Organochlorine Residues in the Riverine Ecosystem of Pakistan
by
Syed-Ali-Musstjab-Akber-Shah, Eqani
A thesis submitted in partial fulfillment of the requirement for the degree of
Doctor of Philosophy in Environment Biology
Department of Plant Sciences Quaid-i-Azam University Islamabad, Pakistan 2012
07-08-2012Page I
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Dedicated
To
MMyy SSwweeeett MMootthheerr
aanndd
CCuuttee SSoonn JJaallaall ((LLaattee))
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DECLERATION
The material contained in this thesis is my original work, except where acknowledge. No part of this thesis has been previously presented elsewhere for any other degree.
Syed-Ali-Musstjab-Akber-Shah, Eqani
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Acknowledgments. All worships and praises are only due to the Almighty Allah, The compassionate, The merciful, Who gave us health, thoughts, strength and potential to achieve the recommended tasks. I owe my deep respect to Hazrat Muhammad (PBUH) and Panjtan Pak (A.S.), who are forever the torch of guidance and light of knowledge for mankind.
I would like to express my deepest gratitude and sincere thanks to Prof Dr. Asghari Bano (Dean/Chairperson) for her keen interest, providing her precious time and valuable insight for this practicum.
Special thanks are due to Dr. Riffat Naseem Malik (Research Supervisor) and Prof. Dr. Gan Zhang(Foreign Supervisor), State Key Laboratory of Organic Geochemistry, Guangzhou Institute of organic Geochemistry, Chinese Academy of Sciences for providing necessary lab facilities regarding the organochlorine residues analysis of water, sediment, and fish samples. Many thanks to Dr. Paromitta, Dr Linthoi, Dr. Jun Li, Dr. Liu Xiang, Mrs. Li Li, Mrs. Jenny, Mr. Chang, and Mr. Lewis for their support and guidance during laboratory work at CAS, China.
I feel most pleasure in expressing my heartiest gratitude for Dr. Ashiq Muhammad (Co- supervisor), Ecotoxicology Research Laboratory, IPEP, NARC Islamabad for his reliable suggestions, affectionate and encouragement during my time at the NARC. I also wish to thank the most respected Dr. Syed Zahoor Hussain, Richard Garstang (NPM/ Chief Organizer, PWP), Dr. Mumtaz H. Taqi, Dr. Rafiq (Director Fisheries), Dr.Azhar Rashid, Dr. Ali RazaGurmani, Dr. Abdul Qadir, Dr. Masood Arshad, Mr. Ahmad Khan (PWP), Mr. Usman (GIS expert), Mr. Naveed Qaiser, Dr. Rashid, Ghulam Abbass Haideri, Saadat Hassan Kunjal, Rabia Riasat and Dr. Maria Ali for their guidance and support during this project.
Special thanks to Jabir Hussain Syed, Mr. Adeel, Mr. Usman Ali, Raja Rizwan Ullaha, Hina Qayyum, M. Nadeem, Samiya Farooq, Mr. Z.A. Malik, Sadia Rasheed, Sidra Waheed, Azmat Zehra, Ambreen Syed, Sidra Rauf Minhas, Naila Zeb, Imran Khosa, Aqeel Kamran,Tabbsum Abbas (Gagoo), Iltaf Chadhar (Tafa), Iltaf Hussain (Shadda) and Usman Khan for their support during field and laboratory work. Deep gratitude to Mr. Azhar Khan (S.D.O. Irrigation Department) for providing necessarily inputs and residence during
Page IV exhaustive field sampling. I am also thankful to Sadqat Hussain (Driver, PWP), Javed Iqbal (Driver PWP), Muhammad Sadiq (Fisherman), Banarus Chacha (Fisherman) and Liaqat Abbasi (Lab Attendant), who really helped me in this project, even more than my expectations.
I ought to submit my sincere feelings to my family members Abu Jan, Shajar Batool (wife), Syed Ali Mushaf (brother), Shabia (sister), Syed Awon Abbas, Baji Kaosar and his family, Sweet Novee, Babboo (Kanita), Naqi Shah, Uncle Syed Mohsin Shah, Uncle Gazanfar, Uncle Hussnain, Uncle Qaiser, Uncle Jafri, Anti Ishtiaq, and Anti Asia for their moral support throughout the course of my research work.
I have no word of appreciation for my sweet cousin Laila Jafri for her moral/technical support throughout my PhD. studies. I ever remember the sweet memories of my respected cousin Syed Ahmad Raza Naqvi (Late), who really support, guide, and encourage me to achieve this mile stone.
EQANI SAMAS
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Table of Contents
Chapter # Title Page # Acknowledgments IV List of Figures X List of Tables XII List of Appendices XIV List of Abbreviations XV Abstract XVIII 1 Introduction and Review of Literature 1.1 General Introduction 1 1.2. Occurrence and distribution of organochlorine contaminants in water, sediment/soil and 3 biota from Pakistan: a review 1.2.1. Organochlorines contamination in the surface and ground waters from Pakistan 3 1.2.2. Organochlorines contamination in the sediments/ soils from Pakistan 5 1.2.3. Organochlorine residues in the biota from Pakistan 8 1.3. Problem Statement 14 1.4. Objectives of the Study 15 1.5. Structure of Thesis 15 2 Materials and Methods 17 2.1. Study area 17 2.2. Field strategies 18 2.3. Sample collection 23 2.3.1. Water 23 2.3.2 Sediment 23 2.3.3. Fish 23 2.4. Experimental section 24 2.4.1. Water 24 2.4.1(a). Physicochemical parameters 24 2.4.1(b). Extraction and clean-up 25 2.4.2. Sediment 25 2.4.2(a). Physicochemical parameters 25
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Chapter # Title Page # 2.4.2(b). Extraction and purification 26 2.4.3. Fish 26 2.4.3(a). Identification, sample preparation, and measurement of biological parameters 26 2.4.3(b). Extraction, lipid (%) determination and purification 27 2.4.3(c). Isotopic analysis 28 2.5. Chromatographic analysis 30 2.6. Quality assurance and Quality control (QA/QC) 30 3 Occurrence, Fingerprinting, and Risk Characterization of OCPs and PCBs in the 32 Surface Water of the River Chenab, Pakistan. 3.1. Material and Methods 32 3.1.1. Statistical analysis 32 3.2. Results 33 3.2.1. Physicochemical parameters of the surface water 34 3.2.2. Remarks on the level of distribution of OCPs and PCBs 35 3.2.3. Classification of the sampling sites 46 3.2.4. Source Identification using Factor Analysis (FA/PCA) 47 3.3. Discussion 53 3.3.1. Organochlorine distributional patterns in a global perspective 53 3.3.1(a). HCHs 53 3.3.1(b). DDTs and their sources 54 3.3.1(c). Chlordanes 55 3.3.1(d). Other OCPs 55 3.3.2. Difference in PCB congener profiles 56 3.3.3. Spatio-Temporal variation of OCPs and PCBs 57 3.4. Toxicity imposed due to OCPs and PCBs 60 3.5. Conclusion 62 4 Occurrence, Distribution, Possible Sources and Ecotoxicological Concern of OCPs 63 and PCBs in the Surface Sediments Collected From River Chenab, Pakistan. 4.1. Materials and Methods 63 4.1.2. Statistical analysis 63 4.2. Results 64
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Chapter # Title Page # 4.2.1. Characterization of sediments 64 4.2.2. Concentration profile of OCPs and PCBs in sediments 65 4.2.3. Classification of sampling sites 77 4.2.4. Source identification using Factor Analysis (FA/PCA) 78 4.3. Discussion 86 4.3.1. Characterization of OCP concentrations in sediment 86 4.3.1(a). HCHs 86 4.3.1(b). DDTs and its extent of pollution 87 4.3.1(c). Chlordanes 89 4.3.1(d). Other OCPs 89 4.3.2. PCB behavior and approximation of their possible sources 90 4.3.3. Spatial and temporal distribution of OCPs and PCBs in the sediment 92 4.4. Ecotoxicological concern for OCPs and PCBs in sediment of River Chenab 95 4.5. Conclusions 98 5 Organochlorine Pesticides (OCPs) and Polychlorinated biphenyles (PCBs) in Fresh 99 Water Fish Collected from the River Chenab, Pakistan: Levels, Accumulation Features, and Risk Assessment 5.1. Materials and Methods 99 5.1.1. Statistical Analysis 99 5.1.2. Bioconcentration factors (BCF) and Biota-sediment accumulation factor (BSAF) 99 5.1.3. Toxic equivalency calculations 100 5.1.4. Risk characterization 100 5.3. Results 101 5.3.1. The biological characteristics and tropic levels of collected fish species 101 5.3.2 OCP loads and their accumulation patterns 104 5.3.3 PCB concentrations and profile distribution 106 5.3.4. Dioxin-like PCBs contribution 107 5.3.5. Seasonal variation of OCPs and PCBs 118 5.3.6. Spatial variation of OCPs and PCBs 120 5.4. Discussion 123
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Chapter # Title Page # 5.4.1. OCP signatures in fish: general observation 123 5.4.1(a). HCHs 123 5.4.1(b). DDTs 124 5.4.1(c). Chlordanes 125 5.4.1(d). Other OCPs 126 5.4.2. Congener-specific profile for PCB and their environmental significance 127 5.4.3. Toxicity imposed due to dioxin-like PCBs 128 5.4.4. Bioaccumulation of OCPs and PCBs in the fish from River Chenab 129 5.4.5 Partitioning behavior of OCPs and PCBs 133 5.5. Risk assessment of OCPs and PCBs in the fish collected from River Chenab 135 5.5.1. Maximum Admissible Concentrations (MACs) 136 5.5.2. Minimal Risk Level (MRL) 136 5.5.3. Potential risk 137 5.6. Conclusion 143 6. General Discussion, Recommendations and Future Thrusts, and Broucher for Policy 144 Makers 6.1. General summary 144 6.1.1. Occurrence, distribution, and finger printing of OCP and PCB in the surface water and 145 sediment from River Chenab, Pakistan 6.1.1(a). Site-specific distributional patterns 146 6.1.1(b). Seasonal variation 148 6.1.2. Bioaccumulation features of organochlorine in the collected fish from River Chenab 149 6.1.3. Ecotoxicological concern of OCP and PCB in different environmental compartments 151 6.2. Concludary remarks 152 6.3. 6. Recommendations and future thrusts 154 6.3.1. For academic and research institutions 154 6.3.2. For national and regional environmental authorities 155 6.3.3. For water and sediment quality 155 References 157 Appendices 181 Research Publication 229
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List of Figures
Fig# Title Page # Fig.1.1. Showing the study locations discussed in the Chapter 1 along with the contribution of OCPs. 10 Fig.1.2. Showing the HCHs, DDTs, OCPs and PCBs levels in the different country parts. 11 Fig.2.1. Showing the sampling locations. 20 Fig.3.1. Showing the spatial distribution of HCHs, Chlordanes, DDTs and PCBs on different studied 40 location of River Chenab, Pakistan. Fig.3.2. Composition (%) of HCHs in water samples from all the sampling locations during (a). summer 41 and (b). winter. Fig.3.3. Composition (%) of DDTs in water samples from all the sampling locations during (a). summer 42 and (b). winter. Fig.3.4. Showing the crossplot of (DDD/DDE)/DDTs and DDD/DDE during both (a. summer and (b. 43 winter season. Fig.3.5. Composition (%) of PCBs in water samples from all the sampling locations during (a). summer 44 and (b). winter. Fig.3.6. Detection frequencies of all the OCPs in the water samples collected from River Chenab, 45 Pakistan. Fig.3.7. Levels of OCs (OCPs and PCBs) in the water samples collected during summer and winter 45 season. Fig.3.8. Hierarchical dendogram of sampling sites obtained using UPMGA as linkage method and 46 Euclidean distance matrix. Fig.4.1. Showing the spatial patterns of HCHs, Chlordanes, DDTs and PCBs detected in the sediments 71 from River Chenab, Pakistan. Fig.4.2. Composition (%) of HCHs in sediments from all the sampling locations during (a). summer and 72 (b). winter. Fig.4.3. Composition (%) of DDTs in sediments from all the sampling locations during (a). summer and 73 (b). winter. Fig.4.4. Showing the cross plot of (DDD/DDE)/DDTs and DDD/DDE during both (a. summer and (b. 74 winter season. Fig.4.5. Composition (%) of PCBs in sediments from all the sampling locations during (a). summer and 75 (b). winter. Fig.4.6. Levels of OCPs in the sediments collected during summer and winter season. 76
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Fig# Title Page # Fig.4.7. Concentrations (ng/g) of ∑PCBs in the collected sediment samples. ERL: the effect range low 76 (Long et al., 1995); TEL: Canadian interim sediment quality guideline for PCBs in fresh water sediment, threshold effect level (CCME, 1999). Fig.4.8. Detection frequencies of all OCPs in the sediments collected from River Chenab, Pakistan. 77
Fig.4.9. Cluster analysis for the sampling sites based on pollution level. 78 Fig.5.1. Concentrations (ng/g WW) of ∑HCHs, ∑Other OCPs, ∑Chlordane, ∑DDTs, ∑OCPs, ∑PCBs, 116 Lipid (%) and δ 15N (%) in collected fish species. Fig.5.2. Showing the composition of (a. HCHs, (b. Chlordane, (c. DDTs and (d. PCBs in the collected 117 fish species from River Chenab, Pakistan. Fig.5.3. OCPs residues (ng/g WW) in the fish species collected from River Chenab 119 Fig.5.3. Showing the levels of different OCP (ng/g ) residues in fish samples from River Chenab. 119 Fig.5.4. Showing the levels of different chlorinated biphenyles residues (ng/g WW) in fish samples from 120 River Chenab. Fig.5.5. Showing the spatial variations of different OCPs (ng/g WW) residues in fish samples . 122 Fig.5.6. Showing the spatial variations of different PCBs (ng/g WW) residues in fish samples from River 122 Chenab, Pakistan. Fig.5.8. Correlations of organochlorine compounds (ng/g WW) in muscle with δ 15N % signatures of 131 individual fish species collected from River Chenab, Pakistan. Fig.5.9. Hazard ratios for the daily fish consumption from River Chenab, Pakistan. a). For non-cancer 142 risks, b). For cancer risks. MEC, measured concentration.
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List of Tables
Table # Title Page # Table 1.1. Organochlorine concentrations (ng/L) in the surface/Ground water collected from the different 12 areas of Pakistan during 1990-2010. Table 1.2. Organochlorine concentrations (ng/g) in the surface Sediment/Soil collected from the different 21 areas of Pakistan during 1990-2010. Table 2.1. Habitat information and general description for all sampling locations. 30 Table 2.2. List of fish species analyzed for OCs contamination during both sampling campaigns (2007-09). 38 Table 3.1. OCP concentrations (ng/L) in surface water collected from River Chenab, Pakistan 46 Table 3.2. PCB concentrations (ng/L) in water samples collected from River Chenab, Pakistan. 47 Table 3.3. Comparison of OCP and PCB concentrations (ng/L) in the different bodies of world. 48 Table 3.4. Values of seven factor loadings for the studied OCs and quality parameters in the water of River 59 Chenab (during two seasons). Table 3.5. Values of seven factor loadings for studied OCs and quality parameters in the waters of river 60 Chenab (collected from the four regions). Table 3.6. Water quality criteria recommended by USEPA, 2002. 67 Table 4.1. Descriptive statistics of OCP concentrations (ng/g) in sediment samples (n = 25) collected from 68 River Chenab, Pakistan. Table 4.2. Descriptive statistics of PCB concentrations (ng/g) in sediment samples (n=25) collected during 69 summer and winter seasons. Table 4.3. Reported levels of OCP in the sediment samples collected from different parts of the world. 70 Table 4.4. Values of seven factor loadings for the studied OCs and quality parameters in the water of River 84 Chenab. Table 4.5. Values of seven factor loadings for the studied OCs and quality parameters in the water of River 85 Chenab (for four spatial groups). Table 4.6. Risk assessment of OCPs and PCBs detected in sediment samples of River Chenab by using the 97 mentioned sediment quality guidelines. Table 5.1. List of collected fish from River Chenab, Pakistan: biological parameters and nitrogen isotope 103 (δ 15N) composition for each species Table 5.2. Minimum-maximum, (mean ± Std) concentration (ng/g WW) of OCPs in freshwater fish species 109 collected from River Chenab, Pakistan. Table 5.3. Minimum-maximum (mean ± Std) concentration (ng/g WW) of PCBs in freshwater fish species 111
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Table# Title Page # Table 5.4. Minimum-maximum (mean ± Std) concentration (ng/g WW) of organochlorines in freshwater 113 fish species from different land uses (i.e. agricultural, urban and industrial) Table 5.5. Showing the level of OCPs and PCBs in the fish from different bodies of the World. 114 Table 5.6. Estimated daily intakes of OCPs and PCBs in fish by human (ng) using average concentrations 139 of OCPs and PCBs (ng/g WW) in fish collected fish samples. Table 5.7. Minimal Risk Level (MRLs) for OCPs and PCBS by Agency for Toxic Substances and Disease 140 Registry (ATSDR, 2005) according to oral exposure. Table 5.8. Cancer Benchmark Concentration (CBC), Oral reference dose (Oral RfD), and cancer slope 141 factor obtained from pervious study funded by USEPA. Table 5.9. Concentrations of dl-PCB congeners (pg WHO-TEQg-1 of wet weight) in fish collected from 140 River Chenab Pakistan.
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List of Appendices
Appendix # Title Page # Appendix 1.1. Major classification and chemical properties of OCPs and PCBs. 181 Appendix 2.1. Detailed description of study sites. 198 Appendix 2.2. Laboratory analysis for physico– chemical parameters of water. 206 Appendix 2.3. Laboratory analysis for physico-chemical parameters of sediments. 208 Appendix 2.4 Showing the chromatograms for each group of studied chemical (OCPs and 212 PCBs). Appendix 3.1. Basic quality parameters (average values) of water samples collected from River 223 Chenab, Pakistan and its adjoining tributaries. Appendix 3.2. Indicative ratios for different OCPs detected in the surface waters of River Chenab. 224 Appendix 3.3. Basic statistics of OCPs and PCBs in the water samples collected from different 225 regions grouped by cluster analysis. Appendix 4.1. Basic quality parameters (average values) of sediment samples collected from 226 River Chenab, Pakistan and its adjoining tributaries. Appendix 4.2. Showing the indicative ratios of different OCPs and their metabolites detected in 227 the sediment samples from River Chenab. Appendix 4.3. Showing the basic statistics of OCPs (ng/g) and PCBs (ng/g) in sediments 228 collected from different four regions separated by cluster analysis.
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LIST OF ABBREVIATIONS
ALK Alkalinity ATSDR Agency of Toxic Substances and Disease Registry CBC Cancer Benchmarks Concentration CB-TECs Consensus Based Threshold Effect Concentrations CCC Criterion Continuous Concentration CMC Criteria Maximum Concentration DO Dissolved Oxygen EC Electrical Conductively ERL Effect Range Low ERM Effect Range Median EU European Union FA/PCA Factor Analysis/ (Principal Component Analysis) FA/PCA Factor Analysis/Principal Component Analysis
FreeCO2 Free carbon dioxide GC/MSD Gas Chromatography/Mass Selective Detector HACA Hierarchal Agglomerative Cluster Analysis HACA Hierarchical Cluster Analysis -HCH -hexachlorocyclohexane -HCH -hexachlorocyclohexane -HCH -hexachlorocyclohexane HRs Hazard Ratios IRIS Integrated Risk Information System ISQG Interim Sediment Quality Guidelines KPK Khyber Paktoon Khaw LEL Lowest Effect Level LOD Limit of Detection LOQ Limit of Quantification MACs Maximum Admissible Concentrations MRLs Minimal Risk Values
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NO3-N Nitrate o, p-DDD ortho, para-Dichlorodiphenyldichloroethane o, p-DDE ortho, para-Dichlorodiphenyltrichloroethane o, p-DDT ortho, para-Dichlorodiphenyltrichloromethane OCPs Organochlorine Pesticides OM Organic Matter p, p-DDD para, para-Dichlorodiphenyldichloroethane p, p-DDE para, para-Dichlorodiphenyltrichloroethane p, p-DDT para, para-Dichlorodiphenyltrichloromethane PAHs Polycyclic Aromatic Hydrocarbons PCBs Polychlorinated Biphenyls. PECs Probable Effect Concentrations pH Hydrogen Potential 3- PO4 Phosphate POPs Persistent Organic Pollutants RfD. Oral reference dose RT Retention Time Sal Salinity SEL Sever Effect Level 2- SO4 Sulphate Spec.Cond Specific Conductivity TDS Total Dissolved Solids TECs Threshold Effect Concentrations Temp. Temperature Turb. Turbidity USEPA United States Environmental Protection Agency
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1. Conflict of Interests, Information on Funding Authority and Ethical Statement:
Higher Education Commission (HEC), Pakistan provided all the funding for this study and there is no conflict of interests among all the authorities of this document. All the experimental work was conducted at State Key Laboratory of Organic Geochemistry Guangzhou (SKLOG), Chinese Academy of Sciences (CAS).The student wish to thanks IRSP (International Research Support Program, HEC Pakistan), for providing supporting funds for foreign visits (PhD. research) and PWP(Pakistan Wetland Programme), UNDP for providing transport during this project. The study was carried out according to the recommendations and provisions of “non-profit Fisheries Development Board of Pakistan (Fish Inspection and Quality Control Act, 1997 and associated rules, 1998) for the protection of fish used for experimental and other scientific purposes.
2. Environmental Impact Statement.
The present manuscript presents the results of a recent study on the occurrence of organochlorine contaminants in the River Chenab, Pakistan, during the period 2007-09. The River Chenab is one of the most important water bodies in central Asia, associated with regions inhabited by more than 100 million people. Notwithstanding its importance for the region, the River Chenab has never been in detail studied for the occurrence of important compounds like PCBs and OCPs. Therefore, this study presents for the first time levels of the aforementioned compounds and offers insight on sources, seasonality, accumulation features, long-range transport and risk assessment. This study can also be an important tool to evaluate the contribution of the Pakistan towards “Global POPs Emissions” and also of the effectiveness of control measures taken by regional and international authorities under the Stockholm convention in developing countries.
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Organochlorine Residues in the Riverine Ecosystem of Pakistan
Abstract
Present study aimed to provide the detailed data on Organochlorine Pesticides (OCPs) and Polychlorinated Biphenyls (PCBs) concentrations, possible sources, their seasonal variation, bioaccumulation and risk assessment. For this purpose; water, sediment and fish samples were collected from 25 site locations at River Chenab, during May 2007 to November 2009. Water samples were extracted by liquid-liquid extraction while sediment and fish samples were Soxhlet extracted, cleaned by column chromatography and finally analyzed by Gas Chromatography/Mass Selective Detector (GC-MSD). Concentration (ng/L) in surface waters (including particulate phase) of River Chenab ranged from 27-1100 and 25-1200 for OCPs and 7.7-110 and 13-99 for PCBs during summer and winter, respectively. Dichlorodiphenyltrichloroethane (DDTs) exhibited the highest concentration in all water samples following by Hexachlorocyclohexane (HCHs), Chlordanes, PCBs and other OCPs in descending order, respectively. DDT and PCB levels in surface water exceeded existing criteria concentration guidelines of USEPA. The concentrations (ng/g, dry weight) in sediment ranged from 29-831 and 11.4-811 for OCPs and 9.3- 129 and 12.5-144 for PCBs during summer and winter seasons. The risk evaluation highlighted the burden of γ-HCH, heptachlor, dieldrin and DDTs levels in 70% sediments, while PCBs concentration in 35% of sediment exceeded Effect Range Low (ERL) and Threshold Effect Level (TEL) values. Different indicative ratios for organochlorine residues in both water, and sediment suggested current use, long range transport along with past application of these chemicals. Statistical analysis highlighted agricultural and industrial activities and municipal waste disposal as main source of OCPs and PCBs in the riverine ecosystem of River Chenab. The level of OCPs and PCBs from feeding tributaries (i.e. S20) was relatively greater as compared to those collected from the River Chenab mainstream. The detection frequencies and concentrations of all OCPs and PCBs in water and sediment were higher in winter than those collected in summer season. OCPs and PCBs concentration (ng/g WW) ranged from 13-106 (mean; 38) and 3.1-93.7 (mean; 20) for five herbivorous fish species and 21.5-365 (mean; 148) and 2.4-108 (mean; 30) for six carnivorous species. DDTs, β-HCH,
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Chlordanes and PCBs detected in fish from distinct trophic levels highlighted biomagnifications. Risk assessments of OCPs and PCBs indicated that fish intake would pose a health risk to human. The findings of present study highlighted the contamination of OCPs and PCBs in River Chenab and there is an urgent need to mitigate the situation. The results can also be helpful for future management of other fresh water resources in the same region.
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Chapter 1 Introduction and Review of Literature: 1.1. General Introduction Environmental behavior and ecotoxicological effects of Persistent Organic Pollutants (POPs), including Polychlorinated Biphenyls (PCBs) and Organochlorine Pesticides (OCPs), such as Dichlorodiphenyl Trichloroethane (DDTs) and its metabolites, Hexachlorocyclohexane (HCHs) isomers and some other OCPs like chlordanes, heptachlor, dieldrin, aldrin, endrin, endosulfan etc., in the freshwater ecosystems are of particular importance due to their negative impacts on quality and integrity of an ecosystem ( Kumi et al., 2010; Malik et al., 2011). Major classification and chemical properties of OCPs and PCBs are given in detail in Appendix 1.1.
Organochlorines (OCs) are toxic to biological organisms due to their high lipophilic properties (Lopez et al., 2005); tend to adsorb on particulate matter due to low water solubility (Yang et al., 2005) and ultimately transferred to higher trophic levels through food chain (Zhou et al., 2006; Malik and Zeb, 2009). Organochlorines also accumulate in abotic matrices such as soil and water sediments which function as both a sink and fresh source of their contamination (Malik et al., 2011). In addition, sediments may also serve as a storage compartment for long- term release, reflecting the history of discharges to an area (Ranjendran et al., 2005).
OCPs along with industrial-based chemicals such as PCBs have been discharged in large quantities into environment for the last 50 years, mainly to control agricultural pests, insect- borne diseases, and termites (Malik et al., 2011). OCPs and PCBs can end up into the aquatic environment through various routes, such as industrial and municipal wastewater discharge, urban and agricultural runoff; oil leaking and atmospheric deposition (Cao et al., 2010). Concerning the aquatic ecosystem, OCPs and PCBs might be distributed into the dissolved or particulate phase. However, the occurrence of OCPs and PCBs into aquatic environment such as riverine ecosystem is of particular importance due to the direct (through consumption of contaminated water) and/or indirect (e.g. through consumption of fish) exposure to human being. In aquatic ecosystem, monitoring of biological organisms (e.g. fish) serve as an important indicator; where there is vertical transport of OCs leading to accumulation in the benthic
Page 1 organisms and ultimately into organisms at higher trophic level via bioconcentration and biomagnifications (Kalyoncu et al., 2010; Olsson et al., 2000).
The exposure of POPs such as OCPs and PCBs to living organisms including humans have been accounted to cause variety of effects such as reproductive, carcinogenic, immunological and neurological problems (Shaw et al., 2006; Kalyoncu et al., 2009; Sharma et al., 2009). Organochlorines are also known as “endocrine disrupting” chemicals, cause hormonal disruption in wildlife and humans, and associated with disorders of reproductive organs and breast cancer in women (Tanabe et al., 2002; Letcher et al., 2010). However, monitoring of chlorinated pesticides and PCBs (dioxin-like compounds) in the environment and foodstuff is of particular concern for the evaluation of their potential health risk to humans and other organisms.
Organochlorinated chemicals are of local, regional and international concern, due to long range global atmospheric transport (Zhang et al., 2008). Beside persistence in environment, organochlorine contaminants move considerable long distances and get accumulated in vegetation, soil and water bodies of high latitudes by global distillation phenomenon, particularly in Polar Regions (Wania and Makay, 1993; Simonich and Hites, 1995; Nizzetto et al., 2008). Realizing the carcinogenicity, long range transport and their toxic behavior; Stockholm convention (2001) banned the use and production of twelve persistent organic pollutants (POPs) including eight pesticide viz. DDT, aldrin, dieldrin, endrin, chlordane, heptachlor, mirex, toxaphene, PCBs, Dioxins and Furans. Being signatory of the Stockholm convention, the use of OCPs and PCBs were banned in Pakistan. However, these chemicals are still in use due to cheapest production and for some purposes POPs remain highly effective. Pakistan also holds one of the largest stockpiles of outdated pesticides in the world. According to Ahad et al. (2010), the quantity of outdated pesticides have been estimated to be 3,805 tonnes in Punjab, 2,016 tonnes in Sindh, 179 tonnes in NWFP and 128 tonnes in Balochistan while stock of 178 tonnes lies with the Federal Department of Plant Protection, out of which 2.54 tonnes are OCPs. On the other hand, demolished factories of OCPs in different parts of the country, where thousand kilograms of DDTs and other OCPs were dumped, are also one of the important sources of the OCPs pollution in the different environmental compartments of Pakistan.
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1.2. Occurrence and distribution of organochlorine contaminants in water, sediment/soil and biota from Pakistan: A review
In Pakistan a limited number of studies have been conducted on POPs contamination of different environmental matrices in Pakistan. Major hindrance to produce the data of POPs is the lack of technical expertise and laboratory facilities in the country. Only few laboratories have sufficient analytical facilities that can measure specific organochlorine chemicals in traces. However, in spite of all such limitations, few studies reported on the level of organochlorine chemicals from Paksitan. During last 40 years, over 47 scientific studies were conducted which reported the residual effect of OCPs and PCBs in different environmental matrices in Pakistan. Among these studies, only 10 studies provided the OCPs-contaminated data from different water bodies in the country. The results of published studies conducted at different locations and intervals from 1990-2011 is discussed in detail below.
1.2.1. Organochlorines contamination in the surface and ground waters
In Pakistan, only few studies have been conducted to investigate the occurrence and distribution of OCPs in surface and ground waters (Table 1.1). For the very first time Praveen and Masud (1988a) reported the organochlorine contamination in the cattle drinking water samples collected from Karachi ranging from traces-16.7 µg/L. Among the studied chemicals, Benzene Hexachloride (BHC) was the dominant of all organochlorine with maximum concentration of 16.7 µg/L and detected in 8% of the total samples. On the other hand, DDTs were detected in 3 % of studied samples. The authors suggested the source of BHC from surrounding industrial activities, while DDE and DDT may come from surrounding contaminated soils. Analysis of shallow ground water samples collected from Faisalabad district resulted in the contamination of endrin (0.1-0.2 µg/L) in 30 % of collected samples (Jabbar et al., 1993). The endrin due to its insecticidal properties is mainly used for crop protection. These samples were collected from cotton growing area, which reflected its large scale use.
Another study by Ahad et al. (2001) from cotton growing area of Multan district reported the contamination of endosulfan (0-0.13 µg/L) and γ-HCH (0.1-0.11 µg/L) in the ground water.
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In this study most of the water samples exceeded the MRLs (maximum residual levels) for each compound, suggesting the extensive use of these pesticides in these areas. In Pakistan, ground water contamination is more likely to happen because of many factors including soil characteristics, shallow water tables, and intensive spraying (Jabbar et al., 1993). For example, ponding irrigation is very common in Pakistan resulted in contamination of surface and ground water due to high water flow infiltration. Tariq et al. (2004) reported the contamination of endosulfans and some other pesticides in the surface and ground water samples collected from different agricultural districts of Pakistan viz; Bhawalnagar, Rajan pur, Muzafarghar, and Dera Ghazi Khan. This study reflected wide scale historical use of these pesticides, which may reach into surface/ground water via surface runoff from agricultural fields, direct spray on water ponds to control mosquitoes and other insects, infiltration of different chemicals via heavily rainfall (Ahad et al., 2001; Tariq et al., 2004). Pesticide application has also been reported from plains of Sindh and KPK (Khyer Paktoon Khaw) provinces. Ahad et al. (2000) conducted a study in the tobacco growing areas of Mardan district in KPK, which showed the relatively higher concentration of different pesticides ranging from 0-0.45 µg/L in ground water samples. Among targeted pesticides in this study, only one organochlorine chemical namely endosulfan was studied which occurred in more than 30% of samples and ranged from 0.0-0.02 µg/L.
OCPs (DDTs in particular), are still in use for sanitation campaigns against vector borne diseases in developing countries including Pakistan, where its illegal use cannot be ignored (Malik et al., 2011). On the other hand, large quantities of obsolete pesticide are also stored at many locations in the country, which resulted in the environmental contamination (Malik et al., 2011). Asi et al. (2008) reported the existence of DDTs and its metabolites in the water samples collected from different areas of Punjab province. The DDTs and its metabolites (17-1006 ng/L) were found frequently in all the collected samples. The high concentration of DDT metabolites in this study reflected the historical use of DDTs in different areas of Punjab, Pakistan. Few samples collected from district Khanewal, a cotton growing area, showed high values of p, p/- DDT (1000 ng/L), which is about 10 times more than the allowable limits set by USEPA and European Union. Ahad et al. (2010) collected water samples in different areas of country near obsolete stores and reported high concentration of OCPs (µg/L) ranging from 0-15.7 (median; 0.29), 0.25-0.78 (median; 0.36), and 0.11-0.83 (median; 0.21) for KPK, Punjab, and Sindh
Page 4 provinces, respectively. Maximum concentration (15.7 µg/L) were found in the samples collected from Amangrah demolished DDTs manufacturing unit which resulted in the sever contamination of DDTs of surrounding areas. Generally, OCPs were mainly imported from Europe to Pakistan, but to meet the local demands of DDTs, it was also produced locally in different parts of country. Some manufacturing units are reported in KPK (Nowshera), Punjab (Lahore) and Karachi (Tariq et al., 2007). After ban on the production and use of DDTs in world, these manufacturing units have been demolished in Pakistan and higher quantities of DDTs and some other pesticides were dumped in the surrounding. On the other hand, improper storage and handling of abandoned pesticide in such demolished unit had resulted greater chances contamination risk. Jan et al. (2009) documented such worst situation in the surrounding area of a demolished factory in Nowshera. In this study, 9 samples were analyzed for DDTs contamination and concentration ranged from 70-400 µg/L. By comparing such high concentration with other studies conducted nearby demolished factories (Asi et al., 2008; Ahad et al., 2010), clearly indicated that the results reported by Jan et al. (2009) are extremely high and do not agree with other studies.
The contamination data of organochlorines from the fresh water resources of Pakistan is still sprase. However, a study conducted by Iram et al. (2009) recorded the high concentrations of p, p/-DDT (0.96-2.87 µg/L) and DDD (0-2.5 µg/L) from the Rawal and Simly Lake located in Islamabad. Such high concentration reflected recent as well as historical use of DDTs in the vicinity of these lakes.
1.2.2. Organochlorines contamination in the sediments/ soils
In Pakistan, only few studies reported the status, distribution, and sources of organochlorine contaminants in soil and sediment collected from different locations of the country (Table 1.2).
During 1991, first systematic study was conducted by Bano and Siddique which documented the concentration of some OCPs (Table 1.2) in sediments collected from Karachi coastal area. Trends of detected OCPs were followed as: dieldrin, DDT, heptachlor, β-HCH and
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DDE in decending order. The samples were collected from five sampling stations viz: Karachi, Korangi, Creek area, Hawksbay and Manora channel, with higher concentration measured from sediment of Hawksbay and Korangi. Another study by Jabbar et al. (1993) also reported similar results as reported by Bano and Siddique, (1991). In this study the samples were collected from cropland of KPK province and results showed the higher concentration of dieldrin (3.1-9.6 ng/g) as compared to any other OCP (Table 1.2). In KPK pesticide are sprayed on different crops (tobacco, maize, sugarcane etc.) from many years and also reported by Tariq et al. 2007. In present study, authors suggested that higher concentration of dieldrin and other OCPs (especially DDT metabolites) may be originated from agricultural wide scale usage of these chemicals.
Pakistan exhibits diverse climatic conditions and is enriched with plenty of freshwater resources. Rivers, canals, streams, and dams are the major water resources in Pakistan, which irrigate most of the cropland and are used for drinking purposes (Qadir and Malik, 2008). Streams locally known as “Nullahs” which feed the main rivers and flow through alluviul plains of the country. These streams are very important especially in flooding season, as these local nullahs collected rain water from plains of Pakistan, which used for irrigation purposes. However, these nullahs receive large amounts of industrial and municipal effluents without prior treatment from different small industrial and municipal drains, resulted in the degradation of natural aquatic ecosystem including streams and rivers (Malik and Nadeem, 2011). Nullah Dehg, an important water feeding tributary of the River Ravi located near Lahore, receives industrial waste from different industrial areas which deteriorates the quality of this stream. Tehseen et al. (1994) collected sediment samples from Dehg Nulla Lahore to present the contamination status of OCPs and reported highest concentrations of DDT ( 62-2041 ng/g), DDD (181-2032 ng/g) and DDE(0.8-82 ng/g). DDT values recorded in this study were higher in comparison to literature reported throughout the world. The authors suggested sources from DDT- formulation factory in vicinity of the sampling area (Kala Shah Kaku) from where DDT entered into the Nullah (stream) system by surface runoff. The DDT manufacturing unit was established at Ittehad Chemicals in 1973 and its annual production was 2,020 metric tons per year. This industry was later on merged into Ittehad Chemical Industries Kala shah Kaku, Lahore. The liquid and powdered DDT products of the industry were used for various anthropogenic activities i.e. to kill insects and in particular to the agricultural activities. In 1994, this
Page 6 manufacturing unit of DDT was closed, as use of OCPs was banned in all over the world. However, the industry is still in operation and most surprisingly thousands kilogram of the chemicals were dumped off in the surrounding soils of Ittehad Chemical Industries and Syed and Malik, (2011) reported very high concentration of DDTs and other OCPs including HCHs and cyclodiens (Table 1.2) in the surrounding area of this factory.
Ahad et al. (2010) reported on the concentration and distribution pattern of OCPs in soil collected from obsolete pesticide stores in three provinces of Pakistan. The results clearly indicated that soil samples mainly contained DDTs followed by lindane and heptachlor. The contamination levels for KPK, Punjab, and Sindh provinces were in range of 247–9,157 mg/kg, 214–10,892 mg/kg, and 86–1,139 mg/kg, respectively. This study evidently indicated the alarming situation regarding the contamination of toxic chemicals. This study also stresses the urgent need to control the emission of these pesticides in the surrounding environment of these chemical warehouses.
Sanpera et al., (2002) measured OCPs levels in the sediments collected from Haleji Lake and Thatta (Sindh) ranging from 0-10 ng/g. DDTs (0-6.5 ng/g) were found predominately in all the collected samples followed by HCB (0-2.5 ng/g). This study indicated intensive agricultural activities in the surrounding area as an important source of the OCPs. This study is unique as this was the first study which reported PCBs contamination in the sediments from Pakistan (Haleji Lake and Thatta of Sindh province).
Malik et al. (2011) also documented the OCPs contamination in sediments of three water bodies of Pakistan viz; River Chenab, River Ravi, and Rawal Lake. Overall pattern of detected contaminants from three locations followed the order as: Rawal Lake, River Ravi and River Chenab in descending order. Residual concentrations of DDT metabolites (N.D. - 64.6 ng/g) were higher when compared to its isomers (N.D. - 42.4 ng/g), showing both recent and historical use of this chemical. The source of high of DDTs concentrations into Rawal Lake are suspected Lake from nearby health center namely NIH, where these chemicals were extensively used and stored in past to eradicate the malarial infection.
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1.2.3. Organochlorine residues in the biota
Organochlorines contamination in various food commodities from Pakistan have been reported including milk (Parveen and Masud, 1988b), fruits and vegetables (Masud and Hassan, 1995; Hussain et al., 2002). Few studies also reported the residues of organochlorine (OCs) in blood serum and fat samples in residents of the Balochistan, Sindh and Punjab Provinces (Naqvi and Jahan, 1999; Parveen and Masud, 2004) and they have also correlated higher incidence of cancer rate in Pakistan during the years 1994 to 2002. In another study, Pesticide Poisoning Center, Nishtar Hospital, Multan, Pakistan (Ahmad et al., 2002) has registered 370 patients that were pesticide victims (73% males and 27% females). Exposure to OCs associated with fish consumption contributed large percentages (67% of chlordane and 60% of PCBs) in the total exposure from all foodstuffs (Dougherty et al., 2000). However, to our knowledge, only few studies were reported on organochlorine-contaminated fish from different water bodies of Pakistan. Munshi et al. (2004) reported the organochlorine accumulation in marine water of Pakistan. This study was carried out at Karachi coastal area including Korangi creek, Bulleji, Manora channel, Keamari and Ghasbunder harbor and two edible fish species (Mugil and Mushka) were analyzed. Generally, the trends of organochlorine chemicals in this study were followed as: DDT (100 µg/g lipid wt.), PCBs (80 µg/g lipid wt.), Cyclodiens (30 µg/g lipid wt.) and HCHs (5 µg/g lipid wt.). DDTs concentration (upto 309 µg/g lipid wt.) in fish samples (n=155) were relatively high but these concentration are still lower than highly contaminated coastal areas of the world. The authors suggested that both urban and industrial activities in vicinity of Karachi harbor are the major sources of pollution. Another study by Saqib et al. (2005) also reported the contamination of organochlorine in the muscle, liver, and gills of the fish (3 Labeo species) collected from Kalri and Haleji lakes, Pakistan. These results are consistence with other studies conducted in the region (Sanpera et al., 2003), showed the dominance of DDTs in all the samples. Some other OCPs including dieldrin and aldrin were also detected in some of samples, and authors descried that agricultural runoff from surrounding cropland is the main source of pollution in this area.
Colonial water birds at the upper level of the food chain make them a suitable indicator of persistent organic environmental contamination (Sanpera et al., 2003; Malik et al., 2011) and
Page 8 have been suggested as useful organisms for monitoring POPs. Ardeidae are important indicators of environmental degradation caused by toxic chemicals in wetlands, and there are only a few studies in Pakistan that use colonial water birds as an ecological indicator of OCP contamination. Sanpera et al. (2003) measured POPs in eggs of Bubulcus ibis (Cattle egrets) from selected wetlands of Pakistan. This study documented the biomagnifications of organochlorine in the food web of Pakistan and showed high levels of DDTs (mean; 750 ng/g; lipid wt.) in the prey and eggs samples of Bubulcus ibis (Cattle egrets). Another recent study by Malik et al. (2011) is extremely important and evidence of the bioaccumulation of these OCPs (especially DDTs) in the food web of Pakistan by using Bubulcus ibis (Cattle egret), a water colonial bird, as bio- indicator. DDT residues can cause both acute and chronic health disorders including thinning of eggshells and reduction of hormonal level necessary for female birds to lay eggs. DDT may cause direct mortality of birds by directly affecting the nervous system even in birds like robins that feed relatively low on the food chain (Burger et al., 2007; Malik et al., 2011).
In Pakistan little documented information is available on PCBs contamination in different environmental matrices. The scenario drew attention that POPs contamination must be considered as an important environmental issue due to their excessive use in agricultural and industrial sector. Detailed studies related to monitoring, assessment, distribution trends, sources identification and ecotoxicological effects on biological organisms are urgently required. This scenario highlighted that proper attention should remunerated to promote the scientific research which provides necessary information on the current levels and distribution of these chemicals in different environmental compartments, alternative products and processes to reduce and eliminate OCPs and PCBs.
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Fig.1.1. Showing the study locations discussed in this chapter along with the contribution of OCPs.
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Fig.1.2. Showing (a. HCHs, (b. DDTs, (c. ∑OCPs and (d. PCBs level in different country parts reported from different studies.
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Table 1.1. Organochlorines (µg/L) in the surface/Ground water collected from the different areas of Pakistan (1990-2011). No. of Sampling location Water type β-HCH γ-HCH BHC Heptachlor Endrin Dieldrin DDE DDD DDT Reference samples Traces- ParveenMasud, Karachi Surface water 79 Traces Traces 16.7 1998a Faisalabad Shallow ground water 10 0.1-0.2 Jabbar et al.,1993
Mardan Ground water 12 Ahad et al., 2000
0.0- Multan Ground water 12 Ahad et al., 2001 0.11 Punjab, Districts Ground water 37 Tariq et al., 2004
Surface and Ground Punjab, Districts 21 0-0.72 0-0.82 0-1.06 Asi et al., 2008 Water Surface and Ground NWFP, Nowshera. 9 70-400 Jan et al., 2009 Water Obsolete Pesticides, Surface and Ground 0.06- 33 0.09 0.16 0.02-0.17 0.05 0.06 0.04 0.03 Ahad et al., 2010 Pakistan. Water 0.11 0.8- 0.96- Rawal Lake, Islamabad. Surface water 0 Iram et al., 2009 2.39 2.87
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Table 1.2. Organochlorines (ng/g) in the surface Sediment/Soil collected from the different areas of Pakistan (1990-2011).
Sampling HCHs HCB Heptachlor Hepta.Epoxide Endrin Dieldrin DDE DDD DDT PCBs References location Coastal area, 0.06- Bano and 1.1-3.5 2.7-9.2 0.5-12.5 2.7-9.2 Karachi 0.16 Siddique,1991 Cropland Soils 0.2 3.1-9.6 0-2 2 0-0.2 Jabbar et al.,1993 Tesheen et al., DeghNullah, Traces 0.1-94 0.8-82 181-2032 62-2041 1994 Lahore
Haleji lake, Sanepra et al., 0.4-1.7 0-6.5 Traces Thatta, Sindh 2003 Sanepra et al., Taunsa barrage 0.3-0.9 2004 Rawal Lake, 0-19.5 0-16.13 0 0-32.4 0-35.8 0-42.2 Islamabad River Chenab, Malik et al., 2011 0-9.2 0-41.6 0-11.3 0-14 0-19.1 0.20 Pakistan River Ravi, 0-8.3 0-42.3 0-13.2 0-24.5 0-19.4 0-28.1 Pakistan Kala Shah Syed and Malik, 0-119 0-31 0-114 0-7.6 47-146 55-212.5 0-1537.9 Kaku soil 2011
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1.3. Problem Statement
The Chenab River being the most important Rivers of Punjab region, the most densely populated province of Pakistan, providing water for domestic, agricultural and industrial purposes (Bhatti and Latif, 2009). The quality of this riverine ecosystem has degraded owing to a number of factors such as less inflow of water due to construction of dams, release of industrial effluents, addition of pesticides and fertilizers through runoff from surrounding agricultural lands etc. putting negative impact on ecological integrity of these aquatic resources, and well-being of human. Untreated toxic industrial effluent and municipal sewage from seven districts of Punjab is being added into the River Chenab everyday through 15 point sources, (among which four points are in district Gujrat, two in district Mandi Bahauddin, two in district Jhang, two in district Chinot, three in district Multan and one each in district Hafizabad and district Sargodha), causing serious threats to a variety of useful uses including irrigation, drinking and sustenance of aquatic life and river ecosystem. On the other hand, catchment area of River Chenab is very important in terms of agriculture which form back bone of country’s economy and major crops i.e. cotton, rice, sugarcane, wheat etc. is cultivated in this area from many years. Pesticides and agrochemicals are used in this area for profitable and sustainable farming, which also resulted in the contamination of surrounding environment. Catchment area of River Chenab, also hold largest stockpiles/heaps of OCPs and demolished pesticides factories with thousand kilograms of DDTs, which is suspected to contaminate the riverine ecosystem (Malik et al., 2011).
Although the occurrence and sources of OCPs and PCBs have been widely studied in most of the developed or developing countries, but this is not the case for Pakistan and research gaps are highlighted comprehensively in Section 1.4. The River Chenab is one of the most important water bodies in central Asia, associated with regions inhabited by more than 100 million people. Notwithstanding its importance for the region, the River Chenab has never been studied in detail for the occurrence of important compounds like PCBs and OCPs. Therefore, this study presents for the first time provide details on status of the aforementioned compounds and offers insight on sources, seasonality, accumulation features, long-range transport and risk assessment. This study can also be an important tool to evaluate the contribution of the Pakistan
Page 14 towards Global POPs emissions and also of the effectiveness of control measures taken by regional and international authorities under the Stockholm convention in developing countries.
1.4. Objectives of the Study
Following are the main objectives which have been focused: 1. To evaluate the occurrence, finger printing, and source apportionment of OCPs and PCBs in surface waters and sediments collected from River Chenab, Pakistan. 2. To investigate the level, distribution and accumulation patterns of OCPs and PCBs in fish collected from River Chenab, Pakistan. 3. Investigation on the potential health risk of OCPs and PCBs measured in water, sediment, and freshwater fish species collected from River Chenab, Pakistan.
1.5. Structure of Thesis
The thesis presents the level, spatial and temporal distribution and potential sources of OCPs and PCBs in the surface water, sediments, and fish collected from the River Chenab during the period, 2007-2009 (summer and winter). Spatio-temporal variations and source identification are assessed by multivariate statistical techniques such as Hierarchical Agglomerative Cluster Analysis (HACA), Factor Analysis/Principal Components Analysis (FA/PCA). Furthermore, integrated multivariate approach applied on the OCP and PCB- contaminated data addresses associated health risks posed to the aquatic and human life, risk managements and restoration of River Chenab, Pakistan. In order to scientifically respond to the above mentioned aspects, the present research thesis is divided into following six chapters; each one will be focusing on specific objectives. Chapter 1 describes the general introduction of organochlorines, background of the research including a worldwide overview of organochlorines, and its status in Pakistan and implication of organochlorines use are discussed along with objectives of research.This chapter also shapes the problem statement in the current study area. Chapter 2 provides detail description of study area in relation to topography, climate, geology, history, drainage pattern, land use, and source of pollution into river body. This chapter also
Page 15 describes the strategies for sample collection and transportation. Moreover, this chapter provides detailed analytical procedure to estimate the physicochemical parameters, OCPs, and PCBs in the water, sediments, and fish. Chapter 3 highlights the occurrence, fingerprinting, and status of OCPs and PCBs in surface water of River Chenab, Pakistan and its adjoining tributaries. Through use of simpler and/or more sophisticated techniques, enable to offer insights on major OCPs and PCBs sources, their seasonality and risk assessment. Chapter 4 provides the data on levels, behavior, spatio-temporal variation, and source appropriation of OCPs and PCBs in surface sediments collected from River Chenab, Pakistan and its adjoining tributaries. Risk assessment is also accomplishes by using different criteria guidelines provided by different organizations and previous studies which showed potential health risks to the aquatic biota and human life. Chapter 5 describes the accumulation features of OCPs and PCBs in different edible fish species collected from selected sites of River Chenab, Pakistan. This chapter also presents ecotoxicological effect of OCPs and PCBs associated with the consumption of contaminated fish; moreover, a brief discussion reflects the trend of organochlorine occurrence in relation to δ 15N % and other biological characteristics of fish. Chapter 6 provides an overview and outcomes of the present study alongwith recommendations for future studies and Broucher for the policy makers.
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Chapter 2 Material and Methods:
2.1. Study area
The River Chenab, one of the main rivers of Punjab province Pakistan, originates from snowcapped peaks of Himalaya and traversed from north–east to south–west direction. The River Chenab is one of the major tributary of Indus River in Pakistan. It enters Pakistan at the upstream of the rim station Marala and total catchment of Marala upstream is nearly 38,000 Km2. Chenab transverse 560 km, through densely polluted and industrial cities (Sialkot, Gujarat, Gujranwala, Faisalabad, Jhang, Khanewal and Multan) of Punjab province of Pakistan.
The catchment area of River Chenab is one of the world’s most intensely farmed areas, comprises the extreme north part of the great Indo-Gangentic plain, a large and fertile plain, encompassing the most populous parts of Pakistan. The plains have been formed by Indus and its tributaries (River Chenab, River Ravi, River Jhelum, and River Sutlej) and known as upper Indus plain. The sediments are loamy in the northern reach and become silty southward. The sediments are characteristically calcareous and of mixed mineral make-up. The soils are typically deep, calcareous, weekly structure, bright coloured and with a zone of secondary lime accumulation at or near one meter depth. In the sub-humid part, upper part of sodium has been decalcified.
The study area has sub-tropical continental type of climate, gradually changing from sub- humid in the north to arid in extremely southern part. The Kharif represents April 01 to September 30 (summer season) and the Rabi is based on flows in October 01 to March 31 (winter season). The climate of the study area fluctuates all over the year; Temperature is highest in May and June over 45 °C, somewhat moderate (30 – 35 °C) in July to mid September and lowest during December and January which may drop to 4 oC. The mean annual rainfall ranges from 225 – 550 mm throughout study area and there are two sources of rainfall in Pakistan i.e., the monsoon and the western depression, the former takes place from July to September and the latter from December to March. Humidity is lowest during May and June and increases
Page 17 substantially from summer monsoon till September and low in October and November while moderate for remaining of year. Sunshine hours are maximum in May and June and minimum in December and January. Dust storms are one of main characteristics of the study area and can be one of the possible source of sedimentation.
River Chenab provides a diverse range of internationally important wetland habitats that support abundant aquatic life i.e. migratory birds, amphibian and fish species. River Chenab is a habitat of a diversity of fresh water fish, a number of which are edible; such as Cirrhinus mrigale, Catla catla, and Culpisoma naziri, etc. River Chenab is not only the provider of water for agriculture and drinking purposes, but also holds up the livelihood for a number of fisher folk communities. The river banks are also breeding grounds for large number of migratory birds (Egrets sp.) as a large no. of colonies of water colonial birds has been reported from River Chenab. The banks are cultivated with crops in both seasons as well as several orchards on the banks depend on the river water.
Agriculture is one of the most important sources in the study area as major per capita income of Pakistan depends exclusively on this source. Present study area is extremely important in terms of productivity and the conventional cropping pattern adopted in the adjoining areas i.e. Kharif and Rabi. The major Kharif cash crop is cotton which contributes a lot in foreign exchange through the export of cotton products. Other Kharif crops are rice, sugarcane, maize, and millet while Rabi crops are wheat, gram, mustered, and toria. The main fruits grown in the study area are mango, orange, guava; date palm and lemon and foremost vegetables are potatoes, chilies, onions, brinjals, turnips, lady fingers etc. To enhance the productivity of crops, local farmers use different pesticides and herbicides to control pests, weeds and diseases, these pesticides and herbicides enters into the river through surface runoff through different non point sources causing water pollution.
2.2. Field strategies
After a detailed preliminary survey of the study area, twenty five sampling sites were selected on the river stretch of about 560 Km (from Marala Headworks to Punjnad Headworks),
Page 18 from where water, sediment and fish samples were collected in summer and winter season during the period of two years (2007-09). The selection of each site location was based upon anthropogenic activities in the catchment and were marked using a GPS (Global Positioning System, Garmin). A mobile Weather Station was also used to record the climatic data at each site and presented in Table 2.1. Among these, 19 sampling sites were located on the Chenab’s mainstream and 6 on the adjoining tributaries i.e. 2 sites on River Ravi, 2 sites on River Jhelum, 1 site on River Sutlej and 1 site is located on the industrial drain dumped into the River Chenab near district Jhang (Fig. 2.1; Table 2.1).
The sampling activities were scheduled by keeping in view the detailed weather forecast to avoid the rainy days. The sampling was carried out in the morning hours (usually from 5.00- 12.00 pm) and in night to ensure the maximum catches of fish samples. Generally, 3-5 sites were sampled per day when the river flow is normal and sampling lasted for 7-10 days. All collected samples were placed on ice after sampling and transferred to the laboratory directly and were kept at recommended conditions (temperature) until further laboratory analysis. The detailed description of each studied sampling site is given in Appendix 2.1.
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Fig. 2.1. Showing the sample collection sites surveyed during summer and winter season, 2007-09.
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Table 2.1. Habitat information and general description for all sampling locations monitored during this study. Survey Sampling Site Site Location Name (District) Latitude Longitude Air Temp Humidity Local land use Condition S-1 Jhelum River, Trimmun upstream (Jhang) 31°25′02.3″ N 72°11′37.3″ E 26°C 44.40% Sunny Agricultural S-2 Trimmun upstream (Jhang) 31°25′81.2″ N 72°20′71.8″ E 25.3°C 37.20% Sunny Industrial Sub-urban and S-3 Trimmun Downstream (Jhang) 31°18′7.5″ N 72°16′46″ E 28°C 31.50% Sunny Agricultural S-4 Islam pura, Chenab mainstream (Jhang) 31°08′27.5″ N 72°14′25.3″E 25.5°C 50.70% Sunny Agricultural S-5 Chenab River, mainstream (Jhang) 30°99′20.7″ N 72°08′65.3″ E 22°C 82.80% Sunny Agricultural S-6 Najafwala, Chenab mainstream (Jhang) 30° 87′90.3″ N 71°97′70″ E 25.8°C 24.40% Sunny Agricultural S-7 Chenab mainstream, Gharmharaja (Jhang) 30° 76′19.3″ N 71°88′23.1″ E 27.2°C 41.50% Partly cloudy Agricultural Sub-urban and S-8 Ravi upstream, Isalmpura (Khanewal) 30°62′14.8″ N 71°82′72.3″ E 25.4°C 26.70% Partly cloudy Agricultural Chenab downstream from the Ravi-Chenab S-9 30°62′61.3″ N 71°80′76.6″ E 20.3°C 40.90% Sunny Agricultural Junction(Khanewal) MauzaMaqsood Pur, Chenab mainstream S-10 30°57′63.3″ N 71°65′99.7″ E 31.3°C 29% Partly cloudy Agricultural (Khanewal) S-11 Rangpur, Chenab mainstream (Khanewal) 30°52′61″ N 71°59′50.1″ E 22.6°C 57.70% Sunny Agricultural S-12 Bastishumali, Chenab mainstream (Multan) 30°45′01″ N 71°50′70.1″ E 26.2°C 84.30% Sunny Agricultural
S-13 Faridabad , Chenab mainstream (Muzafargarh) 30°35′91.8″ N 71°42′32.1″ E 21.3°C 73.30% Sunny Agricultural
S-14 SurajMianiPatan, Chenab mainstream (Multan) 30°44′33″ N 71°50′68″ E 27.3°C 32.50% Partly cloudy Agricultural S-15 Machiwala,Chenab mainstream (Multan) 30°152′06″ N 71°29′03.1″ E 29.7°C 49.90% Sunny Suburban S-16 Shershah bridge, Chenab mainstream (Muzafargarh) 30°07′48.2″ N 71°28′42.7″ E 31.1°C 72.20% Partly cloudy Urban S-17 PulBagar, River Ravi Upstream (Khanewal) 30°62′14.8″ N 71°82′72.3″ E 19.2°C 65.70% Sunny Urban Continued…… ChundPul, Chenab mainstream (Jhang) 31°54′43.3″ N 72°33′12.3″ E 28.8°C 53.90% Sunny Industrial
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S-18 S-19 Chenab mainstream (Chinot) 31°70′61″ N 72°93′53″ E 28.9°C 49.90% Sunny Agricultural S-20 Bhawana, Industrial drain (Jhang) 31°56′45″ N 72°52′52″ E 28°C 43.50% Sunny Industrial S-21 Jhelum City, Jhelum Upstream (Jhelum) 32°95′58″ N 73°75′22″ E 26°C 43.80% Sunny Agricultural Suburban and S-22 Marala, Chenab mainstream (Sialkot) 32°67′09.9″ N 74°47′09.2″ E 26.5°C 34.70% Sunny Agricultural S-23 Khanki, Chenab mainstream (Gujrat) 32°40′29.2″ N 73°97′25.2″ E 22.5°C 40.90% Sunny Industrial S-24 Waisanwala, River Sutlej Upstream (Vehari) 29°79′68″ N 72°28′52.8″ E 23°C 45.60% Sunny Agricultural Sub-urban and S-25 Punjnad, Chenab mainstream (Muzafargarh) 29°35′04″ N 71°02′27.8″ E 29.4°C 62.00% Sunny Agricultural
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2.3. Sample collection 2.3.1. Water
The surface water samples were collected using a metal bucket in prewashed glass bottles from 25 selected sites at the River Chenab and its tributaries (Fig. 2.1). Composite water samples were collected from the river, 30cm below the water surface, covering a range of 500m in each sampling site, using 5L pre-cleaned glass jars with PTFE screw caps (Qadir et al., 2008). From each site, five sub-samples (each of 5 liters) of river water were collected and combined to obtain composite samples. Sampling jars were pre–washed with an organic solvent (methanol) before sampling. Samples were placed on ice after sampling and transferred to the laboratory directly and were kept at 4ºC until further laboratory analysis.
2.3.2. Sediment
Twenty five surface sediments were collected from selected sampling sites. At each site, a composite sample (~2 kg) consisting of 5 sub-samples from upper 0-5cm sediment was manually collected using a stainless steel ladle in pre-washed glass containers in vicinity of 500 meters. Every attempt made to collect representative samples i.e. samples were collected at 100 meter apart from each other on both sides of river. Collected samples were transferred to the laboratory and refrigerated at -20 ºC (Mai et al., 2002). Samples were air-dried, sieved and kept in pre-washed glass bottles for further analysis.
2.3.3. Fish
Five herbivorous species: Cyprinus carpio, Cirrhinus mrigale, Cirrhinus reba, Catla catla, Labeo calbasu while six carnivorous species: Mastacembalis aramtus, Rita rita, Clupisoma naziri, Securicola gora, Gudusia chapra, and Clupisoma garua were selected for the monitoring of OCPs and PCBs. Selection of these fish species, for the analysis of OCPs and PCBs, were made due to their distribution throughout the study and consumed by local people on large scale. Fish samples were collected from each site using variety of fishing nets of varying mesh sizes as suggested by Bhat, (2003). Seine net (8m × 1.5m with mesh size of 1cm), cast nets
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(circumference 15m with 1.5cm mesh size) and dragnets (7m x 1m with mesh size of 1.5cm) were used within 500 meters reach of each sampling site. Cast and dragnets were used to sample fish in water with depth less than 1.5 m, while seine net was used where depth of water was more than 1.5 meter. The intensity of sampling for each site was standardized as 20 efforts for cast net, three hours duration for seine net and 10 trials for drag net covering the stream within 500m of river along longitudinal gradient (Bhat, 2003). All sampling sites were intensively sampled in an effort to capture as many fish species as possible in proportion to their abundance. All fish samples were collected on seasonal basis (summer and winter) from 25 sites located at River Chenab, Pakistan. Out of 25 sampling sites, fish was recorded from only 22 sites and no fish was found from sites (7, 19, 20). The sites from where fishes were grouped into three zones based on the activities in their catchment.
1. Urban sites: (S-8, 11, 13, 17, 10, 16). 2. Industrial sites: (S-2, 18, 23). 3. Agricultural sites: (S-1, 3, 4, 5, 6, 9, 12, 14, 15, 21, 22, 24, 25).
2.4. Experimental section
2.4.1. Water
2.4.1(a). Physicochemical parameters
Water quality parameters i.e. temperature (Temp), pH, specific conductivity (Sp. Cond.), total dissolved solids (TDS), dissolved oxygen (DO), and salinity (Sal) were recorded in field at the time of sampling using a portable Hydro Lab (MS-5, Surveyor Hach Environmental) and turbidity was also measured in field using turbidity meter (2100P, HACH). Furthermore, water 2- quality parameters i.e. alkalinity (ALK), free CO2, sulfates (SO 4), nitrate-nitrogen (NO3-N) and orthro-phosphates (SRP) were measured in the laboratory using standard procedure (APHA, 1998) mentioned in detail in Appendix 2.2.
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2.4.1(b). Extraction and clean-up
Organochlorine compounds (OCPs and PCBs) were extracted using a liquid – liquid extraction method (Ahad et al., 2006). Approximately 1 L of unfiltered water sample (triplicate from each site) was transferred into a separatory funnel where 10g of NaCl was added and then shaken well to completely dissolve NaCl. Known amounts of 2, 4, 5, 6-tetrachloro-m-xylene (TCmX) and PCB-209 (decachlorobiphenyl) were also added in each sample as recovery surrogates. Dichloromethane (DCM) (75 ml) was added to each sample and shaken vigorously for 3-5 minutes for 3 times, collecting the organic phase and dried by passing through a 3 cm of
Na2SO4 placed on glass wool and extract was concentrated and transferred into n-hexane and further purified on an 8 mm i.d. alumina/silica column packed with neutral alumina (6 cm, 3% deactivated), neutral silica gel (10 cm, 3% deactivated), 50% (w/w) sulfuric acid silica (10 cm), and anhydrous sodium sulfate (1 cm). Before use, neutral alumina, neutral silica gel, and anhydrous sodium sulfate were Soxhlet extracted for 48 h with DCM, and baked for purification. The column was eluted with 50 mL of DCM/hexane (1:1) to yield the OCPs and PCBs fraction. The fraction was reduced in volume and solvent exchanged to amber glass vials containing a known quantity of PCB-54 as internal standard prior to chromatographic analysis.
2.4.2. Sediment
2.4.2 (a). Physicochemical parameters
Parameters such as pH, EC, and total dissolved solids (TDS) of each sediment sample were determined by using portable combined meter (Milwaukee, model SM802). Organic matter (OM) was determined by Tyurin’s method (wet oxidation method) as described by Nikolskii (1963). The proportions of sand, silt, and clay were calculated using Bouycous hydrometer, and sediment textural classes were determined on the basis of relative proportion of soil particles using textural triangle (Robert and Frederick, 1995). Total phosphorus (P) and total sulfur (S) percentages were determined by the method described by Allen et al. (1974), and nitrates (NO3–
N) were determined by the procedure described by Metson, (1956). Total P, total S, and NO3–N were finally estimated using spectrophotometer (Agilent 8453). Alkalinity was determined by
Page 25 titration method (AOAC, 1995). The detailed methodology for each parameter is given in Appendix 2.3.
2.4.2 (b). Extraction and purification
OCPs and PCBs were extracted from sediment samples using Soxhlet extraction method (Mai et al., 2002). 10 g of sediment samples were extracted for 48 h with dichloromethane (DCM) at a rate of 4-6 cycles/h using prewashed Soxhlet apparatus. Mixture of 2, 4, 5, 6- tetrachloro-m-xylene (TCmX) and decachlorobiphenyl (PCB-209) was added to each of the samples as surrogate standards prior to extraction. Activated copper granules were also added to remove elemental sulfur. The extract was concentrated and solvent-exchanged to hexane and purified on 8 mm i.d. alumina/silica column packed, from the bottom to top, with neutral alumina (6 cm, 3% deactivated), neutral silica gel (10 cm, 3% deactivated), 50% (on a weight basis) sulfuric acid silica (10 cm), and anhydrous sodium sulfate. Alumina, silica gel, and anhydrous sodium sulfate were Soxhlet extracted for 48 h with DCM before use. The column was eluted with 50 mL of dichloromethane/hexane (1:1). OCPs and PCBs fraction was solvent- exchanged to n-hexane and concentrated to 0.5 mL in gas chromatograph vials under a gentle nitrogen stream and known quantity of PCB-54 was added as an internal standard before chromatographic analysis.
2.4.3. Fish
2.4.3 (a). Identification, sample preparation and measurement of biological parameters
According to method reported by Qadir and Malik, (2011), collected fish samples washed properly and some important parameters were examined immediately in field. All the fish samples were identified in the field and confirmed in the laboratory by fish taxonomist on the basis of morphometric characteristics by following regional keys (Mirza, 2003; Mirza and Bhati, 1993). After inspection all the samples were wrapped in the aluminum foil, packed in plastic bags and immediately frozen in freezer at -20 oC for later analysis.
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In this study muscle samples of each studied fish species, which form the major edible portion, were analyzed as major objective to estimate the dietary intake to human body. Fish samples were taken out from deep freezer, thawed, washed in clean water, and scales removed. Muscle tissues were dissected between the pectoral fin and vent of the fish, minced into pieces and subsamples were taken from homogenate. Each individual of collected species is prepared separately, with varying size. Subsamples were dried to constant weight to determine the water contents.
2.4.3 (b). Extraction, lipid (%) determination and purification
Fish samples were extracted according to method described by Guo et al., (2008). About
10g of muscle tissue was weighted, ground with anhydrous sodium sulphate (Na2SO4. H2O) and mixture was packed in filter paper. Samples were Soxhlet extracted with 150 ml of dichloromethane (DCM) and acetone (2:1 V/V) for 48 hours. Mixture of 2, 4, 5, 6-tetrachloro-m- xylene (TCmX) and decachlorobiphenyl (PCB-209) was also added to each of the samples as surrogate standards prior to extraction. The extracts were concentrated to 10 ml and solvent- exchanged to hexane with rotary evaporator. About 1 ml of aliquot was used to determine lipid content by gravimetric method. All the samples were cleaned by concentrated sulphuric acid (5ml × 3) to remove the bulk of lipids and further purified on an 8 mm i.d. alumina/silica column packed, from the bottom to top, with neutral alumina (6 cm, 3% deactivated), neutral silica gel (10 cm, 3% deactivated), 50% (on a weight basis) sulfuric acid silica (10 cm), and anhydrous sodium sulfate. Alumina, silica gel, and anhydrous sodium sulfate were Soxhlet extracted for 48 h with DCM and then baked for 12 h at 250, 180, and 450 °C, respectively, before use. The column was eluted with 50 mL of dichloromethane/hexane (1:1). OCPs and PCBs fraction was solvent-exchanged to n-hexane and concentrated to 0.5 mL in Gas Chromatograph vials under a gentle nitrogen stream and a known quantity of PCB-54 was added as an internal standard before GC-MS analysis.
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2.4.3 (c). Isotopic analysis
The method described by Guo et al. (2008) was followed for nitrogen isotopic 15 o determination (δ N %). Raw fish muscle samples were dried at 60 C for more than 48 hours and powdered. Approximately, 0.8 mg of fish sample was weighed in tin cup and combusted using elemental analyzer EA (Flash EA 1112, CE) connected to an isotopic ratio mass spectrometer-IRMS (Delta plus XL, Finnigan). Samples and standards were run in a continuous flow with a standard deviation of 0.2 % among replicate standard samples. δ 15N % in the samples are considered as deviation (%) from the nitrogen isotopic composition in air and calculated as:
δ 15N=[(15N/14N)sample/[(15N/14N)air–1]×1000.
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Table 2.2.List of fish species analyzed for OCs contamination during both sampling campaigns (2007-09).
No. of individual caught from different areas Feeding Habit Feeding Habitat (by Mirza and (Total N) Agriculture Urban Industrial S/no. Fish species Family Bhatti, 1993) (N) (N) (N)
1 Cyprinus carpio Cyprinidae Herbivore Bottom feeder 23 12 6 5
2 Cirrhinus reba Cyprinidae Herbivore Column feeder 54 36 10 8
3 Cirrhinus mrigale Cyprinidae Herbivore Column feeder 39 22 8 9
4 Catla catla Cyprinidae Herbivore Column feeder 26 13 8 4
5 Labeo calbasu Cyprinidae Herbivore Column feeder 29 20 5 4
6 Rita rita Bagridae Carnivore Bottom feeder 17 7 5 5
7 Mastacembalis aramtus Mastacembalidae Carnivore Bottom feeder 24 11 7 6
8 Securicola gora Cyprinidae Carnivore Surface feeder 59 32 15 12
9 Gudusia chapra Clupeidae Carnivore Column feeder 68 40 17 11
10 Clupisoma naziri Siluriformes Carnivore Column feeder 64 32 20 12
11 Clupisoma garua Siluriformes Carnivore Column feeder 31 16 8 7 *Agricultural sites (S-1, 3, 4, 5, 6, 9, 12, 14, 15, 21, 22, 24, 25). *Urban sites (S-8, 11, 13, 17, 10, 16). *Industrial sites (S-2, 18, 23).
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2.5. Chromatographic analysis
OCPs including DDTs (o,p/-DDE, p, p/-DDE, o, p/-DDD, p, p/-DDD, o, p/-DDT, p, p/- DDT), HCHs (α-HCH, β-HCH, δ-HCH and γ-HCH),cis-chlordane (CC), trans-chlordane (TC) and trans-nonachlordane (non-trans), some other OCPs (heptachlor, heptachlor epoxide, endrin, aldrin, dieldrin, methoxychlor, α-endosulfan, β-endosulfan, endosulfan sulphate and HCB) and PCBs ( Sum of 31 congeners i.e. PCB-28, PCB-74, PCB-70, PCB-44, PCB-49, PCB-37, PCB- 60,PCB-66, PCB-77, PCB-82, PCB-87, PCB-99, PCB-101, PCB-118, PCB-114, PCB-126, PCB- 105, PCB-138, PCB-153, PCB-187, PCB-179, PCB-169, PCB-156, PCB-128, PCB-166, PCB- 158, PCB-183, PCB-198, PCB-189, PCB-170 and PCB-180) were analyzed by GC-EI-MS applied with a 50 m capillary column (Varian, CP-Sil 8 CB, 50 m, 0.25 mm, 0.25 μm). The injector temperature was 250 °C. The initial oven temperature was set at 150 °C for 3 min, and raised to 290 °C at a rate of 4 °C/min, and held for 10 min. PCBs were determined in selected ion mode (SIM). Temperature of MSD source and quadrupole was 230°C and 150°C. The MS was used in SIM mode with two ions monitored for each target compound group in specific window. The inlet degradation of DDT was checked daily and control within 15 %. All the PCBs were quantified using HP-Chemstation by confirming the peaks.
2.6. Quality assurance and Quality control (QA/QC)
All the samples were subjected to strict quality control procedures. For our QA/QC program, the instruments were calibrated daily with calibration standards. Procedural blanks, solvent blanks and field blanks were analyzed using the same procedure as for real samples. With every batch, a mixture consisting of known concentrations of internal standards was injected into the GC before sample injection. The instruments were calibrated on a daily basis with calibrated standards. In addition surrogate standards (PCB-209 and TCmX) were added to all samples before extraction to monitor the procedural performance and matrix effects. Average surrogate recoveries were ranged between 45-67% (RSD <12) and 76-95 % (RSD < 8) for TCmX and PCB-209, respectively. Reported concentrations of OCPs and PCBs were not corrected for surrogate recoveries. As internal standard, PCB-54 was added prior to the GC-MS analysis. Quantification was done using peak area of internal standard used. Data acquisition and
Page 30 processing was controlled by Agilent MSD Productivity Chemstation software. All the chemicals used in the study were of analytical grade and purchased from Merck, Germany. All the solvents used in the study, were redistilled to purify solvents for reducing any interference effect of solvents. PCBs standards were purchased from Dr. Ehrenstorpher GmbH, Germany. Glassware used was baked at 450 ºC for 6 hours before use.
For quantification purposes, calibration curves based on a set of six standards of concentration 5, 10, 20, 50, 100, 200 ppb were drawn.Limits of detection (LOD) were calculated as 3 times the standard deviation of the OCs level in procedural blanks. For OCs which were not detected in procedural blanks, LODs were calculated as amount of analyte per sample corresponding to lowest calibration standard. In all samples, LOD ranged between 0.01-0.5 ng. Data acquisition and processing was controlled by Agilent MSD Productivity Chemstation software. Standard chromatograms along with method details for each group of studied chemical (OCPs and PCBs) are given in Appendix 2.4.
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Chapter 3 Occurrence, Fingerprinting, and Risk Characterization of OCPs and PCBs in the Surface Water of the River Chenab, Pakistan:
3.1. Material and Methods
Detailed methodology for water sampling and analysis of physicochemical parameters, OCPs and PCBs are given in detail in Chapter 2 (Section 2.3 and 2.4).
3.1.1. Statistical analysis
Basic statistics analysis was performed by using Statistica 5.5 software (Stat Soft, Inc. 1995) and for graphical representations of water contamination data Microsoft excel 2007 (Microsoft Corporation, 2005) was used. Two multivariate techniques such as Hierarchical Agglomerative Cluster Analysis (HACA) and Principal Component Analysis based on Factor Analysis (PCA/FA) were used for the water quality assessment and interpretation of the results (Simonov et al., 2000 ; Wunderlin et al., 2005).These multivariate statistical techniques have been widely used in various studies for the explanation of spatio-temporal variations and interpretation of chemical/physical characteristics of water quality parameters (Simonov et al., 2000; Wunderlin et al., 2001) in comparison to uni-variant techniques that usually fail to give adequate information on multivariate dataset.
The river water quality, OCP, and PCB dataset was subjected to HACA to identify clusters of the sampling sites indicating their similarity based on the pollution level (Appendix
3.3). For this purpose, 11 water quality parameters, OCPs (23 compounds), and Σ31PCBs measured from 25 sites (36 variables x 25 cases) were subjected to HACA analysis. HACA was performed on the mean values of water quality parameters, OCPs, and PCBs. Hierarchical cluster analysis (HACA) was performed using Euclidean distance as a distance matrix and unweighted pair group using arithmetic averages (UPGMA) as a linkage method to extract information
Page 32 regarding spatial similarities/dissimilarities between sampling sites based on physicochemical parameters, OCPs, and PCBs residual level. Euclidean distances were chosen as a measure of linkage that uses analysis of variance to evaluate the distances between clusters, attempting to minimize the sum squares of any two clusters that can be formed at each step (Kent and Coker, 1992). Pearson correlation was used to confirm the results of HACA.
Principal Components Analysis based on Factor Analysis (PCA/FA) to extract a lower dimensional linear structure from the water dataset of 4 spatial groups viz; relatively Region-1 (3 sites x 36 variable), Region-2 (4 site x 38 variables), Region-3 (5 site x 38 variables) and Region-4 (12 sites x 38 variables) separately. Moreover, temporal factor analysis (FA/PCA) was employed for source identification of OCPs and PCBs in the surface water of River Chenab, Pakistan from 25 sites during two sampling season i.e. summer and winter. The main purpose of this analysis was to reduce the contribution of less significant variables of the water monitoring parameters, which was achieved by rotating the axis defined by PCA to produce new group of variables (Varimax factors). PCA technique starts with the covariance matrix describing the dispersion of the original variables and extracting the eigenvalues and eigenvectors (Singh et al., 2005; Hill and Lewicki, 2006). FA attempts to extract a lower dimensional linear structure from the dataset can be used for source identification. It further reduces the contribution of less significant variable obtained from PCA and the new group of variables known as varimax factors (VFs) is extracted through rotating the axis defined by PCA.
3.2. Results
Table 3.1- 3.2 showing the basic statistics for the OCPs and PCBs measured in water samples collected from River Chenab (25 sampling locations, Fig.3.1) during two seasons i.e. summer and winter, 2007-09. A general perspective on the data set of OCPs and PCBs in the surface water of River Chenab is provided by calculating the ratios for different pollutants to identify the sources and distribution of these contaminants over the different zones of river body. The profile distribution of each group of studied compounds including HCHs, Chlordanes, DDTs and PCBs has shown for each sampling location of River Chenab, Pakistan (Fig.3.1). Status and distribution of OCPs and PCBs reported in this study further explained by comparing our dataset
Page 33 with other worldwide fresh water bodies data and guidelines provided by USEPA (United State Environmental Protection Agency) for these pollutants. Different statistical tools including (HACA) and FA/PCA were also used to briefly explain the spatial and temporal variation of studied parameters in the large riverine ecosystem of Pakistan with special reference to Chenab River.
3.2.1. Physicochemical parameters of the surface water
The site wise data of water quality parameters is given in Appendix 3.1. For water samples collected from the River Chenab and its adjoining tributaries (except S-20): pH ranged from 7.3-9.3 and specific conductivity (µS/cm) ranged from 216-594 during both summer and winter seasons, respectively. TDS (ppm) was 180-572 while alkalinity (ppm) was 44-189 during both summer and winter seasons. Salinity (ppt) ranged from 0.09-0.91 for both seasons. NO3-N -1 -2 (ppm) was recorded in the range of BDL-57, PO4 (ppm) was BDL-6.1 and SO4 (ppm) were 12.43-101 during summer and winter season. LDO were recorded with an average of 1.7-13 and turbidity (NTU) ranged from 18-363 in both monitoring seasons. Water was relatively more turbid at all sampling locations in summer because of erosion of banks and due to high flow rate as compared to winter. Temperature of water in summer ranged from 26-39 oC and 16-26 oC in winter and is one of important factor for the long range transport of POPs. All quality parameters (except turbidity) when compared with National Standards for Drinking Water Quality (NSDWQ) by Pakistan EPA and WHO standard guidelines indicated that the measured values of monitored water quality parameters are within the range of set permissible limits, with few exceptions from S-2, S-18 and S-23. These three sites were located after the joining of industrial drains from different cities like Sialkot, Faisalabad and Gujrat (Appendix 3.1).
The water samples collected from adjoining tributaries of River Chenab, which are significantly contributing to deteriorate the water qulity of River Chenab, showing relativley higher levels of monitored quality parameters than those collected from the mainstream of River Chenab. S-20, located on the industrial drain near Jhang, was also monitored for above mentioned water quality parameters and showed very high values of these parameters, due to receiving toxic industrial waste from Faisalabad city (Appendix 3.1).
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3.2.2. Remarks on the level and distribution of OCPs and PCBs
The concentration of each OCP is shown in Table 3.1. The ∑OCPs concentrations (ng/L) were found in the range of 27-1058 (198) for summer and 25-1165 (316) during winter season. Generally, OCP concentrations in water samples collected during the winter season were higher compared to the summer, whereas PCB levels did not exhibit any significant variation during both seasons. Among all studied OCPs, DDTs were the most abundant during both campaigns followed by HCHs, Chlordanes and ∑other OCPs in descending order. Detailed information about individual compounds is given below.
The concentration of HCHs were ranged from 3-204 ng/L with an average value of 55 ng/L for summer and 6.7-330 ng/L with average value of 91 ng/L for winter. Among HCHs, α- HCH and γ-HCH were most frequently found compounds in both seasons followed by β-HCH and δ-HCH with elevated concentrations in low flow winter season (Table 3.1). Trends of HCHs were followed as γ-HCH, α-HCH, β-HCH and δ-HCH (Fig.3.2). / HCH in present study ranged from 0.15-1.77 (0.97) during both sampling seasons, while / ranged from 0.03-1.07 (0.19) showed relativity lower values at most of sampling locations in both seasons (Appendix 3.2). DDTs were also found frequently in the collected water samples during both monitoring seasons. The concentrations were ranged from 0.55-545 ng/L with an average value of 66 ng/L for summer and 0.63-578 ng/L with mean of 95 ng/L for winter season. DDTs occurred in both seasons as: p, p/-DDT, p, p/-DDD, p, p/-DDE, o, p/-DDT, o, p/-DDD, o, p/-DDE (Fig.3.3). The ratios calculated for DDD/DDE were ranged from 0.19-5.27 for summer and 0.11-1.91 for winter. While (DDD+DDE)/ ∑DDTs ranged from 0.18-1and 0.2-0.91 during summer and winter, respectively (Appendix 3.2; Fig.3.4). Chlordane (cis-chlordane, trans-chlordane and non a trans-chlordane) were recorded in the range of 1.5-91 ng/L (mean 19 ng/L) and N.D.-134 ng/L(mean 41 ng/L) for both summer and winter seasons, respectively.
The total concentrations of ∑other OCPs were ranged from 11-218 ng/L for summer and 10-243 ng/L for winter. Trends of other OCPs in water samples were followed as: heptachlor, dieldrin, heptachlor epoxide, Methoxychlor, endrin, aldrin, β-endosulfan, endosulfan sulphate
Page 35 and α-endosulfan in descending order. Higher concentrations of heptachlor were detected in the water samples collected from River Chenab, which ranged from 0.6-112 ng/L for summer and 1.5-143 ng/L for winter season. Heptachlor epoxide concentrations (degradation product of heptachlor) were in the range of 0.29-13 ng/L and 0.7-20 ng/L for both summer and winter season, respectively. The aldrin ranged from 0.8-8.5 ng/L for summer and 0.8-16.2 ng/L for winter season, while its epoxide form ‘dieldrin’ were found in the range of 0.37-27.96 ng/L and 0.48-49.64 ng/L for both summer and winter, respectively. Endrin ranged 0.46-13.54 ng/L in summer and 1.24-65.78 ng/L in winter, with a clear cut elevation in detection frequency and concentration in winter season. Among endosulfans, β-endosulfan found within the range of 0.67-17.2 ng/L in the summer and 0.87-17.8 ng/L in winter season followed by endosulfan sulphate and α-endosulfan in descending order; both in terms of detection frequencies and concentrations (Fig.3.6). Methoxychlor ranged from 1.58-27 ng/L for summer and 0.93-50.4 ng/L for winter. HCB was also detected in some samples, ranged from 0.29-87.54 ng/L for summer and 0.43-87.75 ng/L for winter season.
Minimum, maximum and mean concentrations of each PCB congeners were measured in surface water samples of the River Chenab collected in summer and winter are given in Table 3.2. ∑PCBs ranged from 7.7-109 ng/L (28 ng/L) and 13-99 ng/L (30 ng/L) for summer and winter, respectively, showing no significant variation b/w both seasons. The trends of different classes of PCBs detected in the surface waters of River Chenab in descending order followed as: tetra-CBs (4CBs), penta-CBs (5CBs), tri-CBs (3CBs), hexa-CBs (6CBs), hepta-CBs (7CBs) and octa-CBs (8CBs) in both seasons i.e. summer and winter. Dioxin-like PCB (dl- PCB) congeners including PCB-126,169, 105,114, and 118, which are very important because of their severe toxic effects due to TCDD like carcinogenic properties (Zhao et al., 2006), ranged between 0.43- 2.7 ng/L in both monitoring seasons.
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Table 3.1. OCPs concentration (ng/L) in surface water collected from River Chenab, Pakistan.
Summer Winter Compounds N N (detected) Mean±Std. Min-Max (detected) Mean±Std. Min-Max α-HCH 25 23.6±32.1 0.89-154 25 36±45.5 2.74-177 β-HCH 11 2.9±5.1 0.69-17.5 19 3.7±3.9 0.46-13 γ-HCH 25 25±23.5 1.1-87.6 24 43±42.6 3.75-175 δ-HCH 22 2.8±2.8 0.56-13.2 22 7.6±7.8 1.39-34.6 ∑ HCH 25 54.4±50 3.6-203 25 91±82.64 6.71-330 HCB 19 5.8±17.8 0.29-87.5 17 9.6±22.63 0.43-85 Heptachlor 20 23±27.5 0.57-112 22 27.2±32.54 1.54-143 Heptachlor epoxide. 20 3±3.9 0.29-12.5 18 4.8±5.16 0.72-19.6 Aldrin 22 4±2.7 0.81-8.5 21 4.8±4.46 0.81-16.4 Dieldrin 22 6.8±7.1 0.37-27.9 24 13.1±13.88 0.48-49.6 Endrin 22 3.2±3.8 0.46-13.54 22 10.7±14.77 1.24-65.7 α-endosulfan 12 1.09±1.9 0.62-8.3 17 2.5±2.98 0.92-10.9 β-endosulfan 24 4.3±4.1 0.64-17.2 23 5.6±5.41 0.87-17.5 Endosulfan-sulphate 19 2.1±2.7 0.5-8.7 22 4.4±4.16 0.74-14.8 Methoxychlor 21 4.5±5.6 1.5-27 21 5.6±9.85 0.93-50.4 ∑ other OCPs 25 58.6±46.1 10.6-218 25 88.8±63.7 10.3-243 Cis-chlordane (CC) 25 11±11.5 1.2-49 24 29.9±29.5 0.89-112 Trans-chlordane (TC) 24 5±3.6 1.5-19.1 20 6.9±6.73 0.44-23.9 non-TC 12 2.5±6 0.42-23 17 4.2±5.36 0.43-19.8 ∑ Chlordane 25 18.6±20 1.5-91 25 41 ±38.7 N.D. -133 o,p'-DDE 5 1.2±2.7 2.9-9.2 14 3.6±6.28 1.52-31 p, p'-DDE 15 10.6±21 2.5-91.8 18 20.2±26.8 5.14-126 ∑DDE 15 11.8±22.8 2.17-101 22 23. 6±32.4 2.46-157 o,p'-DDD 14 2.7±6 1-30.1 14 3.7±4.1 1.25-13.9 p, p'-DDD 19 20. 4±40 0.79-181 23 21.9±24 1-82.9 ∑DDD 22 22.92±43.76 0.55-187 25 25.6±27.2 0.62-96 o,p'-DDT 15 4.13±6.57 1.7-28 17 5.5±7.5 1.63-29.9 p, p'-DDT 20 27.2±52 1.5-239 21 40. 6±62 4.27-294 ∑ DDTs 25 66±120 0.55-545 25 95.2±122 0.63-578 ∑ OCPs 25 197 ±224 26.7-1058 25 316.40±280 24.8-1165
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Table 3.2. Summary of PCBs concentration (ng/L) in water samples collected from River Chenab, Pakistan. Summer Winter Compounds N (detected) Mean±Std. Min-Max N (detected) Mean±Std. Min-Max pcb-180 23 0.53±0.33 0.41-1.84 24 0.54±0.30 0.23-1.8 pcb-170 18 0.55±0.44 0.5-2 23 0.61±0.37 0.51-2.1 pcb-189 24 0.99±0.66 0.76-3.04 23 0.96±0.66 0.76-3.04 pcb-198 14 0.32±0.38 0.21-1.58 15 0.33±0.31 0.36-0.98 pcb-183 16 0.86±2.01 0.53-10.32 17 0.81±1.77 0.31-9.12 pcb-166 22 0.39±0.24 0.31-1.24 24 0.41±0.22 0.2-1.32 pcb-158 23 0.70±0.45 0.6-2.44 24 0.78±0.35 0.6-1.97 pcb-128 22 0.59±.42 0.48-2.28 24 0.64±0.39 0.49-2.24 pcb-156 20 0.29±0.32 0.16-1.23 25 0.34±0.18 0.16-0.8 pcb-169 25 0.80±0.65 0.49-3.23 24 0.81±0.59 0.51-2.44 pcb-179 13 0.49±0.93 0.32-4.41 14 0.41±0.43 0.42-1.54 pcb-187 18 0.83±1.98 0.43-10 18 0.68±0.90 0.32-3.56 pcb-153 25 0.79±0.50 0.46-2.32 25 0.76±0.59 0.34-3.21 pcb-138 24 0.77±0.47 0.54-2.44 25 0.76±0.43 0.21-2.4 pcb-105 22 1.28±0.79 0.54-3.2 25 1.25±0.56 0.46-2.58 pcb-126 11 0.26±0.38 0.43-1.72 16 0.34±0.37 0.34-1.77 pcb-114 24 0.67±0.59 0.48-3.12 22 0.67±0.58 0.48-3.2 pcb-99 25 0.67±0.34 0.34-2.16 24 0.65±0.59 0.32-3.4 pcb-87 10 0.72±0.52 0.43-2.34 24 0.88±0.75 0.49-3.47 pcb-82 23 0.56±0.47 0.38-2.43 24 0.60±0.33 0.34-1.52 pcb-118 21 0.56±0.40 0.5-2.04 23 0.60±0.40 0.5-2.04 pcb-77 23 0.83±0.63 0.12-2.34 22 0.74±0.54 0.32-2.5 pcb-101 22 0.56±0.39 0.41-1.76 22 0.57±0.67 0.23-3.56 pcb-66 23 0.66±0.42 0.49-2.08 24 0.83±0.52 0.49-2.61 pcb-60 24 0.70±0.39 0.32-2.12 24 0.76±0.45 0.32-2.1 pcb-37 22 0.73±0.57 0.63-3.21 23 0.72±0.29 0.65-1.34 pcb-49 25 8.74±6.88 0.41-33.84 25 9.63±8.28 0.41-35.88 pcb-44 21 0.56±0.51 0.31-2.32 23 0.68±0.62 0.31-2.73 pcb-70 20 0.65±0.54 0.42-2.16 23 0.71±0.49 0.48-2.4 pcb-74 19 0.57±0.51 0.34-2.32 22 0.80±1.10 0.35-5.24 pcb-28 22 0.62±0.56 0.33-2.8 23 0.72±0.50 0.3-2.54
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Table 3.3. Comparison of OCPs and PCBs concentration (ng/L) in the different bodies of world.
Location HCHs DDTs ∑OCPs PCBs Reference Min-Max Mean Min-Max Mean Min-Max Mean Min-Max Mean Minjiang River, China 52-515 205 40.61-233 142 214-1819 839 203-2473 985 Zhang et al.,2003 Ebro River, Spain 0.22-28.58 3.3 1.97-6.7 3.1 43-108 76.2 Fernandez et al.,1998 Day Bay, China 35.5-1228 285 26.8-975 188.4 143 -5104 929 91-1355 313 Zhou et al., 2001 Baiertang waters, China 0.49-59.1 0.03-2.7 23.4-61 Luoet al. ,2004 Macao harbour, China 3.2-22.31 0.49-27 25.2-67 Luo et al. ,2004 Quintang River 0.79-202 37 0.80-97 10.57 9.6-269 77 Zhou et al.,2006 Damietta Harbour, EGYPET 0.004-3.2 0.002-194 0.02-201 Tarek Othman Said et al. ,2006 Kolleru Lake, India. 544 198 AmaraneniRao and Pillai, 2000 River Kaveri and Coleroon, Ranjendran and Subramanian , India 4.7-182 102 0.8-4.17 2.4 1997 Gomti River, India 2.1 -567 113 Malik et al., 2009 Bosomtwi Lake, Ghana 75 -161 71 23 -176 73 Darko et al., 2008 Ogba river, Edo state Nigeria 713 2360 Ikoro River, Nigeria 591 603 Lyamu et al., 2007 Ovia River, Nigeria 792 1980 Juiling River, China 31.95 -129 62.51 19.48 -96.6 48.69 115 -414 237.7 Zulin et al., 2002 Yangtze River, China 0.55-28 N.D.-16.7 1-46.4 10.55 0.29 -2 1.2 Tang et al., 2008 KucukMenderesRiver,Turkey 187-337 72-120 Turgut et al. ,2003 Nestos River, Greek N.D.-68 N.D.-64 Golfinopouslos et al., 2003
Gomti River, India 0.02-4846 0.2-4578 Singh et al., 2005 Krivoklatsko, Czech Republic 0.12-0.17 0.013-0.12 0.15-0.34 0.153-2.8 Koci et al., 2007 Shatt al-Arab River, Iraq 22-211 115 109-440 285
Tigris River, Iraq 7-90 42 79-261 152 A.AZ DouAbdul et al., 1988
Euphrates River, Iraq 214-533 407 280-710 525 River Chenab, Pakistan 3.6 -203 54.4 0.55-545 66.14 26.7-1058 198 7.7 -108 28.3 This study, (Summer) 2007-09
6.7-330 91.1 0.63-578 95.22 24.8-1165 316.4 13-99 30.1 This study, (Winter) 2007-09
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200 ng/L 500 ng/L
100 ng/L 30 ng/L
Fig.3.1. Showing the spatial distribution of HCHs, Chlordanes, DDTs and PCBs in surface waters collected from River Chenab, Pakistan.
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a-HCH b-HCH g-HCH d-HCH 100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0% a.
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
3 1 2 4 5 6 7 8 9
------
11 22 10 12 13 14 15 16 17 18 19 20 21 23 24 25
------
S S S S S S S S S
S S S S S S S S S S S S S S S S b. Sampling sites
Fig. 3.2. Composition (%) of HCHs in water samples from all the sampling locations during (a). summer and (b). winter.
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p,p'-DDE o,p'-DDE o,p'-DDD p,p'-DDD o,p'-DDT p,p'-DDT 100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0% a.
100% 90% 80% 70% 60% 50% 40% 30% 20% 10%
0%
8 1 2 3 4 5 6 7 9
------
17 10 11 12 13 14 15 16 18 19 20 21 22 23 24 25
------
S S S S S S S S S
S S S S S S S S S S S S S S S S b. Sampling sites
Fig.3.3. Composition (%) of DDTs in water samples during (a). summer and (b). winter.
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1.20 S 10 S 5 1.00 S 16
S 21 S 12 0.80 S 18 S 1 S 17 S 4 S 7 S 23 S 15 0.60 S 6 S 19 S 25 S 20 S 6 0.40 S 8
(DDD+DDE)/DDTs S 13 S 9 0.20 S 24 S 11 S 14 S 22 0.00 0.00 1.00 2.00 3.00 4.00 5.00 6.00 7.00 8.00 DDD/DDE a. 1.20
1.00 S 5 S 12 S 6 S 21 0.80 S 16 S 20 S 22 S 9 S 4 0.60 S 17 S 8 S 3 S 25 S 7 S 10 S 11 S 10 0.40 S23 S-14 S 13 S 9 S 1
S 2 (DDD+DDE)/DDTs 0.20 S 19 S 24 0.00 0.00 0.50 1.00 1.50 2.00 2.50 3.00 DDD/DDE b.
Fig.3.4.Showing the crossplot of (DDD+DDE)/DDTs and DDD/DDE during (a. summer and (b. winter season.
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100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
3CBs 4CBs 5CBs 6CBs 7CBs 8CBs b.
100% 90% 80% 70% 60% 50% 40% 30% 20% 10%
0%
6 7 1 2 3 4 5 8 9
------
21 22 10 11 12 13 14 15 16 17 18 19 20 23 24 25
------
S S S S S S S S S
S S S S S S S S S S S S S S S S
Aroclor1221 Aroclor1232 Aroclor1242 Aroclor1260 Aroclor1262 Aroclor1248 Aroclor1254 Arochlor 1016 Arochlor a. Fig.3.5. Composition (%) of PCBs in waters during (a). summer and (b). winter.
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Summer Winter 30
25
20
15
10
5
0
Fig.3.6. Detection frequencies of all the OCPs in the water samples collected from River Chenab, Pakistan.
Summer Winter 1400.00
1200.00
1000.00
800.00
600.00
400.00 Concentration (ng/l) Concentration
200.00
0.00
8 5 2 3 1 4 6 7 9
------
20 18 23 11 13 17 10 14 15 16 12 19 21 22 24 25
------
S S S S S S S S S
S S S S S S S S S S S S S S S S
Region-1 Region-2 Region-3 Region-4
Fig.3.7. Levels of OCs (OCPs and PCBs) in the water samples collected during summer and winter season.
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3.2.3. Classification of the sampling sites
Cluster analysis grouped the 25 sites into four regions (Fig.3.8) Viz: Region-1 (S-2, 18, 23), Region-2 (S-8, 11, 13, 17), Region-3 (S-3, 10, 14, 15, 16) and Region-4 (S-1, 4, 5, 6, 7, 9, 12, 19, 21, 22, 24, 25). S-20 identified as outlier, whichis most polluted site receiving a higher amount of toxic pollutants from Faisalabad city, which is one of leading industrial city, and different industrial drains from this city is directly dumped into River Chenab resulted in contamination on this site location. Among four regions classified by HACA, Region-1 consisted of most polluted sites with high residual level of ΣOCPs (509-656 ng/L), Σ31PCBs (33-58 ng/L). Region-2 also consisted of polluted sites but not as in Region-1. OCPs and PCBs were recorded frequently with high concentration ranged from (255-394 ng/L) and (21-28 ng/L), respectively. All the sites of Region-3 and 4 showed improved water quality and relatively lower OCP and PCB levels to Region-1 and 2, expect (S-5, 16, 24).The concentration of OCPs and PCBs in these regions were ranged from 183- 236ng/Land 20-33.5 ng/L, respectively (Appendix 3.3).
Unweighted pair-group average Euclidean distances 120
100
80
60
40
(Dlink/Dmax)*100
20
0 S-18 S-2 S-17 S-8 S-14 S-16 S-5 S-24 S-6 S-25 S-21 S-7 S-23 S-13 S-11 S-15 S-10 S-3 S-9 S-12 S-4 S-22 S-19 S-1
Region-1 Region-2 Region-3 Region-4
Fig.3.8. Hierarchical dendogram of sampling sites obtained using UPMGA as linkage method and Euclidean distance matrix.
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3.2.4. Source Identification using Factor Analysis (FA/PCA)
FA/PCA extracted seven varimax factors (VFs) for both monitoring seasons with Eigen value more than 1 explaining 87 % and 84 % of total variance for both seasons, respectively (Table 3.4). For summer season, out of seven VFs, VF1 explained 42 % of total variance with strong positive correlation on specific conductivity, salinity, TDS, Free CO2, alkalinity, phosphates, nitrates, sulphates, α-HCH, δ-HCH, trans-chlordane, cis-chlordane, non- trans chlordane, p, p/-DDD, p, p/-DDT and PCBs, interpreting agricultural and industrial activities (for industrial pollutants like PCBs) as source of these pollutants in the study area. River Chenab transverse, through densely polluted and industrial cities (Sialkot, Gujarat, Gujranwala, Faisalabad, Jhang, Khanewal and Multan) of Punjab province and these area are also worldwide famous for rice, sugarcane and cotton production. Both the industrial (via drains) and agricultural (via surface runoff from surrounding fields) pollutants at different locations the study area resulted in the contamination of river body. VF2 explained 15 % of total variance and showed positive correlation on o, p/- DDD and o, p/-DDT from surrounding agricultural fields entered into riverine ecosystem via surface run-off or from river bed via re-suspension. VF-3 explained 6 % of total variance with positive loading on γ-HCH, heptachlor epoxide, β-endosulfan, and endosulfan sulphate showing the historical use of these pesticides in current study area for agricultural purposes. VF-4 explained 5% of total variance which showed positive correlation on β-HCH suggested the historical use of technical HCH in the study area. Each of VF -5, 6 and 7 explained less than 5% of total variance and strong positive loading on pH and temperature and negative loading on turbidity, respectively.
For winter season out of seven VFs, VF1 explained 43% of total variance with strong positive loading on specific conductivity, salinity, TDS, alkalinity, phosphates, sulphates, heptachlor, o, p/-DDD, p, p/-DDE, p, p/-DDT and PCBs. This factor predicted the source of pollution from industrial drains along with agricultural run-off from surrounding contaminated land. This factor also representing elevated values of aforementioned parameters in the river stretch during winter season. VF2 explained 13% of total variance showing positive loading on γ-HCH, β-endosulfan, endosulfan sulphate and o, p/-DDE and interpreted as long-term historical use of these pesticide in the study area which entered into the river body through surface run-off
Page 47 from nearby agricultural fields. A total of 9% variance explained by VF3 with strong positive loading on α-HCH, HCB, α-endosulfan, p, p/-DDD, o, p/-DDT and p, p/-DDT, while VF4 highlighted a positive correlation on dieldrin and endrin explaining 6 % of total variation. VF5, VF6 and VF7 explained less than 5% with positive loading on δ-HCH and trans-chlordane. For VF-5, negative loading on the Temperature and positive loading on sulphates. These factors suggested the use of pesticide and fertilizers in surrounding agricultural areas which get their way to riverine system by surface run-off or wet deposition. Negative loading on temperature showed the decrease of temperature in winter, which is common in most of the parts of world, like Pakistan.
Factor analysis was performed for all four regions grouped by cluster analysis. Seven factors were extracted for Region-1, explaining the total variance at about 95%, there were six factors for Region-2 and Region-3 with an explanation of total variance 90%, while seven factors for Region-4 explaining total variance 70% (Table 3.5).
For Region-1: out of seven VFs, VF1 explained 42 % of the total variance in which specific conductivity, salinity, TDS, free CO2, alkalinity, phosphate, sulfates, turbidity, heptachlor, p, p/-DDT and PCBs showed positive loadings. This factor explained that high concentration of nutrients, pesticides, and PCBs in this region were mainly related to industrial activities and municipal waste disposal in the river body, which resulted in pollution in this region. VF2 explained 16% of total variance with positive loadings of α-HCH and, p, p/-DDT and negative loadings on the endosulfan sulfate, which suggested the fresh input of technical HCH from surrounding areas. Negative loadings on endosulfan sulfate showed the degradation of endosulfans and may come from sediments and wet deposition.VF3 explained 15 % of the total variance with positive loading on cis-chlordane and γ-HCH and negative loadings on temperature, which suggested the elevated concentrations in water samples during winter season from surrounding agricultural fields by wet deposition and air deposition because of the comparatively high volatility of these OCPs.VF4 explained more than 10% of the total variance with negative loadings on o, p/-DDD. VF-5 highlighted none of the variables while VF-5 and 7 explained less 5 % of the total variance showing positive loadings for methoxychlor, δ-HCH, and
Page 48 nitrates and negative loadings of heptachlor-epoxide, suggesting agricultural activities as the source of these pollutants and these factors expecting the degradation of heptachlor in Region-1.
For Region-2: out of six VFs, VF-1 explained 35% of the total variance showing pH, specific conductivity, salinity, TDS, HCB, heptachlor, endrin, p, p/-DDE, p, p/-DDD, and p, p/- DDT were strongly correlated and suggested that agricultural run-off from fields in the proximities and municipal waste disposal are important source of pollution in this region. VF2 explained 20% of the total variance and positive loading on the γ-HCH, dieldrin, endosulfan sulfate and methoxychlor, while VF-3 explained 14% of the total variance with positive loadings on α-HCH, heptachlor epoxide, aldrin and non-trans chlordane. Both of these factors showed that these pollutants entered into water body through surface run off from adjoining agricultural fields. VF4 explained 12% of the total variance with positive loadings on the alkalinity and negative loadings on sulfates, and o, p/-DDE which explained the increased values of alkalinity indicating the municipal source of pollution. VF-5 and 6 explained less 10% of the total variance and negative loadings on turbidity, trans-chlordane for VF5, and positive loadings on PCBs and negative loading on nitrates for VF6. These circumstances advocated the presence of PCBs in the waters of this region as all the sites of this area located in urban and sub-urban areas. For Region-3: VF1 explained 44% of the total variance showing positive loadings on specific conductivity, TDS, free CO2, γ-HCH, heptachlor epoxide, dieldrin, cis-chlordane, non- trans chlordane, and p, p/-DDE, and negative loadings on heptachlor. VF2 explained 17% of the total variance with positive loadings on nitrates, α-HCH, HCB, endrin, β-endosulfan, endosulfan sulfate and, methoxychlor. VF3 explained 13% of the total variance showing a positive correlation of phosphates, δ-HCH and α-endosulfan. All other factors explained less than 10% of total variance, VF4 showed negative loadings on o, p/-DDE while VF5 and 6 with positive loadings on dieldrin, trans-chlordane and sulfates.
For Region-4: VF-1 explained 23% of total variance with positive loadings on α-HCH, γ- HCH, endrin, endosulfan sulfate, and non-trans chlordane. VF-2 explained 12% of the total variance with positive loadings on o, p/-DDE and o, p/-DDD. VF-3 explained 10% of the total variance with negative loadings on temperature and chloride. VF-4 explained 8% of the total variance with positive loadings on δ-HCH and α-endosulfan. VF-5 explained 7% of the total
Page 49 variance with negative loadings on TDS and methoxychlor. VF-6 and VF-7 showed positive loadings on dieldrin, heptachlor epoxide, and p, p/-DDD explaining less than 7 % of the total variance. In Region 2 and3, source of OCP contamination suggested by FA is mainly related to agricultural activities. Most of the sites included in these regions are located in the agricultural area and use of pesticides is very common for crop protection. FA also suggested the degradation of OCPs in this region which is the evidence for the application of these persistent pesticides from many years ago. In contrast to this, source of pollution in the other regions i.e. Region-1 and 2, mainly related to industrial activities and disposal of municipal waste in river body, while agricultural activities also contributed as a source but not as others.
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Table 3.4. Values of seven factor loadings for the studied OCs and water quality parameters
Summer Winter Parameters VF1 VF2 VF3 VF4 VF5 VF6 VF7 VF1 VF2 VF3 VF4 VF5 VF6 VF7 pH 0.86 0.74 Specific Cond. 0.99 0.97 Sal 0.88 0.97 TDS 0.92 0.96 Free CO2 0.97 0.89 Alkalinity 0.98 0.98 Phosphates 0.97 0.95 Nitrates 0.81 0.77 Sulphates 0.94 0.91 Turbidity -0.81 Temperature 0.91 -0.82 α-HCH 0.83 0.78 β-HCH 0.86 γ-HCH 0.86 0.77 δ-HCH 0.72 0.74 HCB 0.96 0.77 Heptachlor 0.83 Hep-epoxide 0.77 Aldrin 0.84 Dieldrin 0.76 Endrin 0.87 α-endosulfan β-endosulfan 0.79 0.75 Endo- sulphate 0.84 0.80 Methoxychlor 0.90 TC 0.72 CC 0.81 non-TC 0.80 0.78 o,p'-DDE 0.88 0.84 p, p'-DDE 0.80 o,p'-DDD 0.89 0.72 p, p'-DDD 0.84 0.80 o,p'-DDT 0.87 0.77 p, p'-DDT 0.84 0.76 ∑PCBs 0.84 0.76 Eigen value 17.75 5.67 2.32 2.14 2.09 1.34 1.03 16.19 5.10 3.55 2.31 1.71 1.37 1.11 % total Variance 47.96 15.34 6.28 5.77 5.64 3.62 2.80 43.75 13.79 9.60 6.24 4.62 3.71 3.00 Cumulative % Variance 47.96 63.30 69.58 75.35 81.00 84.62 87.42 43.75 57.54 67.14 73.38 78.00 81.71 84.71
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Table 3.5 Table 3.6. Values of seven factor loadings for studied OCs and quality parameters in the waters of river Chenab (collected from the four regions). Region-1 Region-2 Region-3 Region-4 Parameters VF1 VF2 VF3 VF4 VF5 VF6 VF7 VF1 VF2 VF3 VF4 VF5 VF6 VF1 VF2 VF3 VF4 VF5 VF6 VF1 VF2 VF3 VF4 VF5 VF6 VF7 pH 0.83 Specific Cond. 0.98 0.86 0.71 Sal 0.98 0.85 0.7 -0.82 TDS 0.97 0.88 LDO Free CO2 0.95 0.89 Alkalinity 0.98 0.9 Chlorides 0.97 -0.94 -0.8 Phosphates 0.97 0.92 Nitrates 0.83 -0.85 0.72 Sulphates 0.91 0.05 -0.88 0.8 Turbidity 0.78 0.09 -0.89 Temperature -0.91 -0.76 α-HCH 0.83 0.92 0.82 0.89 β-HCH γ-HCH 0.76 0.93 0.95 0.72 δ-HCH 0.85 0.86 0.78 HCB 0.82 0.96 Heptachlor 0.85 0.93 -0.82 Hep-epoxide -0.94 0.92 0.98 0.72 Aldrin 0.88 0.71 Dieldrin 0.88 0.87 0.72 Endrin 0.83 0.77 0.82 α-Endosulfan 0.85 0.78 β-Endosulfan 0.76 0.94 Endo-sulphate. -0.95 0.97 0.73 0.83 Methoxychlor 0.97 0.8 0.73 -0.8 TC -0.84 0.9 CC 0.74 0.86 non-TC 0.93 0.89 0.86 o,p'-DDE -0.82 -0.91 0.76 p, p'-DDE 0.89 0.73 o,p'-DDD -0.79 0.71 p, p'-DDD 0.92 0.84 0.81 o,p'-DDT 0.91 -0.74 p, p'-DDT 0.73 0.88 ∑PCBs 0.87 0.79 Eigen value 15.85 6.06 5.24 4.25 2.38 2.04 1.18 13.28 7.26 5.27 4.55 3.21 2.12 16.4 6.32 4.91 3.39 2.09 1.34 8.53 4.53 3.76 3.08 2.89 2.29 1.88 % Total Variance 42.82 16.38 14.15 11.49 6.44 5.51 3.19 35.89 19.63 14.26 12.29 8.67 5.72 44.34 17.09 13.27 9.16 5.66 3.63 23.06 12.25 10.16 8.33 7.81 6.18 5.09 Cumulative % Variance 42.82 59.21 73.36 84.85 91.29 96.81 100 35.89 55.52 69.78 82.07 90.74 96.45 44.34 61.43 74.7 83.85 89.51 93.15 23.06 35.31 45.47 53.8 61.62 67.8 72.89 *Values of marked loadings more than 0.70 are significant in each factor.
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3.3. Discussion
3.3.1. Organochlorine distributional patterns in a global perspective
In literature, the occurrence of OCPs in various water bodies has been extensively presented. Concentrations of Organochlorines in fresh waters vary widely, depending mainly on the specific sources that exist at the proximities of each sampling site. Compared to data from other surface waters worldwide, presented in Table 3.3, it can be seen that the ∑OCPs concentrations in the surface waters of the River Chenab were found to be of moderate to higher levels, similar to the Rivers of Iraq (79-710 ng/L; DouAbdul et al., 1988) and Juiling River China (115-414; Zulin et al., 2002). Much higher OCP concentrations, to those reported in the present study have been published for the Minjiang river (203-2437 ng/L) and Daya Bay (91.9- 1355 ng/L) of China (Zhou et al., 2001; Zhang et al., 2002). Measured OCP concentrations were far higher to those reported in surface water of various rivers in China (Luo et al., 2004; Zhou et al., 2006), Egypt (Said et al., 2006) and India (Malik et al., 2009).
3.3.1(a). HCHs
The waters of River Chenab can categorize as least to moderate polluted in terms of HCHs. ∑HCH values in other surface waters worldwide (Table 3.3) have been found to vary between not detectable (Nestos River in Greece; Golfinopoulos et al., 2003) or close to detection limit (River Ebro, Spain; Fernandez et al., 1998) and the high of bout 5000 ng/L reported from River Gomti in India (Singh et al., 2005). In Pakistan HCHs were used as technical mixtures containing -isomer (55-80%), -HCH (5-14 %), HCH (8-15 %), and δ-HCH (2-16 %), while lindane has also been used widely for agricultural purposes containing more than 90% of HCH (Yang et al., 2005). Indicative ratios of HCHs can also be useful to indicate the historical application (Kim et al., 2002). / HCH signature values in the present study, during both sampling seasons reflecting the excessive recent use of both technical formulations (HCH and lindane) in the studied stretch (Qiu et al., 2009). The / ratio ranged from 0.03-1.07 (0.19) with relatively low values at most of sampling locations during both seasons. High / HCH ratios
Page 53 suggest isomerization of α-HCH into β-HCH, and indicate that the application of the mixtures in this area was not recent (Zhu et al., 2005). Moreover, lower percentages of β-HCH on most of the sites were recorded, something that probably is indicative of continuous use of HCHs in the vicinity (Tang et al., 2008). Lower β-HCH ratio may also reveal the fact that this isomer, due to high log Kow, may be fractionated towards sediment fraction. In surface waters, HCHs may originate from direct discharges and surface runoff from surrounding agricultural soils, but also from the river bed sediment suspension (Walker et al., 1999), hence the high variability in the HCH ratios.
3.3.1(b). DDTs and their sources
Technical DDT generally contained 75% p, p/-DDT, 15 % o, p/-DDT, 5% p, p/-DDE and less than 5% others (Yang et al., 2005). In Pakistan, it has been used in large quantities during past years to control crop pests and malaria disease, especially in the proximities of River Chenab (Malik et al., 2011; Syed and Malik, 2011). DDTs were routinely analysed in the collected water samples. The present ∑DDTs concentrations are among the highest reported in the last years and suggest that DDT is still widely used in Pakistan. In studies from other developing countries, ∑DDTs have been reported in similar or higher concentrations, notwithstanding the worldwide ban imposed by the Stockholm convention. Such examples are the River Gomti in India (0.2-4578 ng/L; Singh et al., 2005), or various rivers in Nigeria (603- 2360 ng/L; Ize-Lyamu et al., 2007).
Ratios of the DDT concentrations, including DDD/DDE, (DDD+DDE)/ ∑DDTs, p, p/- DDT/ ∑DDTs have been used in the past in order to describe the current status, possible sources, degradation and historical use of DDTs in Riverine ecosystems (Lee et al., 2001; Luoet al., 2004). Fig.3.4 is showing a crossplot of (DDD+DDE)/DDTs and DDD/DDE during both (a) summer and (b) winter season. It can be seen that (when possible to calculate) during winter the DDD/DDE ratio is distributed in the area 0.5 - 2, suggesting that both DDT metabolites are formed evenly; Transformation of DDT to DDE occurs under aerobic conditions, whereas anaerobic conditions favor the formation of DDD (Zhou et al., 2006). During summer, the period that is considered the "wet" period and is characterized by heavy raining, we can observe that the
Page 54 ratio shifts to higher values. This could be either due to the fact that DDE is more volatile than DDD (Katsoyiannis et al., 2006), due to the lower content of dissolved oxygen (Appendix 3.1), or it may be linked with the sediment-water exchange that takes place in the River Chenab. In a recent study, it was shown that River Chenab's sediments are characterized by anaerobic conditions and also high DDD/DDE ratios (Malik et al., 2011). ∑ (DDD+DDE)/∑DDTs values more than 0.5 are indicative of the long term weathering of DDT, originating from surrounding soils and river bed sediments (Zhou et al., 2006). In the present study ranged from 0.18-1and 0.2-0.91 during summer and winter, respectively, suggesting that there are also fresh DDT input in the area, ought to stockpiles of obsolete pesticides and industrial waste (Tang et al., 2008).
3.3.1(c). Chlordanes
Recorded range for Chlordane (cis-chlordane, trans-chlordane and non a trans-chlordane) clearly indicating the long term use of this chemical for crop protection in the current study area. TC/CC values more than 1 also reflect fresh inputs of chlordane (Bidleman et al., 2002, Qiu et al., 2009). The TC/CC ratios calculated for reported concentrations in the waters of River Chenab, which similarly to DDTs can be considered as an indication of existing new sources of chlordanes, together with historic pollution, which is dominant, based on the fact that in all but three sites (S-7, 9 and 19), CC was higher to TC.
3.3.1(d). Other OCPs
Trends of other OCPs occurrence in the water samples reflecting the dominance of heptachlor and its metabolites. Diagnostic ratios of heptachlor and its degraded product clearly indicated the current use of this pesticide in the study area (Singh et al., 2007). Aldrin, dieldrin and endrin belong to the cyclodiens class of organochlorine pesticides, a class that has been used in Pakistan for crop protection due to their strong insecticidal properties. Aldrin in the environment converts rapidly into its epoxide (dieldrin) (Said et al., 2006). Our results indicating the higher concentrations of dieldrin compared to aldrin support the hypothesis of rapid transformation of aldrin into dieldrin in the river system (Said et al., 2006). The high levels of
Page 55 studied cyclodiens reflected the historical large scale use of these toxic chemicals in the agricultural sector and results are consistent with previous studies conducted in the current study area (Tariq et al., 2007; Ahad et al., 2010; Malik et al., 2011).
Concerning endosulfans, β-endosulfan was found predominantly followed by endosulfan sulfate and α-endosulfan in descending order. Endosulfans are widely used in Pakistan for agricultural purposes and are still used at a larger scale to control agricultural pests. Although, α- endosulfan was present in technical mixtures in higher rates than its β-isomer, in the present study, the observed concentration rates are inverse.The physical-chemical properties of the two isomers are similar, however the water solubility of β-endosulfan is markedly higher and thus partitions to aqueous phases more readily (Weber et al., 2010). The latter explains its abundance in the River Chenab. The high levels of endosulfan sulfate are expected, given its persistence and the long-lasting, wide usage of endosulfan in this area (Malik et al., 2009).
HCB has been used as an intermediate product in the preparation of organochlorine pesticides and it has also been widely used in many industrial processes (Tolosa et al., 2010, Zhao et al., 2010).
3.3.2. Difference in PCB congener profiles
As for PCBs, concentration recorded in our samples is comparable to those (5.2-190.8 ng/L) reported by Lana et al. (2008) from Czech Republic surface water which is comparable with present study, while the detected values are far higher than those reported from Venice Lagoon Italy (0.1-1.2 ng/L; Moret et al., 2005) and Bay of Bengal, India (less than 5 ng/L; Ranjendran et al., 2005). PCBs concentrations recorded in the surface waters from River Chenab is much lower (Table 3.3) than those reported from China (Zhou et al., 2001; Zhang et al., 2003) and Spain (Fernandez et al., 1998).
Considering our results, Fig.3.5 presents the PCB profiles in the various samples which can be particularly useful for the understanding of the sources/technical mixtures that have been used in the studied area. Based on Figure 3.5, it can be seen lighter PCBs (tri- and tetra-
Page 56 congeners) have accounted for more than 50% in most River Chenab samples. The lighter PCBs tend to remain in the aqueous phase due to their high water solubility (Hong et al., 2003; Ranjendran et al., 2005). In addition, if PCBs in River Chenab originate from wastewater treatment plants, then it is expected that heavier PCBs will sorb onto solids and be removed through sewage sludge, resulting in lower concentrations in the effluent stream (Katsoyiannis and Samara, 2004; 2005). In addition, PCBs once discharged/deposited in the River Chenab water will tend to sorb on suspended solids and deposit to the sediment. Given that this process is governed by the lipophilic character (Kow) of the compounds, the heavier CBs are expected to deposit to the sediments in favor of the lighter ones. If we take all the aforementioned information in consideration, and the profiles presented in Figure 3.5 (a, b), it can be concluded that the main Aroclor mixtures used in the area should be mainly 1248 and the heavier 1254, 1260 and 1262, however, in lower extents. Never the less, it is known that PCBs, like all POPs may originate also from long range transport, so the comparison of the samples PCB-profiles with the technical mixtures can only give an approximation of the commercial products that have been used in this area. In addition, knowing of the current PCB fingerprint is important, due to the fact the PCB containing materials exist everywhere and in cases of industrial accidents or spills, this information is necessary in order to understand what impact possible accidents have in the region. The presence of dioxin like PCBs in aquatic environment of River Chenab is the evidence of their existence in surrounding environment, which may pose several potential health risks to aquatic life of River Chenab, Pakistan. The significant sources of these super lipophilic PCB congeners suggested from steel manufacturing units and coal burning during iron ore sintering process (Buekens et al., 2001; Biterna et al., 2005) in the many industrial cities of Lahore, Faisalabad, Sialkot and Gujarat in the study area, which may reach into the river ecosystem via run-off from surrounding fields and atmospheric deposition.
3.3.3. Spatio-Temporal variation of OCPs and PCBs
Concerning the spatial patter of OCPs and PCBs in River Chenab, it can be seen from Figure 3.1, that the highest concentrations were observed at the sites S-2, S-8, S-16 S-17, S-18, S-20, S-23, S-24, which were close to industrial and urban areas. Among these sites, S-20 is the most polluted site receiving largest amount of the waste from Faisalabad city, which is an
Page 57 industrial city, and different industrial drains from this city are directly dumped into the River Chenab. Site-2 and 18 was also found to be highly polluted due to industrial discharges, urban run-off, and surrounding agricultural field. Both of these sites were located after mixing of S-20 (industrial drain) with the River Chenab in the Jhang district, before the joining of the River Chenab with Jhelum River on Trimmun barrage (Fig.3.1). All sites located after the joining of the Jhelum River (S-3 to S-7) showed a noticeable difference between the detection frequencies and concentrations of OCP and PCB (Fig.3.1) compared to the three contaminated sites (S-2, S- 18, S-20).
The sites S-8 and S-17 on the Ravi River (Fig.3.1), one of the most polluted rivers of Pakistan, receiving pollutants from the industries of Lahore, Qasoor, and Shiekhupura districts and also from the adjoining agricultural fields showed high OCP and PCB concentrations. All sampling sites located after joining of the Ravi River with the River Chenab (S-9, S-10, S-11, S- 12, and S-13; Fig.3.1), exhibited lower concentrations (as compared to S-8 and S-17) in all studied pollutants, indicating that the main pollution load was due to discharges coming from Ravi upstream. These sampling sites are located in cotton growing areas, where pesticide application takes place; however, there doesn't seem to be a direct impact on the river water pollution.
Among four regions classified by HACA, all the sites of Region-1 are located in the industrial and urban areas, receiving a higher load of toxic chemicals through open waste water drains. Site-2 and 18 located after S-20 in Jhang district before the joining of River Chenab with River Jhelum on Trimmun barrage. Both of these sites, found much polluted because of pollution load from the municipal drains and surrounding agricultural field. S-23 located on Khanki barrage near Sialkot and Gujarat district, also receiving a higher pollutant load via different drains (Nulla Aik and Palku etc.) which openly dumped the industrial wastewater and city waste into fresh water body while agricultural source of pollution in this area also cannot be neglected as this area is world-know for agricultural productivity and comprises the food basket of Pakistan. Region-2 included four sites Viz: S-8, 11, 13, 17 which are also polluted sites but not as in Region-1. S-8 and 17 located on River Ravi before joining with River Chenab. River Ravi is one of most polluted river of Pakistan, receiving pollutants from industries of Lahore, Qasoor
Page 58 and Shiekhupura districts and also from the adjoining agricultural fields due to intensive farming activities. Others S-11 and13 located in cotton belt of Khanewal district and receiving organochlorine residues from the surrounding soil via surface run-off and air depositions as this area is characterized by high temperatures and heavy dust storms. Generally, cyclodiens were found in high concentrations than in all other regions because of excessive use of these pesticides in this area. Region-3 included S-3, 10, 14, 15, 16 which are moderately polluted sites with improved water quality than those of Region-1 and 2. In this region, S-16 needed more attention, located on Shershah Pul Multan. This site exhibited relatively high concentrations of heavier chlorinated biphenyles and very low dissolved oxygen due to receiving the electric waste from a pipeline of nearby electric power plant. Industrial activities, and municipal waste disposal from Multan city is also one of important source of pollution on this site. OCPs also showed high values, which may entered into the River system from some pesticide formulation units in the vicinity of this site. On the other hand, store of obsolete pesticides are also reported in this area which can be the significant source of these toxic chemicals in this area (Ahad et al., 2010). Site-10, 14, 15 located in agricultural areas of Khanewal district and showed high values of p, p/- DDT, aldrin, dieldrin, endrin and cis-chlordane in collected water samples. All these sites are located in the cotton growing area and large scale pesticides application is observed in this area since long for the crop protection, which may contaminate the river environment. Region-4 consisted of 12 sites Viz: S-1, 4, 5, 6, 7, 9, 12, 19, 21, 22, 24, 25 which are considered relatively least polluted than other regions. Among these sites S-24 (located on river Sutlej; a tributary of Chenab) showed high DDTs residues level and reflected the wide scale use of DDTs in the surrounding area. Site-1, 21 located on River Jhelum, least polluted river of Pakistan. Site-4 , 5 ,6 situated after the joining of Jhelum River with Chenab, while S-22 located on head Marala from where Chenab entered into Pakistan territory. All the sites of this region were relatively least polluted sites in the study area except Site-5 which showed elevated levels of PCBs ~ 50 ng/L. These higher values are suspected to originate from surrounding electrical grid station from where electrical waste is being disposed into the river mainstream.
The seasonal variation of OCPs and PCBs in the Chenab River was studied by comparing these concentrations during summer vs the winter season ones, as shown in Fig.3.7. As expected, increased concentrations of ∑OCPs were observed in the winter season as compared to summer
Page 59 at almost all sites. In contrary, PCB levels found in both seasons but showed no significant variation. Although, it is well known that the seasonality of POPs including OCPs and PCBs is mainly attributed to the increased emissions from heating, industrial and agricultural activities in Pakistan, an important parameter might also be the specific climatic conditions, not directly related to the temperature difference between winter and summer. Thus, in Pakistan, the cold season (winter) is also the dry period of the year, whereas the warm period (summer) is characterized by heavy raining. As such, during winter, the water volume of the River is lower, which suggests higher concentrations of organic pollutants because of the low dilution levels (Farooq et al., 2011). However, the dilution caused by the heavy rains might not always “decrease” the contaminants concentration, as the wet deposition of the latter through the rain might tend to maintain equilibrium (Sun et al., 2009; Katsoyiannis et al., 2010). Indeed, there have been cases, where similar OCP and PCBs levels were observed for both periods.
3.4. Toxicity imposed due to OCPs and PCBs
Risk assessment of the surface waters of Chenab River is attempted in the present study. By comparing our dataset with current water quality criteria of USEPA (USEPA, 2006) and our national bottled drinking water standard (PSQCA, 2004). According to USEPA, 2006 the criteria maximum concentration (CMC; is an estimate of the highest concentration of a material in surface water to which an aquatic community can be exposed briefly without resulting in an unacceptable effect) for HCHs (γ-HCH) should be 950ng/L. DDTs allowed upto 1.0 ng/L as criteria continuous concentration (CCC; The Criterion Continuous Concentration (CCC) is an estimate of the highest concentration of a material in surface water to which an aquatic community can be exposed indefinitely without resulting in an unacceptable effect). The criteria maximum concentration (CMC) for Chlordane (less than 4 ng/L); heptachlor (less than 3.8 ng/L, CCC); PCBs (less than 14 ng/L). These guideline values, especially the PCB and DDT ones, were exceeded in big number of occasions especially in Region-1 and 2, highlighting the need for immediate action by the Authorities in the area of Chenab River.
According to PSQCA (2004), permissible limit for γ-HCH is 0.0002 ppm, which when compared with our dataset suggested that few samples (10 % of total samples collected from S-2,
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18, 20) exceeded/closed to set benchmark values. Chlordanes (0.002 ppm), heptachlor (0.0004 ppm), aldrin/dieldrin (0.002 ppm) and methoxychlor (0.04 ppm) were found within the range of set permissible limits of PSQCA, 2004.
Table 3.6. Water quality criteria recommended by USEPA, 2002 and PSQCA, 2004. PSQCA benchmarks Fresh water Human health consumption for (ppm) Compounds Water + organism Organism only CMC (ng/L) CCC (ng/L) (ng/L) (ng/L) α-HCH - - 2.6 4.9 β-HCH - - 2.6 17 γ-HCH 950 980 1800 0.0002
δ-HCH - - - - Heptachlor 520 3.8 0.079 0.079 0.0004 Heptachlor epoxide 520 3.8 0.039 0.039 0.0002 Aldrin 3000 - 0.049 0.05 0.002 Dieldrin - - - - 0.002 a-endosulfan 220 56 62000 89000 b-endosulfan 220 56 62000 89000 Endo-sulphate - - 62000 89000 Methoxychlor - - - - 0.04 ∑ chlordane 2400 4.3 0.8 0.81 0.002 p, p'-DDE 1100 1 0.22 0.22 p, p'-DDD 1100 1 0.31 0.31 o, p'-DDT
p, p'-DDT 1100 1 0.22 0.22 PCBs 14 0.064 0.064
On the other hand, TEQ values for dl- PCB congeners can be calculated to assess the toxic effects of dl-PCBs recorded in abiotic samples to human and other organisms (Yang et al., 2009;
Pan et al., 2010). In our study, total WHO-TEQ dl-PCBs were calculated by using WHO-TEF for each DL-PCBs congener (Van den berg et al., 2006). The dl- PCB-TEQ levels ranged from 0.11-29.59 pg TEQ L-1 with PCB-126 accounting for more than 70 % of the total dioxin-like PCB-TEQ. The PCB-TEQ values were observed at S-20 (29.59 pg TEQ L-1), followed by S-23 (9.66 pg TEQ L-1), S-5 and S-16 (8.7 pg TEQ L-1). All the sites with elevated TEQ values are
Page 61 located in the industrial and urban areas of Pakistan, suggesting that these chemicals occur due to local usage and not because of long range transport.
3.5. Conclusion
Surface waters of the River Chenab were monitored at 25 sites for physicochemical parameters, OCPs and PCBs during the summer and winter of 2007-09. DDTs occurred in higher concentrations and were followed by HCHs, Chlordanes, PCBs and other OCPs. Different indicative ratios for studied organochlorines suggested both current and historical use in the study areas. Cluster analysis separated different sites into four regions i.e. Region-1, Region-2, Region-3 and Region-4 based on the pollution level. Factor Analysis (PCA/FA) identified agricultural, industrial activities and municipal waste disposal from urban areas as the source of studied organochlorines in the complex river system. The dataset obtained from our study was compared with other water bodies of the world and found that the waters of the River Chenab to be moderate to severely polluted in terms of OCPs and PCBs.
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Chapter 4 Occurrence, Distribution, Possible Sources and Ecotoxicological Concern of OCPs and PCBs in the Surface Sediments Collected From River Chenab, Pakistan:
4.1. Materials and Methods Detailed methodology for sediment sampling and analysis of physicochemical parameters, OCPs and PCBs are given in detail in Chapter 2 (Section 2.3 and 2.4).
4.1.2. Statistical analysis
Basic statistics analysis was performed by using Statistica 5.5 software (Stat Soft, Inc. 1995) and for graphical representations of sediment contamination data Microsoft excel 2007 (Microsoft Corporation, 2005) was used. Two multivariate techniques such as Hierarchical Agglomerative Cluster Analysis (HACA) and Principal Component Analysis based on Factor Analysis (PCA/FA) were used for the assessment of sediment contamination and interpretation of the results. The sediment quality, OCP, and PCB dataset was subjected to HACA to identify clusters of the sampling sites indicating their similarity based on the pollution level (Appendix 4.3). For this purpose, 11 sediment quality parameters, OCPs (23 compounds), and Σ31PCBs measured from 25 sites (36 variables x 25 cases) were subjected to HACA analysis. HACA was performed on the mean values of sediment quality parameters, OCPs, and PCBs. Hierarchical Cluster Agglomerative Analysis (HACA) was performed using Euclidean distance as a distance matrix and unweighted pair group using arithmetic averages (UPGMA) as a linkage method to extract information regarding spatial similarities/dissimilarities between sampling sites based on physicochemical parameters, OCPs, and PCBs residual level.
Principal Components Analysis based on Factor Analysis (PCA/FA) to extract a lower dimensional linear structure from the sediment dataset of 4 spatial groups viz; relatively Region- 1 (2 sites x 36 variable), Region-2 (6 site x 38 variables), Region-3 (9 site x 38 variables) and
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Region-4 (7 sites x 38 variables) separately. Moreover, temporal factor analysis (FA/PCA) was employed for source identification of OCPs and PCBs in the sediment of River Chenab, Pakistan from 25 sites during two sampling season i.e. summer and winter.
4.2. Results
OCPs and PCBs were measured in the surface sediments from River Chenab, Pakistan and illustrated in Table 4.1-4.2, and Fig.4.1.
4.2.1. Characterization of sediments
Physicochemical properties of sediment for each site location are presented in Appendix 4.1. Sediments were slightly acidic to moderately alkaline in winter with pH varied from 6.6-8.9 while in summer (6.5-9) most of sediments were moderately alkaline. Sandy, loamy sand and sandy loam was the dominant textural classes. Greater values of EC (0-270 µS/cm), TDS (0-180 ppm) and organic matter (0.18-3.5 %) were recorded in winter in contrast to total P % (0.01- 0.12) and total S% (0.003-0.9) which was measured highest in summer. Greater levels of alkalinity (0.6-1.8 mg/g) and NO3-N (0.02-0.9 mg/g) were measured in winter. High values of all the physicochemical parameters except phosphorus (%) and sulphur (%) in winter than summer season may be due to hydrological conditions (dry and wet season) of Pakistan. In winter, the elevated values of all these parameters mainly related to low water flow, which resulted in high deposition rates of sediments.
The samples collected from adjoining tributires of River Chenab showing the high levels of monitored quality parameters than those collected from the mainstream of River Chenab. S- 20, located on the industrial drain near Jhang, was also monitored for above mentioned qulity parameters and showed very high values of these parameters, due to receiving toxic industrial waste from Faisalabad city (Appendix 4.1).
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4.2.2. Concentration profile of OCPs and PCBs in sediments
The OCP concentrations (ng/g) were found in the range of 29-831 (132) and 11.4-811 (148) for both summer and winter seasons, respectively. Our dataset showed the predominance of DDTs during both monitoring seasons followed by HCHs, chlordanes, and some other OCPs. The detection frequencies of all the OCPs are mentioned in Table 4.1, measured in both monitoring seasons. DDTs and HCHs were most frequently detected OCPs almost found in all the samples, following by chlordanes (90 %), and some other OCPs including endosulfans (60 %), heptachlor (60 %), HCB (50 %), and dieldrin (45%).
The concentration (ng/g) of HCHs in the sediments were ranged from 1.73-64.9 (mean; 13.5) for summer, while 2.54-104 (mean; 18.4) for winter seasons. Trends of HCHs followed descending order as: β-HCH, γ-HCH, α-HCH and δ-HCH. Fig.4.2 a,b showing the composition of HCHs in both monitoring seasons at all the site locations. β-HCH and γ-HCH were contributed 37 % and 29 % of total HCHs in sediment samples during both seasons, followed by α-HCH (24 %) and δ-HCH (9%). The indicative ratios calculated for α/γ HCH ranged from 0.37- 1.44 (0.70) for summer and 0.27-5.37 (0.9) for winter, while β/ γ HCH ranged from 0.19-4.89 (1.8) for summer and 0.59-4.87 (1.65) for winter season (Appendix 4.2). The concentration (ng/g, dry weight) of DDTs were ranged from 5.3-622 (mean; 71.3) and 8.2-478 (mean; 76.8) for both summer and winter seasons, respectively. DDTs occurred in both seasons in descending order as: p, p/-DDE, p, p/-DDD, p, p/-DDT, o, p/-DDD, o, p/-DDE and o, p/-DDT. Fig.4.3 a,b showed the compositional percentage of DDTs in sediments during summer and winter season. In summer season, p, p/-DDE in sediment samples occupied 36% of total DDTs followed by DDDs (30%), and p, p/-DDT (25%). During winter season, p, p/-DDE occupied about 38% of total DDTs while DDDs, and p, p/-DDT occupied 31% and 21%, respectively. DDD/DDE, DDD+DDE /DDTs, p, p/-DDT/DDTs were calculated to predict the current and historical usage of DDTs (Fig.4.4; Appendix 4.2). DDD/DDE were ranged from 0.26-2.2 (0.7) and 0.3-3.14 (1.2) for both summer and winter seasons, respectively. (DDD+DDE) /DDTs were ranged from 0.21-1 (0.52) for summer and 0.3-1 (0.65) for winter, while p, p/-DDT/DDTs ranging from 0.05-0.79 for both summer and winter season.
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Chlordanes (cis-chlordane, non-a-chlordane, and trans-chlordane) concentration (ng/g) was ranged from 3.3-27.5 (mean; 9.4) and 1.5-36 (10) for both summer and winter season, respectively. Chlordanes in sediments followed descending order trends as: TC, non-TC and CC. TC contributed more than 50 % in total composition of chlordanes followed by non-TC (30%), and CC (19%). Generally, TC/CC indicative ratios are used to highlight the potential source and age of application of these chemicals (Bidleman et al., 2002). In our study TC/CC, were ranged from 0.26-2.5 (mean; 1.7) for summer and 0.26-2.5 (mean; 0.41) for winter.
The concentration (ng/g) of ∑other OCPs were ranged from 8.36-128 (mean; 37) and 3.7- 193 (mean; 28) for both summer and winter season, respectively. Trends for other OCPs followed as: heptachlor, endosulfan sulphate, dieldrin, HCB, Methoxychlor, β-endosulfan, heptachlor epoxide, aldrin, α-endosulfan and endrin in descending order. Among some other OCPs, concentrations (ng/g) of heptachlor detected in current study were ranged from 2.2-33.6 (mean; 10) for summer and 1.9-56.7 (mean; 12) for winter. On other hand, heptachlor epoxide (degradation product of heptachlor) in the sediments of River Chenab ranged from 3.6-13.7 (mean; 2.6) and 2.8-21 (mean; 3.2) for both summer and winter seasons, respectively. Heptachlor (more than 70 %) found more frequently than its epoxide which detected in 50 % of total samples during both monitoring seasons. The ratios of heptachlor epoxide/heptachlor ranged from 0-0.29 during both monitoring seasons i.e. summer and winter. Dieldrin, aldrin and endrin were also detected in sediment samples with detection frequencies of 50 %, 40% and 20%, respectively. The concentration (ng/g) of these chemicals were recorded in the range of 1.18-19.5 and 0.77-21.5 for aldrin, 7.5-32.5 and 2.1-19.8 for dieldrin, and 1.3-10.5 and 2.1-23 for endrin during both summer and winter seasons, respectively. In current study, aldrin/dieldrin ranged from 0.15-1.18 (mean; 0.26) and 0.06-0.58 (mean; 0.13) for both summer and winter seasons, respectively. Among endosulfans, the concentration of endosulfan sulphate (ng/g) was recorded in the range of 2.19-16.7 (mean; 4.8) for summer and 1.4-27.5 (mean; 5) for winter with 60 % of detection frequency in both seasons followed by β-endosulfan (1.2-13.2 for summer and 1.46- 16.4 for winter) and α-endosulfan (2.3-7.4 for summer and 1.6-4.3 for winter). HCB and methoxychlor were also detected in many of sediment samples collected from River Chenab. Methoxychlor concentrations (ng/g) were in the range of 1.13-25 (mean; 3) and 0.6-24 (mean; 3.6) during both summer and winter season, respectively. HCB were detected in 50 % of total
Page 66 sediment samples and ranged from 1.8-37.6 (mean; 4) for summer and 1.2-46 (5) for winter season.
Basic statistics for PCB concentrations measured in sediments of the River Chenab in summer and winter are given in Table 4.2. The concentrations (ng/g, dry weight) of ∑PCBs (sum of 31 congeners) in the sediments ranged from 9.3- 129 (mean: 23.8) and 12.5-144 (mean: 28.4), during summer and winter season, respectively. Among the PCB congeners; hepta-CBs, penta- CBs and tetra-CBs were more frequent in sediments during both seasons. The trends of different classes of PCBs follows as hepta-CBs (7CBs), penta-CBs (5CBs), tetra-CBs (4CBs), hexa- CBs (6CBs), tri-CBs (3CBs) and octa-CBs (8CBs) in descending order in both seasons i.e. summer and winter. The composition of PCBs in sediments from the River Chenab are consistent with previous studies (Yang et al., 2009; Zhang et al., 2003; Hong et al., 2003), which also suggested the dominance of tetra-PCBs, penta-PCBs and hepta-PCBs. During summer season (Fig.4.5a), proportions of PCB congeners were as follows: tri-CBs (8.89%), tetra-CBs (18.41%), penta-CBs (21.94%), hexa-CBs (11.3%), hepta-CBs (22 %) and octa-CBs (5.8%), while in winter (Fig.4.5b) it was tri-CBs (11%), tetra-CBs (18%), penta-CBs (22.5%), hexa-CBs (9%), hepta-CBs (26.7%) and Octa-CBs (6.7%). The dioxin- like PCB congeners (PCB-126,169, 105,114, and 118) were also detected (ng/g, dry weight) in collected sediments, ranging from 0.83-19.16 (mean; 2.66) and 0.65-19.16 (mean; 2.68) during both summer and winter seasons, respectively.
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Table 4.1. Descriptive statistics of OCPs concentration (ng/g) in sediment samples (n = 25) collected from River Chenab, Pakistan. Summer Winter OCPs N (detected) Mean±Std. Min-Max N(detected) Mean±Std. Min-Max α-HCH 16 2.9±3.2 1.3-14.2 21 4.82±7.17 1.13-30.9 β-HCH 16 4.9±5.8 4.3-26.9 19 6.83±6.04 2-21.51 γ-HCH 24 4.1±2.7 1.73-15.2 21 5.19±6.53 1.2-33.2 δ-HCH 14 1.4±1.8 0.95-8.5 9 1.57±3.79 1.1-18.4 ∑HCH 25 13.5±12.4 1.73-64.9 24 18.4±20.4 2.5-104 HCB 14 3.7±7.6 1.8-37.6 17 4.9±9.43 1.2-46.6 Heptachlor 18 10±11.8 2.23-33.6 19 12.5±13.5 1.91-56.7 Heptachlor-epoxide 11 2.6±4.7 3.63-13.7 12 3.21±4.94 2.84-21.3 Aldrin 9 3.5±5.2 1.18-19.5 13 2.18±4.53 0.77-22.5 Dieldrin 11 4.7±10 7.5-32.5 17 4.58±6.68 2.19-19.8 Endrin 4 2.1±3.8 1.31-10.5 6 2.55±5.22 2.14-23 a-Endo 13 3.1±2.2 2.3-7.49 15 0.84±1.38 1.59-4.3 b-Endo 11 2.8±3.1 1.26-13.2 14 2.58±3.63 1.46-16.4 Endo-sulphate 16 4.8±4.8 2.19-16.7 18 4.97±6.76 1.4-27.5 Methoxychlor 11 3.12±5.8 1.13-25.1 12 3.6±6.1 0.61-24.6 ∑other OCPs 24 37.8±28 8.63-128 25 42.±41.1 3.73-193 Cis-chlordane (CC) 14 1.8±2.3 1.12-7.8 17 2.45±3.11 1.21-13.9 Trans-chlordane (TC) 21 4.7±4.18 1.34-13.8 22 5.34±4.27 1.12-17.3 Trans-nonachlordane(non-TC) 17 2.9±2.9 0.91-9.8 19 3.1±3.22 1.1-15.6 ∑Chlordanes 24 9.4±7.9 3.3-27.5 25 10.9±9.5 1.5-36 p, p'-DDE 20 25±49 3.7-243 23 29.2±43.5 4.89-181 o,p'-DDE 6 2.3±3.3 1.6-11.7 9 1.91±2.55 0.64-9.3 ∑DDE 25 27.1±52.7 3.7-255 25 31.1±45.2 2.3-189 o,p'-DDD 13 3.2±6.1 1.5-28.4 13 3.53±5.4 1.42-16.7 p, p'-DDD 21 19.27±33.27 2- 136 23 23.11±26.8 1.30-114 ∑DDD 25 22.5±38.2 4.5-164 25 26.6±30 4.4-130 o,p'-DDT 8 4.1±10.77 1.17- 51.3 9 2.53±3.9 0.87-14.3 p, p'-DDT 17 17.59±38.37 3.6-187 19 16.57±29.8 2.3-143 ∑DDTS 25 71±126 5.32-622 25 76.8±101 8.2-478 ∑OCPS 25 132±165 29-831 25 148±165 11.4-811
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Table 4.2. Descriptive statistics of Polychlorinated biphenyles (PCBs) concentrations (ng/g) in sediment samples (n=25) collected from River Chenab, Pakistan. Summer Winter PCB Congeners. N Mean ± Std Min-Max N Mean ± Std Min-Max pcb-180 22/25. 0.60±0.78 0.3-4.2 23/25. 0.69 ±0.77 0.34-4.2 pcb-170 24/25. 0.58±0.21 0.32-1.17 25/25. 0.77 ±0.49 0.17-2.5 pcb-189 21/25. 2.61±5.27 0.32-26.24 24/25. 4.32 ±5.25 0.77-26.24 pcb-198 25/25. 1.38±0.66 0.47-3.2 25/25. 1.62 ±1.11 0.61-4.23 pcb-183 19/25. 0.46±0.27 0.31-1.15 18/25. 0.54 ±0.44 0.31-1.5 pcb-166 20/25. 0.32±0.22 0.21-0.77 21/25 0.32 ±0.20 0.3-0.73 pcb-158 22/25. 0.55±0.28 0.21-1.21 20/25. 0.44 ±0.24 0.49-0.67 pcb-128 20/25. 0.39±0.27 0.16-0.93 23/25. 0.38 ±0.19 0.16-0.62 pcb-156 23/25. 0.41±0.51 0.18-2.05 24/25. 0.54 ±0.51 0.18-2.05 pcb-169 24/25. 0.65±0.43 0.43-2.21 22/25. 0.60 ±0.0.46 0.4-2.21 pcb-179 17/25. 0.67±1.03 0.42-4.97 21/25. 0.86 ± 1.21 0.3-4.97 pcb-187 22/25 0.44±0.21 0.11-0.88 18/25 0.43 ±0.35 0.32-1.4 pcb-153 21/25. 0.39±0.24 0.32-0.93 18/25. 0.41 ±0.39 0.46-1.89 pcb-138 23/25. 0.61±0.37 0.2-1.87 21/25. 0.47 ±0.26 0.53-0.72 pcb-105 23/25. 0.63±0.53 0.12-2.78 24/25. 0.84 ±0.66 0.43-2.78 pcb-126 16/25. 0.41±0.4 0.43-1.97 20/25. 0.53 ±0.46 0.43-1.97 pcb-114 20/25. 0.39±0.21 0.47-0.77 23/25. 0.49 ±0.27 0.44-1.4 pcb-99 21/25. 0.92±1.24 0.42-5.17 25/25. 0.90 ±1.04 0.43-5.17 pcb-87 21/25. 1.04±1.93 0.38-8.67 22/25. 1.08 ±2.07 0.38-8.67 pcb-82 21/25. 0.68±0.91 0.38-4.45 25/25. 0.78 ±0.92 0.33-4.45 pcb-118 22/25. 0.81±1.02 0.5-4.82 25/25. 0.83 ±0.92 0.5-4.82 pcb-77 18/25. 5.57±19.9 0.42-14.62 25/25. 1.54 ±3.16 0.42-14.98 pcb-101 20/25. 0.72±1.09 0.12-5.16 19/25. 0.77 ±1.09 0.42-5.16 pcb-66 24/25. 0.61±0.36 0.49-2.14 25/25. 0.63 ±0.37 0.49-2.14 pcb-60 19/25. 0.50±0.45 0.5-2.27 24/25. 0.75 ±0.49 0.5-2.27 pcb-37 18/25. 1.43±2.16 0.65-9.5 18/25. 1.30 ±2.05 0.48-9.5 pcb-49 17/25. 0.67±1.28 0.33-5.85 25/25. 0.85 ±0.88 0.29-3.43 pcb-44 18/25. 0.94±1.85 0.32-8.59 25/25. 1.11 ±1.76 0.32-8.59 pcb-70 21/25. 1±1.68 0.44-6.41 24/25. 0.87 ±1.24 0.44-6.41 pcb-74 21/25. 0.64±1.03 0.41-5.43 23/25. 0.90 ±1.44 0.12-5.77 pcb-28 12/25. 0.68±1.24 0.53-5.91 21/25. 1.86 ±1.71 0.42-5.91
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Table 4.3. Reported levels (ng/g) of OCPs in the sediment samples collected from different parts of the World.
Location ∑ HCHs ∑ DDTs ∑ OCPs References Range Mean Range Mean Range Mean River Chenab, Pakistan 2- 104 15 7.64 - 660 75 4.23 - 811 140 This Study Lower Mekong River, Thailand. 0.001-9.9 0.027-52 Sudaryano et al., 2011 Huaihe River, China. 1.95-11 4.53 4.07-23 11.07 2 -35.5 16 Sun et al., 2010 Haihe River, China 11.9-1620 547 n.d.-155 18.5 0.997-2447 738 Zhao et al., 2010 Fu River, China 4.7-678 146.3 1.3-51.3 15 5.4-707 161 Guocheng et al., 2010 Lan-yan River, Taiwan 0.16 - 0.59 0.23 - 0.69 - 0.37 - 0.90 - Chang and Doong, (2006) Erh-jen River, Taiwan 0.25 - 1.24 0.16 - 0.64 - 0.17 - 5.04 - Chang and Doong, (2006) Bay of Bengal, India 0.17 - 1.5 - 0.04 - 4.79 - - - BabuRajendran et al., (2005) Haihe River, China 1.88 -18 7.33 0.32 - 80 15.9 - - Yang et al., (2005) Singapore Coast 3.3 - 46 - 2.2 - 11.9 - - - Wurl and Obbard., (2005) Dagu Drainage River, China 33.24 - 141 87 3.6 - 83.4 35.9 - - Yang et al., (2005) Qiantang River, China 9.23 - 152 37.7 1.14 - 100 21.6 23 - 316 93.67 Zhou et al., (2006) Kizilirmak River, Turkey (wet wt.) - 5 - - - - Bakan and Ariman, (2004) Yesilirmak River, Turkey (wet wt .) 7 Bakan and Ariman, (2004) Yangtze River, China - - BDL - 0.6 - - Liu et al., (2003) Minjiang River, China 2.99 - 16.2 8.62 1.57 - 13 6.7 28.7 - 52 36.7 Zhang et al., (2003) Wu-Shi River, Taiwan 0.99 - 14.5 3.78 N.D. - 11.4 1.07 0.99 - 14.5 13.9 Doong et al., (2002a) Da-han River, Taiwan N.D. - 2 0.39 0.06 - 2.91 0.9 0.21 - 7.24 2.3 Doong et al., (2002b) Erh-jen River, Taiwan N.D. - 3.4 1.03 N.D. - 2.03 0.72 0.08 - 8.20 2.67 Doong et al., (2002b) Day Bay, PRC 0.32 - 4.1 0.14 - 0.3 - - Zhou et al., (2001) Casco Bay, USA 0.25 - 0.4 - 0.25 – 20 - - - Lee et al., (2001) Ebro River (Spain) - 0.038 - 51.8 - - Fernandez et al., (1998) Arabian sea, India 0.85 - 7.8 - 1.47 - 25.2 - - - Sarkar et al,. (1997) Xiamen Harbor, PRC 0.14 - 1.12 0.45 4.43 – 311 42.8 - - Hong et al., (1995) Swan River, Australia - 1.2 - 2.1 - - Iwata et al., (1994) Parramatta River, Australia - 7.7 - 26 - - Iwata et al., (1994)
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> 15 ng/g
>500 ng/g
30 ng/g >100 ng/g
Fig.4.1. Showing the spatial patterns of HCHs, Chlordanes, DDTs and PCBs recorded in the sediment from River Chenab, Pakistan.
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a-HCH b-HCH g-HCH d-HCH
100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% a.
100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0%
b. Sampling Sites
Fig.4.2. Composition (%) of HCHs in sediments from all the sampling locations during (a). sumer and (b). winter.
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p,p'-DDE o,p'-DDE o,p'-DDD p,p'-DDD o,p'-DDT p,p'-DDT
100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% a.
100% 90% 80% 70% 60% 50% 40% 30% 20% 10%
0%
2 7 1 3 4 5 6 8 9
------
10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
------
S S S S S S S S S
S S S S S S S S S S S S S S S S b. Sampling Sites
Fig.4.3. Composition (%) of DDTs in sediments from all the sampling locations during (a). summer and (b). winter
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1.20 S 7 S 19 1.00 S 1 S 3,9, 12 S 17 S 2 0.80 S 14 S 4, 21 S 5 S 18 S 11 S 22 0.60 S 20 S 6 S 25 S 16 S 13 S 10 0.40 S 8
S 15 (DDD+DDE)/DDTs 0.20 S 24
0.00 0.00 0.50 1.00 1.50 2.00 2.50 DDD/DDE
a. 1.20 S 23 1.00 S 10 S 8 S 11
S 3 S 21 S 9 S 19 S 18 S 13 0.80 S 2 S 17 S 5 S 4 S 25 S 16 S 1 S 20 S 24 0.60 S 7 S 14 0.40 S 6 S 22
(DDD+DDE)/DDTs S 15 0.20 S 12 0.00 0.00 0.50 1.00 1.50 2.00 2.50 3.00 3.50 DDD/DDE
b.
Fig.4.4. Showing the crossplot of (DDD/DDE)/DDTs and DDD/DDE during both (a. summer and (b. winter season.
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100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0% 3 CBs 4Cbs 5CBs 6CBs 7CBs 8CBs a.
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
9 1 2 3 4 5 6 7 8
------
14 19 10 11 12 13 15 16 17 18 20 21 22 23 24 25
------
S S S S S S S S S
S S S S S S S S S S S S S S S S
Aroclor1260 Aroclor1221 Aroclor1232 Aroclor1242 Aroclor1262 Aroclor1248 Aroclor1254 Arochlor 1016 Arochlor b. Sampling Sites Fig.4.5. Composition (%) of PCBs in sediments from all the sampling locations during (a). summer and (b). winter.
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Summer Winter
900.00 800.00
700.00 600.00 500.00 400.00 300.00 (Concentration ng/g) (Concentration 200.00 Region-4 100.00
0.00
S 9 S S 2 S 5 S 8 S 4 S 1 S 3 S 6 S 7 S
S18
S 23 S 14 S 19 S S 20 S 16 S 17 S 24 S 10 S 11 S 12 S 13 S 15 S 21 S 25 S 22 S
Region-1 Region-2 Region-3
Fig.4.6. Levels of OCPs in the sediments collected during summer and winter season.
Fig.4.7. Concentrations (ng/g) of ∑PCBs in the collected sediment samples. ERL (Long et al., 1995); TEL for PCBs in fresh water sediment, threshold effect level (CCME, 1999).
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Summer Winter
120 100 80 60 40 20
0
TC
TC
CC
-
DDE DDT
HCB
DDE DDT
HCH
HCH
HCH
HCH
Endo
Endo
DDD
DDD
- -
- -
-
-
-
-
-
-
-
-
Aldrin
Detrection frequency (%) frequency Detrection
Endrin
γ
δ
a
β
b
α
epoxide
non
sulphate
Dieldrin
-
-
p,p' o,p' o,p' p,p'
o,p' p,p'
Heptachlor
Methoxychlor
Endo Heptachlor
Fig.4.8. Detection frequencies of all the OCPs in the collected sediments from River Chenab, Pakistan.
4.2.3. Classification of sampling sites To explore similarities between the various sites (Fig.4.9), cluster analysis grouped sites into 4 regions (Region-1, Region-2, Region-3 and Region-4). Site Viz: S-20 was identified as an outlier (Fig.4.9) which showed highest concentration of OCPs and PCBs in the collected sediments and located on the industrial drain receiving untreated effluents from paper, textile, rubber and other industries from Faisalabad city and joined with River Chenab in district Jhang. The basic statistic of each group (region) is mentioned in Appendix 4.3. Among four regions classified by cluster analysis; Region-1 (S-18 and S-23) was categorized as most polluted region and located in industrial areas followed by Region-2 (S-2, S-5, S-8, S-16, S-17 and S-24). All the site location of Region-2 were located in the urban area and receiving a high pollution load from surrounding soils, atmospheric deposition and open dumping of municipal waste. Region-3 (S-9, S-10, S-11, S-12, S-13, S-14, S-15, S-21, S-25) and Region- 4 (S-1, S-3, S-6, S-7, S-19 and S- 22) consisted of agricultural sites in the study area. However, these regions were relatively least polluted than Region-1 and Region-2, but some of the site locations showed high levels of OCP which entered in the river’s ecosystem by surface run-off from surrounding cropland.
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Unw eighted pair-group average Euclidean distances
S-1 S-3 S-4 4 S-6 S-7 S-22 Region S-19 S-9 S-10 S-21 S-12 3 S-13 S-11 S-14 Region S-25 S-15 S-2 2 S-8 S-17 S-5 Region S-16
1 S-24 S-18
S-23 Region 0 20 40 60 80 100 120 (Dlink/Dmax)*100
Fig.4.9. Cluster analysis for sampling sites based on OCPs and PCBs concentration and physicochemical parameters.
4.2.4. Source identification using Factor Analysis (FA/PCA) FA/PCA was also employed for source identification of studied physicochemical parameters, OCP, and PCBs in the sediments collected from 25 sites during two sampling season i.e. summer and winter. Temporal FA/PCA extracted seven varimax factors (VFs) for both monitoring seasons with eigen value more than 1, explaining 86% and 83% of total variance (Table 4.4).
For summer season, out of seven VFs, VF1 explained 48% of total variance with strong positive correlation on pH, EC, TDS, alkalinity, phosphorus, nitrates, organic matter, α-HCH, β- HCH, γ-HCH, δ-HCH, HCB, methoxychlor, p, p/-DDE, p, p/-DDD, o, p/-DDD, p, p/-DDT and PCBs, interpreting agricultural and industrial activities as a source of these pollutants in the study area. Source for highlighted parameters viz; pH, EC, TDS, alkalinity, phosphorus, nitrates,
Page 78 and organic matter mainly related with agricultural run-off from adjoining fields, the natural mineral composition of river sediments, and industrial activities in the study area. Mineral composition of River Chenab’s sediment mainly comprises of thick deposits of calcareous, fine and wind laid sands and silty material, which originates from sand stones and shale rocks of catchment area especially salt range, which resulted in the alkaline like situation and high values of dissolved salts, one of the main characteristics of the water bodies in Pakistan. Source of NO3- N is mainly correlated with the excessive use of nitrogenous fertilizers in the agricultural fields. Nitrogenous fertilizers like urea used excessively in Pakistan, undergo extra cellular enzymatic decomposition to form ammonium compounds, which are either absorbed by the plant roots or converted to nitrates, which are absorbed or lost in leaching or converted to gases in the nitrogen cycle (Singh et al., 1995). The high nitrate rates can increase the nitrate content, which can pollute the river environment by surface runoff and atmospheric deposition (Makrani, 2010). Soils of Pakistan are poor in organic matter (below one per cent) and the use of organic fertilizers is not common, moreover, the rates of chemical fertilizer use have increased the recommended levels. Therefore, under such conditions the nitrate pollution in drinking water and surrounding water bodies cannot be neglected. In toting to this, industrial activities and municipal waste also played important role to add up excessive amounts of these nutrients and organic matter which not only deteriorates for water and sediment quality, but severely affects the ecological integrity of riverine ecosystem. This factor interpreted that among organochlorines, HCHs may come from surrounding agricultural fields and also predicted the aged as well as recent use of these chemicals. Positive loading on HCBs, methoxychlor, p, p/- DDT and PCBs put forward that industrial activities were mainly responsible for the high concentration of these chemicals especially near the industrial areas of Faisalabad, Gujrat, and Sialkot. This factor also predicting the biodegradation of DDTs and p, p/-DDE entering into riverine ecosystem via surface run-off from surrounding agricultural fields, while p, p/-DDD and o, p/-DDD showed the anaerobic condition in River Chenab, Pakistan. VF2 explained 11.6% of total variance and showed positive correlation on endosulfan sulphate and trans-chlordane from surrounding agricultural fields via surface run-off or from river bed re-suspension. This factor highlighting the long-term use of endosulfan for crop protection and dominance of endosulfan sulphate in the sediments of River Chenab. It may be due to more affinity and association of this chemical with particulate material resulted to settle down in the sediment bed. VF-3 explained
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9.6 % of total variance with positive loading on heptachlor and dieldrin showing the widespread use of these pesticides in the study area for agricultural purposes. VF-4 explained 6.3% of total variance; positively correlated on non-a-trans chlordane, while negative correlation on pH. This factor showed a decline of pH value due to high organic matter content which may affect many reactions in the sediment (Cirmo et al., 2000). Acidic conditions accelerated the fluxes of some strong acidic anions (e.g., NO3 -N). On the other hand, increased hydrogen ion concentration also associated with clay particles which resulted in chemical solution of minerals. Hydrogen ion also removed Al3+ ions held within the structure of soil minerals and accelerated the formation of organic complexes (Brady and Weil, 1990).While positive loading on non-a-trans chlordane showing the biodegradation of chlordanes in the River Chenab. Each of VF -5, 6 and 7 explained less than 5% of total variance and very strong positive loading on silt, α-endosulfan, and p, p- DDD and negative loading on sand, respectively.
For winter season out of seven VFs, VF1 explained 48% of total variance with strong positive loading on EC, TDS, nitrates, organic matter, α-HCH, β-HCH, HCB, aldrin, endrin, methoxychlor, cis-chlordane, p, p/-DDD, p, p/-DDE, p, p/-DDT and PCBs. This factor explains the source of pollution from industrial drains along with agricultural run-off. However, the positive loading on aldrin and cis-chlordane showed the recent use of these pesticides for crop protection in agricultural areas. The sites located in agricultural areas receiving pesticides load from adjoining fields due to severely eroded banks and agricultural run-off. On other hand, River Ravi (S-8), River Sutlej (S-24), Khanki barrage (S-23) and Industrial drain (S-20) from Faisalabad city, bring effluents from major industrial cities such as Qasoor, Ravi, Gujarat, which contributed in deterioration of sediment quality. In winter, the elevated values of all these parameters related to low water flow, which resulted in high depositional rates of sediment. OCP dilution and diffusion largely depends upon the temperature and in present study, low temperature and low flow of water in the winter, may be the factors resulted in the high concentration of aldrin and heptachlor endoepoxide in this season. VF2 explained 11% of total variance showing positive loading on silt and negative loading on sand. A total of 10% variance explained by VF3, showing positive loading on nitrates, β-endosulfan, and heptachlor epoxide. VF4 highlighted a positive correlation on endosulfan sulphate and non-TC explaining 6% of total variation. Both of these factors interpreted historical use of these pesticides in the study area
Page 80 which entered into river body through surface run-off from nearby agricultural fields. These factors also suggested the use of fertilizers in surrounding agricultural areas, which get their way into riverine system by surface run-off or wet deposition. VF5, VF6 explained less than 5% of total variance with positive loading on p, p/-DDD and β-HCH.
Spatial FA/PCA extracted five VFs for Region-1, six VFs for Region-2 and Region-3, and five VFs for Region-4 with eigenvalue more than 1 explaining 94%, 92%, 94%, and 63% of total variance, respectively (Table 4.5).
For Region-1, VF1 explained 44% of total variance in which p, p/-DDE, o, p/-DDD, p, p/-DDD, PCBs has strong positive correlation while aldrin, endrin, heptachlor, and β–HCH showed negative correlation. This factor explained that high concentrations of aldrin, endrin, heptachlor and β–HCH in Region-1 indicating long-term use of these chemicals in agricultural sector to protect the trees from insects and also for crop protection. Source of p, p/-DDE, o, p/- DDD, p, p/-DDD, PCBs can be linked with anthropogenic processes in the catchment area. All the sites of this region located in industrial areas and chlorinated pesticides, and PCBs in this region were mainly related to industrial activities and municipal waste disposal in the river body. VF2 explained 17% of total variance and pH, EC, TDS, alkalinity, nitrates, phosphate, organic matter and clay showed strong positive loading while β-endosulfan, and chlordanes with negative loading. This factor interpreting the source of highlighted parameters (pH, EC, TDS, alkalinity, nitrates, phosphate, organic matter and clay) from industrial waste disposal along with natural mineral composition, which ultimately resulted in the high values of these parameters. On the other hand, fertilizer manufacturing units and extensive use of fertilizers in surroundings are also playing major role by dramatically increasing the phosphate and nitrates load in the aquatic ecosystem of Pakistan. VF3 explained 11.41% of total variance showing strong positive loading on β–HCH, p, p/-DDE, PCBs, and organic matter. This factor showed the surface runoff from contaminated land as a main contributing factor. VF4 and VF-5 explained more than 5% of total variance and showed positive loading on PCBs. These factors blamed industrial activities as a source of this sever pollution in this region.
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For Region-2, out of six varimax factors (VFs), VF1 explained 33% of total variance and showed positive loading on EC, TDS, δ–HCH, HCB, heptachlor epoxide, endosulfans, chlordanes, DDT metabolites, and PCBs. This factor interpreting both agricultural and municipal waste as major source of pollution in the study area and upstream flow of industrial pollutants can also be one main reasons for some industrial-based chemicals including PCBs in the study area (Region-2). Positive loading on DDT metabolites may also be the upshot of accelerated chemical reaction rate on these polluted sites. VF2 explained 21% of total variance, positively related to organic matter, clay, α–HCH, p, p/-DDD, PCBs, aldrin, endrin, and negative with sand, representing their source mainly due to agricultural activities in Region- 2 where pesticides are being used to protect the crops from insects and different diseases. Moreover, strong correlation of highlighted organochlorinated contaminants with organic matter and clay content indicating high affinity and lipophilicity (log Kow) to adsorb onto clay and strongly associated with organic matter. VF3 explained 14.37% of total variance and showed positive correlation on heptachlor, showing the wide scale use of this chemical in current study area. VF4 explained 11.4% of total variance, positively loaded on pH, p, p/- DDT. VF5 explained 8.65% of total variance and showed negative loading on nitrates. VF6 explained 3.5% of total variance showing positive loading on p, p/-DDE. Explained factors (VFs) highlighted anthropogenic activities such as agricultural runoff and industrial and municipal played important role OCP contamination. In general, source of pollution in the Region-1 and 2, mainly related to industrial activities and disposal of municipal waste in the river body, while agricultural activities also contributed as a source but not as others.
For Region-3: out of six variables, VF1 explained 46% of total variance and showed positive loading on γ –HCH, δ–HCH, and p, p/-DDE, endrin, endosulfan sulphate, alkalinity, silt and negative on sand. This factor represented the high residue level of γ –HCH, endrin, endosulfan sulphate and DDE from agricultural fields. While the source of alkalinity was the natural occurring minerals in the riverine system.VF2 explained 19% of total variance showing positive loading on alkalinity, cis-chlordane, and negative heptachlor, p, p/-DDE. VF3 explained 11% of total variance and showed positive loading to Clay.VF4 explained 9% of total variance and VF5 and VF6 explained more than 3% of total variance and extracted o, pʹ-DDT and γ –
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HCH as important variables, respectively. VF5 and VF6 interpreted the source of o, p/-DDT and γ –HCH from agricultural activities.
For Region-4: out of five variables, VF1 explained 31.4% of total variance and showed negative loading on β–HCH , γ –HCH, o, p/-DDD, α-endosulfan, non-TC, silt. This factor represented the high residue level of OCPs from agricultural fields and indication to metabolic products of these chemicals reflecting the long term use.VF2 explained 10.3 % of total variance showing positive loading on EC, clay, o, p/-DDD and negative on pH and sand. This factor showed a decline of pH value due to high organic matter content which may affect many reactions in the sediments (Cirmo et al., 2000). Acidic conditions accelerated the fluxes of some -4 strong acidic anions (e.g., SO2 and NO3-N) and increase the rate of OCP-metabolites due to rapid degradation of parent compounds. On the other hand, increased Hydrogen ion concentration also associated with clay particles which resulted in chemical solution of minerals. Hydrogen ion also removed Al3+ ions held within the structure of soil minerals and accelerated the formation of organic complexes (Brady and Weil, 1990). VF3 explained 8.5% of total variance and showed positive loading to phosphate, endosulfan-sulphate, and o, p/-DDE.VF4 explained 8% of total variance and showed positive loading on γ –HCH and heptachlor epoxide. VF5 explained more than 3% of total variance and extracted nitrates. Both of these factors interpreted agricultural activities as main source of this contamination in this region. Regarding, Region-2 and 3, source of organochlorine contamination is mainly related to agricultural activities. As most of the sites included in these regions are located in the agricultural area and use of pesticides is very common for crop protection.
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Table 4.4. Values of seven factor loadings for the studied OCs and quality parameters.
Parameters VF-1 VF-2 VF-3 VF-4 VF-5 VF-6 VF-7 Parameters VF-1 VF-2 VF-3 VF-4 VF-5 VF-6 PH -0.75 EC 0.96 EC 0.98 TDS 0.96 TDS 0.98 Alkalinity 0.73 Nitrates 0.79 Organic Matter 0.39 Phosphate 0.75 Silt 0.89 Nitrates 0.84 Sand -0.89 Organic Matter 0.71 α-HCH 0.79 Silt 0.95 β-HCH 0.93 0.85 Sand -0.88 HCB 0.93 α-HCH 0.77 Heptachlor 0.74 Heptachlor β-HCH 0.84 epoxide 0.86 γ-HCH 0.89 Aldrin 0.95 HCB 0.93 Endrin 0.87 Heptachlor 0.77 β-endosulfan 0.89 Endosulfan Dieldrin 0.75 sulphate 0.78 α-endosulfan 0.91 Methoxychlor 0.78 Endosulfan sulphate 0.84 CC 0.77 Methoxychlor 0.84 non-TC 0.84 TC 0.89 p, p'-DDE 0.81 non-TC 0.82 p, p'-DDD 0.79 0.77 p, p'-DDE 0.93 p, p'-DDT 0.86 o,p'-DDD 0.92 PCBs 0.90 p, p'-DDD 0.78 0.85 p, p'-DDT 0.93 PCBs 0.91 Eigen values 16.8 4.0 3.4 2.2 1.6 1.3 1.0 Eigen values 16.8 3.9 3.5 1.9 1.6 1.4 % Variance % Variance Explained 48.0 11.6 9.6 6.3 4.6 3.7 2.9 Explained 48.0 11.0 10.1 5.6 4.7 3.9 *Values of marked loadings more than 0.70 are significant in each factor.
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Table.Table 4.5. 4.6. Values of Seven factor loadings for the studied OCs and quality parameters in the sediment of River Chenab (for four spatial groups). Region-1 Region-2 Region-3 Region-4 Parameters VF-1 VF-2 VF-3 VF-4 VF-5 VF-1 VF-2 VF-3 VF-4 VF-5 VF-6 VF-1 VF-2 VF-3 VF-4 VF-5 VF-6 VF-1 VF-2 VF-3 VF-4 VF-5 pH 0.83 0.83 -0.9 EC 0.83 0.81 0.72 TDS 0.83 0.83 Alkalinity 0.82 0.91 Chloride 0.85 0.87 0.84 -0.8 Phosphates 0.73 0.79 Nitrates 0.81 -0.96 0.84 Organic Matter 0.77 0.75 0.74 0.75 Clay 0.94 -0.8 0.89 Silt 0.85 0.79 -0.79 Sand -0.71 -0.8 -0.9 α–HCH 0.71 β–HCH -0.91 0.87 -0.93 γ –HCH 0.82 0.85 -0.81 0.75 δ–HCH 0.86 0.87 HCB 0.92 Heptachlor -0.89 0.9 0.79 Hep-epoxide 0.87 0.79 Aldrin -0.97 0.82 Endrin -0.81 0.73 0.73 Dieldrin 0.68 α-endosulfan 0.87 -0.83 β-endosulfan -0.8 0.91 0.9 Endo-sulphate 0.84 0.89 -0.76 Cis-Chlordanes 0.93 0.9 Trans-Chlordanes -0.9 0.9 non-α- Trans Chlordane -0.8 -0.82 p, p'-DDE 0.87 0.82 0.97 0.81 0.74 o, p'-DDE 0.97 0.86 0.77 o, p'-DDD 0.77 -0.8 p, p'-DDD 0.83 0.82 0.82 o, p'-DDT 0.87 -0.79 p, p'-DDT 0.77 0.75 PCBs 0.88 0.76 0.78 Eigen value 19.09 13.9 7.13 6.5 3.5 16.1 9.37 5.43 4.07 3.05 3.04 11 6.98 5.77 4.42 3.7 3.15 12.6 8.04 5.87 4.64 2.53 % Total Variance 44.82 17.4 11.2 9.49 5.32 33.2 21.2 14.4 11.4 8.65 3.98 46.3 19.4 11.5 9.4 4.5 3.3 31.4 10.6 8.43 8.1 5.4 Cumulative % Variance 44.82 68.2 79.4 88.8 94.2 33.2 54.4 68.8 80.2 88.9 92.9 46.3 65.7 77.2 86.6 91.1 94.4 31.4 42 50.5 58.6 63.95
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4.3. Discussion 4.3.1. Characterization of OCP concentrations in sediment and their global comparison
This study provided the first systematic data on the distribution of OCPs and PCBs in sediment of River Chenab, Pakistan and highlighted OCP contamination as one of the important environmental issues due to excessive use of these pesticides in agriculture and industrial sector. The residual concentration (ng/g) of OCPs detected in the current study was considerably high as compared to those measured in sediment from Da-han and Erh-jen Rivers, Taiwan (Doong et al., 2002 a, b), Bay of Bengal, India (Babu et al., 2005), and Huaihe River, China (Sun et al., 2010). The reported of ΣOCP levels from River Chenab’s sediment are comparable to those collected from Qiantang River, China (Zhou et al., 2006) and Fu River, China (Guocheng et al., 2010). Moreover, Zhao et al., (2010) reported far high values of OCPs in the sediments collected from the Haihe River China.
4.3.1(a). HCHs
HCH isomers have different physicochemical properties (Appendix 1.1). Isomerization of α-HCH and γ-HCH into β-HCH in the environment is common process and reported by many studies (Sun et al., 2010; Zhao et al., 2010). As a result, β-HCH adsorbed onto sediment due to relatively more lipophilic nature (high log Kow).The dominance of β-HCH in river sediment, if there is no new input of technical HCH and high concentration of γ-HCH is the reflection of high lindane application rates. Our results showed the high contribution of both β –HCH and γ-HCH in the HCHs composition, reflected the wide scale use of lindane in the study area. Technical HCH, containing α -isomer more than 70% while γ-HCH more than 90% in lindane, have been used in present study like other worldwide use of HCHs (Zhang et al., 2003, Sun et al., 2010). However, β-HCH in the Chenab’s sediment resulted in the isomerization of α-HCH and γ-HCH, which ultimately resulted in the accumulation of this chemical in sediments. Indicative ratios of HCHs can also be useful to indicate the historical application (Kim et al., 2002; Sun et al., 2010). In our study, α/γ HCH reflected the recent excessive use of both lindane as well as technical
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HCH in the study stretch. While high ratios of β/ γ HCH predicting the isomerization of α and γ HCH into β-HCH in the river environment and surrounding soils, resulted in the contamination of β- HCH. Moreover, the elevated concentrations of β-HCH are results of HCHs degradation over long time and reflected the aged usage of HCHs in the study area.
The mean concentration of ∑HCH measured in current study (13.5 and 18.4 ng/g in winter and summer, respectively) are within the range of those detected in sediments of the Wu- Shi River from Taiwan (Doong et al., 2002a), Kizilirmak River from Turkey (Bakan and Ariman, 2004), Haihe River, China (Yang et al., 2005), and Singapore Coast (Wurl and Obbard, 2005). However, sediments from China (Zhou et al., 2006; Zhao et al., 2010; Guocheng et al., 2010) showed far high concentration of ∑HCH as compared to those recorded in current study.
4.3.1(b). DDTs and its extent of pollution
Concentration of DDTs in sediments during both seasons was more prevalent than HCHs, chlordanes and other OCPs. Technical DDT grade contain 75% p, p/-DDT, 15% o, p/-DDT, 5% p, p/-DDE and less than 5% others (Hites and Day, 1992; Yang et al., 2005). Fig.4.3 a,b showed the compositional percentage of DDTs in sediments, indicating recent input of DDTs, aged DDT breakdown products as well as anaerobic conditions in River Chenab. Biodegradation of DDTs into its metabolites in riverine ecosystem cannot be neglected, which may be another reason of high concentration of its metabolites in river sediments (Zhang et al., 1999; Peris et al., 2005). DDT can be biodegraded into DDD via reductive dechlorination under anaerobic condition and to DDE under aerobic condition through dehydrochlorination, an oxidative process (Kalantzi et al., 2001; Luo et al., 2004). In our study, dominance of p, p/-DDE and p, p/-DDD, breakdown product of DDTs, showed the long term weathering of DDTs in the study area. High detection frequency and concentrations of p, p/-DDD in sediments during both seasons, resulted due to anaerobic degradation of DDTs in the river environment, while high contribution of p, p/-DDE in total DDTs attributed to surface runoff from surrounding contaminated soils. Among isomers, p, p/-DDT was more frequently detected compound during both seasons, showed new inputs of DDTs which has not yet degraded. New inputs of DDT can maintained high compositional percentage of DDT (Sarkar et al., 2008), while DDT proportions gradually reduced when there is
Page 87 no more new input of DDT but the concentration of metabolites gradually increased (Doong et al., 2002a).
Different indicative ratios (DDE+DDD/∑DDT, p, p/-DDT /∑DDT and DDD/DDE) are widely used to assess the decomposition of parent compound and recent DDT input (Phuong et al., 1998; Doong et al., 2002b; Sarkar et al., 2008). As seen, by this technique, (DDE+DDD)/∑DDT more than 0.5 during both seasons (Fig.4.4 a, b), suggested that sediments undergo a long term weathering process (Hong et al., 1999; Yang et al., 2005). Alternatively, p, p/-DDT /∑DDT ratios exceeded 0.5 at some sampling stations located in industrial areas (i.e. S- 18, S-20, and S-23), associated with recent input of p, p/-DDT in both season. DDD/DDE ratio (Fig.4.4a,b) also increased more than 1 for 41 % of sediments during both campaigns, highlighting high proportion of DDD and indication of anaerobic environmental conditions (Bossi et al., 1992) in River Chenab, Pakistan. Moreover, greater concentration of DDTs can be linked to its chronological use as well as excessive use of dicofol as a pesticide. DDT is used in manufacturing of dicofol as byproducts (Minh et al., 2007). High residual level of dicofol in sediments is related to its use in agricultural activities as a cheaper pesticide and can also be blamed as additional source of o, p/-DDT (Zhang et al., 2003; Leung et al., 2005; Wie et al., 2008). Mean concentration (ng/g) of ∑DDTs detected in current study in summer (71.3) and winter (76.8) are in the range of those measured in sediment from Chinese water bodies (Hong et al., 1995; Yang et al., 2005; Zhao et al., 2010) and Ebro River from Spain (Fernandez et al.1998). However, measured concentrations (ng/g) are far greater to those found in sediment of Mekong River, Thailand (Sudaryano et al., 2011), Swan River Australia (Iwata et al., 1994), Rivers from Taiwan (Doong et al., 2002a; 2002b).
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4.3.1(c). Chlordanes
The detected level of chlordanes in our sediments are comparable with those of reported by Zhao et al., (2010) from Taihu Lake, China, but far high than those reported from the Thailand’s water bodies (Sudaryano et al., 2011), which reflected the large scale use of chemical in current study area. Generally, TC/CC indicative ratios have been reported to highlight the potential source and age of application of these chemicals (Bidleman et al., 2002). In our study, TC/CC reflected the both historical and recent use of this chemical in the study area. TC were found predominantly in summer season, showed that this chemical entered into the riverine ecosystem from surrounding freshly contaminated soils via surface run-off. In winter, the low flow season, lower ratio predicts the dominance of CC, which is most persistent form of chlordanes and may be converted from TC (less persistent) in the riverine ecosystem (Qiu et al., 2009). Generally, the chlordanes are used in the agricultural sector for the crop protection but high concentration of this chemical from the urban and industrial areas also reflected the high consumption rates for industrial purposes.
4.3.1(d). Other OCPs
Generally, heptachlor found more frequently in sediments than those of its epoxide during both monitoring seasons. The scenario clearly reflected the recent use of this chemical for insect control and in seed and wood preservation. The enrichment of heptachlor in the sediments mainly comes from flooding and surface runoff from agriculture cropland. Moreover, a high concentration of heptachlor from industrial and urban site location, signatures the input of this contaminant in riverine ecosystem from municipal and industrial drains and atmospheric deposition (Zhou et al., 2006; Leong et al., 2007). Aldrin, dieldrin, and endrin belong to cyclodiens group of pesticides, which are used to kill termites and grasshoppers in soil, and to control rodents. These chemicals due to their strong insecticidal properties, widely used in cotton growing areas for the crop protection, from where these toxic chemicals find their way into aquatic ecosystem. In our study, aldrin/dieldrin indicative ratios reflected the dominance of dieldrin in the collected sediments and results are consistence with other studies of Said et al.,
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(2006). Among endosulfans, the dominance of endosulfan sulphate and β-endosulfan in sediment samples showing the historical use of endosulfan in the study area, while due to high persistence and relatively low solubility, found more frequently. However, α-endosulfan also detected in sediments collected near the agricultural areas, which may reach into river body via surface runoff from surrounding cropland. Generally, high concentrations of methoxychlor measured in the sediment samples collected from the industrial site, suggested the main input of this chemical from industrial effluents and urban waste dumping into river body via different entry points. On the other hand, high HCB residual levels measured near industrial site locations (S-18, S-20, S- 23). HCB, which is mainly generated in the production and usage of many agrochemicals and industrial processes, may be suspected to originate mainly from industrial processes and atmospheric deposition (Tolosa et al., 2010). HCB with high octanol-water portioning co- efficient (log Kow ~ 3.4-5.5) likely to adsorbed to suspended particulate material in the water and ultimately, settle down onto the sediment by gravity (Zhao et al., 2010).
4.3.2. PCB behavior and approximation of their possible sources
PCBs used as technical mixture, containing 70% of tri, tetra and penta-chlorinated biphenyles with trichlorinated biphenyles as dominating homologues. The results indicated relatively high proportion of heavier chlorinated PCBs in sediments collected from River Chenab, especially on those sites (S-2, S-8, S-18, S-20, S-23, S-24) located near the industrial drains and urban area, from where these PCB congeners are being added into aquatic system via surface-off and direct disposal of wastewater without prior treatment from industries and municipal drains. The heavier chlorinated congeners have more affinity to get adsorb to particulate material resulting accumulation and deposition onto sediment. These have relatively low mobility, while, lighter PCBs may be subject to microbial degradation and volatilization (Zhang et al., 2003). The concentrations of heavier chlorinated biphenyles i.e. hepta-CBs and octa-CBs showed elevated values in winter season, probably due to hydrological conditions i.e. low flow in winter season which resulted in settlement of particulate matter and increased values of organic matter and clay contents (Yang et al., 2009). High concentration of tri-CBs on three sites i.e. S-5, S-14, S-16 and S-21 originated from electric waste disposal points near these sites. Tri-CBs mainly used in electric appliances and paint additives, the contamination of these
Page 90 congeners may be triggered due to disposal of waste, which directly dumped into aquatic system via drains from these electric power generation located near these sites . As these sites located in urban areas from where different pollutants like paints, oils and other waste material are added into the aquatic system and deteriorating the quality of river sediments (Farooq et al., 2011). On the other hand, tetra-CBs and penta-CBs accounted for major part of total PCB composition, and found with greater frequency on both mainstream and tributaries, with relatively high concentration near industrial areas. Both tetra-CBs and penta-CBs were found in sediments with no significant variation among both seasons i.e. summer and winter. The significant sources of these chlorinated congeners suggested from steel manufacturing units and coal burning during iron ore sintering process (Buekens et al., 2001; Biterna et al., 2005) in the many industrial cities of Lahore, Faisalabad, Sialkot and Gujarat in the study area, which may reached into the river ecosystem via run-off from surrounding fields and atmospheric deposition (Farooq et al., 2011).
Identifying the PCB sources and history of Aroclor mixtures used in the area, is not an easy task and many techniques are usually applied for a better understanding of them. These can include the use of basic or more sophisticated statistics and modeling, use of the PCB fingerprints (contribution of each congener group). The latter can give information about the commercial production and usage of specific chemical mixture, through the assumption that it is known that PCBs, like all POPs may originate also from long range transport, so the comparison of the samples PCB-profiles with the technical mixtures can only give an approximation of the commercial products. By taking into consideration (comparison of the samples PCB-profiles with the technical mixtures, presented in Figure 4.5(a, b), it can be concluded that the main Aroclor mixtures used in the area should be mainly 1248 and the heavier 1254, 1260 and 1262.
The WHO05-TEQ values for three mono-ortho dioxin-like PCB congeners (PCB-105, 118, and 156) were also calculated by using the WHO05-TEF (for three mono-ortho dioxin-like PCBs~0.00003) reported by Van den Berg et al. (2006). The ΣTEQ levels ranged from 0.7-8.65 (1.9) and 1.07-9.8 (1.6) pgTEQg-1 dw during both summer and winter season, respectively. Among individual mono-ortho DL-PCBs, PCB-118 contributed significantly towards the total calculated TEQs ranging from 0.01-0.14 pgTEQg-1 dw with no significant difference (P> 0.005) 18 among both monitoring seasons. High ΣTEQ levels were found on sampling locations S-20
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(0.28 pgTEQg-1 dw), S-18 (0.15 pgTEQg-1 dw), and S-23(0.1 pgTEQg-1 dw), which located near the industrial areas and receiving high quantities via different drains.
Concentrations of PCBs in sediments compared to those reported from different regions worldwide. Total PCBs concentrations (ng/g, dry weight) in the sediments from River Chenab (9.33-144.23, mean: 25) are far high than those reported from the lower reaches of the Yangtze River, China (Shen et al., 2006), Yellow River, China (He et al., 2006) and Bay of Bengal, India (Ranjendran et al., 2005). However, measured concentrations found comparable to those reported in the sediments from the China (Xing et al., 2005) and Hong Kong (Richardson and Zheng, 1999). The PCBs concentrations in the present study found lower than those reported in sediment samples from Narragansett Bay of USA (Hartman et al., 2004), Alexandria Harbor of Egypt (Barakat et al., 2002) , Soulou stream Greece (Katsoyiannis, 2006), Naples Harbour, Italy (Sprovieri et al., 2007).
Correlation analysis of PCBs with other studied physicochemical parameters also played an important role for the identification of sources of these contaminants in the aquatic ecosystem of River Chenab. PCBs showed significant correlation with organic matter (r = 0.68), clay contents (%) (r = 0.51), NO3-N (r = 0.70) and alkalinity (r = 0.61).Among OCPs, DDTs showed strong positive correlation with organic matter (r = 0.70), clay contents (r = 0.62), alkalinity (r =
0.70) and NO3-N (r = 0.70), while HCHs also showed positive correlation with organic matter (r = 0.58) and clay contents (r = 0.69). Sand (%) showed negative correlation with PCBs and DDTs. PCBs and DDTs are hydrophobic organic pollutants, mainly associated with organic matter contents of sediment samples, which justified the results of this study and also consistent with other studies indicating strong adsorption with sediment particles (Honnen et al. 2001).
4.3.3. Spatial and temporal distribution of OCPs and PCBs in the sediment
Generally, the concentrations of OCPs and PCBs in sediments collected from Chenab’s tributaries (S-8, S-17, S-20, S-23, S-24) were much high than those recorded in the mainstream samples (Fig.4.1), suggesting the OCP and PCB contaminant into River Chenab mainly originated from discharge points of industries. However, agricultural run-off, municipal waste
Page 92 disposal, and atmospheric deposition are also important sources of pollution into River Chenab, Pakistan. On the other hand, hydrological conditions of River Chenab also played an important role in the contamination of OCPs and PCBs in the aquatic ecosystem (Yang et al., 2009). A first visual inspection of the Figures 4.1, 4.6, and 4.9, suggested that the River Ravi (S-8), River Sutlej (S-24), Khanki barrage (S-23) and Industrial Drain (S-20) from Faisalabad city, receives a high load of toxic pollutants from different sources than the mainstream sampling locations of River Chenab (before the mixing with aforementioned tributaries). At the same time, both the OCP and PCB concentrations reveal the mixing, that has taken place and clear differences are observed also at the other mixing point where River Jhelum meets with River Chenab. Cluster analysis grouped sites into 4 regions (Region-1, Region-2, Region-3 and Region-4) and the results are similar as presented in Chapter 4, which also showed the almost same distribution of sites among different groups. Region-1 was more contaminated as compared to other regions and comprised of two sites (S-18 and S-23). S-18 is located on the Chenab mainstream in district Jhang after S-20 and also showed highest values of OCPs, PCBs and other physicochemical parameters. The contamination on S-18 was caused due to pollution load added by industrial drain of Faisalabad city which deteriorated the sediment quality and adding up organochlorine contaminants. S23 is located on Khanki barrage in Gujarat district, receiving industrial and municipal waste form two industrial sites, Gujarat and Sialkot. On the hand, the catchment area of this very popular for rice cultivation and agriculture activities were also prominent on this sampling site. The surface run-off from surrounding cropland into aquatic system may also be one of important OCPs pollution sources. Region-2 consists of six sites viz. S-2, S-5, S-8, S-16, S-17 and S-24. These sites also showed relatively high residual levels of OCPs and PCBs as located near urban and sub-urban areas and also receiving high pollution load from upstream flow. Among these sites, S-2 is located in Jhang district receiving pollution load via different municipal drains from Jhang city and also from upstream flow. S-5 is also located in Jhang district and receiving pollution load from nearby fields and an electric supply grid station is also situated near this site, exceeded values of PCBs congeners may come from this station. S-8, S-17 are located on River Ravi, a tributary of River Chenab, one of most polluted river of Pakistan receiving a high load of pollutants from industrial cities i.e. Lahore, Qasoor, Faisalabad etc. Both of these sites are located in urban area of Khanewal district, where these sites receiving municipal waste from nearby towns, agricultural runoff, and upstream flow may also be
Page 93 important source of OCPs and PCBs. S-24 is located on the Sutlej upstream in Vehari district and showed elevated values of OCPs and PCBs contaminants. The high values on this site may come from industrial drains, dumping high quantities of pollutants in the River Sutlej. Sediment samples collected from S-16 also showed high values of OCs and on this site PCBs contamination resulted due to electric power generation plant waste disposal. Region-3 can be categorized as moderately polluted region which included nine sites ( S-9, S-10, S-11, S-12, S- 13, S-14, S-15, S-21, S-25) located in agricultural areas and receiving pollution load from surrounding agricultural lands, however, pollutants load from upstream flow cannot be ignored. The sediments collected from this region showed relatively lower concentration of OCPs and PCBs than those recorded in Region-1 and Region-2. Greater concentration of α-endosulfan, β- endosulfan, dieldrin, heptachlor endoepoxide, and DDTs reflected the widespread use of these pesticides in the region for agricultural purposes. Greater residual level of DDD indicated anaerobic degradation of DDT and also highlighted the widespread historical use of this chemical in these areas. EC, total P and silt showed high values in the sediments collected from Region-3 and sites of this region were situated in cotton belt which receive pesticides load from adjoining fields due to severely eroded banks and agricultural run-off. Upstream pollution load from River Ravi, which meets with River Chenab in this region, cannot be neglected. Region-4 comprised of sites viz: S-1, S-3, S-6, S-7, S-19 and S-22 which are located on main stream of Chenab and agricultural activities were the main land-use types in the catchments. Relatively low levels of OCPs and PCBs were found as compared to other groups. Lower OCPs and PCBs levels can be attributed to high water flow and velocity. Main sources of OCPs and PCBs contaminants may relate to surface run-off from surrounding land, atmospheric deposition and upstream flow.
S-2, S-20, S-18, S-23 located in industrial and urban areas and showed highest value of all the physicochemical properties in the collected sediment samples. The River Chenab transverse through densely populated and industrial cities and receive industrial waste water and municipal effluents, directly disposed into river without any treatment via different tributaries and drains. Industrial and municipal effluents from Rivers (Ravi and Sutlej), streams via Nullah Aik and Palku from Sialkot city and drains from Faisalabad, Chiniot, and Jhang cites have resulted in impairment of sediment quality and showed very high values of all physicochemical
Page 94 parameters and other contaminants. Hence the main inputs of pollutants into the sediment of River Chenab may come from the agricultural runoff from surrounding contaminated soils, atmospheric deposition and directly disposal of industrial and municipal waste through different drains and tributaries. On the other hand, mineral composition of River Chenab’ sediments mainly comprises of thick deposits of calcareous, fine and wind laid sands and silty material, which originates from sand stones and shale rocks of catchment area especially salt range, which resulted in the alkaline like situation and high values of dissolved salts, one of the main characteristics of the water bodies in Pakistan. Source of NO3-N is mainly correlated with the excessive use of nitrogenous fertilizers in the agricultural fields. Soils of Pakistan are characterized by low organic matter and the use of organic fertilizers is not common, moreover the rates of chemical fertilizer use have increased the recommended levels.
Generally, the detection frequencies of all OCPs and PCBs in sediments were high in winter than in summer season, on most sampling locations (Fig.4.8). The values of studied physicochemical parameters were also high in winter. High percentage of organic matter and clay in winter can be one of the reasons that explain the increased levels of OCs in winter (Lee at al. 2001), while lower temperature may also be attributed to less desorption of OCs in winter resulted in high values of OCPs and PCBs (Zhou et al., 2006). In winter, the elevated values of all these parameters may also related to low water flow, which resulted in high deposition rates of sediments. OCPs dilution and diffusion largely depends upon the temperature (Zhang et al., 2008) and in present study, low temperature and low flow of water in the winter, may be the factors resulted in the high concentrations and detection frequencies of OCPs and PCBs in this season.
4.4. Ecotoxicological concern for OCPs and PCBs in sediment of River Chenab
Based on the sediment quality guidelines (SQG) established by Canada (CCME, 2002) and Florida Department of Environment Conservation of America, and some published sediment quality guidelines (Long and Morgan, 1991; Long et al., 1995; McDonald and Ingersoll, 2000; Sun et al., 2010) including, interim sediment quality guidelines (ISQG), probable effect
Page 95 concentrations (PECs), and Threshold effect concentrations (TECs) for freshwater, the potential risk of organochlorine compounds in the sediments from the River Chenab was assessed.
Concentration of OCPs in detected sediment samples in all four regions (Region-1, Region-2, Region-3, and Region-4) were compared with sediment quality guidelines (Table 4.6) for the assessment of relative sediment quality and potential risk to the aquatic life in River Chenab. Among HCHs mean concentrations of α-HCH in the detected samples were ranged from 1.5-5.6 ng/g in all four region of the study area during both seasons, which did not exceed the lowest effect level (LEL) values. Values of α-HCH detected in the sediments of Region-1 (industrial areas) are very close to this benchmark value. In case of γ-HCH, none of the sediment samples showed high values than sever effect level (SEL), but the sediment samples collected from Region-1 (industrial sites) and Region-2 (urban sites) had slightly high values than the consensus based threshold effect concentrations (CB-TECs; below which adverse effects are not expected to occur). Therefore, it can be seen that comparison of HCHs with available quality guidelines, suggesting that risk from HCHs is moderate, however, the concentrations of HCHs should be routinely measured and major sources should be identified and abated.
DDTs were found predominantly in the sediments collected all four regions during both monitoring seasons. Average concentrations (ng/g) of ∑DDTs in all four regions were ranged from 20-146 during both summer and winter seasons. Concentrations of ∑DDTs exceeded than effect range low (ERL; below which adverse biological effects are rarely observed) in all the sediment samples, while sediment samples of Region-1 (industrial sites) and Region-2 (urban sites) exceeded than the values of both sever effect level (SEL) and effect range median (ERM; above this level adverse biological effects are frequently observed). None of the sediment samples from all four regions exceeded consensus based probable effect concentrations (CB- PECs) of 572 for ∑DDTs. Values for p, p/-DDD and p, p/-DDE in all the sediment samples were exceeded than threshold effect level (TEL) and effect range low (ERL). On the other hand, p, p/- DDD and p, p/-DDE concentrations (ng/g) in the sediment samples from Region-1 (industrial sites) and Region-2 (urban sites) also exceeded than census based probable effect concentrations (CB-PECs) for these metabolites. This comparison of DDTs concentrations with available sediment guidelines assessing the contamination of DDTs and its metabolites and indicating the
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adverse biological effects in this studied area especially in Region-1(industrial sites) and Region- 2 (urban sites).
Long et al. (1995) suggested the effect range low value (ERL) of 22.7 ng/g (dry weight) at which PCBs can cause toxic effects on aquatic organisms and the effect range median value (ERM) of 180 ng/g(dry weight) shows high possibility of PCBs posing toxic effects on aquatic organism. According to Canadian Sediment Quality Guidelines (CSQGs), probable effect level (PEL) of 277 ng/g (dry weight) can cause toxic effects on aquatic life frequently whereas at concentration of threshold effect level (TEL) of 34.1 ng/g (dry weight) the effects are negligible (CCME, 1999). In our study, PCBs concentrations in sediments exceeded the TEL and ERL values in 35 % of total samples (Fig.4.7). Sites of Region-1 and 2 viz: S-2, S-8, S-18, S-20, and S-23) located near industrial areas showed values above these guidelines. However, none of the samples in this study exceeded ERM and PEL, with the exception of S-20 (144.23 ng/g, dry weight) that showed a value far high to those of ERL and TEL. Sediments collected from the Region-1(industrial sites) and Region-2 (urban sites) near the industrial and urban area indicated the serious potential risks posed to the aquatic life of River Chenab.
Table 4.6.Risk assessment of OCPs and PCBs detected in sediment samples of River Chenab by using the mentioned sediment quality guidelines.
TECs PECs OCPs concentration in sediment Chemicals CB- CB- TEL LEL ERL TEC PEL SEL ERM PEC Region-1 Region-2 Region-3 Region-4 α-HCH - 6 ------5.6 4 2.2 1.5 γ-HCH 0.94 3 - 2.37 1.38 10 - 4.99 4.3 5 3.7 2.6 p, p'-DDD 3.54 8 2 4.88 8.51 60 20 28 45 27 10 7 p, p'-DDE 1.42 5 2.2 3.16 6.75 190 27 31.3 61 31 9 10 p, p'-DDT - - 1 4.16 - 710 7 62.9 20 18 7 5 ∑DDT 3.89 7 1.58 5.28 51.7 120 46.1 572 146 70 28 20 ∑HCH 0.32 - - - 0.99 - - - 24 18 11 7.4 ∑PCBs 34.1 - 22.7 - 277 - 180 - 56 24 18 13
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4.5. Conclusions
The present study provided the first systematic data on the distribution of organochlorine pesticides (OCPs) and polychlorinated biphenyles (PCBs) in sediments of River Chenab, Pakistan. This study documented the occurrence, spatio-temporal trends and source identification of OCPs and PCBs in the river sediment. DDTs, PCBs, HCHs, and heptachlor were the dominant OCs measured in the sediments of River Chenab. High concentration of p, p/-DDT in sediments in both seasons, reflected the recent use of parent DDT compound while presence of DDE and DDD in most of the sediment samples suggested its contamination mainly from aged agricultural soils and anaerobic environmental conditions of riverine ecosystem of River Chenab. Cluster analysis grouped all the sampling sites into four regions (Region-1, Region-2, Region-3, and Region-4) based on the pollution level. Sediment collected from adjoining tributaries (such as S- 8, S-17, S-20, S-23, S-24) were relatively more contaminated as compared to those collected from main stream. Statistical tools and diagnostic ratios suggested agricultural and industrial activities, municipal waste disposal into river system and atmospheric deposition as main sources. This risk assessment of OCP and PCB concentrations with available sediment guidelines assessing the contamination of DDTs (including breakdown products) and indicating the adverse biological effects in this studied area especially in Region-1(industrial sites) and Region-2 (urban sites). Highest ΣTEQ levels for dioxin-like PCB congeners were found on sampling locations S-20 (0.28 pgTEQg-1 dw), S-18 (0.15 pgTEQg-1 dw), and S-23(0.1 pgTEQg-1 dw), which located near the industrial areas and receiving high quantities of contaminants via different drains.
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Chapter 5 Organochlorine Pesticides (OCPs) and Polychlorinated biphenyles (PCBs) in Fresh Water Fish Collected from the River Chenab, Pakistan: Levels, Accumulation Features, and Risk Assessment:
5.1. Materials and Methods
Detailed methodology for fish sampling and laboratory analysis of OCPs and PCBs are given in detail in Chapter 2 (Section 2.3 and 2.4). Moreover, methods for nitrogen isotopic (δ 15N %) analysis is also given in Chapter 2 (Section 2.4).
5.1.1. Statistical Analysis
Analytical results were compiled to form a multi elemental database using Excel software. Seasonal, spatial, and specie-specific basic statistics of OCPs and PCBs congeners (only for detectable values) were calculated by using statistical software package Statistica (version 5.5). Moreover, Microsoft Excel was also used for graphical representation of dataset and indicative ratios for different OCPs. Analysis of variance (ANOVA) was also employed to evaluate mean significant differences (P < 0.05) for individual OCPs and PCBs residual levels among different species, seasons, and sites.
5.1.2. Bioconcentration factors (BCF) and Biota-sediment accumulation factor (BSAF)
BCFs were used to predict the partitioning behavior of OCPs and PCBs in water and fish and were estimated based on the ratio of the concentration of OCPs and PCBs in the fish to that in water samples. BCFs can be calculated by following equation.
BCF= Cbiota/ Cw,
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Cbiota is analyte concentration in fish, ng/g (WW)
While, Cwis analyte concentrations in the water (ng/L) The BSAF is used to predict the potential accumulation and partitioning of lipophilic compounds from the organic fraction of sediment into fish (lipid fraction). BASF for each fish sample were calculated as ratio of a contaminant’s lipid normalized concentration in tissue of fish to its organic fraction normalized concentration in the sediment. BSAF can be calculated as:
BSAF= C biota (lipid)/ C sed. (organic matter)
Here, and C biota (lipid)is analyte concentration in fish (lipid normalized), and C sed. (organic matter) is analyte concentration in sediment (ng/g, organic matter normalized).
5.1.3. Toxic equivalency calculations
The WHO-TEQ (Toxic Equivalents) values for dl-PCBs (dioxin like-PCBs) were calculated for each species by multiplying the mean value for each species with the toxic equivalency factor (TEF). WHO-TEF values for each mono-ortho dl-PCBs and non-ortho dl- PCBs (Van den Berg et al., 2006) were utilized for this purpose. Total TEQs were calculated but summing up each congener-TEQ values calculated for each fish species.
5.1.4. Risk characterization
Risk characterization was carried out firstly by comparing the data recorded (average concentrations of OCPs and PCBs) from the fish samples of River Chenab with admissible limits and guideline of different world’s organizations including European Union (EU, 1999/788), U.S. Environmental Protection Agency (USEPA, 2000) and Chinese Government (GB 18421-2001) to screen out the initial risks associated with the consumption of contaminated fish. MRLs by Agency of Toxic Substances and Disease Registry (ATSDR, 2005) were also used on the basis of exposure time, exposure route and most sensitive substance-induced end point, which are considerable in the relevance to humans.
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Hazard Ratios (HZs) were calculated to evaluate the cancer and noncancerous risks associated with the consumption of fish to human beings. HZs were obtained by dividing average daily exposure by benchmark concentrations and if these ratios found greater than unity indicate some potential risks to the human health due to excessive levels of OCPs and PCBs. A benchmark concentration (BMC), cancer slope factor and Oral reference dose (RfD) value were used by following a study (Dougherty et al., 2000) conducted by USEPA.
Hazard Ratio (HR) = Average daily exposure/ Benchmark concentration.
“Benchmark concentration can be defined as the daily exposure concentration below which there is a high probability of no adverse health effect and statistically derived by using oral RfD for noncancerous effects”. And Average daily exposure (ng/Kg body weight) = Fish consumption (g/Kg body weight) × Contaminant concentration (ng/g). Note: Fish consumption was expressed as daily consumption divided by body weight (set to an average of 60 kg for an adult). Daily consumption is used as 8 g/Kg body weight-day by surveying 200 people in the field. Cancer benchmark concentrations (CBCs) and oral RfD for each compound obtained from a study of USEPA (Dougherty, 2000) to evaluate the both cancer and noncancerous risk for studied compounds based on 50 th and 95 th percentile of the exposure concentrations.
5.3. Results
5.3.1. The biological characteristics and tropic level of collected fish species
The biological parameters (moisture, lipid contents) and δ 15N % of all studied fish species are mentioned in Table 5.1. Length (cm) of collected fish species ranged from 4.8-53.1 with mean value of 22. Gudusia chapra significantly differ from all other species with length (cm) ranged from 4.8-17.5 with mean value of 11.6, while the maximum length of Mastacembalis aramtus recorded with mean value of 48.4 following Catla catla (mean: 25.9),
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Rita rita (mean: 24.6) and Clupisoma naziri (mean: 22.6). Moisture (%) were ranged from 68- 87.3 (with mean value; 79.3) among all the collected fish samples. Generally, no significant difference of moisture was found among different species, while relatively low moisture content recorded in all the carnivorous species. Width (cm) ranged from 1.9-8 (with mean value; 4.8), with highest value recorded in Catla catla (mean value; 7.4).
Lipid fraction (%), one of important biological parameters, measured for all the targeted fish samples and ranged from 0.23-9.1 (with mean value; 3.1). Generally, a significant difference of lipid fraction (%) was found b/w carnivorous and herbivorous species, with high values found in carnivorous species especially in species with benthic habitat i.e. Mastacembalis aramtus (mean value; 8.6) and Rita rita (mean value; 7.6). Among herbivorous species, Catla catla and Cyprinus carpio showed high lipid content (%) with mean value of 3.2 and 2.3, respectively.
15 The δ N % signature values are presented in Table 5.1. For each fish species, this is used to predict the trophic position of collected fish species. On the other hand, increase in contaminants concentration among different fish species at distinct trophic levels can be defined 15 using these signature values of naturally occurring stable isotopes i.e. δ N %. The herbivorous fish species i.e. Cyprinus carpio, Catla catla, Cirrhinus reba, Cirrhinus mrigale and Labeo calbasu showed relatively lower δ 15N % ranged from 5.1-8 (mean value; 6) while carnivorous fish species i.e. Rita rita, Mastacembalis aramtus, Securicola gora, Gudusia chapra, Clupisoma naziri, Clupisoma garua showed δ 15N % ranging from 6.4-12 (mean value; 9). Among 15 carnivorous fish species, Mastacembalis aramtus and Rita rita showed highest δ N % values with mean values of 10.5 and 11, respectively. Lipid fraction (%) showed increasing trend with surface feeder to bottom feeder and same trend was found from herbivorous to carnivorous fish 15 species which may be the reason of positive correlation b/w lipid fraction (%) and δ N %.
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Table 5.1. List of collected fishe from River Chenab, Pakistan: Biological parameters and nitrogen isotope (δ 15N) composition for each species.
S/No Fish Species No.of individuals Feeding Habit Moisture % Lipid % δ 15N% Length[cm] Width[cm] Mean ±STD 83.2±2.4 2.3±1.4 5.6±1.2 1 Cyprinus carpio 23 Herbivorous 18.3±11.1 3.9±1.7 Min-Max 83.1-85.6 0.23-4.2 4.7-6.1 9.5-43.5 2.3-5 Mean ±STD 82.2±0.9 1.24±0.8 5.26±0.5 2 Cirrhinus reba 54 Herbivorous 18±3.24 3.8±1.2 Min-Max 81.6-83.2 0.94-2.4 4.56-6.06 11.8-20 2.1-4.9 Mean ±STD 81.7±4.5 1.4±1.8 5.99±0.09 3 Cirrhinus mrigale 39 Herbivorous 18±4.98 3.97±0.7 Min-Max 80.9-82.4 0.43-3.5 5.92-6.06 8.5-23 3.3-5.4 Mean ±STD 84.2±1.3 1.23±2.2 7.48±0.8 4 Catla catla 26 Herbivorous 25.9±13.6 7.4±2.2 Min-Max 83.2-85.2 0.64-4.1 6.92-8.05 25.4-26.5 7-7.8 Mean ±STD 84.5±2.7 0.91±1.3 5.1±0.5 5 Labeo calbasu 29 Herbivorous 17.7±11.6 2.8±1.6 Min-Max 81.9-87.3 0.63-2.1 4.11-6.2 11.13-28.5 1.7-3.5 Mean ±STD 73±1.4 5.61±3.6 9.54±1.6 6 Rita rita 17 Carnivorous 24.9±12.5 5.6±2. 8 Min-Max 72-75.4 4.5-8.2 7.24-11.09 24.6-25.2 5.4-5.9 Mean ±STD 71±2.76 5.6±3.4 7.99±1.8 7 Mastacembali saramtus 24 Carnivorous 48.4±17.6 4.4±1.7 Min-Max 68-76.4 4.4-9.10 6.87-11.9 43.7-53.1 4.1-4.8 Mean ±STD 78±3.1 4.4±2.8 7.01±1.03 8 Securico lagora 59 Carnivorous 23±3.93 4.1±0.55 Min-Max 75.6-81.8 3.2-6.21 5.28-9.2 18.5-34 3.3-5.2 Mean ±STD 76.2±1.7 3.5±2.3 7.27±1.3 9 Gudusia chapra 68 Carnivorous 11.6±6.5 7.7±5.3 Min-Max 75.4-77. 5 2.3-5.5 5.96-9.8 4.8-17.5 3.2-18 Mean ±STD 78.4±2.8 4.9±1.5 7.60±1.4 10 Clupisoma naziri 64 Carnivorous 22.6±3.76 4.3±1.2 Min-Max 75-1.2 3.34-6. 6 6.32-10.9 15.5-28 2.9-5.7 Mean ±STD 79.2±3.5 3.9±2.5 7.22±2.1 11 Clupisoma garua 31 Carnivorous 20 ±4.51 4.7±2.1 Min-Max 78.9-81.4 0.84-6.2 4.41-9.8 16-31 1.9-6.2
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5.3.2. OCP loads and their accumulation patterns
The basic statistic of measured concentrations (ng/g WW) for individual OCP in eleven studied fish species (five herbivorous and six carnivorous) are shown in Table 5.2. Generally the trends of detected OCP contaminants in all fish samples followed the order; DDTs, Other OCPs, chlordanes and HCHs in descending order. Total OCPs concentration (ng/g WW) ranged from 13-106 (mean; 38) for five herbivorous fish species and 21.5-365 (mean; 148) for six carnivorous species. A significant variation (p< 0.05) in OCPs contamination was observed b/w herbivorous and carnivorous species with considerably highest values recorded in carnivorous species.
The average HCHs concentrations (ng/g WW) ranged from 0.72-7.9 (mean; 3.6) for herbivorous and 0.65-15.8 (mean; 6.5) for carnivorous fish species. No significant variations were found among all collected fish species expect Mastacembalis aramtus and Rita rita with highest mean values of HCHs i.e. 7.5 and 8.6, respectively. Compositional analysis of HCHs showing (Fig.5.2a) that β-HCH and γ-HCH formed the major portion of total HCHs in all fish sample, with contribution of 55.5% for γ-HCH, 39% for β-HCH followed by α-HCH (17%) and δ-HCH (8%). The dominance of γ-HCH and β-HCH were observed in all the collected fish samples. In all analyzed fish samples, γ-HCH and β-HCH found more frequently in more than 80% of total samples, while α-HCH and δ-HCH were rare. Generally, the concentrations (ng/g WW) of γ-HCH in all fish samples were ranged from 0.2-8.6 (mean: 3.4) with high recorded values in Securicola gora (mean value; 4.3). Concentrations (ng/g WW) of α-HCH, β-HCH and δ-HCH in all monitored samples were in range of 0.1-2.5(mean: 1.7), 0.3-6.2 (mean: 2.1) and 0.1-1.5 (mean: 0.45), respectively. Generally, α-HCH, β-HCH and δ-HCH showed no significant variation among all the collected fish species except β-HCH (ng/g WW) which showed elevated values in two of carnivorous fish species i.e. Mastacembalis aramtus and Rita rita with mean value of 3.2 and 2.6 in, respectively.
Concentrations of DDTs were much high in all the collected fish samples than any other OCPs i.e. HCHs, chlordanes and other OCPs and detected more than 90% of analyzed fish samples. ∑DDTs (ng/g, WW) concentrations ranged from 4.6-88.5 (mean; 29.2) in herbivorous
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species with highest values recorded in Cyprinus carpio (mean; 57.7) and Cirrhinus mrigale (mean; 34.5) while Catla catla (mean; 8.8) with lowest values among all the collected fish samples. A significant variation for DDTs was observed among herbivorous and carnivorous fish species with highest concentrations (ng/g WW) of ∑DDTs recorded in carnivorous fish samples ranged from 17.8-308 (mean; 120). Highest values recorded in Mastacembalis aramtus (mean; 190.8) and Rita rita (mean; 155). The compositional trends of DDTs (metabolites and isomers) in all the collected fish samples (Fig.5.2c) followed the pattern in descending order as: p, p/ - DDE (68%), p, p/ -DDT (12%), p, p/ -DDD (10%), o, p/ -DDT (3%), o, p/ -DDE (3%) and o, p/ - DDE (2%). The concentrations (ng/g WW) of p, p/ -DDE showed the maximum values in all the fish samples ranged from 3.2-58.9 (mean; 19) and 8.7-215 (mean; 70) for both herbivorous and carnivorous fish species, respectively. Highest average values of p, p/ -DDE were observed in Mastacembalis aramtus (mean; 190.8, ng/g WW), while minimum in Catla catla (mean; 4.5, ng/g WW). The maximum average values (ng/g WW) of p, p/ -DDT were detected in Mastacembalis aramtus (mean; 31.5), Rita rita (mean; 17) and Securicola gora (mean; 15) while lowest in Catla calta (mean; 2) and Labeo calbasu (mean; 3.6). The maximum average p, p/ - DDD values (ng/g WW ) were recorded in carnivorous fish species (mean; 7) as compared to herbivorous fish samples (mean; 4.5), with elevated concentrations (mean; 15) recorded in Mastacembalis aramtus, Rita rita and Clupisoma naziri while lowest values were observed in Catla catla (mean; 0.7) and Labeo calbasu (mean;1.3). Generally, concentrations (ng/g WW) of o, p/ -DDT (mean; 1.2), o, p/ -DDE (mean; 2) and o, p/ -DDE (mean; 1.2) were not significantly varied among all the fish samples, but Mastacembalis aramtus and Rita rita showed relatively high values of these contaminants.
Recorded concentrations (ng/g WW) for chlordanes among all fish species ranged from 1.1-14.4 (mean; 4.5), with highest average values were found in Rita rita (mean; 6.5), Securicola gora (mean; 6.64) and Clupisoma naziri (mean; 5) while lowest in Labeo calbasu (mean; 1.9). Among chlordanes, TC (48%) contributed mainly in the composition of total chlordanes, followed by non-TC (40%) and CC (12%) in all collected fish species (Fig.5.2b) The concentrations (ng/g WW) of TC (mean; 1.8), non-TC (mean; 1.5) and CC (mean; 0.8) were recorded in all fish samples with relatively high values found in Securicola gora, Rita rita and Mastacembalis aramtus and lowest in Labeo calbasu.
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Values of ∑Other OCPs varied significantly b/w herbivorous and carnivorous fish species collected from River Chenab, Pakistan. The concentrations of ∑Other OCPs (ng/g WW) ranged from 1.62-22.6 (mean; 8.1) and 1.4-52.8 (mean; 20) for both herbivorous and carnivorous fish species, respectively. For endosulfans, endosulfan sulphate (mean; 3.2 ng/g WW) and β- endosulfan (mean; 1.2 ng/g WW) were found in most of the fish samples with detection frequency of 67% and 47%, respectively. α-endosulfan (mean; 0.8 ng/g WW) was also detected but in few samples with low detection frequency of about 32%. Generally, high values (ng/g WW) of ∑endosulfan were recorded in Mastacembalis aramtus (mean; 12) and Securicola gora (mean; 9.3). Heptachlor (mean; 1.2, ng/g WW) and its epoxide (mean; 0.5, ng/g WW) were found with no significant variation among collected fish species, with detection frequency of 53% and 42 %, respectively. Methoxychlor, were detected with relatively high concentrations (mean; 2.5 ng/g WW) and detection frequency (68%), in all the collected fish samples. The maximum average concentrations (ng/g WW) of methoxychlor were found in Mastacembalis aramtus (mean; 3.4), Securicola gora (mean; 3.56) and Gudusia chapra (mean; 3.2) and lowest in Catla catla (mean; 0.61). Recorded concentrations (ng/g WW) of aldrin (mean; 0.7), dieldrin (mean; 1.8) and endrin (mean; 0.45) were found in all the collected fish with detection frequencies of 28%, 47% and 17%, respectively. Among these three cyclodiens, dieldrin (epoxide of aldrin) was recorded in relatively elevated values with maximum average in Mastacembalis aramtus (mean; 5.3), Securicola gora (mean; 6.5), Gudusia chapra (mean; 5.31) and Cyprinus carpio (mean; 3.2). HCB were also detected in 43% of collected fish samples with average value of 0.92 ng/g (WW), with maximum mean concentration of 2.43 ng/g (WW) in Securicola gora individuals.
5.3.3. PCB concentrations and profile distribution
The mean±std and min-max of the concentrations (ng/g WW) of individual PCB congeners are given in Table 5.3. Generally, the concentrations (ng/g WW) of ∑31PCBs ranged from 3.1-93.7 (mean; 20) in five herbivorous species and 2.4-108 (mean; 30) for six carnivorous fish species. A significant variation was found among the herbivorous and carnivorous fish species, with highest concentrations were recorded in carnivorous fish species expect Cirrhinus
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mrigale, herbivorous fish, which showed elevated values of PCBs at some sampling location. The compositional analysis of individual classes of PCBs showed the major contribution of heavier chlorinated biphenyles (more than 50 %) towards the total PCBs composition in all collected fish samples from River Chenab, Pakistan. The trends of individual PCBs congeners (Fig.5.2d) followed the descending order as: hepta-CBs (26%), penta-CBs (21%), octa-CBs (15%), hexa-CBs (14%), tetra-CBs (12%), tri-CBs (10%). The average concentrations (ng/g WW) of tri-CBs (mean; 2.72), tetra-CBs (mean; 3.1), penta-CBs (mean; 4.5), hepta-CBs (mean; 6.1), hexa-CBs (mean; 2.89) and octa-CBs (mean; 3.7) were recorded in all collected fish species. Rita rita, a carnivorous fish species, in contrast to all other species showed elevated values of tri-CBs with mean of 11.5 ng/g (WW). Generally, the concentration (ng/g WW) of heavier chlorinated biphenyls were found high in all the collected fish samples with maximum average for hepta-CBs in Mastacembalis aramtus (mean; 16), Rita rita (mean; 9.35) and Cirrhinus mrigale (mean; 8.6); for hexa-CBs in Mastacembalis aramtus (mean; 6), Rita rita (mean; 4) and Cirrhinus mrigale (mean; 3.8); and for octa-CBs in Mastacembalis aramtus (mean; 6), Rita rita (mean;5), Securicola gora (mean; 4.7) and Cirrhinus mrigale (mean; 4.5).
5.3.4. Dioxin-like PCBs contribution
In this study, the levels of ∑7 dl-PCBs ranged from 2.6-6.4 (mean; 3.9 ng/g WW). Among different studied fish species elevated average concentration (ng/g WW) of these toxic contaminants were recorded from Mastacembalis aramtus (6.5), Gudusia chapra (5.3), and
Cirrhinus mrigale (4.9). Among ∑7dl-PCBs, concentrations (ng/g WW) of three non-ortho dioxin- like PCB congeners (PCB-77, 126,169), were ranged from 0.19-6.58 (mean; 1.7). The maximum average concentration (ng/g WW) of these contaminants was found in Mastacembalis aramtus (mean; 2.43) and Cyprinus carpio (mean; 2.1). While mono-ortho PCBs (PCB-105, 114, 118, 156) were ranged from 1.55-4 (mean; 2.46 ng/g) in all fish samples, with maximum average values (ng/g) reported from Mastacembalis aramtus (4.1), Gudusia chapra (3.33), and Cirrhinus mrigale (3.1). The toxicity of these ∑7 dl-PCBs was calculated as sum of WHO-PCB TEQs for all the mono-ortho and non-ortho congeners for each fish species. The combined mono-ortho and non-ortho WHO- PCB TEQs for studied fish samples were ranged from 0.96-2.9 pg TEQ g-1 (WW). In all fish species, non-ortho congeners in respect to mono-ortho congeners contributed
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predominantly (more than 90%) towards total WHO-TEQs. Among non-ortho congeners, PCB- 126 contribution is noticeable in all species ranged from 0.005-2.85 pgTEQg-1. Specie-specific trends clearly reflected the high WHO-PCBs TEQ values in Mastacembalis aramtus (2.5 pg TEQ g-1; WW), Rita rita (2.47 pg TEQ g-1; WW), Securicola gora (2.98 pg TEQ g-1; WW) and Cirrhinus mrigale (2.44 pg TEQ g-1; WW).
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Table 5.2. Minimum-maximum (Mean±std) concentration (ng/gWW) of OCPs in fish species collected from River Chenab, Pakistan.
Compounds Cyprinus carpio(N=23) Cirrhinus reba(N=54) Cirrhinus mrigale(N=39) Catla catla(N=26) Labeo calbasu(N=29) a-HCH 0.12-1.13 (0.71±0.23) 0.20-0.66 (0.39±0.14) 0.2-0.57 (0.34±0.1) 0.1-0.3 (0.21±0.08) 0.14-0.7 (0.4±0.2) b-HCH 0.33-2.43 (1.83±0.9) 0.41-3.11 (1.9±0.7) 0.4-3.45 (2.1±0.7) 0.31-2.3 (1.2±0.7) 0.6-1.22 (0.8±0.12) g-HCH 0.22-2.29(1.47±0.55) 0.32-3.41 (2.1±1.5) 0.38-2.91 (1.6±0.95) 0.7-4.3 (2.44±1.8) 0.64-2 (1.5±0.5) d-HCH 0.11-0.76 (0.23±0.1) 0.1-0.45 (0.23±0.09) 0.1-0.5 (0.2±0.06) 0.2-1 (0.55±0.4) 0.13-0.3 (0.22±0.1) ∑HCH 0.72-5.34 (3.91±1.2) 1.16-6.63 (3.94±1.36) 1.3-6.1 (3.97±1.8) 0.91-7.93 (3.9±3.5) 1.84-5.30 (2.54±0.63) HCB 0.6-1.5(0.85±0.19) 0.33-1.91(1.08±0.4) 0.38-1.78(1.01±0.55) 0.46-0.8(0.66±0.2) 0.22-1.35(0.63±0.26) Heptachlor 0.45-2.4(1.42±0.39) 1.29-1.93(1.67±0.3) 0.4-1.22(0.57±0.1) 0.22-1.8(0.7±0.6) 0.35-1.67(0.61±0.2) Aldrin 0.26-0.51(0.4±0.13) N.D. 0.17-1(0.3±0.04) 0.12-0.5(0.2±0.06) 0.2-0.7(0.4±0.05) Hep-exo 0.22-1.35(0.79±0.21) 0.13-0.54(0.3±0.1) 0.22-1.25(0.45±0.2) 0.16-0.48(0.22±0.24) 0.15-1.5(0.45±0.12) Dieldrin 2.83-4.6(3.4±0.19) 2.36-3.78(2.9±0.92) 0.38-3.72(1.31±0.45) 0.8-1.43(1.17±0.1) 0.3-2.26(1.14±0.64) Endrin 0.25-0.9(0.5±0.1) 0.19-1.3(0.7±0.23) 0.14-0.56(0.4±0.2) N.D. 0.46-1.7(1.1±0.4) a-Endo N.D. N.D. 0.16-1.23(0.51±0.29) 0.18-1.4(0.5±0.2) N.D. b-Endo 0.91-1.10(0.96±0.14) 0.13-0.6(0.32±0.04) 0.24-0.9(0.45±0.3) 0.17-0.6(0.4±0.21) 0.21-0.95(0.5±0.14) Endo-sulphate 0.48-2.39(1.45±0.6) 1.46-1.7(1.55±0.5) 0.29-8.91(1.94±3.4) 0.32-1.31(0.76±0.6) 0.4-1.7(0.65±0.05) Methoxychlor 0.26-1.7(1.1±0.36) 0.78-1.8(0.7±0.32) 0.4-4.5(1.5±0.92) 0.28-0.97(0.64±0.33) 0.2-2.7(1.17±0.65) ∑ other OCPs 5.3-12.5(8.6±2.06) 4.63-7.6(5±1.4) 2.2-22.6(7.08±13.8) 3.5-6.5(4.6±1.2) 1.62-10.4(5.10±4.13) TC 1.42-4.5(2.8±0.49) 0.65-3.5 (1.5±0.54) 0.72-2.56 (1.2±0.5) 1.1-3.39 (1.9±2.1) 0.51-1.6 (0.7±0.04) CC 0.30-1.6(0.7±0.11) 0.21-0.9(0.45±0.06) 0.19-1.5 (0.8±0.7) 0.4-0.9 (0.5±0.1) 0.6-1.5 (0.9±0.2) non-TC 1.29-2.45 (1.45±0.49) 0.5-2.6(1.3±0.3) 0.8-1.14 (0.95±0.5) 0.5-2.82 (1.4±1.1) 0.69-2.5(1.24±0.5) ∑Chlordane 2.87-5.85(4.82±0.92) 1.1-4.5 (2.8±1.3) 1.8-4.14 (2.4±1.7) 1.6-6.2 (3±2.63) 1.2-3(1.9±0.4) p, p'-DDE 6.7-58.9 (40.65±9.26) 5.2-25.7 (16.5±6.1) 7-45.5 (26.5±17.8) 3.6-7 (4.5±2.3) 3.2-13.2 (6.3±0.82) o ,p'-DDE 0.29-3.5 (2.5±0.42) 0.35-1.75(0.95±0.23) 0.29-4 (1.4±1.8) 0.41-1.8 (0.7±0.2) 0.2-2.3 (1±0.71) o, p'-DDD 0.42-0.7 (0.5±0.18) 0.2-0.7(0.45±0.1) 0.24-1.28 (0.46±0.6) 0.2-0.57(0.35±0.1) 0.1-0.7 (0.4±0.21) p, p'-DDD 0.93-16.44 (10.4±1.16) 2.8-7(3.4±2.5) 1.2-12.15 (6.8±4.3) 0.5-1.17(0.7±0.2) 0.55-2.5 (1.3±0.34) o, p'-DDT 0.87-2.5 (1.1±0.22) 0.6-2 (1.3±0.6) 0.7-2.25 (1.7±0.74) 0.62-1.2(0.84±0.4) 0.6-1.4 (0.95±0.03) p ,p'-DDT 0.31-15.2 (8.7±4.3) 0.2-14.5 (6.6±3.3) 0.4-5.2 (3.9±1.4) 0.2-3.6(2±1.1) 1.37-7.6 (3.6±0.45) ∑DDTs 8.6-88.5 (57.7±13.05) 9.4-32.4 (22.7±10.4) 8.6-66.4 (38.6±25.7) 4.6-13.7 (8.8±3.1) 6.8-32.7(17.2±12.4) ∑OCPs 16.5-106 (61.1±40.5) 16.2-48.3 (34.55±18.2) 13.01-98.9 (44.1±29.7) 8.7-32.2 (21.5±5.6) 11.6-44.8(29.1±17.3) Continued…
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Compounds Rita rita(N=17) Mastacembalis aramtus(N=24) Securicola gora(N=59) Gudusia chapra(N=68) Clupisoma naziri(N=64) Clupisoma garua(N=31) a-HCH 0.4-3.2 (2±1.2) 0.5-1.4 (0.6±0.45) 0.3-2.2 (1.2±0.51) 0.1-1.6 (0.85±0.28) 0.35-2.5 (1.9±0.23) 0.5-1.5 (0.9±0.52) b-HCH 0.9-5.8 (2.6±3.1) 1.3-6.2(3.2±2.7) 0.7-4(2.5±1.75) 0.6-2.5 (1.3±0.8) 1.1-4.2 (2.2±0.44) 0.9-3.5 (2±1.2) g-HCH 0.6-7.18 (4±2.5) 1.8-6.5(3.5±1.21) 1.5-8.6(4.3±1.14) 0.2-5.5 (3±2.2) 0.2-6 (3.6±0.51) 0.6-7.5 (4±0.78) d-HCH 0.3-1.5 (0.8±0.2) 0.5-1.2(0.5±0.76) 0.2-0.85(0.4±0.65) 0.1-0.8 (0.5±0.2) 0.1-0.66(0.25±0.38) 0.1-0.9(0.6±1.13) ∑HCH 1.95-17.9 (8.56±6.3) 3.88-15.8 (7.45±3.4) 2.75-15.3 (6.90±2.85) 0.65-9.72(4.65±3.29) 1.26-12.2(5.27±4.85) 1.8-12.8(6.33±4.3) HCB 0.17-2.77(1.28±1.1) 0.5-2.15(1.3±1.2) 0.72-4.11(2.43±1.6) 0.75-2.82(0.59±0.46) 0.41-2.74 (0.97±0.78) 0.85-1.33(1.17±0.27) Heptachlor 0.27-2.1(1±0.6) 0.7-2.4(1.2±0.8) 0.3-1.91(0.77±0.9) 0.66-1.22(0.87±0.13) 0.28-2.40(1.2±0.71) 1.05-2.04(1.64±0.52) Aldrin 0.2-1.17(0.56±0.23) 1.6-2.15(1.5±0.21) 0.4-1.21(0.5±0.1) 0.3-1.5(0.9±0.09) 0.25 -0.79(0.3±0.27) 0.6-1.7 (0.8±0.43) Hep-exo 0.1-1.53(0.57±0.39) 0.2-2.8(1.9±0.31) 0.19-2.55(1.57±0.75) 0.4-2.2(0.71±0.26) 0.22-1.1(0.43±0.32) 0.27-0.94(0.68±0.33) Dieldrin 0.22-5.30(2.7±3.78) 1.5-9.5(5.3±2.1) 0.67-12.4(6.5±6.05) 0.81-14.33(5.31±3.4) 0.2-5.61(1.87±1.65) 0.84-3.51(1.89±1.42) Endrin 0.9-2.3(1.4±0.35) 0.45-2.8(1.6±0.64) 0.5-1.91(0.93±0.92) 0.30-1.15(0.5±0.19) 0.34-1.28(0.34±0.43) 0.4-1.10 (0.47±0.56) a-Endo 0.5-3.5(1.7±0.06) 0.8-3.4(2.4±1.3) 0.4-2.99(1.2±1.63) 0.2-1.56(0.76±0.43) 0.2-0.74(0.30±0.25) 0.38-1.31(1±0.53) b-Endo N.D. 1.25-4.2(2.5±0.56) 0.25-3.6(1.35±0.73) 0.4-2.37(1.4±0.6) 0.1-0.58 (0.18±0.32) 0.1-0.62(0.21±0.35) Endo-sulphate 0.73-5.48(2.5±1.4) 1.7-13.4(5.1±4.31) 0.57-10.9(4.95±5.09) 0.85-2.94(1.31±0.51) 0.3-7.9(2.32±2.42) 0.84-3.36(2.14±1.26) Methoxychlor 0.32-7.47(2±3.4) 0.6-6.4(3.4±2.31) 1.26-5.32(3.56±2.01) 0.9-8.78(3.10±1.3) 0.25-3.85(1.73±1.13) 0.83-1.94(1.52±0.6) ∑ other OCPs 1.8-30.6(13.5±15.8) 5.4-45.8(31.3±16.3) 6.84-52.8(28.3±17.8) 1.44-28.1(12.9±9.3) 2.18-25.7(9.4±6.7) 6.5-35.2(20±9.5) TC 1.84-5.7 (2.4±1.5) 1.6-2.6 (2.2±0.3) 0.26-5.75(2.72±2.38) 0.51-2.9(1.7±0.8) 0.65-8.5(2.4±0.71) 0.99-2.15(1.64±0.61) CC 0.29-4.14 (1.7±.86) 0.56-1.37 (0.8±0.65) 0.5-2.69 (1.66±1.41) 0.3-2.29(0.85±0.51) 0.4-2.57(0.58±0.47) 0.3-0.68(0.43±0.39) non-TC 1.8-3.4(2.8±1.1) 1.33-4.66 (2±1.02) 1.5-3.24 (2.26±0.72) 1.10-2.9(1.98±0.58) 0.41-3.84(1.68±0.79) 0.98-2.19(1.5±0.66) ∑Chlordane 3.69-13.15(6.5±3.4) 2.88-7.8 (4.6±1.58) 1.99-10.36 (6.64±4.17) 2.23-6.23(4.45±1.51) 1.19-14.47(4.66±2.69) 1.96-4.10(3.22±1.19) p, p'-DDE 30.3-181 (85.8±91.02) 96.56-215 (138.9±151.3) 17.54-163 (92.10±76.8) 38.8-98.6 (55.4±5.2) 8.67-187 (65.7±49.84) 35.7-106 (62.9±38.9) o, p'-DDE 1.47-13.6 (3.4±9.3) 1.76-5.4(3.6±1.2) 0.48-2.93(1.05±5.97) 2.64-7.10 (3.9±1.1) 0.34-1.8 (0.6±0.67) 0.49-3.76 (2.5±1.64) o, p'-DDD 2.25-8.5 (3±0.76) 0.43-11.5(5±3.37) 0.53-2.12(1.3±0.82) 0.61-4.2(2.5±1.42) 2.6-6.8 (3.35±2.44) 1.10-1.8(1.5±0.38) p, p'-DDD 2.01-28.5 (6.5±13.4) 8.5-35.7(15.3±7.8) 6.5-20.8(10.1±12.05) 7-21.5 (14.5±6.19) 3.49-24.4 (15±8.03) 4.92-13(6.8±4.22) o, p'-DDT 1.12-16.3 (4.1±3.5) 0.8-6.19 (4±1.7) 0.90-4.38 (3±2.56) 1.10-3.5(2.3±0.9) 0.71-5.2 (3.5±1.76) 0.4-2.7(1.3±1.32) p, p'-DDT 3.43-42.5 (17.5±20.6) 2.5-47.4(31.5±8.41) 4.03-31.17 (14.4±5.16) 1.04-22.60 (7.6±3.56) 4.37-18 (7±2.15) 1.88-8.2 (5.7±1.51) ∑DDTs 39.2-260 (115.8±87.5) 106-308 (190±143.6) 28.1-226(105.±101.9) 48-148(86.12) 17.8- 237(143.5±113.) 40.2-132(75.2±46.7) ∑OCPs 45.7-295(136.3±88.6) 115-365(225±96.6) 45.5-305(143±94.7) 52.5-160(102.7± 43.6) 21.5-282(168.5± 112.5) 49.4-175(112.±45.3)
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Table 5.3.Minimum-maximum (Mean±std) concentration of Polychlorinated Biphenyles (PCBs) in freshwater fish species from River Chenab.
Compounds Cyprinus carpio Cirrhinus reba Cirrhinus mrigale Catla catla Labeo calbasu pcb-28 0.52-1.35(0.86±0.37) 0.1-0.97(0.59±0.21) 0.65-1.29(0.77±0.17) 0.1-1.56(0.92±0.44) 0.14-0.7(0.53±0.21) pcb-37 0.11-0.73(0.49±0.28) 0.11-1.97(0.96±0.6) 0.54-6.5(1.58±1.67) 0.43-1.44(1.1±0.62) 0.54-1.33(0.76±0.24) pcb-66 0.1-0.56(0.38±0.21) 0.1-2.32(0.65±0.46) 0.49-2.27(0.67±0.56) 0.2-0.49(0.4`±0.05) 0.17-0.62(0.38±0.2) pcb-60 0.16-0.54(0.4±0.18) 0.2-2.37(0.63±0.58) 0.5-3.81(0.93±0.97) 0.14-1.2(0.76±0.22) 0.1-0.68(0.39±0.2) pcb-49 0.1-0.61(0.35±0.21) 0.1-0.49(0.36±0.11) 0.36-1.9(0.56±0.26) 0.14-0.72(0.49±0.32) 0.1-0.41(0.27±0.15) pcb-44 0.1-0.68(0.4±0.27) 0.1-0.64(0.33±0.12) 0.3-0.94(0.39±0.22) 0.17-1.2(0.73±0.56) 0.1-0.45(0.26±0.15) pcb-70 0.17-0.87(0.46±0.32) 0.1-1.08(0.52±0.23) 0.4-.79(0.47±0.18) 0.1-0.54(0.4±0.06) 0.1-0.8(0.37±0.24) pcb-74 0.1-0.88(0.46±0.3) 0.1-1.08(0.52±0.22) 0.44-0.87(0.47±0.18) 0.31-1.22(1.1±0.51) 0.1-0.8(0.4±0.24) pcb-105 0.31-0.97(0.6±0.27) 0.11-0.8(0.53±0.15) 0.52-3.03(0.88±0.73) 0.11-0.74(0.61±0.43) 0.46-2.12(0.69±0.55) pcb-101 0.35-0.55(0.45±0.1) 0.1-0.69(0.41±0.17) 0.47-0.72(0.51±0.07) 0.5-1.52(1.1±0.67) 0.4-0.62(0.48±0.25) pcb-114 0.3-0.48(0.34±0.25) 0.21-0.89(0.43±0.2) 0.43-2.9(0.93±0.81) 0.15-0.82(0.55±0.17) 0.13-0.53(0.4±0.15) pcb-99 0.19-0.64(0.45±0.19) 0.1-0.58(0.42±0.16) 0.48-2.24(0.59±0.62) 0.23-1.21(0.76±0.39) 0.2-0.98(0.47±0.3) pcb-87 0.2-0.57(0.42±0.16) 0.13-0.49(0.31±0.16) 0.38-0.99(0.44±0.21) 0.2-1.7(0.51±0.6) 0.12-0.8(0.36±0.27) pcb-82 0.1-0.45(0.3±0.17) 0.13-0.82(0.55±0.15) 0.5-4.37(1.09±1.10) 0.11-0.42(0.33±0.27) 0.1-0.54(0.33±0.14) pcb-118 0.33-0.62(0.49±0.12) 0.11-0.9(0.46±0.24) 0.42-4.28(1.03±1.09) 0.23-1.02(0.54±0.42) 0.41-0.79(0.55±0.12) pcb-166 0.4-0.62(0.52±0.1) 0.11-1.55(0.56±0.35) 0.31-7.33(1.54±1.97) 0.16-0.65(0.31±0.15) 0.31-0.74(0.41±0.22) pcb-158 0.4-0.85(0.51±0.37) 0.16-1.65(0.7±0.33) 0.6-7.5(1.74±1.95) 0.45-1.23(1.1±0.22) 0.6-1.12(0.65±0.28) pcb-128 0.4-0.68(0.39±0.29) 0.12-1.28(0.54±0.26) 0.49-5.59(1.3±1.46) 0.21-0.98(0.57±0.38) 0.49-0.88(0.55±0.21) pcb-156 0.1-0.16(0.12±0.08) 0.1-0.29(0.14±0.09) 0.16-0.22(0.17±0.06) 0.1-1.14(0.7±0.27) 0.16-0.21(0.17±0.1) pcb-153 0.56-0.75(0.65±0.08) 0.14-1.71(0.67±0.37) 0.46-7.5(1.69±1.98) 0.1-0.67(0.44±0.31) 0.46-0.9(0.55±0.25) pcb-138 0.6-0.85(0.65±0.37) 0.16-1.65(0.72±0.34) 0.6-7.5(1.74±1.95) 0.6-1.15(0.81±0.47) 0.6-1.12(0.65±0.28) pcb-180 0.48-0.56(0.49±0.24) 0.1-1.19(0.48±0.26) 0.45-3(0.91±0.73) 0.14-1.56(0.69±0.24) 0.1-0.5(0.25±0.22) pcb-170 0.1-0.59(0.39±0.22) 0.12-0.98(0.48±0.27) 0.51-1.83(0.72±0.44) 0.2-0.63 (0.34±0.41) 0.12-0.63(0.33±0.24) pcb-189 0.12-0.77(0.27±0.36) 0.12-0.87(0.55±0.32) 0.76-0.9(0.8±0.36) 0.12-1.47(0.36±0.14) 0.15-0.8(0.47±0.36) pcb-183 0.1-0.53(0.28±0.27) 0.1-0.68(0.34±0.25) 0.53-1.69(0.67±0.4) 0.11-2.53(1.28±0.75) 0.1-0.69(0.22±0.27) pcb-179 0.1-0.43(0.22) 0.12-0.44(0.21±0.25) 0.43-0.61(0.49±0.21) 0.28-1.24(0.52) 0.1-0.46(0.16±0.2) pcb-187 0.19-0.56(0.3±0.26) 0.1-0.58(0.31±0.23) 0.47-1.59(0.65±0.42) 0.73-1.36(1.03±0.69) 0.15-0.61(0.2±0.23) pcb-198 1.49-8.78(4.32±3.12) 0.44-8.99(3.2±2.5) 1.34-9.75(5.04±3.09) 0.93-6.51(2.32±1.2) 0.41-6.28(2.87±1.83) pcb-169 0.2-0.52(0.29±0.24) 0.18-0.6(0.41±0.18) 0.51-1.46(0.61±0.35) 0.39-1.26(0.5±0.42) 0.17-0.66(0.35±0.24) pcb-126 0.1-0.43(0.22±0.24) 0.1-0.44(0.21±0.34) 0.43-0.61(0.46±0.21) 0.1-0.56(0.27±0.12) 0.1-0.56(0.18±0.22) pcb-77 0.24-0.64(0.48±0.17) 0.11-0.81(0.56±0.17) 0.5-4.78(1.13±1.22) 0.6-0.84(0.68±0.37) 0.2-0.74(0.44±0.23) ∑PCBs 9.3-24.9(16.8±6.4) 3-29.6(17.47±6.48) 12.7-93.7 (30.4±21.96) 8.2-28.4(19.64±5.43) 3.58-20.8(14.3±5.89) Continued…
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Compounds Rita rita Mastacembalis aramtus Securicola gora Gudusia chapra Clupisoma naziri Clupisoma garua pcb-28 0.65 - 30.83(6.4±10.5) 0.65-1.23(0.86±0.29) 0.1-1.62(0.56±0.44) 0.57-4.37(1.48±0.99) 0.52-1.35(0.86±0.37) 0.11-0.66(0.39±0.28) pcb-37 0.56 -16.8(5.59±5.23) 0.55-4.75(2.57±1.84) 0.1-9.83(2.14±2.71) 0.12-0.75(0.57±0.19) 0.11-0.73(0.49±0.28) 0.17-1.88(0.89±0.56) pcb-66 0.49 -0.86(0.59±0.12) 0.5-1.24(0.86±0.33) 0.12-1.79(0.53±0.49) 0.49-2.08(0.86±0.43) 0.1-0.56(0.38±0.21) 0.15-0.72(0.39±0.22) pcb-60 0.5 -1.34(0.79±0.34) 0.5-1.09(0.82±0.24) 0.1-1.77(0.61±0.46) 0.5-2.11(0.83±0.43) 0.16-0.54(0.4±0.18) 0.21-0.56(0.4±0.15) pcb-49 0.38 - 1.61(0.73±0.37) 0.36-1.58(0.88±0.57) 0.16-0.86(0.35±0.23) 0.36-1.07(0.56±0.21) 0.1-0.61(0.35±0.21) 0.1-0.52(0.28±0.2) pcb-44 0.35 -1.5(0.67±0.34) 0.32-0.75(0.54±0.18) 0.14-1.13(0.37±0.31) 0.25-0.79(0.47±0.14) 0.1-0.68(0.4±0.27) 0.07-0.42(0.23±0.15) pcb-70 0.46 -1.25(0.89±0.37) 0.44-1.94(1.02±0.75) 0.11-1.33(0.49±0.35) 0.44-0.998(0.64±0.21) 0.17-0.87(0.46±0.32) 0.1-0.52(0.32±0.18) pcb-74 0.46 -1.25(0.89±0.37) 0.14-1.13(0.73±0.33) 0.1-1.78(0.56±0.46) 0.43-0.9(0.64±0.32) 0.1-0.88(0.46±0.3) 0.11-0.52(0.32±0.19) pcb-105 0.53 -5.4(1.56±1.43) 0.6-2.27(1.5±0.77) 0.11-2.21(0.73±0.54) 0.52-6.86(1.53±1.64) 0.31-0.97(0.6±0.27) 0.22-1.3(0.65±0.36) pcb-101 0.47 -4.56(1.23±1.35) 0.47-0.53(0.5±0.02) 0.1-0.49(0.31±0.19) 0.13-0.53(0.29±0.23) 0.35-0.55(0.45±0.1) 0.17-0.48(0.27±0.21) pcb-114 0.43 -0.94(0.59±0.16) 0.56-1.32(0.96±0.34) 0.18-1.66(0.55±0.43) 0.27-6.27(1.3±1.53) 0.3-0.48(0.34±0.25) 0.16-0.68(0.4±0.23) pcb-99 0.48 -1.82(0.87±0.42) 0.55-1.33(0.99±0.35) 0.16-2.1(0.64±0.53) 0.41-1.16(0.68±0.22) 0.19-0.64(0.45±0.19) 0.2-0.6(0.38±0.16) pcb-87 0.38 -1.57(0.63±0.38) 0.2-0.49(0.33±0.18) 0.16-0.85(0.29±0.24) 0.25-0.65(0.38±0.14) 0.2-0.57(0.42±0.16) 0.18-0.5(0.27±0.2) pcb-82 0.5 - 6.8(1.99±1.5) 0.5-2.19(1.45±0.71) 0.11-3.8(1.04±0.98) 0.41-1.46(0.76±0.35) 0.1-0.45(0.3±0.17) 0.33-1.07(0.6±0.25) pcb-118 0.42 - 1.99(0.97±0.49) 0.49-2.24(1.39±0.69) 0.11-3.36(0.92±0.88) 0.42-1.7(0.82±0.41) 0.33-0.62(0.49±0.12) 0.29-0.98(0.58±0.26) pcb-166 0.5 - 3.2(1.7±1.16) 0.59-4.75(2.98±1.75) 0.1-4.76(1.4±1.23) 0.37-3.34(1.22±0.78) 0.4-0.62(0.52±0.1) 0.42-1.28(0.84±0.29) pcb-158 0.6 - 3.82(2.1±1.27) 0.77-5.22(3.35±1.89) 0.11-5.75(1.71±1.47) 0.61-3.71(1.45±0.85) 0.4-0.85(0.51±0.37) 0.6-1.83(1.07±0.45) pcb-128 0.49 - 2.93(1.55±0.96) 0.49-3.86(2.47±1.4) 0.1-4.36(1.31±1.11) 0.49-2.83(1.07±0.67) 0.4-0.68(0.39±0.29) 0.54-1.41(0.84±0.33) pcb-156 0.17 - 0.32(0.21±0.05) 0.16-0.19(0.17±0.1) 0.13-0.42(0.12±0.12) 0.16-0.4(0.16±0.12) 0.1-0.16(0.12±0.08) 0.1-0.16(0.15±0.07) pcb-153 0.66 - 3.37(1.86±1.16) 0.72-4.91(3.13±1.76) 0.11-4.92(1/49±1.26) 0.53-3.5(1.35±0.79) 0.56-0.75(0.65±0.08) 0.58-1.43(0.95±0.3) pcb-138 0.6 - 3.82(2.01±1.27) 0.77-5.22(3.35±1.89) 0.11-5.75(1.72±1.46) 0.61-3.71(1.46±0.84) 0.6-0.85(0.65±0.37) 0.71-1.83(1.1±0.43) pcb-180 0.45 - 1.02(0.7±0.21) 0.64-2.57(1.7±0.81) 0.17-1.46(0.57±0.34) 0.1-0.78(0.48±0.15) 0.48-0.56(0.49±0.24) 0.35-0.57(0.45±0.07) pcb-170 0.5 -1.77(0.73±0.42) 0.5-1.24(0.8±0.36) 0.5-1.16(0.49±0.33) 0.11-0.71(0.52±0.14) 0.1-0.59(0.39±0.22) 0.14-0.68(0.4±0.21) pcb-189 0.76 -1.32(0.8±0.41) 0.76-0.96(0.86±0.08) 0.13-0.94(0.45±0.36) 0.15-0.98(0.66±0.26) 0.12-0.77(0.27±0.36) 0.12-0.81(0.45±0.35) pcb-183 0.53 - 0.9(0.67±0.24) 0.61-1.1(0.83±0.22) 0.1-0.51(0.22±0.21) 0.11-0.89(0.42±0.22) 0.1-0.53(0.28±0.27) 0.1-0.55(0.32±0.23) pcb-179 0.43 - 0.63(0.49±0.28) 0.43-0.54(0.47±0.05) 0.16-1.22(-0.5±0.4) 0.12-0.53(0.24±0.22) 0.1-0.43(0.22) 0.1-0.44(0.18±0.2) pcb-187 0.47 - 1.29(0.83±0.33) 0.58-1.45(1.02±0.39) 0.19-0.87(0.39±0.3) 0.27-0.65(0.43±0.17) 0.19-0.56(0.3±0.26) 0.16-0.52(0.34±0.17) pcb-198 1.13-8.7(4.54±2.84) 2.59-8.19(6.02±2.8) 0.24-5.42(2.23±1.95) 0.41-10.66(4.42±3.13) 1.49-8.78(4.32±3.12) 0.39-2.84(1.43±1.05) pcb-169 0.5 - 0.97(0.54±0.36) 0.75-1.37(0.98±0.24) 0.1-1.41(0.53±0.34) 0.11-1.03(0.55±0.23) 0.2-0.52(0.29±0.24) 0.1-0.44(0.18±0.2) pcb-126 0.43 -0.54(0.47±0.25) 0.43-0.54(0.47±0.05) 0.1-1.46(0.57±0.39) 0.12-0.53(0.24±0.22) 0.1-0.43(0.22±0.24) 0.16-0.6(0.37±0.2) pcb-77 0.56 -1.4(0.96±0.38) 0.58-1.5(1.01±0.41) 0.1-0.51(0.22±0.21) 0.34-2.22(0.76±0.45) 0.24-0.64(0.48±0.17) 0.22-0.62(0.44±0.16) ∑PCBs 15.8-108(42.4±28.6) 18.6-66.9(45.1±20.12) 2.4-73.8(24.06±18.58) 16.7-49 (26.77±9.08) 9.3-24.9(16.8±6.4) 7.6-22.7(15.4±6.1)
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Table 5.4. Minimum-maximum (Mean±std) concentration (ng/g WW) of organochlorines in freshwater fish species from different land uses (i.e. agricultural, urban and industrial).
Compounds Land Use Season Agricultural Urban Industrial ∑HCH Summer 0.87-4.3 (2.8±2.5) 2.7-8.2(3.3±2.87) 3.9-15.8 (12.2±6.5) Winter 0.65-7.2 (4.4±3.8) 1.9-11.7(7.4±3.44) 3.5-8.9(6.5±3.7) Summer 3.8-24.5(14.3±9.6) 5.3-34.7(21.84±14.82) 4.6-41(33±15.84) ∑ other OCPs Winter 3.8-28.5(19.1±12.4) 6.1-42.1(27.47±15.29) 7.4-52.6(39±22.54) Summer 1.8-4.17 (2.1±1.18) 2.5-8.5(5.21±2.5) 2.25-11.2(7.4±3.28) ∑Chlordane Winter 1.4-5.3 (3.6±2.3) 2.1-13.6(7.72±3.52) 4.9-13.1(9.1±3.95) Summer 18.6-149 (85.9±39.2) 54-214 (113±67.2) 76.8-248 (138±98.8) ∑DDTs Winter 29.3-169 (105±91) 45.6-269(143±117.37) 14.6-308 (219±119.3) Summer 32.7-188 (98.3±75.6) 41.1-267.8(117±112) 85.2-285 (160±116.5) ∑OCPs Winter 35.19-206 (112±89.2) 52.8-290 (195±102) 64.1-344 (248±196.1) Summer 2.9-39 (14.3±28.1) 11.3-73.8(43.7±32.2) 13.8-108 (59.4±42.7) ∑PCBs Winter 3.9-41(19.1±23.4) 9.17±93.7(39.8±54.8) 15-66 (49.6±27.2)
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Table 5.5.Showing the levels of OCPs and PCBs in the fish from different water bodies of the World.
Location HCHs ( ng/g WW) DDTs ( ng/g WW) PCBs ( ng/g WW) References Cochin and Rameshwaram > 200 Muralidharan et al., 2009 Konya, Turkey > 20 Kalyoncu et al., 2009 Xiamen Island, China 0.18-345(d.w.) Chen et al., 2002 Korean coast 0.38-5.6 0.84 -27 Yim et al., 2005 European eel from Poland 0.6-6 0.4-23.8 Richert et al,. 2010 Huairou Reservoir and Gaobeidian lake, China 0.58-8.4 7.5-88 N.D. - 22.7 Li et al., 2008 Day Bay, China 1.7-462 Pearl River estuary , China 0.2 1.8-287 Guo et al., 2008 Tibetan fish 2.5 (d.w.) 0.84-10 Yang et al., 2010 Pearl River Delta 0.01-7.8 (lipid wt.) Zhou and Wong, 2004 Quanzhou and Xinghua Bay China 0.53 -109 Yatawara et al., 2010 Garigliano River Italy 36-52 167 ng/g Ferrante et al., 2010 Fish of Chinese coastal water 0.4-5-8.6 0.24-1.24 Jiang et al. 2005 Arungen Lake, Norway 444 Sharma et al., 2009 Turia River Spain 29.3 Bordajandi et al., 2003
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∑HCH (ng/g WW) ∑ other OCPs(ng/g WW)
Clupisoma garua Clupisoma naziri Gudusia chapra Securicula gora Mastacembalis aramtus Rita rita Labeo calbasu Catla catla Cirrhinus mrigala Cirrhinus reba Cyprinus carpio 0 2 4 6 8 10 0 5 10 15 20 25 30 35
∑Chlordanes (ng/g WW) ∑DDTs (ng/g WW)
Clupisoma garua Clupisoma naziri Gudusia chapra Securicula gora Mastacembalis aramtus Rita rita Labeo calbasu Catla catla Cirrhinus mrigala Cirrhinus reba Cyprinus carpio 0 1 2 3 4 5 6 7 0 50 100 150 200 250
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∑OCPs (ng/g WW) PCB (ng/g WW) Clupisoma garua Clupisoma naziri Gudusia chapra Securicula gora Mastacembalis aramtus Rita rita Labeo calbasu Catla catla Cirrhinus mrigala Cirrhinus reba Cyprinus carpio 0 10 20 30 40 50 0 50 100 150 200 250 δ 15N (%) Lipid (%)
Clupisoma garua Clupisoma naziri Gudusia chapra Securicula gora Mastacembalis aramtus Rita rita Labeo calbasu Catla catla Cirrhinus mrigala Cirrhinus reba Cyprinus carpio 0.00 1.00 2.00 3.00 4.00 5.00 6.00 0.00 2.00 4.00 6.00 8.00 10.00 12.00
Fig.5.1. Concentrations (ng/g WW) of ∑HCHs, ∑Other OCPs, ∑Chlordane, ∑DDTs, ∑OCPs, ∑PCBs, Lipid (%) and δ15N (%).
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Clupisoma garua Clupisoma naziri Gudusia chapra Securicula gora Mastacembalis aramtus Rita rita Labeo calbasu Catla catla Cirrhinus mrigala Cirrhinus reba Cyprinus carpio 0% 20% 40% 60% 80% 100% 0% 20% 40% 60% 80% 100%
a-HCH b-HCH g-HCH d-HCH TC CC non-TC
Clupisoma garua Clupisoma naziri Gudusia chapra Securicula gora Mastacembalis aramtus Rita rita Labeo calbasu Catla catla Cirrhinus mrigala Cirrhinus reba Cyprinus carpio
0% 20% 40% 60% 80% 100% 0% 20% 40% 60% 80% 100%
p,p'-DDE o,p'-DDE o,p'-DDD p,p'-DDD o,p'-DDT p,p'-DDT 3CBs 4CBs 5CBs 6CBs 7CBs 8CBs
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5.3.5. Seasonal variation of OCPs and PCBs
Generally, seasonal variation cannot be explained clearly for OCs-contamination in fish. For persistent organic pollutants such as PCBs, chlorinated pesticides, accumulation is age related as well as fat content of fish. Concentrations (ng/g WW) of ∑OCPs in all the collected fish species (Fig.5.3) ranged from 8.7-283 (mean; 85) and 12-365 (mean; 110) in summer and winter seasons, respectively. A significant seasonal variation of ∑OCPs (ng/g WW) in collected fish species were observed in Cyprinus carpio [mean; summer (47.7) and winter (70)], Cirrhinus reba [mean; summer (19) and winter (32.34)], Cirrhinus mrigale [mean; summer (31)and winter (57.8)], Rita rita [mean; summer (117) and winter (189)], Mastacembalis aramtus [mean; summer (169) and winter (284)], Securicola gora [mean; summer (100) and winter (232)] and Clupisoma naziri [mean; summer (206) and winter (96)]. All of these fish species showed relatively high values in winter season than in summer with exception of Clupisoma naziri which showed elevated values of ∑OCPs in summer season. Among individual OCPs, the concentrations (ng/g WW) of ∑other OCPs [mean; summer (9.6) and winter (17)] and DDTs [mean; summer (60) and winter (88.4)] showed significantly high values in winter season than in summer. While HCHs (mean; 5.4; ng/g, WW) and chlordanes (mean; 4.2; ng/g, WW) showed almost similar levels in both summer and winter seasons (Fig.5.4). Polychlorinated biphenyls (PCBs) concentration (ng/g WW) in all the collected fish species (Fig.5.5) were ranged from 3.1- 108 (mean; 29.38) and 3.58-98 (mean; 30) in summer and winter seasons, respectively. No significant variation of PCBs levels (ng/g WW) were shown by all the collected fish samples, with exception of Cirrhinus mrigale [mean; summer (23) and winter (38)] and Rita rita [mean; summer (50) and winter (30)]. Among individual classes of PCBs, only tri-CBs showed significant variation with relatively high values (mean; 15.6 ng/g WW) in Rita rita during summer season, while all other PCBs classes showed no significant seasonal variation between two seasons i.e. summer and winter (Fig.5.5).
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Summer Winter 450 400 350 300 250 200
150 (ng/gWW) 100 50 0 -50
Fish species
Fig.5.3. OCPs residues (ng/g WW) in the fish species collected from River Chenab.
Summer Winter
250
200
150
ng/gWW 100
50
0 ∑HCH ∑ other OCPs ∑Chlordanes ∑DDTs ∑OCPs
Fig.5.4. Showing the levels of different OCPs (ng/g WW) residues in fish samples from River Chenab.
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Summer Winter 12
10
8
6 ng/gWW) 4
2
0 3-CBs 4- CBs 5-CBs 6-CBs 7-CBs 8-CB PCB classes
Fig.5.5. Showing the levels of different chlorinated biphenyles residues (ng/g WW) in fish samples from River Chenab.
5.3.6. Spatial variation of OCPs and PCBs
The mean±std and min-max of the concentrations (ng/g WW) of OCPs and PCBs recorded in the all analyzed fish species were collected from different land use types i.e. Agricultural, Urban and Industrial are given in Table 5.4. A significant spatial variation of OCPs and PCBs were observed among all the fish species collected from different land use types i.e. agricultural, urban and industrial in the study stretch. Generally, the concentration (ng/g WW) of OCPs (Fig.5.6) and PCBs (Fig.5.7) were recorded relatively high in all the fish species collected from industrial zone [OCPs: 46-321 (mean ;205) and PCBs: 10.87-98.5 (mean; 54.4)] following by urban site locations [OCPs: 34.4-270 (mean ;156) and PCBs: 5.7-63.7 (mean; 41.5)], while fish samples collected from agricultural areas [OCPs: 12.7-196 (mean ;105) and PCBs: 2.1-31.3 (mean; 16.4)] showed low levels.
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The reason of high concentration in the fish samples collected from all those sites (S-2, 18, 20, and 23) located near industrial areas, receiving a high load of toxic chemicals through open waste water drains from surrounding world known industrial cities including Sialkot, Gujarat, Gujranwala, Faisalabad, Jhang, Khanewal, and Multan, which resulted in the accumulation of these contaminants in food web of River Chenab. On the other hand, S-8, 11, 13, 17, 3, 10, and 16 can be categorized as urban and fish samples collected from these site location showed high levels of OCs contaminants, may be due to receiving organochlorine residues from the surrounding soil via surface run-off and air depositions as these areas were characterized by high temperatures and heavy dust storms, but upstream flow contamination of OCPs and PCBs from industrial areas also cannot be neglected. Fish samples collected from those sites located near agricultural area showed relatively lower levels than other areas i.e. industrial and urban, but some of the samples from the cotton belt showed high residual levels of cyclodiens and DDTs, which signature the extensive use of these pesticides for the crop protection.
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Agricultural Urban Industrial 300
250
200
150
(ng/g WW) (ng/g 100
50
0 ∑HCH ∑ other OCPs ∑Chlordanes ∑DDTs
Fig.5.6. Showing the spatial variations of different OCPs (ng/g WW) residues in fish samples from River Chenab, Pakistan.
80 70 60
50
40
30 (ng/gWW 20 10 0 Agricultural Urban Industrial -10
Fig.5.7. Showing the spatial variations of different PCBs (ng/g WW) residues in fish samples from River Chenab, Pakistan.
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5.4. Discussion
5.4.1. OCP signatures in fish: general observation
Organochlorines (OCs) due to its persistence and lipophilic properties have potential to accumulate in the bottom sediment and associated biota, which resulted in biomagnifications of these contaminants in the fish tissues (Malik et al., 2011). The results of our study showed that DDTs and its metabolites and heavier PCB congeners were found predominantly in all fish samples, which is consistent with other studies; the latter also reported similar trends for these chemicals (Ferrante et al., 2010; Sharma et al., 2009).
5.4.1(a). HCHs
Technical HCH usually contains α-HCH (55-80%), β-HCH (5-14%), γ -HCH (8-15%) and δ-HCH (2-16%) and lindane (90% of γ -HCH) is being used for the sanitation and in agriculture sector to control insects (Muralidharan et al., 2009, Yang et al., 2010). The results of our study showed the dominance of β-HCH and γ-HCH in all the collected fish samples from River Chenab waters. The ratios of α/γ-HCH ranged in all the fish samples 0.08-0.52 (mean; 0.29), showing the dominance of γ-HCH in most of the fish samples and indicated lindane as potential source of these low ratios, while some of fish samples also showed high ratios which is evidence of application of technical HCH in the study area (Yang et al., 2010). β / γ -HCH ranged from 0.43-1.24 (0.74), indicating historical use of technical HCH as well as the past and recent use of lindane in surrounding areas (Li et al., 2008; Yang et al., 2006). Dominance of β- HCH in most of fish samples than others i.e. α-HCH and δ-HCH is due to its high persistence and stability in the environment and also high potential to accumulate in fish tissue due to more lipophilic nature (Walker et al., 1999; Malik et al., 2007). Moreover, presence of β-HCH in the biota from River Chenab waters is also the evidence of the use of technical HCH in the past which resulted in the isomerization of α-HCH and γ-HCH into β -HCH in River water and sediment (Li et al., 2008; Andersen et al., 2001; Rajendran et al., 2005; Zhou et al., 2004), which is resistant to microbial degradation and have ability to magnify in the high trophic level (Howard, 1992; Burke et al., 2003, Malik et al., 2011). The presence of β-HCH in the present
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study is consistent with other studies which also reported the considerable residual level of β- HCH in the biological samples (Kumari et al., 2001; Kole et al., 2001 Li et al., 2008; Muralidharan et al., 2009; Yang et al., 2010). On the other hand, β –HCH has been reported to accumulate more than 10-30 times in the food chain than lindane (Muralidharan et al., 2009). γ- HCH also detected in high concentration which signatures the current use of lindane for crop protection (Mdegela et al., 2009). Generally, our results showed the dominance of β-HCH in the fish species i.e. Mastacembalis aramtus and Rita rita, collected from benthic region while γ- HCH found with high levels from Securicola gorawhich feed in the water column and accumulate via gills routes. The elevated values of β-HCH in benthic species may be due to association of β-HCH with sediment and low volatility, which resulted in its accumulation into the fish (ATSDR, 2005). The concentration of HCHs in the fish samples collected were low when compared with reported levels of mean more than 200 ng/g (WW) in fish samples from India (Muralidharan et al., 2009), mean more tahn 20 ng/g (WW) in fish from, Turkey (Kalyoncu et al., 2009) and from Xiamen Island, China (Chen et al., 2002). The reported concentration of HCHs from our study is comparable with those reported from fish of Korean coast (Yim et al., 2005) and European eel from Poland (Richert et al., 2010).
5.4.1(b). DDTs
The results of our study showed the dominance of DDTs, in all collected fish samples than any other OCP, indicating its historical as well as large scale current usage. Our results are consistent with some other studied reported high concentration of DDTs in eggs (Malik et al., 2011), sediment (Sanepra et al., 203), water and soil (Tariq et al., 2007; Syed and Malik, 2011; Ahad et al., 2010) from different parts of Pakistan and briefly presented in chapter 1. Although, DDT is banned in most countries including Pakistan, but is still used in the country in the health, agricultural and industrial sectors due to its low cost and easy availability in the market (Malik et al., 2011). Jan et al., (2008), also reported one of the world’s large stockpiles of OCPs at different locations in the country, with thousand kilograms of DDTs in chemical warehouses, which resulted in the contamination of these toxic chemicals in different environmental matrices including fish.
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Generally different indicative ratios of DDTs and its metabolites in water, sediment, soil and biota were used to predict the historical trends of DDTs in the surrounding environment (Richert et al., 2010; Ozkoc et al., 2007; McHugh et al., 2010). Reported values of (DDD+DDE) / DDTs (1.97-10.52; mean: 5.46), p, p/-DDT / DDTs (0.04-0.29; mean: 0.14), DDD / DDE (0.1- 0.26; mean: 0.2) and p, p/-DDE / p, p/-DDT (1.75-11.75 mean; 5.58) showed the historical as well as recent wide scale use of DDTs in the study area. Most of the fish samples showed high values of the p, p/-DDE /p, p/-DDT, reflected the predominance of p, p/-DDE, which is consistence with results reported by Storelli et al., (2009). p, p/-DDE, which recorded in high concentrations and frequencies, signatures the long-term use of DDTs in the study area, which may brought into River’s ecosystem via surface run-off and erosion from surrounding soils (Tang et al., 2008). In biological organisms like fish, metabolic functions via mixed oxygenase enzyme may also play important role in the conversion of p, p/-DDT into p, p/-DDE (Muralidharan et al., 2009), which also contributed significantly to the elevated values of p, p/- DDE. On the other hand, bio-concentration factor may also played important role in this process depending upon the feeding habit of individual species which cannot be neglected as source of high p, p/-DDE levels (Wang and Simpson, 1996). DDTs due to their lipophilic properties have potential to accumulate in the bottom sediment and associated biota, which resulted in biomagnifications of DDTs in the fish tissues (Tanabe et al., 1998; Zhou et al., 2006; Sarkar et al., 2008). Among other DDTs, p, p/-DDT also detected in many of fish samples reflected the existence of new inputs in our study area while DDD, which is anaerobic metabolic product of DDTs (Kalantzi et al., 2001; Luo et al., 2004), also detected in fish samples may come from sediment and accumulate in fish tissue. The DDT concentrations in fish measured in our study is comparable to those reported by Guo et al. (2008) from Pearl River estuary and Day Bay, China and from Garigliano River, Italy (Ferrante et al., 2010). The measured concentrations of DDTs from the fish of Chenab River is far high than those reported from Poland waters (Szlinder et al., 2010), Tibetan Plateau (Yang et al., 2010) and Korean waters (Yim et al., 2005).
5.4.1(c). Chlordanes
Chlordanes were also detected in 60% of fish samples and the results followed similar trends as reported by Guo et al. (2008), with almost comparable concentration of ∑chlordanes
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ranged from 0.8-4.8 ng/g (WW). Yim et al. (2005) reported 0.17-4.24 ng/g WW of ∑chlordanes from the coast of Korea which is also comparable with those measured in fish samples collected from River Chenab. While a mean value of 13.83 ng/g (WW) of ∑chlordanes has been reported in the fish from tropical Asia and Oceania (Kannan et al., 1999), which showed relatively high ∑chlordanes concentration from the Chenab’s fish samples. TC/CC ratio commonly used in many studies to predict the age and history of application of the chlordanes (Bidleman et al., 2002; Guo et al., 2008). In our study TC/CC ranged 0.77-4.13 which reflected its inputs in River Chenab. High concentrations of trans-chlordane in the collected fish samples reflected that still this is in use in agricultural and industrial sector and may entered into riverine ecosystem through surface run-off, erosion from surrounding soils and atmospheric deposition (Bidleman et al., 2002). Some of the analyzed fish samples showed lower values of TC/CC, which reflected the aged application of chlordanes in the study area (Qiu et al., 2009).
5.4.1(d). Other OCPs
Among other OCPs, endosulfan sulphate, β-endosulfan, heptachlor, dieldrin and methoxychlor were found more frequently and in relatively high concentrations than others including aldrin, heptachlor epoxide, endrin and α-endosulfan. Among endosulfans, β-endosulfan and endosulfan sulphate were found in more of the fish samples collected from different location of River Chenab, while α-endosulfan also found in some samples but relatively in smaller concentration. The presence of β-endosulfan and endosulfan sulphate may be due to its high persistence and stability in environment and high affinity with sediment, which resulted in the accumulation of these chemicals into fish tissues. This trend of detection of endosulfans reflected the existence of these chemicals in the current study and comparable with concentrations reported from biota of India (Malik et al., 2007) and Turkey (Kalyoncu et al., 2009) but the concentrations are much high than those recorded from Chinese fish (Jiang et al., 2005). Most of studies (Kalyoncu et al., 2009; Malik et al., 2007) reported the dominance of heptachlor epoxide Viz; degradation byproduct of heptachlor. In contrary to these reports, our study showed the frequent detection of heptachlor than its degradation byproduct i.e. heptachlor epoxide. The indicative ratios of heptachlor and its epoxide in our study also reflected the current inputs of heptachlor in the River Chenab from surrounding contaminated soils, which resulted in
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the high detection frequency of heptachlor in our fish samples. Aldrin and its epoxide dieldrin were found in many fish samples with relatively high concentrations of dieldrin which reflected the both continued and recent inputs of these chemicals in current study. Generally, aldrin is converted rapidly into dieldrin by plants, animals and insects and similar trends of results were showed by the fish samples collected from River Chenab, Pakistan. The concentrations of dieldrin in the current study is similar to those reported from fish of Konya water, Turkey (Kalyoncu et al., 2009) and Garigliano River, Italy (Ferrante et al., 2010) while Jiang et al., (2005) reported lower values for dieldrin than those measured in the fish from River Chenab, Pakistan. HCB was also found in many fish samples in concentrations comparable with those detected from the biota of Arungen, Norway (Sharma et al., 2009) and Chinese fish (Jiang et al., 2005).
5.4.2. Congener-specific profile for PCB and their environmental significance
As for PCBs, the distribution of individual PCB classes in the fish tissue largely depends upon the species type, feeding habit and source of PCBs contamination into the aquatic system (Olsson et al., 2000; Zhou and Wong, 2004; Yang et al., 2010). Generally, results of study showed the dominance of heavier-chlorinated biphenyls i.e. penta-CBs, hepta-CBs, hexa-CBs and octa-CBs in most of fish samples, which can be justified by the fact of more lipid loving nature of heavier chlorinated biphenyls (Kidd et al., 1998; Yang et al., 2006), resulted in accumulation of these congeners in fish tissue than lighter ones (have more ability to pass through body’s cell membrane), thus showed low levels in biota tissue (Li et al., 2008). Moreover, heavier-chlorinated biphenyls have relatively low mobility and biodegradability and more affinity to get adsorb to particulate material resulted in sedimentary deposition (Zhou et al., 1999), ultimately accumulated and magnified into the food web (Sharma et al., 2009; McHugh et al., 2010) while lighter PCB congeners may be subject to microbial degradation and volatilization (Fisher et al., 1995; Zhou and Wong, 2004).
The comparison of PCBs measured in the fish samples from River Chenab, Pakistan showed the similar levels of PCBs to those reported from fish of Huairou Reservoir and Gaobeidian Lake, Beijing, China (Li et al., 2008), Turia River Spain (Bordajandi et al., 2003)
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and from the fish species of Hong Kong waters (Hung et al., 2006). Reported values of PCBs from River Chenab’s fish are much high than those found in the fish samples collected from coastal areas of China (Jiang et al., 2005) and streams of New Zeeland (Holmqvist et al., 2006) while recorded values are comparatively lower than those reported from the fish of Garigliano River Italy (Ferrante et al., 2010), Pearl River estuary, China (Nie et al., 2005) and Flanders, Belgium (Maes et al., 2008).
Congener-specific distributional patterns for PCBs clearly indicating that dl-PCBs were found in all fish samples. These super lipophilic compounds are of particular concern because of their severe toxic effects due to TCDD like carcinogenic properties (Zhao et al., 2006). Moreover, dl-PCB congeners have also been considered because of numerous reproductive, neurological, and hormonal disruptions. In our study, generally ∑7 dl-PCBs found in considerable levels in large fish individual having high fat contents generally collected near urban and industrial areas. Mastacembalis aramtus, Rita rita, Securicola gora and Cirrhinus mrigale showed maximum values of these toxic contaminants. Relatively the individual of these fish species contained high lipid contents (%) with high trophic position resulted in the accumulation these super lipophilic contaminants (Storelli et al., 2009). In contrary, Cirrhinus mrigale, which is herbivorous fish, also showed extremely high values of dl-PCBs. The reason for such high concentrations can be justified as the most contaminated individuals caught from industrial site locations with large size and high lipid content (%). The significant sources of these PCBs congeners into riverine ecosystem of Chenab, suggested from steel manufacturing units and coal burning during iron ore sintering process in the many industrial cities of Lahore, Faisalabad, Sialkot and Gujarat in the study area, which may reached in the river ecosystem via run-off from surrounding fields, direct disposal of municipal waste and atmospheric deposition (Buekens et al., 2001; Biterna et al., 2005).
5.4.3. Toxicity imposed due to dioxin-like PCBs
Many studies reported the toxicity of these dl-PCBs using WHO-PCB TEQ approach to reflect the risk of these mono-ortho and non-ortho congeners (Adu-Kumi et al., 2010; Szlinder- Richert et al., 2010; McHugh et al., 2010). The results of our study are also consistent with
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these studies showing the pre-dominance of non-ortho congeners with highest contribution of PCB-126, which may be due to high WHO-TEF values for this congener (Table 5.9). Our calculated TEQs were well below the current permissible established by WHO food legislative maximum levels. However, McHugh et al., (2010) and Szlinder et al., (2010) reported the comparable values from Irish water fish samples (0.17-1.24 pgTEQg-1) and Poland fish (1.55- 7.11 pgTEQg-1) which are within range of the values reported in our study. On the other hand, Stachel et al., (2007) reported fish WHO-PCBs TEQ between 8.5-59 pgTEQg-1, which exceeded the maximum value of 2.9 pgTEQg-1 for Mastacembalis aramtus in our study.
5.4.4. Bioaccumulation of OCPs and PCBs in the fish from River Chenab
Biological characteristics (age, lipid contents etc.), habitat preferences, feeding behaviors and differences in trophic position are reported to largely affect the accumulation of both OCPs and PCBs in the fish tissues and resulted the specie-specific (both inter and intra) dependent accumulation of these lipophilic compounds (Shaw et al., 2006; Sharma et al., 2009). Many studies (Kidd et al., 1995; Olsson et al., 2000; Burreau et al., 2004; Guo et al., 2008; Sharma et al., 2009) suggested the estimation of δ 15N % (naturally occurring nitrogen isotope of nitrogen) to monitor the time integrated snapshot of the food preferences and δ 15N % values are considered to be effective approach to measure a continuous trophic level accumulation of organochlorine contaminants. Generally, a difference of 3-5% of δ 15N is reported between two trophic levels (Minigawa and Wada, 1984), though predator diet often consist of prey from different trophic level and varies with availability of prey organism or cannot determined. In our study, herbivorous fish species showed lower levels of δ 15N than carnivorous fish species in food web of River Chenab body and clearly indicated the different trophic levels for both groups. However, significant correlations of ∑PCBs, ∑DDTs, chlordanes and β-HCH with δ 15N% in current study indicated the strong biomagnifications (Fig.5.8) and increases with different trophic levels and these trends are consistent with other studies conducted by (Rognerud et al., 2002; Sharma et al., 2009). The possible explanation for this strong correlation is that, these compounds were detected in both carnivorous and herbivorous species, having clearly distinct trophic levels (Kidd et al., 1998; Olsson et al., 2000). The strong biomagnifications for DDTs, β- HCH and PCBs in the food web of River Chenab, may be due to their high lipophilic properties
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and less biodegradability, resulted in the accumulation in the food web (Fisk et al., 1998;
ATSDR, 2005). HCHs owing to low octanol-water partition coefficients (log Kow ~4), generally showed lack of accumulation in the food webs (Guo et al., 2008) and the results of our study also validate this assumption, though β-HCH showed high bioaccumulation due to more lipophilic properties (Malik et al., 2011) and also reported in various biological matrices. On the other hand, lipid content and habitat differences i.e. column feeder, surface feeder and bottom feeder are also strongly correlated with the δ 15N % of all the species (both inter and intra specific) which also affect the accumulation of these lipophilic contaminants (Olsson et al., 2000). The elevated values of δ 15N% in two species showed a clear cut trend of food and habitat preference, which reflected maximum accumulation of OCPs and PCBs in the tissues of these species. Among carnivorous fish species, both Mastacembalis aramtus and Rita rita feed on the fish and zooplankton with the habitat preference of benthic zone. OCPs and PCBs are mainly associated with sediment in aquatic ecosystem due to their hydrophobic properties (Sarkar et al., 2008), which are ingested by benthic organism (invertebrates etc.), ultimately resulted in the active transport to their predator and accumulated in the high trophic level in the food web (Fisher, 1995; Zhou et al., 2004).
Although the monitoring of δ 15N reported to be useful to predict the bioaccumulation of organic contaminants in the food chain of aquatic ecosystem, but some studies also showed the low effectiveness of δ 15N as a predictor of OCs accumulation in the food web (Campbell., 2000; Guo et al., 2008). Some physiological process of fish, in contrary to feeding habit, resulted in the elimination of organic contaminants in young individuals to compensate the food intake for maximum growth while in large individual with low metabolic rates may occupied high trophic levels resulted in high accumulation of organic pollutants (Campfens et al., 1997; Olsson et al., 2000). Generally, possible routes of elimination are via gills or excreta, which are reported to estimate with log Kow of 5-8, lipophilicity range of DDE and OCs with log Kow of 6-7, is considered to have low elimination rates (Sijm et al., 1992; Fox et al., 1994).
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2.5 y = 0.1751x + 0.536 1.8 y = 0.0786x + 0.8073 R² = 0.305 1.6 R² = 0.4198
2 1.4
1.2 1.5 1 0.8
1 Log ∑PCBs Log Log ∑DDTs ∑DDTs Log 0.6
0.5 0.4 0.2 0 0 0.00 2.00 4.00 6.00 8.00 10.00 12.00 0.00 2.00 4.00 6.00 8.00 10.00 12.00
1 y = 0.0808x + 0.0181 3.5 y = 0.2488x + 0.2331 0.9 R² = 0.2719 R² = 0.4385 3 0.8
0.7 2.5 0.6 2
0.5 HCH - 0.4 β 1.5
0.3 Log Log ∑Chlordanes Log 0.2 1 0.1 0.5 0 0.00 2.00 4.00 6.00 8.00 10.00 12.00 0 δ 15N % 0.00 2.00 4.00 6.00 8.00 10.00 12.00 δ 15N %
15 Fig.5.8. Correlations of organochlorine compounds (ng/gWW) in muscle with δ N % signatures of individual fish species collected from River Chenab, Pakistan.
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Habitat preferences (surface feeders, column feeders and bottom feeder) also reported to largely affect the kinetics of bioaccumulation process (Zhou et al., 1999). Generally, as we discussed earlier that fish species with high trophic level i.e. carnivorous fish species showed more bioaccumulation of OCPs and PCBs as compared to those of herbivorous species. Among the fish species of both these groups i.e. carnivorous and herbivorous, habitat preference are very important to determine the trends of accumulation of OCPs and patterns of PCBs homologue distribution. In our study, Rita rita and Mastacembalis aramtus, with feeding habit from benthic region of aquatic system , showed maximum accumulation of OCPs and PCBs (especially high chlorinated homologue) while two other carnivorous i.e. Securicola gora and Clupisoma naziri also showed considerable high concentration of OCPs and PCBs, with feeding habit from column to bottom region, which can be the possible explanation of high bioaccumulation of OCPs and PCBs in the muscle tissues of these fish species (Zhou and Wong, 2004). Another possible reason of high accumulation of OCPs and PCBs in two carnivorous species i.e. Mastacembalis aramtus and Rita rita is high values of biological parameters i.e. lipid fraction (%) with narrow range, which suggested to affect both bioconcentration and biomagnifications of OCPs and PCBs with high efficiency of converting food into body weight. Some other factors like change in size of smaller individual into larger individual affect the relationship of gill area with body mass ratio resulted in more exchange area for contaminants which ultimately increased bioconcentration rates (Lessmark, 1983; Satora et al., 1995). Similarly, larger individuals of Cirrhinus mrigale and Cyprinus carpio from industrial sites showed high concentration of OCPs and PCBs, reflected the more uptakes of these contaminants from water via gills and also magnified high quantities of lipophilic compounds from aquatic food web. Moreover, distribution pattern of PCB congeners also affected by the habitat preference of fish species. Generally, the proportions of heavier chlorinated PCBs congeners are high in the fish species with feeding from bottom region of aquatic system except a few samples of Rita rita collected near a electric grid station, which showed high proportion of tri-CBs may be due to high rates of bioconcentration reaction via large gill area. Generally, explanation of dominance of heavier chlorinated congeners, more association of these congeners with particulate material due to low water solubility and more hydrophobicity resulted in the ingestion by benthic organisms and accumulated in the food web (Larsson et al., 1993; Zhou et al., 2004). On the other hand, lighter chlorinated congeners have relatively more water solubility than heavier ones
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and perhaps due to this reason lighter congeners contributed relatively high in surface feeder fish species due to active transport of these chemicals via gills route through contaminated water.
Besides all of other ecological characteristics such as feeding habit, trophic position, habit etc., lipid fraction (%) of fish species (both inter and intra specific) contributed significantly towards the accumulation of OCPs and PCBs in the fish tissues. One of the most striking observations made in this programme is greater variations of organochlorines among different species (carnivores and herbivores), as well as within same specie (depending upon size). Results of our study exhibit the elevated values of OCPs and PCBs with increased values of lipid fraction (%) which is positively correlated with δ 15N %, especially in larger/aged individual. Lipid fraction (%) of carnivorous fish was generally very high when compared with herbivores, which can be possible explanation of accumulation of aforementioned lipophilic chemicals in carnivore fish tissue. On the other hand, accumulation patterns for individuals of same specie also varied, depending upon the size of each fish. Generally, larger individuals (greater than 15 cm) of each studied fish species showed more accumulation potential mainly due to high fatty tissues and it can be seen very clearly for Mastacembalis aramtus and Rita rita (showing maximum accumulation for OCs), which is accounted for high values lipid fraction (%) with narrow range and very well explained earlier.
5.4.5. Partitioning behavior of OCPs and PCBs
Pakistan lies in the temperate zone, immediately above the tropic of cancer. The climate varies from tropical to temperate. Arid conditions exist in the coastal south, characterized by a monsoon season with adequate rainfall and a dry season with lesser rainfall, while abundant rainfall is experienced by the province of Punjab, and wide variations between extremes of temperature at given locations. Current study area, lies in Punjab, is characterized with high temperature, which enhances the process of transportation of organochlorine chemicals into multiple phase environment system. The scenario reflecting the significance of the area in terms global POPs contamination by long-range transport of these semi-volatile chemicals (Zhang et al., 2008). Kannan et al., (1995) reported the fractionation of HCHs among air and water in tropical and temperate regions, due to low water-octanol co-efficient (Kow) which tend them to
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remains in air-aqueous phase rather than in soil/sediment and fish. In contrary to this, DDTs and heavier PCBs due to low volatility and high Kow and relatively low levels of Henry law constant
(H values) and H/Kow, tend to remain in particulate phase and biota (Kannan et al., 1995). However, this situation enhances the DDTs fractionation between particulate phase/sediment and biota.
The BASF and BCF is screening tool used to predict the partitioning behavior of organochlorines among different environmental media and bioaccumulation potential of these lipophilic compounds from water and sediment into fish. Generally, a theoretical value of BCF less than 250, considered as having low potential for bioconcentration. In our study, p, p/-DDE (120), endosulfans (56) and lighter chlorinated congeners (85) showed high BCF values for all the fish species at different locations. Maximum BCF values for lighter chlorinated congeners were observed in Rita rita, Catla catla and Cirrhinus mrigale collected near the electric grid station. Such high values reflected the fresh input of these contaminants in aquatic ecosystem. Mastacembalis aramtus showed maximum BCF value for p, p/-DDE, possibly due to large gill size, which acts as active route of chemical transport from contaminated water into fish. On the other hand BASF value more than 1.7 is considered as cut-off point for biota-sediment accumulation of organochlorines. BASF values reflected the strong accumulation potential for DDTs and its metabolites (2.4) and heavier chlorinated congeners (2.65) in almost all the fish species. DDTs and heavier chlorinated congeners are reported to strongly associate with sediment due to their high hydrophobicity and high log Kow values, which resulted in stronger biota-sediment accumulation of these chemicals. Site-specific differences in sediment contamination, organic fraction of sediment and taxonomic-specific differences in exposure, also largely affect the accumulation patterns of organochlorines in aquatic system. Our results also reflected similar trends for fish species collected from benthic region Viz; Rita rita, Mastacembalis aramtus showed maximum values of BASF. HCHs and some other OCPs showed less potential of accumulation with low values for both BCF and BASF, which may be due physical and chemical properties of these compounds.
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5.5. Risk assessment of OCPs and PCBs in the fish collected from River Chenab
Organochlorines might pose several potential health risks to both wildlife and human beings, due to their persistency, lipophilicity, and potential toxicity (Tanabe et al., 1998). The present study was conducted to evaluate the residual levels of OCPs and PCBs in the fresh water fish from River Chenab, Pakistan. Hence, the risk estimation of these toxic contaminants detected in the fish samples from River Chenab is very important for the suitability of human consumption. Many studies (Dougherty et al., 2000; Jiang et al., 2005; Guo et al., 2007; Li et al., 2008) reported different national and international criteria guidelines for fish consumption to evaluate the potential health risks of OCPs and PCBs by different organization (EU, W.H.O. and USEPA). In our study, we used three guidelines to assess the health risk for studied OCPs and PCBs in fish.
1. Firstly, risk assessment is carried out by comparing our dataset of OCPs and PCBs in the fish samples of River Chenab with allowable limits set by laws of European Union (EU) and United States Environmental Protection Agency (USEPA). Generally, both of these organization set more stringent values which are safer and reliable for the protection of world’s population health even with different feeding preferences and different consumption rates.
2. ATSDR, 2005 provided Minimal Risk Values (MRLs) to assess the adverse effects of studied contaminants with different exposure times i.e. chronic, intermediate, and acute.
3. One of the most important approach to assess both non-cancer and cancer risks by relevant oral RfDs and cancer benchmarks concentration (CBC) obtained from USEPA’s Integrated Risk Information System (IRIS). Cancer hazard were estimated by calculating hazard ratios of each contaminant with 50 th and 95 th percentile of the relevant data.
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5.5.1. Maximum Admissible Concentrations (MACs)
Generally, DDTs were found predominately in most of fish samples collected from River Chenab. Following the maximum admissible limit of DDTs (50 ng/g WW) for fish consumption established by European Union (Binelli and Provini, 2004), about 60 % of our fish samples from River Chenab considered overloaded. While comparison of our dataset with relatively strict limits for DDTs (14.4 ng/g; WW) by USEPA, 2000 showed that more than 80% of fish samples exceeded this limit. On the other hand, Chinese government (GB 18421-2001) has set most stringent values (10 ng/g; WW) for fish consumption and 90 % of fish samples from River Chenab showed excessive values of DDTs. A maximum admissible concentration of 10 ng/g (WW) for HCHs in fish is established by European Union. In our samples HCHs levels are relatively lower and only 5% of total samples found overloaded, which may be due to lower persistence, high volatility and biodegradation of these contaminants. As for PCBs, European Union Directive 1999/788 establish a maximum value of 200 ng/g (lipid weight) for seven PCBs congeners (PCB-28, 52,101, 138, 153, 180) in food stuffs ( not particularly for fish). Samples exceeded this limit, were individuals of Rita rita, Mastacembalis aramtus, and Cirrhinus mrigale showed maximum values of PCBs up to 108 ng/g (WW).
5.5.2. Minimal Risk Level (MRL)
MRL is an estimate of the daily human exposure to a hazardous substance that is likely to be without appreciable risk of adverse noncancerous health effects over a specified duration of exposure. However, daily intake of contaminants and time of exposure considered as important factors to evaluate the potential health risk for human being. ATSDR derived MRL using no observed adverse effect level/uncertainty factor (NOAEL/UF) approach for the toxic chemicals. MRLs are derived for acute (1-14 days), intermediate (more than 14-364 days), and chronic (365 days and longer) exposure durations, and for the oral and inhalation routes of exposure. MRLs are generally based on the most sensitive substance-induced end point considered to be of relevance to humans. ATSDR does not use serious health effects (such as irreparable damage to the liver or kidneys, or birth defects) as a basis for establishing MRLs. Exposure to a level above the MRL does not mean that adverse health effects will occur. Table 5.6 showed the estimated
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intake of studied contaminants with maximum intake of DDTs (10.6 ng/kg/day) and PCBs (3.3 ng/Kg/ day), which when compared with MRL values by ATSDR, 2005 (Table 5.7) found safe and did not exceed the MRL values of studied compounds.
5.5.3. Potential risk
In order to estimate the potential risk to human health, Hazard ratios (HRs) were calculated by following Jiang et al., (2005), using estimated daily intake divided by Cancer Benchmark Concentration (CBC; the benchmark concentration is derived by setting the cancer risk to 1,000,000 for lifetime exposure). Relevant Oral (RfD) values, cancer slope factors and Cancer Benchmark Concentrations (CBCs) were used by following USEPA, Integrated Risk Information System (IRIS) (Table 5.8). The 50th and 95th percentile of measured concentration for HCB, dieldrin, heptachlor, chlordane, DDTs and PCBs were calculated. The 95th percentile concentrations for DDTs (DDT and its metabolites) and PCBs were found highest among all organochlorine followed by chlordane, dieldrin, HCB and heptachlor. All the measured concentration of OCPs and PCBs were less their corresponding oral RfD values except 95th percentile value of PCBs (43.77 ng/g; WW).
Two Hazard Ratios (HRs) with 50th and 95th percentile were calculated to assess both non-cancerous and cancer risks associated with the fish consumption containing OCs contaminants and summarized (Fig.5.9 a, b). The HZs ratios calculated for noncancerous risk assessment using 50th and 95th percentile of the OCs data were all less than unity, due to lower fish consumption rates (8g/person-day) and relatively low concentration of studied OCs in the fish samples collected from River Chenab, Pakistan. Similar results were reported for Chinese coastal population (Fung et al., 2004; Jiang et al., 2005), Huairou and Gaobeidian Lake of Beijing, China (Li et al., 2008). On the other hand, HZs for cancer risk assessment based on 95th percentile concentrations of dieldrin, chlordane, heptachlor, DDTs and PCBs were greater than unity and suggested that daily intake of fish contained these contaminants had a lifetime cancer risk of greater than 1 in 1,000,000. The risk assessment of OCPs and PCBs were estimated for human through different guidelines and results indicated that fish consumption with this average daily intake (8 g/person-
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day) will not be risky according to Minimal Risk Level (MRL). Suggested safety may be due to lower consumption rates of fish in Pakistan than other countries like China (30 g/person-day; Yang et al., 2004 and 105±182 g/person-day; Jaing et al., 2005) and India (47 g/person-day; Muralidharanet al., 2009). However, 95th percentile of OCPs (Dieldrin, Chlordane, Heptachlor, and DDTs) and PCBs data indicated some serious chronic and carcinogenic effects, due to consumption of contaminated fish of River Chenab. Still, there are many limitations in this initial screening study, but it provides the comprehensive and appropriate reflection of the scenario and stresses to conduct more detailed studies to evaluate the carcinogenic effects of these contaminants found frequently in the fish of River Chenab Pakistan.
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Table 5.6. Estimated daily intakes of OCPs and PCBs in fish by human (ng) using average concentrations of OCPs and PCBs (ng/g, WW) in fish collected fish samples. Compounds Average Concentration (ng/g, WW) EDI (ng/kg- day)
HCB 1.1 0.14 α-HCH 1 0.13 β-HCH 2 0.27 γ-HCH 2.5 0.33 Aldrin 0.75 0.10 Endosulfans 3.6 0.45 Heptachlor 1.05 0.13 Dieldrin 2.95 0.40 Endrin 1 0.13 Chlordane 4.1 0.53 p, p'-DDT 20 2.67 HCHs 6 0.80 DDTs 80 10.67 PCBs 25 3.33 *Average rates of fish consumption and weight were 8g/person day and 60kg, respectively) in Pakistan.
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Table 5.7. Minimal Risk Level (MRLs) for OCPs and PCBS by Agency for Toxic Substances and Disease Registry (ATSDR, 2005) according to oral exposure.
Compounds Duration MRL (ng/kg day) Factor of uncertainty Endpoint Acute 8000 300 Develop. HCB Int. 100 90 Repro. Chr. 50 300 Develop.
a-HCH Chr. 800 100 Heptic Acute 50,000 100 Neurol. b-HCH Int. 600 300 Heptic Acute 300 300 Develpo. g-HCH Int. 10 1000 Immuno. Int. 20,000 1000 Develop. Aldrin Chr. 30 1000 Heptic Int. 50,000 100 Immuno. Endosulfans Chr. 20,000 100 Heptic Acute 600 3000 Repro. Heptachlor Int. 100 300 Immuno. Int. 100 100 Neurol. Dieldrin Chr. 500 100 Heptic Int. 20,000 100 Neurol. Endrin Chr. 300 100 Neurol. Acute 1000 100 Develop. Chlordane Int. 600 100 Heptic Chr. 600 100 Heptic Acute 500 1000 Develpo. p, p'-DDT Int. 500 100 Heptic Int. 30 300 Neurol. PCBs Chr. 20 300 Immuno.
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Table 5.8. Cancer Benchmark Concentration (CBC), Oral reference dose (Oral RfD), and cancer slope factor obtained from pervious study funded by USEPA. Compounds Measured concentrations in fish from River USEPA, guidelines Chenab Oral RfD Cancer slope factor Cancer Benchmark concentration (µg/kg-day) [per (mg/kg-day] (µg/kg -day) 50 th MEC (ng/g, WW) 95 th MEC (ng/g, WW) HCB 0.8 1.6 0.001 1.01 1.86 Dieldrin 0.05 16 0.0000625 2.7 5.9 Chlordane 0.06 0.35 0.001 4.45 6.57 Heptachlor 0.5 9.1 0.00022 1 1.65 DDTs 0.5 0.34 0.003 75.5 167.23 PCBs 0.02 2 0.00013 19.46 43.77 *50 th and 90 th percentile MEC (measured concentration) of OCPs and PCBs were used in risk assessment posed to human health by the consumption of contaminated fish of River Chenab, Pakistan.
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100
10
Hazard ratio Hazard 1
0.1 b).
50 % MEC 95 % MEC 1
0.1
0.01 Hazard ratio Hazard 0.001
0.0001 HCB Dieldrin Chlordane Heptachlor DDTs PCBs
a).
Fig.5.9. Hazard ratios for the daily fish consumption from River Chenab, Pakistan. a). for non- cancer risks, b). for cancer risks. MEC, measured concentration.
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5.6. Conclusion Information regarding the occurrence and distribution of OCPs and PCBs in freshwater fish species from River Chenab, Pakistan is not available to date. This is the first study to record concentrations, accumulation patterns and potential human health risks from OCPs and PCBs in fish samples collected from River Chenab, Pakistan. A significant variation (p< 0.05) in OCPs contamination was observed b/w herbivorous and carnivorous species with considerably highest values recorded in carnivorous species. DDTs and its metabolites and heavier chlorinated biphenyles were predominant contaminants in fish muscles found with high detection frequencies and elevated concentrations. The concentration (ng/g WW) of OCPs and PCBs were recorded relatively high in all the fish species collected from those sites located near industrial areas following by urban and agricultural areas, respectively. All these fish species showed relatively high values in winter season than in summer with exception of Clupisoma naziri and Cirrhinus mrigale which showed elevated values of OCs in summer season. The trophic position cannot clearly explain the trends of observed concentrations of OCs in smaller individual (less than 15 cm) of all the collected species while the larger individual (more than 15 cm) showed strong biomagnifications for OCs contaminants and increased with trophic position. Risk assessment against various standards showed that fish samples were highly contaminated with DDTs and PCBs and may pose health threats to local residents as well as consumers all over the world. Furthermore, other OCs including dieldrin, heptachlor, chlordanes, also pose life time cancer risk to consumers of fish collected from River Chenab, Pakistan. Therefore, continuous monitoring of OCPs and PCBs and other contaminants such as polychlorinated dibenzo-p- dioxins/ dibenzofurans (PCDD/Fs), polybrominateddiphenyl ethers (PBDEs) and perfluorinated compounds (PFCs) are needed to mitigate the impacts of these contaminants on human health and ecological environment. Therefore, continual monitoring of OCPs and PCBs and other contaminants such as polychlorinated dibenzo-p-dioxins/ dibenzofurans (PCDD/Fs), polybrominateddiphenyl ethers (PBDEs) and perfluorinated compounds (PFCs) are needed to mitigate the impacts of these contaminants on human health and ecological environment.
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Chapter 6 General Discussion, Recommendations and Future Thrusts:
6.1. General summary
This study presents the results of comprehensive survey of studied stretch of River Chenab Pakistan, provided a baseline data of OCPs and PCBs in the surface water (including particulate material), sediment and biota (11 fish species) of River Chenab. This is first study to report OCPs and PCBs data from River Chenab, Pakistan: a major tributary to River Indus; associated with more than 100 million inhabitants. Current study not limited in its simple presentation of OCP(s) and PCB(s) concentrations, but through use of simpler and/or more sophisticated techniques, enable to offer insights on major OCPs and PCBs sources, their seasonality, bioaccumulation features and risk assessment. Comparison of residual data of OCPs and PCBs in the water, sediment, and fish samples with available criteria guidelines and existing literature reflecting the significance of the study. The output of screening level showed the potential health risks for OCPs and PCBs to human and aquatic life associated with River Chenab. By our results, it is concluded that globally banned insecticides, like DDT are still used/emitted into River Chenab and its surrounding, in contrast to the demands of the Stockholm Convention on POPs. The scenario highlights dire need for management and remedial action to control the possible sources of OCPs and PCBs to reduce the contamination of these organic pollutants in the River Chenab. Moreover, this study can also play significant role to evaluate the contribution of Pakistan towards POPs emission in global environment and effectiveness of control measures taken by regional and international authorities under Stockholm convention in Pakistan.
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6.1.1. Occurrence, distribution, and finger printing of OCP and PCB in the surface water and sediment from River Chenab, Pakistan
OCPs and PCBs are ubiquitous in the environment and have been detected in various environmental media and discussed in detail in Chapter 1. Concerning the aquatic ecosystem, OCP and PCB might be distributed into the dissolved or particulate phase. OCPs and PCBs can end up into the aquatic environment through various route(s) (Cao et al., 2010; Malik et al., 2011), ultimately accumulating and settling down in bottom sediment, which act as a sink and resulted in the accumulation of these contaminants in the associated biota (Sarkaret al., 2008).The occurrence of OCP and PCB into aquatic environment such as rivers is of particular importance due to the direct (through consumption of contaminated water) and/or indirect (e.g. through consumption of fish) exposure to humans.
The first objective of study was to evaluate the occurrence, sources and spatio-temporal distribution trends of OCPs and PCBs in the surface water and sediment collected from River Chenab, Pakistan. Surface water (including particulate phase) and sediment were monitored at 25 sites for physicochemical parameters, OCPs and PCBs. The River Chenab is moderately to severely polluted in terms OCP and PCB contaminants, when compared with other waterbodies worldwide. DDTs were found predominately in most of water and sediment samples followed by HCHs, Chlordanes, PCBs and other OCPs. High concentration(s) of DDTs in the water, and sediment during both campaigns can be linked to its chronological excessive use. In our study, dominance of p, p/-DDE and p, p/-DDD (breakdown product of DDTs) showed the long term weathering of DDTs, while presence p, p/-DDT in most of samples also reflected the recent use of DDTs. On the other hand, trends of HCHs in the water samples in descending order as γ- HCH, α-HCH, β-HCH and δ-HCH. In sediment β-HCH and γ-HCH were contributed mainly in total composition of HCHs. The dominance of β-HCH in sediment may reveal the fact that this isomer, due to relatively more lipophilic nature and low water solubility (high log Kow), may be fractionated towards sediment fraction. Indicative ratio(s) of HCHs indicated both excessive recent and historical use of this pesticide mainly for the crop protection and sanitation purpose. Trends of other OCPs in water samples were descending order as: heptachlor, dieldrin, endosulfan sulphate, HCB, Methoxychlor, β-endosulfan, heptachlor epoxide, aldrin, α-
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endosulfan, and endrin. The detection levels of other studied pesticides reflected the historical large scale use of these toxic chemicals in the agricultural sector and the results are consistent with previous studies conducted in the current study area (Tariq et al., 2007; Ahad et al., 2010; Malik et al., 2011).
As for PCB, the dominance of lighter chlorinated biphenyle (tri and tetra congener) occupied more than 50% of total PCB in waters, while tetra-CB, penta-CB and hepta-CB were dominant in sediments. PCB compositional trends in water and sediment is consistent with previous studies (Yang et al. 2009; Zhang et al., 2003; Hong et al., 2003), which also suggested the dominance of heavier chlorinated congeners in sedimentary fraction. In contrary to this, dominance of lighter chlorinated congener(s) in water happened due to their high solubility (Hong et al., 2003; Ranjendranet al., 2005). Given that this process is governed by the lipophilic character (Kow) of the compounds, the heavier CBs are expected to deposit to the sediments in favor of the lighter ones. If we take all the aforementioned information in consideration, it can be concluded that the main Aroclor mixtures used in the area should be mainly 1248 and the heavier 1254, 1260 and 1262, however in lower extents. The dl- PCB-TEQ levels ranged from 0.11- -1 -1 29.59 pg TEQΣ7dl-PCBs L (for water) and 0.01-0.28 pgTEQ Σ3dl-PCBs g (dw) (for sediment) , with PCB-126 and118 accounting for more than 70 % to total dioxin-like PCB-TEQs. All the sites with elevated TEQ values are located in the industrial and urban areas of Pakistan, suggesting that these chemicals occurred due to local usage and not because of long range transport.
6.1.1(a). Site-specific distributional patterns
Environmental fate, distribution and dispersion rate of organochlorine is influenced by various metrological factors such as temperature and rainfall (Sanepraet al., 2004). OCP and PCB are suspected into aquatic ecosystem, as a result of widespread industrial and agricultural activities (Salvadoet al., 2005). The combined effect of temporal and spatial magnitude of pollutants, concentrations, and fluxes in large integrating river systems is largely unknown (Farooq et al, 2011). Chemical reaction(s) and physical displacement(s) influence the persistence of the chemicals in the various environmental matrices, but with different environmental implications. The use of multivariate statistical tools is reported to provide better understanding
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of spatial and temporal trends and source identification of such toxic contaminants (Qadiret al., 2008). Furthermore, integrated multivariate approach applied on the organochlorine- contaminated data addresses associated health risks posed to the aquatic life, risk managements and restoration of aquatic system (Farooq et al., 2008). However, the spatio-temporal variation and source identification of OCP and PCB in River Chenab were accomplished by using multivariate statistical techniques such as Cluster Analysis (CA), Factor Analysis/Principal Components Analysis (FA/PCA).
Generally, the concentrations of OCPs and PCBs in samples collected from Chenab’s tributaries (S-8, S-17, S-20, S-23, S-24) were much high than those recorded in the mainstream samples (Fig.4.2), suggesting the OCP and PCB contaminant into River Chenab mainly originated from discharge points of industries. However, agricultural run-off, municipal waste disposal, and atmospheric deposition are also important sources of pollution into River Chenab, Pakistan. On the other hand, hydrological conditions of River Chenab also played an important role in the contamination of OCPs and PCBs in the aquatic ecosystem (Yang et al., 2009). Results suggest that the River Ravi (S-8), River Sutlej (S-24), Khanki barrage (S-23) and Industrial Drain (S-20) from Faisalabad city, receives a load of toxic pollutants from different sources than the mainstream sampling locations of River Chenab (before the mixing with aforementioned tributaries). At the same time, both the OCP and PCB concentrations reveal the mixing, that has taken place and clear differences are observed also at the other mixing point where River Jhelum meets with River Chenab. Cluster analysis grouped the 25 sites into four regions viz: Region-1, Region-2, Region-3 and Region-4 based on pollution level. S-20 is identified as outlier, which is one of most polluted sites, receiving a high amount of toxic pollutants from Faisalabad city, one of leading industrial city, and different industrial drains from this city is directly dumped into River Chenab near this site. Region-1 consisted of most contaminated sites (S-18 and S-23) located in industrial area and receiving high amount of pollutants via open drains from different industries. Region-2, which also showed relatively high residual levels of OCP and PCB, as these sites located near urban and sub-urban area and also receiving high pollution load from upstream flow. Region-3 and 4 consisted of sites located in agricultural area and can be categorized as moderately polluted regions. Spatial FA/PCA, explained the spatial variation more briefly and discussed in detail in Chapter 3 and 4. In Region
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2 and 3, source of OC-contamination is mainly related to agricultural activities. Most of the sites included in these region(s) are located in agricultural area and use of pesticides is very common for crop protection. FA also suggested the degradation of OCP in this region, which is the evidence for the application of these persistent pesticides from many years ago. In contrast to Region 2 and 3, source of pollution in the other regions i.e. Region-1 and 2, is mainly related to industrial activities and disposal of municipal waste in the river body, while agricultural activities also contributed as a source but not as other(s).
6.1.1(b). Seasonal variation
The seasonal variation of OCPs and PCBs in the Chenab River was studied by comparing these concentrations during summer vs. the winter season ones, as discussed in detail in Chapter 3 and 4. As expected, increased concentrations of ∑OCPs were observed in the winter season as compared to summer at almost all sites. In contrary, PCBs level found in both seasons showed no significant variation. Although it is well known that the seasonality of POPs including OCPs and PCBs is mainly attributed to the increased emissions from heating, industrial and agricultural activities in Pakistan, an important parameter might also be the specific climatic conditions, not directly related to the temperature difference between winter and summer. Thus, in Pakistan, the cold season (winter) is also the dry period of the year, whereas the warm period (summer) is characterized by heavy raining. As such, during winter, the water volume of the River is lower, which suggests high concentrations of organic pollutants because of the low dilution levels (Farooq et al., 2011). However, the dilution caused by the heavy rains might not always “decrease” the contaminants concentration, as the wet deposition of the latter through the rain might tend to maintain equilibrium (Sun et al., 2009; Katsoyianniset al, 2010). Indeed, there have been cases, where similar OCP and PCBs levels were observed for both periods. As for sediment, high percentage of organic matter and clay in winter can be one of the reasons that explain the increased levels of OCs in winter (Lee at al. 2001), while lower temperature may also be attributed to less desorption of organochlorine in winter, resulted in high values of OCPs and PCBs (Zhou et al., 2006). Temporal Factor Analysis (PCA/FA) also gives beautiful picture of all chlorinated contaminants, interpreting agricultural, industrial activities and municipal waste
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disposal from urban areas as the source of studied organochlorines in the complex river system during both sampling periods.
6.1.2. Bioaccumulation features of organochlorine in the collected fish from River Chenab
Organochlorines due to their low water solubility and high octanol-water partition coefficient strongly tend to associate with suspended particulate matter in the aquatic environment (Ranjendranet al., 2005), ultimately accumulate in the bottom sediment which acts as sink for these kinds of pollutants and resulted in the injuries to aquatic organisms and accumulate in the food web (Malik et al., 2011). In aquatic ecosystem, monitoring of fish serves as an important indicator; where there is vertical transport of OCs leading to accumulation in the benthic organisms and ultimately into fish organs via bioconcentration and biomagnifications (Kalyoncuet al., 2009; Olsson et al., 2000). The second objective of our study was to investigate the occurrence and accumulation patterns of organochlorines (OCs) in fish species collected from River Chenab, Pakistan. Considering the importance of fish as protein source for humans and limited data on OCs in fish from rivers in Pakistan, this section provides some insight into the levels of OC contamination in fish from Chenab River, Pakistan. Results imply that bioaccumulation of organochlorines is specie-specific with predominance of DDTs (along with their breakdown products) and heavier-chlorinated biphenyle(s) followed by Other OCPs, Chlordanes and HCHs in descending order. A significant variation (p<0.05) in OCs contamination were observed b/w herbivorous and carnivorous species with considerably highest values recorded in carnivores.
Estimation of δ 15N % (naturally occurring isotope of nitrogen) to monitor the time integrated snapshot of the food preferences and δ 15N % values are considered to be effective approach to measure a continuous trophic level accumulation of organochlorine contaminants. In our study, herbivorous fish species showed lower levels of δ 15N than carnivorous fish species in food web of River Chenab body and clearly indicated the specie-specific organochlorine accumulation, with clearly distinct trophic levels (Kidd et al., 1998; Olsson et al., 2000). The trophic position cannot clearly explain the trend of observed concentrations of OCs in smaller
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individuals (less 15 cm) of all the collected species, while larger individual (more than 15 cm) showed strong biomagnifications. Generally, larger individuals (more than 15 cm) of each studied fish species showed more accumulation potential mainly due to high fatty tissues and it can be seen very clearly for Mastacembalis aramtusand Rita rita (showing maximum accumulation for OCs), which is accounted for high values of biological parameters i.e. length, δ 15N and lipid fraction (%) with narrow range and very well explained earlier. Beside the feeding habit, habitat preference is also very important to determine the patterns of OCP and PCB homologue distribution. In our study, Rita rita and Mastacembalis aramtus, with feeding habit from benthic region of aquatic system, showed maximum accumulation of OCPs and PCBs (especially heavier chlorinated congeners), while two other carnivore fish i.e. Securicola gora and Clupisoma naziri also showed considerable high concentration of OCPs and PCBs with feeding habit from column to bottom region, which can be the possible explanation of high bioaccumulation of OCPs and PCBs in the muscle tissues of these fish species (Zhou and Wong, 2004). However, significant correlations of ∑PCBs, ∑DDTs, chlordanes and β-HCH with δ 15N % in current study indicated the strong biomagnifications and increases with high trophic levels and these trends are consistent with other studies conducted by (Rognerud et al., 2002; Sharma et al., 2009). The strong biomagnifications for DDTs, β-HCH and PCBs in the food web of River Chenab, may be due to their high lipophilic properties and less biodegradability, resulted in the accumulation in the food web (Malik et al., 2011).
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6.1.3. Ecotoxicological concern of OCP and PCB in different environmental compartments of River Chenab, Pakistan
The third objective of our study was to investigate on the potential risks of OCP and PCB measured in water, sediment, and twelve edible fish species collected from different locations along the stretch of River Chenab, Pakistan.
Evaluating the risk of OCPs and PCBs in the surface water of River Chenab by comparing our dataset with current USEPA national recommended water quality criteria (USEPA, 2006). PCBs and DDTs were exceeded the limits for human health risk standards in 80 % of total water samples during both seasons. These exceedances suggested that high concentrations of OCPs and PCBs, especially at industrial and urban sites, might cause several potential health risks to the human and aquatic organism.
As for sediment the comparison of HCHs with available quality guidelines, suggesting that risk from HCHs is moderate, while severe contamination of DDTs and its metabolites indicating the adverse biological effects in this studied area especially in Region-1(industrial sites) and Region-2 (urban sites). As for PCBs, the concentration exceeded the TEL and ERL values in more than 35% of total sediment samples. Generally, the sediment collected from the Region-1(industrial sites) and Region-2 (urban sites) near the industrial and urban area indicated that serious potential risks posed to the aquatic life of River Chenab.
Although, chlorinated chemicals including OCPs and PCBs might pose several potential health risks to both wildlife and human beings, due to their persistency, lipophilicity, and potential toxicity (Tanabe et al., 1998). Hence the risk estimation of these toxic contaminants detected in the fish samples from River Chenab is very important for their suitability of human consumption. In our study, we used three criteria guidelines to assess the human health risk of studied OCPs and PCBs associated with consumption of River Chenab’s fish. Firstly, Risk assessment was carried out by comparing our dataset of OCPs and PCBs in the fish samples of River Chenab with allowable limits set by laws of European Union (EU) and U.S. Environmental Protection Agency (USEPA). The results showed that more than 60% of total samples, exceeded
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the guideline values for DDTs while samples were not severely contaminated with HCHs and only 5 % of total samples were overloaded in term of HCHs. As for PCBs, benchmark value of 200 (ng/g lipid wt.), is considered overloaded for Rita rita and Mastacembalis aramtus, and Cirrhinus mrigale showed maximum values of PCBs up to 108 (ng/g WW). On the other hand, two Hazard Ratios (HRs) with 50th and 95th percentile were also calculated to assess both non- cancer and cancer risks associated with the fish consumption containing OCs contaminant. The results showed that 95th percentile of OCPs (Dieldrin, chlordane, heptachlor, and DDTs) and PCBs data indicated some serious chronic and carcinogenic effects, due to consumption of contaminated fish of River Chenab.
6.2. Concludary remarks
In general, the present study provided the first systematic data on the distribution of organochlorine pesticides (OCPs) and polychlorinated biphenyles (PCBs) in the water, sediment, and fish collected from River Chenab, Pakistan. OCPs and PCBs contamination is one of the important environmental issues due to excessive use of these chemicals in agriculture and industrial sector. This study showed that DDTs were relatively more prevalent in the aquatic ecosystem of Pakistan, followed by PCBs, HCHs, Chlordanes and ∑other OCPs in descending order. Extensive industrial activities, illegal use of banned chemicals, and existence of biggest stockpiles of obsolete pesticides in the Punjab province of Pakistan, are sources of these toxic chemicals in different environmental matrices of River Chenab, Pakistan. The results manifested that reported levels of OCP and PCB are moderate to highly polluted, when compared to other reported studies of freshwater bodies of world. Concerning the spatial patters of OCPs and PCBs, it can be concluded that the highest concentrations were observed from adjoining tributaries (S-2, S-8, S-17, S-18, S-20, S-23, S-24), which are close to industrial and urban areas. Among these most polluted sites, S-20 is most polluted site receiving a high amount of toxic pollutants from Faisalabad city, which is one of leading industrial city, and different industrial drains from this city is directly dumped into River Chenab. Site-2 and 18 also found much polluted because of pollution load from the industrial drains, urban run-off, and surrounding agricultural field. These both sites were located after mixing of S-20 (industrial drain) with River Chenab in Jhang district, before the joining of River Chenab with River Jhelum on Trimmun barrage. As a part of
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pilot project “Status of POPs in the riverine ecosystem of Pakistan” this study provided a first baseline dataset on the spatial and temporal distribution of OCPs and PCBs contaminant in the water, sediment, and fish collected from River Chenab. This study played a significant role to evaluate the contribution of Pakistan towards POPs emission in global environment and effectiveness of control measures taken by regional and international authorities under Stockholm convention in developing countries. Furthermore, integrated multivariate approach applied on the OCP and PCB-contamination data addresses associated health risks posed to the aquatic life, risk managements and restoration of River Chenab, Pakistan. However, this study provides the comprehensive and appropriate reflection of the scenario and stresses to conduct more detailed studies to evaluate the ecotoxicological effects of these contaminants in the aquatic ecosystem of River Chenab Pakistan. Still, there are many limitations in this initial screening study, therefore continual monitoring of OCPs and PCBs and other contaminants such as polychlorinated dibenzo-p-dioxins/ dibenzofurans (PCDD/Fs), polybrominateddiphenyl ethers (PBDEs) and perfluorinated compounds (PFCs) are needed to mitigate the impacts of these contaminants on human health and ecological environment.
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6.3. Recommendations and future thrusts.
Although, the study was conducted successfully and reported the first hand data on organochlorine contaminants from the important water body (River Chenab) of Pakistan. Our results drew attention that POPs contamination must be considered as an important environmental issue due to their excessive use in agricultural and industrial sector. Present work can be a way forward for very useful future studies for the sustainability of aquatic ecosystem of the country. In this context, future recommendations are as follows:
6.3.1. For academic and research institutions
1. Detailed studies from other freshwater bodies of Pakistan, related to monitoring, assessment, distributional trends, sources identification and ecotoxicological effects of POPs, are urgently required.
2. Comprehensive monitoring of organochlorine residues should be conducted in different crop ecologies keeping in view biotic and abiotic environmental variables.
3. Highly contaminated areas in different parts of country should be identified and cultivation of food crop and fish etc., especially exportable food commodities should be avoided in these areas.
4. Foremost obstacle to produce the data of POPs, is be short of technical expertise and laboratory facilities in the country. Hence, establishment of modern laboratories to produced trained and technical people is very important.
5. An extensive awareness programme for safe use of pesticides through potential paper and electronic media i.e. Television, radio, school, and colleges, agricultural and municipal staff. Government and other agencies should educate the farmers on Good Agriculture Practices (GAP) in the use of pesticides in agriculture.
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6.3.2. For national and regional environmental authorities
1. The results of this study stress the need for adequate regulations and proper management of industrial and municipal waste especially from Faisalabad and Sialkot city, by the national environmental protection authorities to protect the aquatic ecosystem of river Chenab, Pakistan.
2. Proper management of toxic chemicals stored at different location in stores and demolished factory units of OCPs, threatened human health, our natural water resources (including River Chenab and its adjoining tributaries) and other environmental compartments.
3. To implement the strict policies regarding the use of banned/ restricted chemicals in both agricultural and industrial sector, by local and national environmental agencies, to reduce the pollution in riverine ecosystem of Pakistan.
4. To conduct more detailed studies in future in present study area and some others, to from baseline data of organochlorine residues in different environmental matrices i.e. air, soils, sediments, biota etc. which is of great concern on both national and international level.
6.3.3. For water and sediment quality
1. Effective measures should be adapted to reduce the deleterious contribution of point and non-point sources.
2. The strict criteria should be applied to treat the effluents at their generation site rather than in river management.
3. Waste treatment plants should be established for the effective treatment of industrial and municipal waste.
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4. There should be a regular monitoring of industrial units, municipal sewage and in River water.
5. All industrial units should be encouraged to adopt the effluent treatment techniques.
6. People belonging to various fields of the community should be educated about the deleterious effects that are causing the degradation of water quality.
7. Guidelines of river water should be developed not only for River Chenab but also for regional streams and rivers.
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Appendix 1.1. Chemical properties and classification of Organochlorine Pesticides (OCPs) and Polychlorinated Biphenyls (PCBs):
Organochlorine Pesticides (OCPs) and its Classification
Primary role of agriculture is to produce a reliable supply of wholesome food to feed the burgeoningworld population, safely and without adverse effects on the environment. Intensified agriculture around the world has, therefore dictated increasing use of agrochemicals to meet the growing food, feed and fiber demands. Modern era of chemical pest control began around the time of World War II with the discovery of most famous Organochlorine, DDT (Zhang et al., 2009). The DDT compound was rediscovered as an insecticide in 1939 and its discoverer, Paul Müller was awarded the Nobel Prize for medicine in 1948 (Banuri, 1999). Commercial production of DDT began in 1943 (Beatty, 1973). At that time, DDT was considered as blessing due to low cost production, high toxicity to insects, and low toxicity to mammals and was used widely in public health and agriculture sector as an all-purpose insecticide. Afterwards, many other compounds were formulated between 1945 and 1953, including BHC, chlordane, toxaphene, aldrin, dieldrin, endrin, heptachlor, parathion, methyl parathion, and tetraethyl paraphosphate (Banuri, 1999). These pesticides had been used for many years in public health sector to control mosquitoes and as broad-spectrum insecticide against insect pests of food and fiber crops in agriculture sector. Over the years, use of pesticides and other agrochemical has increased many folds in agriculture sector especially in the developing countries with no or negative impact on crop yields (Khan et al., 2002). Presently, over 500 compounds are registered worldwide as pesticides or metabolites of pesticides (Van Der Hoff and Van Zoonen, 1999).
Pesticides are grouped according to purpose of use, formulations and chemical structure of pesticides. According to purpose of pesticide use, different groups include: insecticides (insect killers), herbicides (plant killers), fungicides (controlling fungi), molluscicides (controlling molluscs), nematicides (controlling nematodes), rodenticides (controlling rodents), bactericides (bacteria killers), defoliants (removing plants leaves), acaricides (killers of ticks and mites),
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wood preservatives, repellents (substances repugnant to pest), attractants (substances attracting insects, rodents and other pests), and chemosterilants (substances inhibiting reproduction of insects) (Skoglundet al., 2006). A formulation is a mixture of the active ingredient in a pesticide with other inert (inactive) substances. Different formulations may be used differently. These include, aerosol, baits, dry flowable, dusts, emulsifiable concentrate, flowable, granule, low concentrate solution, micro-encapsulation, soluble powder, solution, tracking powders, water soluble packets and wettable powder (Buffington and McDonald, 2006).
Classification of the pesticides on the basis of chemical structure is another very important criterion. Based on the chemical structure, there are three main classes of pesticides viz. inorganic, botanical and synthetic organic pesticides. Inorganic pesticides constitute elements, minerals or chemical compounds derived from deposits in nature. Common inorganic pesticides are copper, mercury, sulfur silica aerogel, boric acid, borates, diatomaceous earth and cryolite. Botanical pesticides are extracts of different parts of plant species. These pesticides have a short residual activity and do not accumulate in the biotic and abiotic environments. Common examples of botanical pesticides are pyrethrins, rotenone, nicotine, neem, limonene etc. Synthetic organic insecticides are synthesized having carbon and hydrogen atoms as the basis of their molecule. Synthetic organic insecticides are grouped into six basic types viz. Organochlorine (hydrocarbon compounds containing multiple chlorine substitutions), organophosphates (esters of phosphoric acid), carbamates (slats or esters of carbamic acid), pyrethroids (synthetic compounds similar to naturally occurring pesticide pyrethrin, which is found in chrysanthemum), insect growth regulators affecting the normal growth and maturity of insects and microbial pesticides (formulations of pathogens of insect pests).
Organochlorine is an organic compound that contains carbon, hydrogen and through sharing of electron pair forms covalent bond with one or more chlorine atoms. Several groups of chemicals including industrial chemicals viz. polychlorinated biphenyls (PCBs), chlorofluorocarbons (CFCs), polychlorinated dibenzodioxins (PCDDs), or dioxins and many pesticides contain chlorinated hydrocarbons and are classified as Organochlorine. Organochlorine pesticides vary in their chemical structures and mechanisms of toxicity. Persistence of Organochlorine compounds in the environment is well known. They are
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hydrophobic and lipophilic in nature and, tend to accumulate in the fatty tissues of marine and wildlife animals. Beside persistence in the environment, some Organochlorine pesticides move considerable long distances and get accumulated in vegetation, soil and bodies of water of high latitudes by global distillation phenomenon (Simonich and Hites, 1995). Organochlorine pesticides can be classified into four categories: diphenyl aliphatic (e.g. DDT, dicofol), cyclodiens (e.g. heptachlor, dieldrin), chlorinated benzenes (e.g. Hexachlorobenzene [HCB]), and cyclohexanes (e.g. hexachlorocyclohexane [HCH]).
Dichloro-Diphenyl-Trichloroethane (DDT)
DDT (1, 1, 1-trichloro-2, 2-bis (p-chlorophenyl) ethane), most famous insecticide was the first Organochlorine pesticide developed and was discovered to be an insecticide in 1939 by Nobel laureate Paul Muller. It is persistent, non-systemic with contact and stomach mode of action. After its wide use in World War II against mosquitoes in the malaria eradication programme, DDT was introduced as insecticide in agricultural sector. According to the estimates more than 2 million tons of DDT has been produced and applied since 1940 for the management of insect pests throughout the world.
Commercially available DDT (technical grade) is white, crystalline, tasteless and almost odorless solid and a mixture many isomers. The major component p, p/-DDT constitute 77% and o, p/-DDT is about 15% of the mixture and the rest contains o, o/-DDT and sometime breakdown
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products. The chemistry of the main isomers and metabolites of DDTs has been given in Table 1.1. DDT and its metabolites are highly toxic and can persist in the environment for several decades after application and their affects could be magnified through the food chain. Being lipophilic DDT gets accumulated into fatty tissues of birds and animals. In soil environment due to hydrophobic properties, it remains adhered to soil particles and does not reach ground water quickly. DDT is semi-volatile compound and enter atmosphere through volatilization from plant, soil and water surfaces. Half-life of DDT ranges from 2–15 years and breaks down by the affects of sunlight or microorganisms in environment. In environment DDT breaks down to more persistent compounds DDE (1,1-dichloro-2,2-bis(p-chlorophenyl)ethylene) and DDD (1,1- dichloro-2,2-bis(p-chlorophenylethane). In the residue analysis, the term "total DDT or Σ DDT" is used to refer to the sum of all DDT related compounds (p, p/-DDT, o, p/-DDT, DDE, and DDD). Generally, in the aquatic ecosystem, DDT can be biodegraded into DDD via reductive dechlorination under anaerobic conditions and to DDE under aerobic conditions through dehydrochlorination, an oxidative process (Kalantziet al. 2001; Luoet al. 2004). DDD was also manufactured and used as an insecticide, but to a much lesser extent than DDT. DDE has no commercial use, but is commonly detected along with DDT at concentrations in the environment that often exceed those measured for DDT.
Table 1.1a.Chemical properties of isomers and metabolites of DDTs. -3 3 -1 DDT(Molecular Weight PL/Pa SWL/ mol.m H/Pa.m /mol Log Kow Log KOA p, p/-DDD (320) 0.0012 0.0023 0.5 6.33 10.03 p, p/-DDE (318) 0.0032 0.00079 4.2 6.93 9.70 p, p/-DDT (354.5) 0.00048 0.00042 1.1 6.39 9.73 o, p/-DDT (354.5) 0.0031 0.00024 5.65 - 5.50 × 10-6
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Hexachlorohexanes (HCHs)
The chemical name for HCH is 1, 2, 3, 4, 5, 6-hexachlorocyclohexanes and the chemical formula is C6H6Cl6 and the molecular weight is 290.85. The differences in the physicochemical and biological properties of the different HCH isomers derive from the differences in structure that arise from the placement of the six chlorine atoms in either axial or equatorial positions at each carbon of the rings. The chemistry of the four main isomers of HCH has been given in Table 1.2. Lindane/γ-HCH and α-HCH have relatively similar structure patterns (AAAEEE versus AAEEE), while β-HCH has all six chlorine atoms in equatorial positions, a configuration which greatly reduces the susceptibility of the β-isomer to chemical and biological transformation.
The physical, chemical properties, transportation, and fates of the isomers tend to follow a similar pattern in that the behaviors of lindane and α-HCH are more closely related to each
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other than they are to those of β-HCH isomer. The positions of the axial and equatorial chlorine atoms in α-HCH allow for mirror image symmetry and, as a consequence, this isomer has two enantiomers. The physical properties of the two α-HCH stereomers look identical, however their interactions with biological systems which are also chiral, are not. Studies on Arctic microbes (Bidleman et al, 2002; Harner et al, 1999) show that they preferentially transform the (+) enantiomers.
The β-HCH isomer has a structural symmetry, which is not shared by the other isomer and correspondingly greater crystal lattice energy. This results in the far higher melting point and the lower solubility and vapour pressure for the solid phase and consequent decreased volatilization when compared with the other isomers. The structural symmetry of β-HCH has far less influence on liquid and gas- phase physical chemistry. Liquid-phase solubility and vapour pressure, which rive redeposition, are similar for lindane and β-HCH, and approx. 1-2 orders of magnitude higher for α-HCH.
HCHs has been commercialized in two predominant products; technical HCH and purified gamma isomer, lindane. Technical HCH contains about 60-70 % alpha-HCH, 5-12 % beta HCH, and 10-15% gamma HCH. These are the three most environmentally significant isomers. The manufacturing of the technical grade HCH involves the photochlorination of benzene, which yields an isomeric mixture consisting of alpha-HCH, beta-HCH, gamma-HCH, delta- HCH, epsilon-HCH , and inerts (IARC, 1979); this reaction can be started by free-radical initiators such as visual or ultraviolet light, X-rays, or gamma-rays (Kirk- Othmer,1985). Treatment with methanol or acetic acid, followed by fractional crystallization, concentrates gamma-HCH to the 99.9% required in the technical grade of gama-HCH (IARC, 1979); nitric acid is used to remove odor (SRI, 1987).
HCH isomers (including alpha- and beta-HCH) are a by-product of lindane manufacture. Lindane and α-HCH are considered to have low solubility in water (solid-phase β-HCH is considered only sparingly soluble) under general classification schemes but are relatively soluble compared to other Organochlorine pesticides such as DDT. None of the isomers absorbs light at wavelengths (less 250 nm), so direct photolysis is unlikely in any medium. Some photo
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transformation in water is possible as a result of indirect photolysis, especially in the presence of dissolved organic matter. Solid-phase pressures are relatively high for lindane and α-HCH and two orders of magnitude lower for the –isomers. Sub cooled liquid-phase vapour pressures of all three isomers are high enough to indicate intermediate to high volatility under general classification schemes: α-HCH vapour pressure is far greater than that of the other two isomers.
Henry’s Law constants describe the volatility of a substance from an aqueous phase and are deprived from both the water solubility and the vapour pressure. These indicate that all three isomers will be rapidly lost from a water surface to the air, with α-HCH being the most volatile and β-HCH the least volatile. Under real-world field conditions, it is the differences in Henry’s
Law constant and not differential partitioning to soil (log Koc and Kow for the three solid isomers are very similar and show moderate partitioning to soil and moderate lipophilicity) that control differences in the transportation patterns of the three isomers. The Henry’s Law constant of the β-HCH isomer is an order of magnitude less than α-HCH and γ-HCH suggesting that transport of β-HCH to the arctic and subsequent out gassing from Arctic Ocean waters will be slower than for the other HCH isomers (Li et al., 2004).Although concentrations of β-HCH are much lower than α-HCH and γ -HCH in Canadian Arctic waters (Li et al.,2004), β-HCH is found in increasing concentrations at higher tropic levels.
Volatilization from a moist surface and transport via air is the over whelming driving mechanism for the removal of HCHs from the site of application, although long-distance transportation by rivers and ocean currents as dissolved and particle-sorbed phases is also important, especially for β-HCH. This has important consequences for field studies and the fate under real conditions of use, as the rate of volatilization of lindane and the contaminant isomers is affected not only by temperature but also by soil moisture content. Losses of lindane to the air from moist or wet soils are known to be more rapid than from drier soils. The vapor pressures of liquid-phase HCHs decrease with decreasing temperature while the liquid-phase water solubility increase with decreasing temperature. As a result, the Henry’s Law constants for all three isomers are correspondingly lower at 0-5 oC than at 20-25 oC. This leads to the “cold condensation” effect.
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Table 1.1b.Chemical properties of HCHs isomers. -3 3 -1 HCH(Molecular Weight PL/Pa SWL/ mol.m H/Pa.m /mol Log Kow Log KOA α- HCH (290.8) 0.003 0.0052 0.55 3.81 7.46 β-HCH (290.8) 0.00004 0.00035 0.036 3.8 8.64 γ- HCH (290.8) 0.0071 0.025 0.24 3.78 7.75
Heptachlor
Heptachlor (1, 4, 5, 6, 7, 8, 8-heptachloro-3a, 4, 7, 7a-tetrahydro-4, 7-methanoindene) is an insecticide in the form of white powder and sometimes due to impurities as tan color powder. Commercial heptachlor (technical grade) contains 72% heptachlor and 28% related compounds. It is a non-systemic insecticide with contact, stomach and to some extent respiratory mode of action. Heptachlor resembles chlordane and was isolated from technical chlordane. In efficacy as insecticide, heptachlor is 4-5 time more effective than chlordane.
Heptachlor is one of the persistent organic pollutants (POPs) and is toxic and extremely persistent in the environment. Through biomagnifications accumulates in the fatty tissues of humans and animals and can move to remote locations after volatilization and can deposit in high altitudes via global distillation. Heptachlor persists in soils for long time and metabolizes
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into a more toxic compound heptachlor epoxide. Heptachlor epoxide residues are more likely to occur in soil than its parent compound either due to rapid degradation compared to heptachlor or accumulation from other sources like chlordane. Heptachlor epoxide is solubilized in water easily and can get adsorbed with soil particle to persist soil and water for longer periods. Due to environmental persistence they can find routs into human food chain by deposition in edible fish, dairy products, and meats exposed to the compounds, breast milk, and drinking water. Direct inhalation and contact with contaminated soil at disposal sites are some other human exposure routes.
Aldrin
Aldrin (1, 2, 3, 4, 10, 10-Hexachloro-1, 4, 4a, 5, 8, 8a-Hexahydro-exo-1, 4-endo-5, 8- Dimethanonaphthalene) is used as an insecticide in agriculture sector. It was named after the German chemist Kurt Alder. Aldrin has been effectively used against soil born insects such as termites and grasshoppers to protect field crops such as cotton, corn and potatoes. Aldrin itself is non-toxic to insects. In insect body, it is oxidized to dieldrin which is neurotoxin and carries out insecticidal function. After agricultural application aldrin either volatilizes from soil or is converted rapidly to dieldrin after oxidization. Aldrin is also categorized as persistent organic pollutant (POP) due to persistence of parent and breakdown products. After inhalation or some direct or indirect physical contact, aldrin enters the body, and metabolizes to dieldrin. Dieldrin has the ability to accumulate in the fatty tissues and its metabolites are excreted in bile and feces. It is also excreted in breast milk.
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Dieldrin
Dieldrin (1, 2, 3, 4, 10, 10-hexachloro-6, 7-epoxy-1, 4, 4a, 5, 6, 7, 8, 8a-octahydro-endo- 1, 4, -exo-5, 8-dimethanonaphthalene) is closely related to aldrin which breaks down to form dieldrin. Dieldrin was developed in the 1950s as an alternative to DDT and proved to be a highly effective insecticide and was very widely used during the 1950s to early 1970s.
Dieldrin is extremely persistent in the environment and is also categorized as persistent organic pollutant (POP). Dieldrin is hydrophobic and adheres very strongly to soil particles. Volatilization of dieldrin from soil to air is very slow. Dieldrin breaks down in the soil at very slowly rate and can accumulate into the plants from soil. It can be biomagnified to pass into the human food chain. Being lipophilic, it deposits in the body fat and leaves the body very slowly. Dieldrin and its metabolites are excreted in bile and feces from body. It is also excreted in human breast milk. Long-term exposure to dieldrin has proven toxic to a very wide range of animals including humans, far greater than to the original insect targets. The elimination half-life of dieldrin is approximately 1 year. At high doses, dieldrin affect central nervous system by blocking inhibitory neurotransmitters which leads to symptoms like headache, confusion, muscle twitching, nausea, vomiting, and seizures.
Chlordanes (CHLs)
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The chemical name of chlordane is 1.2.3.4.5.6,7,8,8-octachloro-2,3,3a,4,7,7,a-hexahydro-
4,7-methano-1H-indene and the chemical formula is C10H6Cl8 and molecular weight is 409.78. The basic properties of chlordane are as follows: melting point is less than 25oC, boiling point is 165oC at 2mm Hg; KH: 4.8 × 10-5atm m3/mol at 25oC; vapor pressure: 10-6 mm Hg at 20 oC (Montgomery, 1993).
Chlordane is a mixture of chlorinated hydrocarbons containing chlordane, heptachlor, nonachlor and related compounds. Technical chlordane typically contains 64-67% chlorine (NRCC, 1974).Chlordane is highly insoluble in water, and is soluble in organic solvents. It is semi-volatile and can be expected to partition into the atmosphere as a result. It binds readily to aquatic sediments and bioconcentration in the fat of organisms as a result of its high partition coefficient (log Kow=6.00). It is produced by reacting hexachlorocyclopentadiene with cyclopentadiene to from chlordane, which is then chlorinated to form chlordane (IARC, 1991). The chemistry of isomers and metabolite of chlordane is given in Table 1.3.
Table 1.1c.Chemical properties of Chlordane. -3 3 -1 Chlordane(Molecular Weight) PL/Pa SWL/ mol.m H/Pa.m /mol Log Kow Log KOA Heptachlor (373.4) 0.13 0.0035 38 5.94 7.76 cis- Chlordane (409.8) 0.0073 0.00013 5.7 6.2 8.83 trans-Chlordane (409.8) 0.01 0.0015 6.8 6.27 8.83 Heptachlor Epoxide (398.2) 0.022 0.013 1.7 5.42 8.59
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Endrin
Endrin (1aR, 2S, 2aS, 3S, 6R, 6aR, 7R, 7aS)-3, 4, 5, 6, 9, 9-hexachloro-1a, 2, 2a, 3, 6, 6a, 7, 7a-octahydro-2, 7:3, 6-dimethanonaphtho [2, 3-b] oxirene) is an insecticide used on cotton, maize, and rice. It also acts as an avicide and rodenticide. It is a solid, cream to light tan to white, almost odorless substance. Endrin is a stereoisomer of dieldrin and is structurally similar to aldrin, and heptachlor epoxide. Due to high toxicity and persistence, endrin was banned in many countries. It was mainly used as aerial spray to control insect pests of cotton besides it was also used on rice, sugar cane, grain crops and sugar beet, and tobacco.
Endrin is highly persistent in the environment and is likely to be absorbed into the sediments in surface water. It is very toxic to aquatic organisms and has potential of bioaccumulation into fatty tissues of aquatic animals. In soil its half life is over 10 years. Human exposure is through skin adsorption or inhalation of dust or vapors. Endrin is highly lipophilic and can deposit in fatty tissues for long time. Acute endrin poisoning in humans affects primarily the nervous system.
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Hexachlorobenzene (HCB)
The molecular formula of HCB is C6Cl6 and molecular weight is 284.78. The basic properties of HCB are: melting point is 227-230oC; boiling point is 323-326oC (sublimes); KH: 7.1 × 10-3 atm m3/mol at 20oC; log Koc is 2.56-4.54; log Kow is 3.03-6.42; solubility in water is 40µg/L at 20oC; vapor pressure is 1.089 × 10-5 mm Hg at 20oC.
HCB is also a byproduct of the manufacture of industrial chemicals including carbon tetrachloride, perchlorethylene, trichloroethylene and pentachlorobenzene. It is a known impurity in several pesticide formulations, including pentachlorophenol and dicloram and may be present as an impurity in others (Tobin, 1986). HCB is highly insoluble in water, and is soluble in organic solvents. It is quite volatile and can be expected to partition into the atmosphere as a result. It is very resistant to breakdown and has a high partition coefficient (log Kow = 3.03 - 6.42), and is known to bioconcentration in the fat of living organisms as a result. HCB is formed during the liquid phase substitute reaction of chlorine and benzene, with a ferric oxide catalyst, at temperatures greater than 150oC. It can also be produced from the unwanted stereoisomer of hexachlorocyclohexane (those not needed in lindane manufacture) by treatment with metal chlorides or anhydrous sulfuric acid (Vancouver Proceedings).
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Endosulfans (ENDOs)
The chemical name for endosulfan is 6,7,8,9,10,10-hexachloro-1,5,5a,6,9,9a-hexahydro- 6,9-methano-2,4,3-benzo(e)-dioxathiepin-3-oxide;endosulfantechnical;-norbornene-2,3- dimethanol-1,4,5,6,7,7-hexachlorocyclic sulfite and the chemical formula is C9H6Cl6O3S and molecular weight is 406.95.
Technical grade endosulfan contains atleast 94% of two pure isomers, α- and β- endosulfan (Maier- bode, 1968). The α- and β- isomers of endosulfan are present in the ratio of 7:3, respectively .Technical grade endosulfan may also contain up to 2 % endosulfan alcohol and 1% endosulfan ether. Endosulfan sulfate is a reaction product found in technical endosulfan; it is also found in the environment due to photolysis and in organisms as a result of oxidant by biotransformation (Coleman PF, 1982; EPA, 1979). The chemistry of isomers and metabolite of endosulfan has been given in Table 1.4.
Table 1.1d. Chemical properties of Endosulfans. -3 3 -1 ENDOs (Molecular Weight) PL/Pa SWL/ mol.m H/Pa.m /mol Log Kow Log KOA α- Endosulfans (406.9) 0.0044 0.0063 0.7 4.94 8.49 β- Endosulfans (406.9) 0.0040 0.089 0.045 4.78 Endosulfans sulphate (406.95) 4.7 3.66
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Polychlorinated biphenyls (PCBs)
PCBs consist of a biphenyl (two benzene rings with a carbon to carbon bond between carbon 1 on one ring and carbon 1 on the second ring) with a varying number of chlorines. The common nomenclature used for identifying the location of chlorine atoms on the biphenyl rings is as follows: 1. Chlorines on carbons 2 or 2’ and/or 6 or 6’are in the ortho position (the chlorine is bonded to a carbon immediately adjacent to carbon 1 or 1’). 2. Chlorines are carbon 3 or 3’and /or 5 or 5’ are in the meta position (there is a carbon between the carbon on which the chlorine is bonded and carbon 1 or 1’). 3. Chlorines on carbon 4 or 4’ are in the para position (the chlorine is bonded to the carbon that is directly across from carbon 1 or 1’).
If there are only chlorines, one in the ortho position (carbon 2) and one on the ortho position (carbon 2’) and one on the ortho position (carbon 2’). The fact that there are only two chlorines, and that both of the chlorines is in an ortho position would tend to make this congener of PCB less toxic than the case in which the congener contains 6 chlorines, but chlorines are lacking in all of the four ortho positions (carbon2, 6, 2’ and 6’). The large number of chlorine present coupled with the fact that there are no ortho carbon makes this congener a particularly toxic and bioaccumulative species of PCB. The toxicity of a PCB is dependent not only upon the number of chlorines present on the biphenyl structures, but the position of the chlorines. For instance congeners with chlorines in both para position (4 and 4’) and at least 2 chlorines at the meta positions (3, 5, 3’, 5’) are considered to be “dioxin like “and are particularly toxic. When there is just 1 or no substitute in the ortho position, the atoms of the congener are able to line up in a single plane (sometimes
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referred to as coplanar). The planar or “flat configuration” is particularly toxic. Furthermore, biodegradability is related to the amount of chlorination of a specific PCB. The higher the chlorine content of a PCB, the less the biodegradability. The lack of degradability of PCB compounds results in bioaccumulation of PCBs in the environment.
Polychlorinated biphenyls have been used extensively since 1930 in a variety of industrial uses, including as dielectrics in transformers and large capacitors, as heat exchange fluids, as paint additives, in carbonless copy paper and in plastics. The value of PCBs for industrial applications is related to their chemical inertness, resistance to heat, non-flammability, and low vapour pressure and high dielectric constant (WHO, 1993). PCBs are produced by the chlorination of biphenyl by anhydrous chloride, under heated reaction conditions and in the presence of suitable catalysts. The degree of chlorination varies depending on the reaction conditions, and ranges from 21 % to 68 %( w/w). The result is a mixture of different congeners, and contains many impurities, including polychlorinated dibenzofurans (PCDFs) (WHO, 1993).
Commercial PCB mixtures were sold based on the percentage of chlorine by weight, with each manufacturer utilizing their own system for identifying their products. In the Aroclor series, a ($)-digit code is used; biphenyls are generally indicated by 12 in the first 2 positions, while the last 2 numbers indicate the percentage of chlorine in the mixture; i.e. Aroclor 1260 is a polychlorinated biphenyl mixture containing 60% chlorine (WHO,1993). There are 209 possible PCBs, from three mono chlorinated isomers to the fully chlorination and substitution position. They were typically manufactured as mixtures of 60 to 90 different congeners. In the concentrated form, PCBs are either oily liquids or solids with discernable taste or odor. As the number of chlorines in a PCB mixture increases the flash point rises and the substance becomes less combustible. Also, PCBs with large numbers of chlorines are more stable and thus resistant to biodegradation. The most highly favored PCBs tended to be the ones with large numbers of chlorines. These congeners are also proving to be the ones that present the greatest environmental and health risks.
Generally, the water solubility and vapour pressure decrease as the degree of substitution increases, and the lipid solubility increases with increasing chlorine substitution. PCBs in the
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environment may be expected to associate with the organic components of soils, sediments, and biological tissues or with dissolved organic carbon in aquatic systems, rather than being in solution in water. PCBs volatilize from water surfaces in spite of their low vapour pressure, and partly as a result of their hydrophobicity; atmospheric transport may therefore be a significant pathway for the distributions of PCBs in the environment (Dalzell et al., 1994).
The chemical properties of PCBs (low water solubility, high stability, and semi-volatility) favor their long range transport. One of the qualities that make PCBs so desirable is one of the characteristics that make PCBs so hazardous to the environment. The high thermal and chemical resistance of PCBs means that they do not readily break down when exposed to heat or chemical treatment. This is a very desirable trait for lubricants. However, since PCBs do not break down they remain in the environment and continue to build up as more are introduced into the environment.
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Appendix 2.1. Detailed description of studied sampling sites:
Site 1: (31°25′02.3″ N, 72°11′37.3″ E)
This sampling site (S-1) is situated at Jhelum upstream, 10km above Trimmun Headwork (confluence point at which River Jhelum joins River Chenab) in district Jhang. Agricultural activities were observed in the catchment area of this site. Rice and sugarcane are commonly cultivated in the vicinity of sampling site. Anthropogenic activities are relatively less here on this sampling location, while fishing, boating etc. were observed on the time of sampling. Hence, the banks were not stable and eroded into river.
Site 2: (31°25′81.2″ N, 72°20′71.8″ E)
This sampling site (S-2) is situated, at River Chenab upstream, 10km above Trimmun Headwork. This site is located in an urban area near Jhang city, hence it is encountered by enormous industrial and municipal anthropogenic activities, besides this that these effluent containing huge amount of pollutants (both organic and inorganic) enters directly into the river through different drains, due to which river at this site is highly polluted having blackish green color of water with bad odor. This site is also receiving a huge load of pollutants by upstream flow from the industrial drains of Chinot and Faisalabad city (Plate 3.6), which resulted in the degradation of natural aquatic system. Coal burning was observed on this site, which along with atmospheric deposition can also be the source of Organochlorine contaminants.
Site 3: (31°18′7.5″ N, 72°16′46″ E)
This sampling site (S-3) situated at the downstream of Trimmun headworks (joining point of River Jhelum and River Chenab) and very famous for tourism in the country. This site can be categorized as sub-urban on the basis of activities observed on this site. Tourism activities were maximum, which is one of the sources of pollution on this site. This site location is also receiving pollution load from upstream flow of River Chenab, however River Jhelum which
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joined the Chenab, played important role to dilute this load. Extensive fishing and boating activities were observed on this site. Agricultural practices in the vicinity of this site were also observed, which may be the one of important source of pesticides contamination of River ecosystem. Generally, the banks were stable, but in flooding season, large area is slashed down into river water.
Site 4: (31°08′27.5″ N, 72°14′25.3″ E)
This sampling site (S-4) is situated at Nikokara (town) 10km below Trimmun Headwork. This area is well known for agricultural productivity. Sugarcane, rice, wheat, and maize are the main crops of this area. Generally, the banks were not stable and eroded into river with the high water flow. Fishing and boating were also observed on this site and many brick Killens were also found in the area. Run-off from surrounding agricultural fields, upstream flow and atmospheric deposition can be suspected as sources of Organochlorine contamination.
Site 5: (30°99′20.7″ N, 72°08′65.3″ E)
This sampling site (S-5) is located in the rural area of the Jhang district and can be categorized as agricultural. Generally, on this site agricultural activities were observed while some local brick manufacturing kilns were also found in the vicinity of this site. An electric power grid station was situated near this site and boating activities for the transportation was also observed on this site.
Site 6: (30° 87′90.3″ N, 71°97′70″ E)
This sampling site (S-6) is located in agriculture area of the Jhang district. Sugarcane crop was observed on the banks of this site and water is more turbid due to erosion of the bank material into river body, while construction of the bridge was also observed at the time of sampling, which may be one of the important sources of pollution on this site location. Fishing and boating were also observed on this site.
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Site 7: (30° 76′19.3″ N, 71°88′23.1″ E)
This sampling site (S-7) located in the agricultural area on the border line of the Jhang and Khanewal district, 10km above on the Chenab’s upstream before it joins with River Ravi. Although, this site is located in the agricultural area but the cropping pattern is entirely different from the above mentioned sampling locations. Cotton is the main crop of this area, while wheat is also cultivated and this area is world known cotton growing areas. Fishing and boating were also observed on this sampling location.
Site 8: (30°62′14.8″ N, 71°82′72.3″ E)
This sampling site (S-8) is situated at River Ravi in district Khanewal, at Ravi upstream, 10 Km above from the joining point of River Ravi and River Chenab. River Ravi, one of the most polluted river of Pakistan, receives huge quantities of pollutants (both organic and inorganic) from many industrial cities of Punjab i.e. Lahore, Qasoor, Faisalabad etc. Moreover, this site is located in the cotton belt of Pakistan from where pesticides may enter into the river body. Many small towns were also observed on the banks of this site while extensive fishing and boating are the common practices on this site. Colonies of migratory birds i.e. little egrets, cattle egrets, and pond herons were also found in this region.
Site 9: (30°62′61.3″ N, 71°80′76.6″ E)
This sampling site (S-9) is situated on the Chenab’s downstream 10 km below the joining point of the River Ravi and River Chenab. Generally, the banks were moderately eroded and mango orchards were observed on the banks. This area consisted of very fertile agricultural soils and very important for cotton, wheat and sunflower production. Fishing and boating were also observed on this site.
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Site 10: (30°57′63.3″ N, 71°65′99.7″ E)
This sampling site (S-10) is situated at mainstream of River Chenab in district Khanewal. The land use of catchments’ area is mainly agricultural and sub-urban, while boating was also observed on this site. The banks were severally eroded and during flooding period a huge part of surrounding agricultural land slashed into river water.
Site 11: (30°52′61″ N, 71°59′50.1″ E)
This sampling site (S-11) is also situated in the agriculture area of the Khanewal district. This area is categorized as agricultural as farming activities were observed in the surrounding area. Boating and fishing were the main activities on this site, while large colonies of migratory birds i.e. little egret, cattle egret, and pond heron etc. were also found on the bank of this site. The source of pollution on this site was the run-off from surrounding agricultural fields.
Site 12: (30°45′01″ N, 71°50′70.1″ E)
This sampling site (S-12) is located in the cotton growing area of Multan district and can be categorized as agricultural area. The main activity observed over there at the time of sampling, was the construction of bridge. Generally, the banks were more stable and the agricultural crops have been growing for many years on the banks of the river while boating and fishing were also the main activities of the local population.
Site 13: (30°35′91.8″ N, 71°42′32.1″ E)
This sampling site (S-13) is also located in Muzaffarghar district and land use of this site can be categorized as agricultural and sub-urban. Indus link canal also joined the River Chenab on this site location which largely affects the water flow and chemistry of river ecosystem. Indus link canal is 38 miles long link canal which provides 1 Lac cusec water to River Chenab. Banks were generally stable with agricultural crops and mango orchards on the both sides of the river
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body. Fishing and boating were also observed on this site at the time of sampling and local people also using the river water to irrigate the crops grown on the banks.
Site 14: (30°44′33″ N, 71°50′68″ E)
This sampling site (S-14) is also situated at River Chenab mainstream and can be categorized as sub– urban area. This site also located in cotton belt area of district Multan, receiving municipal waste from SurajMiani drain and local land use is agricultural.
Site 15: (30°152′06″ N, 71°29′03.1″ E)
This sampling site (S-15) is located in the Multan district in sub-urban area of the district. Rapid urbanization is in progress in this area and many small drains from surrounding area are directly dumping municipal water into the river body. Water quality appears poor on this site with more turbidity. Agricultural crops were also observed in the surrounding areas. Poultry farms were also observed near the river banks, while boating and fishing were the main activities on this site.
Site 16: (30°07′48.2″ N, 71°28′42.7″ E)
This sampling site (S-16) is situated at Shershah Bridge near Multan city. This site is located in urban area and a huge amount of untreated toxic and hazardous municipal and industrial effluent of Multan and Muzaffarghar, enters into river at this site from different point and non point sources. An electric waste water supply line from a Grid station was also present at this site, through which all waste water is being disposed into river. The banks were moderately stable and water quality appears very poor on this site due to severe pollution load from different surrounding urban area, electric grid station and industrial and municipal drains from both cities i.e. Multan and Muzaffarghar located on the bank of the River Chenab.
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Site 17: (30°62′14.8″ N, 71°82′72.3″ E)
This sampling site (S-17) is situated at PulBaghar in district Khanewal, at Ravi upstream, 20 Km above from the joining point of River Ravi and River Chenab. River Ravi, one of the most polluted river of Pakistan, receives huge quantities of pollutants (both organic and inorganic) from many industrial cities of Punjab i.e. Lahore, Qasoor, Faisalabad etc. Moreover, this site is also located near the urban area locally known PulBaghar, from where municipal waste containing huge amount of pollutants especially from automobile workshops and automobile service stations entering into the river ecosystem via open drains and deteriorating the quality of the surface water of River Chenab.
Site 18: (31°54′43.3″ N, 72°33′12.3″ E)
This sampling site (S-18) is located on the Chenab mainstream in district Jhang after the joining of industrial drain from Faisalabad city (S-20). This site is located in the urbanized area of district Jhang having extreme anthropogenic activities. Water is highly polluted receiving huge amount of toxic effluents from industries and automobiles service stations, municipal sewage from different point and non-point sources. At this location, water quality of river appears very poor having light greenish water color due to intense pollution.
Site 19: (31°70′61″ N, 72°93′53″ E)
This sampling site (S-19) is situated on the mainstream of River Chenab in the agricultural area of Chinot district. On this site, banks were severally eroded which slashed off a huge portion of surrounding agricultural land into river every year. Generally, sugarcane, rice and wheat are the main crops of this area and pesticides are used in large quantities for crop protection. Fishing and boating were also observed on this site.
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Site 20: (31°56′45″ N, 72°52′52″ E)
This sampling site (S-20) is located on the industrial drain, joined with River Chenab in district Jhang. This most polluted site location receiving a huge amount of toxic pollutants from Faisalabad city, which is one of leading industrial city, and different industrial drains from this city, is directly dumped into River Chenab. Faisalabad has a large number of textile and dying mills, where generally weaving, dying, printing and finishing of cloth is carried out. Moreover, large agro-processing plants, and fertilizers producing units are also working in the city. These operations usually produce intensely alkaline liquor high in dissolved materials and suspended soil. In the absence of adequate treatment facilities and effective drainage system, bulk of the effluent from these industrial units flow into open land and low lying areas via small drains which finally dumped into River Chenab, with consequential severe damage to flora and fauna. The offensive smell of stagnant pools of waste water is great source of nuisance to the local people. Generally, the banks on this site are moderately eroded with poor quality of river water and revealed that effluents from all industries causing severe toxic material pollution.
Site 21 (32°95′58″ N, 73°75′22″ E)
This sampling site (S-21) is situated on River Jhelum near the Jhelum city. Agricultural fields were observed along with banks of river. Anthropogenic activities including car washing, fishing, construction to expand urbanization and non-point sources include agricultural runoff. As Jhelum city is densely urbanized, it was observed that residential colonies were in progress utilizing natural habitat along with river. The land was rough and broken. Overall, water quality appears good as locals utilize this water for their needs and is considerably fast flowing.
Site-22 (32°67′09.9″ N, 74°47′09.2″ E)
This sampling site (S-22) is located on upstream of rim station Marala where Chenab enters Pakistan, near the Sialkot city. Marala headwork has a capacity of discharge of 1.1 million cusecs. Two major water channels originate at the Marala Headwork’s the Marala-Ravi Link Canal and the Upper Chenab canal. Width of river Chenab estimates about 800-1400m and depth
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varies to 4-8m. River banks were slightly eroded and river bed constitutes of sand and clay. This site attracts tourists, hunter and poacher for its natural scenic. Fishing and boating were observed on this at the time of sampling.
Site-23 (Khanki Barrage) (32°40′29.2″ N, 73°97′25.2″ E)
This sampling site (S-23) is located at Khanki Barrage near Gujranwala district and the catchment area of this site is known worldwide for rice production. However, this site receiving pollution load from a number of small tributaries like Bhimber, Halsi, Aik and Palku covers area of 3,437 Km2, finally joined Chenab River at the upstream of this site. These tributaries carried a huge load of pollutants from the Sialkot, Gujarat, and Gujranwala, which are world known industrial cities. These cities are prominent for leather industry, tanneries, surgical instruments manufacturing industry, musical instruments and sportswear manufacturing industry, textile mills, cutlery manufacturing and large agro-processing plants, from where River Chenab receives industrial and municipal effluents without prior treatment from different drains, nullahs (streams) resulted in the degradation of natural aquatic ecosystem.
Site-24 (29°79′68″ N, 72°28′52.8″ E)
This sampling site (S-24) is located at Sutlej River (downstream of Head Islam). River Sutlej, one of main tributaries of River Chenab, originates from India and transverse through many densely polluted and industrial cities of Pakistan from where it receives huge pollution load via surface run-off, industrial drain and atmospheric deposition. However, this site is located in the rural area of Vehari district and can be categorized as agricultural. Main crops cultivated in the area are; wheat, cotton, sunflower, and maize etc.
Site-25 (Punjnad Barrage) (29°35′04″ N, 71°02′27.8″ E)
This sampling site (S-25) is located at downstream of River Chenab about a kilometer away from PunjnadHeadworks. River banks at this site location were relatively stable by placing heavy rocks and thick vegetation of Thypha was also present on both sides of river banks.
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Appendix 2.2. Laboratory analysis for physico– chemical parameters of water:
Free CO2was analyzed by titration method using sodium hydroxide (0.02N) titrant and phenolphthalein was used as an indicator. Free CO2 was calculated by using following equation. (Garget al., 2002). volume of titrant used Free CO2 (ppm) = 1000 voulme of sample
Alkalinity (ALK) was measured through titrometry method with the standard solution of strong acid (sulphuric acid) using phenolphthalein (carbonate alkalinity) and methyl orange (bicarbonate alkalinity) as an indicator. ALK concentration was calculated by following equation. (Garget al., 2002).
volume of titrant A Phenolphthalein Alkalinity (ppm) = 1000 voulme of sample
volume of titrant B Total Alkalinity (ppm) = 1000 volume of sample
Titrant A= volume of titrant used using phenolphthalein indicator. Titrant B= total volume of titrant used.
3– Phosphates (PO4 ) were determined by using the ammonium molybdenum method. Filtered water sample of 25ml was taken, 1ml of ammonium molybdate was added along with 3 drops of stannous chloride and contents were left for 10 minutes for the appearance of blue color. The readings were measured spectrophotmetrically at 690nm (HACH Spectrometer, Model: DR 5000) (Garget al., 2002).
Nitrates (NO3– N) were determined spectrophotmetrically by using the phenol– disulphonic acid method. Filtered water sample of 25ml was dried on hot plate; 0.5ml of phenol– disulphonic
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acid was added, followed by 25ml of distilled water and 2ml of 12N solution of KOH. Contents were mixed well and residues were dissolved. Values were determined at 410nm using spectrophotometer (HACH spectrometer, Model: DR 5000) (Garget al., 2002).
2– Sulphates (SO4 ) were determined spectrophotmetrically by using barium chloride method. 50ml of filtered water sample was taken; 10ml each of NaCl– HCL and glycerol– ethanol solution were added along with 0.15 grams of barium chloride. Contents were mixed for 30 minutes and readings were taken at 420nm wavelength on spectrophotometer (HACH spectrometer, Model: DR 5000) (Garget al., 2002).
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Appendix 2.3. Laboratory analysis for physico-chemical parameters of sediments: pH, EC, TDS Parameters such as pH, EC, and total dissolved solids (TDS) of sediment sample were determined by using portable combined meter (Milwaukee, model, SM802). Prepare 1: 10 suspension of sediment samples w/v in distilled water and determine pH, ECe and TDS (Garg et al., 2002).
Organic matter
Organic matter (OM) was determined by Tyurin’s method (wet oxidation method). In this method OM (%) were calculated by following formula (a b)C volume of titrant used A = 1000 voulme of sample Where A= weight of humus % in dry soil. a= ml of Mohr salt in blank titration. b= ml of Mohr salt in sample titration. D= weight of sample used.
C, H2 O= conversion factor for the water contents of soil to express the results on oven dry basis. C Mohr salt= conversion factor for exactly 0.2 N Mohr salt. 0.0010362= Co-efficient of Ischereko.
Sand, Silt and Clay Contents (%)
The proportions of sand, silt, and clay were calculated using Bouycous hydrometer, and sediment textural classes were determined on the basis of relative proportion of soil particles using textural triangle. Their calculations were done by following method,
First reading (after 5 minutes) = (Silt+ Clay) % Second reading (after 5 hours) = Clay%
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100− (Silt+ Clay) = sand % (Silt+ Clay)−Clay= Silt %
Alkalinity (ALK)
For sediments fresh suspension was made by taking 10g of sediments in 100 ml of distilled water. The suspension was filtered using Whatman filter paper No.40 and used for the analysis. Alkalinity is estimating by titrating the suspension (50 ml) with the standard solution of strong acid (H2SO4), first to pH 8.3 using phenolphthalein as an indicator (carbonate alkalinity) and then further to second end point to pH 4.5 using methyl orange as an indicator (bicarbonate alkalinity). The calculations were done as follows ml of titrant A Phenolphthalein ALK (ppm) = 1000 ml of sample
ml of titrant B Total ALK (ppm) = 1000 ml of sample Titrant A= volume (ml) of titrant used using phenolphthalein indicator Titrant B= total volume of titrant used to reach the pH 4.6 endpoint
Phosphates (PO43-) Phosphates were estimated by Ammonium Molybdate method in which sample (10 ml) react with acidified Ammonium Molybdate solution and forms molybdophosphoric acid which was reduced to a blue complex in the presence of stannous chloride (SnCl2). This was measured spectrophotmetrically (HACH, DR 5000) at 700 nm. The value of SRP mg/L was calculated with the help of standard curve. In sediments the samples were prior treated with 0.02N H2SO4. The suspension was shaken for about 30 minutes and filtered through Whatman filter paper No.40.
Standard curve
KH2PO4 (219.5 mg) was dissolved in distilled water and volume raised up to 1000ml to make standard solution of 50 mg/L strength. By plotting the standard curve between
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concentration and absorbency concentration of samples was noted and final concentration of SRP was calculated as under:
3- C 1000 PO4 (ppm) = volume of sample Where, C= concentration calculated from graph. Final calculation done as follows 3- 3- PO4 = volume of PO4 infiltrate X volume of suspension 1000 wt of sample taken
Nitrate-nitrogen (NO3-N) For nitrate, the sample (25ml) were dried on a hot plate /water bath and dissolved in phenol disulphonic acid which firms nitro acid complex and give yellow color with strong alkali salt (KOH). The absorbance is taken at 410 nm and value deduced with the help of standard curve. For the extraction of nitrates from sediments sample (25g were taken for 15 minutes with Nitrate extraction solution (NES). It was further shaken with solid calcium hydroxide. Pinch of solid magnesium carbonate was added and filtered using Whatman filter paper No.40. First 20ml of filtrate was discarded. 25 ml was evaporated up to the dryness and continued for nitrate determination employing the same as described.
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Standard curve
3.61g of KNO3 in 500ml of distilled water to make stock solution of 1000 mg/L strength. The solution was diluted up to 50 mg/L. B plotting the standard curve between concentration and absorbency concentration of samples was noted and final concentration of nitrate was calculated as
NO3-N (ppm) = C X 1000/ml of sample Where, C= concentration calculated from the graph Calculations were calculated as follows;
NO3-N (ppm) = A X V/1000 X W Where
A= NO3-N in the filtrate. V= volume of total sediment extracted. W= weight of dried sediment sample (g).
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Appendix 2.4. Showing the chromatograms for each group of studied chemical (OCPs and PCBs)
tri-CBs
C:\Xcalibur\...\cm27 5/15/2009 3:21:11 AM Std1 May2009
RT: 14.83 - 20.11 31/28 18.32 NL: 100 22 3.32E5 19.08 m/z= 80 18 18.37 255.60- 16.52 256.60 F: 60 M S cm27 40 30 15.99 20 Relative Abundance Relative 17.79 19.84 14.93 15.67 15.84 16.10 16.97 17.71 18.65 19.38 0 18.32 NL: 100 3.10E5 19.08 m/z= 80 18.37 257.60- 258.60 F: 16.52 60 M S cm27
40 15.99 20 15.24 15.84 16.12 16.95 17.58 17.79 18.64 19.31 19.84 0 15.0 15.5 16.0 16.5 17.0 17.5 18.0 18.5 19.0 19.5 20.0 Time (min)
tetra-CBs
C:\Xcalibur\...\cm27 5/15/2009 3:21:11 AM Std1 May2009
RT: 16.55 - 25.99 41/64 21.47 NL: 100 2.97E5 54 60/56 m/z= 80 24.36 291.60- 17.79 52 49 292.60 F: 44 74 70 M S cm27 60 19.84 20.04 20.82 22.82 40
20 Relative Abundance Relative 20.54 24.23 24.91 17.63 18.67 19.45 22.29 25.29 0 21.47 NL: 100 2.42E5 m/z= 24.36 289.60- 80 17.79 290.60 F: M S cm27 60 19.84 20.04 22.82 20.82 23.05 40 24.23 20 24.91 20.54 23.30 25.27 17.30 18.24 19.02 19.45 22.29 0 17 18 19 20 21 22 23 24 25 Time (min)
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penta-CBs
C:\Xcalibur\...\cm27 5/15/2009 3:21:11 AM Std1 May2009
RT: 19.89 - 10433.50 90/101 24.89 NL: 100 20.61 2.47E5 m/z= 80 114 325.60- 95 99 123 118 31.21 105 326.60 F: 23.29 110 M S cm27 60 25.27 87 30.08 32.44 26.96 27.82 40
20 31.66 Relative Abundance Relative 33.23 20.75 22.66 24.49 25.75 29.03 0 24.89 NL: 100 20.61 1.59E5 m/z= 80 323.60- 324.60 F: 31.21 60 23.29 30.08 M S cm27 25.27 27.83 32.44 40 26.96 31.66
20 29.01 33.21 20.75 22.66 24.23 25.49 0 20 21 22 23 24 25 26 27 28 29 30 31 32 33 Time (min)
C:\Xcalibur\...\cm27 hexa-CBs 5/15/2009 3:21:11 AM Std1 May2009
RT: 31.02 - 35.92 NL: C:\Xcalibur\...\cm27 32.23 5/15/2009 3:21:11 AM 34.28Std1 May2009 C:\Xcalibur\...\cm27100 5/15/2009 3:21:11 AM Std1 May2009 2.10E5 m/z= RT: 23.41 - 39.23 80 371.30- RT: 23.41155 - 39.23 372.30 F: 156 157 NL: 24.23 NL: M S cm27 100 24.23 60 2.05E5 100 153/132 37.7637.76 2.05E5 138 158 m/z= m/z= 40 32.25 34.49 167 359.60- 80 80 149 32.25141 34.49 359.60- 34.31 36.36 360.60 F: 33.19 34.31 36.36 360.60 F: 30.11 M S cm27 60 20 151 30.11 M S cm27 Relative Abundance Relative 60 31.32 29.0332.0329.03 32.43 32.76 33.44 33.85 34.72 35.13 35.50 40 0 40 32.23 NL: 100 34.29 1.69E5 20 m/z= Relative Abundance Relative 20 35.56 37.07 Relative Abundance Relative 35.56 37.07 24.39 25.8080 26.87 28.06 31.7831.78 373.30- 0 24.39 25.80 26.87 28.06 374.30 F: 0 NL: 24.21 NL: M S cm27 100 24.21 60 1.67E5 100 37.76 1.67E5 37.76 m/z= m/z= 40 34.49 361.60- 80 80 34.49 36.34 361.60- 32.25 34.31 36.34 362.60 F: 32.2533.19 34.31 362.60 F: M S cm27 60 20 M S cm27 60 30.1130.11 31.30 31.8029.0129.01 32.57 32.92 33.44 34.01 34.40 34.83 35.45 35.84 0 40 40 31.5 32.0 32.5 33.0 33.5 34.0 34.5 35.0 35.5 20 20 Time (min) 35.56 37.07 38.85 24.36 31.78 35.56 37.07 38.85 24.36 26.7326.7328.1028.10 31.78 0 0 24 24 26 26 28 28 30 30 32 32 34 34 36 36 38 38 TimeTime (min) (min)
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hepta-CBs
C:\Xcalibur\...\cm27 5/15/2009 3:21:11 AM Std1 May2009
RT: 30.49 - 43.70 189 188 180 42.14 NL: 100 31.66 187 183 174 38.85 1.79E5 35.56 35.90 37.07 170 m/z= 80 40.51 393.50- 394.50 F: 60 M S cm27
40
20
Relative Abundance Relative 41.32 31.21 32.21 33.32 35.36 37.32 39.51 42.39 0 42.14 NL: 100 1.72E5 31.66 38.85 35.56 37.07 m/z= 80 40.51 395.50- 396.50 F: 60 M S cm27
40
20 41.32 31.20 32.21 33.30 35.36 37.66 39.51 42.45 0 31 32 33 34 35 36 37 38 39 40 41 42 43 Time (min)
octa-CBs
C:\Xcalibur\...\cm27 5/15/2009 3:21:11 AM Std1 May2009
RT: 37.98 - 45.60 199 203 41.32 NL: 100 39.51 6.13E4 194 m/z= 80 43.98 425.50- 426.50 F: 60 M S cm27
40
20 Relative Abundance Relative 38.55 39.12 39.67 40.52 40.84 41.47 42.30 42.77 43.31 45.02 45.47 0 39.51 NL: 100 41.32 1.37E5 m/z= 80 43.98 427.50- 428.50 F: 60 M S cm27
40
20 38.42 39.24 39.71 40.19 41.08 41.50 42.62 43.11 43.65 44.71 45.58 0 38 39 40 41 42 43 44 45 Time (min)
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13C PCB-28
C:\Xcalibur\...\cm27 5/15/2009 3:21:11 AM Std1 May2009
13 RT: 17.23 - 19.71 C PCB-28 18.37 NL: 100 2.80E5 m/z= 80 267.50- 268.50 F: 60 M S cm27
40
20 Relative Abundance Relative 19.00 17.40 17.53 17.73 17.89 18.21 18.67 18.79 19.10 19.33 19.71 0 18.37 NL: 100 2.48E5 m/z= 80 269.50- 270.50 F: 60 M S cm27
40 18.98 20 17.35 17.58 17.76 17.97 18.27 18.60 18.79 19.23 19.38 19.63 0 17.5 18.0 18.5 19.0 19.5 Time (min)
13C PCB-52
C:\Xcalibur\...\cm27 5/15/2009 3:21:11 AM Std1 May2009
13 RT: 18.76 - 20.84 C PCB-52 19.83 NL: 100 1.59E5 m/z= 80 303.50- 304.50 F: 60 M S cm27
40
20 Relative Abundance Relative 18.83 18.98 19.13 19.36 19.56 20.11 20.19 20.37 20.54 20.62 0 19.83 NL: 100 1.31E5 m/z= 80 301.50- 302.50 F: 60 M S cm27
40
20 20.52 18.92 18.97 19.17 19.40 19.60 19.99 20.18 20.34 20.79 0 18.8 19.0 19.2 19.4 19.6 19.8 20.0 20.2 20.4 20.6 20.8 Time (min)
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13C PCB-101
C:\Xcalibur\...\cm27 5/15/2009 3:21:11 AM Std1 May2009
13 RT: 23.82 - 26.00 C PCB-101 24.87 NL: 100 1.73E5 m/z= 80 337.50- 338.50 F: 60 M S cm27
40
20 Relative Abundance Relative 24.05 24.16 24.34 24.63 24.99 25.27 25.50 25.80 25.93 0 24.87 NL: 100 1.10E5 m/z= 80 335.50- 336.50 F: 60 M S cm27
40
20 24.18 25.27 24.08 24.33 24.46 24.73 25.07 25.50 25.60 25.92 0 24.0 24.2 24.4 24.6 24.8 25.0 25.2 25.4 25.6 25.8 26.0 Time (min)
13C hexa-PCBs
C:\Xcalibur\...\cm27 5/15/2009 3:21:11 AM Std1 May2009
13 13 RT: 31.02 - 35.92 C PCB-153 C PCB-138 NL: 32.23 34.28 100 2.10E5 m/z= 80 371.30- 372.30 F: 60 M S cm27 13 40 C PCB-141 33.19
20 Relative Abundance Relative 31.32 32.03 32.43 32.76 33.44 33.85 34.72 35.13 35.50 0 32.23 NL: 100 34.29 1.69E5 m/z= 80 373.30- 374.30 F: 60 M S cm27
40 33.19 20 31.30 31.80 32.57 32.92 33.44 34.01 34.40 34.83 35.45 35.84 0 31.5 32.0 32.5 33.0 33.5 34.0 34.5 35.0 35.5 Time (min)
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13C PCB-180
C:\Xcalibur\...\cm27 5/15/2009 3:21:11 AM Std1 May2009
13 RT: 37.57 - 40.16 C PCB-180 38.83 NL: 100 1.79E5 m/z= 80 407.30- 408.30 F: 60 M S cm27
40
20 Relative Abundance Relative 37.68 37.96 38.10 38.26 38.60 39.06 39.28 39.51 39.85 39.94 0 38.83 NL: 100 1.91E5 m/z= 80 405.30- 406.30 F: 60 M S cm27
40
20 39.94 37.68 37.96 38.17 38.49 38.58 39.01 39.22 39.49 39.85 0 38.0 38.5 39.0 39.5 40.0 Time (min)
13C PCB-208
C:\Xcalibur\...\cm27 5/15/2009 3:21:11 AM Std1 May2009
13 RT: 41.46 - 43.99 C PCB-208 42.84 NL: 100 5.70E4 m/z= 80 473.20- 474.20 F: 60 M S cm27
40
20 Relative Abundance Relative 41.59 41.82 42.00 42.20 42.43 42.54 42.98 0 42.84 NL: 100 5.50E4 m/z= 80 475.20- 476.20 F: 60 M S cm27
40
20 41.49 41.68 41.89 42.13 42.38 42.48 42.96 0 41.5 42.0 42.5 43.0 43.5 Time (min)
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13C PCB-209
C:\Xcalibur\...\cm27 5/15/2009 3:21:11 AM Std1 May2009
13 RT: 45.95 - 48.49 C PCB-209 47.21 NL: 100 1.43E5 m/z= 80 506.90- 507.90 F: 60 M S cm27
40
20 Relative Abundance Relative 46.19 46.51 46.74 47.08 47.32 47.51 47.84 48.09 48.29 0 47.21 NL: 100 1.67E5 m/z= 80 509.50- 510.50 F: 60 M S cm27
40
20 46.00 46.26 46.58 46.82 47.05 47.39 47.61 47.76 48.00 48.19 48.42 0 46.0 46.5 47.0 47.5 48.0 Time (min)
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HCHs
C:\Xcalibur\...\cm27 5/15/2009 3:21:11 AM Std1 May2009
RT: 14.21 - 17.53 α-HCHs 14.91 NL: 100 β-HCHs γ-HCHs δ-HCHs 2.07E5 15.84 16.12 m/z= 80 16.97 218.50-219.50 F: M S cm27 60
40
20 Relative Abundance Relative 14.35 14.52 15.21 15.34 15.69 16.20 16.52 16.70 17.11 17.36 0 14.91 NL: 100 1.66E5 15.84 16.12 m/z= 80 16.97 216.50-217.50 F: M S cm27 60
40
20 17.05 14.35 14.75 15.24 15.71 16.34 16.88 17.46 0 14.5 15.0 15.5 16.0 16.5 17.0 17.5 Time (min)
HCB
C:\Xcalibur\...\cm27 5/15/2009 3:21:11 AM Std1 May2009
RT: 14.47 - 16.04 HCB 15.24 NL: 100 4.05E5 m/z= 80 283.50- 284.50 F: 60 M S cm27
40
20 Relative Abundance Relative 14.52 14.67 14.75 14.98 15.03 15.44 15.61 15.67 15.79 15.96 0 15.24 NL: 100 3.19E5 m/z= 80 285.50- 286.50 F: 60 M S cm27
40
20 14.53 14.62 14.83 14.96 15.03 15.44 15.56 15.67 15.81 15.94 0 14.6 14.8 15.0 15.2 15.4 15.6 15.8 16.0 Time (min)
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Chlordanes
C:\Xcalibur\...\cm27 5/15/2009 3:21:11 AM Std1 May2009
RT: 23.01 - 26.68 γ-chlordane α-chlordane 24.16 NL: 100 2.57E5 25.27 m/z= 80 374.50- 375.50 F: 60 M S cm27
40
20 Relative Abundance Relative 23.39 23.52 23.65 24.34 24.74 25.04 25.40 25.80 0 24.16 NL: 100 2.76E5 m/z= 80 25.25 372.50- 373.50 F: 60 M S cm27
40
20 23.09 23.48 23.62 24.01 24.38 24.76 25.01 25.42 25.90 0 23.5 24.0 24.5 25.0 25.5 26.0 26.5 Time (min)
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DDEs
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RT: 23.28 - 28.72 o,p’-DDE 24.69 NL: 100 5.63E5 m/z= 80 245.50- p,p’-DDE 246.50 F: M S cm27 60 27.25 (tri-BDEs) 40 27.65
20 Relative Abundance Relative 23.30 23.85 24.20 25.01 25.27 25.65 26.18 26.57 27.03 27.92 28.22 0 24.69 NL: 100 3.60E5 m/z= 80 247.50- 248.50 F: 60 M S cm27 27.25 27.65 40
20 27.92 23.32 24.18 24.86 25.27 25.64 26.27 27.03 28.12 0 23.5 24.0 24.5 25.0 25.5 26.0 26.5 27.0 27.5 28.0 28.5 Time (min) Note: the p,p’-DDE peak is smaller than the o,p’-DDE because it is detected in a different window with a shorter dwell time.
DDDs, DDTs, Endosulfans, Endosulfan sulfate
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RT: 26.41 - 35.26 o,p’-DDD p,p’-DDD 31.04 NL: 100 27.92 p,p’-DDT 3.73E5 o,p’-DDT 34.04 m/z= 80 234.50- 31.29 235.50 F: 60 M S cm27
40
20 Relative Abundance Relative 33.35 27.03 28.88 29.74 30.31 32.30 34.35 0 31.04 NL: 100 2.69E5 27.92 m/z= 80 34.03 236.50- 237.50 F: 31.29 60 M S cm27
40 α-Endosulfan β-Endosulfan Endosulfansulfate 20 33.35 27.03 29.76 28.88 30.32 31.43 32.28 32.85 34.28 0 27 28 29 30 31 32 33 34 35 Time (min)
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Appendix 3.1. Basic quality parameters (average values) of water samples collected from River Chenab, Pakistan and its adjoining tributries:
Spec. Cond. TDS LDO Free CO2 Site location pH (µS/cm) Sal (ppt) (ppm) (ppm) (ppm) Alkalinity(ppm) Phosphates(ppm) Nitrates(ppm) Sulphates(ppm) Turbidity(NTU) Temp (oC) S-1 8.2 216 0.2 109 8.5 68 116 4.8 5.4 56 263 24 S-2 8.9 667 0.6 275 8.4 106 189 3.4 4.4 46 18 27.4 S-3 8.8 354 0.2 250 10.3 41 128 5.3 2.0 38 173 24.8 S-4 8.7 407 0.2 300 10.4 27 127 2.3 1.8 49 213 25.4 S-5 8.9 357 0.2 250 11.0 37 122 2.5 29.6 50 170 26.2 S-6 8.5 364 0.2 250 9.2 39 121 3.3 7.4 14 246 26.3 S-7 8.9 375 0.2 250 7.8 43 123 2.4 4.2 40 134 27.7 S-8 8.9 594 0.3 390 6.0 41 108 2.9 2.9 36 157 27.3 S-9 8.5 379 0.2 230 8.2 36 115 2.5 1.5 30 265 26.5 S-10 8.9 386 0.2 250 6.4 80 135 2.1 3.6 27 219 27.0 S-11 8.6 391 0.2 250 12.3 41 119 2.1 2.4 24 117 26.1 S-12 8.9 393 0.2 250 9.7 44 129 2.8 7.8 28 337 25.9 S-13 8.9 419 0.2 300 8.3 26 117 2.8 1.9 17 352 25.4 S-14 8.9 392 0.2 250 9.2 45 155 3.0 4.7 34 358 26.0 S-15 9.0 354 0.2 250 11.0 42 121 4.0 1.9 18 121 26.9 S-16 8.9 338 0.2 250 7.9 31 110 2.7 5.1 36 363 26.7 S-17 8.9 374 0.2 250 7.6 46 123 3.8 16.3 21 96 25.8 S-18 9.0 1428 0.8 920 1.7 118 391 6.1 6.9 27 52 25.7 S-19 9.1 316 0.2 200 8.8 103 147 3.5 1.2 7.8 18 25.2 S-20 9.0 5537 3.1 3550 0.7 425 1113 33.6 15.2 460 107 26.7 S-21 7.8 270 0.5 180 12.6 10 60 0.0 1.7 65 9.2 28.6 S-22 8.0 285 0.4 180 8.6 10 84 0.2 4.5 58 100 22.5 S-23 8.2 330 0.2 220 9.2 5 111 0.0 7.3 48 65 31.2 S-24 8.3 475 0.5 315 8.0 5 120 0.0 2.6 65 52 28.3 S-25 7.6 233 0.9 572 11.1 10 121 0.0 1.3 101 178 32.5
Appendices Page 222
Appendix 3.2.Indicative ratios for different OCPs detected in the surface waters of River Chenab:
Summer Winter p,p'- p, p'- Sites Aldrin/ ∑(DDD+DDE)/ Aldrin/ ∑(DDD+DDE)/ α/γ β/γ TC/CC DDD/DDE DDT/ α/γ β/γ TC/CC DDD/DDE DDT/ Dieldrin ∑DDTs Dieldrin ∑DDTs ∑DDTs ∑DDTs S-1 0 0.95 0.09 0.51 5.27 0.76 0.24 0.68 0 0 0.23 0.77 0.43 0.57 S-2 0.26 1.29 0 0.57 3.77 0.5 0.41 1.14 0.11 0.14 0.06 0.32 0.4 0.5 S-3 0.27 0.75 0 0.67 0 0.19 0.68 1.53 0.24 0.31 0.16 1.87 0.54 0.34 S-4 0.87 0.63 0.06 0.51 0 0.68 0.32 0.55 0.12 0 0.54 1.42 0.67 0.33 S-5 0.95 1.01 0.86 0.61 1.08 1 0 0 0.62 0.38 0 1 0
S-6 1.19 0.62 0 0.83 0 0.53 0.39 1.6 0.04 3.92 0.42 2.14 0.9 0 S-7 0.98 0.82 0 1.24 0 0.71 0 1.17 1.82 0.17 0.79 0.11 0.49 0.51 S-8 0.7 0.63 0 0.88 1.36 0.51 0.45 1.58 0.12 1.45 0.19 1.3 0.58 0.38 S-9 0.95 0.65 0 1.1 2.8 0.34 0.51 0.95 0 1.43 0.37 0.33 0.64 0.28 S-10 0.83 0.82 0 0.42 0 1 0 0.49 0.03 0.27 0.11 2.63 0.5 0.41 S-11 0.26 0.66 0 0.74 1.4 0.22 0.57 0.22 0 0.28 0.29 1.6 0.45 0.51 S-12 0 1.05 0 0.92 0 0 1 0.18 0 0.59 0 1.24 0.92 0 S-13 0.28 0.94 0.32 0 0.73 0.39 0.49 0.99 0.08 0.51 0.1 1.1 0.39 0.56 S-14 1.08 0.6 0 0.6 0 0.21 0.73 0.62 0.05 0.67 0.37 0.8 0.44 0.5 S-15 0.82 1 1.07 0.35 0 0.54 0.46 0.82 0.45 0.29 0.22 1.57 0.45 0.55 S-16 0.99 1.1 0.11 0.42 0 1 0 1.77 0.1 0.42 0.38 24.23 0.75 0.2 S-17 0.09 0.52 0.17 0.34 2.06 0.66 0.29 0.39 0.08 0.09 0.07 0.89 0.67 0.29 S-18 0 0.19 0.07 0.48 0.72 0.82 0.18 0.15 0.04 0.83 0.27 0.59 0.62 0.34 S-19 1.28 0.99 0.08 1.23 3.16 0.58 0.25 0.54 0 0 0 0.54 0.26 0.57 S-20 0 5.53 0.27 0.39 1.86 0.53 0.44 2.93 0.16 0 0.43 0.62 0.44 0.51 S-21 1.18 0.54 0.21 0.78 0 1 0 0.73 0.37 0.89 0.5 0 1 0 S-22 1.51 0.77 0.91 0.27 0 0.19 0.39 0.74 0 0 0 0.74 0.6 0.29 S-23 0 0.68 0 0.19 5.49 0.7 0.22 1.34 0.1 0.43 0.39 1.99 0.43 0.49 S-24 1.05 1.72 0 0.3 0 0.22 0.78 0.75 0.2 0.83 0.41 0 0.09 0.91 S-25 1.78 0.6 0.42 0.87 0.54 0.45 0.46 1.24 0 0 0.27 0.62 0.51 0.49
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Appendix 3.3.OCPs (ng/L) and PCBs (ng/L) in water collected from different regions grouped by cluster analysis: Region-1 Region-2 Region-3 Region-4 Parameters Mean±STD Min-Max Mean±STD Min-Max Mean±STD Min-Max Mean±STD Min-Max α-HCH 66.92±44.86 21-111 36.75±14.81 24.25-57.38 21.91±7.19 11.99-31.68 10.71±6.27 1.87-21.14 β-HCH 5.36±1.96 3-6.5 4.99±5.26 1.52-11.47 4.22±6.05 1.12-15.04 1.44±1.92 0.23-6.68 γ-HCH 96.01±37.99 55-131 57.10±11.54 40.11-64.65 27.08±14.53 9.88-47.64 13.15±7.63 2.5-25.20 δ-HCH 9.55±8.72 2.82-19.40 5.161±2.63 2.80-8.92 6.22±1.44 4.74-7.72 2.65±2.35 0.27-7.92 HCB 27.05±26.47 1.15-54.06 2.37±2.10 1.21-4.23 1.69±1.91 0.41-4.68 1.04±0.54 0.39-2.08 Heptachlor 53.70±33.14 18.11-83.68 14.61±11.98 3.21-29.95 30.95±9.98 22.26-41.95 11.08±9.98 0-30.38 Hep-epoxide 5.36±3.01 3.32-8.82 6.95±6.16 3.06-16.09 2.87±1.19 1.65-4.72 2.73±3.52 0.15-12.05 Aldrin 3.015±1.12 1.83-4.06 5.84±4.30 2.25-12.08 7.22±3.13 1.83-9.47 3.55±1.51 1.02-6.21 Dieldrin 13.11±8.51 3.28-18.28 20.46±12.23 3.62-29.9 15.15±7.19 6.26-22.59 4.31±2.99 1.05-8.98 Endrin 18.18±19.08 3.18-39.66 15.57±3.16 12.24-18.71 4.75±1.62 2.01-6.24 2.88±2.09 0.59-7.74 α -endosulfan 2.58±1.6 0.76-3.75 1.66±1.05 0.46-2.88 1.41±0.79 0.95-2.81 1.84±2.38 0.54-6.05 β -endosulfan 11.20±0.55 10.58-11.61 7.37±6.22 0.9-14.66 4.94±3.11 1.07-9.79 2.89±1.57 0.97-5.54 Endo-sulphate 6.92±4.15 4.33-11.71 5.80±1.60 4.12-7.78 2.98±1.83 0.35-5.23 1.72±1.41 0.42-4.32 Methoxychlor 9.61±13.54 0.92-25.22 5.72±2.48 2.04-7.40 3.55±1.68 1.48-5.56 4.44±4.91 0.79-15.98 TC 13.35±6.72 5.78-8.63 4.87±1.40 3.21-6.63 4.75±1.96 2.51-7.66 3.91±2.64 0.16-8.11 CC 56.70±5.37 50.36-60.86 29.45±15.66 14.18-43 17.32±5.58 10.76-22.85 7.55±4.75 0.62-16.80 non-TC 11.04±7.84 4.799.85 5.045±5.87 0.64-13.69 1.47±1.80 0.48-3.44 0.79±0.91 0.21-3.27 o, p'-DDE 3.99±3.35 3.72-6.72 3.28±2.39 3.31-5.64 1.21±1.20 1.23-2.44 0.82±0.8 0.76-2.12 p, p'-DDE 34.04±16.37 22.45-52.78 20.37±9.86 9.69-32.3 6.39±4.45 4.38-10.96 5.08±5.61 1.09-18.87 o, p'-DDD 10.10±7.30 4.14-18.26 3.97±1.49 2.08-5.70 1.96±1.56 0.52-3.23 1.25±1.59 0.51-4.9536 p, p'-DDD 49.36±18.23 30.17-67.14 24.19±15.23 11.10-43.7 14.35±9.00 5.32-28.10 6.37±6.01 0.31-18.16 o, p'-DDT 13.22±9.49 2.34-19.79 5.52±1.88 3.57-8.10 3.56±2.94 2.11-7.96 1.57±1.82 0.9-5.85 p, p'-DDT 70.76±33.73 32-94.026 42.16±15.14 27.01-60.65 21.99±11.05 8.12-36.47 7±5.89 2.92-20.87 ∑PCBs 49.79±14.72 32.79-58.47 25.34±3.25 20.55-27.65 26.1±5.90 20.09-33.55 22.59±10.26 10.64-51.78
Appendices Page 224
Appendix 4.1. Basic quality parameters (average values) of sediment samples collected from River Chenab, Pakistan and its adjoining tributries:
EC TDS Textural Sites pH(units) (µS/cm) (ppm) Alkalinity(mg/g) Phosphates(%) Nitrates(mg/g) O.M. (%) Class S1-1 8.8 10 0 0.24 0.038 0.19 0.17 sand S1-2 8.7 60 40 0.25 0.048 0.17 0.54 sandy loam S1-3 8.7 0 0 0.26 0.05 0.7 0.06 sand S1-4 8.8 20 10 0.4 0.03 0.1 0.58 loamy sand S1-5 8.6 40 30 0.44 0.039 0.12 1.04 sandy loam S1-6 8.5 0 0 0.16 0.045 0.14 0.06 sand S1-7 7.7 0 0 0.16 0.051 0.13 0.15 sand S1-8 8.1 190 120 0.54 0.044 0.49 1.37 sandy loam S1-9 8.8 20 10 0.32 0.068 0.17 0.5 sand S1-10 8.7 40 20 0.48 0.05 0.21 0.62 sandy loam S1-11 8.7 40 20 0.4 0.046 0.13 0.35 sandy loam S1-12 8.8 10 10 0.34 0.05 0.9 1.31 sand S1-13 8.8 30 20 0.36 0.053 0.16 0.27 loamy sand S1-14 8.8 0 0 0.22 0.039 0.11 0.27 sand S1-15 8.8 10 0 0.24 0.057 0.1 0.48 sand S1-16 9 30 20 0.44 0.045 0.8 0.33 loamy sand S1-17 8.8 80 50 1.03 0.033 0.63 0.39 sand S1-18 7.1 130 80 0.5 0.057 0.18 0.19 sand S1-19 6.5 0 10 0.53 0.028 0.15 2.82 sand S1-20 8.2 2250 1190 1.8 0.12 2.1 5.1 sandy loam S1-21 8.3 270 180 0 0.01 0.02 0.33 sand S1-22 8.2 130 90 0 0.017 0.4 0.95 sand S1-23 8.5 80 50 0 0.017 0.2 1.02 loamy sand S1-24 9 90 60 0.8 0.025 0.5 0.87 sand S1-25 8.9 100 70 1 0.023 0.2 3.50 sandy loam
Appendices Page 225
Appendix 4.2.Showing the indicative ratios of different OCPs and their metabolites detected in the sediment samples:
Summer Season Winter Season Sites DDD+DDE p, p'- DDD+DDE p, p'- α/γ β/γ aldrin/dieldrin TC/CC DDD/DDE α/γ β/γ aldrin/dieldrin TC/CC DDD/DDE /DDTs DDT/DDTs /DDTs DDT/DDTs S-1 0.77 1.29 0.00 4.17 0.80 1.00 0.00 1.52 4.87 0.00 0.50 3.14 0.69 0.25 S-2 0.37 0.98 0.00 1.78 1.09 0.92 0.06 5.37 2.33 0.00 0.64 0.78 0.79 0.18 S-3 0.00 0.00 0.00 1.54 0.00 1.00 0.00 1.03 0.00 0.00 0.00 0.27 0.93 0.07 S-4 0.00 0.00 0.00 0.00 0.00 0.56 0.44 0.00 0.00 0.00 0.72 0.00 0.76 0.00 S-5 1.44 3.01 0.00 0.00 2.11 0.76 0.19 0.63 1.22 0.00 0.00 1.58 0.78 0.22 S-6 0.00 0.00 0.00 0.00 0.00 1.00 0.00 2.71 0.00 0.00 0.00 0.00 0.39 0.61 S-7 0.00 0.00 0.00 0.00 0.28 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.57 0.00 S-8 1.46 2.30 0.58 3.28 0.26 0.41 0.18 3.42 1.90 0.29 0.59 0.74 0.85 0.15 S-9 1.29 2.23 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.44 2.22 0.91 0.09 S-10 0.76 0.00 0.60 0.00 2.29 0.48 0.43 0.14 0.19 0.24 0.26 0.67 0.93 0.07 S-11 0.75 0.11 0.00 0.00 0.33 0.73 0.27 0.61 3.61 0.63 0.00 0.90 0.89 0.02 S-12 0.00 1.98 0.00 0.00 0.00 1.00 0.00 0.00 1.34 0.00 0.52 0.00 0.00 0.00 S-13 0.00 0.00 0.00 0.00 0.00 0.41 0.59 0.75 2.22 1.18 0.00 1.09 0.87 0.13 S-14 1.05 0.00 0.00 1.31 1.02 0.88 0.12 0.46 1.44 0.00 0.23 2.12 0.47 0.28 S-15 0.00 0.00 0.00 0.00 0.00 0.35 0.00 0.00 0.00 0.00 0.33 2.37 0.33 0.67 S-16 2.81 4.89 0.00 0.00 1.42 0.56 0.42 0.39 2.92 0.00 0.00 2.76 0.72 0.28 S-17 0.66 1.32 0.22 2.36 0.50 0.93 0.05 0.27 0.59 0.15 0.41 0.72 0.89 0.06 S-18 0.93 1.76 0.00 2.07 1.52 0.76 0.24 1.22 5.28 0.00 0.32 0.75 0.84 0.16 S-19 0.41 0.00 0.00 1.81 0.48 1.00 0.00 1.17 2.30 0.00 0.35 0.30 0.80 0.11 S-20 0.93 1.76 0.00 1.25 0.65 0.68 0.30 0.93 0.65 0.00 0.80 0.69 0.67 0.30 S-21 0.00 1.88 0.00 1.64 0.73 0.68 0.00 0.46 1.14 0.00 0.56 1.57 0.83 0.14 S-22 0.00 2.03 0.00 0.00 0.79 0.69 0.24 0.37 1.70 0.00 0.52 1.30 0.40 0.60 S-23 2.52 1.76 0.00 2.07 1.52 0.76 0.24 1.23 3.17 0.00 0.25 0.69 1.00 0.00 S-24 0.94 1.54 0.00 2.39 0.96 0.21 0.79 2.12 3.58 0.75 0.41 1.83 0.65 0.29 S-25 0.49 0.66 0.00 1.19 0.57 0.64 0.36 0.45 0.79 0.00 2.51 1.58 0.72 0.28
Appendices Page 226
Appendix 4.3. Showing the basic statistics of OCPs (ng/g) and PCBs (ng/g) in sediments collected from different four regions separated by cluster analysis: Parameters Region-1 Region-2 Region-3 Region-4 Mean±STD Min-Max Mean±STD Min-Max Mean±STD Min-Max Mean±STD Min-Max α -HCH 5.6±1.3 4.7-6.56 5.8±4.03 2.6-12.8 1.9±1.3 0-3.75 1.53±1.3 0-3.55 β-HCH 11.9±1.8 10.7-13.2 7.65±2.4 5-11.2 3.8±2.4 0-7.4 2.6±3.9 0-8.6 γ-HCH 4.2±0.45 3.9-4.6 4.37±1.35 2.4-6.1 4.3±2.2 1.6-9.5 2.6±1.16 0-4.4 δ-HCH 2.7±0.77 2.2-3.3 0.82±0.65 0.5-1.85 1.05±1.3 0-3.4 0.64±0.8 0-2 ∑HCHs 24.6±1.7 23.4-25.8 18.6±5.6 11.4-27.7 11.1±4.5 3.0-17.3 7.4±5.9 0.86-16.1 HCB 6±3.2 3.7-8.32 5.06±3.7 1.43-12.25 1.5±1.8 0-5.7 1.49±1.46 0-3.8 Heptachlor 8.4±5.5 4.5-12.3 14.6±6.5 5.71-23.4 8.4±5.1 0-16.6 8.5±8.7 0-19.7 Heptachlor-epoxide 2.8±2 1.4-4.2 6.8±7.2 0.4-17.5 0.99±1.2 0-3.15 2.4±2.4 0-5.8 Aldrin 3.2±2.8 1.2-5.3 2.44±3.4 1.8-8.9 2.6±2.7 0-8.1 N.D. N.D. Dieldrin N.D. N.D. 8.3±8.24 1.2-18.5 7.4±8.4 0-19.5 0.8±1 0-2.3 Endrin 7.5±4.9 4.1-11 1.97±2.3 0-5.1 1.06±1.9 0-5.9 0.76±1.3 0-2.7 α-Endo N.D. N.D. 1.55±1.85 0-4.9 1.01±1.6 0-4.9 0.97±1.3 0-2.8 β-Endo 1.6±2.29 N.D.-3.2 4±5.5 0.4-14.8 1.5±1.3 0-3.4 2.08±1.9 0-5.2 Endo-sulphate 13.8±9.2 7.3-20.3 7.3±4 2.7-13.3 1.8±1.7 0-4.8 2.7±2.7 0-6.6 Methoxychlor 9.3±5.9 5.1-13.5 1.33±0.64 0.56-2.2 2.2±2.9 0-8.1 1.7±2.1 0-5.4 ∑ Other OCPs 53±35.9 27.6-78.5 53.6±24 23.2-82.7 28.7±17.3 9.3-58.6 21.5±10 0-30.8 Cis-chlordane (CC) 3.4±2.17 1.9-5 3.1±3 0-8.2 0.96±0.8 0-1.9 1.2±1.04 0-2.6 Trans-chlordane (TC) 8.76±5 5.1-12.3 7.7±3.7 2.4-13.7 2.61±1.7 0-5.1 3.6±2.6 0-7 non-TC 8±5.32 4.2-11.7 3.4±0.85 2.4-4.8 1.6±1.5 0-5.1 2.8±3.1 0-7.5 p, p'-DDE 61.3±27.7 41.7-80.9 31.5±28.9 6.9-82.4 9.5±7.1 0-23.1 10.8±10.2 0-30.1 o, p'-DDE 1.7±0.05 1.7-1.8 2.4±2.9 0-7.6 1.2±1.6 0-4.6 0.23±0.4 0-1.3 o, p'-DDD 9.9±3.4 7.5-12.4 2.97±3 0.75-8.9 1.6±2 0-6.6 1.3±2.3 0-6.4 p, p'-DDD 45.6±24.3 28.4-62 27.9±28.7 1.7-84.2 10.3±4.3 3.8-17.3 7.4±4.5 2-14.9 o, p'-DDT N.D. N.D. 5.7±9.8 0.58-25.6 2.8±3.1 0-8.7 1.2±1.06 0-2.8 p, p'-DDT 20.6±21.2 5.6-35.6 18.5±12.1 2.8-37.8 7.6±7.9 0-26.8 5.7±7.4 0-21.7 ∑PCBs 56.2±3.23 53.9-58.5 24.9±6.2 13.8-31.5 18.6±1.3 17-21.2 13.7±1.8 10.7-15.91
Appendices Page 227
CirriculumVitea: Syed Ali Musstjab Akber Shah Eqani (Ph.D Scholar). E-mail:[email protected]; [email protected]. Phone No: +92-331-5176512. Address: Environmental and Ecotoxicology Laboratory, Department of Environmental Sciences, Quaid-I-Azam University, Islamabad.
Research Interests; Fate and behavior of organic chemicals; primarily in aquatic environments.Bioaccumulation of POPs in the aquatic food web.Trends in environmental POPs contamination and the implications for sources.Biological exposure to organic chemicals.
Research Experience;
1. 2005-2006: Research Fellow, Land Resources Research Programme, NARC, Islamabad. 2. 2006-2011: PhD. Fellow at Quaid-I-Azam University, Islamabad. 3. 2008-2009: Visiting Research Fellow, Ecotoxicology Research Programme, NARC, Islamabad. 4. July, 2009-Dec, 2009 Visiting Research Fellow, Chinese Academy of Sciences, China. 5. Sep, 2011-Present: Visiting Faculty; Department of Environmental Sciences, QAU, Islamabad.
Current Research Project and its Impact; This study “Status of POPs in the riverine ecosystem of Pakistan“ is conducted in a region with almost no data on organic contaminants. It is the very first research activity, which report persistent organic pollutants (POPs) levels from the Chenab River, Pakistan, a major tributary of the Indus River, associated with >100 million inhabitants. The results provide insights on OCPs, PCBs, PBDEs, PAHs concentrations, possible sources, their seasonal variation and risk assessment. Finally, the importance of current study is highlighted by the magnitude of the total annual fluxes of POPs from the Chenab to the Indus River.
Research Publications;
[1]. S Farooq, SAMAS Eqani, RN Malik, A Katsoyiannis, G Zhang,Y Zhang, J Li, L Xiang, KC Jones ZKShinwari (2011). Occurrence, finger printing and ecological risk assessment of polycyclic aromatic hydrocarbons (PAHs) in the Chenab River, Pakistan. Journal of Environmental Mointoring, 13: 3207-3215.
Appendices Page 228
[2]. RN Malik, S Rauf, A Mohammad, SAMAS Eqani, K Ahad (2011). Organochlorine residual concentrations in cattle egret from the Punjab Province, Pakistan. Environmenal Monitoring and Assessment,173: 325-341.
[3].SAMAS Eqani, RN Malik, A Mohammad (2011). The level and distribution of selected organochlorine pesticides in the sediments from River Chenab, Pakistan. Environmental Geochemistry and Health, 33: 33-47.
[4].SAMAS Eqani, RN Malik, G Zhang, A Mohammad, P Chakraborty (2012). Polychlorinated Biphenyls (PCBs) in the Sediments of the River Chenab, Pakistan: Current levels and Their Toxicological Concerns. Chemistry and Ecology, [GCHE-2011-0155, Accepted].
[5].SAMAS Eqani, RN Malik, A Alamdar, H Faheem. Status of Organochlorine Contaminants in the different environmental compartments of Pakistan: A Review on Occurrence and levels (2011). Bulletin Environmental Contamination and Toxicology, DOI: 10.1007/s00128-011-0496-4.
[6].SAMAS Eqani, RN Malik, A Katsoyiannis, G Zhang, P Chakraborty, A Mohammad and KC Jones (2012). Distribution and Risk Assessment of Organochlorine Contaminants in Surface Water from River Chenab, Pakistan. Journal of Environmental Mointoring, 14: 1645-1654..
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