -299

103 ± 38

26.7-190

81 ± 36

13-190 143 ± 82

16-330 139 ± 43

89-205

Penta-CBs 66 ± 29

22-1448

48 ± 23

14 -108 29 ± 27

2.7-103 57 ± 34

9.4 - 148

76 ± 27

12-160

Hexa-CBs 21 ± 9.5

1.4-50 15 ± 10

1.1-39.6 13 ±17

1-60 21 ±14

1.9-59 56 ± 31

20-108

Hepta CBs 10 ± 8

0.8-40 4.2 ± 2

0.7-12.4 8 ± 5

0.7-28 6.5 ± 7

0.4-30 17 ±9

3.8-33

Octa CBS 2.2 ± 2.9

0.1-10 1.1 ±1

0-2.9 0.6 ±0.6

0-2 0.7 ± 0.5

0-1.4 3 ± 3.6

0.4-9

Deca CBs 1.3 ± 1.5

0.1-4.4 2.6 ± 2.4

0.3-8.9 0.8 ±1.6

0-5 2.7 ±3.5

0.1-12 2 ± 0.9

0.4-3

∑PCBs 295 ± 111

87-1905

190 ± 82

51-392 146 ± 92

24-411 252 ± 153

45-619 311 ± 120

137-544

Table II: Toxic Equivalent (TEQs) calculated

for maternal blood serum samples using WHO’s 2005 Toxicity Equivalency Factors (TEFs)

Compound WHO 2005

TEF TEQs

Non-ortho substituted PCB PCB 77 0.0001 1.2 x 10-3 PCB 126 0.1 0.202 PCB 169 0.03 0.042 Mono-ortho substituted PCB PCB 105 0.00003 1.46 x 10-4 PCB 114 0.00003 1.17 x 10-4 PCB 118 0.00003 1.79 x 10-4 PCB 156 0.00003 4.22 x 10-5 PCB 189 0.00003 3.88 x 10-5 ∑8PCBs 0.245 Toxicity Equivalent Fluxes of Dioxin like PCBs

The toxicity equivalent fluxes of dioxin like PCBs are given in Table-II. The mean TEQ concentration of dioxin like PCBs was recorded as 0.03 ng/g l.w. (ranged between 3.88 x 10-5 ng/g l.w. to 0.202 ng/g l.w). The results for TEQ values in present study were higher than those

reported from Taiwan (0.0045ng/g l.w; Wang et al., 2004). Spatial Distribution patterns of Homolog PCBs with potential sources Spatial trend of PCB homologs in sampling matrix from selected sampling zones is represented in Figure 2. The overall prevalence of tetra-CBs followed by Penta-CBs and Hexa-CBs was observed in all sampling populations. This trend was different from the previous studies on serum samples from Pakistan, Bangladesh and China (Ali et al., 2013; Mamun et al., 2007; Bi et al., 2007), where higher chlorinated PCBs were predominant. Levels of Tetra-CBs in the present study were predominant in maternal serum from Lahore. This trend is consistent with the results of a previous study on PCB concentrations reported in food commodities, particularly in rice plant and grains from different areas of Punjab province (Mahmood et al., 2014; Mumtaz et al., 2016). The concentration of Tetra-CBs was observed as highest in all the serum samples of the present study might be due to their higher volatilization into the environment and higher concentration in the food chain supplements as reported in previous studies. Lahore and Sialkot showed an increasing trend of urbanization and the hub of many industrial activities including chemical manufacturing, paints, and dyes

A. NAQVI ET AL. 256

manufacturing units. Therefore, exposure of lower chlorinated biphenyls was higher in these urban areas than those in rural areas. Levels of PCBs contamination were also reported in other environmental matrices such as freshwater resources, sediments, fish, indoor dust, food commodities and in human body matrices viz. hair, serum and milk in recent studies (Eqani et al, 2012; Eqani et al, 2013; Ali et al, 2014; Ali et al, 2013; Mahmood et al, 2014; Khawaja et al, 2010). Generally, lower chlorinated PCBs were dominated in urban air and the probability of

human exposure to these contaminants resulted accumulation in the human body. Among all the homolog groups, highest levels of Tetra-CBs (18.7 ng/g l.w.) was detected in the samples collected from Lahore, whereas, Deca-CB with least concentrations of 0.76 ng/g l.w. were found in Okara. The urban population showed higher concentrations of low chlorinated biphenyls as compared to the rural population in blood samples. A similar trend was observed by Turci et al., (2007) in women populations living in different environments of Italy.

(a) (b)

(c) (d)

(e)

Figure 2: Spatial distribution patterns of homolog PCBs in maternalserum (a).Lahore, (b). Khanewal, (c). Okara, (d). Sialkot and (e). Chakwal.


The lower chlorinated biphenyls are unintentionally formed as byproducts in paints and pigments industry. The disposal and decomposition of PCB containing materials is one of the sources and it enters the human body through inhalation (Rodriguez, 2016). These semi volatile lower chlorinated biphenyls have been detected in substantial quantities in outdoor and indoor air in cities, and older buildings having sealants containing PCBs (Turci et al., 2007; Herrick et al., 2004). Levels of high chlorinated PCBs were higher in Sialkot and Chakwal in comparison to other sampling districts. Consumption of food, particularly meat, fish, oil, dairy products and water contaminated with PCBs and cement industry exhaust are

main exposure routes of high chlorination biphenyls (Rodriguez, 2016; Xing et al., 2009). Increasing chlorine atoms in biphenyls rings tend to increase the lipophilicity of these PCB congeners (Xing et al., 2009). Therefore, higher concentrations in Chakwal may be linked with multiple sources viz; food, contaminated air, burning of solid waste and indoor PCBs exposure. Demographic Characteristics The mother and infant demographic characteristics are represented in Table III. The age of the volunteers ranged between 19 to 45 years and their BMI was 25.8 kg/m2.


Sampling Districts

2

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10

12

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-2

0

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a C

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Figure-3: Box whisker plots showing Homolog PCBs patterns in maternal serum (a).TriCBs, (b).Tetra CBs, (c). Penta-CBs, (d). Hexa-CBs

A. NAQVI ET AL. 258

Most of the women population was comprised of multiparae (65 %) with the baby birth weight of 2.21 kg, belonged to rural areas (61 %) and preferred to eat dairy products (34 %) and take milk daily (68 %). The results of one way ANOVA on the sum of PCBs, for the socio-demographic parameters viz; locality, age, BMI, the number of children, eating habits and fish intake are given in Table III. PCBs showed a significant relationship (P < 0.05) for milk intake, indicating the affinities of PCBs with lipophilic substances. A comparison of the present study with other studies on PCBs residues in maternal serum is given in Table IV. The mean concentration of ∑34 PCBs in the present study was 232.30 ng/g l.w. There exists limited literature about the PCBs residues in human

serum in India (Sharma et al., 2014) where most of the studies were focused on DDT, HCHs, and OCPs, however, Eguchi et al., (2012) reported PCB s concentration of 187 pg/g wet wt. which is less than the results of the present study. The concentration in the present study is comparable to the study reported from Japan (292 ng/g l.w.) and Korea (127.0 ng/g l.w.). The mean serum concentration of PCBs in the current study is lower than the reported from Belgium (595 ng/g l.w.) and Romania (680 ng/g l.w.; Table IV) indicating the indiscriminate past exposure of PCBs in European countries (Dirtu et al, 2009). The mean values of Tri-CBs were higher in Lahore (12.4 ng/g l.w) followed by Sialkot (10.47 ng/g l.w), Chakwal (9.1 ng/g l.w), Khanewal (8.04 ng/g l.w) and Okara (6.84 ng/g l.w).


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(g) Figure-4: Box whisker plots showing Homolog PCBs patterns in maternal serum (e). Hepta CBs,

(f). Octa CBs and (g). Deca CBs


The highest levels of Tetra-CBs were detected in maternal serum from Lahore followed by Penta-CBs and Hexa-CBs in district Chakwal (Figure 3). The box whiskers showed the higher prevalence of lower PCBs in urban areas decreasing towards rural population. A similar trend was observed in a previous study conducted in urban air and soil in Punjab Province (Syed et al., 2013). CONCLUSION

This study was among the first detailed efforts to report the concentrations of PCB in maternal serum from Punjab province of Pakistan. The mean concentration of PCB in the present study was 232.30 ng/g l.w. Among

different PCB homolog, Tetra-CBs were the major contaminants in blood serum of sampling population. The levels of PCBs were laid down at the lower end of the concentration range when compared with some of the developed countries. Sources of PCBs in the environment may include industrial leakage and volatilization from municipal dumping sites; therefore, levels of PCB in the present study were higher in the urban environment as in comparison to rural areas. The results of this study are the potential database for future studies and highlight the need for large scale assessment of PCB residues in mothers from Pakistan and their carryover to the fetus.

Table III: Socio-demographic characteristics and their relationship with PCBs concentrations

Subject Characteristics Mean± SD F value p Value

Age (years) 29.32 ± 5.77 0.563 0.901 Body Mass Index (kg/m2) 25.77 ± 5.3 0.594 0.848 Infant Birth weight (kg) 2.21 ±1.02 0.563 0.864 Total Lipids g/L 4.4±1.18 Parity 0.565 0.456 Primiparae 15 (34%) Multiparae 29 (65%) Location 1.257 0.269 Urban 27 (39%) Rural 17 (61%) Fish Intake 0.547 0.702 Never 9 (20%) Once in a week 4 (9%) Once in a month 14 (32%) Quarterly 17 (39%) Milk Intake 3.218 0.050* Never 3 (7%) Daily 30 (68%) Weekly 11 (25%) Eating Habits 0.176 0.982 Meat 2 (4%) Vegetable 13 (30%) Dairy 15 (34%) All of them 3 (7%) Vegetable and Dairy 9 (21%) Meat and Dairy 1 (2%) Meat and Vegetable 1 (2%)

* Correlation is significant at the 0.05 level (one way ANOVA).

A. NAQVI ET AL. 260

Table IV: Comparison of PCBs concentrations (ng/g l.w.) in maternal blood serum with other

studies from developed and developing countries

*Concentration in wet weight

Country Name

Sample Size

PCB congeners studied PCB Concentrations

References

Median Mean Pakistan 44 30, 37, 44, 49, 52, 54, 60, 66, 70, 74,

77, 82, 87, 99, 101, 105 114, 118,126, 128,138, 153, 156, 158, 166, 169, 170, 179, 180, 183 187, 189, 198, and 209

- 232.3 Present study

Sweden 201 105, 118, 138, 153, 156, 167, and 180

552 Glynn et al., 2003

Japan 89 74, 99, 118, 138, 146,153, 156, 163/164, 170, 180, 182/187, 194,199, 206, and 209

38 232

Inoue et al., 2006

Romania 53 101, 99, 118, 146, 153, 105, 138, 187, 184, 156, and180

- 680 Dirtu et al., 2009

Belgium 20 101, 99, 118, 146, 153, 105, 138, 187, 184, 156, and 180

- 595 Dirtu et al., 2009

Poland 22 28, 52, 74, 99,101, 105, 118, 138, 153, 156, 170, 180, 183, 187, 194, 196/203 and 199

79.4 83.0 Jaraczewska et al., 2006

Pakistan 34 118, 153, 180 and 170 13 34 Ali et al., 2013 Bangladesh 24 138, 146, 153 and180 26 - Mamun et al.,

2007 India - - - 0.187* Eguchi et al.,

2012 China 47 - 115 134 Bi et al., 2007 Taiwan 20 28, 52, 101, 138, 153, 180, 77, 81,

126, 169, 105, 114, 118, 123, 156, 157, 167, 189

36.41 28.2

Wang et al., 2004

Korea 40 18, 20, 28, 52, 66, 70, 74, 99, 101, 105, 111, 118, 138, 146, 153, 156, 164, 167, 170, 178, 180, 183 and 187

127 Kang et al., 2008


ACKNOWLEDGEMENT We are grateful to the State Key

Laboratory of Organic Geochemistry, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou, China for providing support for PCB analysis.

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