HEAVY METAL FRACTIONATION IN RIVEER SEDIMENTS OF CAUVERY DELTA REGION

Thesis submitted to

Bharathidasan University for the award of the Degree of

DOCTOR OF PHILOSOPHY IN ENVIRONMENTAL MANAGEMENT

By

Mr. S. DHANAKUMAR

Under the Supervision of Dr. R. MOHANRAJ

DEPARTMENT OF ENVIRONMENTAL MANAGEMENT SCHOOL OF ENVIRONMENTAL SCIENCES BHARATHIDASAN UNIVERSITY – 620 024 TAMILNADU

MAY – 2012

Department of Environmental Management School of Environmental Sciences Bharathidasan University Tiruchirappalli- 620 024 , India

Dr. R. MOHANRAJ E-mail : [email protected] Assistant Professor Phone : 91-431-2407071 Extn: 640

CERTIFICATE

This is to certify that this thesis entitled “HEAVY METAL FRACTIONATION IN RIVER SEDIMENTS OF CAUVERY DELTA REGION” submitted by Mr. S. DHANAKUMAR, for the ddegree of Doctor of Philosophy to the Bharathidasan University is based on the results of studies carried out by him under my supervision and guidance. This thesis or any part thereof has not been submitted elsewhere for any other degree.

Place: Tiruchirappalli

Date: (R. MOHANRAJ)

DECLARATION

I do hereby declare that this work has been originally carried out by me under the supervision and guidance of Dr. R. Mohanraj, Assistant Professor, Department of Environmental Management, Bharathidasan University, Tiruchirappalli and this work has not been submitted elsewhere for any other degree, diploma or other similar titles.

Place: Tiruchirappalli

Date : (S. DHANAKUMAR)

ACKNOWLEDGEMENT

My foremost thanks and offerings are due to the Insurmountable, Invincible and Irrepressible power of the Almighty for being an able ally and helping me to cross many boundaries to reach the present position. I hope it would continue to serve as a beacon in all my future endeavors.

I would like to express my sincere gratitude to my Research Supervisor Dr. R. Mohanraj, Assistant Professor, Department of Environmental Management for providing me an opportunity to do research under his supervision, mentoring and being a constant source of inspiration for me throughout the research period. It is an honor to be his first Ph.D. student and the intellectual inputs and training imparted by him during my stint under his guidance has resolved me to face my future career with confidence. His methodological approach has imbibed in me the quality to work in various research aspects with clarity and execute autonomously.

My profound sense of thanks is to Dr. M. Ravichandran, Professor and Head, Department of Environmental Management for the care and enthusiasm he has shown for my research which was the motivational factor, even during tough times in the Ph.D. pursuit.

I express my gratitude to my doctoral committee member Dr. N.D. Shrinithivihahshini for her moral support and timely help throughout my research period. I would like to thank Dr. M. Prashanthi Devi for lending a helping hand by valuable inputs, discussions and motivation during the course of the study.

Also I would like to express my sincere thanks to Dr. M. Krishnan, Chair, School of Environmental Sciences for his timing help to my research work. My deep sense of gratitude are due to Dr. K. Kumaraswamy, Professor, Dept of Geography, Bharathidasan University, Dr. R. Babu Rajendran, Associate Professor, Dr. M. Govindaraju, Assistant Professor, Department of Environmental Biotechnology for the support rendered by them in completion of my research work.

I would like to thank Dr. R. Arthur James, Assistant Professor, Department of Marine Science, Dr. C. Lakshumanan, Assistant Professor, Center for Remote sensing, Dr. S. Rajakumar, Assistant Professor, Department of Marine Biotechnology, Dr. N. Jawahar Raj, Assistant Professor, Department of Geology, National College, Tiruchirappalli for their timely support, motivation and care during the study period.

I am grateful to Dr. P. Lakshmanaperumalsamy, Registrar, Karpagam University, Coimbatore, Dr. M. Usharani, Professor and Head, Department of Environmental Sciences, Bharathiar University, Dr. M. Muthukumar, Assistant Professor, Department of Environmental Sciences, Bharathiar University, Coimbatore, Dr. K. Kalaivani, Associate Professor, Department of Biochemistry, Dr. K. Krishnamurthy, Associate Professor, Department of Chemistry, Kongunadu Arts and Science College, Coimbatore for their outstanding teaching skills which motivated me to carryout higher studies.

I feel worthy to record my thanks to Dr. S. Pattabhi, Professor and Head, Department of Environmental Sciences, PSG College, Coimbatore, Dr. U. Mani and Dr. R.C. Murthy, IITR, Lucknow for extending their instrumental facilities and valuable inputs.

I feel wordless to thank my academic brothers Dr. S. Somasundaram, Dr. K. Thangaraj for their constructive role in my education career since from under graduation. I would like to extend my sincere thanks to my seniors Dr. M.V. Sureshkumar, Dr. A. Kannan, Dr. A. Noorjahan, Dr. A. Williams, Dr. V.R. Muthukumaran, Dr. C. Kanagaraj, Dr. K. Padmanaban, Dr. S. Karthik, Mr. Kishore for their valuable inputs and suggestions.

I have no word to express my greatness to Mr. K. Rutharvel Murthy, Research Scholar, Department of Geography for churning out moments of scholarly discussion, inputs, memorable support and being alongside by me during on and off field happenings during this research work.

My thanks to Mr. Veeramani, Mr. Seenivasan, Mr. Faize, Mr. Solaraj, Mr. Sirajuddin, Mr. Arivoli, Mrs. Manjula Menon, Ms. Brindha, Ms. Vidyalakshmi, Ms. Chitra Devi, Mr. Balamurugan, Ms. Sheela Mary, Mr. Saravanan, Mr. Mahamuni, Ms. Nithya and Mr. Viji Research Scholars, Department of Environmental Management, Bharathidasan University for their help and cooperation.

Special thanks are due to my pals Mr. Ganeshkumar, Mr. Vivek, Mr. Dass Prakash, Mr. Krishnakumar, Mr. Pandiyarajan, Mr. G. Shivaprabhu, Mr. Prabharkaran, Mr. Rajesh, Mr. Vignesh, Mr. Muthukumar, Mr. Rameshkumar, Mr. Karthikeyan, Mr. Kanagaraj, Mr. Srimurali, Mr. Krishnakumar, Mr. Puhalendi, Mr. Suryakumar, Ms. Machlein Rani, Mrs. Jayalakshmi and Mrs. Dhanamani for being the Stress-busters and staying by me through the thick and thin of things. Thanks to them for transforming moments of uncertainties and sulkiness into uplifting ones.

I would like to express my sincere thanks to MBA students, Mr. Suresh, Mr. Hariram, Mr. Manikannan, Mr. Alwin, Mr. Sudhakar, Mr. Pandiyarajan, Mr. Ramkumar, Ms. Buguna, Mr. Elencharan, Mr. Pounraj, Mr. Benedict, Mr. Anjalin, Mr. Arun Shankar and Mr. Anbarasan for their timely help.

I take this opportunity to thank Mr. Kumaraesan, Mrs. Chitra, Mr. Thirunavukarsu, Mr. Thiyagarajan, Non-teaching staff members, Department of Environmental Management for their official assistance throughout the research work. Also I would like to mention the financial assistance provided by the Bharathidasan University in the form of University Research Fellowship during my tenure of the study and thank them profoundly for the timely help.

I am at loss of words to describe the support extended by my family members. But for the unflinching faith and unbridled love reposed on me by my parents, Mr. R. Selvaraj and Mrs. S. Kalavathi, my brother Mr. Sivakumar, my sister Mrs. Leelavathi, my brother in law Mr. Kuppuraj, and cuties R.K. Praveenkumar and S. Dhanavarshini. I am sure that I would not have had the strength to complete the task successfully. I thank them for their patience and bearing with me for shunning most of the domestic responsibilities during this period. Finally I would like to thank everyone who had helped me in macro and micro level all along my research period.

S. DHANAKUMAR

DDeeddiiccaatteedd ttoo mmyy FFaammiillyy aanndd MMeennttoorrss

CONTENTS

CHAPTER 1 General introduction 1.1. River basin degradation and pollution 1 1.2. Metal chemistry in sediments 3 1.3. Metal toxicity 7 1.4. Ecological effects 8 1.5. Objective of the current study 9 1.6. Structure of the thesis 9 CHAPTER 2 Study area 2.1. Background 10 2.2. 10 2.3. 11 2.4. Amaravathi River 11 2.5. Cauvery delta region 12 2.6. Geology 13 2.7. Climate 14 2.8. Rainfall 14 2.9. Water flow 15 2.10.Temperature 16 2.11.Agriculture 17 2.12. Sampling locations for the current study 18 CHAPTER-3 Surface water quality of River Cauvery in delta region 3.1. Background 25 3.1.1. Surface water in Indian context 25 3.1.2. Surface water resources and economy 27 3.1.3. Surface water scarcity 27 3.1.4. Surface water pollution 28 3.1.5. Water quality in River Cauvery and its tributaries 33 3.2. Materials and Methods 35

3.3. Results and discussion 3.3.1. pH, Electrical conductivity and total solids 36 3.3.2. Major anions and fluoride 37 3.3.3. Major cations 39 3.3.4. Dissolved oxygen and organic load 40 3.3.5. Nutrients 41 3.3.6. Correlation analysis 42 3.3.7. Factor analysis 3.3.7.1. Dry season 45 3.3.7.2. Wet season 47 3.3.8. Cluster analysis 49 3.4. Conclusion 49

CHAPTER-4 Surface sediment characteristics and heavy metal distribution in River Cauvery in delta region 4.1. Background 51 4.1.1. Ecological significance of river sediments 52 4.1.2. Sediment dynamics and Pollution 53 4.1.3. Heavy metals in sediments 55 4.1.4. Heavy metal distribution in Cauvery delta region 59 4.2. Materials and methods 61 4.3. Results and discussion 4.3.1. Sediment characteristics 61 4.3.2. Organic carbon 62 4.3.3. Nutrients 67 4.3.4. Nutrient ratios 68 4.3.5. Heavy metal distribution in surface sediments 70 4.3.6. Assessment of risk by the presence of heavy metals 4.3.6.1. Sediment quality guidelines 75 4.3.6.2. Geo accumulation Index (Igeo) 76 4.3.7. Principal component analysis 77 4.4. Conclusion 78

Chapter-5 Heavy metal fractionation in surface sediments of River Cauvery in delta region 5.1. Background 79 5.1.1. Metal speciation and fractionation 80 5.1.2. Sequential extraction scheme 83 5.1.3. Role of metal fractionation studies on source 84 apportionment 5.1.4. Metal fractionation in aquatic sediments and its 85 influencing factors 5.1.5. Status in India 86 5.1.6. Metal fractionation studies in Cauvery River in delta 89 regions 5.2. Materials and methods 5.2.1. Metal fractionation analysis 89 5.2.2. Assessment of risk assessment code and mobility factor 93 5.3. Iron fractionation 5.3.1. Background 93 5.3.2. Fractionation of Iron in River Cauvery in delta region 94 5.4. Manganese fractionation 5.4.1. Background 98 5.4.2. Fractionation of Manganese in River Cauvery in delta 98 region 5.5. Copper fractionation 5.5.1. Background 103 5.5.2. Fractionation of Copper in River Cauvery in delta 104 region 5.6. Chromium fractionation 5.6.1. Background 109 5.6.2. Fractionation of Chromium in River Cauvery in delta 110 region 5.7. Zinc fractionation 5.7.1. Background 115 5.7.2. Fractionation of Zinc in River Cauvery in delta region 116 5.8. Lead fractionation 5.8.1. Background 121 5.8.2. Fractionation of Lead in River Cauvery in delta region 122 5.9. Nickel fractionation 5.9.1. Background 126 5.9.2. Fractionation of Nickel in River Cauvery in delta region 127 CHAPTER-6 Bioaccumulation of metals in edible fishes of select reservoirs in Cauvery delta region 6.1. Background 132 6.1.1. Bioaccumulation of heavy metals in fish population 134 6.1.2. Bioaccumulation studies in various freshwater and 137 marine environment of India 6.2. Materials and methods 6.2.1 Sampling of sediments and fish 141 6.2.2. Metal fractionation 142 6.3. Results and discussion 6.3.1. Physico-chemical characteristics of reservoir sediments 143 6.3.2. Heavy metal concentration in surface water 143 6.3.3. Heavy metal fractionation in Reservoir sediments 144 6.3.4. Heavy metals content in fish 147 6.3.5. Metal fractionation and bioaccumulation 149 6.4. Conclusion 150

7. Summary and Conclusion 151 References

LIST OF TABLES

Table 2.1. Rainfall during different seasons in Cauvery delta region (1979- 15 2009) Table 2.2. Sampling site characteristics 20 Table 3.1. BOD levels in various rivers of India 32 Table 3.2. Comparison of some physico-chemical parameters of other rivers 33 with present observations Table 3.3 Surface water quality in River Cauvery and its tributaries 34 Table 3.4. Surface water quality characteristics of River Cauvery in delta 37 and Estuarine region during dry seasons (both 2008–2009 and 2009–2010). Table 3.5. Surface water quality characteristics of River Cauvery in delta 38 and Estuarine region during wet seasons (both 2008–2009 and 2009–2010). Table 3.6. Correlation between physico-chemical characteristics of surface 43 water in Cauvery delta during dry season Table 3.7. Correlation between physico-chemical characteristics of surface 44 water in Cauvery delta during wet season Table 3.8. Total variance explained in dry season 46 Table 3.9. Rotated component matrix for dry season 46 Table 3.10. Total variance explained in wet season 48 Table 3.11. Rotated component matrix for wet season 48 Table 4.1. Heavy metal distribution in River Cauvery and its tributaries 59 Table 4.2. Comparison of organic carbon and total nitrogen level in the 63 sediments of various aquatic environments. Table 4.3. Correlation analysis for heavy metal concentration in Cauvery 74 delta region Table 4.4. Classification of sediment quality guidelines (SQGs) and its 75 effects Table 4.5. Comparison between sediment quality guidelines (SQGs) and 75 heavy metals concentration (µg/g) in the present study Table 4.6. Geo accumulation Index (Igeo) for total heavy metal distribution 77 in sediments Table 4.7. Total variance explained 77 Table 5.1.1. Metal fractionation studies in various aquatic environments of 86 India Table 5.2.1. List of Sampling sites and its landuse patterns 90 Table 5.3.1. Analysis of variance of Iron fractionation in various comparisons 95 Table 5.3.2. Correlation matrix for Iron fractionation profile in Cauvery delta 96 regions Table 5.3.3. Percentage of samples fall under various classes as per risk 97 assessment code and summary of mobility factor assessment for Fe fractionation Table 5.4.1. Analysis of variance of Manganese fractionation in various 100 comparisons Table 5.4.2. Correlation matrix for Manganese fractionation profile in 101 Cauvery delta regions Table 5.4.3. Percentage of samples fall under various classes as per risk 102 assessment code and summary of mobility factor assessment for Manganese fractionation Table 5.5.1. Analysis of variance of Copper fractionation in various 105 comparisons Table 5.5.2. Correlation matrix for Copper fractionation profile in Cauvery 107 delta regions Table 5.5.3. Percentage of samples falls under various classes as per risk 108 assessment code and summary of mobility factor assessment for Copper fractionation Table 5.6.1. Analysis of variance of Chromium fractionation in various 111 comparisons Table 5.6.2. Correlation matrix for Chromium fractionation profile in 113 Cauvery delta regions Table 5.6.3. Percentage of samples fall under various classes as per risk 114 assessment code and summary of mobility factor assessment for Chromium fractionation Table 5.7.1. Analysis of variance of Zinc fractionation in various comparisons 117 Table 5.7.2. Correlation matrix for Zinc fractionation profile in Cauvery delta 119 regions Table 5.7.3. Percentage of samples fall under various classes as per risk 120 assessment code and summary of mobility factor assessment for Zinc fractionation Table 5.8.1. Analysis of variance of Lead fractionation in various 123 comparisons Table 5.8.2. Correlation matrix for Lead fractionation profile in Cauvery delta 125 regions

Table 5.8.3. Percentage of samples fall under various classes as per risk 126 assessment code and summary of mobility factor assessment for Lead fractionation Table 5.9.1. Analysis of variance of Nickel fractionation in various 128 comparisons Table 5.9.2. Correlation matrix for Nickel fractionation profile in Cauvery 129 delta regions Table 5.9.3. Percentage of samples fall under various classes as per risk 130 assessment code and summary of mobility factor assessment for Nickel fractionation Table 6.1. Bioaccumulation of metals in various lakes, rivers and marine 137 environment Table 6.2. Details of sequential extraction scheme followed in the present 142 study Table 6.3. Physico-chemical characteristics of reservoir sediments 143 Table 6.4. Concentration of metals in surface water 144 Table 6.5. Concentration of heavy metals (µg/g) in sum of all fractions 145 Table 6.6. Risk Assessment Code (RAC) 146 Table 6.7. Concentration of heavy metals in various tissue systems of six 148 select fish species Table 6.8. Correlation between metal accumulation in various fish organs 149 and metal fractionation in sediments

LIST OF FIGURES

Figure 1.1. Various forms of metal species in the environment 4 Figure 1.2. Environmental fate and bioavailability of metals 6 Figure 2.1. Study area – Cauvery delta region 12 Figure 2.2. Annual rainfall trend in Cauvery Delta Region 15 Figure 2.3. Water flow rate in River Cauvery at Musiri 16 Figure 2.4. Annual agricultural yield in Cauvery delta during last 25 years 17 (1985 to 2009) Figure 2.5. Sampling location map 19 Figure 3.1. Human activities across the River Basin 26 Figure 3.2. Cluster analysis for surface water quality of River Cauvery in 49 delta region Figure 4.1. Sediment quality and quantity: interfacing systems 52 Figure 4.2. Spatial and temporal variation of A) Total organic carbon B) 64 Total phosphorous and C) Total nitrogen in sediment samples of River Cauvery in delta region Figure 4.3. Spatial and temporal variation of C:N and N:P ratio in river 65 sediments of CDR during A) Dry period (2008) B) Wet period (2009). Figure 4.4. Spatial and temporal variation of C:N and N:P ratio in river 65 sediments of CDR during A) Dry period (2009) B) Wet period (2010). Figure 4.5. Spatial and temporal variation of total available phosphorus 66 and C: P ratio in river sediments of CDR during A) Dry period (2008), B) Wet period (2009). Figure 4.6. Spatial and temporal variation of total available phosphorus 66 and C: P ratio in river sediments of CDR during A) Dry period (2009), B) Wet period (2010).

Figure 4.7. Total heavy metal distribution in sediments of River Cauvery 72 in delta region A) Distribution of various heavy metals during dry season B) Distribution of various heavy metals during wet season C) Fe and Mn concentration during dry season (Fe-D and Mn- D) and wet season (Fe-W and Mn-W) Figure 4.8. Factor loadings of principal component analysis 78 Figure 5.1.1. Common associations of metals in sediments 82 Figure 5.1.2. Relationship between metal mobility in the different 83 operationally-defined phases and leachant strength of common chemical reagents used for sequential extraction Figure 5.2.1. Study area- River Cauvery in delta region with sample 90 locations Figure 5.2.2. Flow chart of the sequential extraction scheme followed in the 92 present study Figure 5.3.1. Iron fractionation in river sediments of Cauvery delta region 94 Figure 5.4.1. Manganese fractionation in river sediments of Cauvery delta 99 region

Figure 5.5.1. Copper fractionation in river sediments of Cauvery delta 104 region Figure 5.6.1. Chromium fractionation in river sediments of Cauvery delta 110 region Figure 5.7.1. Zinc fractionation in river sediments of Cauvery delta region 116 Figure 5.8.1. Lead fractionation in river sediments of Cauvery delta region 122 Figure 5.9.1. Nickel fractionation in river sediments of Cauvery delta region 127 Figure 6.1. Study area (S1) Upper Anicut (S2) Grand Anicut 141 (S3) Anaikarai Figure 6.2. Metal fractionation in reservoir sediments 146

LIST OF PLATES

Plate 1. River Cauvery at Musiri 22 Plate 2. Sewage discharge into River Cauvery at Tiruchirappalli 22 Mambala Salai Plate 3. River Cauvery at Grand Anicut 23 Plate 4. River Coleroon at Anaikarai 23 Plate 5. River Cauvery at Killiyur 24 Plate 6. Vettar Estuary at Nagore 24

LIST OF ABBREVIATIONS

AAS Atomic Absorption Spectrophotometer ANOVA Analysis of Variance APHA American Public Health Association ATSDR Agency for Toxic Substances and Disease Registry BIS Bureau of Indian Standard BOD Biochemical Oxygen Demand COD Chemical Oxygen Demand CPCB Central Pollution Control Board DO Dissolved Oxygen DOM Dissolve Organic Matter EC Electrical Conductivity FAO Food and Agriculture Organization GDP Gross Domestic Product IARC International Agency for Research on Cancer Igeo Geoaccumulation Index IGES Institute for Global Environmental Strategies mg/kg Milligram/Kilogram mg/l Milligram/Liter NYSDEC New York State Department of Environmental Conservation PCA Principal Component Analysis SD Standard Deviation SOM Soil Organic Matter TAP Total Available Phosphorous TDS Total Dissolved Solids TH Total Hardness TN Total Nitrogen TOC Total Organic Carbon TOM Total Organic Matter TP Total Phosphorous UN -WWAP United Nations World Water Assessment Programme US-EPA United States Environmental Protection Agency WHO World health Organizations

Chapter -1 General Introduction

1.1. River basin degradation and pollution During the last half century, Indian economy has witnessed growth, intensive agriculture, rapid urbanisation, and industrialisation. Paradigmatic shift in economic policies and globalization had further fueled the growth. Although India is currently the world's 12th largest economy (and the third largest in Asia behind Japan and China) with a total GDP of around USD 1 trillion, some have predicted that by 2050 India will be the third largest economy in the world (IGES 2010). All these drivers of economic growth have affected the rivers system in different ways. In most of the Indian River basins, hundreds of multi-purpose projects have been completed for water supply, irrigation, hydropower, fisheries development and water diversion (Agrawal and Chak 1991). Many floodplains have been cut out from rivers by embankments and remaining riparian lands are under intensive agriculture and grazing pressure. Human settlements, deforestation, mining and other activities have degraded the river catchments and increased sediment loads of all rivers. In last few decades, rivers have received increasingly large discharges of industrial effluents, fertilizers and pesticides from agricultural practices and domestic wastes (CPCB 1996).

Evidently, due to their extensive utilisation and long term development activities, nine Indian River basins including Cauvery, -Brahmaputra, Godavari, Indus, Krishna, Mahanadi, Narmada, Pennar and Tapi were selected under global level priorities for the protection of aquatic biodiversity (Groombridge and Jenkins 1998). Among them, except Ganges-Brahmaputra all the above basins have also been categorized as “strongly affected” by flow fragmentation and regulation (Nilsson et al. 2005). Conservation and restoration of rivers have become vital for the overall sustainable development of the country. Ironically, conservation of river basins has been limited to cleaning of rivers by treatment of wastewater, occasional symbolic removal of garbage and enforcing the treatment of industrial effluents (Gopal and Chauhan 2003). Since, the ecological integrity of the river basins highly depends upon their physical, chemical, biological characteristics and interactions with their

1 catchment. These efforts have not yielded any major improvements in the river basin quality.

The majority of pollutants discharged into any aquatic environment eventually end up in sediments. Sediments are ecologically imperative components of the aquatic habitat which play a significant role in maintaining the trophic status of any water body (Singh et al. 1999). Pollutants in water column have strong affinity for the particulate phase, which facilities their elimination from the water column and their accumulation in sediments (Ramamoorthy and Rust 1978). In the aquatic environment, less than 0.1% of the contaminants are actually dissolved in the water and more than 99.9% are stored in sediments due to its high storage capacity (Salomons 1998). Because of their accumulating potential sediments are known as a sinks; however in response to changing environmental conditions it also can act as source of secondary pollution when pollutants release back into water column from sediment column (Forstner and Wittmann 1983). Therefore, sediments can be considered as a heterogeneous mixture of different organic and inorganic components. In this context, sediment analysis helps in the understanding of pollution effect as the residence time of pollutants in sediment of impacted area is long.

Among the inorganic pollutants in water and sediments, heavy metals are ubiquitous in nature and gaining scientific interest due to its unique characteristics such as high reactivity, lithophilic nature, toxicity and non-biodegradability. The term “heavy metal” refers to any metallic chemical element that has a relatively high density and is toxic at low concentrations. Examples of heavy metals include mercury (Hg), cadmium (Cd), arsenic (As), chromium (Cr), and lead (Pb). These pollutants are metallic elements that have a relatively high density and are toxic even at low concentration (Duruibe et al. 2007). Heavy metals are natural constituents of the freshwater environment, generally found in very low concentrations due to geogenic process including weathering and erosion. Apart from natural sources, anthropogenic activities in recent decades have lead to elevated levels of these pollutants causing environmental toxic concerns. Anthropogenic sources of heavy metals includes emissions from industries (electroplating, metal processing, tanneries, automobile,

2 foundries, petrochemical processing, pesticide and fertilizer manufacturing industries), untreated or partially treated urban sewage, agrochemicals (pesticides and fertilizers), atmospheric deposition includes vehicular emissions and airborne suspended particulate matter are significant sources of these pollutants in fresh water environment (Okoye et al. 1991; Kabata-Pendias and Pendias 1992; Mwamburi 2003; Sarkar et al. 2004; Singh et al. 2005c; Sharmin et al. 2010; Liu et al. 2011b).

Some of the metals (e.g., copper, selenium and zinc) are nutritionally essential to maintain the metabolism of the human body elements at low levels but toxic at higher levels, and others (e.g., lead, arsenic and mercury) have no known biological functions. These elements are widespread pollutants of environmental concern as they are nondegradable, toxic and persistent with serious ecological ramifications on aquatic ecology (Aboud and Nandini 2009). Heavy metals may cause toxic effects such as carcinogenic, endocrine disruption and non-carcinogenic effects on human and animal population by various biochemical processes. Because of its high toxic nature it has been classified under priority pollutants category by the US Environmental Protection Agency and Agency for Toxic Substances and Disease Registry (ATSDR). Environmental Protection Agency’s “Top 20 Hazardous Substance Priority List” has ranked arsenic as 1st, mercury 6th, cadmium 7th, chromium 8th, and nickel 13th in their list. In this regard, environmental monitoring of heavy metals proves vital in the decision making and establishment of standard levels of particular metal in the environmental matrix.

1.2. Metal chemistry in sediments Heavy metals take part in biological and geochemical cycles and are not permanently fixed to soils. They are usually present in different chemical forms; 1) easily exchangeable ions 2) metal carbonates 3) oxides, 4) sulphides, 5) organometallics compounds, and 6) ions in crystal lattices of mineral. Heavy metals are bound to different phases of soil particles by a variety of mechanisms, such as absorption, ion exchange, co-precipitation and complexation. In addition, soil properties such as contents of organic matter, carbonates, oxides as well as soil structure and profile development influence the heavy metal mobility (Kabata-Pendias 2001). The knowledge of the binding of metals with the different soil phases and

3 components is of major interest to assess the connections with toxicity. The concentrations of heavy metals in soils and their mobility within different chemical fractions are influenced by anthropogenic activities, such as mining, agricultural practices, transport, industrial activities, waste disposal.

Due to its highly reactive nature they are more quickly converted into other species depending upon the characteristics of the receiving environment (Khan 2001). As hinted earlier, metals from natural and manmade sources transported to river system both in the form of suspended particles or dissolved form are subsequentlly removed from the water column by precipitation, adsorbed onto particulate matter and biota and deposited in bottom sediments (Loska and Wiechula 2003). In the aqueous phase, metal can react/bind with dissolved ligands according to the pH, Eh, ionic strength, and abundance of ligands (Miao et al. 2006). Whereas, sediment phase reactions are mainly controlled by Fe-Mn oxyhydroxides that exist as colloids, surface coatings of particles and organic matter. As a result of redox chemistry, metals can undergo seasonal redox- driven cycling between the water column and sediments or within the sediments, depending on the position of the redoxycline. Metals in the aquatic environment may exist in form of different species and its each metal species are considered as a specific form of an element defined as to isotopic composition, electronic or oxidation state, complex or molecular structure and phase (Templeton et al. 2000) (Figure 1.1).

Figure 1.1. Various forms of metal species in the environment (Source: www.ecotest.univ.kiev.ua)

4 The total concentration of a given metal in sediment phase is equal to the summation of the concentrations of its free ion, complexes and metal associated with various organic and inorganic legends. The mobility, bioavailability and toxicity of metals highly depend on the chemical forms of a metal existing in the environmental compartment. Conventional analysis of total metal concentrations is insufficient in information about toxicity and bioavailability of the metals (Tessier et al. 1979). The concept of elemental speciation dates back to 1954, when Goldberg introduced the concept of oxidized versus reduced, chelated versus free metal ions of trace elements in seawater (Jain 2004). Since then speciation studies attracted worldwide environmentalists and chemists and became very prominent when Tessier et al (1979) described the sequential extraction analytical procedure for heavy metals.

Speciation analysis is an analytical activity focused to identify and/or measure the quantities of one or more individual chemical species in a particular sample or matrix (Templeton et al. 2000). Although, importance of speciation analysis are well understood, in some cases metal species present in the environment are not stable enough to be determines in the original form and analytical procedures influenced species changes also affects the accuracy of measurement and fails to imitate the metal species present in the environment. Even, in many cases the large numbers of individual species (e.g., in metal-humic acid complexes) makes it impossible to determine the speciation. In this regard, the new practice has emerged to identify various classes of species of an element and to determine the sum of its concentrations in each class. Identification and quantification of metals associated with predefined phases was termed as "metal fractionation” where metal portioning was carried out based on its physical and chemical characteristics (Templeton et al. 2000). Fractionation studies are carried out through sequential extractions of operationally defined metal fractions using specific reagents at various conditions. According to metal fractionation, based on the metal accumulation on sediments metal fractions can be categorized into five classes, viz., adsorptive/exchangeable, bound to carbonate phases, bound to reducible phases (Fe and Mn oxides), bound to organic matter and sulfides, and residual phase (Tessier et al. 1979; Salomons and Forstner 1980). In

5 general, the mobility and bioavailability of metals decrease approximately in the order of sequence (Prusty et al. 1994).

Figure 1.2. Environmental fate and bioavailability of metals (Source: modified Fairbrother et al. 2007)

It is important to know which part of the total concentration of a given metal is available to living organisms as it determines the real toxic effect of the particular metal (Bernhard and Neff 2001). The concept of metal bioavailability is known to as a measure of the potential for entry of a metal into ecological or human receptors and is specific to the receptor, the route of entry, time of exposure, and the environmental matrix containing the metal (Figure 1.2) (Peijnenburg and Jager 2003). Metal bioavailability and bioaccessibility are mainly influenced by numerous factors that includes metal speciation, fractionation, physico-chemical characteristics of water and soil/sediments and etc. The process of accumulation of these metals in plant or animal tissues is called bioaccumulation. Finally, a process called biomagnification takes place whereby metal concentrations increase in organisms of each successive trophic level. Key determinants of metal bioavailability and bioaccumulation are speciation and fractionation. Besides, numerous studies have concluded that the bioavailability of metals is directly correlated to concentrations of free metal ions rather than to total or complexed metal concentrations (Campbell 1995). On the other hand, metal

6 fractionation studies reveals that the exchangeable form of metal fraction is considered as an easily bioavailable one and fractions bound to carbonate, Fe-Mn oxide and organic matter could be relatively active depending on the physical and chemical properties of the medium (Prusty et al. 1994; Yuan et al. 2004).

1.3. Metal toxicity Humans have evolved in the presence of metals and are adapted to various levels of essential and non-essential metals. Metals from dietary intake and environmental exposure eventually reach their target organs (brain, liver and kidney). The fate of metal inside the body is determined by its ability to modify these systems. Excess metals in body are excreted through urine and faeces or accumulated in various tissues. At higher concentrations metals become toxic. Some metals act additively when they are present together, others act independently of each other, and still others are antagonistic or synergistic (Khan 2001). In general, lipophilic metal species are absorbed more readily than hydrophilic ones. Absorption may vary dramatically for different forms of the same metal, for the same form of metal in different matrices, among different species, and across different routes of exposure (Walker et al. 2001). The distribution of metals reflects their transport and accumulation in the body within tissues, blood or plasma, or other extracellular space. Retention in tissues of metals or metal compounds generally is related to formation of inorganic complexes or metal protein complexes (Newman 2010). Metal binding to proteins is capacity-limited, and toxicity to target organs occurs when the binding capacity is exceeded.

Heavy metals toxicity can be categorized into three classes viz., i) non- carcinogenic effects ii) carcinogenic effects iii) endocrine disruption. Metal causes numerous non-carcinogenic effects including liver and kidney damage, hyper sensation, bronchial asthma, contact dermatitis, nausea, vomiting, abdominal cramps, ataxia, paralysis, diarrhea, respiratory and dermal disorders (Alabaster and Lloyd 1980; Reilly 1991; EPA 1999; ATSDR 2005). Some of the metals such as As, Cd, Cr (VI), Be, and Ni are reported as human carcinogens in one form or another or in particular routes of exposure (IARC 2004). Even, few heavy metals (Cd, Hg, As, Pb, Mn, and Zn) can affect the endocrine system by producing alterations in physiological functions.

7 1.4. Ecological effects In developing countries like India, sewage and industrial effluents generated by urban conglomerates generally find their way into streams arid rivers and the contaminated water is often used to irrigate crop lands. Though rich in plant nutrients, the contaminated waters often have high concentrations of metals like Cu, Zn, Pb, Cd, Ni, Cr, etc. Continuous application of metal contaminated irrigation water leads to the built up of toxic concentrations of these metals in soils and get transferred to plants grown on them (Nair and Siddaramappa l999), rendering the edible tissues of these plants unfit for animal or human consumption. Plants can be exposed to metals via aerial deposition onto leaf surfaces, trapping metals in hairs or rough cuticular surfaces. The phytotoxicity of the heavy metals has serious implications in soil degradation (Friedland 1990). This may reduce both the quality and productivity of plants. For example, Zn is an essential element to plants, however at excess Zn concentration it can inhibits plant growth and impairs some important cellular processes. Further, metal contaminated plant species may act as exposure route for herbivores. In general, strong acidic soils increase plant uptake of Zn, Cd, Ni, Mn, and Co and increase the potential for phytotoxicity from Cu, Zn, and Ni (Chaney and Ryan 1993).

Heavy metals show harmful effects even at very low concentration on the aquatic organisms including plankton, aquatic plants, invertebrates and vertebrates (Schuurmann and Markert 1998). The studies carried out on freshwater and marine fishes have shown that heavy metals may alter the physiological activities and biochemical parameters both in tissues and in blood (Canli 1995; Basa and Rani 2003). Episodes such as Minamata have been linked to mercury poisoning in man due to the consumption of contaminated fish. Even, metals in the diet may also alter wildlife behavior patterns. For example, Copper concentration in plants apparently influenced the selection of nest sites by Abert’s squirrels (Snyder and Linhart 1994).

The context of unprecedented increase in river basin pollution and escalating metal toxicity in many parts of India forms basis of this study with following objectives

8 1.5. Objectives of the current study To assess • Surface water quality of River Cauvery in delta region. • Surface sediments characteristics with emphasis on nutrients and total heavy metals distribution. • Heavy metal fractionation in surface sediments (Fe, Mn, Cu, Cr, Zn Pb and Ni). • Influence of physico-chemical parameters (pH, redox potential, organic carbon, etc.) on heavy metal fractionation. • Bioaccumulation of metals in fish species in Cauvery delta reservoirs.

1.6. Structure of the thesis The thesis is structured into 8 chapters. 1st Chapter introduces the topic of river system in India, its degradation and heavy metal pollution. 2nd Chapter describes the Cauvery delta region (study area) including information on geology, meteorology, river water flow and etc. The Chapter 3 provides details about the surface water quality of River Cauvery in delta region with emphasis on spatio-temporal variation with the background information on river water pollution, water scarcity issues, and economic importance. The Chapter 4 addresses the distribution of nutrients and total heavy metal in surface sediments of Cauvery delta region and its variation on spatial as well as temporal scale. The Chapter 5 explains the geochemical fractionation of select heavy metals (Fe, Mn, Cu, Cr, Zn, Pb and Ni) in surface sediments and influences of various physico-chemical factors on metal fractionation. This chapter also relates the metal fractionation with risk assessment code and mobility factor which is important in determining the extent of metal toxicity to biological system. The Chapter 6 focuses metal fractionation in certain reservoir sediments of Cauvery delta region, metal bioaccumulation in fish population of select reservoirs in the delta region and correlates the metal fractionation and metal bioaccumulation. Finally consolidated summary presenting the salient feature of the entire thesis is appended.

9 Chapter-2 Study area – Cauvery Delta Region

2.1. Background River Cauvery is known as "Dakshin Ganga" (the Ganges of the South) and fourth largest river in Indian peninsula. Cauvery river rises at on the Brahmagiri range in the in at an elevation of about 1,341 m and traverses a total length 800 km (320 km is in Karnataka state, 416 km in Tamil Nadu State) and falls into the Bay of Bengal. The River Cauvery is one of the major rivers of the Peninsular India and runs across three of the southern Indian states i.e. Karnataka, Tamil Nadu, and Union Territory of Pondicherry towards the east before it drains into the Bay of Bengal. Cauvery basin extends over an area of 87,900 km2 which is nearly 2.7% of total geographical area of the country. The culturable area of the River basin is about 58,000 km2, which is about three percent of the culturable area of the country. In terms of discharge, River Cauvery stands as a eighth largest river in the Indian subcontinent. The basin receives about 75% of the annual rainfall during monsoon period (July to December) (Vaithiyanathan et al. 1992). An average annual surface water potential of river basin is measured as 21.4 km3. Out of this, 19.0 km3 is utilizable water. Present use of surface water in the basin is 18.0 km3. Live storage capacity in the basin has increased significantly since independence. From just about 4.1 km3 in the pre-plan period, the total live storage capacity of the completed projects has increased to 7.4 km3. Physiographically, the basin can be divided into three parts - the Western Ghats, the Plateau of and the Delta. The important tributaries joining the Cauvery are the Harangi, Hemavati, Kabini, Suvarnavathi, Bhavani, Noyyal and Amaravathi. Among the tributaries, R. Bhavani, R. Noyyal and R. Amaravathi are the notable contributors of River Cauvery in the Tamil Nadu state.

2.2. Bhavani River The Bhavani River is one of the tributaries of Cauvery River in its mid-reach whose sub-basin lies between latitudes 10°56’03” N and 11°46’14” N and longitudes 76°24’41” E and 77°41’11”E. The Bhavani River rises at an altitude of about 2,634 m in the Billimala range of Nilgiri hills in Tamil Nadu. The total drainage area of the river is 6,154 km² and it flows for a distance of 216 km before joining the Cauvery from the

10 right at Bhavani in Coimbatore district. The important tributaries of the Bhavani are the Siruvani, the Kundah, the Coonoor and the Moyar.

2.3. Noyyal River The Noyyal River is a tributary of the River Cauvery and originates from the Vellingiri Hills of the Western Ghats in the Coimbatore district of Tamil Nadu, South India. It lies between latitudes 10o54’ N and 11o19’ N and longitudes 76o39’ E and 77o56’ E. The river flows entirely in Tamil Nadu and the basin is spread over the districts of Coimbatore, Tiruppur and Tiruchirappalli. The area of the basin is 2,999 km2, which constitutes 3.7% of the area of the Cauvery basin. The river has moderate to good flow for a short period during the Northeast and Southwest monsoons and flows over a distance of 180 km in an area of 3,510 km2. Cultivated land in the basin amounts to 1,800 km² while the population density is 120 people per km² in the countryside and 1,000 people per km² in the cities. About 700 dyeing and bleaching industries are located on the banks of river Noyyal river in Tirupur city and it discharges about 7,5000 to 10,0000 m3 of their effluent into the river per day.

2.4. Amaravathi River River Amaravathi is one among the main tributaries of the Cauvery River in its mid reach. It is a right bank tributary next to Noyyal, downstream of dam in Tamil Nadu. The Amaravathi River originates from Naimakad at an elevation of 2,300 m in the Southern Ghat in Devikulam taluk of Iddukki district of Kerala State. A number of streams join the river in Kerala before its entry into Tamil Nadu. The Amaravathi sub-basin lies between latitudes 10o6’ N and 11o2’ N and longitudes 77o3’E and 78o6’ E. It is bounded by Noyil sub-basin in the north, Vaigai basin in south, the southern part of the Western Ghats in the west, and the Cauvery River in the east. Except that part of the upper hilly catchment of the sub-basin which lies in Kerala, the rest of the sub-basin is spread over Tamil Nadu. The area of the basin is 8,280 km2 which constitutes 10.2% of the area of the Cauvery basin.

11 2.5. Cauvery delta region The Cauvery delta region is the most fertile tract in the basin. River Cauvery at downstream of the Grand Anicut (Tiruchirappalli) subdivides itself into two main branches namely, Cauvery and Vennar System, which get further sub-divided into 36 rivers to feed the delta through a network of channels and branches, distributaries and sub-distributaries (Figure 2.1). The area irrigated by the River Cauvery called as old delta and those by the Grand Anicut canal and its branches irrigated area is known as new delta. The Cauvery delta system below upper Anicut covering about 8,000 sq.km has 36 distributaries whose total length is 1607 km and 2988 channels running to a length of 18,395 km (Kandaswamy 1986). The delta region is spread over 6,566 km2 which is 8.09% of the total area of the Cauvery basin. It has a total geographic land area of 14.47 lakh ha which is equivalent to 11.13 percent of the state area and lies in the eastern part of Tamil Nadu between 10o00’-11o30’, North latitude and between 78o15’ – 79o45’ longitude. It is bounded by the Bay of Bengal on the East and the Palk straight on the South, on the west, Perambalur, districts on the North West, district on the North and Puddukkottai district on the South West.

Figure 2.1. Study area – Cauvery delta region

12

The Cauvery flows through the entire districts of , and in different names through its tributaries and branches-Grand Anicut canal, Adapparu, Arasalaru, Ayyanaru, Cholasudamani, Harichandranathi, Kaduvaiyar, Kattar, Kirtimanar, Kodamurtiyar, Koraiyar, Mahimalayaru, Manajalaru, Mudikondan Aru, Mullaiyaaru, Nandalaru, Nattaru, Noolaru, Odambogiyaru, Palavaru, Pamaniyaru, Pandavaiyaru, Pannaiyaru, Putharu, Thirumalairajanaru,Vadavaru, Valapparu, Valavaikkal Aru, Vanjiaru, Veerasozhanaru, Vellaiyaru, Vennaru, Vettaru, Vikaraman Aru and all these branch off into a number of small streams. Some of these tributaries and branches enter the sea while others are lost in the wide expense of the agricultural fields (Madras Science Foundation 2000).

The principal soil types found in the basin are black soils, red soils, laterites, alluvial soils, forest soils and mixed soils. Red soils occupy large areas in the basin. Alluvial soils are found in the delta areas and its thickness extends up to 35 mm in far to the west of Tiruchirappalli in a highly fertile belt. Because of its fertile nature, Cauvery delta region is known for its agricultural productivity and called as a “rice bowl” of southern India (Ramanathan et al. 1993). In this region, two rice crops are typically grown each year in cropping systems of either rice-rice-fallow or rice-rice- pulses, depending on the availability of irrigation water. The first rice crop, with highest yield potential, is cultivated in Kuruvai, the pre-monsoon dry season (June- September), with predominantly short duration varieties. The second rice crop is grown in Thaladi, the wet season (October-February), with medium duration rice varieties. The entire delta is categorised into old and new delta with clay loam to clay soils dominating in old delta and sandy loam to clay loam soils with good drainage characteristics existing in new delta (Rajendran et al. 2010).

2.6. Geology The geology of the Cauvery basin is predominantly formed from Precambrian rocks, principally the Dharwars, Peninsular granitic Gneiss, Charnockites and the Closepet Granite. The Dharwar metamorphics mainly comprise of phyllites, slates, schists with chlorite, biotite, garnet and hornblende. Accompanying these are

13 greenstones and quartzite. The Closepet Granite of the upper reaches of the Cauvery basin is pink granite consisting mainly of quartz, plagioclase, microcline, perthite, and subordinate hornblende. Over the main basin, the peninsular granites and gneisses comprising of biotite granitic gneiss, hornblende granitic gneiss are widely found. The Charnockites are confined to the Nilgiri Range in the central part of the drainage basin. These are represented by gabbros, olivine norites, and pyroxene. Cretaceous sediments crop out in the coastal region and consist of conglomeratic sandstone, coralline limestone, and shale (Ramanathan et al. 1988; Madras Science Foundation 2000).

2.7. Climate Cauvery basin experiences tropical climate. The influence of southwest monsoon is more pronounced in the northern part of the basin especially in the areas of northwest and southwest. Whereas the southern part of the basin, especially the delta areas are more under the influence of north east monsoon. The north-east monsoon provides the greater portion of the annual precipitation. The far north-western part of the drainage basin has a per-humid climate which passes eastwards into humid, moist sub-humid, dry sub-humid and semi-arid zones. The recorded maximum and minimum temperatures are 38°C and 18°C respectively. The basin experiences four distinct seasons, namely: southwest monsoon, northeast monsoon, winter and summer. These four seasons fall from June to September, October to December, January to February, and March to May respectively.

2.8. Rainfall Over the basin, the highest rainfall is received along the western border of the basin during the southwest monsoon. The eastern side of the basin gets most of the rain during the northeast monsoon. The mean annual rainfall indicates a clear pattern of high rainfall areas around the hilly zones in northwest and southwest part of the basin. The central part of the basin is landlocked and hence it suffers from inadequate of rainfall. The northern part of the basin receives copious of rainfall due to southwest monsoon, enriching the catchment capacity of northern tributaries. The average annual rainfall in the Cauvery delta region is 1,078 mm with a standard deviation of 259.4 mm

14 (Figure 2.2). Seasonal rainfall contributions to the annual rainfall in Cauvery delta region is given in Table 2.1. Table 2.1. Rainfall (in mm) during different seasons in Cauvery delta region (1979-2009)

1980 - 1985- 1990- 1995- 2000- 2005- Season 1984 1989 1994 1999 2004 2009 Winter 5.2 6 4.7 2.2 8.9 2.5 Summer 8 7.2 5.9 9.5 11.3 14.6 Southwest monsoon 29.2 31.8 25.5 25.9 25.8 21.5 Northeast monsoon 57.6 55 63.8 62.4 53.9 61.4

1800

1500

1200

900

600 Rainfall (mm)

300

0 1979 1984 1989 1994 1999 2004 2009 Years Figure 2.2. Annual rainfall trend in Cauvery delta region (Source: Department of Economics and Statistics, Government of Tamil Nadu)

Northeast monsoon significantly contributes to rainfall in Cauvery delta region. Depressions in the Bay of Bengal affect the delta regions in the monsoon, causing cyclones and widespread heavy rains. Cyclonic rainfall and flooding in low lying areas are common during the northeast monsoon period in the eastern part of the delta. Notable raise in summer rainfall also observed in the delta region.

2.9. Water flow The water flow rate data for River Cauvery at Musiri (which is located about 15 km in upstream from , where river gets bifurcated and diverted for irrigation canals) was obtained for the period of ten years (2000 to 2009) from Central Water Commission, Government of India. The mean water flow level was recorded as 235.5

15 m3/second, maximum flow was recorded as 1,202 m3/second in October (2005-06) followed by 1,128 m3/second in November (2005-06) (Figure 2.3). In general, water flow rate is very low or nil during the months of February to May. Maximum water flow was recorded during the months from August to November. About 66% of annual water discharge was recorded during this monsoonal period itself. Since, the Cauvery river basin receives major proportion of rainfall during these months the water flow was also substantially higher than normal flow.

1400 2000-01 1200 2001-02

1000 2002-03

800 2003-04 2004-05 600 2005-06 400 2006-07

200 2007-08

0 2008-09 Water flow (cubic meter meter Water flow(cubic second) per Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Months Figure 2.3. Water flow rate in River Cauvery at Musiri (Source: Central Water Commission, Government of India)

2.10. Temperature The temperature varies from upper reaches of the basin to the plain region of the Cauvery delta, mainly because of altitude variation. The average annual temperature is 38oC. The recorded maximum and minimum temperatures are 38oC and 18oC. However, the maximum monthly temperature is found varying from 36.5oC in June to 27.8oC in December.

16 2.11. Agriculture

1900000 Paddy Food crops 1700000

1500000

1300000

1100000

900000

Yield (Metric Yield tons) 700000

500000 1985-86 1986-87 1987-88 1988-89 1989-90 1990-91 1991-92 1992-93 1993-94 1994-95 1995-96 1996-97 1997-98 1998-99 1999-00 2000-01 2001-02 2002-03 2003-04 2004-05 2005-06 2006-07 2007-08 2008-09

Year Figure 2.4. Annual agricultural yield in Cauvery delta during last 25 years (1985 to 2009). (Source: Department of Economics and Statistics, Government of Tamil Nadu)

In Cauvery delta region, rice is the principal crop. In the rice based cropping system, it is either single or double cropped. Third crop rice is grown during summer in some parts. Annual yield of food crops and paddy’s share in total yield is shown in Figure 2.4. According to Department of Economics and Statistics (Government of Tamil Nadu), the total yield of food crop and paddy was recorded as 1408.6 and 830.7 metric tons during the year of 2008-2009, respectively. The delta region have been experienced by significant decline of food crop yield from 1906.3 to 1408.6 thousand metric tons during the past 25 years due to water sharing dispute between states, drought and lack of integrated water management plan for irrigation.

Pulses blackgram and greengram are next important crops grown in the rice fallows throughout the delta region from January onwards under no tillage condition. Summer irrigated blackgram is grown with splash irrigation, sowings commencing in April. In many places fruits like mangoes, guava, citrus fruits, jack fruits, banana and cashew are grown as longer duration crops occupying the land for more than one year

17 for successive returns. Coconut gardens, bamboo and wood lots are scattered in the delta in different densities.

2.12. Sampling locations for the current study For the present study, forty sampling sites (Thirty freshwater sites and ten estuarine sites) were selected along the River Cauvery in delta region. The sampling locations are shown in Figure 2.5. The locations were chosen to represent various land use practices and anthropogenic activities. Details of the land-use pattern and possible sources of contamination at the vicinity of each sampling sites are given in Table 2.2.

18

Figure 2.5. Sampling location map

19 Table 2.2. Sampling site characteristics

Sample Land use pattern in the vicinity of Possible sources of Name of station No sampling station contamination Freshwater sampling sites S1 Urban and commercial mixed Sewage and urban runoff Agricultural runoff and sand S2 Thirumukudallur Agricultural and residential mixed mining Kulithalai Sewage and agricultural S3 Agricultural and commercial mixed (plate 1) runoff S4 Upper Anicut Predominantly agricultural area Agricultural runoff Tiruchirappalli- S5 Mambala Salai Urban and commercial mixed Sewage and urban runoff (plate 2) Grand Anicut S6 Predominantly agricultural area Agricultural runoff (plate 3) S7 Pundi Agricultural and residential mixed Agricultural runoff S8 Sathanur Agricultural and residential mixed Agricultural runoff S9 Ammapalli Agricultural and residential mixed Agricultural runoff S10 Thiruperambium Agricultural and residential mixed Agricultural runoff Anaikarai Agricultural runoff, sewage S11 Agricultural and commercial mixed (plate 4) and solid waste dumping Agricultural runoff and sand S12 Mannalmedu Agricultural and residential mixed mining S16 Thirukattupalai Agricultural and residential mixed Agricultural runoff S17 Agricultural and residential mixed Agricultural runoff Sewage and solid waste S18 Urban and commercial mixed dumping Sewage and agricultural S19 Aduthurai Agricultural and commercial mixed runoff Sewage and solid waste S20 Mayavaram Urban and commercial mixed dumping S21 Kanjanagaram Agricultural and residential mixed Agricultural runoff S24 Thenperambur Agricultural and residential mixed Agricultural runoff S25 Thiruvankadu Agricultural and residential mixed Agricultural runoff S26 Padakacherry Agricultural and residential mixed Agricultural runoff S27 Sellur Agricultural and residential mixed Agricultural runoff S28 Okkur Agricultural and residential mixed Agricultural runoff Sewage and solid waste S30 Thiruvarur Urban and commercial mixed dumping S33 Utharamangalam Agricultural and residential mixed Agricultural runoff S34 Semanallur Agricultural and residential mixed Agricultural runoff

20 S35 Rajapuram Agricultural and residential mixed Agricultural runoff S36 Vadapadi Agricultural and residential mixed Agricultural runoff S37 Arasalur Agricultural and residential mixed Agricultural runoff S38 Krishnapuram Agricultural and residential mixed Agricultural runoff Estuarine sampling sites S13 Kollidam MID Aqua culture Aqua culture waste S14 Kollidam estuary Aqua culture Aqua culture waste Diesel powered boat and S15 Pichavaram Recreational and Mangrove forest solid waste Killiyur S22 Agricultural and residential mixed Agricultural runoff (plate 5) Agricultural runoff and S23 Poompuhar Recreational and agricultural mixed Diesel powered boat Nagore Sewage and solid waste S29 Urban and commercial mixed (plate 6) dumping Sewage and solid waste S31 Nagapattinam Urban and commercial mixed dumping Sewage and solid waste S32 Seruthur Estuary and residential dumping S39 Pallingamedu Agricultural and residential mixed Agricultural runoff S40 Muthupet Agricultural and residential mixed Agricultural runoff

21

Plate 1. River Cauvery at Musiri

Plate 2. Sewage discharge into River Cauvery at Tiruchirappalli Mambala Salai

22

Plate 3. River Cauvery at Grand Anicut

Plate 4. River Coleroon at Anaikarai

23

Plate 5. River Cauvery at Killiyur

Plate 6. Vettar Estuary at Nagore

24 Chapter-3 Surface water quality of River Cauvery in delta region

3.1. Background Human activities cause a wide ranging impact on distribution, quantity and quality deterioration of water resources. Such effects may be short term or long term ranging from years to decades and also spatial (Local scale, sub regional and regional scales ranging from tens to thousands of square miles). Agriculture has been the significant driver of landscapes changes throughout the world. Surface-water irrigation systems including a network of canals of varying size and capacity to a considerable distance, are some the examples of distribution changes in water resources engineered by humans (Figure 3.1). Significant changes in water quality accompany the movement of water through agricultural fields. The evapotranspiration in agricultural fields also left behind saline soils. These continue to increase as irrigation continues, resulting in the dissolved-solids concentration. Apart from agricultural activities point sources such as industrial effluents also cause substantial impact in surface water quality. Examples of point sources include direct discharges from sewage-treatment plants, industrial facilities and storm water drains. These facilities and structures commonly add sufficient loads of a variety of contaminants to streams to strongly affect the quality of the stream for long distances downstream. The focus of this chapter is to highlight physico-chemical characteristics of surface water in River Cauvery at the delta region with the background information on economic importance of river water, water scarcity, pollution and its sources.

3.1.1. Surface water in Indian context India receives about 4,000 billion m3 as an annual rainfall, where 1,869 billion m3 is lost in natural run-off in streams and rivers, 432 billion m3 goes for recharging the ground water and only 690 billion m3 of the surface water is available for various uses. There are thirteen major river basins (catchment area over 20,000 sq.km) in the country, which occupy 82.4% of total drainage basins, contribute 85% of total surface flow and house eighty percent of the country's population. Major river basins are

25 Brahmaputra, Ganga, Indus, Godavari, Krishna, Mahanadi, Narmada, Cauvery, Brahmini, Tapi, Mahi, Pennar and Sabarmati. Freshwater resources and its river systems in India can be grouped into four categories – the Himalayan, the rivers traversing the , the Coastal and those in the inland drainage basin based on the geographical settings (Shukla 2009). The Himalayan Rivers are perennial as they are fed by melting glaciers every summer. The Gangetic basin is the largest river system in India, draining almost a quarter of the country. The rivers of the Indian peninsular plateau are mainly fed by rain. The Godavari basin in the peninsula is the largest, spanning an area of almost one-tenth of the country. Most of the peninsular Indian west-flowing rivers are highly seasonal in discharge and shorter, although they form an effective water realm in their basin and major sources of sediment transport to the Arabian Sea. The per capita average annual freshwater availability in India, has reduced from 5,177 cubic meters from 1951 to about 1,869 cubic meters in 2001 and is estimated to further come down to 1,341 cubic meters in 2025 and 1,140 cubic meters in 2050 due to growing population, monsoonal variation and lack of integrated water resource management (Kumar 2003).

Figure 3.1. Human activities across the River basin (Source: U.S. Geological Survey Circular 1139 (1998)

26 3.1.2. Surface water resources and economy Water resources provide important commodity and environmental benefits to society. Any particular use of water will be associated with opportunity costs, which are the benefits foregone from possible alternative uses of the resource. India has only 4% of available fresh water, although the country hosts 16 per cent of the world's population. About 85% of the water is used for irrigation, whereas drinking water accounts for 7% and the rest is used by industry including the energy sector. Agriculture and its related sectors accounted for 15.7% of the GDP in 2009–10, employed 52.1% of the total workers. Although a steady decline of its share in the GDP was noted in recent years, still it stands as the largest economic sector and a significant piece of the overall socio-economic development of India. On other hand, pollution in fresh water resources pose a major environmental health hazard, which cause significant economic loss in India. A study conducted by World Bank has estimated that the total cost of environmental damage amounts to US$9.7 billion annually, or 4.5 per cent of the gross domestic product in India. Of this, 59% results from the health impacts of water pollution (World Bank 1999). Sewage and waste water from rapidly growing cities and effluents from industries have turned many rivers into fetid sewers. These findings emphasized a need for massive investments in sewers and wastewater treatment plants to protect people’s health and improve the economy.

3.1.3. Surface water scarcity The unprecedented increase in population demands increased allocations of surface water and groundwater for the domestic, agriculture and industrial sectors, leading to tensions, conflicts among users, and excessive pressure on the environment worldwide. By 2025, 1,800 million people will be living in countries or regions with absolute water scarcity, and two-thirds of the world population could be under stress conditions. India is facing a serious crisis of fresh water scarcity, especially in the view of population growth and economic development. Water flow in 80–90% of the Indian rivers, is restricted to monsoon season (June to September) and the rest of the non- monsoon seasons solely depend on water availability in reservoirs and tanks for domestic as well as agricultural practices (Garg and Hassan 2007). Due to uneven variation in precipitation and fresh water resources distribution, nearly 200 million

27 people do not have access to safe and clean drinking water and 90% of the country’s water resources are polluted (Chavan et al. 2009). River Cauvery is one among the water scarce river basin situated in the states of Kerala, Karnataka, Tamil Nadu, and the Union Territory of Puducherry in southern India. Being a “closed” river basin, (i.e. a basin in which the water uses exceeds the amount of renewable water available), the river’s water is heavily used and much needed. The irrigation of almost 1.2 million hectares of agricultural land (Bohle 2004) in Karnataka and Tamil Nadu requires over 90% of the Cauvery’s water, mainly for the cultivation of water-intensive crops, like paddy and sugarcane (Prasad 2007). Additionally, water demands from other stakeholders and sectors are increasing, namely, the growing urban population of cities which is located in the banks of River Cauvery that requires water for drinking and household purposes. Water scarcity in the basin often triggers conflicts between the states on water sharing issues.

Some of the major reasons for water scarcity are; • Erratic monsoon • Population growth and food production (Agriculture) • Increasing infrastructure development activities • Massive urbanization and industrialization • Climatic change and variability • Lack of implementation of effective water management systems.

3.1.4. Surface water pollution Every day, 2 million tons of sewage, industrial and agricultural waste are discharged into the world’s water (UN-WWAP 2003), the equivalent of the weight of the entire human population of 6.8 billion people. The UN estimates that the amount of wastewater produced annually is about 1,500 km3, six times more water than that exists in all the rivers of the world (UN-WWAP 2003). Most of the Indian rivers and their tributaries are also polluted due to discharge of untreated sewage and industrial effluents directly into the rivers. Out of about 34,000 MLD (million liters per day) of sewage generated treatment capacity exists for only about 12,000 MLD. It shows large gap between generation and treatment of wastewater in India (CPCB 2008).

28 Pollutants can enter into freshwater system from point sources and non-point sources. As the name indicates, in point-source pollution, a contaminant or nutrient enters the water at an identifiable point. Point sources pollution usually originates from numerous sources includes industrial or municipal waste outfalls, leachate from municipal and hazardous waste dumpsites, hazardous spills, underground storage tanks, storage piles of chemicals, mine-waste ponds and septic tanks (Loague and Corwin 2005).

In contrast, nonpoint source pollution comes from many diffuse sources. Among them agriculture and forestry are two major sources of this kind pollution, however urban storm runoff and sewer overflows also contribute significantly (Rogers 1991). Numerous studies on non-point source pollution have focused on the effects from runoff over agricultural land and also noted increasing levels of nutrients in streams, specifically phosphorous and nitrogen, resulting from agricultural runoff (Allan et al. 1997; Chambers et al. 2000). Rivers receiving moderate nutrient enrichment from sewage and agriculture have shown increases in biological productivity. Increased nutrient levels can have negative effects on downstream ecosystems (Wernick et al. 1998).

Apart from point and non-point sources of water pollution, land use and land cover change is one among the most significant determinants of water quality in river basin (Griffith et al. 2002). Land use is characterised by the arrangements, activities and inputs people undertake in a certain land cover type to produce, change or maintain it (FAO 1997). It affects the quantity and quality of runoff water and the water budget, water chemistry and biodiversity of aquatic organisms in receiving waters (Environment Canada 2001). Some of the landscape properties such as riparian zone condition, channel slope, geology, vegetation and hydrography are essential components capable to influence structure and function of aquatic environment (Tong and Chen 2002). According to UN- WWAP (2003), about sixty percent of the world’s biggest rivers have interrupted stream flows due to dams and other infrastructure. These interruptions ultimately affects stream flow, dramatically decrease sediment and

29 nutrient transport to downstream stretches, reducing water quality and impairing ecosystem health.

Among the range of land use category, agricultural land use has caused significant impact on surface water quality (Paustian et al. 1997; Kucharik et al. 2001; Subramani et al. 2010). Evidently, most of prior studies conclude that the agricultural land use has strong influences on stream water enrichment by nitrogen (Arheimer and Liden 2000; Smart et al. 1998), phosphorus and sediments (Johnson et al. 1997). As an outcome of this enrichment, elevated algal levels in agriculturally impacted rivers are reported in worldwide (Moreau et al. 1998; Carr and Chambers 1998; Hatch 2002). Nikhil Raj and Azeez (2009) had highlighted the effects of land use changes and irrigation projects especially in the catchments on spatial variations in the water discharge, water quality and elemental load in River Bharathapuzha, India. In order to manage the agricultural influences on surface water quality, the use of perennial vegetation as a riparian buffer to scavenge excess water and nutrients from annual row crop fields has been recognised as a control strategy (Cey et al. 1999; Nassauer et al. 2002; Vache et al. 2002; Lee et al. 2003; Sileika et al. 2006).

Urban areas are another major source of organic and inorganic pollutants in river water (Baker 2003; Wayland et al. 2003). Though urban areas (buildup areas) cover a relatively small proportion of the earth and it contains much of the world’s population, untreated domestic and solid waste discharged indiscriminately into the water bodies, deteriorate the surface water to a large extent (Khadse et al. 2008; Juang et al. 2009; Venugopal et al. 2009). According to reports, most of the major river systems in India are heavily polluted by about 50 million cubic meters of untreated sewage discharged annually (Mehrotra 1990). In general, nutrients level in urban area streams are generally higher than in streams in rural areas (Winger and Duthie 2000; Brett et al. 2005). Maximum BOD, Kjeldahl nitrogen, and total phosphorus were recorded river stretch located downstream of the town (Cerqueira et al. 2005). Sewage contamination is reportedly known to increase the total coliform and fecal coliform in river water causing health hazard. Metropolitan city Delhi alone contributes around

30 3,296 MLD of sewage by virtue of drains out falling in River . This is more than that of all the class 2 cities of India put together (Jain et al. 2007a).

Some of natural processes (precipitation inputs, erosion, weathering of crustal materials, self-purification and etc) also affect the surface water quality in detrimental ways (Zhang and Huang 1993; Jarvie et al. 1998; Simeonov et al. 2003; Singh et al. 2005a; Ma et al. 2009; Subramani et al. 2010; Ilijevic et al. 2012). Among them, weathering and erosion is capable to influence the water chemistry, particularly dissolved trace metals (Ma et al. 2009). Subramani et al. (2010) found weathering of carbonate and silicate minerals and ion exchange reactions to predominantly regulate major ion chemistry in Chithar River.

Mining activities generate a diversity of contaminants that have been shown to have adverse effects of varying intensity on surface waters, surface water biota and submerged sediments (Pulles et al. 1999). Impact of mine wastewater and its potential impacts on fresh water system are reported by numerous researches in worldwide (Tiwary and Dhar 1994; Gupta et al. 1995; Banks et al. 1997; Pulles et al. 1999; Reza et al. 2009). Kitetu and Rowan (1997) stated that the mining of river sand and gravel many folds higher than natural replenishments can impart serious offsite and onsite impacts. Further, it causes changes in channel form, physical habitats food webs and structures associated with river channels and inland sediment supply to coastal and near shore environments (Gaillot and Piegay 1999).

Although the industrial sector only accounts for three per cent of the annual water withdrawals in India, it causes water pollution in most of the river basins (Singh and Singh 1990; Mehrotra 1990; Prakasan and Joseph 2000; Jayaprakash et al. 2005; Tiwari et al. 2005; Panda et al. 2006; Alam et al. 2007; Kannel et al. 2007; Akan et al. 2009; Varunprasath and Daniel 2010; Shraddha et al. 2011). According to Ministry of Water Resources (2000), wastewater generation from industrial sector is about 55,000 million m3 per day, of which 68.5 million m3 are dumped directly into rivers and streams without prior treatment. Disposal of treated and untreated industrial effluents containing toxic metals as well as metal chelates (Ammann et al. 2002) from different industries, e.g., tannery, steel plants, battery industries, thermal power plants, etc. may

31 lead to serious environmental problems posing threat on human beings (Akhtar et al. 2009). Effluent from tanneries and textile are reportedly known to increase high Chemical Oxygen Demand (COD) (upto 3439 mg/l) and total organic carbon (upto 5.54 %) which is manifold higher than the World Health Organization limits (Akan et al. 2009) recorded. Massive accumulation of various organic and inorganic pollutants in R. Rapti due to the impact of sugar industry and distillery effluents was reported by Chaurasia and Tiwari (2011).

Biochemical Oxygen Demand (BOD), one of the key pollution indicators was recorded very high level in some of river basin in response to massive discharge of organic rich industrial and domestic waste water. Maximum level of BOD recorded in various Indian river basins were given in Table 3.1. Due to the high organic load, dissolved oxygen in these basins was very low in most of the time. Presence of certain physico-chemical pollution indicators in some Indian rivers are given in Table 3.2.

Table 3.1. BOD levels in various rivers of India River BOD (mg/l) Reference R. Challawa 678 Akan et al. (2009) R. Narmada 220 Sharma et al. (2008) R. Ganga 121 Akhtar et al. (2009) R. Noyyal 120 Ramesh (2006) R. Rapti 115 Chaurasia and Tiwari (2011) R. Coovum 105 CPCB (2008) R. Yamuna 103 CPCB (2009) R. Satluj 55 CPCB (2008) R. Hindon 54 Suthar et al. (2010) R. Kosi 54 Yadav and Rajesh (2011) R. Adyar 43 CPCB (2008) R. Sabarmati 46 CPCB (2009) R. Godavari 26 CPCB (2009) R. Gomti 24 Singh et al. (2005b) R. Cauvery 23 CPCB (2009) R. Tapi 21 CPCB (2008) R. Krishna 18 CPCB (2009) R. Brahmani–Koel 14 Sundaray et al. (2010)

32 Table 3.2. Comparison of some physico-chemical parameters of other rivers with present observations

Rivers TDS Alkalinity Cl Ca Mg Na K SO4 Reference Mahanadi Subramanian 224 122 23 24 13 14 8 3 River (2004) Subramanian Ganga River 241 128 10 25 8 11 3 13 (2004) Subramanian Cauvery River 272 135 20 21 9 43 4 13 (2004) Gupta and Gomti River 394 274 9 30 19 27 5 15 Subramanian (1994) Subramanian Narmada River 322 225 20 14 20 27 2 5.0 (1983) Subramanian Tapti River 322 150 65 19 22 48 3 0.6 (1983) Biksham and Godavari 181 105 17 22 5 12 3 8.0 Subramanian River (1988) Ramesh and Krishna River 360 178 38 29 8 30 2 49.0 Subramanian (1988) Brahmani- Sundaray et al. 205.6 93.5 60.2 - - - - 27.1 Koel River (2010) Subramanian Indian average 159 74 15 30 7 12 3 13.0 (2004) River Cauvery The present 300.7 129.2 257.3 74.2 27.770.5 6.6 26 in delta study

3.1.5. Water Quality in River Cauvery and its tributaries Surface water pollution in Cauvery River and its tributaries due to various anthropogenic activities has been reported by numerous studies. According to these studies, industrial effluents (pulp and paper manufacturing, dyeing and bleaching industries), agrochemicals and sewage are the main anthropogenic sources of water pollution in Cauvery River (Table 3.3).

33 Table 3.3 Surface water quality in River Cauvery and its tributaries

Study area Observations Author Cauvery Electrical conductivity, total dissolved solids Ramanathan et al. (1993) Estuary and total suspended matter increased conservatively with increasing chlorinity. SO4, Na, K, Ca, and Mg showed an increasing trend while H4SiO4 and phosphate showed a decreasing trend toward the sea. Cauvery Higher levels of hexachlorocyclohexane Rajendran and Estuary (HCH) during pre-monsoon (July to Subramanian (1997) September) and monsoon (October to December) months, reflected the HCH usage during that season for paddy crops. River Noyyal The surface water quality of River Noyyal is Senthilnathan (1998) highly polluted by dyeing and bleaching industries which are located on the banks of river Noyyal on both sides. The heavy metal level in surface water samples decreased in the order of Mn>Fe>Zn>Cu. River Noyyal The study recorded higher concentration of Ramesh (2006) some physico-chemical parameters due to dyeing and bleaching effluents discharge in surface water samples of River Noyyal in Tirupur (dissolved solids (upto 6250 mg/l), total suspended solids (1686 mg/l), alkalinity (675 mg/l), total hardness (2600 mg/l), BOD (120 mg/l), COD (365 mg/l) and chloride (2750 mg/l). Cauvery River Low faunal diversity and negative impact on Begum and Harikrishna in Mandya the biotic and abiotic environment was (2008) District observed due to the discharge of industrial effluents from sugar and brewery distilleries. Upstream of Massive additions of industrial effluents, Begum et al. (2009a) River Cauvery agrochemical effluents and municipal sewage (Karnataka) into the river. Major ions and nutrients were observed in the following order: Na > HCO3 > Mg > K > Ca> Cl > SO4 whereas heavy metal distribution in surface water was observed in the following order Cr > Cu = Mn > Co > Ni > Pb > Zn. Upper reaches Monsoonal rains and the subsequent increase Solaraj et al. (2010) of Cauvery in river flow rate influences certain delta region parameters like dissolved solids, phosphate, and dissolved oxygen.

34 River Bhavani Impact of bleaching and dyeing effluents was Varunprasath and Daniel noted in surface water quality in terms of (2010) physico-chemical parameters (temperature, turbidity, total hardness, BOD). Cauvery River Significant variation between pre-monsoon Venkatesharaju et al. in Karnataka and post-monsoon samples in parameters (2010) (TDS, TH, HCO3, Na and K). Inverse relations of DO with other physical chemical parameters signify the role of dissolved oxygen in determining surface water quality.

Although many investigations had reported surface water pollution in River Cauvery and its tributaries, there is no detailed study in Cauvery delta and estuarine regions where River Cauvery breaks into thirty six distributaries and canals. In that context, present study is a significant attempt to assess water quality in delta region and estuarine stretch for a period of two years.

3.2. Materials and Methods Surface water samples were collected in acid washed polythene bottles from 40 stations during dry season (June 2008 and 2009) and wet season (January 2009 and 2010) in fresh water and estuarine regions of Cauvery delta. The sampling locations are shown in Figure 2.5. Details of the land-use pattern and possible sources of contamination at the vicinity of each sampling sites are given in Table 2.2. Water samples were analyzed for pH, Electrical Conductivity (EC), Total Dissolved Solids (TDS), Total Suspended Solids (TSS), Dissolved Oxygen (DO), Chemical Oxygen Demand (COD), Biological Oxygen Demand (BOD), total hardness, chloride (Cl), alkalinity, calcium (Ca), magnesium (Mg), sodium (Na), potassium (K), nitrates (NO3), sulfate (SO4), phosphate (PO4) and fluoride (F) using standard methods (APHA 1995). Briefly, the pH, EC and TDS were measured using Water analyzer (Make: Systronics) instantly at the field during the time of sampling. DO and BOD was measured using

Winkler’s method and the BOD5 was calculated from the difference of DO concentration after 5 days of incubation at 20oC. COD was determined by open reflux method.

35 Total hardness, Ca and Mg were estimated titrimetrically using standard ethylenediamine tetraacetic acid. Alkalinity and Cl was estimated by standard sulfuric acid titration method and silver nitrate titration method, respectively. Sodium and potassium was measured using flame photometer. Sulfate was measured spectrophotometrically by precipitating as BaSO4 using BaCl2. PO4 and NO3 were determined as per stannous chloride method and brucine sulphate method with spectrophotometer. Fluoride was estimated using SPADNS method. Results obtained were subjected to statistical analysis using Statistical Package for Social Scientists (SPSS) 11th version.

3.3. Results and discussion 3.3.1. pH, Electrical conductivity and total solids Surface water quality of forty samples representing River Cauvery in delta and Estuarine region during dry season and wet season is given in Table 3.4 and 3.5. The pH level of river water samples ranged between 7.06 and 8.91 indicating slightly alkaline nature. In many sampling stations, pH was recorded above 8.5 exceeding the WHO permissible limit (6.5–8.5). Electrical Conductivity (EC) was observed in the range from 412 to 1,850 µS/cm in delta region and 645 to 61500 µS/cm in estuarine region. Maximum level of EC was found at Nagapattinam (S31) and minimum was recorded at Tiruchirappalli Mambala Salai (S5). Mean concentrations of total dissolved solids in dry season and wet season was observed as 221 and 386 mg/l in delta samples, and 17,747 and 13,421 mg/l in estuarine samples. Maximum level of total suspended solids (352 mg/l) was recorded at Nagore (S29) during wet season (2009-2010). Mean level of total suspended solids content in estuarine samples (111 mg/l) are comparatively higher than the delta water samples (55 mg/l) throughout the study period. Comparatively, higher level of total suspended solids recorded in estuarine region indicates accretion of suspended matter which is carried over from upstream and middle delta region.

36 Table 3.4. Surface water quality characteristics of River Cauvery in delta and Estuarine region during dry seasons (both 2008–2009 and 2009–2010)

Parameters Unit Delta region Estuarine region WHO BIS Min Max Mean Min Max Mean (1993) (1991) pH - 7.5 8.8 8.1 7.3 8.4 7.8 6.5–9.2 6.5–8.5 EC µS/cm 412.0 755.0 521.0 12800.0 60900.0 46468.1 - - TDS mg/l 160.0 338.0 220.8 212.0 26800.0 17747.4 1000 500 TSS mg/l 2.0 100.0 26.2 4.0 182.0 52.1 - - TH mg/l 126.8 570.0 193.7 140.0 7700.0 5219.1 100 300 Ca mg/l 26.3 189.0 63.0 42.0 519.8 328.7 75 75 Mg mg/l 3.0 82.4 26.4 13.6 397.0 201.9 30 30 Na mg/l 25.3 240.5 43.7 27.6 16675.0 3189.9 200 - K mg/l 1.2 21.5 6.8 3.7 163.8 33.4 - - Chloride mg/l 12.2 317.6 87.3 158.8 30841.5 17598.2 250 250 Alkalinity mg/l 22.5 360.0 146.6 45.0 247.5 122.6 500 - DO mg/l 2.8 10.5 6.3 4.5 8.5 6.6 - 6 BOD mg/l 0.2 26.8 5.7 0.4 22.6 8.9 5 - COD mg/l 8.0 146.0 46.2 12.9 168.0 78.3 10 -

PO4 mg/l 0.1 0.9 0.6 0.2 0.7 0.4 - -

NO3 mg/l 0.1 4.9 1.5 0.5 2.2 1.1 50 45

SO4 mg/l 2.7 93.3 32.4 18.0 163.7 68.6 250 200 F mg/l 0.5 0.7 0.7 0.5 0.7 0.6 1.5 1.0

3.3.2. Major anions and fluoride Alkalinity is a measure of base concentration in water. Mean alkalinity level in fresh water samples recorded as 146.6 and 111.6 mg/l in dry season and wet season, respectively. The carbonate alkalinity is absent in most of the stations throughout the study. This result shows that the alkaline pH is particularly due to bicarbonate and not due to carbonate alkalinity. Comparatively, higher concentration of bicarbonate observed during dry season is probably attributed to chemical weathering in the delta region (Table 3.4 and 3.5). Bicarbonate content of all samples falls within the

37 permissible limit (500 mg/l) of World Health Organization. The alkalinity level increases towards downstream during the study period. Similar kind of downstream variations of major ions have been reported by others also (Subramanian 1983; Gupta and Subramanian 1994; Datta and Subramanian 1998).

Table 3.5. Surface water quality characteristics of River Cauvery in delta and Estuarine region during wet seasons (both 2008–2009 and 2009–2010)

Parameters Unit Delta region Estuarine region WHO BIS Min Max Mean Min Max Mean (1993) (1991) pH - 7.5 8.8 8.1 7.3 8.4 7.8 6.5–9.2 6.5–8.5 EC µS/cm 412.0 755.0 521.0 12800.0 60900.0 46468.1 - - TDS mg/l 160.0 338.0 220.8 212.0 26800.0 17747.4 1000 500 TSS mg/l 2.0 100.0 26.2 4.0 182.0 52.1 - - TH mg/l 126.8 570.0 193.7 140.0 7700.0 5219.1 100 300 Ca mg/l 26.3 189.0 63.0 42.0 519.8 328.7 75 75 Mg mg/l 3.0 82.4 26.4 13.6 397.0 201.9 30 30 Na mg/l 25.3 240.5 43.7 27.6 16675.0 3189.9 200 - K mg/l 1.2 21.5 6.8 3.7 163.8 33.4 - - Chloride mg/l 12.2 317.6 87.3 158.8 30841.5 17598.2 250 250 Alkalinity mg/l 22.5 360.0 146.6 45.0 247.5 122.6 500 - DO mg/l 2.8 10.5 6.3 4.5 8.5 6.6 - 6 BOD mg/l 0.2 26.8 5.7 0.4 22.6 8.9 5 - COD mg/l 8.0 146.0 46.2 12.9 168.0 78.3 10 -

PO4 mg/l 0.1 0.9 0.6 0.2 0.7 0.4 - -

NO3 mg/l 0.1 4.9 1.5 0.5 2.2 1.1 50 45

SO4 mg/l 2.7 93.3 32.4 18.0 163.7 68.6 250 200 F mg/l 0.5 0.7 0.7 0.5 0.7 0.6 1.5 1.0

Chloride (Cl) levels in samples were found ranging from 12.2 to 317.6 mg/l and 53.8 to 297.7 mg/l in freshwater zone during dry season and wet season, respectively. Except estuarine water samples and few freshwater sampling sites [Karur (S1), Upper Anicut (S4) and Arasalur (S37)], Cl content in most of the samples were found within the permissible limit of 250 mg/l (WHO 1997). Higher concentrations of Cl observed in Karur (S1) and Kulithalai (S3) indicates likely sources from dyeing and bleaching

38 operating at Karur District. In addition to these textile processing sources at Karur, seasonal discharges from one of the most polluted Noyyal River (a tributary to Cauvery River) is also largely suspected for Cl sources in R. Cauvery at initial stretches in Karur District. R. Noyyal while flowing through Tiruppur district, receives about 75,000 m3 to 10,0000 m3 per day of effluents containing Cl of more than 24,000 mg/l respectively from 700 odd dyeing and bleaching industries operating in the vicinity.

SO4 levels in River Cauvery ranged from 2.7 to 163.7 mg/l. Maximum level of

SO4 (163.7 mg/l) was recorded at Nagapattinam (S31) during dry season (2009-2010) and minimum level was recorded at Vadapadi (S36) in wet season (2008). Level of SO4 in all sampling stations was within the prescribed limits of Bureau of Indian standards (400 mg/l) in both seasons. Industrial effluents are known as a prime source of sulphate

(SO4) in fresh water environment and its level may be found in few to several thousand milligrams per liter in natural waters (APHA 1998).

Fluoride levels in samples varied from 0.06 to 1.05 mg/l. Wet season recorded comparatively higher mean level of F (0.7 mg/l) than the dry season samples (0.63 mg/l), indicating the influence of agricultural runoff from the vicinity (Jameel and Sirajudeen 2006). Overall, F content in all water samples were recorded within the optimum concentration of 1.5 mg/l (WHO 1997). Weathering of rocks, phosphatic fertilizers usage and discharge of sewage sludge are reportedly known as major sources of fluoride (F) in fresh water system (Oelschlager 1971; Jameel and Sirajudeen 2006). Low level of F (less than 1 mg/l) in drinking water has been considered beneficial but high level may lead to dental fluorosis and more seriously skeletal fluorosis (Teotia et al. 1971; Reddy 1979). Further, prolonged exposure of high-concentration of F may lead to thyroid functional disorders (Balabolkin et al. 1995) and neurological effects (Mullenix et al. 1995).

3.3.3. Major cations Hardness is due to the presence of divalent metallic cations like Ca, Mg, strontium and manganese ions. Hardness is commonly classified in terms of degree of hardness as (1) soft (0 to 60 mg/l), (2) moderately hard (60 to 120 mg/l), (3) hard

39 (120 to 180 mg/l) and (4) very hard (>180 mg/l) (Durfer and Backer 1964). It is the property of water which prevents the lather formation with soap and increases the boiling point of water. Hardness in water is also derived from the solution of CO2 released from the bacterial action in percolating water (Sawyer and McCarty 1967). In the present study, total hardness in samples varied from 127 to 570 mg/l and 36.5 to 437 mg/l in dry season and wet season, respectively. As per this classification, more than half the number of samples falls under hard category in both the season. Desirable limit of total hardness is 300 mg/l, however in the absence of alternate source it is permissible up to 600 mg/l (De 2002). High hardness level in water samples may lead to high risk for urinary and salivary stone formation in human (Mudryi 1999). Ca and Mg that imparts hardness ranged from 26.3 to 189 mg/l & 3.0 to 82.4 mg/l and 31.5 to 126 mg/l & 0.7 to 83.2 mg/l during dry season and wet season, respectively. Low level of Mg was observed in the both seasons. The desirable limits for calcium and magnesium for drinking water are 75 and 30 mg/l, respectively (BIS 1991).

Mean concentration of sodium (Na) and potassium (K) was recorded as 63.6 and 6.6 mg/l in delta samples and 3,400 and 84.4 mg/l in estuarine samples respectively. Comparatively, low concentration of K recorded in the surface water samples is probably attributed to its preferential absorption and incorporation onto silicates (Ramanathan et al. 1993). In the estuarine regions, sodium was observed as major cation, indicating the influence of sea water. In general, concentration of Na, K, Ca and Mg are high in downstream (estuarine regions) of the delta region where sea water influences were noted. Although Na plays a vital role in electrolyte regulation and water balance maintenance (Howard and Schrier 1990), however at elevated levels it adversely affects the cardiac, renal and circulatory functions (Srivastava 2007).

3.3.4. Dissolved oxygen and organic load Dissolved Oxygen (DO) is known as a vital indicator to assess the effect of industrial and municipal effluents on freshwater system (Rudolf et al. 2002). DO content in surface water samples ranged from 2.8 to 10.5 mg/l and 2.8 to 8.9 during dry and wet season, respectively. Minimum level of DO (2.76 mg/l) was found at Poompuhar (S23) in wet season (2009-2010) followed by 2.8 mg/l at Utharamangalam

40 (S33) during dry season (2009-2010). The lowest DO recorded at these locations would have been attributed to sewage discharge from the vicinity into the river. Moreover, minimum fresh water flows in these regions also aggravate the DO levels. More than half the number of surface water samples recorded DO less than the permissible limit of Bureau of Indian standards (6 mg/l) in wet seasons.

Organic load, investigated for COD and Biological Oxygen Demand (BOD) ranged from 8 to 168 mg/l and 0.2 to 26.8 mg/l, respectively. In freshwater zone, mean level of COD and BOD was found in the range of 46.2 and 34.2 mg/l, 5.7 and 3.4 mg/l during dry and wet season, respectively. Paired T-test yielded statistically significant difference in BOD content in surface water samples between seasons in study period. Except few sampling stations, COD level in surface water samples exceeded the standard limit of 10 mg/l throughout the study period. COD and BOD in elevated indicates high organic load in surface water which depletes oxygen for its oxidation process. Higher water flow rate and monsoonal dilution is a probable reason for significant reduction in BOD level during wet season period. Mukherjee et al. (1993) opined that about 30% of BOD and 47% of COD are contributed by industrial sources in Indian River system.

3.3.5. Nutrients

Phosphate (PO4) and nitrate (NO3) are the two important nutrients essential for the growth and reproduction of all organisms; nevertheless, an excessive concentration of these nutrients may lead to accelerated eutrophication and algae growth (Niemi

1998). PO4 level was observed in the range 0.12 to 0.86 mg/l and 0.12 to 0.98 mg/l during dry season and wet season, respectively. In all the sampling sites, PO4 content in surface water exceeded the permissible limit (0.1 mg/l) of US Public Health Standards (De 2002) in both seasons. Although phosphates naturally occur in water due to leaching of phosphorus rich bedrock, in most of the freshwater system anthropogenic sources also plays a key role in PO4 enrichment (Girija et al. 2007). Elevated levels of

PO4 observed in the entire Cauvery delta region indicate probable origin of PO4 from Cretaceous formation of Tiruchirappalli (which has rich phosphatic nodules), untreated sewage discharge and fertilizer runoff (Vaithiyanathan et al. 1989; Robbins and

41 Birkenholtz 2003). According to Department of Environment (2001), Government of Tamil Nadu, the usage pattern of nitrogenous, phosphatic and potasic fertilizers were recorded as 52.84%, 22.47% and 24.69%, respectively. Cauvery delta regions have been experienced by extensive usage of diammonium phosphate fertilizers. Significant increase in usage of fertilizers from 31,107 to 54,306 metric tons during the past 15 years may be a significant anthropogenic source of phosphorous and nitrogen in the river system. Higher concentration of PO4 in the urban samplings sites might be attributed to the mixing of municipal sewage into River Cauvery. Paired T-test revealed significant difference in phosphate content in surface water samples between the seasons.

In the present study, NO3 levels in water samples were found ranging from 0.09 to 4.87 mg/l and 0.44 to 30.92 mg/l during dry season and wet season, respectively.

Mean concentration of NO3 in wet season (7.84 mg/l) was significantly higher than the dry season (1.30 mg/l) indicating notable enrichment in the wet season. As hinted earlier, fertilizer runoff from agricultural field might have contributed higher levels in the wet season. NO3 level of 10 mg/l is considered as contamination (Hounslow 1995), while 8.5 mg/l as a low level contamination (Hallberg 1989). In the present investigation more than half the number of surface water samples was observed to be contaminated (10 mg/l). Higher levels of NO3 were recorded in downstream sampling sites, Kollidam (S13), Pichavaram (S15), Okkur (S28), Nagore (S29) and Nagapattinam

(S31). Paired T-test yielded significant difference in NO3 concentration between the seasons.

3.3.6. Correlation analysis Correlation among the physico-chemical characteristics is given in the Table 3.6 and 3.7. In dry season, the negative correlation between PO4 and DO reveals the role of phosphate in eutrophication. TDS is strongly correlated with total hardness, Ca, Mg and Cl (dry season and wet season).

42 Table 3.6. Correlation between physico-chemical characteristics of surface water in Cauvery delta during dry season

pH EC TDS TSS TH Ca Mg Na K Cl Alkalinity DO BOD COD PO4 NO3 SO4 F pH 1.00 EC .35 1.00 TDS -.34 .96** 1.00 TSS -.23 .44** .44** 1.00 TH -.34 .98** .94** .43** 1.00 Ca -.31 .86** .81** .36** .85** 1.00 Mg -.33 .96** .92** .36** .97** .82** 1.00 Na -.16 .59** .59** .18 .62** .53** .54* 1.00 K -.02 .45** .51** .08 .39** .17 .44** .23* 1.00 Cl -.29 .97** .93** .43** .95** .81** .93** .63** .56** 1.00 Alkalinity -.16 -.10 -.05 -.07 -.10 .05 -.09 -.11 -.02 -.08 1.00 DO -.23 .06 .06 .01 .04 .01 .03 .08 .12 .11 .25* 1.00 BOD .04 .28* .18 .15 .29** .18 .30** .18 .05 .25* -.49 -.39 1.00 COD -.09 .42** .32** .23* .44** .42** .44** .14 -.01 .35** -.28 -.34 0.77** 1.00

PO4 .50** -.29 -.35 .01 -.28 -.24 -.28 -.28 -.29 -.29 -.27 -.43 .17 .05 1.00

NO3 -.01 -.13 -.11 .00 -.12 -.04 -.12 -.04 -.13 -.13 .37** -.11 -.11 .07 .04 1.00

SO4 -.22 .40** .46** .07 .33** .51** .34** .09 .33** .37** .19 .03 -.03 .11 -.28 .04 1.0 F .30** -.42 -.41 -.18 -.44 -.33 -.50 -.11 -.14 -.39 .04 -.25 -.07 -.23 .34** -.03 -.07 1.0 ** Correlation is significant at the 0.01 level (2-tailed). * Correlation is significant at the 0.05 level (2-tailed).

43 Table 3.7. Correlation between physico-chemical characteristics of surface water in Cauvery delta during wet season

pH EC TDS TSS TH Ca Mg Na K Cl Alkalinity DO BOD COD PO4 NO3 SO4 F pH 1.0 EC -.23 1.0 TDS -.26 .90** 1.0 TSS -.17 .53** .58** 1.0 TH -.15 .75** .58** .30** 1.0 Ca -.17 .42** .22* .13 .30** 1.0 Mg -.14 .69** .51** .27* .98** .36** 1.0 Na -.08 .48** .58** .21 .39** -.18 .23* 1.0 K -.06 .49** .59** .24* .39** -.17 .22* .99 1.0 Cl -.21 .80** .66** .42** .65** .59** .64** .33** .33** 1.0 Alkalinity -.11 .21 .26* .18 .08 .19 .09 -.07 -.06 .18 1.0 DO .06 .01 .04 .08 -.08 .05 -.07 -.06 -.05 .06 -.02 1.0 BOD -.28 .35** .31** .14 .44** .40** .50** -.11 -.13 .32** .33** -.16 1.0 COD -.24 .30** .24* .15 .41** .36** .47** -.07 -.10 .35** .22* -.09 .83** 1.0

PO4 .17 -.22 -.19 -.28 -.19 -.14 -.18 -.12 -.14 -.29 -.05 .15 -.13 -.18 1.0

NO3 -.17 .04 .12 -.10 -.01 -.17 -.08 .36** .33** -.04 -.16 .08 .02 .04 .23* 1.0

SO4 -.22 .39** .44** .38** .32** .28* .29** .17 .17 .33** .35** .21 .26* .25* -0.20 -.06 1.0 F .16 -.11 -.12 .03 -.01 .04 .05 -.22 -.20 -.06 .04 -.17 .18 .14 -.18 -.42 -.03 1.0

* Correlation is significant at the 0.05 level (2-tailed). ** Correlation is significant at the 0.01 level (2-tailed).

44 3.3.7. Factor analysis Factor analysis is a multivariate statistical tool for pattern recognition. It attempts to explain the variance of a large set of inter-related variables with smaller set of independent variables (Simeonov et al. 2003). Factors are obtained by multiplying the original variables with the eigenvector, which is a list of coefficients (loading and weightings) and are thus, weighted linear combinations of the original variables. Factors can provide information on the most meaningful parameters, which describe whole data set affording data reduction with minimum loss of original information. In the present study, factor analysis was performed on normalized variables and 40 observations were used for this analysis. The factors with eigen values more than one have been extracted from the principal component matrix after varimax rotation.

3.3.7.1. Dry season The factor analysis extracted five significant factors which explained 75.38% of the variance in data sets (Table 3.8 and 3.9). The following factors were indicated considering the hydrochemical aspects of the water: Factor 1: EC, TDS, TH, Ca, Mg, Na, K, Cl Factor 2: BOD and COD

Factor 3: DO, PO4, F

Factor 4: Alkalinity, NO3

Factor 5: K, SO4

Factor 1 accounted for 34.99% of representation with high loadings of EC, TDS, TH, Ca, Mg, Na and Cl. This factor loading suggests diverse role of weathering and anthropogenic influences on these parameters (Shrestha et al. 2008). Factor 2 accounted for 12.78% of the total variance with loadings of BOD and COD. This can be explained as biochemical pollution and contributions from anthropogenic activities, including industrial and domestic wastewater (Zhou et al. 2006). Factor 3 explains 11.77% of total variance with positive loading of DO and moderate negative loading of

PO4. Negative loading of PO4, NO3 and BOD with DO implies high organic pollution along with high microbial activity in the Cauvery delta region. Factor 4 yielded 8.22% of total variance with higher loadings of alkalinity and NO3. Level of NO3 in the water

45 Table 3.8. Total variance explained in dry season

Factor Initial Eigen values Extraction Sums of Squared Rotation Sums of Squared Loadings Loadings Total % of Cumulative Total % of Cumulative Total % of Cumulative Variance % Variance % Variance % 1 7.29 40.53 40.53 7.30 40.53 40.53 6.30 34.99 34.99 2 2.59 14.37 54.90 2.59 14.37 54.90 2.30 12.78 47.77 3 1.39 7.71 62.62 1.39 7.71 62.62 2.12 11.77 59.54 4 1.27 7.07 69.69 1.27 7.07 69.69 1.48 8.22 67.76 5 1.03 5.70 75.38 1.03 5.70 75.38 1.37 7.62 75.38

Table 3.9. Rotated component Matrix for dry season

Factor Parameters 1 2 3 4 5 pH -.206 -.011 -.729 -.178 .025 EC .936 .168 .219 -.056 .132 TDS .924 .077 .210 -.029 .196 TH .932 .184 .228 -.053 .066 Ca .825 .190 .188 .198 .128 Mg .886 .216 .256 -.066 .136 Na .694 -.047 -.017 -.109 -.016 K .436 -.158 -.021 -.300 .585 Cl .945 .079 .170 -.104 .160 Alkalinity -.049 -.446 .150 .679 .183 DO .019 -.586 .545 -.161 -.004 BOD .157 .856 -.056 -.244 -.027 COD .272 .875 .128 .030 -.019

PO4 -.159 .223 -.713 .031 -.368

NO3 -.072 .034 -.047 .794 -.064

SO4 .322 .067 .112 .306 .701 F -.285 -.139 -.668 .056 .100 Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization. a Rotation converged in 5 iterations.

46 is may be attributed to anthropogenic (irrigation return flow, fertilizers, and domestic wastes). Loading of alkalinity and NO3 is an indication of the hydro geochemical effect of runoff leakage on the water (Wang and Luo 2001). Factor 5 explains 7.62 % of total variance with moderate positive loadings of K and SO4. Factor 3 to 5 represents anthropogenic nutrient pollution, from sources such as waste disposal, organic pollution from domestic wastewater, and agricultural activities (Singh et al. 2005b).

3.3.7.2. Factor analysis for Wet season The factor analysis extracted five significant factors which explained 70.8% of the variance in data sets (Table 3.10 and 3.11). The following factors were indicated considering the hydrochemical aspects of the water: Factor 1: EC, TDS, TH, Ca, Mg, Cl Factor 2: Na, K

Factor 3: Alkalinity, SO4 Factor 4: COD, BOD

Factor 5: NO3, SO4

Factor 1 accounted for 22.5% of representation with high loadings of EC, total hardness, Mg, Cl and moderate loadings of Ca and TDS. This factor explains complex nature of hydro geochemical processes and its influences factors (natural and anthrogenic) on these parameters. Factor 2 accounted for 15.8% of the total variance with loadings of Na and K. The contribution of Na and K to this factor can be considered as a result of cation exchange process at soil water interface (Guo and Wang

2004). Factor 3 explains 12.5% of total variance with loadings of alkalinity and SO4. Factor 4 yielded 10.6% of total variance with higher loadings of BOD and COD. Like dry seasons, these loadings also can be explained as biochemical pollution attributed to man-made sources. Factor 5 explains 9.4 % of total variance with moderate positive loadings of PO4 and NO3 whereas moderate negative loadings of F. It reveals notable contributions from agrochemicals runoff during wet season.

47 Table 3.10. Total variance explained in wet season Factor Initial Eigen values Extraction Sums of Squared Rotation Sums of Squared Loadings Loadings Total % of Cumulative Total % of Cumulative Total % of Cumulative Variance % Variance % Variance % 1 5.7 31.8 31.8 5.7 31.8 31.8 4.0 22.5 22.5 2 2.8 15.5 47.4 2.8 15.5 47.4 2.8 15.8 38.2 3 1.5 8.5 55.9 1.5 8.5 55.9 2.3 12.5 50.8 4 1.5 8.1 64.0 1.5 8.1 64.0 1.9 10.6 61.4 5 1.2 6.8 70.8 1.2 6.8 70.8 1.7 9.4 70.8

Table 3.11. Rotated component matrix for wet season

Factor

1 2 3 4 5 pH .027 -.142 -.368 -.559 -.171 EC .770 .373 .385 .071 .016 TDS .551 .510 .503 .099 .045 TH .875 .264 .010 .179 -.078 Ca .578 -.409 .270 .122 .029 Mg .893 .103 -.012 .205 -.112 Na .218 .913 .058 -.055 .117 K .220 .912 .079 -.094 .083 Cl .791 .152 .338 .033 -.009 Alkalinity .003 -.150 .595 .267 -.083 DO .094 -.257 .325 -.447 .512 BOD .435 -.216 .134 .764 -.093 COD .429 -.193 .095 .718 -.069

PO4 -.089 -.205 -.320 -.109 .568

NO3 -.098 .401 -.165 .301 .675

SO4 .242 .031 .711 .091 .042 F .023 -.218 -.067 .017 -.725 Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization. a Rotation converged in 12 iterations.

48 3.3.8. Cluster analysis Cluster analysis (CA) was computed to identify the similarity groups between the sampling sites. In this study, CA was performed on the standardized dataset (mean of observations over the whole period) using the Ward’s method with squared Euclidean distances as a measure of similarity (Singh et al. 2005b) (Figure 3.2). Group I was related with a large number of stations which may be considered as more influenced by river run-off. The second group consists of sea water influenced stations.

Euclidean distances

S1 S4 S6 S38 S2 S5 S3 S22 S25 S34 S26 S27 S7 S8 S10 S11 S12 S16 S9 S17 S19 S24 S18 S33 S20 S21 S30 S35 S36 S37 S28 S39 S13 S23 S40 S14 S31 S15 S29 S32

0 1E52E53E54E55E56E5 Linkage Distance

Figure 3.2. Cluster analysis for surface water quality of River Cauvery in delta region

3.4. Conclusion The present study reveals that the surface water quality assessment of River Cauvery in delta regions is potentially contaminated by anthropogenic activities. Since River Cauvery is predominantly monsoon fed, some of physico-chemical parameters demonstrate significant changes in response to the river flow intensity. Elevated concentration of COD and BOD indicates organic load enrichment in River Cauvery in the delta region. Higher phosphate level recorded in water samples indicate pollution from fertilizer runoff from agricultural fields, sewage, and other non-point sources. Results of factor analysis implies role of natural as well as anthropogenic influences on

49 water chemistry. Dumping of solid waste in river and river banks, industrial effluents, sewage let out by inhabitants in many pockets, and agricultural runoff containing fertilizers were suspected as probable sources of pollution in River Cauvery in delta region. In conclusion, surface water in the delta regions of Cauvery River showed contamination of total hardness, COD, BOD, DO and PO4 if compared with drinking water standards (BIS) and WHO guidelines.

50 Chapter-4 Surface sediment Characteristics and Heavy metal distribution of Cauvery Delta region

4.1. Background Sediments play a key role in freshwater environment by providing substrate for living organisms. Interaction between sediments and overlying water phase is considered as prime factor in nutrient cycling (Tiedemann et al. 1988). Interestingly, pollutants that enter into the river systems in water column shows strong affinity with sediments, this process further facilitates to absorb/adsorb pollutants from water column and gets deposited in sediments and thereby it flee from water quality monitoring schemes. Further, pollutants in sediments not necessarily remain permanently fixed but it may be recycled through biological and chemical agents within both the sedimentary compartment and the water column. Therefore, sediments can act as sink as well as source of pollutants in the hydrological cycle (Campbell and Tessier 1991). Because of this unique character, sediment plays a vital role in the framework of environmental investigations related to short-term or long term pollution which is not adequately addressed by water analysis (Salomons and Foerstner 1984).

The concentration of pollutants in sediments not only depends on anthropogenic and lithogenic sources but also upon the textural characteristics, organic matter contents, mineralogical composition and depositional environment of the sediments (Trefry and Parsley 1976). The bioavailability of pollutants, for example, is not only species-specific but also depends on sediment characteristics, sediment deposition and erosion. Therefore sediment analysis is paramount important to estimate pollutants that are being discharged into surface water system. This chapter deals about surface sediment characteristics and total heavy metal distribution in River Cauvery at the delta region with the background information on ecological significance, sediment dynamics, nutrient and heavy metal pollution and its possible origins.

51 4.1.1. Ecological significance of river sediments Freshwater sediments are complex ecosystems comprising multifaceted structures of geological origin and biological provenance as well as a variety of autotrophic and heterotrophic communities. It plays a vital role in various ecosystem functions covers; provisioning (e.g. food, clean drinking water); regulating (e.g. carbon sequestration, decomposition, detoxification, self-purification); and supporting (e.g. biogeochemical cycles, biostabilisation) services, which are important for the river system health (Gerbersdorf et al. 2011) (Figure 4.1).

Figure 4.1. Sediment quality and quantity: interfacing systems Source: European sediment research network (www.sednet.org)

Interestingly, sediments are considered as a socio-economic, environmental and geomorphological resource, as well as a tool of nature. It plays a key role in fundamental control on the hydrological and geomorphological behaviour of a river, and contributes to biological functioning by contributing to habitat quality and quantity (for example, creation of mud flats etc.). Further, huge amounts of sediment are being used for flood protection (e.g. beach nourishment), coastal zone management, habitat and wetland protection (Owens et al. 2005). Conversely, changes in human activities within the catchment can have detrimental effects on both sediment quantity and quality. Subsequently, these changes may lead to significant impact on range of social, economic and environmental systems.

52 4.1.2. Sediment dynamics and pollution In the hydrological cycle, less than 0.1% of the contaminants are actually dissolved in the water and more than 99.9% are stored in sediments due to its high storage capacity (Salomons 1998). Therefore, sediments can be considered as a heterogeneous mixture of dissimilar particles and these particles can be measured in turn as a complex assemblage of different organic and inorganic components. The organic matter in sediments, expressed as total organic carbon and total nitrogen, represents an important reservoir for the global carbon cycle (Weston and Joye 2005). Major source of riverine dissolved organic carbon and particulate organic carbon is attributed to the carbon pool of the terrestrial biosphere, which is known as biogenic organic carbon, including the living biomass, litter, and soil organic carbon. According to estimates made by Schlunz and Schneider (2000), global riverine organic carbon flux converged to about 0.4 Gt/year (0.33–0.43 Gt/year), of which 45% is particulate and 55% is dissolved. Organic carbon in riverine and estuarine sediments is controlled mostly by the rate of organic to inorganic constituents, primary productivity, composition and texture of the sediments. Moreover, riverine organic carbon flux is very sensitive to anthropogenic activities. It has been reported that fluxes of organic carbon changed in response to the anthropogenic disturbances of river basin, i.e. increased in response to deforestation and road construction (Kao and Liu 1996), and decreased in response to the construction of dams/reservoirs (Wu et al. 2007). Organic carbon transport via fluvial systems from land to the oceans provides an integrated measure of natural processes and anthropogenic activities within the drainage basin.

The sources of particulate organic matter and its ability to transport nutrients to riverine system is of increasing global concern because of increasing urban input and agricultural pollution (Meybeck 1982; Howarth et al. 1996; Vitousek et al. 1997; Mayer et al. 1998). Several studies have been carried out on the organic carbon and nutrients behavior in the wetland and lake sediments (Eckerrot and Petterson 1993). However, spatial and temporal changes in the organic carbon and nutrients studies are very limited in world and Indian rivers (Meybeck 1982; Cuffney 1988; Meyer et al. 1998; Ferreira et al. 1996; Howarth et al. 1996; Vitousek et al. 1997; Westerhoff and Anning 2000; Rivas et al. 2000). Large rivers like Himalayan rivers often derive significant

53 fractions of their organic carbon from their flood plain, especially during seasonal flooding (Cuffney 1988). Similarly, Meyer et al. (1998) opined that the concentrations of Dissolved Organic Carbon (DOC) and particulate organic carbon increase with increasing stream flow, as a greater portion of the water is derived from overland flow that carries organic material to the natural waterways. Westerhoff and Anning (2000) observed that effluents from wastewater treatment plants and reservoirs were higher in DOC concentration with less variability than inflows and autochthonous DOC dominated rivers compared to natural rivers. Meyers (2003) point out that the total organic carbon level commonly increases as sediment grain size decreases.

Sediment bound nutrients, phosphorous and nitrogen is considered as prime components due to its vital role in any biological system. In most of the aquatic systems, phosphorus limits freshwater systems and nitrogen limits marine systems (Nixon et al. 1996). In the aquatic environment, weathering of rock, fixation of atmospheric nitrogen by plants, decomposition of biological material and leaching of soils are natural sources of nutrients. Apart from natural origin of these nutrients, agricultural and urban disturbance in the riverine environment may lead to massive accumulation of these nutrients in water and sediment phase of the freshwater system (Turner et al. 2003). Irrespective of the sources, sediment bound nutrients may not be everlastingly fixed with sediment phase, however under changing physical-chemical condition it may be released into the water phase.

Nutrient fluxes are also influenced by river run-off, where large rivers may show low fluxes because of dilution effect, while small streams may show high fluxes owing to lack of dilution (Nasnolkar et al. 1996; Rivas et al. 2000; Subramanian 2008; Zhigang et al.2009). Among them, Rivas et al. (2000) studied the nitrogen enrichment in connection with level of water flow in the Catatumbo River in Venezuela and found nitrogen to vary directly with the increase in high flow periods. Nasnolkar et al. (1996) found the relationship of carbon and nutrients with sediment characteristics that indicate significant linear variation with clay and silt in Mandovi estuary. Therefore, sediment bound nutrients may act as a sink as well as passive sources of nutrient enrichment in an aquatic environment. Nutrient enrichment affects the aquatic system

54 in detrimental ways such as hypoxia, frequent harmful algal blooms, and losses in fishery production. Further, it can have negative effects on downstream ecosystems (Wernick et al. 1998).

Evidently, a study conducted by Howarth et al (1988) found the release of primary compounds of nitrogen and phosphorus from the sediments to the water phase may cause secondary eutrophication. Moreover, load of nutrients which travels from the river toward lakes and seas gives an estimate of the degree of eutrophication of the ecosystems (Seniche and Takanobn 1991; Hu et al. 2001; Diaz and Rosenberg 2008; Zhigang et al. 2009). Earlier studies conducted by Zhigang et al. (2009) revealed that the eutrophication of the basin is mainly because of high total nitrogen and total organic carbon content in sediments. Although the external nutrient loading has been reduced, nutrients could gradually be released back into the water column from the contaminated sediments and delay improvement of the water quality (Hu et al. 2001). A recent study found that there are more than 400 dead zones in the world’s coastal areas, most of them formed in the past half century. These dead zones, which cover a quarter of a million square kilometres, are usually found where rivers discharge large amounts of fertilizers and sewage into relatively enclosed ocean areas (Diaz and Rosenberg 2008). Some of the studies also hinted the role of sediment resuspension on active transport of nutrients in sediment interstitial water and adsorbed on sediment particles (Sondergaard et al. 1992; Wainright and Hopkinson 1997; Golterman 2001). Among them, Wainright and Hopkinson (1997) established that resuspension causes release of nutrients, an increase in carbonates and increased oxygen consumption in the water column. Further, resuspension may lead to oxidation of iron to ferric oxides, which adsorb orthophosphates, removing them from the water column (Golterman 2001).

4.1.3. Heavy metals in sediments Among the inorganic pollutants, heavy metals are ubiquitous in nature and gaining scientific interest due to its characteristics such as high reactivity, lithophilic nature, toxicity and non-biodegradability. These pollutants are metallic elements that have a relatively high density and are toxic even at low concentration (Duruibe et al. 2007). Heavy metals are natural constituents of the freshwater environment, generally

55 found in very low concentrations due to geogenic process including weathering and erosion. They are transported to river system both in the form of suspended particles or dissolved form that are subsequently removed from the water column by precipitation, adsorbed onto particulate matter and biota and deposited in bottom sediments (Loska and Wiechula 2003).

Contamination of freshwater sediments by heavy metals is a worldwide problem stemming from natural and numerous anthropogenic activities such as industrial, domestic and agricultural sector. In general, heavy metals are naturally occurring constituents in the environment and vary in concentrations across geographic regions. Metals are mainly introduced into the water body by the natural process of weathering and erosion (Singh et al. 2003; Wang et al. 2007; Rauf et al. 2009a; Hejabi et al. 2010). Notably, Singh et al. (2003) found the extensive physical weathering of the Himalayas and monsoon-controlled fluvial process resulting in the strong homogenization of heavy metal distribution in the Ganga river sediments. Wang et al. (2007) demonstrated terrigenous sources role in trace metal enrichment and the lithology of parent material in Pearl River Estuary. A study conducted by Rauf et al. (2009a) found heavy metals accumulated over the years in the river bed sediments could act as secondary source of pollution to the overlying water column in the contaminated sediments.

Anthropogenic enrichment of heavy metal in freshwater system is mainly contributed by emissions from industries like electroplating, metal processing, tanneries, automobile, foundries, petrochemical processing, pesticide and fertilizer manufacturing industries, etc. (Okoye et al. 1991; Khwaja et al. 2001; Mwamburi 2003; Sarkar et al. 2004; Singh et al. 2005c; Gaur et al. 2005; Jonathan et al. 2007; Beg and Ali 2008; Sharmin et al. 2010; Liu et al. 2011a). For example, electroplating industries use metals like nickel, cadmium, copper, zinc, silver, chromium, and lead for plating process. About 4% of total amount of these metals used in this process goes as waste in sludge and effluents. Discharge of these sludge and effluents causes significant enrichment of heavy metals in the river basin (Tang and Zhang 2004; Kaushik et al. 2009; Liu et al. 2011a). Khwaja et al. (2001) found alarmingly 10-fold increase of

56 chromium concentration in the upstream and downstream of River Ganga sediments due to the discharge of untreated tannery effluent.

Industrial processes, particularly those concerned with the mining and processing of metal ores, the finishing and plating of metals and the manufacture of metal objects are major sources for heavy metals in environment. In addition, metallic compounds which are widely used in other industries as pigments in paint and dye manufacture; in the manufacture of leather, rubber, textiles, paint, paper and chromium factories forms another major anthropogenic sources (WHO 1998; Lohani et al. 2008). Mining wastes and its untreated effluents disposal contributes significant level of heavy metals into the freshwater environment (Upadhyay and Gupta 1995; Singh et al. 1999; Nagaraju and Marimulla 2001; Ogola et al. 2002; Macklin et al. 2003; Nagorski et al. 2003; Khan et al. 2005; Alagarsamy 2006; Cesar et al. 2011). Among them, Singh et al. (1999) found higher level of Cu, Ni, Zn and Cr in the coarse fraction of sediments in mining area. Khan et al. (2005) observed the mean value of Cu, Zn, Mn, Fe, Ni, Cd, Cr, Co, Pb, and Hg in the coal mine affected area are respectively 2, 6, 17, 75.5, 200, 260, 275, 350, 484, and 1200 times higher than the average levels in the world’s rivers. Similarly, Cesar et al. (2011) found higher concentration of Zn, Cu and Hg in the vicinity of Gold mining area in Brazil.

Accumulation of heavy metals in surface sediments from untreated or partially treated urban sewage discharged into the river was reported by numerous studies (Scancar et al. 2000; Dauvalter and Rognerud 2001; Mwamburi 2003; Huang and Lin 2003; Wu et al. 2008). For example, Mwamburi (2003) found high correlation between metal concentration in sediments and waste input from municipal sewage in the Kasat River. Wu et al. (2008) observed substantial level of Hg, Cd, Zn and Pb enrichment in sediments of urban sewage contaminated channel. Scancar et al. (2000) measured metals such as Cr, Ni and Zn concentration up to 856, 621 and 2032 µg/g respectively in sewage sludges.

Agricultural discharge containing residual of pesticides and fertilizers also which contains metals also is considered as another significant source. Important

57 sources of Zn, Cu and Cd pollution are also related to fungicide, pesticides and herbicides applications, which usually contain high levels of Zn salts and Cu arsenates (Kabata-Pendias and Pendias 1992; Chen et al. 1999; Sharma et al. 2007). Notably, some phosphate fertilizers contain potentially toxic elements, including As, Cd, Cr, Pd, Hg, Ni, and V (Mortvedt 1996) and some pesticides have contained Cu and As as part of their formulation. For Cr, inorganic fertilizers were the largest single source followed by atmospheric deposition and sewage sludge.

Atmospheric deposition is also one of the substantial sources for metals in soil/sediments. Volcanic eruptions and emissions, entrainment of soil and dust, entrainment of sea salt spray, and natural forest fires are significant natural emission sources of heavy metals (Chekushin et al. 1993; Wong et al. 2003). Airborne Suspended Particulate Matter (SPM) released into the atmosphere by automobile and industrial sources on deposition become source of metal pollution in water bodies. Higher level of metals associated with the suspended particulate matter arising from various sources including automobiles in the atmosphere also can enrich metals in freshwater system (Sharma et al. 2008). The world-wide emissions of metals to the atmosphere by anthropogenic sources are as follows: Pb: 450, Zn: 320, Ni: 47, Cu: 56, As: 24, Cd: 7.5, Se: 1.1 (thousand tons/year) (Clark et al. 1997). Wong et al. (2003) found that atmospheric deposition of Cu, Cr and Zn was generally higher in the summer than that in the winter, which could be caused by the washout effect of the rainy season in Pearl River Delta.

A range of influencing factors (organic matter, grain size and water flow) in relation to the accumulation of heavy metals to the sediments has been emphasized by numerous researches (Davies et al. 1991; Seidemann 1991; Singh et al. 1999; Mzimela et al. 2003; Gaur et al. 2005). For example, Mzimela et al. (2003) recorded high concentration of some metals (Al, Fe and Mn) in water and sediment in high water inflow period and lower level in the reduced riverine runoff period in Mhlathuze Estuary, South Africa. Strong correlation between the percentage of organic carbon and metal pollution in sediment of an urban estuary was reported by Seidemann (1991) and

58 role of organic matter and sediment grain size in metal accumulation was made by Davies et al. (1991).

4.1.4. Heavy metal distribution in Cauvery delta region Distribution of heavy metals in various parts of River Cauvery and its tributaries has been reported in earlier studies (Table 4.1). These studies also noted the role of various anthropogenic sources such as industrial effluents, municipal sewage discharge and agrochemicals in heavy metals enrichment.

Table 4.1. Heavy metal distribution in River Cauvery and its tributaries

Location Findings Reference Cauvery Increase of heavy metals towards the finer Subramanian et al. (1989) Estuary fractions (bed) as well as the increase of total concentration of heavy metals (bed and suspended) towards the downstream implies similar mobility of metals during transport processes. Cauvery Heavy metals enrichment in the suspended Ramanathan et al. (1993) Estuary sediments compared to the surficial bottom sediments as follows: Fe = 3.5, Mn = 7.4, Pb = 1.1, Zn = 15.2, Cu = 7.4, and Cr = 4.0. Significant levels of Cd, Cr, Zn, and Fe increased up to the middle reaches and then decreased toward the sea due to urban effluent and fertilizer input. Cauvery The study found wide variations in heavy Vaithiyanathan et al. River basin metals concentration in surface sediments (1993) due to the non-uniform grain size distribution of the sediments. Some of the metals (Cd, Zn, Cr, Pb, Cu and Ni) are enriched in sediments several times over background values. Significant contribution of heavy metal load from tributaries (Hemevathi and Kabini) to the Cauvery river. Cauvery basin Heavy metal concentration decreases with Ramanathan et al. (1996) the increase of depth. The heavy metal concentrations at certain depths are attributed to the irregular input of metals and their remobilization.

59 Cauvery Heavy metal concentrations were relatively Ramesh et al.(1999) Estuary higher than the other Indian estuaries and were uniformly distributed, indicating a predominantly geogenic origin. Cauvery High rates of chemical weathering and Dekov et al. (1998) River basin human activity (irrigation, deforestation, construction of dams and reservoirs, industrial waste release) throughout the basin leading to disbalanced transport of elements mainly in dissolved phase. Poompuhar The concentration of heavy metals was Prasath and Khan (2008) observed in the order of Mn > Fe > Cu > Zn > Pb ≈ Co ≈ Cd ≈ Ni in sediments. The variation in heavy metal concentration was probably due to the continuous stirring of sediments by the high speed tidal actions. Upstream of Heavy metal concentration in sediment Begum et al. (2009a) River samples was Co > Cr > Ni ≈ Cu > Mn > Zn Cauvery >Pb. Metal concentrations in the (Karnataka) downstream indicate an increase in the pollution load due to movement of fertilizers, agricultural ashes, industrial effluents and anthropogenic wastes. , Significant correlation between Cu, Zn and Hejabi et al. (2010) tributary of Pb revels Cu content in sediment was not River only due to its presence in the parent rocks Cauvery but also due to anthropogenic effluents of industrial area, and confirms the combination of metal affiliation of varied origin. Upper The total metal concentrations of Cr, Mn, Dhanakumar et al. (2011) reaches of Fe, Cu, Zn and Pb were recorded up to the Cauvery maximum levels of 102.6, 341.7, 4860, River delta 12.8, 37.7 and 4.04 mg/kg respectively. River Mean concentration of metals such as Fe, Venkatesharaju et al. Cauvery in Mn, Zn, Cr, Ni, Cu, Pb, Co and Cd were (2012) Karnataka recorded as 11144, 1763.3, 93.1, 389, 27.7, 11.2, 4.3, 1.9 and 1.3 μg/g, respectively.

However, most of these studies focused on upper stretch and estuarine regions. Studies in Delta region and its distributaries are negligible and hence the present work focused in this region to bring out spatial and temporal changes in nutrients and heavy metals distribution.

60 4.2. Materials and methods For the present study, surface sediments (top 15cm) were collected from 40 locations in the River Cauvery in delta region during dry and wet season of year 2008-2009 and 2009- 2010. The collected sediment samples were air dried at room temperature and homogenized using an agate mortar and pestle and sieved using a 63 µm sieve. The particles with size less than 63 µm are retained in pre-cleaned plastic bottles for further laboratory analysis. Soil texture analysis was carried out as per standard sieve method using mechanical sieves of different mesh sizes. Sediment particles in the range from 2000 to 63 µm were taken as coarse fractions and less than 63 µm was taken as fine fraction. The portion of the sample meant for chemical characterization was analyzed for the following parameters viz., pH, electrical conductivity, total dissolved solids and oxidation reduction potential were analysed from the sediment water suspension in the ratio extract in the ratio of 1:5 using respective electrodes of water analyzer (Model : 371, Make: Systronics).

Total Organic Carbon (TOC) was estimated as per Walkley and Black method (1934), wherein the organic matter in the sediment is oxidized with a mixture of Potassium

Dichromate (K2Cr2O7) and concentrated Sulphuric Acid (H2SO4). The unspent potassium dichromate was back titrated against Ferrous Ammonium Sulphate solution. Total nitrogen (TN) content in sediment samples were estimated as per Jackson et al. (1958). Total Available Phosphorus was estimated as per standard Olsen’s method after extracting with 1M Sodium bicarbonate solution (pH: 8.5). Available form of phosphate in the solution was estimated as per ascorbic acid method spectrophotometrically using Systronics spectrophotometer, model SL 171 (Jackson et al. 1958). The nutrient ratios, C/N, C/P and N/P were empirically estimated. Aqua regia digestion was adopted to estimate the total metal content. Briefly, one gram of sediment from each sample was digested with aqua regia (HNO3/HCl= 1:3) and HClO4 for two hours to determine the concentrations of Fe, Mn, Cu, Cr, Pb, Zn, and Ni as per Chen et al. (2006).

4.3. Results and discussion 4.3.1. Sediment characteristics The pH of the sediment samples ranged from 6.89 to 9.08 revealing slightly acidic to strong alkaline nature. Slight acidic pH observed in certain sampling stations during dry

61 season might be due to the decomposition of organic matter and subsequent formation of carbonic acid (Ahmad et al. 1996). Electrical Conductivity (EC) ranged from 8.6 to 3910 µS/cm. Maximum level of EC was noted at Muthupet (S40) and minimum was observed in Thiruperambium (S10) in dry season (June 2008). Total Dissolved Solids (TDS) level in sediment samples ranged from 3.99 to 1800 mg/kg. Maximum level of TDS was observed at Muthupet (S40) and minimum at Thiruperambium (S10) during dry season (2008). Oxidation Reduction Potential (ORP) ranged from -152 to 53 mV with a mean level of -86 mV. Grain size distribution in River Cauvery is a low gradient and coarse-grained system. Most of the fine sediments are trapped in dams/reservoirs, thus the river bed-sediments usually contain low level of fine fraction. The 500-250 and 250–150 µm sizes are the dominant fractions, accounting for about 60–75% of the sediments.

4.3.2. Organic carbon The soil organic carbon is a common constituent of all organic matters. Total Organic Carbon (TOC) and Organic Matter (OM) level in sediments was observed in the range 0.08 - 5.2 % and 0.07 - 8.97 %, respectively (Figure 4.2A). Maximum level of TOC was recorded at Vadapadi (S36) and minimum at Arasalur (S38). Higher organic carbon content recorded during dry season suggests active organic decomposition in stagnant waters and mineralization of organic matter deposited in the sediment (Sharma 1994). In most of the unpolluted estuaries, TOC content of the sediments is <5% except where water column is anaerobic or vegetal matter is abundant. However in areas, where organic pollutants are high TOC often exceeds 5% (Alagarsamy 1991). Highest TOC found in the present study was 5.2%, which is slightly higher than the standard limit for a polluted estuary. Paired T test yielded significant difference in TOC content in sediments between seasons and studied years.

The association of TOC is crucially dependent on the sediment grain size, whereby higher organic content represents in the finer particles (Marchand et al. 2008). As hinted earlier, grain size distribution in the study area is dominated by coarse-grained system. Thus, low organic carbon level in the study area may be due to the sediment texture which was dominated by sand (90 - 95%). Coppola (2007) and Soto-Jimenez (2002) reported about 10-20% of organic carbon in the fine grain sediments of river and mangrove region.

62 Nichols and Biggs (1985) has demonstrated that the concentration of absorbed organic carbon is directly proportional to clay content in a soil. The comparison of present results with existing reports is given in Table 4.2. While compared with other riverine observations of organic carbon and total nitrogen level is higher than Hugli River, India.

Table 4.2. Comparison of organic carbon and total nitrogen level in the sediments of various aquatic environments.

River TOC (%) TN (%) Reference Jacui River, Brazil 0.62 - 2.11 0.01-0.22 Baisch (1994) Mandovi River, India 0.1- 3.28 0.05- 0.67 Nasnolkar et al. (1996) Cullacan River, Mexico 0.3-3.0 0.04 - 0.29 Fernandez et al. (2002) Ore River, Sweden 1 - 3 0.09 - 0.54 Franciskovic-Bilinski et al. (2003) Pearl River, China 0.3 - 0.5 0.1 - 0.6 Cheung et al. (2003) Hugli River, India 0.31 - 0.61 0.06 - 0.07 Sarkar et al. (2004) Mirim lagoon, Uruguay 0.2 - 2.37 0.03 - 0.30 Santos et al. (2004) Daliao river, China 0.06 -1.19 --- Wang et al. (2009) East China Sea shelf 0.2 - 1.5 0.02 - 0.19 Kao et al. (2001) River Cauvery in delta region 0.08- 5.20 0.01 - 0.26 The present study

63 1800 4 1600 3.5 Dry season Wet season Dry season Wet season 1400 3 1200 2.5 1000 2 800

TOC (%) TOC 1.5 TP (mg/kg) TP 600 1 400 0.5 200 0 0 S1S4S7S10S13S16S19S22S25S28S31S34S37S40 S1 S4 S7 S10 S13 S16 S19 S22 S25 S28 S31 S34 S37 S40 Sampling sites Sampling sites 0.25 A B 0.2 Dry season Wet season

0.15

0.1 TN (%) TN

0.05

0 S1 S4 S7 S10 S13 S16 S19 S22 S25 S28 S31 S34 S37 S40 Sampling sites C

Figure 4.2. Spatial and temporal variation of A) Total organic carbon B) Total phosphorous and C) Total nitrogen in sediment samples of River Cauvery in delta region.

64 40 10 24 7 9

6 20 32 8 A C:N N:P B C:N N:P 5 7 16 24 6 4 ) ) ) ) 5 12 3 16 4 N:P ratioN:P (% C:N ratioC:N (% ratioC:N (% 8 N:P ratio (% 3 2 8 2 4 1 1

0 0 0 0 S1 S4 S7 S10 S13 S16 S19 S22 S25 S28 S31 S34 S37 S40 S1 S4 S7 S10 S13 S16 S19 S22 S25 S28 S31 S34 S37 S40

Sampling sites Sampling sites

Figure 4.3. Spatial and temporal variation of C:N and N:P ratio in river sediments of CDR during A) Dry period (2008) B) Wet period (2009). 60 8 9

100 8 7 50 A C:N N:P B C:N N:P 7 6 80 40 6 5 ) ) ) 60 5 ) 30 4 4 3 C:N ratio (% ratioN:P (% N:P ratio (% 40 ratioC:N (% 20 3 2 2 20 10 1 1

0 0 0 0 S1 S4 S7 S10 S13 S16 S19 S22 S25 S28 S31 S34 S37 S40 S1 S4 S7 S10 S13 S16 S19 S22 S25 S28 S31 S34 S37 S40 Sampling sites Sampling sites Figure 4.4. Spatial and temporal variation of C:N and N:P ratio in river sediments of CDR during A) Dry period (2009) B) Wet period (2010).

65 100 100 40 100

80 32 80 A 75 TAP C:P TAP C:P B

60 24 60 ) 50 )

40 16 40 C:P ratio (% C:P ratio (% TAP (mg/kg) TAP TAP (mg/kg) TAP 25 20 8 20

0 0 0 0 S1 S4 S7 S10 S13 S16 S19 S22 S25 S28 S31 S34 S37 S40 S1 S4 S7 S10 S13 S16 S19 S22 S25 S28 S31 S34 S37 S40 Sampling sites Sampling sites

Figure 4.5. Spatial and temporal variation of total available phosphorus and 90C: P ratio in river sediments of CDR during A) Dry period (2008),150 50 B) Wet300 period (2009).

75 250 A TAP C:P 120 B 40 TAP C:P 200 60 30 90 )

v ) 150 45

20 60 C:P ratio (%

100 30 (% ratio C:P TAP (mg/kg) TAP (mg/kg) TAP

10 30 50 15

0 0 0 0 S1 S4 S7 S10 S13 S16 S19 S22 S25 S28 S31 S34 S37 S40 S1 S4 S7 S10 S13 S16 S19 S22 S25 S28 S31 S34 S37 S40 Sampling sites Sampling sites Figure 4.6. Spatial and temporal variation of total available phosphorus and C: P ratio in river sediments of CDR during A) Dry period (2009), B) Wet period (2010).

66 4.3.3. Nutrients Concentration of Total Nitrogen (TN) in sediment samples in the study area was measured in the range between 0.01 - 0.26% with an average of 0.13% (Figure 4.2C). Maximum level (0.26%) of TN was recorded at Thiruvarur (S30) in wet season where as minimum level (0.01%) was recorded at Kumbakonam (S18). Decomposition of organic matter during transport by rivers leads to variable presence of sediment-bound nitrogen in the river system (Subramanian 2008). Earlier study by Rivas et al. (2000) found the concentration of nitrogen varying directly with the increase in high flow periods, whereas in the present study there was no statistically significant difference (P >0.05) in TN level between seasons studied. Nitrogen retention in freshwater system has a significant impact on ecosystem processes and the importance of nitrogen as a limiting nutrient is increasingly recognized (Elser et al. 1990; Downing and McCauley 1992). Total Nitrogen (TN) concentration was generally found in the range 0.2 to 0.4% of sediments dry weight (Alongi et al. 1992).

Total Phosphorus (TP) level in River Cauvery sediment samples varied from 418 to 1600 and 402 to 1660 mg/kg during dry season and wet season, respectively (Figure 4.2B). Maximum level of TP was recorded at Poompuhar (S23) and minimum at Nagore (S29). Mean concentration of TP was recorded as 925 and 893 mg/kg in dry and wet season, respectively. Slightly high concentration of TP observed in the dry season than wet season can probably be attributed to the leaching of phosphorus from sediments to the overlying water column that gives relatively high values of dissolved phosphate in wet season (Nasnolkar et al. 1996). Concentration of TP in Indian river sediments was observed in range from 700 to 1400 mg/kg (Vaithiyanathan et al. 1989). Paired t-test yielded significant difference in TP level between years studied but not seasons.

The total available phosphate (TAP) content of sediments varied between 0.6 and 88.3 mg/kg with an average of 19.7 mg/kg (Figure 4.5 and 4.6). Maximum level of TAP was observed at Semanallur (S34) and minimum at Muthupet (S40). The discharges from agriculture drains contain substantial remains of phosphorus fertilizers which may contribute to the higher phosphorus concentrations noted in low flow period samples. In general, remarkable variation of TAP concentration was noted in the entire

67 study area. This pattern may be due to varying levels of sewage dumping and agrochemicals input in the basin. Flushing of the sediment during high flow period may lead to the lower concentration observed in the wet season samples (Mathews and Chandramohanakumar 2003). Alike TP, Paired t-test yielded significant difference in TAP concentration in sediments between years studied but not between seasons. Higher level of total available phosphorous recorded at few sampling sites reveals notable contribution from sewage and agricultural runoff. However, high concentration of available phosphorous in the sediments may probably be influenced by sewage discharge which has been reported as a major anthropogenic source of phosphorous throughout the delta region (Tandon 1987). Earlier study by Solaraj et al. (2010) also reported the significant nutrients input into Cauvery River system from agricultural runoff. Mainstone and Parr (2002) found that the point sources usually contain highly available phosphorus derived from sewage treatment works, whereas diffuse sources of contamination are highly delivered from surface runoff, whose availability is more complicated and highly seasonal. Bulut and Aksoy (2008) pointed out that only 10–15% of applied phosphate fertilizer is absorbed by the crops, while the rest is lost through various processes.

4.3.4. Nutrient ratios The C/N ratio is a well known natural source indicators of sedimentary Particulate Organic Matter (POM). It can be used to identify organic matter derivation in fresh water and estuarine environments, allowing discriminating the origin of pollutants associated to the sedimentary organic material (Thornton and MacManus 1994). In the Cauvery delta region, C/N ratio figured between 0.5 and 103.5 with an average of 7.45. Maximum level of C/N ratio (103.5) was recorded at Vadapadi (S36) during dry season period (2009) (Figure 4.3 and 4.4). It implies that suspended matter transported by the river has being deposited in the lower region of delta due to the process of agglomeration and aggregation (Nichols and Biggs 1985). The C/N ratios above 20 suggest a terrestrial source, whereas lower C/N ratios imply a mixture of non- vascular and autochthonous organic matter sources (Meyers and Ishiwatari 1993).

In the present study, about half the number of sediment samples C/N ratio was recorded below 8 in wet season. It suggests the dominant contribution from in-situ

68 phytoplankton growth to the riverine POM. The low C/N values may be due to the complete degradation of the organic matter. The present study findings are similar observations made by Paramasivam and Breitenbeck (1994). Gao et al. (2007) opined that the autochthonous source may overwhelm allochthonous source in some clean, slow flowing waters, making C/N ratio less than eight. Thorp et al. (1998) reported C/N values that ranged from about eight to ten for benthic algae and about 11 to 12 for aquatic macrophytes in the Ohio River. Hamilton and Lewis (1992) report C/N ratios of different size-fractions of algal-derived POM from the Orinoco River floodplain in Venezuela in the range from 6 and 9.

The C/N ratio higher than eight recorded in Tiruchirappalli Mambala Salai (S5), Sathanur (S8), Thiruperambium (S10), Papanasam (S17), Mayavaram (S20), Kanjanagaram (S21), Thenperambur (S24), Thiruvankadu (S25) and downstream regions (Semanallur (S34), Rajapuram (S35), Vadapadi (S36), Arasalur (S37), Krishnapuram (S38), Pallingamedu (S39) and Muthupet (S40) might be due to dominant contribution from allochthonous source to riverine POC. High C/N ratios observed in the dry season samples are on par with higher total suspended solids concentration in the surface water samples. Meybeck (1982) stated that C/N ratio for riverine particulate organic matter (POC) lies in the range between 10 to 12, which is close to that of soil organic matter.

The C/P ratio was observed in the range 1.4 to 289.2 and 0.6 to 144.6 during dry and wet period, respectively (Figure 4.5 and 4.6). Maximum level was recorded at Vadapadi (S36) during dry period and minimum level in wet season at Arasalur (S37). Mean level of C/P ratio is significantly high during dry period (29.2) than wet period (15.3). The lower ratios recorded in Upper Anicut (S4), Tiruchirappalli Mambala Salai (S5), Grand Anicut (S6), Pundi (S7), Ammapalli (S9), Kollidam (S13) and few sampling sites during wet and dry season indicates substantial input of phosphorus in the delta region whereas, the higher ratios recorded in the dry period attributed to the cumulative effect of high organic carbon and low phosphorus content of sediments. Paired T-test yielded significant difference in C/P ratio between dry and wet season samples (P<0.05).

69 The N/P ratio in sediment samples of Cauvery delta was recorded in the range from 0.20 to 9.02. In general, high N/P ratio observed in the estuarine sample sites such as Kollidam (S13), Killiyur (S22) and Pallingamedu (S39) reveals notable accumulation of nitrogen from allochthonous and autochthonous sources. High N/P ratio recorded in more than half number of samples suggests low phosphorous content in sediments. There was no significant difference in N/P ratio between wet and dry season sediment samples (P >0.05). Notably, high level of C/P and N/P ratios recorded in sediment samples indicates substantial anthropogenic input of nitrate and phosphate. Earlier study conducted by Nasnolkar et al. (1996) also found similar trend of nutrient ratios in Mandovi River, India.

4.3.5. Heavy metal distribution in surface sediments The relative abundance of heavy metals in sediments of River Cauvery in delta region was found in the order of: Fe > Mn > Cr > Zn > Cu > Ni > Pb.

Iron (Fe) is the most abundant metal in all sediments. The average abundance of Fe in the earth’s crust is 47,000 μg/g. Fe content in sediment samples ranged from 4291 to 6023 μg/g and 3906 to 5740 μg/g in dry season and wet season, respectively (Figure 4.7). Maximum level (6023 μg/g) was recorded at Sathanur (S8) in dry season (2008) and minimum at Semanallur (S34) during wet season (2010). The dry season period recorded higher concentrations of Fe than wet season period. Earlier study conducted by Venkatesharaju et al. (2012) in the upper region of River Cauvery also found same trend of observation. Paired T-test yielded significant difference in Fe concentration between seasons (P<0.05) throughout the study period.

Manganese (Mn) is the 11th most abundant element in the earth crust (Anschutz et al. 2005). Mn level in the present study was observed in the range 55 - 1057 μg/g with mean level of 234 μg/g. Mean concentration of Mn was found as 220 and 260 μg/g in dry season and wet season, respectively. These values are much lower than the average value of 605 μg/g for Indian rivers (Subramanian et al. 1985). Maximum concentration (1057 μg/g) of Mn was observed at Upper Anicut (S4) and minimum (55 μg/g) at Kulithalai (S3) during wet season (2008-2009) and dry season (2009- 2010), respectively. Apart from the natural mineralogical sources, the only source of

70 Mn is macronutrient fertilizers application (Wolters et al. 1989). According to Allen (1989), sediment in unpolluted environment contains Mn in the range from 5 to 500 μg/g.

Chromium (Cr) is one among the 14 most noxious heavy metals and seventh most abundant element on the earth. In sediment samples of River Cauvery, Cr level ranged from 23 to 540 μg/g and 2 to 76 μg/g during dry season and wet season, respectively. Maximum concentration of Cr was recorded at Upper Anicut (S4) and minimum at Thenperambur (S24) in wet season. Comparatively, mean Cr level (109 μg/g) in dry season samples were many fold higher than the levels (13 μg/g) obtained in the wet season sediment samples. It suggests accumulation of Cr in the river bed during low flow period in dry season probably due to less dilution effect. Singh et al. (2002) reported upto 817 μg/g of Cr in sediments of River of Ganga, India. Paired T-test yielded significant difference in Cr level was observed between seasons (P<0.05) throughout the study period. An average value of 87 μg/g of Cr has been reported by Subramanian et al. (1985) for sediments of Indian Rivers.

Copper (Cu) content in sediment samples ranged between 0.1 and 335 μg/g in the study period. Maximum level was found at Tiruchirappalli Mambala Salai (S5) and minimum at Mayavaram (S20). Mean level of Cu in wet season samples (69 μg/g) were six fold higher than the dry season sediments samples. Paired T-test yielded significant difference in Cu level observed between seasons (P<0.05) throughout the study period. Mean Cu content in sediments of River Cauvery is slightly higher than the mean concentration of Cu (28 μg/g) in River sediments of India (Subramanian et al. 1985). Anthropogenic sources play a significant role in enrichment Cu particularly in river sediments (Kabata-Pendias 2004).

Lead (Pb) is second among the top 20 priority list of hazardous substances (ATSDR 2005). Pb level in sediment samples varied from 0.1 to 68.6 and 0.5 to 59.6 μg/g during dry season and wet season, respectively. Maximum level was found at Tiruchirappalli Mambala Salai (S5) and minimum at Mayavaram (S20). Pb derived from discarded batteries and their migration to water bodies through storm water runoff and sewage is suspected as a probable source in addition to leaded gasoline (Khan

71 2001). The concentration of Pb in unpolluted sediments of United Kingdom recorded as 20 μg/g, where as concentration was reported more than 2700 μg/g in heavily contaminated sediments (Bryan and Langston 1992). Paired T-test yielded significant difference in Pb concentration between seasons (P< 0.05).

600 600

500 500

400 400

300 300

200 200 Concentration (ug/g) Concentration (ug/g)

100 100

0 0 Cr Cu Pb Zn Ni Cr Cu Pb Zn Ni

A Metals B Metals

7000

6000

5000

4000

3000

Concentration (ug/g) Concentration 2000

1000

0 Fe-D Mn-D Fe-W Mn-D C Metals Figure 4.7. Total heavy metal distribution in sediments of River Cauvery in delta region A) Distribution of various heavy metals during dry season B) Distribution of various heavy metals during wet season (All values in µg/g) C) Fe and Mn concentration during dry season (Fe-D and Mn- D) and wet season (Fe-W and Mn-W)

Zinc (Zn) content in sediment samples varied between 5.7 to 526.9 μg/g. Maximum Zn content was recorded at Nagapattinam (S31) and minimum at Krishnapuram (S38). Higher concentrations of Zn and Cu were recorded during wet season. Industrial emissions, composted materials and agrochemicals are major sources of Zn in aquatic environment (Romic and Romic, 2003). Zn can enter the aquatic

72 environment from a number of sources including sewage effluent and runoff. Besides, input of organic wastes into the river basin, which comes from sewage also contributes Zn enrichment in sediments (Boxall et al. 2000; Alagarsamy 2006).

Nickel (Ni) in the present study, was observed in the range 0.2 to 47.9 µg/g. Maximum Ni content was recorded at Kollidam (S13) and minimum at Thirumukudallur (S2). The present observation of Ni distribution was similar to the reported level of Ni (7 to 50 µg/g) in non-contaminated soils (Merian 1991). Mean level of Ni (15.1 µg/g) in sediment samples of River Cauvery is well below than the Indian rivers mean level of 37 µg/g (Subramanian et al. 1985).

Variation in levels of heavy metals between samples could be probably attributed to the grain size variation, difference in sediment components responsible for sorption and varying amounts of anthropogenic contributions (Vaithiyanathan et al. 1993). Untreated or partially treated industrial effluents, municipal sewage, agrochemical runoff and storm water runoff are suspected sources of metal pollution in the study area. The continuous application of organic and inorganic fertilizers For example, phosphate fertilizer containing heavy metals such as Cd, Cu, Hg, Ni, Pb and Zn (Jones and Johnston 1989), and other soil amendments potentially enrich heavy metals in agricultural soils (Huang et al. 2007; Lambert et al. 2007). The pesticide consumption in mid and lower delta region is also higher (45 kg/ha) than the upper reaches of River Cauvery (38.2 kg/ha) (Rajendran and Subramanian 1999).

The present observation is similar to other polluted rivers and contaminated sediments elsewhere. Correlation matrix of the total heavy metal concentration is shown in Table 4.3. Significant positive relationship observed between Cu, Cr, Pb and Zn, suggests the anthropogenic origin from industrial waste discharge and municipal sewage.

73 Table 4.3. Correlation analysis for heavy metal concentration in Cauvery delta region

Fe Mn Cr Cu Pb Zn Ni Dry season Fe 1.0 Mn .056 1.0 Cr .108 .317** 1.0 Cu -.182 -.037 -.161 1.0 Pb -.075 .221* -.006 .321** 1.0 Zn -.221* .071 .062 .669** .307** 1.0 Ni -.288** -.064 .143 .268* .136 .498** 1.0 Wet season Fe 1.0 Mn .248* 1.0 Cr .082 .441** 1.0 Cu .165 .089 .036 1.0 Pb -.102 .112 .281* -.172 1.0 Zn -.125 .070 .253* -.058 .508* 1.0 Ni .153 .370** .453** .101 .036 .183 1.0 * Correlation is significant at the 0.05 level (2-tailed). ** Correlation is significant at the 0.01 level (2-tailed).

74 4.3.6. Assessment of risk by the presence of heavy metals 4.3.6.1. Sediment quality guidelines Table 4.4. Classification of sediment quality guidelines (SQGs) and its effects

Sediment quality guidelines Effect Reference TEL and PEL PEL Frequently associated with adverse biological effects LEL and SEL SEL Several impacted ERL and ERM ERM Effects would frequently occur

Table 4.5. Comparison between sediment quality guidelines (SQGs) and heavy metals concentration (µg/g) in the present study. Sediment quality guidelines Fe Mn Cu Cr Zn Pb Ni Threshold Effects Level (TEL) - - 18.7 52.3 124 30.2 15.9 Probable Effects Level (PEL) - - 108 160 271 112 42.8 Lowest Effect Level (LEL) 20000 460 16 26 120 31 16 Severe Effect Level (SEL) 40000 1100 110 110 270 110 50 Effects Range Low (ERL) - - 34 81 150 46.7 20.9 Effects Range Medium (ERM) - - 270 370 410 218 51.6 Measured levels in this study Dry season Range 4291-6023 23-579 0.1-128 4-539 14-97 0.1-68 0.2-42 Average 5259 208 11 109 36 16 15 Wet season Range 3906-5740 34-1056 6-335 2-76 6-527 0.5-60 0.3-48 Average 4758 260 69 13 47 9 15

75 In order to assess the status of sediment health in River Cauvery in delta region, the present observation of total metal concentration was compared with three sets of Sediment Quality Guidelines (SQG). The classification of SQG along with its effects and comparison results are presented in Table 4.4 and 4.5. According to SQG, 71.25%, 46.25%, 40% and 17.5% of sediment samples falls under the category between TEL- PEL of Cu, Cr, Ni and Pb, respectively. Cr level in three sample sites Kulithalai (S3), Upper Anicut (S4) and Ammapalli (S9) exceeded the ERM level in the dry season period. Although a small variation has been observed among the three sediment quality guidelines, the results reveals that most of the heavy metals studied (Cu, Cr, Zn, Ni and Pb) are of concern in the Cauvery delta region and it may connected with adverse biological effects.

4.3.6.2. Geo accumulation index (Igeo) Geo Accumulation Index (Igeo) proposed by Muller (1979) was used to evaluate the possible sediment enrichment and quantitative check of metal pollution in aquatic sediments. It can be determined using a formula: Igeo = log2 (Cn/1.5×Bn).

Where, Cn is the concentration of the metal in sediments, and Bn is the background value of the element. 1.5 is the correction factor to minimize the effect of possible variation in the background value due to lithologenic effect in the sediment (Turekian and Wedepohl 1961). Igeo values for the Cauvery River sediments are given in Table 4.6. As per Igeo index calculation Fe, Mn and Ni were recorded under unpolluted class, while Pb falls under unpolluted to moderate polluted category. Metals such as Zn, Cr and Cu were recorded in moderately polluted category. In general, on the basis of the Igeo mean values, the studied heavy metals enrichment are in order of Cr > Zn > Cu > Pb > Mn > Ni > Fe.

76 Table 4.6. Geo accumulation Index (Igeo) for total heavy metal distribution in sediments Igeo Value Class Sediment Quality Metals < 0 0 Unpolluted Fe, Mn, Ni 0- 1 1 Unpolluted to moderate polluted Pb 1-2 2 Moderate polluted Cr, Zn, Cu 2-3 3 Moderate to strongly polluted - 3-4 4 Strongly polluted - 4-5 5 Strongly to extremely polluted - >6 6 Extremely polluted -

4.3.7. Principal component analysis Principal Component Analysis (PCA) is one of the widely used multivariate statistical tools applied to assess the source of origin and metal behaviour in sediments (Simeonov et al. 2003; Zhou et al. 2006). In the present study, PCA has extracted three components with eigen values > 1 explaining 61.67 % of the total variance of the dataset (Table 4.7 and Figure 4.8). First factor accounted for 24.29% of total variance with high loadings of Fe and Cr. Fe - Cr combination in this factor suggested natural origin of these elements. Factor 2, accounted for 18.93% of total variance describing anthropogenic sources for Pb and Zn. Factor 3 containing loadings of Mn Ni and Cu, suggested natural as well as anthropogenic sources.

Table 4.7. Total variance explained Factor Initial Eigen values Extraction Sums of Squared Rotation Sums of Squared Loadings Loadings Total % of Cumulative Total % of Cumulative Total % of Cumulative Variance % Variance % Variance % 1 1.72 24.54 24.54 1.72 24.54 24.54 1.70 24.29 24.29 2 1.50 21.46 46.01 1.50 21.46 46.01 1.32 18.93 43.22 3 1.09 15.67 61.67 1.10 15.67 61.67 1.29 18.45 61.67 4 0.84 11.96 73.63 5 0.64 9.22 82.85 6 0.64 9.18 92.03 7 0.56 7.97 100.0

Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization. a Rotation converged in 6 iterations.

77 1.0

.8 zn pb

.6

.4 ni Factor 2 cr .2 mn

0.0 fe -.2 cu

1.0 .8 .8 1.0 .6 .4 .6 .4 0.0.2 .2 -.4 -.2 Factor 3 0.0 -.6 Factor 1

Figure 4.8. Factor loadings of principal component analysis

4.4. Conclusion In the present investigation, spatial and temporal variation of nutrients and heavy metals distribution in surface sediment samples of River Cauvery in delta region was carried out as per standard protocols. Higher level of nutrient ratios in the dry season implies substantial deposition of suspended matters which are transferred by the river. Considerable influence of water flow rate on organic carbon and nutrients distribution was noted in the entire delta region. Correlation analysis shows significant positive relationship between Cu, Cr, Pb and Zn suggesting a common source of enrichment of these metals. Alarmingly, sediment quality guidelines reveal that the most of the heavy metals studied (Cu Cr, Zn, Ni and Pb) are of concern in terms of adverse health risks to the exposed population. Principal component analysis also highlights substantial input of heavy metals by manmade activities. In conclusion, organic carbon nutrients and heavy metals distribution was significantly influenced by various anthropogenic inputs. Industrial effluents, municipal sewage discharge and agrochemical runoff are the suspected contributors of nutrients and heavy metals in the Cauvery delta region.

78 Chapter-5 Heavy metal fractionation in surface sediments of River Cauvery in delta region

5.1. Background Analyzing the heavy metals speciation in the environment has become paramount necessity in terms of human health in particular and ecosystem sustenance in general. Metals in the environment occur naturally as result of weathering process. However, since industrialization anthropogenic activities such as industrial emissions, combustion by-products, automobile emissions, mining activities, foundries and smelters, and diffuse sources such as piping, constituents of products and etc (Kubin and Lippo 1996) causes elevated levels of heavy metals emissions into the ecosystems exceeding the natural emission rate (El-Hassan and Jiries 2001; Everrarts 1989; Homady et al. 2002). Around 15 times more cadmium (Cd), 100 times more lead (Pb), 13 times more copper (Cu) and 21 times more zinc (Zn) are emitted to the atmosphere by human activities than by natural processes. In urban areas, various stationary and automobile sources release large quantities of heavy metals into the atmosphere and soil, exceeding the natural emission rates (Nriagu 1989; Bilos et al. 2001).

Depending on their origin, metals in ecosystem exist in different mineral forms and chemical compounds, and in different combinations with mineral and organic components of sediments (Berner and Berner 1996; Tokalioglu et al. 2000). Bioavailability, toxicity and mobility heavy metals in the environment depend largely on its association with different chemical forms or otherwise known as speciation (Hamelink et al. 1994). To understand these properties of heavy metals, conventional analysis of total metal concentrations is insufficient. Chemical speciation based on sequential extractions can provide important information about the distribution of metals in sediments which has been increasingly felt significant worldwide for predicting and modeling the fate, risk and their toxic effects in the environment. The vital component of metal exposure is bioavailability; it is defined as the fraction of total

79 metal that is available to exert action and effect within respondent organism (Baker et al. 2003). It is widely believed that easily mobile or free metal ion forms and exchangeable forms are bioavailable forms and causes biological toxicity in an ecosystem by diffusion into cell membrane, sorption/complexation of metal at binding sites in cell membrane and uptake into the organism (Morel 1983). It is also believed that metals in adsorbed, carbonate, sulfide and organic bonds are highly correlated to pollution and higher risk of bioavailability (Karbassi et al. 2008).

5.1.1. Metal speciation and fractionation The concept of elemental speciation dates back to 1954, when Goldberg introduced the concept of oxidized versus reduced, chelated versus free metal ions of trace elements in seawater (Jain 2004). Since then speciation studies attracted worldwide environmentalists and chemists and became very prominent when Tessier et al (1979) described the sequential extraction analytical procedure for heavy metals. Trace metals speciation occurring in a given environment predominantly depend on the geochemical structure of the area and underlying rock. However anthropogenic activities (urban runoff and industrial effluents) which represent a fraction of heavy metals that are transported in the soluble form also influence the speciation (Ramesh et al. 1988). Sediments of aquatic ecosystems in many regions besides acting as a sink of heavy metals also act as source for bioavailable fraction of metals. Sediments act as traps for most of the heavy metals by forming stable complexes with sedimented organic matter, carbonates, and iron-manganese oxides (Duzzin et al. 1988; Rajendran et al. 1992).

Due to its highly reactive nature of heavy metals, they are more quickly converted into other species depends upon the characteristics of the receiving environment (Khan 2001). The term “speciation” which deals about the distribution of metal species in a particular sample or matrix is emerged as a key to address these issues. Speciation analysis is an analytical activity focused to identify and/or measure the quantities of one or more individual chemical species in a particular sample or matrix (Templeton et al. 2000). As hinted earlier, each metal species of the same metal

80 illustrate its distinct characteristics in terms of mobility, bioavailability and toxicity, speciation studies stands as a decision making tool to evaluate the status of metal contamination in the environment. For example, Free Cu2+ ion in the water column is likely to disperse from the site of release through diffusion and through physical movement such as currents, while solid CuS is likely to settle in the sediment where it may remain for long periods of time.

Although, importance of speciation analysis are well recognized, in some cases metal species present in the environment are not stable enough to be determined in the original form and analytical procedures influenced species changes also affects the accuracy of measurement and fails to replicate the metal species present in the environment. Even, in many cases the large numbers of individual species (e.g., in metal-humic acid complexes) makes it impossible to determine the speciation. In this regard, the new practice has been emerged to identify various classes of species of an element and to determine the sum of its concentrations in each class. Identification and quantification of metals associated with predefined phases was termed as "metal fractionation” where metal portioning was performed based on its physical and chemical characteristics (Templeton et al. 2000).

Sediments act as traps for most of the heavy metals by forming stable complexes with sediment organic matter, carbonates, and iron-manganese oxides (Duzzin et al. 1988; Rajendran et al. 1992). Based on the primary accumulation mechanism of heavy metals on sediments, it can be classified into six categories such as 1) easily exchangeable ions 2) metal carbonates 3) oxides, 4) sulphides 5) organometallics compounds, and 6) ions in crystal lattices of mineral (Lopez- Sanchez et al. 1993; Weisz et al. 2000; Kuang-Chung et al. 2001) (Figure 5.1.1).

81

Figure 5.1.1. Common associations of metals in sediments (Gunn et al. 1988)

In this background, metal fractionation studies are carried out through sequential extractions of operationally defined metal fractions using specific reagents at various conditions. In general, the mobility and bioavailability of metals decrease in the order of extraction sequence of exchangeable > carbonate bound > Fe-Mn oxide bound > organic matter bound > lithogenic fraction (Prusty et al. 1994) and hence the strength of the chemical reagents increases in the sequence (Figure 5.1.2). Metals present in these categories have different remobilization behaviors under changing environmental conditions (Forstner 1985) and their solubilities, which directly influence their bioavailability (Xian 1987). It is also believed that metals in adsorbed, carbonate, sulfide and organic bonds are highly correlated to pollution and higher risk of bioavailability (Karbassi et al. 2008).

82

Figure 5.1.2. Relationship between metal mobility in the different operationally- defined phases and leachant strength of common chemical reagents used for sequential extraction (Filgueiras et al. 2004)

5.1.2. Sequential extraction scheme In order to portion the sediments bound metals into operationally defined factions, the sequential extraction schemes have been developed by several workers (Tessier et al. 1979; Li et al. 2001a; Svete et al. 2001; Pustisek et al. 2001; Rauret et al. 1999; Sutherland and Tack 2003; Tokalioglu et al. 2003). The most appropriate sequence can be determined by the type of sediment being examined, the horizon involved, the elements of interest and so on (Pickering 1986). The two important factors should be taken into the account for sequential extraction method development is thermodynamic stability and kinetic inertness of the target species (Lobinski and Potin-Gautier 1998). Since, slight changes in ambient conditions frequently disturbs the existing physico-chemical equilibria in such cases finally determined metal species may not imitate the existing original species of the sample. Among the sequential extraction schemes, five step extraction scheme developed by Tessier et al. (1979) and the Community Bureau of Reference (BCR) procedures have been widely used due to its reagents selectivity, operating conditions and reliable results for the targeted phases (Shiowatana et al. 2001). Element specific methods have also been developed by several workers. For example, Polton and Canfield (2005) developed extraction scheme of iron. According to protocol developed by Tessier et al. (1979), metals fractioned into

83 five major geochemical forms such as exchangeable, carbonate bound, Fe-Mn oxides bound, organic matter and sulfide bound and residual fraction. Based on Tessier’s sequential extraction procedure, metal fractionation studies are being extensively carried out across the world. In many such investigations extraction procedures are modified for different kinds of soils/sediments to increase the extraction efficiency by changing reagents used and the time of their action (Kersten and Forstener 1986; Horowitz 1991). The European Community Bureau of Reference (BCR) proposed the three-step modified sequential extraction scheme, focusing on the reproducibility of the extraction and the selectivity of the extractants (Rauret et al. 1999).

Most sequential extraction schemes follow similar fractional degradation with small variation. Ure et al. (1993) extracted the exchangeable and carbonate-bound fractions in a single step versus the two steps used in the Tessier procedure. A method developed by Geological Survey of Canada divides the Fe-Mn oxide fraction into the amorphous oxyhydroxides and crystalline oxides, thereby increasing sequential fractionation from five to six steps (Hall et al. 1996). Some extraction methods modified the reagent and its strength for gaining more information, for example seven step procedure developed by Zeien and Brummer (1989) which included EDTA extractable, moderately reducible, and strongly reducible fractions and Miller et al. (1986) developed method nine step fractionation designed to test waste amended and agriculturally polluted sediments. Even though, problems like re-adsorption and poor reproducibility has been reported in sequential extraction schemes by numerous authors, metal fractionation studies still stands as a key tool to address the extent of metal pollution in the aquatic environment (Kheboian and Bauer 1987; Sahuquillo et al. 1999).

5.1.3. Role of metal fractionation studies on source apportionment Metal fractionation studies may give indications of the origin of the metals. In general, high proportion of exchangeable, acid soluble and easily reducible fractions may point out sources from anthropogenic origin (Hung et al. 1993; Vicente-Beckett 1992). Even elevated level of more resistant fractions (oxidisable and reducible fractions), except residual fraction may be significant in the long term perspective (Clevenger 1990). Fractionation of metals in sediments and overlying waters in

84 estuaries can allow us to track the fate of pollutants. For example, a study conducted by Meranger and Lett (1988) reported the high Cu and Zn levels were found in the exchangeable and acid soluble fractions of coastal sediments as compared to ocean sediments, indicating pollution problem arising from discharges. Similarly, Mesuere et al. (1991) observed high total metal level in riverine clay minerals of the Yangtze river. Further analysis on fractionation showed major portion of metals in the residual fraction suggesting sources of natural origin.

5.1.4. Metal fractionation in aquatic sediments and its influencing factors Metal fractionation is mainly influenced by the physico-chemical conditions in the local microenvironment such as pH, redox conditions, and competition for absorption sites among metal ions and absorptive area (Rajendran et al. 1992; Jones and Turki 1997; Gundersen and Steinne 2001; Tsai et al. 2003; Korfali and Davies 2004; Korfali and Jurdi 2007; Zakir and Shikazono 2011). For example, chloride metal complexes increase with increasing salinity and carbonate metal complexes increase with increasing pH and alkalinity (Gundersen and Steinnes 2001). Gundersen and Steinne (2001) while experimenting on the influence of pH, alkalinity and Ca on the fractionation and concentration of heavy metals observed that low pH generally induces dissolution, ion exchange and desorption reactions. Rajendran et al. (1992) found increased reduction in Mn-oxyhydrates resulted in significant enrichment rate on Mn in pore water.

Fe- Mn oxides and organic matter plays excellent scavengers role for removal of heavy metals from water column in the aquatic environment. In general, Fe and Mn oxides exist as nodules, concretions, and cement between particles, or simply as coating on particles. However, these oxides can be mobilized under reducing and acidic conditions (Tessier et al. 1979). For example, Skvarla (1998) found about 27 to 44% of heavy metals associated with Fe-Mn oxides in Ruzin reservoir. Organic matter’s scavenging role also reported by numerous workers (Elsokkary and Muller 1990; Pliesovska et al. 1997). For example, about 25% of Pb associated with the organic matter is reported by Pliesovska et al. (1997) in sediments of Hornad river basin.

85 5.1.5. Status in India Understanding metal fractionation in the environment is important in developing countries like India since traffic, urbanization, industrial development and mining operations continue to accelerate. It is extremely essential to determine how the anthropogenic derived metals fractionate in sediments in the river basin. In addition, association of metals with various organic and inorganic complex in sediments act as a useful indicator of long and medium term metal flux in industrialized estuaries and rivers, and they help to improve management strategies as well as to assess the success of recent pollution control programs (Goldberg et al. 1977; Ravichandran et al. 1995). Since, most of the heavy metal contamination studies conducted in various Indian river basins, estuaries and lakes are mainly concentrated on total metal concentration comparatively very few of studies only concentrated on metal fraction studies (Roy and Upadhyaya 1985; Iyer and Sarin 1989; Jha et al. 1990). Following are few studies on fractionation of heavy metals in river sediments in India (Table 5.1.1).

Table 5.1.1. Metal fractionation studies in various aquatic environments of India

Study area Findings Reference Mandovi During monsoon an increase in adsorbed Jayakumar and Estuary manganese was observed while a major Rajendran (1990) proportion of iron was found in residual fraction. The study claims as first of its kind on speciation of these metals in Indian waters. Cauvery The abundance of heavy metals was measured Ramanathan et al. Estuary in the order of residual > organic/sulfide > (1993) carbonate > Fe/Mn oxide > exchangeable fractions. Mahanadi Chemical fractionation studies on suspended Chakrapani and River and bed sediments show Fe, Zn, Cu, and Pb are Subramanian associated with the residual fraction and Mn (1993) with the exchangeable fraction. Jhanji River About 30–50% of cadmium and lead in mobile Baruah et al. fraction (either exchangeable or carbonate (1996) bound) and therefore can easily enter the food chain. Significant association with the residual fraction and a scavenging action by the Fe-Mn oxide fraction of the sediments were observed

86 River About 30–50% of lead at most of the sites exist Jain (2004) Yamuna in exchangeable fraction while 30–50% of cadmium at almost all the sites is either exchangeable or carbonate bound. Fractionation pattern of zinc shows low to medium risk to aquatic environment. Gomti River Most fractions in the sediments associated with Singh et al. the carbonate and the exchangeable fractions (2005c) were between 11 and 30% except in a few cases where it was more than 50%. It implies Most of the metals are associated in the carbonate and exchangeable fraction, posing medium risk in terms of contamination. Kolleru Lake Large fractions of Zn, Cd and Cu were Sekhar et al.(2004) associated with mobile fraction of sediment and showed greater bioaccumulation in fish whereas Ni and Co were least mobilisable. Achankovil Metal distribution in the surficial sediments is Prasad et al. River mostly concentrated in the residual and (2006) carbonate phases. Few metals such as Cu, Cd and Pb are also present in the exchangeable phase and may they become potential pollutants in the river system River The sum of the heavy metals associated with the Jain et al. (2008) Narmada first three fractions (exchangeable, carbonate and reducible) is quite high and it represents the proportion of heavy metals that can be easily remobilized in response to changes in the physico-chemical characteristics.

87 Ganga River Residual fractions constitute more than 60% of Purushothaman total metals, except Zn, Cu and Cr. However, and Chakrapani the reducible and organic and sulfide (2007) components also act as major sinks for metals in the down stretches of the river. Zuari River Mn and Co are potentially available in the Dessai and Nayak bioavailable fractions indicating their (2009) importance in toxicity whereas rest of the metals viz.,. Fe, Cu, Zn and to some extent Cr are largely available in residual phase although they are available in other fractions. Sundarban The strong association with residual part and Mukherjee et al. Estuary poor association with exchangeable portion of (2009) most of the metals studied indicate low availability and thereby bio-availability/mobility was not in alarming level to biotic community. Mahanadi Higher proportion of heavy metals in the form Sundaray et al. River of exchangeable fraction (Cd, Ni, Co and Pb), (2011) which poses an adverse impact on exposed population and aquatic biota. The study also suggests that colloids of Fe-Mn oxides act as efficient scavengers for metals like Zn, Pb, Cu, Cr, Co, and Ni. Whereas, organic matter and

CaCO3 have been found to be more effective scavenger for Cu and Cd respectively. Bharali River Few heavy metals such as Cd, Cu, and Pb Hoque et al. (tributary of exhibit considerable presence in the (2011) River exchangeable and carbonate fraction, shows Brahmaputra) higher mobility and bioavailability. In contrast, Ni, Mn, and Fe exhibit greater presence in the residual fraction and Zn was dominant in the Fe–Mn oxide phase. Upper Significant levels of heavy metals in Dhanakumar et al. Reaches of exchangeable and carbonate fraction. The risk (2011) Cauvery assessment code analysis shows 51% of Zn in Delta the labile fractions signifies that the sediments fall under high and very high risk category. In the case of Iron, manganese, copper, and lead, about 30% was noticed in labile fractions putting the sediments under moderate risk category.

88 5.1.6. Metal fractionation studies in Cauvery River in delta regions Although heavy metal contamination in the environment is widely being reported in India, the studies on fractional distribution of heavy metals are very scarce. It will be further interesting to know the chemical fractionation of heavy metals in the Cauvery Delta Zone (CDZ) of River Cauvery lying in the eastern part of Tamil Nadu between 10o00’-11o30’ North latitude and between 78o15’- 79o45’ longitudes made of alluvial and tertiary deposits. River Cauvery on its course also receives various tributaries, urban drainages and industrial effluents which would also be reflected in the speciation study. Dekov et al. (1998) had reported that sediments flux and mass accumulation rates for heavy metals are enhanced at the Cauvery Delta. Except a study in 1993 at Cauvery estuary (Ramanathan et al. 1993), which states the abundance of metals in the order of residual > organic/sulfide > carbonate > Fe/Mn oxide > exchangeable fractions, no notable studies are available in the delta region. The study also did not highlight the seasonal variations and mobility profile of heavy metal. Hence the present study is envisaged in the entire Cauvery delta region including estuary.

5.2. Materials and methods 5.2.1. Metal fractionation analysis The 40 sampling stations mentioned in chapter 2, has been categorized into three namely; upper delta (U1 to U6), mid delta (M1 to M24) and estuarine region (E1 to E10) for metal fractionation study (Figure 5.2.1 and Table 5.2.1). One gram of sediment (grain size < 63 µm) from each sampling point was subjected to sequential extraction of heavy metals (Fe, Mn, Cu, Cr, Zn, Pb and Ni) as per Tessier et al. protocol (1979). Schematic illustration of the same is elaborated in Figure 5.4. This scheme extracts different chemical form of metals in five sequential steps, extracting the exchangeable (F1), carbonate-bound (F2), Fe-Mn oxides-bound (F3), organic- bound (F4) and residual fractions (F5) of the metals/elements. All Glass wares and polypropylene centrifuge tubes used were previously soaked in 50% (v/v) HNO3 overnight and extensively rinsed with double distilled water. Room temperature was about 30oC, while extractions were carried out. After the each extraction, the sediment was washed by shaking with double distilled water and centrifuged for supernatant removal, prior to each subsequent extraction. The concentrations of Fe, Mn, Cu, Cr, Zn,

89 Pb and Ni in each of the supernatant were determined by Atomic Absorption Spectrophotometer (GBC SenSAA Spectrometer, Australia) in flame mode. In order to verify the precision of the method and accuracy simultaneously blanks and internal standards were run regularly.

Figure 5.2.1. Study area- River Cauvery in delta region with sample locations

Table 5.2.1. List of Sampling sites and its landuse patterns

Sample No Name of station Landuse pattern in the vicinity of sampling station Upper delta region U1 Karur Urban and commercial mixed U2 Thirumukudallur Agricultural and residential mixed U3 Kulithalai Agricultural and commercial mixed U4 Upper Anicut Predominantly agricultural area U5 Tiruchirappalli-Mambala salai Urban and commercial mixed U6 Grand Anicut Predominantly agricultural area

90 Mid delta region S7 Pundi Agricultural and residential mixed S8 Sathanur Agricultural and residential mixed S9 Ammapalli Agricultural and residential mixed S10 Thiruperambium Agricultural and residential mixed S11 Anaikarai Agricultural and commercial mixed S12 Mannalmedu Agricultural and residential mixed S16 Thirukattupalai Agricultural and residential mixed S17 Papanasam Agricultural and residential mixed S18 Kumbakonam Urban and commercial mixed S19 Aduthurai Agricultural and commercial mixed S20 Mayavaram Urban and commercial mixed S21 Kanjanagaram Agricultural and residential mixed S24 Thenperambur Agricultural and residential mixed S25 Thiruvankadu Agricultural and residential mixed S26 Padakacherry Agricultural and residential mixed S27 Sellur Agricultural and residential mixed S28 Okkur Agricultural and residential mixed S30 Thiruvarur Urban and commercial mixed S33 Utharamangalam Agricultural and residential mixed S34 Semanallur Agricultural and residential mixed S35 Rajapuram Agricultural and residential mixed S36 Vadapadi Agricultural and residential mixed S37 Arasalur Agricultural and residential mixed S38 Krishnapuram Agricultural and residential mixed Estuarine region E1 Kollidam MID Aqua culture E2 Kollidam estuary Aqua culture and E3 Pichavaram Recreational and Mangrove forest E4 Killiyur Agricultural and residential mixed E5 Poompuhar Recreational and agricultural mixed E6 Nagore Urban and commercial mixed E7 Nagapattinam Urban and commercial mixed E8 Seruthur Estuary and residential E9 Pallingamedu Agricultural and residential mixed E10 Muthupet Agricultural and residential mixed

91 Figure 5.2.2. Flow chart of the sequential extraction scheme followed in the present study

1 g dried and powdered sediment

1 M MgCl2 (pH 7) + Continuous agitation for 1 hr at 30OC

Centrifuge Exchangeable fraction (F1)

Residue + 1 M NaOAc (pH 5.0) + Continuous agitation for 5 hr at 30OC

Supernatant Bound to carbonate fraction (F2) Centrifuge

Residue + 0.04M NH2OH.HCl in 25% (v/v) Acetic acid and refluxed for 6 hr at 95OC

Supernatant Centrifuge Fe-Mn oxides bounded fraction (F3)

Residue + 0.02 M HNO3 + 30% H2O2 was digested at 85º C in for 5 hours with occasional

shaking +5 ml of 3.2-M NH4OAc was added and agitated for 30 minutes

Supernatant Centrifuge Organic matter bounded fraction (F4)

Residue + Aqua regia (3HCl:1HNO3)

Filtration

Residual fraction (F5)

92 5.2.2. Assessment of risk assessment code and mobility factor The extent of metals availability and metal mobility associated risk connected with their presence in the study area Risk Assessment Code (RAC) and Mobility Factor (MF) was calculated from sequential extraction scheme results. As per RAC classification, sum of exchangeable and carbonate bound fractions if figured <1% of the total metal is considered as no risk for the environment, 1–10% shows low risk, 11– 30% indicates medium risk, 31–50% high risk and >50% corresponds to very high risk. To assess metal mobility, earlier studies have used the relative index, which was calculated as a “mobility factor (MF)” on the basis of the ratio of exchangeable, carbonate-bound fractions and reducible fractions to the sum of all fractions (Kabala and Singh 2001; Olajire et al. 2003). The high MF values have been interpreted as symptoms of relatively high mobility and biological availability of heavy metals in soils (Ma and Rao 1997).

5.3. Iron Fractionation 5.3.1. Background Iron (Fe) distribution in aquatic sediments receives considerable attention in view of its significant influences on the regulation of other heavy metal abundance in natural waters (Rajendran et al. 1992). The redox chemistry of Fe plays a major role in the geochemical cycling of numerous elements (anions and cations) in uncontaminated and contaminated aquatic systems. Under oxidizing condition Fe predominantly exit as Fe (III) oxides (Delaune et al. 1997), whereas in reduced conditions the Fe3+ is solubilized into Fe (II) form and then changed into insoluble Fe sulfide and Fe carbonate (Delaune et al. 1997). Moreover, surface sediments under anoxic conditions (reducing) are characterized by extensive microbial sulfate reduction in the presence of reducible Fe (Fe3+), which allows formation of both sulfide and carbonate minerals (Clark et al. 1998). During early diagenesis, microbial mediated redox reactions quickly result in the reduction of insoluble Fe (III) oxides and release of Fe (II) species to pore water (Canfield 1989). Dissolution leads to release of Fe and its associated metals to the pore water and overlying water followed by surface sediments (Petersen et al. 1995). Relationship between Fe and other heavy metals are mainly influenced by high adsorption capacity of its oxide (Niencheski et al. 2002; Algan et al. 2004).

93 5.3.2. Fractionation of Iron in River Cauvery in delta region In Iron(Fe) fractionation profile, major proportion of Fe is found to be associated with residual fraction (F5 fraction) and least with the exchangeable fraction (F1 fraction) in all three regions of Cauvery River delta (Figure 5.3.1). Lithogenous fraction of Fe (F5) is the major source of Fe contributed to the oceans by rivers (Duce and Tindale 1991). Fe was primarily associated in the residual fraction (54 to 97%) with maximum detected at Rajapuram (M21) and minimum at Pichavaram (E3). Higher level of Fe distribution in this fraction suggests considerable conversion of the amorphous Fe oxides into more stable, residual crystalline Fe oxides (Staelens et al. 2000). Similarly, in uncontaminated water bodies maximum level of Fe was found in the F5 fraction. Usero et al. (1998) and Yuan et al. (2004) reported about 89% of Fe in the fraction F5 in uncontaminated water body. Metals associated with F5 fraction cannot be remobilized under normal conditions existing in nature. Analysis of variance yielded significant difference in F5 fraction of Fe between years but not seasons (Table 5.3.1).

100

80

60

40 Fe concentration (%) concentration Fe

20

0 F1 F2 F3 F4 F5

Metal fractions

Figure 5.3.1. Iron fractionation in river sediments of Cauvery delta region

Metal association with non-residual fractions (F1 to F4) has been used as an indicator of anthropogenic enrichment (Sutherland and Tack 2000; Zakir 2008). F4

94 fraction deals about organic matter bounded Fe was recorded in the range from 0.2 to 22%. Maximum level was recorded at Pallingamedu (E9) and minimum at Poompuhar (E5) in wet season (2010). Significant proportion of Fe associated with F4 fraction implies key role of organic matter in distribution of Fe in sediments. Analysis of variance yielded significant difference in F4 fraction of Fe between years.

Table 5.3.1. Analysis of variance of Iron fractionation in various comparisons Fe Comparisons df F Significance Fraction F1 Between season 1 12.6 .000 (wet season and dry season) Between years (2008-09 and 2009-10) 1 31.7 .000 Between three regions 2 0.2 .979 (upper, mid and estuarine regions) Between reservoir sites 5 0.8 .575 Between urban, sub urban and rural sites 2 1.5 .220 F2 Between season 1 26.6 .000 Between years 1 3.6 .060 Between three regions 2 2.5 .084 Between reservoir sites 5 0.5 .768 Between urban, sub urban and rural sites 2 0.3 .705 F3 Between season 1 4.7 .031 Between years 1 4.5 .035 Between three regions 2 0.2 .776 Between reservoir sites 5 0.7 .595 Between urban, sub urban and rural sites 2 1.2 .294 F4 Between season 1 0.1 .847 Between years 1 92.3 .000 Between three regions 2 0.6 .541 Between reservoir sites 5 0.5 .763 Between urban, sub urban and rural sites 2 0.4 .611 F5 Between season 1 0.2 .614 Between years 1 93.6 .000 Between three regions 2 1.0 .354 Between reservoir sites 5 0.2 .929 Between urban, sub urban and rural sites 2 0.4 .681 *Bolded values are significant at the 0.05 level

95 Fe in F3 fraction was found to be in range 0 to 28.6%. Maximum level was recorded at Utharamangalam (M19) in wet season (2010) and minimum at Rajapuram (M21) during dry season (2009). Mean level of F3 fraction was recorded as 18.2% and 16.5% during dry season and wet season, respectively. Since, the collected samples represent the oxidized surface sediments and alkaline nature of surface water in River Cauvery ultimately facilitates the Fe occurrence as oxides and oxyhydroxides (Korfali and Davies 2000). Significant level of Fe accumulation in fraction F3 is probably attributed to competition between iron organic complexes and hydrous iron oxide forms. This condition is complex in nature because hydrous iron oxides themselves can form complex with organics, especially humic substances in sediments (Smith and Milne 1979; Nembrini 1982). Correlation between metal fraction and various characteristics of surface sediments for wet and dry seasons is presented in the table (Tables 5.3.2). Analysis of variance yielded significant difference in F3 fractions of Fe between season and years.

Table 5.3.2. Correlation matrix for Iron fractionation profile in Cauvery delta regions

F1 F2 F3 F4 F5 pH ORP OM CaCO3 Dry season F1 1.00 F2 -0.14 1.00 F3 -0.13 0.16 1.00 F4 0.35** -0.17 -0.13 1.00 F5 -0.26* -0.14 -0.48** -0.79** 1.00 pH 0.09 -0.21 -0.10 0.25* -0.13 1.00 ORP -0.19 0.11 0.11 -0.39** 0.26* -0.84** 1.00 OM 0.23* -0.04 -0.12 0.20 -0.12 0.14 -0.26* 1.00

CaCO3 -0.19 -0.15 0.10 -0.19 0.15 0.07 0.08 0.03 1.00 Wet season F1 1.00 F2 0.70** 1.00 F3 0.11 0.18 1.00 F4 0.28* 0.14 -0.18 1.00 F5 -0.54** -0.58** -0.63** -0.57** 1.00 pH 0.32 0.21 0.13 0.20 -0.31** 1.00 ORP -0.18 -0.11 -0.11 0.08 0.12 -0.66** 1.00 OM 0.31** 0.16 -0.07 0.10 -0.09 -0.01 0.04 1.00

CaCO3 -0.18 -0.05 0.07 -0.09 0.04 -0.06 0.06 -0.01 1.00 * Correlation is significant at the 0.05 level (2-tailed). ** Correlation is significant at the 0.01 level (2-tailed).

96 Fe distribution in F2 (carbonate bound) and F1 fraction (exchangeable) was comparatively less in all stations throughout the study period. Analysis of variance yielded significant difference in F2 fraction of Fe between seasons. Except few samples, most of the samples contains very small amount (<1%) of F1 fraction. Mean level of F1 fraction recorded as 0.3% and 0.7% during dry and wet season, respectively. Fe associated with F1 fraction shows significant variation between seasons and years. Since, Fe distribution in F1 and F2 fractions were relatively very low, it ultimately implies very trace level of Fe enrichment by manmade sources in the study area.

Table 5.3.3. Percentage of samples falls under various classes as per risk assessment code and summary of mobility factor assessment for Iron fractionation Risk class Upper delta Mid delta Estuarine Dry Wet Dry Wet Dry Wet season season season season season season Risk Assessment Code No risk 42 33 35 22 25 15 Low risk 58 58 65 76 75 85 Medium risk - 9 - 2 - - High risk ------Very high risk ------Mobility Factor Minimum 17.1 1.6 0.0 0.5 17.7 2.2 Maximum 24.7 34.2 25.0 35.8 28.2 28.9 Mean 20.8 18.5 18.9 20.3 21.4 21.2

Except few sampling sites in wet season, most of the sediment samples fall under the category of no risk to very low risk category as per risk assessment code (Table 5.3.3). In medium risk category, about 9% and 2% of samples recorded in upper delta and mid delta regions, respectively. Based on the no risk category the three regions of Cauvery delta may recorded as upper delta > mid delta > Estuarine region. To evaluate the metal mobility, several researchers have been applied the relative index to calculate the mobility factor (MF) on the basis of the ratio of F1, F2 and F3 fractions to the sum of all fractions (Kabala and Singh 2001; Olajire et al. 2003). The high MF values have been known as a symptom of relatively high mobility and biological availability of heavy metals in sediments (Ma and Rao 1997). In the present study,

97 mobility of Fe decreased in the order estuarine region > mid delta = upper delta (Table 5.3.3). Maximum level of mobility (35.8%) was recorded in mid delta region.

5.4. Manganese Fractionation 5.4.1. Background Manganese (Mn) is ubiquitous in the environment and it comprises about 0.1% of the Earth’s crust (Graedel 1978). Major anthropogenic sources of Mn in aquatic environment includes municipal wastewater discharges, sewage sludge, mining and mineral processing (particularly nickel), emissions from alloy, steel, and iron production and combustion of fossil fuels (WHO 2004). Alike Fe, environmental chemistry of Mn is largely governed by pH and redox conditions for example, Mn (II) dominates at lower pH and redox potential (LaZerte and Burling 1990). In aquatic environment, Mn mainly exists in two main forms: Mn (II) and Mn (IV) (Nealson 1983). Oxidized form of Mn is high reactive in nature and have a strong capacity for adsorption of other heavy metals (Neretin et al. 2003). However, Mn oxidation is a much slower process in aquatic environment Mn mostly exists in reduced form (Tessier et al. 1979).

The amounts of total Mn and its distribution in different fractions in the sediments were primarily influenced by anthropogenic influences and physico-chemical characteristics of water and sediment column. For example, Barreto et al. (2008) found larger proportion of exchangeable Mn due acidic pH of the sediments, which favored partitioning toward the more soluble Mn species. The presence of chlorides, nitrates, and sulfates may increase Mn solubility and thus increase aqueous mobility and uptake by plants (Reimer 1999). Key chemical factors that controlling sedimentary Mn cycle are the oxygen content of the over lying water, the penetration of oxygen into the sediments, and benthic organic carbon supply (Balzer 1982; Sundby et al. 1986; Hunt and Kelly 1988).

5.4.2. Fractionation of Manganese in River Cauvery in delta region Geochemical association of Manganese (Mn) in river sediments of Cauvery delta is given in Figure 5.4.1. Mn fractionation profile indicates that it is mostly found in F5 fraction followed by F3 fraction. F5 fraction was recorded in the range between 21.5 and 92.8%. Maximum level was recorded at Poompuhar (E5) in dry season (2008)

98 and minimum at Semanallur (M20) during wet season (2010). Mean level of F5 fraction was found as 57.5% and 50.4% in dry and wet season, respectively. Analysis of variance yielded significant difference in F5 fraction of Mn between seasons implies significant natural influences on Mn fractionation (Table 5.4.1). High level of Mn accumulation was found in F5 fraction and this fraction cannot be easily released in the aquatic environment since they are bound to the crystal lattice of the minerals in the sediments (Savvides et al. 1995).

100

80

60

40 Mn concentration (%) concentration Mn

20

0 F1 F2 F3 F4 F5

Metal fractions

Figure 5.4.1. Manganese fractionation in river sediments of Cauvery delta region

Mn oxides plays vital role as an electron donor and acceptor in redox processes of aquatic environments (Neretin et al. 2003). F4 fraction was recorded in the range 1.2 to 38.8%. Mean level of this fraction did not show any significant variation between dry (10.8%) and wet season (9.4%). Comparatively, low level of Mn in F4 fraction recorded in half the number of samples is probably due to the competition between Fe- Mn-organic complex and hydrous Fe-Mn oxide forms (Sundaray 2007). In this study F4 fraction of Mn was recorded in the order: upper delta > mid delta > estuarine region (Table 5.4.1).

In Mn fractionation profile, F3 fraction stands as a second largest fraction (up to 39.1%) in all three regions of River Cauvery in delta region. In aquatic environment, Mn commonly exist as oxides, hydroxides or in association with iron oxides/hydroxides, Mn ions can be adsorbed and/or partially ion-exchanged on the

99 surface of MnO2 and they can be environmentally mobile in certain conditions (Weisz et al. 2000). Comparatively, high concentration of Mn recorded in reducible fraction is probably due to the reduced form of metal existence (Tessier et al. 1979). Mn exists as oxides may be released if the sediment is subjected to more reducing conditions (Panda et al. 1995). Correlation between metal fraction and various characteristics of surface sediments for wet and dry seasons is presented in the table (Tables 5.4.2). Analysis of variance yielded significant difference in F3 fraction of Mn between three regions of delta region (upper delta, mid delta and estuarine) implying manmade influences on Mn fractionation.

Table 5.4.1. Analysis of variance of Manganese fractionation in various comparisons Mn Comparisons df F Significance Fraction F1 Between season 1 8.3 .004 Between years 1 0.3 .574 Between three regions 2 0.4 .618 Between reservoir sites 5 1.2 .329 Between urban, sub urban and rural sites 2 0.2 .827 F2 Between season 1 41.2 .000 Between years 1 0.4 .540 Between three regions 2 1.2 .313 Between reservoir sites 5 0.5 .739 Between urban, sub urban and rural sites 2 1.9 .148 F3 Between season 1 0.0 .986 Between years 1 0.2 .640 Between three regions 2 4.5 .013 Between reservoir sites 5 0.4 .804 Between urban, sub urban and rural sites 2 2.2 .117 F4 Between season 1 1.2 .274 Between years 1 2.9 .088 Between three regions 2 1.8 .161 Between reservoir sites 5 0.3 .879 Between urban, sub urban and rural sites 2 0.5 .601 F5 Between season 1 8.9 .003 Between years 1 0.8 .356 Between three regions 2 1.3 .263 Between reservoir sites 5 1.0 .445 Between urban, sub urban and rural sites 2 1.6 .187 *Bolded values are significant at the 0.05 level

100

Fraction F2 (carbonate bound) stands as third largest in the Mn fractionation profile. Mean level of F2 fraction was detected as 6.9 and 13.2% during dry and wet season, respectively. Analysis of variance yielded significant variation of Mn associated with F1 fractions between seasons. The higher level of Mn in this fraction is most likely due to the similarity in ionic radii to that of calcium which allows them to substitute for Ca in carbonate phase (Pedersen and Price 1982; Zhang et al. 1988). The result indicates that considerable amount of Mn may be released into environment if conditions become more acidic (Thomas et al. 1994). The significant association of Mn with carbonates (F2 fraction) has been reported elsewhere (Panda et al. 1995; Kiratli and Ergin 1996; Marin and Giresse 2001). In the present study, F2 fraction was distributed in the order of: upper delta > mid delta > estuarine region (Table 5.4.1). Other studies also reported notable level of F2 fraction in Mn fractionation profile (Wartel et al. 1991; Bougheriet et al. 1994). Table 5.4.2. Correlation matrix for Manganese fractionation profile in Cauvery delta regions

F1 F2 F3 F4 F5 pH ORP OM CaCO3 Dry season F1 1.00 F2 -0.06 1.00 F3 0.01 0.20 1.00 F4 0.31** 0.20 0.18 1.00 F5 -0.38** -0.52** -0.70** -0.72** 1.00 pH -0.10 0.20 0.14 -0.19 -0.03 1.00 ORP 0.16 -0.19 -0.13 0.22* -0.01 -0.84** 1.00 OM -0.28* 0.21 0.22* -0.16 -0.04 0.14 -0.26 1.00

CaCO3 0.18 -0.08 -0.07 0.08 -0.02 0.07 0.08 0.03 1.00 Wet season F1 1 F2 0.35** 1.00 F3 0.05 0.40** 1.00 F4 0.08 -0.20 -0.22 1.00 F5 -0.55** -0.70** -0.61** -0.33** 1.00 pH 0.11 -0.02 -0.04 0.09 -0.05 1.00 ORP -0.09 -0.01 0.02 0.03 0.01 -0.66** 1.00 OM 0.37** -0.03 0.09 -0.01 -0.14 -0.01 0.04 1.00

CaCO3 0.03 0.12 0.18 -0.18 -0.06 -0.06 0.06 -0.01 1 * Correlation is significant at the 0.05 level (2-tailed). ** Correlation is significant at the 0.01 level (2-tailed).

101 Fraction F1 was recorded in the range from 0.4 to 26.5%. Maximum level was recorded at Thiruperambium (M4) and minimum at Ammapalli (M3), both during wet period (2009). Mn in this fraction is retained on sediment surface by relatively weak electrostatic interactions and may be released by ion exchange processes and dissociation of Mn carbonate phase (Yuan et al. 2004; Caplat et al. 2005). Mean level of F1 fraction during wet season (6.6%) was higher than the observations made in dry period (4.6%). Analysis of variance yielded significant variation of Mn associated with F1 fractions between seasons. In few locations [Grand Anicut (U6), Mannalmedu (M6), Thiruvarur (M18), Pallingamedu (E9) and Muthupet (E10)] large proportion of Mn in F1 fraction suggests Mn existence as Mn (II) due to the slow oxidation processes of Mn (Huang et al. 2007; Jain et al. 2007b). Besides the natural sources, macronutrient fertilizers application is the significant anthropogenic source of Mn (Wolters et al. 1989). In the present study, F1 fraction was distributed in the order of: upper delta > estuarine region > mid delta. More than 30% of the Mn was associated with first three fractions (F1, F2 and F3 fraction) may pose risk to aquatic life.

Table 5.4.3. Percentage of samples falls under various classes as per risk assessment code and summary of mobility factor assessment for Manganese

Risk class Upper delta Mid delta Estuarine Dry Wet Dry Wet Dry season Wet season season season season season Risk Assessment Code No risk ------Low risk 42 17 20 20 50 10 Medium risk 58 58 63 63 50 70 High risk - 25 17 17 - 20 Very high risk ------Mobility Factor Minimum 23.5 20.7 11.8 9.4 4.3 13.8 Maximum 48.5 61.8 61.4 71.9 48.9 68.3 Mean 34.2 34.1 33.0 39.8 27.6 44.3

As per risk assessment code, Mn fractionation in the River Cauvery in delta region falls under the category of low risk to high risk (Table 5.4.3). In wet season,

102 25%, 17% and 20% of sediment samples in the upper, mid and estuarine region recorded in the high risk category. However, about 63% and 70% of sediment samples are fall under medium risk category at mid delta and estuarine regions in wet season. In general, mobility and bioavailability risk is generally high in wet season than the dry season. Based on the high risk category the three regions of Cauvery delta may recorded as upper delta > estuarine region > mid delta. In order to estimate the mobility of Mn, mobility factor (MF) was computed on the basics of the ratio of liable fractions (F1, F2 and F3 fractions) to the sum of all fractions. The high MF value may denote the mobility potential and bioavailability of metals in sediments. In the present study, mobility of Mn found in the order estuarine region ≈ mid delta > upper delta (Table 5.4.3). Maximum level of mobility (71.9%) was recorded in mid delta region and minimum level (4.3%) also recorded in estuarine during wet and dry season, respectively. Elevated level of mobility factor was recorded in numerous sampling sites in all three regions of Cauvery delta implies mobile nature of Mn in the study area.

5.5. Copper Fractionation 5.5.1. Background Copper (Cu) is an essential micronutrient for living organism, but it is potentially toxic at elevated concentrations (Sunda and Hanson 1987). Smelters, industrial and incinerator emissions, tyre abrasion, lubricants and agrochemical and municipal sewage are common sources Cu in the freshwater environments (Markus and McBratney 1996; Moller et al. 2005; Tiller 1989). Intensive usage and unregulated discharge of Cu leads to widespread accumulation in aquatic sediment where it often remains in surface layers (Brun et al. 1998; Miko et al. 2003). Therefore, Cu deposited in sediments may be directly available to benthic fauna (Zarcinas and Rogers 2002) or be released to the overlying water through sediment resuspension, adsorption/desorption reactions, reduction/oxidation reactions, and degradation of organism (Santschi et al. 1990). The pH, hardness, dissolved organic matter concentration, and ionic strength significantly influence the partitioning of Cu within and between sediment and water phases (Depledge et al. 1994). Among them, free ionic copper (Cu2+) is the readily bioavailable and toxic metal species in water and interstitial water followed by hydroxyl complexes.

103 In sediments, Cu adsorption on Fe and Mn oxides and oxyhydroxides and to organic matter may be an efficient retention process in oxic sediment, precipitation of Cu sulphide may constitute a major sink in anoxic sediments. Extractable Cu in polluted sediments is mainly associated with oxidizable phases, where it is likely to occur as organic-metal complexes (Samanidou and Fytianos 1987; Pardo et al. 1993; Marin et al. 1997). This behavior can be explained by the high affinity of Cu to humic substances that are a fraction of natural organic matter very active chemically in the process of complexing metals (Pempkowiak et al. 1999). In unpolluted sediments, high proportion of Cu fixed in the crystal lattice of silicate minerals implies Cu mainly originates from geochemical background rather than anthropogenic source (Wu et al. 2011).

5.5.2. Fractionation of Copper in River Cauvery in delta region In Cu fractionation profile, F5 fraction of Cu was observed in the range from 28.3 to 75.7 % and 23.7 to 76% during dry and wet season, respectively. The association of Cu in various geochemical fractions was presented in Figure 5.5.1. Further, elevated level of Cu in the fraction F5 indicates that sediments are relatively unpolluted and it mainly derived from lithogenic origin (Passos et al. 2011). Analysis of variance yielded significant difference in F5 fraction of Cu between season and years (Table 5.5.1).

100

80

60

40 Cu concentration (%) concentration Cu

20

0 F1 F2 F3 F4 F5

Metal fractions

Figure 5.5.1. Copper fractionation in river sediments of Cauvery delta region

104 F4 fraction (organic matter bound) stands as second largest in dry season and third largest in wet season in the study period. The mean level of F4 fraction was observed as 19.5 and 15.7 % during dry and wet season, respectively. Notable decline in F4 fraction in wet season samples suggests substantial particulate organic matter transport into the ocean during high water flow period in the Cauvery delta region. And also due to the reduction in the sediment pH during the period of wet season (Iwegbue et al. 2012). Significant portion of Cu associated F4 fraction reveals strong association of Cu with organic matter in all most all the sampling sites. Earlier studies also hinted that the trend is typical of this metal in sediments (Salomons and Forstner 1980). This behavior can be explained by the well known high affinity of copper to humic substances, which are a fraction of natural organic matter and chemically very active in complexing such metals (Pempkowiak et al. 1999). The tendency of Cu to be associated with the F4 has been widely reported by other researchers also (Sauvea et al. 1997; Li et al. 2001c; Imperato et al. 2003; Yang et al. 2006). Correlation analysis also reveals key role of organic matter in the distribution of Cu in surface sediments due to the high affinity of metal to humic substances (Table 5.5.2). Cu associated with F4 fraction shows significant variation between seasons and years.

Table 5.5.1. Analysis of variance of Copper fractionation in various comparisons

Cu Comparisons Df F Significance Fraction F1 Between season 1 0.0 .991 Between years 1 2.6 .107 Between three regions 2 1.3 .259 Between reservoir sites 5 1.4 .244 Between urban, sub urban and rural 2 1.2 .290 sites F2 Between season 1 3.2 .073 Between years 1 1.0 .301 Between three regions 2 1.1 .328 Between reservoir sites 5 1.4 .254 Between urban, sub urban and rural 2 1.4 .249 sites

105 F3 Between season 1 18.2 .000 Between years 1 0.8 .358 Between three regions 2 4.8 .009 Between reservoir sites 5 0.4 .794 Between urban, sub urban and rural 2 2.6 .075 sites F4 Between season 1 4.6 .032 Between years 1 6.4 .012 Between three regions 2 1.5 .220 Between reservoir sites 5 0.3 .893 Between urban, sub urban and rural 2 2.6 .072 sites F5 Between season 1 5.7 .018 Between years 1 5.6 .018 Between three regions 2 0.1 .904 Between reservoir sites 5 0.5 .801 Between urban, sub urban and rural 2 3.2 .043 sites *Bolded values are significant at the 0.05 level

Fe-Mn oxides bounded fraction F3 was stands as a second largest and third largest fraction in Cu fractionation profile in wet season and dry season samples, respectively. In the present investigation, F3 faction was recorded in the range from 0.1 to 61.2% with a mean level of 18.3%. Maximum level was measured at Pundi (M7) and minimum level was found at Arasalur (M23). Mean level of Cu in fraction F3 recorded slightly higher during wet season (21.6%) than the dry season samples (14.9%). Significant level of Cu in F3 fraction suggests sorption of Cu on hydrous Fe-Mn oxides due to co-precipitation of Cu in the Fe–Mn oxide lattice (Kotoky et al. 2003). The present results indicate Fe-Mn oxides fraction is the important binding site for all three regions of Cauvery delta. Cu associated with F3 fraction shows significant variation between seasons and three regions of delta region. The high proportions of Cu associated with F3 and F4 fractions in most of the sampling sites indicate its low mobility and bioavailability in the sediments of River Cauvery.

106 Table 5.5.2. Correlation matrix for Copper fractionation profile in Cauvery delta regions

F1 F2 F3 F4 F5 pH OM CaCO3 Fe- F3 Mn-F3 Fe Mn Dry season F1 1.00 F2 0.17 1.00 F3 -0.29** -0.13 1.00 F4 -0.36** -0.16 -0.11 1.00 F5 -0.16 -0.64** -0.21 -0.41** 1.00 pH 0.27* -0.08 -0.36** 0.06 0.09 1.00 OM 0.14 -0.19 -0.20 0.72* 0.16 0.14 1.00

CaCO3 0.09 -0.04 -0.14 -0.05 0.09 0.07 0.03 1.00 Fe- F3 -0.20 -0.05 0.08 -0.10 0.17 -0.10 -0.12 0.10 1.00 Mn-F3 0.06 -0.25* 0.03 0.04 0.13 0.14 0.22* -0.07 -0.19 1.00 Fe -0.04 -0.06 0.12 -0.01 0.02 0.06 0.10 -0.22* 0.07 -0.11 1.00 Mn -0.15 -0.07 0.16 -0.18 0.19 -0.17 -0.27* -0.18 -0.14 0.05 0.06 1.00 Wet season F1 1 F2 0.15 1.00 F3 0.05 0.22 1.00 F4 -0.20 -0.25* -0.49** 1.00 F5 -0.36** -0.65** -0.59** -0.06 1.00 pH 0.06 0.05 0.01 0.05 -0.09 1.00 OM -0.01 -0.08 -0.08 0.07 0.07 -0.01 1.00 CaCO3 -0.05 0.04 -0.20 0.01 0.14 0.06 0.19 1.00 Fe- F3 -0.13 -0.04 -0.25* 0.22* 0.11 0.13 -0.07 0.14 1.00 Mn-F3 0.12 0.22* -0.02 -0.12 -0.08 0.03 0.09 0.02 0.08 1.00 Fe -0.14 0.00 -0.09 0.02 0.10 -0.28* 0.06 0.34* 0.22* -0.16 1.00 Mn 0.03 -0.03 0.01 -0.11 0.08 -0.04 -0.06 0.16 0.16 -0.01 0.25* 1.00 * Correlation is significant at the 0.05 level (2-tailed). ** Correlation is significant at the 0.01 level (2-tailed).

In polluted sediments, Cu mainly associated with non-lithogenic fractions (F1 and F2 fractions). Cu gets accumulated in sediment by the mechanism of ion exchange and precipitation (Pagnanelli et al. 2004). The highest percentage contribution of Cu in the F2 fraction was up to 33.4 and 48.9% of total Cu concentration during dry and wet season, respectively. Maximum level of Fraction F2 (48.9%) was recorded at Kulithalai (U3) and minimum level (0.2%) at Thirumukudallur (U2). Significant portion of Cu

107 found in F2 fractions suggests its special affinity towards carbonate and may co- precipitate with its minerals (Sundaray 2007). Major anthropogenic sources of Cu include agrochemicals (fertilizers and pesticides), wood preservatives, electroplating and antifouling paints (Schuler et al. 2008). In variably, in most of the sample sites Cu was seen in lowest in the fraction F1 followed by fraction F2. Maximum level of F1 (33.8%) was recorded at Semanallur (M20) followed by 24.8% at Rajapuram (M21) during dry season (June 2008). Cu associated with F1 and F2 fractions are particularly important, because they can be easily remobilized in response to the changes in environmental conditions (Perez et al. 1991). In the present study, F1 and F2 fractions of Cu not yielded any significant variation between seasons and years.

Table 5.5.3. Percentage of samples that falls under various classes as per risk assessment code and summary of mobility factor assessment for Copper fractionation

Risk class Upper delta Mid delta Estuarine Dry Wet Dry Wet Dry Wet season season season season season season Risk Assessment Code No risk ------Low risk 25 8 26 30 30 30 Medium risk 58 67 59 57 65 55 High risk 17 17 11 13 5 10 Very high risk - 8 4 - - 5 Mobility Factor Minimum 17.2 1.3 9.1 0.1 17.3 2.0 Maximum 43.2 30.6 65.1 61.2 49.5 48.0 Mean 31.1 15.5 33.4 24.4 30.1 18.5

The order of the Cu fractions in terms of concentration was observed in order of F5 > F4 > F3 > F2 > F1 and F5 > F3> F4 > F2 > F1 in dry season and wet season, respectively. Variably high levels of Cu were noted in the F3, F2 and F1 fractions at some sampling sites indicating notable anthropogenic input of Cu. The high proportions of Cu in the fractions can be regarded as a common scavenger of Cu in the river sediments of Cauvery delta region. Even Cu occurred predominantly in F4 and F5

108 fractions, known for its low mobility and bioavailability other micro environmental conditions may influence the metal mobility.

The Risk Assessment Code (RAC) values of Cu for three regions of Cauvery delta and seasons are given in Table 5.5.3. According to RAC, Cu falls under the category of low to very high risk category. Very high risk class was noted in 8 and 4% of sediment samples in upper delta and mid delta during wet and dry season, respectively. Maximum of 17%, 13% and 10% of samples fall under high risk category in upper delta, mid and estuarine regions, respectively. The Mobility Factor for Cu in all three regions of Cauvery delta and seasons is given in Table 5.5.3. The mobility factor of Cu in the three regions of Cauvery delta follows the order: mid delta > estuarine region > upper delta. The results indicate that the metals were relatively mobile at mid and estuarine regions of the study area as compared to upper reaches of delta.

5.6. Chromium Fractionation 5.6.1. Background Chromium (Cr) is a low mobility element, especially under moderately oxidizing and reducing conditions and near-neutral pH values. Cr in excess of naturally occurring background levels are prevalent in urbanized and industrialized river and estuaries sediments due to the runoff from road surfaces, combined sewer overflows, and municipal and industrial discharges (Paul et al. 2002; USEPA 2004). In aquatic sediments Cr exists in multiple oxidation states, primarily trivalent chromium [Cr(III)] and hexavalent chromium [Cr(VI)], which shows varying geochemical and ecotoxicologic properties (Kabata-Pendias 2001). Both Cr(III) and Cr(VI) may adsorb onto clay minerals, Fe(III), Mn(IV), and Al(III) oxides and hydroxides (Koppelman and Dillard 1980; James and Bartlett 1983; Rai et al. 1987; Zachara et al. 1987).

Cr (III) gets solubilized by the process of complexation with organic matter (James and Bartlett 1983). Cr is relatively low mobile element under moderately oxidizing and reducing conditions and near-neutral pH in sediments. Cr6+ adsorption decreases with increasing pH, and Cr3+ adsorption increases with increasing pH. In anaerobic sediments, rapid reduction of Cr(VI) to Cr(III) facilitated by several organic

109 and inorganic constituents (sulfides, ferrous iron, and organic matter) (Schmieman et al. 1998). Although the hexavalent of Cr is thermodynamically stabile under aerobic conditions, surprisingly it is rarely found in the aquatic environment (Barnhart 1997). In general, Clay particles, humus colloids and Fe and Mn oxyhydroxides are capable to co-precipitate Cr in aquatic sediments. Since, Cr is not a divalent metal and does not form a insoluble metal sulfide complex, however in the presence of acid volatile sulphur Cr exist as Cr(III) (Berry et al. 2004; Besser et al. 2004; Becker et al. 2006).

5.6.2. Fractionation of Cr in River Cauvery in delta region The association of Cr to various geochemical fractions in sediments of river Cauvery in delta region is presented in Figure 5.6.1. In Cr fractionation profile, F5 fraction is the main carrier for Cr in all the three regions of Cauvery delta region in which Cr content varies from 81 to 94%, 41 to 75.2% and 33.4 to 65.1%, respectively in upper delta, mid delta and estuarine region. In uncontaminated sediments, major proportion of Cr was recorded in the F5. The Cr-F5 fraction was observed in the order of upper delta > mid delta > estuarine region. Analysis of variance yielded significant difference in F5 fraction of Cr between seasons, reservoir sites and three regions of delta region (Table 5.6.1).

100

80

60

40 Cr concentration (%) Cr

20

0 F1 F2 F3 F4 F5

Metal fractions

Figure 5.6.1. Chromium fractionation in river sediments of Cauvery delta region

110 The affinity of heavy metals with organic substances and their decomposition products are of great importance for the release of the metals into water. Organic matter plays an important role in the distribution and dispersion of heavy metals, by mechanisms of chelation and cation exchange processes (Purushothaman and Chakrapani 2007).

Table 5.6.1. Analysis of variance of Chromium fractionation in various comparisons

Cr Comparisons df F Significance Fraction F1 Between season 1 22.4 .000 Between years 1 1.9 .169 Between three regions 2 38.6 .000 Between reservoir sites 5 0.8 .542 Between urban, sub urban and rural sites 2 5.6 .005 F2 Between season 1 46.7 .000 Between years 1 4.3 .040 Between three regions 2 42.5 .000 Between reservoir sites 5 2.7 .056 Between urban, sub urban and rural sites 2 0.4 .695 F3 Between season 1 4.43 .037 Between years 1 0.7 .397 Between three regions 2 92.3 .000 Between reservoir sites 5 19.1 .000 Between urban, sub urban and rural sites 2 2.2 .111 F4 Between season 1 4.09 .045 Between years 1 1.7 .188 Between three regions 2 118.7 .000 Between reservoir sites 5 29.3 .000 Between urban, sub urban and rural sites 2 9.9 .000 F5 Between season 1 6.99 .009 Between years 1 0.003 .958 Between three regions 2 200.9 .000 Between reservoir sites 5 28.3 .000 Between urban, sub urban and rural sites 2 2.0 .132 *Bolded values are significant at the 0.05 level

111 In the present study, Cr bound to organic matter (fraction F4) stands second largest fraction in the fractionation profile. The Cr concentration in F4 ranged from 2.0 to 30.4% among samples. Maximum level was observed at Thiruvankadu (M14) in dry season (2009) and minimum at Kulithalai (U3) during wet season (2009). The order of Cr-F4 fraction distribution in the River Cauvery in delta region was observed as, estuarine region≈ mid delta > upper delta. Analysis of variance yielded significant difference in F4 fraction of Cr between seasons, reservoir sites, three regions of delta and urban, sub urban and rural sites implies notable variation in the F4 associated Cr in the study area. Correlation between metal fraction and various characteristics of surface sediments for wet and dry seasons is presented in the table (Tables 5.6.2).

Fraction F3 (Fe-Mn oxide matrix) was found to carry 0.5 to 26% of Cr in the study area. The association of Cr in F3 fraction may be result of the insoluble Cr+3 hydroxide (Eary and Rai 1988; Morel 1983), which eventually is incorporated into during precipitation and as Fe containing Mn phase of the particle provide more surface sites for Cr to be adsorbed (Sager 1992). Maximum level of Cr in F3 was recorded at Thenperambur (M13) during wet season (2010) and minimum at Grand Anicut (U6) in wet season (2009). F5 and F3 being the major carrier of Cr has been reported by Jain et al. (2010) in Hussainsagar Lake, Hyderabad, India. It has been reported that metals absorbed by Fe-Mn oxides tend to decrease in the order of Cr > Zn > Cu (Badarudeen et al. 1996). It is known that oxides are amphoteric in nature and their charges change according to the pH of the environment. Significant difference in F3 bounded Cr was observed between seasons, three regions of delta and reservoir sites.

112 Table 5.6.2. Correlation matrix for Chromium fractionation in Cauvery delta regions

F1 F2 F3 F4 F5 pH OM CaCO3 Fe- F3 Mn-F3 Fe Mn Dry season F1 1 F2 0.44** 1.00 F3 -0.09 0.28* 1.00 F4 -0.33** 0.20 0.62** 1.00 F5 -0.07 -0.60** -0.84** -0.83** 1.00 pH -0.13 -0.27* -0.13 -0.03 0.17 1.00 OM -0.12 -0.01* 0.06 -0.01 0.01 0.14 1.00

CaCO3 -0.11 0.02 0.28* 0.28* -0.26* 0.07 0.03 1.00 Fe- F3 0.03 0.09 0.07 -0.02 -0.05 -0.10 -0.12 0.10 1.00 Mn-F3 0.06 0.06 -0.10 -0.04 0.04 0.14 0.22* -0.07 -0.19 1.00 Fe -0.21 -0.27* -0.30** -0.17 0.34** 0.06 0.10 -0.22* 0.07 -0.11 1.00 Mn 0.23* 0.06 -0.03 -0.22 0.08 -0.17 -0.27* -0.18 -0.14 0.05 0.06 1.0 Wet season F1 1 F2 0.54* 1.00 F3 0.33* 0.49* 1.00 F4 0.26* 0.43* 0.72* 1.00 F5 -0.57* -0.74* -0.88* -0.86* 1.00 pH -0.16 -0.07 -0.20 -0.29* 0.25* 1.00 OM -0.04 0.12 -0.15 0.02 0.02 -0.01 1.00

CaCO3 0.30* 0.17 0.13 0.17 -0.22* -0.06 -0.01 1.00 Fe- F3 -0.09 0.16 -0.24* -0.21 0.24* 0.13 -0.07 0.07 1.00 Mn-F3 0.12 0.05 -0.02 0.16 -0.10 0.03 0.09 0.07 0.08 1.00 Fe -0.22* -0.20 -0.10 -0.08 0.17 -0.28* 0.06 -0.10 0.22* -0.16 1.00 Mn -0.01 -0.13 -0.08 -0.04 0.08 -0.04 -0.06 -0.04 0.16 -0.01 0.25* 1.0 * Correlation is significant at the 0.05 level (2-tailed). ** Correlation is significant at the 0.01 level (2-tailed).

113 Fraction F2 associated Cr was distributed in the range from 0 to 16%. Mean level of F2 fraction was notably high in wet season (7.5%) than the dry season samples (3.8%). Comparatively, low level of Cr associated with this fraction may occur due to the inability of Cr3+ to form a precipitate or complex with carbonates (Sundaray et al. 2011). The order of Cr-F2 fraction was recorded as, estuarine region > mid delta > upper delta. Analysis of variance yielded significant variation of F2 fraction between season, year and three regions.

In the present study, fraction F1 was recorded in the range from 0 to 16.2%. Cr emitted from chrome plating, tanning, paint and pigment production, agrochemicals and metallurgy wastes are main anthropogenic sources of Cr which have the potential to contaminate the aquatic environment (Duran et al. 2007). Maximum level was recorded at Nagapattinam (E7) and minimum at Sellur (M16) in wet period of 2009 and 2010, respectively. Mean level of F1 fraction was relatively high in wet season (4.0%) than the dry season samples (2.3%). Low level of Cr associated with F1 fraction in most of the dry season samples probably due to the solubility of Cr in response to the reducing conditions prevailing under anoxic environment (Singh et al. 2008). The Cr- F1 fraction was recorded in the order: estuarine region > upper delta > mid delta. Statistically significant difference in F1 associated Cr was observed between seasons, three regions of delta and urban, sub urban and rural sites.

Table 5.6.3. Percentage of samples falls under various classes as per risk assessment code and summary of mobility factor assessment for Chromium fractionation Risk class Upper delta Mid delta Estuarine Dry Wet Dry Wet Dry Wet season season season season season season Risk Assessment Code No risk - - 8 - - - Low risk 100 100 92 50 30 5 Medium risk - - - 50 70 95 High risk ------Very high risk ------Mobility Factor Minimum 5.0 2.0 10.2 16.0 17.0 22.0 Maximum 16.0 12.0 27.7 36.6 34.7 45.6 Mean 9.7 8.3 17.1 26.3 26.5 33.1

114 In all three regions of Cauvery delta, the order of fraction in terms of Cr concentration was observed as residual (F5) > organic bound (F4) > Fe–Mn oxide bound (F3) > carbonate bound (F2) > exchangeable (F1). As per risk assessment code, upper delta regions fall under low risk category and about 50% samples are recorded in medium risk category in the wet season (Table 5.6.3). In the case of estuarine region, 70 and 95% samples were recorded as medium risk category in dry and wet season samples, respectively. In order to evaluate the mobility potential of Cr in the River sediments of Cauvery delta region mobility factor was calculated (Table 5.6.3). In mobility factor assessment, maximum level of mobility (45.6%) was recorded at Nagore (E6) and minimum level (10.2%) was found at Sathanur (M2). High mobile nature of Cr in these regions may pose risk to the aquatic biota and humanity. The order of mobility was observed as, estuarine region > mid delta > upper delta.

5.7. Zinc Fractionation 5.7.1. Background Zinc (Zn) is an essential nutrient element, but toxic to living organisms at elevated concentrations. Zn is released into the aquatic environment from both natural and anthropogenic sources; however, anthropogenic emissions are greater than those from natural sources. Anthropogenic sources of Zn in aquatic environment includes liquid manure, composted materials, agrochemicals, mine tailings, wood preservatives, processing of Zn-bearing raw materials, coal and fuel combustion and iron and steel production (Ragaini et al. 1977; Prusty et al. 2008). Once Zn introduced into aquatic environment, it gets incorporated at the surface of the sediment particles by different mechanisms, such as adsorption on Fe and Mn hydrated oxides, ionic exchange on clay minerals, binding on organic matter and adsorption on colloid particles existing in water (Menon et al. 1997). In Zn fractionation in aquatic sediments, co-precipitation with carbonate minerals is of importance for control of this element (Forstner and Wittmann 1983; Alloway 1990). The stability constant of Zn adsorbed onto Fe-Mn oxides is greater than that of Zn adsorbed onto carbonates (Fernandes 1997; Ramos et al. 1999). In case of high Fe hydroxides and less organic matter in the sediments, Zn tends to be co-precipitated with carbonates (Forstner and Wittmann 1979).

115 5.7.2. Fractionation of Zinc in River Cauvery in delta region The fractionation profile of Zn indicates that major portion of Zn is associated with residual fraction (F5). Zn level in F5 fraction was observed in the range from 24 to 62% and 23 to 71% during dry and wet season, respectively (Figure 5.7.1). Analysis of variance yielded significant difference in F5 fraction of Zn between years and three regions (Table 5.7.1). Since, the concentration of metal in this fraction is largely controlled by the mineralogy and extent of weathering trace metals in the form which is not soluble under experimental condition and considered to be detained in the mineral matrix.

100

80

60

40 Zn concentration (%) concentration Zn

20

0 F1 F2 F3 F4 F5

Metal fractions

Figure 5.7.1. Zinc fractionation in river sediments of Cauvery delta region

Zn bound F4 fraction (Organic matter bounded) was observed as second largest carrier holding 3- 35%. Maximum level was figured at Aduthurai (M10) and minimum at Tiruchirappalli Mambala Salai (U5). Analysis of variance noted significant variation of F4 fraction between season and years. The higher proportion of Zn in the organic and residual fractions indicated the low mobility and bioavailability of Zn in the sediments of the River Cauvery in delta region. Fraction F4 is relatively stable in nature, but it may mobilize under strong oxidizing conditions due to organic matter degradation, leading to a release of the soluble metal (Tessier et al. 1979).

F3 fraction contained 1.2 to 32.1% Zn. Maximum level was recorded at Kanjanagaram (M12) during wet season (2010) followed by 31.8% at Mayavaram

116 (M11) in dry season (2009). Hydrous oxides of Fe and Mn on particulate surfaces are significant carriers for Zn in aquatic systems (Howard and Vandenbrink 1999). The present observations are in agreement with the known ability of Fe-Mn oxides to scavenge Zn from water column (Pardo et al. 1990; Ranu Gadh et al. 1993). Although the attachment of metals to Fe-Mn oxides indicates their relative immobilization, the condition is largely dependent on the micro environmental conditions. However, changing pH and redox conditions may lead to remobilization and uptake by biota. The present observation also agrees with the general pattern of Zn association with Fe-Mn oxide bound (F3) and organic fraction (F4) (Li and Thornton 2001b; Hickey and Kittrick 1984). The stability constant of Zn adsorbed onto Fe-Mn oxides is greater than that of Zn adsorbed onto carbonates (Fernandes 1997; Ramos et al. 1999). In case there are more iron hydroxides and less organic matter in the sediments, Zn tends to be co- precipitated with carbonates (Forstner and Wittmann 1979).

Figure 5.7.1. Analysis of variance of Zinc fractionation in various comparisons Zn Fraction Comparisons df F Significance F1 Between season 1 12.5 .001 Between years 1 4.3 .038 Between three regions 2 2.5 .084 Between reservoir sites 5 2.4 .073 Between urban, sub urban and rural 2 0.1 .939 sites F2 Between season 1 24.5 .000 Between years 1 0.2 .624 Between three regions 2 10.6 .000 Between reservoir sites 5 1.0 .416 Between urban, sub urban and rural 2 1.3 .264 sites F3 Between season 1 1.3 .247 Between years 1 1.8 .210 Between three regions 2 0.5 .570 Between reservoir sites 5 0.8 .509 Between urban, sub urban and rural 2 0.1 .827 sites

117 F4 Between season 1 10.8 .001 Between years 1 9.8 .002 Between three regions 2 0.6 .548 Between reservoir sites 5 0.8 .538 Between urban, sub urban and rural 2 0.2 .777 sites F5 Between season 1 2.2 .133 Between years 1 20.1 .000 Between three regions 2 4.5 .012 Between reservoir sites 5 0.8 .546 Between urban, sub urban and rural 2 0.5 .602 sites *Bolded values are significant at the 0.05 level

Fraction F2 carried about 1 to 34% of Zn irrespective of samples. Maximum level of F2 was recorded at Thiruvarur (M18) and minimum at Karur (U1). Zn bound to

F2 fraction can be related to the concentration of HCO3 in sediments. Carbonate association with Zn is due to the relatively high stability of ZnCO3 under the pH–Eh conditions of common inland waters, as well as its characteristic co-precipitation with

CaCO3 (Salomons and Mook 1978; Deurer et al. 1978). Relatively higher level of F2 fraction of Zn is probably due to similarity in their ionic radii to that of calcium in the carbonate phase (Pederson and Price 1982; Zhang et al. 1988). The association of Zn with the carbonate fraction is also reported by many workers (Modak et al. 1992; Panda et al. 1995). Earlier investigation done by Vaithiyanathan et al. (1993) in River Cauvery also found substantial level of F2 fraction in surface sediments. In general, F2 fraction distribution was recorded in the sequence of: mid delta > upper delta > estuary. Analysis of variance yielded significant variation of F2 fraction between season and three regions. Correlation between metal fraction and various characteristics of surface sediments for wet and dry seasons is presented in the table (Tables 5.7.2).

118 Table 5.7.2. Correlation matrix for Zinc fractionation profile in Cauvery delta regions

F1 F2 F3 F4 F5 pH OM CaCO3 Fe- F3 Mn-F3 Fe Mn Dry season F1 1.00 F2 0.08 1.00 F3 -0.08 0.11 1.00 F4 -0.32** -0.47** -0.35** 1.00 F5 -0.31** -0.48** -0.43** -0.13 1.00 pH -0.07 0.34** -0.07 0.08 -0.25* 1.00 OM -0.08 0.01 -0.08 0.13 -0.03 -0.01 1.00

CaCO3 0.06 0.17 -0.11 -0.04 -0.06 0.07 0.14 1.00 Fe- F3 -0.05 0.08 0.16 0.00 -0.15 0.13 -0.07 -0.26* 1.00 Mn-F3 -0.14 0.16 0.13 -0.09 -0.04 0.03 0.09 0.10 0.08 1.00 Fe -0.04 -0.30** 0.11 0.14 0.05 -0.28* 0.06 -0.02 0.22* -0.16 1.00 Mn -0.15 -0.17 0.16 0.17 -0.06 -0.04 -0.06 0.00 0.16 -0.01 0.25* 1.00 Wet season F1 1.00 F2 0.35** 1.00 F3 -0.22 0.05 1.00 F4 0.13 -0.05 -0.08 1.00 F5 -0.50** -0.59** -0.39** -0.58** 1.00 pH 0.18 -0.04 0.15 0.13 -0.20 1.00 OM -0.05 0.01 0.10 0.04 -0.05 -0.01 1.00

CaCO3 -0.06 0.12 0.28* 0.16 -0.27* 0.06 0.19 1.00 Fe- F3 0.05 0.17 0.07 0.19 -0.24* 0.13 -0.07 0.14 1.00 Mn-F3 0.01 -0.02 0.13 -0.07 -0.02 0.03 0.09 0.02 0.08 1.00 Fe -0.10 0.25* 0.13 0.06 -0.18 -0.28* 0.06 0.34* 0.22* -0.16 1.00 Mn 0.14 0.06 -0.09 0.20 -0.14 -0.04 -0.06 0.16 0.16 -0.01 0.25* 1.00 * Correlation is significant at the 0.05 level (2-tailed). ** Correlation is significant at the 0.01 level (2-tailed).

F1 fraction of Zn was found in the range 1 - 25.7% with a mean of 9% with Maximum levels at Grand Anicut (U6) followed by 24.4% at Thirumukudallur (U2). Significant level of Zn in F1 fraction in few sampling sites indicates its likely origin from agrochemical and municipal sewage in certain pockets of the study area. Among the three portions of delta region, maximum level of F1 fraction was observed in the upper region during dry period. Mean level of F1 fraction recorded during dry season

119 (10.5%) is slightly higher than the wet season samples (7.6%) suggesting influence of factors such as pH and redox conditions. Zn can enter the aquatic environment from a number of sources including industrial discharges, liquid manure, composted materials, sewage effluent and agrochemical runoff (Boxall et al. 2000). Recently, Passos et al (2010) reported about 34% of Zn associated with F1 fraction in contaminated sediments of Poxim river estuary, Brazil. Analysis of variance yielded significant variation of F1 between season and years. This fraction can be easily released back into water column, which may cause secondary pollution and subsequent health risk to the exposed population. Numerous earlier reports also considered F1 fraction to be the most mobile and bioavailable phase present in the sediments followed by the F2 fraction (Tessier et al. 1979; Ahumada et al. 1999; Howari and Banat 2001). The association of Zn with different fractions followed the order:

Dry season: Residual (F5) > carbonate bound (F2) > Fe–Mn oxide bound (F3) > organic bound (F4) > exchangeable (F1)

Wet season: Residual (F5) > organic bound (F4) > Fe–Mn oxide bound (F3) > carbonate bound (F2) > exchangeable (F1)

Table 5.7.3. Percentage of samples falls under various classes as per risk assessment code and summary of mobility factor assessment for Zinc fractionation

Risk class Upper delta Mid delta Estuarine Dry Wet Dry Wet Dry Wet season season season season season season Risk Assessment Code No risk ------Low risk - 25 2 7 20 60 Medium risk 42 67 50 76 50 35 High risk 58 8 48 17 30 5 Very high risk ------Mobility Factor Minimum 35.9 19.0 21.9 15.3 14.8 7.7 Maximum 62.5 60.7 67.7 62.9 66.0 45.4 Mean 47.3 32.1 43.9 40.7 42.3 31.5

120 Due to significant proportion Zn in F1 and F2 fractions, RAC finds 58% of samples under high risk class particularly in mid delta and estuarine region during wet season (Table 5.7.3). The mobility factor of Zn in the three regions of Cauvery delta follows the order: mid delta > upper delta > estuarine region (Table 5.7.3). Maximum level of mobility factor (67.7%) was recorded in mid delta region and minimum level (7.7%) also recorded in estuarine region during dry and wet season, respectively.

5.8. Lead Fractionation 5.8.1. Background Lead (Pb) is known for its lithophilic and chalcophilic characteristics (Itoh et al. 2004). It manly enters into the aquatic environment from the diffuse pollution sources, including mining and smelting, recycling of sewage sludge and from motor vehicle exhausts (Small et al. 1995). Pb may mobilize from soil when lead-bearing soil particles run off to surface waters during heavy rains and soil to the atmosphere by downwind transport. Since, Pb is non-biodegradable in nature and once sediment has become contaminated by Pb it remains a long-term source of exposure (Pate et al. 2006). Pb occurs in naturally as insoluble element, but under reducing conditions it is readily absorbed by organic matter. Mobility of Pb is relatively low, restricted by tendency for adsorption on to Fe and Mn-oxides and insoluble organic matter. Lead fractionation is generally very variable probably because of a relatively large proportion of Pb entering the aquatic environment through atmospheric deposition (Davidson et al. 1994; Marin et al. 1997).

Anthropogenic accumulation of Pb is mainly associated with Fe-Mn oxide fraction. Hydrous oxides are important scavengers of Pb in the aquatic environment and it plays a vital role in controlling its mobility in the environment. Therefore, Pb associated to Fe-Mn oxides has been proved to be sensitive to anthropogenic inputs (Modak et al. 1992 Burt et al. 2003; Kaasalainen and Yli-Halla 2003; Singh et al. 2005c; Davidson et al. 2006; Jensen et al. 2006; The Cuong and Obbard 2006). For example, Ettler et al. (2006) found up to 71% of the Pb associated with Fe–Mn oxides in stream sediments from the Ag–Pb and U mining in Pribram district, Czech Republic. Even though Pb is strongly sorbed to oxides, such association does not assure immobilization of Pb in the subsurface (Hering 1995). Shockingly, high proportion of Pb in mobile fraction can be transferred down through the soil profile and supplied to

121 the plant roots (Ponizovsky and Mironenko 2001). However, low mobile fraction of Pb in the sediment leads to Pb remaining in the upper layer of the sediment profile. It can also be transported to bodies of water by overland flow and pollute waterways during storm events, which is one type of nonpoint source pollution.

5.8.2. Fractionation of Lead in River Cauvery in delta region In the present investigation, the predominant fractionation forms of Pb were residual, Fe-Mn oxide bound and carbonate bound fractions. Fraction F5 was found in the range from 22 to 93.5% (Figure 5.8.1). Maximum level was measured at Ammapalli (M3) in dry season (2008) and minimum at Killiyur (E4) in wet season (2009). F4 and F3 fraction stands as a second and third largest fraction accounting for 13.6 and 14.6%, respectively. The F4 fraction was observed in the range from 1.8 to 33.9%. Maximum level of F4 fraction was recorded at Kulithalai (U3) and minimum at Poompuhar (E5) in wet season (2009). Pb forms stable and insoluble chelates together with organic substances (Bradl 2005). Sorption of Pb by sediments has been reported to be the processes correlated with organic content, grain size and anthropogenic pollutants (Muniz et al. 2004). Therefore, insoluble and stable Pb was thought to be bound to organic matter (F4 fraction). In most of the aquatic environments, organic material exhibit a high degree of selectivity for divalent ions and the probable order of binding strength for metal ions onto organic matter is Cu > Pb > Zn (Jonasson 1977).

100

80

60

40 Pb concentration (%) Pb

20

0 F1 F2 F3 F4 F5

Metal fractions

Figure 5.8.1. Lead fractionation in river sediments of Cauvery delta region

122 Pb bound to Fe and Mn oxides (F3) in the samples ranged between 0.4 to 42.3%. It is also found that Pb associated with F3 was comparatively higher in alkaline pH. The organic matter and Fe-Mn oxide have a scavenging affect and may provide a sink for Pb (Fytianos et al. 1995; Yu et al. 2001). Analysis of variance showed significant variation of F3 associated Pb between season and years (Table 5.8.1). Pb bound to carbonates (F2) was observed in range 0.4 - 38.8% in samples. Mean level of F2 fractions in dry season (12.8%) is slightly lower than the wet season (14.4%) samples. Since, carbonates can be readily dissolve in response to slight changes in water-sediment equilibrium may leads to release of Pb from sediment matrix to water column. According to Gambrell et al. (1991), reducible (F3) and carbonate fraction (F2) of Pb may acts as a mobile fraction. Pb in F2 fraction threats the aquatic biota and exposed population (Polic and Pfendt 1992). In polluted sediments, Pb is mainly found associated with carbonate fractions (Klavins et al. 2000).

Table 5.8.1. Analysis of variance of Lead fractionation in various comparisons

Pb Comparisons df F Significance Fraction F1 Between season 1 0.6 .432 Between years 1 0.1 .746 Between three regions 2 0.5 .601 Between reservoir sites 5 1.3 .300 Between urban, sub urban and rural sites 2 0.6 .511 F2 Between season 1 1.5 .216 Between years 1 2.6 .108 Between three regions 2 0.5 .587 Between reservoir sites 5 0.3 .912 Between urban, sub urban and rural sites 2 1.5 .208 F3 Between season 1 7.6 .006 Between years 1 8.9 .003 Between three regions 2 0.6 .540 Between reservoir sites 5 0.6 .679 Between urban, sub urban and rural sites 2 0.4 .625

123 F4 Between season 1 4.8 .030 Between years 1 1.2 .273 Between three regions 2 1.0 .365 Between reservoir sites 5 0.9 .478 Between urban, sub urban and rural sites 2 0.3 .728 F5 Between season 1 2.2 .137 Between years 1 0.1 .714 Between three regions 2 0.8 .421 Between reservoir sites 5 0.3 .879 Between urban, sub urban and rural sites 2 1.2 .291 *Bolded values are significant at the 0.05 level

Pb in F1 fraction recorded in the range 0.4 - 19.7 % and 0.1 - 29.4% during dry season and wet season, respectively. Maximum level of F1 fraction was found at Nagapattinam (E7) and minimum at Grand Anicut (U6). The slight variation Pb in F1 between dry and wet season can be attributed to anthropogenic enrichment from the vicinity. Pb and its compounds from industrial effluents, sewage sludge, domestic wastes, pigments, petrol (gasoline) additives, steel products and the combustion of fossil fuels are likely reach the aquatic environment ultimately (Fergusson 1990). F1 and F2 fractions consist of weakly bonded Pb which may equilibrate with the aqueous phase and thus become more rapidly bioavailable (Salomons and Forstner 1980; Pardo et al. 1990). Correlation between metal fraction and various characteristics of surface sediments for wet and dry seasons is presented in the table (Tables 5.8.2).

124 Table 5.8.2. Correlation matrix for Lead fractionation profile in Cauvery delta regions

F1 F2 F3 F4 F5 pH OM CaCO3 Fe- F3 Mn-F3 Fe Mn Dry season F1 1.00 F2 0.13 1.00 F3 -0.26* 0.05 1.00 F4 -0.01 -0.08 0.01 1.00 F5 -0.30** -0.60** -0.45** -0.55** 1.00 pH 0.00 0.11 -0.17 0.15 -0.07 1.00 OM -0.17 0.04 0.02 0.03 0.02 0.14 1.00

CaCO3 0.11 0.09 0.03 0.05 -0.14 0.07 0.03 1.00 Fe- F3 0.08 0.03 0.03 -0.11 0.00 -0.10 -0.12 0.10 1.00 Mn-F3 0.02 -0.02 -0.05 -0.04 0.06 0.14 0.22* -0.07 -0.19 1.00 Fe -0.19 0.08 -0.31** -0.12 0.24* 0.06 0.10 -0.22* 0.07 -0.11 1.00 Mn -0.01 -0.07 0.08 0.02 0.00 -0.17 -0.27* -0.18 -0.14 0.05 0.06 1.00 Wet season F1 1 F2 0.04 1.00 F3 -0.10 -0.34** 1.00 F4 -0.17 -0.14 0.04 1.00 F5 -0.33** -0.33** -0.49** -0.46** 1.00 pH 0.06 0.13 -0.05 0.13 -0.16 1.00 OM -0.10 0.18 -0.01 -0.01 -0.05 -0.01 1.00

CaCO3 0.23* 0.25* -0.31** 0.10 -0.10 0.38** 0.13 1.00 Fe- F3 0.15 -0.08 0.06 -0.02 -0.05 0.13 -0.07 0.34** 1.00 Mn-F3 -0.14 0.18 0.13 0.15 -0.23* 0.03 0.09 -0.05 0.08 1.00 Fe 0.18 -0.17 0.11 -0.04 -0.04 -0.28* 0.06 0.11 0.22 -0.16 1.00 Mn 0.09 -0.19 0.19 -0.01 -0.06 -0.04 -0.06 -0.09 0.16 -0.01 0.25* 1.00 * Correlation is significant at the 0.05 level (2-tailed). ** Correlation is significant at the 0.01 level (2-tailed)

Distribution of Pb in various geochemical fractions observed were as follows, Dry season: Residual (F5) > organic bound (F4) > Fe–Mn oxide bound (F3) > carbonate bound (F2) > exchangeable (F1) Wet season: Residual (F5) > Fe–Mn oxide bound (F3) > carbonate bound (F2) > organic bound (F4) > exchangeable (F1)

125 Table 5.8.3. Percentage of samples falls under various classes as per risk assessment code and summary of mobility factor assessment for Lead fractionation

Risk class Upper delta Mid delta Estuarine Dry Wet Dry Wet Dry Wet season season season season season season Risk Assessment Code No risk ------Low risk - 25 20 4 20 10 Medium risk 83 58 65 72 75 60 High risk 17 17 15 22 5 25 Very high risk - - - 2 - 5 Mobility Factor Minimum 18.0 10.2 3.6 18.1 16.6 13.2 Maximum 54.1 63.4 57.1 65.6 57.0 67.1 Mean 41.4 38.2 32.4 39.0 31.7 42.0

As per risk assessment code, Pb fractionation observed in this study suggests that many regions in Cauvery delta may fall under low risk to very high risk category in case of Pb toxicity (Table 5.8.3). About 2 and 5% of sediment samples come under very high risk category in mid delta and estuarine regions during wet season (2010), respectively. Mobility factor (MF) assessment implies high mobility potential of Pb in the estuarine region followed by upper delta and mid delta (Table 5.8.3). Maximum level of mobility (65.6%) was recorded mid delta during wet season.

5.9. Nickel Fractionation 5.9.1. Background The main sources of Nickel (Ni) in freshwater environment are metallurgical industries, burning of fossil fuels, municipal wastewater, and geological weathering (Nriagu and Pacyna 1988). It has been demonstrated that nickel of natural origin deposited in sediments, also in very high concentrations, has low potential mobility and low ecological risk (Glosinska et al. 2005; Saulnier and Gagnon 2006), whereas, Ni of anthropogenic origin has higher potential mobility (Glosinska et al. 2005; Farkas et al. 2007). Concerning its chemical fractionation, Ni occurs predominantly in the divalent

126 form Ni (II), which forms strong complexes with organic ligands (carboxylates, fulvates and humates). Ni forms soluble salts of chloride, sulfate and nitrate, whereas Ni oxide is only soluble under acid conditions, in contrast to Ni hydroxides, sulphides, arsenides, arsenates, and silicates, which are almost insoluble. Depends on the sediment Eh changes, Fe and Mn oxides are excellent scavengers of Ni in the aquatic environment (Feijtel et al. 1988; Delaune et al. 1997).

5.9.2. Fractionation of Nickel in River Cauvery in delta region

100

80

60

40 Ni concentration (%) concentration Ni

20

0 F1 F2 F3 F4 F5

Metal fractions

Figure 5.9.1. Nickel fractionation in river sediments of Cauvery delta region

The association of Ni in various geochemical fractions was presented in Figure 5.9.1. In the River sediments of Cauvery delta, Ni was mainly bound to the residual fraction (F5) ranging from 55 to 78.6% and 48.1 to 78% during dry and wet season, respectively. Analysis of variance showed significant difference in F5 fraction of Ni between years, three regions and reservoir sites (Table 5.9.1). The Ni-F5 fraction was found in the order of upper delta > mid delta > estuarine region. F4 fraction stands as a second largest fraction in the Ni fractionation profile. It recorded in the range from 8.0 - 25.5% with a mean level of 15.7%. Maximum level was figured at Thirukattupalai (M7) in dry season (2008) and minimum at Anaikarai (M5) during wet season (2009). Analysis of variance yielded significant difference in F4 fraction of Ni between seasons. Organic matter in sediments is generally known to influence Ni bound to F4

127 fraction. Earlier studies had also reported significant level of F4 bounded Ni in the organic matter enriched lake (Sekhar et al. 2004) and in contaminated aquatic environment (Farkas et al. 2007). Ni associated with F4 fraction may be released back to the water column in response to organic matter decomposition and oxidation of sulfides to SO2 (Zoumis et al. 2001). The order of Ni-F4 fraction distribution in the River Cauvery in delta region was observed as, estuarine region ≈ mid delta > upper delta. In general, earlier studies also found Ni associated with F4 and F5 fractions as dominant in aquatic sediments (Staelens et al. 2000; Zhai et al. 2003).

Table 5.9.1. Analysis of variance of Nickel fractionation in various comparisons Ni Comparisons df F Significance Fraction F1 Between season 1 21.6 .000 Between years 1 0.5 .499 Between three regions 2 75.0 .000 Between reservoir sites 5 5.1 .004 Between urban, sub urban and rural sites 2 2.9 .053 F2 Between season 1 55.0 .000 Between years 1 1.6 .203 Between three regions 2 12.3 .000 Between reservoir sites 5 1.3 .285 Between urban, sub urban and rural sites 2 0.4 .666 F3 Between season 1 1.6 .210 Between years 1 2.0 .156 Between three regions 2 0.2 .827 Between reservoir sites 5 2.7 .050 Between urban, sub urban and rural sites 2 1.1 .339 F4 Between season 1 6.3 .013 Between years 1 2.4 .125 Between three regions 2 1.7 .186 Between reservoir sites 5 2.2 .094 Between urban, sub urban and rural sites 2 0.6 .848 F5 Between season 1 3.0 .082 Between years 1 5.5 .020 Between three regions 2 11.3 .000 Between reservoir sites 5 4.7 .006 Between urban, sub urban and rural sites 2 1.1 .339 *Bolded values are significant at the 0.05 level

128 Fraction F3 was noted as a third largest fraction containing 6.2 to 28% of Ni. Maximum level was recorded at Aduthurai (M10) and minimum at Arasalur (M23) in wet season (2010). Positive correlation between F3 fraction and Mn-F3 fraction suggests Mn oxides potential to retain Ni in the sediments (Singh et al. 2008) (Table 5.9.2). F2 fraction was observed to be fourth largest carrier of Ni ranging from 0.0 to 13%. Maximum level was found at Nagapattinam (E10) in wet season of 2010. Analysis of variance showed significant difference in F2 fraction of Ni between season and three regions. A greater portion of Ni associated with F2 fraction indicates contaminated sediments (Kwapulinski et al. 1991; Loska and Wiechula 2003).

Table 5.9.2. Correlation matrix for Nickel fractionation profile in Cauvery delta regions

F1 F2 F3 F4 F5 pH OM CaCO3 Fe- F3 Mn-F3 Fe Mn Dry season F1 1.00 F2 0.70** 1.00 F3 0.07 -0.05 1.00 F4 -0.20** -0.01 0.32 1.00 F5 -0.30** -0.37** -0.73** -0.74** 1.00 pH -0.07 -0.05 -0.21 -0.04 0.16 1.00 OM 0.00 0.06 0.06 0.04 -0.07 -0.01 1.00

CaCO3 -0.13 -0.06 0.06 0.14 -0.07 0.07 0.14 1.00 Fe- F3 0.08 0.04 -0.09 -0.02 0.03 0.13 -0.07 -0.26* 1.00 Mn-F3 0.00 0.17 0.30 0.20 -0.17 0.03 0.09 0.10 0.08 1.00 Fe -0.25* -0.18 -0.02 0.01 0.10 -0.28* 0.06 -0.02 0.22* -0.16 1.00 Mn 0.06 0.02 -0.02 -0.16 0.09 -0.04 -0.06 0.00 0.16 -0.01 0.25* 1.00 Wet season F1 1.00 F2 0.46* 1.00 F3 0.20 0.12 1.00 F4 0.19 -0.10 0.02 1.00 F5 -0.63* -0.55** -0.65* -0.53* 1.00 pH -0.13 -0.01 -0.05 -0.07 0.09 1.00 OM -0.05 -0.02 -0.08 -0.08 0.10 -0.01 1.00

CaCO3 -0.32* -0.23* -0.09 -0.07 0.26* 0.06 0.19 1.00 Fe- F3 0.02 -0.09 -0.04 0.20 -0.05 0.13 -0.07 0.14 1.00 Mn-F3 0.10 0.06 0.18 -0.08 -0.11 0.03 0.09 0.02 0.08 1.00 Fe -0.31* -0.25* -0.11 0.01 0.23* -0.28* 0.06 0.34* 0.22* -0.16 1.00 Mn 0.01 -0.03 -0.17 -0.06 0.13 -0.04 -0.06 0.16 0.16 -0.01 0.25* 1.00 * Correlation is significant at the 0.05 level (2-tailed). ** Correlation is significant at the 0.01 level (2-tailed).

129 F1 fraction of Ni corresponds to the metal species that are most easily available under changing ionic strength of the medium (Norvell 1984). In the present study, F1 fraction in sediments contained up to 6.9%. Maximum level of F1 was recorded at Poompuhar (E7) in wet season of 2010. Comparatively, higher level of F1 and F2 fraction of Ni recorded in estuarine sampling sites [Kollidam (E4), Killiyur (E6), Poompuhar (E7), Nagore (E8) and Nagapattinam (E9)] suggests anthropogenic origin of Ni besides natural influences like sediment diagenesis processes (Kim et al. 1998). The Ni-F1 fraction was found in the order of estuarine region > mid delta > upper delta. Analysis of variance yielded significant difference in F1 fraction of Ni between season and three regions and reservoir sites. In overall, the order of fraction in terms of Ni concentration was observed as residual (F5) > organic bound (F4) > Fe-Mn oxide bound (F3) > carbonate bound (F2) > exchangeable (F1). Main sources of Ni in aquatic environment are electroplating and metallurgical industries, burning of fossil fuels, municipal wastewater, and geological weathering (Nriagu and Pacyna 1988). Hang et al. (2009) found high proportion of Ni in F1 fraction in the electroplating contaminated sediments.

Table 5.9.3. Percentage of samples falls under various classes as per risk assessment code and summary of mobility factor assessment for Nickel fractionation

Risk class Upper delta Mid delta Estuarine Dry Wet Dry Wet Dry Wet season season season season season season Risk Assessment Code No risk - - 19 2 - - Low risk 100 100 81 84 100 70 Medium risk - - - 14 - 30 High risk ------Very high risk ------Mobility Factor Minimum 13.5 13.8 7.9 10.0 13.5 16.7 Maximum 20.7 20.3 22.8 35.9 27.0 34.9 Mean 17.5 17.6 15.5 18.8 19.8 23.5

130 The risk assessment code and mobility factor of Ni fractionation in the River sediments of Cauvery delta region is given in Table 5.9.3. As per risk assessment code, upper delta regions fall under low risk category in throughout the study period. In mobility factor assessment, maximum level of mobility (35.9%) was recorded at Aduthurai (M10) and minimum level 7.9% was recorded at Vadapadi (M22) in mid delta region. The order of Ni mobility was observed as, estuarine region > upper delta > mid delta.

131 Chapter-6 Bioaccumulation of metals in edible fishes of select reservoirs in Cauvery delta region

6.1. Background Metal fractionation occurring in the sediments is in turn likely to influence metal bioavailability, and thereby metal content in biota, in particular in the soft tissues of fish and mussels (Pempkowiak et al. 1999; Yap et al. 2002). Metals bound to various fractions can act differently in the sedimentary and diagenetic environment and attributed to different potential of remobilization and uptake by biota. Since, determination of total metal concentration failed to provide any precious information about bioavailability and toxicity; metal fractionation studies are highly essential to understand the ecotoxicity (Sekhar et al. 2004).

Among the five metal fractions, exchangeable and carbonates bound fractions are considered as a most bioavailable and consequently toxic to the surrounding biota (Hickey and Kittrick 1984; Ma and Rao 1997; Li et al. 2001a). These fractions are believed to be in equilibrium with the aqueous phase and can be affected easily by the changes of different environmental parameters, such as pH, redox potential, and the concentration of metals and ligands, etc (Athalye and Gokhale 1991). In contrast, residual fractions are strongly bound within the crystalline structure of the primary and secondary minerals of the sediment. This fraction may be released during the weathering process of the minerals and therefore considered to be the least mobile and not bioavailable to the surrounding biota in the natural environmental conditions. On the basis of the binding strength and solubility in different geochemical fractions of heavy metals, the bioavailability and potential toxicity are expected to decrease in the order of exchangeable > bound to carbonates > reducible > oxidizable > residual metal phase (Ma and Rao 1997).

In general, heavy metals in aquatic biota are mainly derived from three major sources, including water, sediments and food chains (Amiard-Triquet et al. 1992). However, the extent of heavy metal bioaccumulation in the biota from these sources is

132 highly dependent on several factors, such as fractionation, physiological conditions of the biota and environmental characteristics (Athalye and Gokhale 1991). In general, the high concentration of heavy metals in the bioavailable fraction may facilitate the high accumulation in the biota (Sekhar et al. 2004).

The term bioaccumulation may be defined as “accumulation of any substances (e.g., heavy metals) by the living organisms into their biological systems through the processes of direct or indirect absorption from water, sediments and/or food chains”. Bioaccumulation studies are helpful to elucidate the accumulation effect on living organisms along the trophic levels in the food chains (Lau and Chu 2000).

Interaction of heavy metals with living organisms highly depend on three broad areas of concern: 1) metal fractionation in the external environment 2) metal interaction with the biological membrane and 3) metal portioning within the organism and the accompanying biological effects (Sunda 1991). The biological response of organisms is more or less proportional to the activity of the free metal ions. Free ionic form of metals in aquatic system is believed to be responsible for toxicity. For example, Cu2+ has been directly linked to toxicity in fish and invertebrates while Cu complexed by dissolved organic matter does not induce toxicity to the same degree (Erickson et al. 1996; Ma et al. 1999) because bioavailability for uptake is reduced. The free metal ion activity is a measure of its chemical reactivity at cell surface and it also allows to determining the extent of uptake, nutrition of cationic metals (Campbell 1995). Free metal ions in the non-complex form with organic matter can express its action on cell membrane through three steps: 1) advection or diffusion of the metal in the bulk solution to the cell membrane surface 2) sorption or surface complexation of the metal at binding sites on the cell membrane surface 3) uptake of the metal through the cell membrane into the organism. Therefore, a metal must first interact with or traverse the cell membrane surface to elicit a biological membrane.

Metals differ significantly in the extent of their bioavailability or its ability to enter organisms and cause toxicity. According to Caussy et al (2003), certain metal fractions are likely to get attached to the cell surface and get transported inside whereas

133 major portion of the metals cannot enter the organism. Therefore, the type of bioavailability is differentiated into external availability and internal bioavailability. External availability is largely determined by ability of the metals to be solubilized and released from environmental matrix such as soil, food, water and air, an ability synonymously called bioaccessibility. Internal bioavailability decides the ability of the metals to be absorbed and reach target organ, where it exerts its toxic effects. As highlighted earlier the availability of metals to the organisms is largely determined not exclusively by the external ambient conditions but also by the physico-chemical characteristics of the microenvironment of the organism. Some organisms change the microenvironment to restrict the toxicant entering their body through extracellular ligands and extracellular metal binding organic matter in response to the stress from the excess of uncomplexed metals. These compounds are known as complexants, can form a complex with metals and prevent their cellular uptake (Wood et al. 1985). Plants are known to influence the solubility and speciation of metals in the rhizosphere by exuding chelators and manipulating rhizosphere pH (Reichman 2002).

6.1.1. Bioaccumulation of heavy metals in fish population Among the aquatic biota, fish population is frequently used as indicators of heavy metals contamination in the aquatic ecosystem because they occupy high trophic levels and are important food source (Blasco et al. 1998; Agah et al. 2009). It is also known for its metal accumulation potential in their bodies from the surrounding medium (Forstner and Wittman 1981; Ademoroti 1996). Metal content in fish tissues competent to imitate the previous exposure via water and/or food and demonstrate the existing condition of its population before toxicity affects the ecological balance of populations in the aquatic environment (Forstner and Wittmann 1983).

Bioaccumulation of heavy metals in fishes is a complex process, which is manly controlled by exogenous and endogenous factors. Exogenous factors reflects the environmental parameters such as metal bioavailability, temperature and alkalinity of ambient aquatic surroundings, whereas endogenous factors include species, age, size, physiological state and type of feeding of fish (Elder and Collins 1991; Moiseenko and Kudryavtseva 2001). Stow et al. (1994) and Bentzen et al. (1996) found some factors

134 such as proximity of fish to contaminated sediment, magnitude of contamination in their habitats and the trophic status may contribute to the degree and exposure of chemical contaminants in fish. An investigation carried out by Eisler (2000) opined that the increase of temperature in the aquatic medium is known to causing the increase of heavy metal toxicity while the increase of water hardness causes the reduction of toxicity.

In fishes, heavy metals exposure takes place in two main routes: (1) The primary route of intake of these chemicals in fish species is via gills or transport of dissolved contaminants in water across biological membranes and ionic exchange (2) The secondary route is through ingestion of food or sediment particles with subsequent transport across the gut (Newarman 1998; Bervotes et al. 2001; Burger et al. 2002). Among the fish organs, liver and kidney appeared to have significantly higher tendency for the accumulation of most of the metals where as gills and muscles had minimum concentrations of these metals (Javed 2005; Agarwal et al. 2007; Rauf et al. 2009b). Metals accumulation in gill may possibly reflect the metal levels in water environment where fish lived in, whereas those in fish liver represent the metals storage in fish (Farkas et al. 2003). Pollutants transformed in the liver may be stored there or excreted in bile or transported to other excretory organs such as gills, skin or kidneys for elimination or stored in fat which is an extra hepatic tissue (Nussey et al. 2000).

Heavy metal accumulation in fish organs are highly influenced by spatial and seasonal variations and have been studied worldwide (Barlas 1999; Chong and Wong 2000; Lee et al. 2000; Karadede et al. 2004; Licata et al. 2004; Durrieu et al. 2005; Agarwal et al. 2007; Mendil and Uluozlu 2007; Ploetz et al. 2007; Yang et al. 2007; Triebskorn et al. 2008; Eneji et al. 2011; Eslami et al. 2011). Among them, Kojadinovic et al. (2007) found higher level of trace elements (Cd, Hg, Pb, and Zn) in muscle of commercially important fish from Western Indian Ocean. In general, high level of metal bioaccumulation in fishes were observed in industrial effluents contaminated fresh water and marine environment (Agarwal et al. 2007; Besser et al. 2007; Agarwal and Saxena 2011; Bharti and Banerjee 2011; Olowoyo et al. 2012) and very trace level of accumulation was detected in remote areas. Olowoyo et al. (2012) observed

135 significantly higher concentration of metals such as zinc, iron, chromium and nickel in C.gariepinus and C.carpiofish species in wastewater affected lake Pretoria, South Africa. Durrieu et al. (2005) found notable heavy metal contamination differences between fish species and organs, as well as heavy metals in Gironde Estuary, France.

Mining effluents are the main source of metals in the surrounding aquatic environment and it also facilitates metal bioaccumulation in aquatic biota (Besser et al. 2007; Moiseenko and Kudryavtseva 2001; Borgmann et al. 2007; Mayes et al. 2010; Bharti and Banerjee 2011; Ruelas-Inzunza et al. 2011; Liu et al. 2011b). Among them, Besser et al. (2007) highlighted significant level of spatial and temporal variations in heavy metal concentration in stream biota from mining areas of United States. Coal mine effluent exposed fishes shows significantly high concentration of Fe, Cd, Pb and Cr in most of the tissues above the permissible limits (Bharti and Banerjee 2011). Ruelas-Inzunza et al. (2011) found higher level of metal contamination (Cd, Cr, Hg and Pb) in fishes of mining activities affected Baluarte River basin in dry season.

Even though relationship between metal bioaccumulation and metal fractionation was well understood by last few decades, findings relevant to metal levels in the living biota and metal fractionation profile in the sediments are vary scare (Ismail and Rosniza 1997; Pempkowiak et al. 1999; Fan et al. 2002; Sekhar et al. 2004; Baumann and Fisher 2011). Among them, significant correlation between the Cd assimilation efficiency in mussels (P. viridis) and clams (R. philippinarum) and Cd partitioning in the easily exchangeable and reducible phases in contaminated sediments of Chinese bay was found by Fan et al. (2002). Similarly, Baumann and Fisher (2011) reported influences of sediment geochemistry on bioavailability of As, Cd, and Cr in deposit-feeding polychaetes and observed positive relationship between metal assimilation efficiencies and exchangeable as well as carbonate sedimentary fractions.

Few studies also focused about the influences of various ligands on metal fractionation (Elder and Collins 1991; Eisler 2000; Voets et al. 2004). Influences of Cd bioavailability in the Zebra mussel in the presence of humic acid and dissolved organic matter concentration was examined by Voets et al. (2004). Bourgoin et al. (1991)

136 investigated the factors influencing Pb bioavailability to M. edulis collected near lead smelter. Their study indicates that the total sulphur content in the sediment played an important role in defining the Pb accumulation in the mussel tissues.

6.1.2. Bioaccumulation studies in various freshwater and marine environment of India Bioaccumulation of heavy metal in aquatic biota was widely reported in Indian rivers, lakes and marine environment. Elevated level of metal bioaccumulation was reported in aquatic systems which are contaminated by industrial, domestic and agricultural pollutants; whereas low level of accumulation was found in undisturbed areas (Table 6.1).

Table 6.1. Bioaccumulation of metals in various lakes, rivers and marine environment Location Name of the aquatic observations Author species studied Kolleru Lake C. striata The concentrations of Sekhar et al. (2004) C. catla metals were found to be O. mossambicus higher in gills followed by liver and muscle of fish. Notable relationship between labile fractions and metal accumulation in biota was noted. Cochin M. cephalus,C. Metal selectivity index Nair et al. (2006) backwaters elongatus, P. niger, C. found in the order: Zn > malabaricus, S. jello, Mn > Pb > Cd. The organ G. giuris, C. elongates, of choice for estimating C. malabaricus, S. bioaccumulation potential carutta, N. japonicus, is the liver in a weakly R. kanagurta, N. polluted area, whereas in a japonicus, R. heavily polluted area, the kanagurta, G. tissue of choice is the gill. filamentosus Port Blair S. turnbil, E. tauvina Significant correlation was Kaladharan et al. and Kochi R. kanagurta, S. observed between metal (2006) gibbosa accumulation in water and L. parsia, E. tauvina and S. longiceps as well as P. longimanus and R. kanagurta with sediment from Kochi.

137 River Gomti, C.batrachus, M. The study showed that the Agarwal et al. Lucknow cavasius, R. rita, C. accumulation pattern of (2007) punctatus, H. fossilis, M. total mercury in the fish armatus, L. rohita and species in order, M. N.notopterus armatus > C. batrachus > M. cavasius > N. notopterus > R. rita = H.fossilis > C. punctatus > L. rohita. Poompuhar M. cephalus Accumulation of heavy Prasath and Khan Coast metals was observed in the (2008) order of Sediments > Fish > Water. In fish, the order was found to be Fe > Zn > Mn > Cu > Ni > Co ≈ Pb≈ Cd.

Parangipettai U. vittatus, Cr and Zn concentrations Lakshmanan et al. Coast A. commersonii were maximum in all (2009) P. maculates studied fish species L. adetii, A. followed by Pb Pb > Cu > Cr > Cd. Data indicated that Zn accumulated maximally in the sediment as well as muscles of both of the fish species in comparison to other metals River O. mosambicus and In fish muscles, metal Begum et al. Cauvery in P. niger concentration was (2009a) Karnataka observed in the order of: Cr > Mn > Cu > Ni > Co > Pb ≈ Zn Madivala O. niloticus, E. The maximum Begum et al. Lake suratensis concentration of heavy (2009b) C. marulius, H. fossilis metals was found in C.catla Kidney and liver, the order of heavy metal level in various argons is muscle

138 >Gills>liver>kidney. Concentration of Pb and Cd exceeds the permissible limits for human Upper Lake L. rohita and C. idella Different organs of the Malik et al. (2010) of Bhopal fishes accumulated varying quantities of different heavy metals Chaliyar H. fossilis The concentration of Radhakrishnan River, Kerala metals in sediment was (2010) found higher than those of water. Gill showed maximum accumulation of the heavy metals and muscles were the site of least accumulation Southwest L.calcarifer, N. Elevated metal levels in Rejomon et al. coast of India japonicus, C. fishes along the southwest (2010) melampygus, R. Indian coast likely reflects kanagurta and C. the associated food chain macrostomus bioaccumulation of metals from the enhanced bioavailability of metals in up welled waters during the summer monsoon Aligarh C. punctatus, The Fe and Zn Javed and Usmani C. gariepinus and concentrations were the (2011) L. rohita highest in all tissues analyzed, followed by Ni, Cu, Co, Mn and Cr in almost all the three species. In the muscles of C. punctatus the order of accumulation is Fe >Zn > Ni > Cu > Co> Mn, whereas in C. gariepinus it was Fe > Zn > Ni > Cu=Mn> Co > Cr. In L. rohita the pattern of accumulation was Zn > Fe > Ni > Cu > Co > Mn. Kolkata C. catla, O.nilotica, O. The concentration of Kumar et al. (2011) wetlands mossambica, L.rohita, heavy metals was in order H.molitrix, P. ticto and of, Fe > Zn > Cu > Mn > C.marulius Ni > As > Hg > Cd. The bioaccumulation pattern of

139 metals in different species of fishes was as: H. molitrix > O. nilotica > L. rohita >O. mossambica > C. marulius > C. catla > P. ticto Kollidam M. vittatus, The distribution of heavy Ambedkar and River T. mossambica, metals in selected organs Muniyan (2011) H. fossilis, C.idella and analyzed were in the order S. undosquamis of magnitude as liver > Gill > Kidney > Intestine > Muscle. The distribution of heavy metal in the all fish organs analyzed were in the order of Cu > Cd > Pb > Cr and Zn Lower C. melanosoma, S. Mean level of Co, Cr, Mn Bhuvaneshwari et al. Anicut, wynaadensis, M. and Zn were found to be (2012) Cauvery delta cephalus, higher in gills, but Cu, Ni O.mosambicus, P. niger and Fe were more in liver and S. of all fishes. Mean erythrophthalamus concentrations of Cr (11.8 μg/g), Mn (4.4 μg/g) and Fe (139 μg/g) in the muscle were exceeding the permissible limit of FAO and WHO

Literature survey on bioaccumulation studies in River Cauvery in delta region yielded only two published reports which focused on few locations of the study area with small sample size (Ambedkar and Muniyan 2011; Bhuvaneshwari et al. 2012). In this background, present study focused to investigate i) status of heavy metal fractionation in reservoir sediments ii) bioaccumulation of heavy metals in select fish species iii) relationship between metal fractionation and bioaccumulation of metals in fishes of River Cauvery in delta region.

140 6.2. Materials and methods

Figure 6.1. Study area -(S1) Upper Anicut (S2) Grand Anicut (S3) Anaikarai

6.2.1 Sampling of sediments and fish Surface water and sediment samples were collected in three reservoirs (S1- Upper Anicut, S2- Grand Anicut and S3-Anaikarai) in the Cauvery delta region during January 2011 (Figure 6.1). In each site five sediment samples were collected with a clean acid-washed plastic scoop and returned to the laboratory in polyethylene bags and frozen until analysis. Six edible fish species (Channa striata, Catla catla, Oreochromis mossambicus, Etroplus suratensis, Mystus vittatus, Cirrhinus mrigala) which are predominantly abundant and mainly consumed were collected freshly with the help of fisher man in all three sampling sites and immediately frozen until analysis. Maximum care was taken to collect fish samples at the same locations where the sediments and water samples were collected. For heavy metal analysis, eight samples from each species with similar age and weight were selected and its organs were dissected (muscles, gills, kidney and liver). Subsequently, dissected fish samples were homogenized and dried in oven at 65°C and then powdered. The powdered fish samples were subjected to acid digestion as per Sreedevi et al. (1992). Briefly, one

141 gram of each sample was digested using 1:5:1 mixture of 70% perchloric acid concentrated nitric acid and concentrated sulphuric acid at 80 ± 5°C, until clear solution was obtained. Each digested sample was made up to 25 ml with de-ionized water and analyzed for heavy metals in an Atomic Absorption Spectrophotometer (GBC SenSAA Spectrometer, Australia). Values of heavy metals were recorded in μg/g dry weight. Atomic absorption spectrometer with hallow cathode lamp and a deuterium background corrector was used for all metal analysis. Recoveries were carried out by the addition of the standards of each element at different levels. Blanks were analysed in each batch of analysis. Calibration of standards was regularly performed to assess the accuracy of the analytical method. Working calibration standards of iron (Fe), manganese (Mn), copper (Cu), chromium (Cr), zinc (Zn), lead (Pb) and nickel (Ni) were prepared by serial dilution of concentrated stock solution (Merck, Germany) of 1,000 mg/l. These standards and blank solutions were analyzed in the same way as for the digested samples.

6.2.2. Metal fractionation The air dried sediment samples were sieved to obtain 63 micron sediment fraction. Tessier et al. (1979) developed sequential extraction scheme was followed for partitioning the sediment bound heavy metals into the five fractions viz.,. exchangeable, carbonates bound, Fe–Mn oxides bound, organic matter bound and residual form. Details of reagent used and other extraction conditions are given in Table 6.2.

Table 6.2. Details of sequential extraction scheme followed in the present study Extraction Details of reagent used and its other Sediment step extraction conditions phase 1 1.0 M MgCl2 (pH 7.0) + 1 hour exchangeable 2 1.0 M Sodium acetate (pH 5.0) + 5 hours bound to carbonates 3 0.04 M NH2OH. HCl in 25% (v/v) acetic acid+ reflux at bound to Fe–Mn 95oC for 6 hours oxides o 4 0.02 M HNO3 + 30% H2O2 + refluxed at 100 C bound to organic for 2 h + room temperature + 3.2 M ammonium acetate matter in 20% (v/v) HNO3 5 Aqua regia digestion (3HCl: 1 HNO3) residual

142 6.3. Results and discussion 6.3.1. Physico-chemical characteristics of reservoir sediments The pH of the reservoir sediments varied from 6.95 to 8.4 representing neutral to alkaline nature (Table 6.3). The total dissolved solids and oxidation reduction potential varied from 25.8 to 46.8 mg/kg and -83 to 68 mV, respectively. Mean maximum of bulk density (1.49 g/cm3) was recorded in Upper Anicut followed by Anaikarai (1.45 g/cm3). Total organic carbon and organic matter ranged from 0.08 to 1.36 % and 0.7 to 2.34%, respectively. Low level of organic carbon content in the reservoir sediments may be due to domination of coarse particles (> 99%). Organic carbon in sediment is crucially dependent on the grain size, whereby higher organic content is represented in the finer particles and vice versa (Marchand et al. 2008). Mean maximum of organic matter (1.55%) was recorded at Grand Anicut.

Table 6.3. Physico-Chemical characteristics of reservoir sediments

Parameters Mean ± Standard deviation Upper Anicut Grand Anicut Anaikarai Bulk density (g/cm3) 1.49 ± 0.04 1.43 ± 0.02 1.45 ± 0.07 Coarse particle (%) 99.31± 0.72 99.50± 0.43 99.80± 0.16 Fine particle (%) 0.67± 0.72 0.50± 0.43 0.20 ± 0.16 pH 8.27± 0.15 7.86± 0.33 7.43± 0.61 TDS 29.42± 2.29 34.72± 5.11 45.98± 8.24 ORP (mV) -76.80± 4.45 -59.60± 6.15 -10.00± 51.40 Conductivity(µS/cm) 50.30± 3.11 60.54± 11.20 77.74± 14.07 TOC (%) 0.23± 0.14 0.90± 0.29 0.33± 0.20 OM (%) 0.39± 0.24 1.55± 0.50 0.56± 0.34

6.3.2. Heavy metal concentration in surface water Concentration of iron (Fe), manganese (Mn), chromium (Cr), copper (Cu), lead (Pb) and nickel (Ni) in reservoir water samples are shown in Table 6.4. Fe and Mn content in most of the samples were found to exceed their limits (Bureau of Indian Standards 1991). Except few sampling points at Anaikarai (S3), Pb level was below the guideline value of 0.1 mg/l (BIS 1991). Cu content in water samples exceeded the standard limit of 0.05 mg/l in all three reservoirs. In the case of Cr, half the number of

143 samples exceeded the standard limit of 0.05 mg/l. The mean concentrations of the metals in reservoir water samples were observed in the order: Fe > Zn >Mn > Cu > Cr = Pb > Ni. Heavy metal abundance in reservoirs varied in following order: Grand Anicut (S2) > Anaikarai (S3) > Upper Anicut (S1). The probable sources of heavy metals entering these reservoir as per previous studies (Ramanathan et al. 1993; Ambedkar and Muniyan 2011; Bhuvaneshwari et al. 2012) are: (a) municipal sewage outlets, (b) municipal solid waste dumping, (c) industrial effluents (dyeing and tanning, distilleries and paper), and (d) agricultural runoff water (e) urban runoff and atmospheric deposition (Ramanathan et al. 1993; Ambedkar and Muniyan 2011; Bhuvaneshwari et al. 2012).

Table 6.4. Concentration of metals in surface water

Metals Metal level in water samples (mg/l)

Upper Anicut Grand Anicut Lower Anicut

Fe 0.37 ± 0.35 0.57 ± 0.16 0.35 ± 0.21 Mn 0.23 ± 0.17 0.83 ± 1.87 BDL Cu 0.24 ± 0.02 0.38 ± 0.31 0.17 ± 0.11 Cr 0.06 ± 0.03 0.04 ± 0.02 0.05 ± 0.02 Pb 0.02 ± 0.01 0.05 ± 0.02 0.11 ± 0.10

Zn 0.07 ± 0.02 0.66 ± 0.90 0.24 ± 0.21

Ni 0.03 ± 0.01 0.03 ± 0.01 0.03 ± 0.01 * BDL below detectable level

6.3.3. Heavy metal fractionation in Reservoir sediments The sum of five metal fractions of heavy metals studied is given in Table 6.5. The sum of the concentrations in all the metal fractions was observed in the order: Fe > Mn > Cr > Zn > Cu > Ni > Pb. Analysis of variance revealed significant difference (p<0.05) in total heavy metal concentration between reservoirs studied. Mean level of Fe, Mn, Cu, Cr, Pb, Zn and Ni in reservoir sediments sample recorded as 5513.7, 252.9, 24.3, 112.6, 2.9, 53.7 and 4 µg/g, respectively. Majority of heavy metals recording higher concentrations at Grand Anicut (S2) apparently implies influence of urban activities such as industrialization, storm water runoff, sewage and wastes dump (Table 6.5).

144 Table 6.5. Concentration of heavy metals (µg/g) in sum of all fractions Metals Upper Anicut Grand Anicut Lower Anicut (S1) (S2) (S3) Fe 5466.2 5933 5141.9 Mn 252.5 354.2 152 Cu 22.8 39.9 10.3 Cr 153.1 139.4 45.4 Pb 1.14 5.4 2.2 Zn 50.8 85.6 24.7 Ni 2.2 7.7 2.0

The heavy metal fractionation profile of Upper Anicut, Grand Anicut, Lower Anicut reservoirs sediments are showed in Figure 6.2. Results of metal fractionation studies suggest that the residual fraction (F-V) dominated Fe (35.4 - 73.4%), Mn (33.2 - 52%), Cu (49.6 - 68.2%) and Cr (49.6 - 68.2%) and Pb (41.9 - 80.2%). The exchangeable fraction of metals concentration observed in the order of Pb> Zn> Cu > Mn > Cr >Ni > Fe. High concentration of Pb, Cu and Zn in the F1 fraction can have an impact on aquatic biota. Maximum level of F1 fraction of Pb (16.5%) was measured at Grand Anicut (S2), whereas Cu (11.7%) and Zn (14.7%) were found at Upper Anicut (S1). Higher concentration of Cu and Zn in F1 fractions at Upper Anicut (S1) suggests influence of industrial effluents, urban runoff and municipal sewage.

Considerable portion of metals such as Pb, Zn, Mn, Cu and Fe associated with F2 fractions suggested special affinity of these metals towards carbonate phase (Sundaray 2007). Moderate level of Cu is found in F4 (8 - 13%) in all three reservoir sediment samples shows its strong affinity to organic matter. Significant level of Mn, Cu, Cr, Pb and Ni associated in F3 and F4 fractions indicates scavenging effect of Fe- Mn oxides and organic matters. Comparatively, high level of Mn found (13.5-22.7%) in the F3 fraction implies that the existence of metal in the reduced form due to the slower process of Mn (II) oxidation than Fe (II) oxidation (Tessier et al. 1979).

145 F5 F4 F3 F2 F1 100%

75%

50%

25% Metal concentration 0% S1 S2 S3 S1 S2 S3 S1 S2 S3 S1 S2 S3 S1 S2 S3 S1 S2 S3 S1 S2 S3

Fe Mn Cu Cr Zn Pb Ni Sampling site and metal

Figure 6.2. Metal fractionation in reservoir sediments

Among the risk assessment tools, Risk Assessment Code (RAC) developed by Perin et al. (1985) based on the percentage of metal associated in the exchangeable (F1) and carbonate fraction (F2) of sediment is a widely used (Table 6.6). The RAC applied to the present study reveals that Fe, Mn and Cu in all three reservoirs come under medium risk category. High level of Zn (33.5- 46.7%) accumulation in exchangeable and carbonate fractions indicates high risk and possible transfer into food chain in all three sampling sites, whereas Pb level (31.7%) at Lower Anicut is also found to be high risk to exposed population.

Table 6.6. Risk Assessment Code (RAC)

RAC Criteria Metals with corresponding sampling site No risk <1% - Low risk 1-10 % Cr (Upper Anicut and Grand Anicut) Ni (Upper Anicut and Lower Anicut) Medium risk 11-30% Fe, Mn and Cu (All three sites) Pb (Upper Anicut and Grand Anicut) Cr (Lower Anicut)

Ni (Grand Anicut)

High risk 31-50% Zn (All three sites) Pb (Lower Anicut) Very high risk >50% -

146

6.3.4. Heavy metals content in fish The metal accumulation in fish species are given in Table 6.7. Iron concentration was highest in fish samples analyzed in this study. Mean metal content in the fish samples in all three reservoir samples followed in the order: Fe > Zn > Pb > Cr > Mn > Cu > Ni. Mean maximum Fe level was detected in Kidney (417.7 µg/g) in M. vittatus and minimum level was measured in C. mrigala in muscles (9.4 µg/g). While comparing Fe accumulation in other organs, muscle contained low level of Fe than rest of the organs analyzed. Mean maximum concentration of Mn was detected in Kidney in E. suratensis (16.8 µg/g) followed by liver (6.52 µg/g) of the same species. In muscles, mean maximum level of Zn was recorded in E. suratensis (47.23µg/g) followed by C. catla (19.63 µg/g). Notable level of Cu content in the liver tissues in C. catla (6.0 µg/g) and E. suratensis (3.55 µg/g) indicated the liver as the target organ for the element. Cu may cause an additive toxic effect on fish in association with Zn and Hg (Rompala et al. 1984; Schmitt and Brumbaugh 1990). High mean level of Cr (0.33- 10.83 µg/g) was found in kidney, than rest of the organs in fish population indicates its major accumulating potential. Mean maximum level of Pb was detected in Kidney (15.95 µg/g) in E. suratensis followed by Kidney (14.45 µg/g) in C. mrigala. In general, Pb level was significantly high in liver and kidney in studied fish species except C. striata and O. mossambicus. Another study in River Yamuna reported upto 24 µg/g of Pb in M. vittatus (Sen et al. 2011). The order of heavy metals accumulation in fish species were as follows:

Fe: M. vittatus >O.mossambicus > E. suratensis >C. catla > C. mrigala > C. striata Mn: E. suratensis > O.mossambicus > C. catla >M. vittatus > C. striata> C. mrigala Cu: C. catla > M. vittatus> E. suratensis >O.mossambicus> C. mrigala > C. striata Cr: C. catla > E. suratensis > O.mossambicus> M. vittatus > C. striata > C. mrigala Pb: C. catla > E. suratensis> C. mrigala> M. vittatus>O.mossambicus > C. striata Zn: E. suratensis > C. catla> C. mrigala> O.mossambicus >M. vittatus > C. striata Ni: C. catla > O.mossambicus >E. suratensis > C. mrigala > C. striata>M. vittatus

147 Table 6.7. Concentration of heavy metals in various tissue systems of six select fish species

Metal Organ Metal content (µg/g, dry weight) with standard deviation Channa Catla Oreochromis Etroplus Mystus Cirrhinus striata catla mossambicus suratensis vittatus mrigala Fe Muscle 12.50±2.21 19.33±17.12 108.10±93.56 30.37±29.00 21.17±16.78 9.42±8.77 Gills 22.87±8.75 85.45±73.53 244.1±162 96.63±46.21 113.54±98.6 46.26±44.22 Kidney 203.2±151 222.6±107 125.27±121.1 168.37±88.22 417.70±117.4 188.41±131.43 Liver 248.2±176 225.64±207 132.93±69.16 246.83±233.86 397.83±86.03 315.22±268.43 Mn Muscle BDL 6.12±3.53 0.87±0.64 3.07±2.73 BDL BDL Gills 0.79±0.46 0.75±0.43 1.95±1.92 3.04±1.75 1.17±0.68 BDL Kidney BDL BDL 4.88±4.24 16.80±9.90 BDL BDL Liver BDL BDL 1.11±0.64 6.52±3.77 BDL BDL Cu Muscle BDL 0.03±0.02 0.05±0.01 0.03±0.01 0.04±0.03 0.02±0.01 Gills 0.04±0.01 0.26± 0.20 0.31± 0.24 0.21± 0.16 0.75±0.66 0.10± 0.08 Kidney 0.24 ± 0.21 6.45±3.75 3.69±2.57 2.29±1.46 1.50±1.31 0.28±0.17 Liver 0.03±0.02 6.0±3.47 0.99±0.66 3.55±2.10 1.80±1.41 0.46±0.4 Cr Muscle 0.50±0.32 1.27±1.23 1.10±0.83 1.10±0.87 1.56±1.30 0.73±0.58 Gills 1.21±1.0 1.67±1.47 1.40±1.3 1.50±1.3 1.63±0.58 0.37±0.29 Kidney 0.80±0.52 10.83±0.64 6.33±0.81 6.97±4.97 2.87±0.46 0.33±0.29 Liver 3.06±1.77 6.93±6.00 6.18±5.37 7.17±1.67 2.81±0.58 1.93±1.79 Zn Muscle 14.73±3.84 19.63±1.31 12.50±3.27 47.23±40.7 16.87±7.22 11.57±4.92 Gills 22.17±7.51 123.33±85.3 21.43±18.95 37.0±21.92 26.2±15.76 12.0±9.37 Kidney 17.93±10.64 43.00±23.96 57.47±10.81 50.73±20.13 47.23±36.26 182.56±135.40 Liver 45.93±24.0 37.03±15.70 43.73±12.34 169.32±122.83 38.83±21.19 21.77±4.30 Pb Muscle 0.29±0.12 1.04± 0.7 0.42 ± 0.14 1.9 ± 1.1 2.92±0.7 1.26±0.22 Gills 1.24 ± 0.87 1.34± 0.37 0.89±0.12 1.67±.0.88 1.61±0.61 0.53± 0.32 Kidney 1.93± 0.21 11.92± 8.44 0.94±0.33 15.95±12.54 11.97±7.79 14.45±12.52 Liver 1.47± 0.85 17.5 ±15.2 6.5± 5.1 6.83±5.06 4.16± 2.61 4.86± 3.12 Ni Muscle 0.42±0.40 0.29±0.15 0.24±0.15 0.20±0.05 0.24±0.10 0.20±0.02 Gills 0.52±0.50 0.36±0.16 0.43±0.37 0.34±0.18 0.33±0.21 0.19±0.02 Kidney 0.65±0.58 1.58±1.53 2.63±2.14 1.18±0.74 0.74±0.37 0.78±0.49 Liver 0.95±0.85 2.39±1.71 0.93±0.15 2.14±1.98 0.63±0.37 2.31±1.51 *BDL-below detectable level

148 Metal content in fish samples revealed that the major accumulation had taken place in gills, liver and kidney compared to muscles. This trend of accumulation shows that the target tissues of heavy metals are metabolically active ones (liver, kidney and gill) than metabolically less active tissues like muscle (Serra et al. 1993; Roesijadi and Robinson 1994; Canli et al. 1998). In the present study, Pb, Cr and Zn level in most of the fish samples exceeded the permissible limits (Pb: 4 µg/g, Cr: 2 µg/g and Zn: 50 µg/g) proposed by FAO (1983) for India. Cr and Pb are toxic elements that have no known biological role and exhibit their carcinogenic impact on aquatic biota and humans (Malik et al. 2010). Consumption of the aquatic food enriched with toxic metals may cause serious health hazards through food chain magnification. Higher level of Cu, Cr, Pb and Ni accumulation was found in C.Catla indicates great risk for human consumption. The entry of metals into food chain is a considerable hazard because of their high toxicity.

6.3.5. Metal fractionation and bioaccumulation

Significant correlations (p<0.1 and p<0.05) between metal levels in fishes and various metal fractions are observed (Table 6.8). Positive correlation was observed between F-I fraction of Cu and Pb with the respective concentrations in muscles/kidney. F1 fraction of Ni and Mn showed positive correlation with kidney and liver. F1 fraction of Mn, Cr and Zn showed significant positive correlation with liver. Pb, Cr, Cu and Zn associated with the labile fractions (F1 and F2) of the reservoir sediments apparently influenced the bioaccumulation (Table 6.8).

Table 6.8. Correlation between metal accumulation in various fish organs and metal fractionation in sediments

Metal Muscle Gills Kidney Liver Metal Muscle Gills Kidney Liver Fe Zn F1 .352 .298 -.315 -.082 F1 .097 .217* -.313 .188 F2 .334 .202 .076 -.128 F2 -.350 -.051 .714 -.409 F3 -.340 -.482 -.322 -.351 F3 .314 -.399 -.518 .380 F4 -.363 -.165 .959** .717 F4 -.258 .011 -.657 -.243

F5 .087 .209 -.231 .007 F5 .153 .178 -.140 .091

T-Fe .097 .109 .296 .455 T- Zn -.353 -.273 .936** -.380 W- Fe .520 .597 .203 .024 W-Zn -.113 -.211 -.574 -.056

149 Mn Pb F1 -.306 .361 .445 .430 F1 .715* .047 .530* -.208 F2 -.214 -.794 -.818* -.755 F2 -.618 -.252 -.527 -.479 F3 .636 .060 .160 .120 F3 -.063 .467 .276 -.014

F4 -.566 .331 .447 .444 F4 .167 .136 .201 .198

F5 .225 .821* .519 .478 F5 .178 -.205 -.069 .390 T-Mn -.587 .097 .212 .241 T- Pb -.476 -.137 -.585 .115 W- Mn -.339 -.559 -.238 -.220 W- Pb -.360 -.001 -.251 -.050 Cu Ni F1 .584 -.112 .225 -.143 F1 -.635 -.220 .370 .437 F2 .346 .803 -.450 -.402 F2 .048 .576 .539 -.160 F3 -.650 -.535 .295 .518 F3 .622 .510 .343 .103

F4 -.012 .463 -.781 -.554 F4 .198 .205 .204 .450

F5 -.117 -.659 .490 .306 F5 -.424 -.453 -.397 -.270 T-Cu .330 .499 -.481 -.340 T-Ni -.392 -.666 -.292 -.082 W-Cu .221 -.016 -.166 -.140 W- Ni -.148 .304 .888* -.383 Cr * Correlation is significant at the 0.05 level F1 -.436 -.026 -.206 .344 ** Correlation is significant at the 0.01 level. F2 -.499 -.276 -.373 -.506 F3 .220 -.371 .209 .207 F4 -.054 -.042 .067 .200

F5 .097 .130 -.057 -.159

T- Cr .086 -.344 -.309 -.108

W-Cr -.267 .174 -.138 .092

6.4. Conclusion This chapter provides information on metal bioaccumulation and metal fractionation in sediments of selected three reservoirs which are located in the Cauvery delta region. Among the three reservoirs studied Grand Anicut stands as a highly polluted one followed by Upper Anicut. High proportion of Pb, Cu and Zn in the exchangeable fraction has an adverse impact on aquatic biota. Risk assessment code reveals that Fe, Mn and Cu in all three reservoirs come under medium risk category. Metal content in fish samples reveal that the major accumulation has taken place in gills, liver and kidney whereas metal level in muscles were relatively low except few samples. Higher level of Cu, Cr, Pb and Ni accumulation was observed in C.Catla indicates great risk for human consumption. Pb, Cr and Zn level in most of the fish samples exceeded the Food and Agriculture Organization permissible limits for India. Pb, Cr, Cu and Zn associated with the labile fractions (F1 and F2) of the reservoir sediments apparently influenced the bioaccumulation.

150 7. Summary and conclusion

River ecosystems are essential and integral part of the biosphere. More importantly river sediments play a crucial role in providing substrate for living organisms, nutrient cycling and waste assimilation. However, since industrial revolution, indiscriminate discharge and dumping of waste in many river systems renders it contaminated. About 70% of rivers are polluted in India. The pollutants that enter the water column or solution could be absorbed rapidly by particulate matters and thereby they also could escape any detection by water monitoring schemes. These contaminants are not permanently fixed to sediments, but they can remobilize back to water column and act as a source of pollution at times. Hence, besides water analysis, sediment data are important factors that should be taken into account while conducting environmental monitoring programs in river ecosystems.

This study focuses on environmental monitoring of River Cauvery in delta region of Southern India with main emphasis on sediment bound heavy metals and their speciation chemistry in surficial sediments. An assessment of as water chemistry, sediment physico-chemical characters, nutrients, total metal distribution was made a basic step to study trend as well as their influencing role in metal fractionation. The aim fractionation chemistry of sediment bound heavy metals is to reveal the metal toxicity, source, bioavailability, environmental mobility, and potential risk to the biological system. Another component of study investigates metal bioaccumulation in selected edible fish population in relation to metal fractionation.

Surface water and sediment samples were collected at 40 locations along the River Cauvery in delta region during dry and wet season of year 2008-2009 and 2009- 2010. These samples were analyzed for select physico-chemical parameters following standard methods. The significant findings are given below:

• Surface water quality of the Cauvery delta region is highly influenced by land use practices and river flow intensity. Elevated concentration of COD and BOD

suggests organic load enrichment in the entire delta region. The role of PO4 in

151 eutrophication apparently seen along the delta region. Some of the water quality

parameters (total hardness, COD, BOD, DO and PO4) exceeded the prescribed standard limit for drinking water.

• Considerable influence of water flow rate on organic carbon and nutrients distribution in sediments was noted in the entire delta region. High C:P ratio recorded in the dry period indicates the cumulative effect of high organic carbon and low phosphorus content of sediments. Significant variation in C:N ratio reveals varying level of autochthonous and allochthonous influence on the nutrient distribution along the delta region.

• Total heavy metal distribution in surface sediments of River Cauvery in delta region was found in the order of: Fe > Mn > Cr > Zn > Cu > Ni > Pb. High concentration of few heavy metals found in certain pockets (Upper Anicut (S4), Tiruchirappalli Mambala Salai (S5), Kollidam (S13) and Nagapattinam (S31) of Cauvery delta region suggests significant anthropogenic enrichment. Industrial effluents, municipal sewage discharge and agrochemical runoff are the suspected contributors of heavy metals in the Cauvery delta region. Noteworthy correlation noted between few metal such as Cu, Cr, Pb and Zn suggests its similar origin. Outcome of sediment quality guidelines and geoaccumulation index reveals most of the heavy metals studied (Cu Cr, Zn, Ni and Pb) are of concern in terms of environmental and human health risks.

• In Fe fractionation profile, major portion of Fe is associated with residual fraction (F5) followed by reducible (F3) and organic fraction (F4). Considerable portion of Fe found in Fe-Mn oxide (up to 28.6%) and organic matter bound fraction (up to 22%) is probably attributed to competition between iron organic complexes and hydrous iron oxide forms. In most of the sediment samples, insignificant portion of Fe (less than 2%) was recorded in the exchangeable (F1) and carbonate fraction (F2) suggesting very trace level of Fe enrichment by anthropogenic sources.

• Mn was predominantly found in residual fraction (21.5 to 92.8%) and Fe-Mn oxide bound fractions (1.1 to 41.1%). High concentration of Mn observed in the

152 exchangeable fraction (F1) in certain pockets (Grand Anicut (U6), Mannalmedu (M6), Thiruvarur (M18), Pallingamedu (E9) and Muthupet (E10) of River Cauvery in delta region implies significant input from anthrogenic source. Exchangeable fraction of Mn was found in the order of: upper delta > estuarine region > mid delta. More than 30% of the Mn associated with first three fractions (exchangeable, carbonate bound and reducible fraction) may pose risk to aquatic biota.

• Residual, organic matter and Fe-Mn oxide bound fractions are predominant forms in Cu fractionation profile. Major proportion of Cu in the organic and residual fraction indicates its low mobility and bioavailability in the sediments of River Cauvery in the delta region. Pronounced tendency of Cu for complexation with organic matter was found in the study area showing good agreement with the previous results that the high level of Cu associated with organic matter in sediments. Higher level of Cu exchangeable (up to 34%) and carbonate fractions (up to 49%) recorded in few samples suggests risk to aquatic biota in respective regions.

• In Cr fractionation profile, easily mobile fractions such as exchangeable (F1) and carbonate bound fractions (F2) are recorded up to 16.2 and 16%, respectively. Higher level of Cr associated with these factions in few sampling sites suggests anthropogenic enrichment of Cr in the study area. The order of Cr mobility was observed as, estuarine region > mid delta > upper delta. Statistically significant difference in exchangeable (F1) and carbonate bound fractions (F2) associated Cr was observed between seasons, three regions of delta and urban, sub urban and rural sites. Significant proportion of Cr associated with Fe-Mn oxides and organic matter fraction suggests scavenging role of these components in Cr fractionation in the sediments of River Cauvery in delta region. The order of Cr fraction in terms of concentration was observed as residual (F5) > organic bound (F4) > Fe-Mn oxide bound (F3) > carbonate bound (F2) > exchangeable (F1).

153 • In the present study, the predominant fractionation forms of Pb were residual (F5), Fe-Mn oxide bound (F3) and carbonate bound fraction (F2). Exchangeable fraction of Pb (F1) was recorded in the range from 0.1 to 29.4% suggesting non- lithogenic origin of Pb in the study area. High proportion of Pb in F1 fraction in few sampling sites (Nagapattinam (E9), Killiyur (E6), Anaikarai (M5) and Karur (U1) suggests their source from the vicinity.

• Zn fractionation profile reveals significant portion of this metal associated with the exchangeable fraction (F1) in certain pockets of River Cauvery in delta region. The source could be traced to agrochemical runoff and discharge of municipal sewage. In addition, substantial level of Zn associated with F2 fraction (0.9 to 33.7%) is particularly important, because this fraction can be easily remobilized in response to the changes in environmental conditions. The present study suggests that colloids of Fe-Mn oxides (1.2-32.1%) and organic matter (2.7- 35.3%) act as efficient scavenger of Zn in River Cauvery in delta regions.

• In Ni fractionation profile, one third portion of Ni associated with Fe-Mn oxide and organic matter fraction revealing scavenging role of these components in the study area. Noteworthy proportion of Ni bound to F4 fraction (8.0 to 25.5%) could be due to high organic matter content in the sediments of River Cauvery in delta region. Comparatively, low level of non-lithogenic fractions such as exchangeable (0.0 to 6.9%) and carbonate bound (0.0 to 13%) fraction recorded in most of the sampling sites samples in suggests low risk to aquatic biota. The order of Ni mobility was observed as, estuarine region > upper delta > mid delta. The order of fraction in terms of Ni concentration was observed as residual (F5) > organic bound (F4) > Fe-Mn oxide bound (F3) > carbonate bound (F2) > exchangeable (F1).

• Metal concentrations in fishes at three reservoir sites (Upper Anicut, Grand Anicut and Lower Anicut) of the Delta region was analyzed to find out if there is any relationship between metal fractionation and bioaccumulation. Among the three sites, Grand Anicut was identified as most polluted in terms of metal

154 fractionation and bioaccumulation pattern in fish. Since Grand Anicut is the first reservoir located in the downstream of Tiruchirappalli City it receives substantial quantity of industrial effluents, storm water runoff, municipal sewage and solid wastes besides agrochemical runoff. The order of metals in terms of bioavailable fraction concentration in the reservoir sediments was observed as Pb > Zn > Cr > Cu > Mn > Ni > Fe. In metal bioaccumulation study, Pb, Cr and Zn content in most of the fish samples exceeded the Food and Agriculture Organization permissible limits. Accumulation of Pb, Cr, Cu and Zn were found to be high in fish organs, which is in good agreement with the sequential extraction schemes as these metals were mainly associated with the highly mobile fractions (exchangeable and carbonate bound) of the sediments.

155 References

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