studies on environmental geochemistry of river damodar...

STUDIES ON ENVIRONMENTAL GEOCHEMISTRY OF RIVERDAMODAR ALONG THE STRETCH OF DISHERGARH TO

BURDWAN, WEST BENGAL, INDIA

THESIS SUBMITTED FOR THE DEGREE OF DOCTOR OF PHILOSOPHY IN SCIENCE(ENVIRONMENTAL SCIENCE) OF THE UNIVERSITY OF BURDWAN (MARCH, 2013)

UDAY SANKAR BANERJEE, M.Sc., M.PhilDEPARTMENT OF ENVIRONMENTAL SCIENCETHE UNIVERSITY OF BURDWANBURDWAN – 713104WEST BENGAL, INDIA

Department of Environmental Science THE UNIVERSITY OF BURDWAN

GOLAPBAG, BURDWAN-713104 WEST BENGAL, INDIA

Phone No. : (0342)26559255

Date: Dr. Srimanta Gupta Assistant Professor Dept. of Environmental Science The University of Burdwan Burdwan, West Bengal, India

Certificate

This is to certify that Mr. Udaysankar Banerjee (M.Sc. M.Phil) has carried out the research

work entitled “STUDIES ON ENVIRONMENTAL GEOCHEMISTRY OF RIVER

DAMODAR ALONG THE STRETCH OF DISHERGARH TO BURDWAN, WEST

BENGAL, INDIA” under my supervision and guidance. Mr. Banerjee has fulfilled all the

requirements (including Course work and presentation of seminar talk) and followed the

rules and regulations relating to the nature and prescribed period of research as lay down by

the University. This thesis representing the results of original investigation made by Mr.

Banerjee is submitted for the partial fulfillment of the degree of Doctor of Philosophy in

Science (Environmental Science) of The University of Burdwan. This work has not been

submitted previously anywhere for any degree whatsoever by him or anyone else.

[Srimanta Gupta]

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DEDICATED�TO�MY�PARENTS�

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ACKNOWLEDGEMENTS

It gives me immense pleasure in acknowledging the valuable assistance, help and support

which I have received from various people for whom I am highly grateful and thankful.

I am heartily grateful to my supervisor and teacher Dr. Srimanta Gupta, Assistant

Professor, The University of Burdwan, not only for his constant guidance, inspiration, advice and

suggestions but also utmost support and care throughout the period of my research work. It is the

greatest opportunity to me to carry on the research work under his supervision not only for

achieving the Degree but also to acquire the in-depth knowledge about the research work and I

feel privileged to be his student.

I also express my deep regards to Mr. Prabir Gupta and his family for nourishing and

taking care of myself and for their constant support.

I am cordially greatful to Professor Jayanta Datta, Professor Apurba Ratan Ghosh and

Mrs. Sampa Dutta, Department of Environmental Science, The University of Burdwan for giving

me the valuable suggestions and carefull support as and when required, and also to Dr. N.K.

Mondal, Teacher-In Charge, for giving me the permission to utilize the laboratory facilities

during his headship, and providing me the their valuable suggestions and support.

I also express my deep sense of gratitude to Professor Goutam Chandra, Department of Zoology,

The University of Burdwan and Professor Chittaranjan Sinha, Department of Chemistry,

Jadavpur University for helping me in every step of my work with their valuable suggestions and

proper support.

I owe my indebtedness to my respected Dr. S. Dan, Pro-Vice Chancellor, The University

of Burdwan, for his important suggestions and constant help during this research.

I also express my immense gratitude and special respect to the Honourable Vice-

Chancellor, the Registrar and Dean (Science) of this University for their kind permission and co-

operation in all official procedures to do this research.

I would also like to give thanks to Mr. Shankar Prasad Nag, Mr. Gobinda Baidya and Mr.

Budhadeb Mukhopadhya, Miss Sanchari De, Mr. Arunavo Roy and Tarakeswar Senapati

Department of Environmental Science, The University of Burdwan for their help.

Constant help and encouragement extended from the research scholars, Dr. Babuji Das, Mr.

Biswajit Das, Mr. Aditya Pathak, Mr Sudipta Banerjee, Mrs. Subrata Chaterjee, Dr Sandipan Pal,

Mr. Aloke Kumar Mukherjee, Mr. Koushik Das, Mr. Tirthankar Mallick, Dr Arnab Banerjee, Dr.

Sumanta Nayek, Miss Moupriya Roy, Uttia Dey, Miss Dolly Mondal, Mrs. Ruma Banerjee and

other seniors, juniors and all well wishers gave me constant encouragement to accomplish my

work successfully.

I express my sincere thanks, Mr Nishit Chatterjee, ASC UGC, The University of

Burdwan for getting his constant expertise for my computational works.

I also express my sincere thanks to Mr. Pranjit Roy for his immense expertise to prepare

the manuscript.

I express my sincere thanks to Mr. Bamacharan Banerjee, teacher of Mohanpur High

School for giving me the support to do the research work and for his valuable suggestion in

every step of the research work.

I am greatly thankful to Mr. Pathik Kumar Rakshit, Headmaster of Mohanpur High

School and Mr. Pradipta Kumar Mondal, Secretary Managing Committee for giving me the

permission to do the research work successfully, and also to the Members of the Managing

Committee, all teachers and Staffs of Mohanpur High School especially Mr. Chandramohan Das

and Mr. Jagadish Mondal for their constant help and good wishes in performing my research.

I want to express my gratitude to my father, Dr Sibsankar Banerjee; my mother, Srimati

Sushama Banerjee; Mr. Sukumar Banerjee and Mrs. Dipali Banerjee (uncle and aunt), my

brother, Dr Siddharthasankar Banerjee and my sister-in-law Mrs. Bratati Banerjee (Manti), my

wife Ruma Banerjee and my brothers and sisters and relatives Mrs Umarani Chattopadhy,

Padmanabha Chattopadhy, Uddalok Chattopadhy, Sandip Chatterjee and Arpita Chatterjee for

their constant support and guidance for the research work.

Place: Burdwan

Date: [UDAY SANKAR BANERJEE]

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CONTENTS

List of Tables i

List of Figures ii-iii

List of Annexure iv

1.0 INTRODUCTION 1-13 1.1 River water hydrogeochemistry 2 1.1.1 Natural input 3 1.1.2 Anthropogenic input 3 1.2 Heavy metals and its environmental significance in the

riverine system 4

1.2.1 Evolution of heavy metal due to sediment-water interaction:

6

1.3 Assessment criteria of pollution load 7 1.4 Drinking and irrigation water suitability criteria of the

river water 7

1.5 Origin of research 8 1.6 Objective of the research 9

2.0 DAMODAR RIVER BASIN – A BRIEF REVIEW 14-21 2.1 About the region 14 2.1.1 Physiography: 14 2.1.2 Geological setting: 15 2.1.2.1 Tectonic framework of Gondwana

basins:15

2.1.2.2 Damodar valley basin-fill succession:

16

2.1.3 Drainage system: 17 2.1.4 Climate: 19 2.1.5 Rainfall: 19 2.1.6 Soil: 19 2.1.7 Vegetation: 20

3.0 REVIEW OF LITRATURE 22-43 3.1 Weathering and geochemical processes controlling river

water/sediment chemistry 22

3.2 Influence on river water/sediment chemistry due to anthropogenic activities

25

3.2.1 Mining activities: 26 3.2.2 Treated and untreated discharge of municipal

and industrial discharges:26

3.2.3 Dam construction: 28 3.2.4 Influence of sandbar-regulated hydrodynamic

on river hydrochemistry: 28

3.2.5 Effects of land use: 29

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3.3 Spatio-temporal distribution of heavy metals in river bottom sediments

30

3.4 Ecological risk due to heavy metal 38 3.5 River water quality 38 3.6 Assessment of natural and anthropogenic sources of

chemical element in the river water/sediment through multivariate statistical methods and pollution indices

40

4.0 MATERIALS AND METHODS 44-58 4.1 Collection of the river water Samples 44 4.2 Quality Control Assurance 44 4.3 Physico-chemical analysis of the river water samples: 45 4.3.1 Determination of pH [Standard Methods

(APHA 1998)]:45

4.3.2 Electrical Conductivity [EC] [Standard Methods (APHA 1998)]

45

4.3.3 Total Dissolved Solids [TDS] [Standard Methods (APHA 1998)]

46

4.3.4 Estimation of Bicarbonate [Titrimetric Method (APHA 1998)]

46

4.3.5 Estimation of Calcium [Titrimetric Method (APHA 1998)]

48

4.3.6 Estimation of Magnesium (Titrimetric Method (APHA 1998)]

48

4.3.7 Estimation of Sodium [Flame photometric Method (APHA 1998)]

49

4.3.8 Estimation of Potassium [Flame photometric Method (APHA 1998)]

49

4.3.9 Estimation of Chloride (Titrimetric Method (APHA 1998)]

50

4.3.10 Estimation of Sulfate [Turbidimetric Method (APHA 1998)]

50

4.3.11 Estimation of Phosphate [Spectrophotometric Method (APHA 1998)]

51

4.3.12 Estimation of Nitrate Nitrogen [Spectrophotometric Method (APHA 1998)]

52

4.3.13 Estimation of Silica [Spectrophotometric Method (APHA 1998)]

53

4.4 Collection, preparation and analysis of sediment samples 53 4.4.1 Metal Speciation in BCR Sequential Extraction

Process54

4.4.2 Estimation of Heavy Metals 55 4.4.3 Infrared spectroscopic analysis of Bottom

Sediments: 55

4.5 Statistical analysis 55 4.5.1 Descriptive statistical analysis: 55 4.5.2 Pearson Correlation coefficient analysis: 56 4.5.3 Multivariate statistical analysis: 56

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4.6 GIS Methodology 57 4.6.1 Supervised classification: 57 4.6.2 Digital Elevation Model (DEM): 57

5.0 RESULTS AND DISCUSSION 59-168 5.1 Computation of ion balance and analytical precision 59 5.2 Spatio-temporal variations in hydrochemistry 59 5.3 Spatio-temporal distribution of heavy metals in the river

water66

5.4 Statistical analysis 69 5.4.1 Descriptive data analysis: 69 5.4.2 Pearson correlation coefficient: 69 5.5 Multivariate statistical analysis 71 5.6 Hydrochemistry of the river Damodar – role of

weathering and anthropogenic input on dissolved load 72

5.6.1 Ionic ratio – an indicative of weathering and ion exchange input:

73

5.6.2 Ionic ratio- an indicative of anthropogenic input:

74

5.7 Scatter diagram representing chemical weathering and ion exchange processes of the Damodar river

74

5.7.1 Ionic relationship between (Ca2++Mg2+) versus (HCO3

–+SO42–):

74

5.7.2 Ionic relationship between (Ca2++Mg2+)/ HCO3–

:75

5.7.3 Ionic relationship between Ca2++Mg2+ versusTZ+:

75

5.7.4 Ionic relationship between Na+ versus Cl–: 75 5.7.5 Ionic relationship between Na versus Ca2+: 75 5.7.6 Ionic relationship between Na++K+ versus TZ+: 75 5.8 Ternary diagram – an index of weathering 76 5.9 Geochemical relationship and hydrogeochemical facies 76 5.10 Mechanisms controlling the river water chemistry 77 5.11 Suitability for drinking, domestic and livestock uses 78 5.12 Suitability of the river water for irrigation use 79 5.12.1 Suitability on the basis of pH, electrical

conductivity, bicarbonate, sodium, chloride, sulphate and nitrate:

80

5.12.2 Sodium adsorption ratio (SAR): 81 5.12.3 Sodium percentage Na%: 83 5.12.4 Permeability index (PI): 83 5.12.5 Magnesium hazard (MH): 84 5.12.6 Residual Sodium Carbonate (RSC): 85 5.12.7 Suitability on the basis of metal content: 86 5.12.8 US Salinity Laboratory Diagram (USSL 1954): 86 5.12.9 Wilcox diagram (Wilcox 1955): 87 5.13 Sediment geochemistry 87 5.13.1 Distribution of heavy metals in the river bottom 88

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sediments: 5.13.2 Metal speciation and its retention in bottom

sediments: 90

5.13.3 Partitioning co-efficient (Kd) of heavy metals: 92 5.13.4 Recalcitrant Factor (RF): 93 5.13.5 Infrared spectroscopic evaluation of the bottom

sediments: 94

5.14 Geo-chemical assessment of the river sediments in relation to metal contamination

94

5.14.1 Enrichment factor (EF): 94 5.14.2 Index of geoaccumulation (Igeo): 97 5.14.2.1 Spatial interpolation of

Geoaccumulation Index in a GIS environment

99

5.14.3 Pollution load index (PLI): 99 5.14.3.1 Spatial interpolation of Pollution

Load Index in a GIS environment 100

5.14.4 Eco-toxicological assessment of the river sediments in relation to metal contamination

100

5.14.5 Evaluation of the environmental significance of metals in the river sediment by comparison with sediment quality guideline (SQGs):

101

6.0 CONCLUSION 169-173

7.0 REFERENCES 174-198

Annexure v-xxi

List of Publications xxii

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LIST OF TABLES

Page Nos.

Table 1 Existing land use pattern around the sampling locations 11 Table 2 Constituents of the Damodar river basin 20 Table 3 Stratigraphic succession of Gondwana sediments in

Damodar valley (Raja Rao 1987). 21

Table 4 Extraction protocol (BCR) 57 Table 5.1 Descriptive statistical analysis of physico-chemical

parameters 103-106

Table 5.2 Factor pattern (after varimax rotation) 107 Table 5.3 Average ionic ratio of three years (2007, 2008 and 2009) and

in three seasons 108

Table 5.4 Descriptive statistical analysis of irrigation water quality parameters

109

Table 5.5 Spatio-temporal distribution of manganese (Mn) (µg/g) in the Damodar river bottom sediments

110

Table 5.6 Spatio-temporal distribution of cadmium (Cd) (µg/g) in the Damodar river bottom sediments

111

Table 5.7 Spatio-temporal distribution of iron (Fe) (µg/g) in the Damodar river bottom sediments

112

Table 5.8 Spatio-temporal distribution of lead (Pb) (µg/g) in the Damodar river bottom sediments

113

Table 5.9 Assignment of the principle descriptive IR absorption bands 114-118Table 5.10 Spatio-temporal variation of Enrichment Factor of

manganese in the Damodar river bottom sediments 119

Table 5.11 Spatio-temporal variation of Enrichment Factor of cadmium in the Damodar river bottom sediments

120

Table 5.12 Spatio-temporal variation of Enrichment Factor of iron in the Damodar river bottom sediments

121

Table 5.13 Spatio-temporal variation of Enrichment Factor of lead in the Damodar river bottom sediments

122

Table 5.14 Spatio-temporal variation of Igeo of lead (Pb) in the Damodar river bottom ediments

123

Table 5.15 Spatio-temporal variation of Igeo of cadmiium (Cd) in the Damodar river bottom ediments

124

Table 5.16 Spatio-temporal variation of Igeo of manganese (Mn) in the Damodar river bottom ediments

125

Table 5.17 Spatio-temporal variation of Igeo of iron (Fe) in the Damodar river bottom ediments

126

Table 5.18 Spatio-temporal variation of Pollution Load Index (PLI) in the Damodar river bottom sediments

127

ii�

LIST OF FIGURES

Page Nos.

Figure 1 Map showing different land use pattern along the stretch of the Damodar river

12

Figure 2 Sampling location along the stretch of the river Damodar (location detail plotted on satellite image (Resourcesat-1)

13

Figure 3.1 a – c scatter diagram representing (Ca2++Mg2+) vs (HCO3–

+SO42–) and d – f representing (Ca2++Mg2+) vs HCO3

–128

Figure 3.2 a – c scatter diagram representing Ca2++Mg2+ vs TZ+ and d – f representing Na+ vs Cl–

129

Figure 3.3 a – c scatter diagram representing Na+ vs Ca2+ and d – f representing Na++K+ vs TZ+

130

Figure 4.1 Ternary diagram showing relationship among (SO42–+Cl–)-

HCO3– -SiO2. in 2007 a – premonsoon, b – monsoon, c –

postmonsoon and d – all seasons

131


HCO3– -SiO2. in 2008 a– premonsoon, b – monsoon, c –


132


HCO3– -SiO2. in 2009 a– premonsoon, b – monsoon, c –


133

Figure 4.4 Ternary diagram showing relationship among (Na++K+) – (Ca2++Mg2+)-SiO2 in 2007 a – premonsoon, b – monsoon, c – postmonsoon and d – all seasons

134

Figure 4.5 Ternary diagram showing relationship among (Na++K+) –(Ca2++Mg2+)-SiO2 in 2008 a– premonsoon, b – monsoon, c – postmonsoon and d – all seasons

135

Figure 4.6 Ternary diagram showing relationship among (Na++K+) – (Ca2++Mg2+)-SiO2 in 2009 a– premonsoon, b – monsoon, c – postmonsoon and d – all seasons

136

Figure 5.1 Hydrochemical classification (Piper 1953) of the Damodar river water in�2007�a�–�premonsoon,�b�–�monsoon,�c�–�postmonsoon�and�d�–�all�seasons

137

Figure 5.2 Hydrochemical classification (Piper 1953) of the Damodar river water�2008�a�–�premonsoon,�b�–�monsoon,�c�–�postmonsoon�and�d�–�all�seasons

138

Figure 5.3 Hydrochemical classification (Piper 1953) of the Damodar river water��2009�a�–�premonsoon,�b�–�monsoon,�c�–�postmonsoon�and�d�–�all�seasons

139

Figure 6.1 Mechanism controlling river water chemistry (Gibbs 1970) [Na+/ (Na++Ca2+)] in 2007

140

Figure 6.2 Mechanism controlling river water chemistry (Gibbs 1970) [Na+/ (Na++Ca2+)] in 2008

141

Figure 6.3 Mechanism controlling river water chemistry (Gibbs 1970) [Na+/ (Na++Ca2+)] in 2009 a – premonsoon, b – monsoon, c – postmonsoon and d – all seasons

142

iii�

Page Nos.

Figure 6.4 Mechanism controlling river water chemistry (Gibbs 1970) Cl–/(Cl–+ HCO3

–)] in 2007a – premonsoon, b – monsoon, c – postmonsoon and d – all seasons

143


–)] in 2008 a – premonsoon, b – monsoon, c – postmonsoon and d – all seasons

144


–)] in 2009 a – premonsoon, b – monsoon, c – postmonsoon and d – all seasons

145

Figure 7.1 Figure 7.1: Diagram for classification of irrigation water (after U.S. Salinity Laboratory Stuff 1954)�in 2007 a – premonsoon, b – monsoon, c – postmonsoon and d – all seasons

146

Figure 7.2 Figure 7.2: Diagram for classification of irrigation water (after U.S. Salinity Laboratory Stuff 1954)� in 2008 a – premonsoon, b – monsoon, c – postmonsoon and d – all seasons

147

Figure 7.3 Diagram for classification of irrigation water (after U.S. Salinity Laboratory Stuff 1954)� in 2009 a – premonsoon, b – monsoon, c – postmonsoon and d – all seasons

148

Figure 8.1 Classification of irrigation water (after Wilcox 1953) in 2007 a– premonsoon, b – monsoon, c – postmonsoon and d – all seasons

149


150


151

Figure 9 Speciation of metals in the bottom sediment (after BCR extraction)

152

Figure 10 FTIR spectrum of Damodar river sediment (10.1-10.27) 153-166Figure 11.1 Spatial interpolation of Igeo of Cd 166 Figure 11.2 Spatial interpolation of Igeo of Fe 167 Figure 11.3 Spatial interpolation of Igeo of Mn 167 Figure 11.4 Spatial interpolation of Igeo of Pb 168 Figure 12 Thematic zonation of study area with respect to PLI 168

iv�

LIST OF ANNEXURES

Page Nos.

Annexure I Spatio-temporal variation of pH in the Damodar river water

x

Annexure II Spatio-temporal variation of Electrical conductivity (µS/cm) in the Damodar river water

xi

Annexure III Spatio-temporal variation of Total Dissolved Solids (mg/l) in the Damodar river water

xii

Annexure IV Spatio-temporal variation of Bicarbonate (mg/l) concentration in the Damodar river water

xiii

Annexure V Spatio-temporal variation of Sulphate (mg/l) concentration in the Damodar river water

xiv

Annexure VI Spatio-temporal variation of Chloride (mg/l) concentration in the Damodar river water

xv

Annexure VII Spatio-temporal variation of Nitrate (mg/l) concentration in the Damodar river water

xvi

Annexure VIII Spatio-temporal variation of Phosphate (mg/l) concentration in the Damodar river water

xvii

Annexure IX Spatio-temporal variation of Dissolved silica (mg/l) concentration in the Damodar river water

xviii

Annexure X Spatio-temporal variation of Calcium (mg/l) concentration in the Damodar river water

xix

Annexure XI Spatio-temporal variation of Magnesium (mg/l) concentration in the Damodar river water

xx

Annexure XII Spatio-temporal variation of Sodium (mg/l) concentration in the Damodar river water

xxi

Annexure XIII Spatio-temporal variation of Potassium (mg/l) concentration in the Damodar river water

xxii

Annexure XIV Spatio-temporal variation of Lead (µg/l) concentration in the Damodar river water

xxiii

Annexure XV Spatio-temporal variation of Iron (mg/l) concentration in the Damodar in the Damodar river water

xxiv

Annexure XVI Spatio-temporal variation of Manganese (µg/l) concentration in the Damodar river water

xxv

Annexure XVII Spatio-temporal variation of Cadmium (µg/l) concentration in the Damodar river water

xxvi

INTRODUCTION

[1]

1.0 INTRODUCTION

nvironmental geochemistry focuses on the processes involved in the distribution

and transport of chemical substances, as well as the identification of element

sources. The studies of river water are important due to the need to understand the

weathering, hydrological, seasonal, and various anthropogenic factors which influence

the water quality. The chemical properties of rivers are reflections of complex natural

and interdependent relationships involving the chemistry of precipitation, the

weathering of minerals, and the evolution or history of its water. Chemical weathering

is an extremely important component of many basic geochemical processes on the

surface of the earth by which simpler dissolved ions and secondary clay minerals are

released from primary minerals and ultimately transported to ocean by rivers. Minerals

present in the rocks completely or partially dissolve in water according to the resistance

of chemical weathering and make the chemical composition of the river water. The

solute concentration of the river water system is proportional to the reactivity of the

bedrock minerals constituting the catchment. Moreover, the changes in river water

chemistry can reflect the influence of anthropogenic activities on water environment to

some extent. Urbanization also affects the processes that control stream flow of the

river channels (Rose and Peters 2001) and water quality (Reza et al. 2010). Climatic

influence on chemical weathering might also be the reason for the differences in the

thermodynamic interactions between minerals and solutions (Das and Kaur 2001).

Geochemical processes, occurring within the river water and their reactions with the

sediment materials are responsible for changes in the river water chemistry and a

number of studies of river water chemistry and geochemistry focused on identifying the

various contributions of the different sources to the water solutes, and estimating

weathering rates of continental crust (Reeder et al. 1972; Stallard and Edmond 1987;

Gaillardet et al. 1997; Fairchild et al. 1999; Liu and Zhao 2000; Xu and Liu 2007).

Pollution of surface water with toxic chemicals and eutrophication of rivers

with excess nutrients are of great environmental concern worldwide. Empirical

evidences related to the negative effects of the degrading aquatic environmental

conditions have been noted from different rivers all over the world (Ayotamuno 1994;

Singh et al. 2007; Budambula and Mwachiro 2006; Djuikom et al. 2006; Sood et al.

E

INTRODUCTION

[2]

2008; Sharma et al. 2008; Jonathan et al. 2008, Zheng et al. 2008; Wang et al. 2008;

Chang 2008). The geochemical study of stream and river reveals the pattern and

linkage between evaporation, chemical weathering, precipitation and anthropogenic

impacts (Gibbs 1970; Meybeck 1987; Brennan and Lowenstein 2002). The major

element chemistry of many of the world’s major rivers has been studied by various

workers, notably the Amazon (Gibbs 1972; Stallard and Edmond 1981; 1983; 1987),

the Ganges–Brahmaputra (Sarin et al. 1989), the Lena (Gordeev and Sidorov 1993;

Huh et al. 1998a, b) and the Godavari (Biksham and Subramanian 1988). Deteriorating

fresh water quality thus limiting its various uses, exacerbated by a continuous increase

in population and socio-economic development will result in water scarcity and

degrade aquatic ecosystem. In Indian context, rapid urbanization and industrialization,

intensive agriculture, and growing demands for energy during the last few decades have

affected the physicochemical parameters.

1.1 River water hydrogeochemistry

The dissolved ions in river water are derived from various sources and

compositional relations among them can reveal the origin of solutes and the processes

that generated the observed water compositions. Chemistry of rivers is also dependent

upon their watershed features namely vegetation, geology, temporal and spatial

variation in climate and topographical features. The interaction of all factors leads to

various water types. In the recent years, the growth of industrial technologies,

population, and water usage has increased the stress upon the river water resources.

Rivers play an important role in human civilization and are important natural potential

sources of different uses. Agriculture is a major source of several nonpoint source

pollutants, including nutrients, sediment, pesticides, and salts. The impact of point

source pollution in rivers can be localized and well-defined, whereas the influence of

non-point pollution is less obvious because of the poorly defined origins, volume, and

frequency of loading. Regardless of origin, both the source loads typically find their

way to rivers and streams and potentially lead to substantive pollution (Berankova and

Ungerman 1996; Carpenter et al. 1998; Fytianos et al. 2002). Various studies have

examined the major ion concentrations such as Ca2+, Mg2+, Na+ and K+, Cl� and SO42–

in urban streams and rivers and their mutual relationship in the urbanized watersheds

INTRODUCTION

[3]

(Hoare 1984; Wahl et al. 1997; Wernick et al. 1998; Kaushal et al. 2005; Williams et al.

2005; Bhatt and McDowell 2007; Lewis et al. 2007; Bahar and Yamamuro 2008).

1.1.1 Natural input: Natural waters, having a contact with different chemical variations

of rocks, inevitably gain a specific composition. Geochemical study of the natural river

water gives significant information on chemical weathering of rock as well as

soil, chemical and isotopic compositions of drainage and even of the upper

continental crust (UCC), and on the elements cycled in the continent–river–ocean

system (Reeder et al. 1972; Hu et al. 1982; Stallard and Edmond 1983; Zhang et al.

1995).

1.1.2 Anthropogenic input: The anthropogenic inputs of sewage, without prior

treatment, in aquatic environments, affect the geochemical composition of receiving

water bodies. Indiscriminate and unscientific disposal of municipal sewage has severely

deteriorated the aquatic environment leading nutrient enrichment of the receiving water

body (Akpan 2004) which in turn affects environmental health worldwide. Massive

amount of wastewater from municipal sewage if treated properly can be certainly used

for fish production, irrigation aquaculture and for many other plilanthronic purposes.

Nutrient enrichment of lakes, reservoirs, wetlands, rivers and streams is one of the most

prevalent environmental problems responsible for freshwater quality degradation on a

worldwide scale (Smith et al. 1999; Dodds and Welch 2000). Anthropogenic activities

at basin scales cause increased waterborne pollution from point and diffuse sources,

affecting aquatic ecosystems. Various works regarding water quality influenced by

municipal sewage and effluents have been carried out on Ganga (Sinha et al. 1991),

Kathajodi river in Cuttack city in Orissa (Das and Acharya 2003, Girija et al. 2007) in

India. Various kinds of anthropogenic activities in a river basin result in inputs via

point and non-point sources which may degrade surface waters and impair their use for

potable supply, industrial, agricultural or other purposes (Simeonova et al. 2003;

Kepner et al. 2004).

Fresh water contamination with a wide range of pollutants has become a matter

of concern (Canli et al. 1998; Dirilgen 2001; Vutukuru 2005). Improper disposal of

industrial effluents and other wastes may contribute greatly to the poor quality of the

receiving water bodies (Furtado et al. 1998; Ugochukwu 2004; Chindah et al. 2004;

INTRODUCTION

[4]

Emongor et al. 2005). Various Indian rivers carry effluents from sewage, industries,

agricultural and urban areas (Chakrapani 2005; Jameel and Hussain 2005, 2007; Rani

and Sherine 2007). Pollution of river water with toxic chemicals and the eutrophication

of streams and rivers with excess nutrients are the areas of great environmental concern

worldwide. Nutrient input in a large scale mainly nitrates and phosphates in to river

waters causes eutrophication and its related effects (House and Denison 1997). Rapid

urbanization and industrialization have resulted in increased waste loads which are

discharged into rivers without any prior treatment. River water contamination due to

wastewater discharge is a major environmental concern.

1.2 Heavy metals and its environmental significance in the riverine system

Metals represent a threat to the aquatic organisms because of their toxicity,

persistence and bioaccumulation (Tekin-Ozan and Kir 2006). The sources of pollution

with heavy metals of the environment can be natural and anthropogenic. The natural

sources include mother rocks and minerals of the metals and anthropogenic sources are

agriculture, and industrial activities. Release of heavy metals form domestic, industrial

and other man-made activities may extensively affect the natural aquatic systems

(Conacher et al. 1993; Velez and Montoro 1998).

Heavy metals in drinking water such as lead and cadmium have some

carcinogenic effects. Lead is an extremely pervasive and toxic environmental

contaminant. Acute or chronic exposure to lead can cause several types of neurological,

neurophysical and metabolic disorders. Metals like manganese, chromium, copper,

nickel and zinc are essential to human nutrition at low doses, but demonstrate adverse

health effect at higher doses (NAS SDWC 1977; N.R.C. 1989). Various

pathophysiological effects, including interference with haeme synthesis, anemia,

kidney damage, and elevations in blood pressure occure due to lead exposure

(U.S.E.P.A. 1990). In living systems essential metals like iron, nickel, zinc, vanadium,

manganese, molybdenum, cobalt, chromium, tin, and copper are required in micro

amounts although at higher concentrations the metal ions are toxic. Non essential

metals like cadmium, mercury, lead, titanium, arsenic, antimony, and bismuth are not

required by living systems. Cadmium is the best known toxic metal and it is used in

electroplating, battery, paints and plastic industry. According to Bowen (1966) the lead

INTRODUCTION

[5]

is not essential as a trace metal to nutrition in animals, as it is a cumulative poison.

Lead is used in piping, building materials, paint, ammunition, castings, storage

batteries, metal products, chemicals and pigments. Effects of lead include anaemia,

severe abdominal pain, diarrhoea, sleep disorders, neurobehavioral inconsistency,

cardiotoxicity, impairment of the thyroid and adrenal functions. The objective of the

study is to characterize the effluents discharged into the riverine system and to

determine the river water quality using a number of parameters and to compare it with

Water Quality Guidelines. According to Marschner 1995 and Bruins et al. 2000 the

heavymetal lead (Pb) has no known physiological activity, but it is detrimental beyond

a certain limit. Therefore, monitoring of lead is important for safety assessment of the

environment in general and human health in particular. Cadmium (Cd) has a negative

effect on the environment where it accumulates throughout the food chain posing a

serious threat to human health.

Cadmium, iron, manganese and lead along with some physicochemical

parameters were assessed in water in four areas along the river Damodar in West

Bengal, India. The mining and its related operations are the most significant

anthropogenic sources of heavy metals that negatively influence the nearby

environment (Vanek et al. 2005; Vanderlinden et al. 2006; Conesa et al. 2007).

Widespread use of heavy metals in industries as well as intensive agriculture has

resulted in a variety of heavy metals being released into the environment with

concentrations in excess of the natural background levels (De Groot et al. 1976;

Dryssen and Wedborg 1980). Both the deficiency and excess of certain trace elements

in irrigation water have great significance as they can retard growth and metabolic

activities. The trace elements in water, especially heavy metals, can impact on human

health. So neither the nutrient value nor the toxicity of trace elements in irrigation water

can be ignored. Heavy metal residues in contaminated sediments may accumulate in

microorganisms, aquatic flora and fauna which in turn, may enter into the food chain

and eventually causes various human health problems (Cook et al. 1990; Deniseger et

al. 1990). The poor quality of water can also adversely affect the plant growth and

human health (Wilcox 1948; US Salinity Laboratory Staff 1954; Holden 1971; Todd

1980; Hem 1991; Karanth 1997) and causes various environmental consequences.

INTRODUCTION

[6]

1.2.1 Evolution of heavy metal due to sediment-water interaction: The river sediment

can act both as source and sink for the nutrients and other elements (Thornton et al.

1975; Förstner and Wittmann 1983) and is also important for the assessment of

anthropogenic contamination in riverine environment. Surface sediment acts as a metal

pool that can release metals to the overlaying water through natural and anthropogenic

processes and pose potential adverse health effects in the ecosystems (Howarth and

Nombela 2003; McCready et al. 2006). Metals are associated with sediments in aquatic

systems largely due to processes of adsorption onto mineral surfaces, absorption into

organic matter, ion-exchange in riverine environments. Several environmental

pollutants that enter water bodies may remain suspended in the water column, be taken

up by aquatic biota, or settle at the bottom and ultimately become incorporated into the

sediments. Sediments are significant environmental compartment for aquatic system,

since they may accumulate contaminants in higher concentration of pollutants than

those observed in the water column. Like other toxic pollutants, heavy metals have

been of great concern due to their environmental persistence, toxicity, and ability to be

incorporated into food chains (Shriadah 1999; Gangaiya et al. 2001; Gladyshev et al.

2001; Nasr et al. 2006). Therefore, assessment of heavy metal in surface sediments is

important in order to estimate the extent of pollution or identify pollution sources.

Heavy metals including different contaminants in the aquatic system can lead to

elevated sediment concentrations which ultimately cause potential toxicity to aquatic

system and residues may enter the human food chain. Analyses of sediment carried out

by various workers (Singh et al. 1999; Bhattacharyay et al. 2005; Banerjee and Gupta

2011) indicate the metal pollution of the river Damodar. The elemental concentration of

sediments not only depends on anthropogenic and lithogenic sources, but also upon the

textural characteristics. Heavy metals in aquatic ecosystems are considered as serious

pollutants due to their environmental persistence, toxicity and ability to be incorporated

into food chains. Sediment represents one of the ultimate sinks for heavy metals

discharged into the aquatic environment (Gibbs 1972; Jones 1974; Luoma and Bryan

1981; Arjonilla et al. 1994; Singh et al. 2005; Davies and Abowei 2009). Discharge of

industrial effluents and toxic compounds into riverine systems represents an ongoing

environmental problem and so poses a potential threat to human health. The present

study deals with the quality assessment of industrial effluent and its impact on the

INTRODUCTION

[7]

receiving river. Metals in the environment have increased tremendously as a result of

rapid anthropogenic activities. Increasing industrial activity has continuously

introduced pollutants into the riverine environment, and many researchers have

attempted to assess chemical behavior of metals and potentially toxic inorganic

pollutants (Li and Thornton 2001; Silveira et al. 2006; Morillo et al. 2008).

Generally, it has been recognized that natural aquatic sediments absorb

persistent and toxic chemicals to levels many times higher than the water column

concentration (Vermeulen and Wepener 1999; Casper et al. 2004). Under changing

environmental conditions contaminants may be released from the sediments in the

water system by various processes of remobilisation. Therefore, comprehensive

environmental management programme is becoming a necessity in order to safeguard

public health and to protect the valuable resources. The presence of heavy metals in the

water and sediments and the aquatic environmental conditions have been reported by

various workers (Subramanian 1979; Loska and Wiechula 2003; Jonnalagadda and

Mhere 2001; Koukal et al. 2004).

1.3 Assessment criteria of pollution load

The concentrations of metals in sediments can be sensitive indicators of

contaminants in hydrological systems. To assess the degree of contamination of heavy

metals in the sediments the enrichment factor (EF), geoaccumulation index (Igeo) and

pollution load index (PLI) are applied for the study. Index of geoaccumulation (Igeo) is

an assessment tool to assess the contamination by comparing the current and

preindustrial concentrations (Muller 1969). It can also be applied for the assessment of

soil and sediment contamination. Pollution load index (PLI), has been calculated for a

particular site following the method proposed by Tomlison et al. (1980). PLI is

represented as geometric mean of Cf value of n number of metals estimated at each site.

1.4 Drinking and irrigation water suitability criteria of the river water

The river waters are most vernarable to chemical and microbial pollution. The local

communities around the river channel use the water for drinking purposes and so the

study of the river water quality as drinking purpose is very significant. Generally

sodium content in irrigation water causes exchange of Na+ in water for Ca2+ and Mg2+

in soil and reduces the permeability and eventually results in soil with poor internal

INTRODUCTION

[8]

drainage (Saleh et al. 1999). Sodium adsorption ratio (SAR) is a significant parameter

for determining the suitability of river water for irrigation because it is a measure of

alkali/sodium hazard to crops. In all natural waters, sodium percentage Na % is the

most important parameter in determining the suitability of water for irrigation use

(Wilcox 1948). Elevated level of sodium percent causes deflocculation and

impairment of the tilth and permeability of soils (Karanth 1987) and may produce

harmful levels of exchangeable sodium in most soils that will require special soil

management like good drainage, high leaching, and organic matter additions. As per

the Bureau of Indian Standards (BIS) (1991) a sodium percentage of 60 is the

maximum recommended limit for irrigation water. The high sodium saturation in the

irrigation water samples directly causes the calcium deficiency.

1.5 Origin of research

The river Damodar is one of the prominent tributaries of our holy river Ganga.

The river originating from the Khamarpet hill, Palamou district of Chotonagpur Plateue

of Jharkhand travels about 541 km in the eastern part of India and ends to the river

Hooghly at lower Ganga near Syampur at 55 kms downstream of Howrah. During its

course the river flows through the large cities like Ramgarh, Bokaro, Dhanbad,

Asansol, Durgapur, Burdwan and Howrah. Industrial discharges from coke oven plants,

sponge iron industries and several coal washeries discharge their thick effluents directly

/ indirectly into the river at different points in its course (Ramaswamy and Erkman

2001). The river basin area extends from 22�45'N to 24�30'N and 84�45'E to 88�00'E

and circumscribes parts of Jharkhand and West Bengal. Basin geology is mainly

characterised by rocks consisting of granites and granitic gneisses of Archean age,

sandstones and shales of the Gondwana age and the recent alluvials. Seasonal rainfall

occurs due to the South-Western monsoon every year and floods occur depending on

the intensity of the storms. Being a peninsular Indian river, the Damodar tributaries are

used to serve a variety of purposes including drinking, recreation, irrigation, and

industry. Such an indispensable vital water course is affected by the changing land use

pattern (Fig. 1, Table 1), together with the discharge of excessively huge volume of

industrial effluents and silt load from sand and coal mining activities. Tamlanala is a

natural water channel that ultimately drains into the river Damodar near Durgapur

industrial complex. Along its course, it receives effluents from various industries such

INTRODUCTION

[9]

as iron and steel plant, thermal power plant, chemical plant, etc., as well as untreated

sewage water from various settlements along it. Industrial effluent and wastewater are

used for irrigation purposes for growing vegetables beside the Tamlanala. Several

studies on the distribution of heavy metals and toxic chemicals and their effects on

aquatic environment have been noted from different rivers (Downing 1971; Wang et al.

2008; Zheng et al. 2008). The local communities around the effluent channel and the

main river use the water for domestic, fishing, and agricultural purposes. Extensive use

of industrial wastewater for irrigation is a common practice in this area and so the study

of this open channel and the main river is very significant. Except for some studies on

the water quality aspects of the upper part of the Damodar river (Dey 1981 1985; Dey

et al. 1987; Tiwary and Dhar 1994, Singh and Hasnain 1999), no attempt has been

made to study controlling factors of surface water chemistry and practically no

information is available on the heavy metal content and partitioning coefficient of the

Damodar river sediments, particularly in the stretch of Disergarh (upstream) to

Pallareoad (downstream).

Study area detail is represented in Fig. 2. On this backdrop the present research

work has been undertaken within the mentioned stretch in order to fulfil the objectives

which are mentioned in the later section.

1.6 Objective of the research:

� Study of spatio-temporal variation of solute load and the elemental

chemistry.

� Study of hydrogeochemical facies and various ionic relationships and

linking up to weathering and anthropogenic constraint.

� Multivariate statistical analysis for identifying major factors controlling

hydrogeochemistry.

� Assessment of Damodar river water with respect to drinking and irrigation

water suitability.

� Assessment of spatio-temporal distribution of heavy metals in river bottom

sediment and their quantitative speciation.

INTRODUCTION

[10]

� Evaluation of functional groups of bottom sediments with respect to infra-

red spectroscopic study.

� Determination partition coefficient of heavy metals in sediment phase and

aqueous phase.

� Metal pollution assessment.

� Thematic zonation of the Damodar river on the basis of various pollution

indices in GIS environment.

INT

RO

DU

CT

ION

[11]

Tab

le 1

: Exi

stin

g la

nd u

se p

atte

rn a

roun

d th

e sa

mpl

ing

loca

tions

Site

No

Sam

plin

gst

atio

nsL

and

use

in a

djoi

ning

are

a Si

te

No

Sam

plin

gst

atio

nsL

and

use

in a

djoi

ning

are

a

S1

Dis

herg

arh

Con

fluen

ce p

oint

of t

he ri

ver

Bar

akar

S1

5 D

urga

pur

barr

age

Bar

rage

S2

Purb

anch

al

Mun

icip

al a

rea

S16

Shya

mpu

r In

dust

rial d

isch

arge

S3

R

amgh

at

Dis

char

ge p

oint

of t

herm

al p

ower

pl

ant c

oal m

ines

S1

7 M

ajhe

r Man

a D

isch

arge

poi

nt o

f Tam

la n

ala

S4

Chi

naku

ri C

oal m

ines

are

a S1

8 D

hobi

ghat

In

dust

rial e

fflu

ent a

nd se

wag

e di

scha

rge

S5

Dam

odar

railw

ay

stat

ion

Coa

l min

es a

rea

S19

Sila

mpu

r A

gric

ultu

ral l

and

S6

Dih

ika

Indu

stria

l are

a, m

ainl

y iro

n in

dust

ries

S20

Ran

diha

C

onst

ract

ed D

am a

rea

S7

Mad

an D

ihi

Indu

stria

l are

a S2

1 Si

llagh

at

Agr

icul

tura

l are

a S8

B

urnp

ur ri

ver s

ide

Urb

an a

rea

S22

Goh

ogra

m

Agr

icul

tura

l and

low

den

sity

resi

dent

ial

area

S9

N

aray

anku

ri D

isch

arge

poi

nt o

f Nun

ia n

ala

S23

Sika

rpur

A

gric

ultu

ral a

nd lo

w d

ensi

ty re

side

ntia

l ar

ea

S10

Mej

hiag

hat

Coa

l min

es a

nd th

erm

al p

ower

pla

nt

S24

Sada

rgha

t M

unic

ipal

are

a al

ong

with

ext

ensi

ve

agric

ultu

ral a

rea

S11

Mad

anpu

r C

oal m

ines

are

a S2

5 Pa

la S

riram

pur

Mun

icip

al a

rea

alon

g w

ith e

xten

sive

ag

ricul

tura

l are

a S1

2 B

aska

C

oal m

ines

are

a S2

6 B

arsu

l A

gric

ultu

ral a

rea

S1

3 Pu

rsa

U

rban

are

a S2

7 Pa

lla R

oad

Agr

icul

tura

l are

a S1

4 A

shis

hnag

ar

Indu

stria

l eff

luen

t and

sew

age

disc

harg

e

INT

RO

DU

CT

ION

[12]

Figu

re 1

: Map

show

ing

diff

eren

t lan

d us

e pa

tter

n al

ong

the

stre

tch

of th

e D

amod

ar r

iver

INT

RO

DU

CT

ION

[13]

Figu

re 2

: Sam

plin

g lo

catio

n al

ong

the

stre

tch

of th

e ri

ver

Dam

odar

(loc

atio

n de

tail

plot

ted

on sa

telli

te im

age

(Res

ourc

esat

-1)

DAMODAR RIVER BASIN – A BRIEF REVIEW

[14]

2.0 Damodar river basin – a brief review

2.1 About the region

The Damodar River Basin (DRB) is a sub-basin and part of the Ganges River

basin spreading over an area of about 23,370.98 sq.km in the states of Jharkhand and

West Bengal in India. The geographical extremity lies between 22�15' to 24�30' N

latitude and 84�30' to 88�15' E longitude. The Damodar river in its upper reaches

flowes over plateau followed by a flat alluvial plain in the south east and east ward

towards the Bay of Bengal. The river basin traverses conjointly over five districts of

West Bengal, viz., Purulia, Bankura, Burdwan, Hooghly and Howrah and six districts

of Jharkhand viz., Palamau, Hazaribagh, Giridih, Dhanbad, Santhal Pargana

respectively. The coverage of each constituent district is shown in Table 2. A few

districts of Bengal-Jharkhand belt like Giridih and Santhal Pargana transbounded the

Damodar river basin in the north; Hazaribagh and Palamau districts in the west;

Ranchi, Purulia and Bankura districts in the south; and Hooghly and Howrah districts

in the east and southeast representing about 8.1% and 10.4% of the total population of

Undivided Bihar and West Bengal, respectively. The Damodar river basin represents

about three-fourth of its area as the upper catchment situated in Jharkhand, while the

low-lying flood plains entirely lie in West Bengal. The region is richly endowed with

varied mineral resources. Consequently, the region supports several economic

activities related to mining and mine-based industries (311 coalmines, 182 non-coal

mines, 78 urban centers and 82 industrial centers).

2.1.1 Physiography: The river basin geology constructed by variety of rocks ranging

from Archaean about 2/3rd of the deposits in upper and middle basin upto recent age

rocks with the Gondwana and Vindhyan deposits, covering considerable areas are in

the middle part of the basin with valuable mineral deposits like mica and coal etc.

The lower basin is characterized by alluvium soil. At most places, the crystalline and

Gondwana areas are criss-crossed by post-Gondwana intrusions and are punctuated by

multi-directional faults and lineaments. The lithology is dominated by the quartzites,

quartzmica-schists, biotite-gneisse, biotite-schist, garnetiferousgneiss and schist, acid

granulites with hornblend and amphibolites of Archean age (Ghose 1983). Gondwana

rocks consisting of sandstones, shales and fire clays with coal seams are forming the


[15]

part of the catchments of Tenughat, Panchet and Durgapur Barrage. Though not rich

in metallic minerals, the Damodar basin is the storehouse of Indian coal. Other than

coal, fire clay, bauxite, mica, limestones are associated with the geological formation

of the basin.

2.1.2 Geological setting:

2.1.2.1 Tectonic framework of Gondwana basins: The Indian plate is thought to be

an assembly of microcontinents, sutured along Proterozoic mobile belts acting as

zones of rift propagation, and reactivation of palaeo-sutures and graben formation

inferring to have generated the intra-cratonic Gondwana basins (Mitra 1994; Tewari

and Casshyap 1996). Before separation of the east-west Gondwana terrains in the

Permo-Triassic, intra-continental extensional tectonics was active and responsible for

the formation of the sag basins of the Gondwana period; most of the continental

Gondwana sediments in India were deposited during this extensional regime. These

successions of Gondwana sedimentary overlie Late Archaean or Middle-to-Late

Proterozoic basement rocks flanked by regional dislocation zones (Narula et al. 2000).

The Syn-sedimentary subsidence events due to repeated sediment accumulations of

great thickness, dislocation along the intra-basinal faults and asymmetric basin-fills

indicate faulting-induced subsidence to provide the necessary accommodation

(Ramanamurthy and Parthasarathy 1988; Mishra et al. 1999; Chakraborty and Ghosh

2005). The Central, eastern and south-central parts of India are subjected to the

continental Gondwana sedimentary successions, and the basins are mainly aligned

along three river valleys- the Narmada–Son–Damodar, the Pranhita–Godavari and the

Mahanadi. These three Permianto Jurassic-aged riftogenic continental basins filled

with Gondwana sediments converge to meet at the Satpura area in central India

(Narain 1994; Chakraborty and Ghosh 2005). The Raniganj basin of the Damodar

valley is an elongated, semi-elliptical basin, situated between Damodar and Ajoy

rivers (Ghosh 2002). The sedimentary fill of the Raniganj basin comprises a

Gondwana succession from the Lower Gondwana Group (Permian) to the Upper

Gondwana Group (Triassic to Lower Cretaceous) (Ghosh 2002).

The southern basin boundary is east–west trending, steep down-displacement

dip-slip fault zone, led to a half-graben geometry with accumulation of sediment


[16]

towards the south (Ghosh 2002) and also indicative of an extensional tectonic setting

(Gibbs 1984). Transverse normal faults, regarded as the transfer faults (Gibbs 1984),

are distributed along the basin margin and have affected the contact of Gondwana

sedimentary successions with the basement rocks. Such faults have dislocated the

basin boundary fault and are thus younger and were probably initiated after the

beginning of sedimentation. Conjugate sets of the intrabasinal normal faults

transverse to the basinal trend are common and have truncated entire Gondwana

sediment package as well as the basement rocks. Other intrabasinal normal faults

parallel to the basin margin are thought to have been active during the sedimentation

(Ghosh 2002).

2.1.2.2 Damodar valley basin-fill succession: The Gondwana sediments overlie the

Chhotanagpur Granite Gneiss Complex (CGC) showing broad concordance with the

regional structure of the surrounding basement in Damodar valley. The Gondwana

basins are presumed to extend also beneath the Cenozoic sediments of the eastern

Bengal basin (Uddin 1996). Phanerozoic sedimentation on Neoproterozoic basement

was initiated with the deposition of Late Carboniferous Gondwana sediments in

Damodar valley basins. The stratigraphic configuration of the Gondwana sediments of

the Damodar valley is presented in Table 3 (after Raja Rao 1987).

The early Permian Talchir formation, the lowermost formation of the Lower

Gondwana Group is mainly glacigenic in origin in nature. According to Krishnan

1982 the lowermost Tillite member of the Talchir formation unconformably overlies

the Precambrian basement gneisses and correlated with the Dwyka Tillite of South

Africa and the Buckeye Tillite of Antarctica region. The Talchir formation is overlain

successively by the Barakar formation, Barren Measure formation and Raniganj

formation, from bottom to top respectively. The formation of Talchir has a

conformable contact with the overlying sandstones of the Barakar formation that pass

conformably into the ironstone-shale of the Barren Measures formation. The topmost

unit of the Lower Gondwana Group is the upper Permian Raniganj formation. The

Panchet formation is the lowermost unit of the Upper Gondwana Group in the

Raniganj basin conformably overlies the Raniganj formation which is overlain by the

Supra Panchet formation which is composed of coarse sandstones and conglomerates.

According to Bandyopadhyay et al. 2002 the Supra Panchet formation overlies the


[17]

underlying formations as well as the crystalline basement rocks with a pronounced

unconformity. Variou authors (Ghosh and Mukhopadhyay 1986) reported that Soft-

sediment deformation structures are reported from both Upper and Lower Gondwana

sediments.

2.1.3 Drainage system: The core drainage system of the Damodar river basin

contructed by the Damodar river and its principal tributary, the river Barakar, that

drains about 23,370.98 sq. km. area of Jharkhand and West Bengal states. In its upper

reaches the Damodar is known as the Deonad, and originates in Khamarpet hill range

(1,062 m) near Chandwa in Palamau district and drains into a fan shaped catchment

area of about 25,820 sq km. The waters of the Deonad traverse through the steep

slope of the pat region to descend on the gneissic flat plain of Chandwa basin and the

sluggish flow of the river over the flat top surface, which later on got dissected into

tabular blocks by fluvial action. The river Damodar enters the Gondwana Basin after

the confluence of the Dharamauti near Mahuamilan, and the topography around the

river changes. The gradient of the stream becomes steeper and waterfalls abound the

course traverses through the hilly region and woody country carved out of hard

sandstone and grit of the Gondwana age. In this section, the Damodar receives a

number of tributaries both from the southern and the northern slopes. The southern

tributaries like Chati, Saphi, Batuka, Dainkata, Nalkari and Dhobdhab and flow over

the granite-gneissic surface of Ranchi plateau, while the northern tributaries are

Haharo (W), Bakri-Garhi, Haharo (E) and Marmarhar originate from the Hazaribagh

plateau and flow for considerable distance over the Archaean gneiss before entering

the Gondwana basin. The Konar and Bokaro setrams originate in the Hazaribagh

plateau near Hazaribagh town flows over the Archaean gneiss country while Bokaro

traverses through the Archaean gneiss country for some distance and finally enters the

Gondwana basin near Bokaro coalfields.

The combined courses of the Konar and Bokaro rivers meet the Damodar near

Tenughat. The Damodar flows eastward from Tenughat and receives a few other

tributaries from the north and south before reaching Panchet. From the north the

Jamunia and the Khudia join the Damodar after flowing over the Jharia coalfields,

while from the south Ijri and the Gowai meander eastward to meet the Damodar near

the western end of Panchet hill reservoir. The Barakar river basin is a sub-basin and


[18]

part of the Damodar rier basin has an area of 7026 sq. km. rising from the Koderma

plateau and runs for a long distance to meet the Damodar near Dishergarh and

traverses through a steep sided valley. The Barakar river has two important tributaries

the Barsoti and the Usri. After Dishergarh, the Damodar river enters flat alluvial

plains and runs eastward upto Barsul in Burdwan and the flow of the river becomes

very sluggish at this stage. In this portion Damodar receives its last tributary, the Sali

from the south and after-wards the Damodar river takes a sharp turn towards south

near the village Chachai, 24 km south-east of Burdwan. Within its elbow shaped area

several spill channels, are found to carry surplus water of the Damodar during

monsoon months. Aftre traverse some area the river turning towards south and it has a

distributary named the Kana Damodar, which ultimately drains out water in the

Hooghly. Traversing further towards south Damodar splits into two important

channels, the Mundeswari and the Damodar. After Burdwan subdivision the Damodar

river flows over the Arambagh sub-division of Hooghly district and Uluberia sub-

division of Howrah district to meet the Hooghly opposite Falta. At present 75% of the

runoff from the Damodar river is carried by the river Mundeswari through the Begor

and the Mushir hanas and drains out water in the Rupnarayan. This channel cannot

carry the total discharge of flood of the Damodar and as a result the elbow area of the

Damodar gets inundated occasionally notwithstanding the construction of the barrage

and dams over the Damodar in its upstream area.

In the downstream area the flood protection embankments have been

constructed along the banks of the Damodar, but are not sufficient to cope up with the

steadily rising river bed due to silting. According to Hora 1947 the entire Damodar

valley can be divided into the upper, middle and lower valleys depending to the

gradient of the river. The undulating upper and the middle valleys are wider than the

flat lower valley. The river has a total length of 540 km, out of which 380 km is in

Jharkhand and the next 160 km is in West Bengal. The river slope is 1.86 m per km

for 241 km, 57 m per km in the next 167 km and 16 m per km in the last reach. In

final 145 km the Damodar takes a southward course before joining the river Hooghly.

The upper and middle catchment area, constituting over 4/5th of the total catchment

area is a hilly terrain with a steep slope while the lower valley is strikingly narrow and

flat. Thus, in the event of heavy rain in the upper valley, there is a natural tendency


[19]

for water to overflow in the lower alluvial plain where most of the farm lands and

human habitats are located.

The river originates in the Khamarpet hill, Palamou district of Chotonagpur Plateue of

Jharkhand in the eastern part of India and ends to the river Hooghly at lower Ganga

near Syampur at 55 kms downstream of Howrah. During its course the river flows

through the large cities like Ramgarh, Bokaro, Dhanbad, Asansol, Durgapur,

Burdwan and Howrah. Industrial discharges from coke oven plants, sponge iron

industries and several coal washeries discharge their thick effluents directly /indirectly

into the river at different points in its course.

2.1.4 Climate: The Damodar is referred to as a tropical river as it flows through a

tropical environment. The tropicality of environment is primarily a product of thermal

criteria. Damodar river basin exists in the tropical climatic zone with the hottest

summer and the coldest winter. The month of May is the peak of summer season with

an average maximum temperature of 43�C and minimum of 30�C, while December

and January are the coldest months. Temperatures during the winter season fall below

4�C at some locations in the Damodar river basin (DRB).

2.1.5 Rainfall: Seasonal rainfall occurs due to the South-Western monsoon every year

and floods occur depending on the intensity of the storms. Over the basin, the annual

rainfall varies between 765 and 1607 mm with an average of 1200 mm of which 80%

occurs during the monsoon season. The average annual rainfall in the three sub-

catchments namely Barakar, Damodar and lower basin are approximately 1200 mm,

1250 mm and 1400 mm, respectively. The rainfall is the highest in the southern part

and decreases gradually towards the northern part of the Barakar catchment. Rainfall

due to squalls in upper basin are not uncommon during summer season. The

evaporation is maximum during the summer season (21 mm) and minimum in

monsoon season (2.5 mm). The Damodar is seasonal and flood prone mainly on

account of reasons, which are physiographic and meteorological in nature. Frequent

floods ravage the lower valley area, which is not only very fertile owing to its alluvial

plain suitable for irrigation and agriculture but also used for various industrial

activities.


[20]

2.1.6 Soil: The soil has been grouped into major red and yellow loam sedimentary

types in upper and middle basin of Jharkhand region. They have a tendency of

laterisation; are highly leached; neutral to acidic in reaction; deficient in organic

matter, nitrogen and available phosphorus acid but the potash content is high.

2.1.7 Vegetation: Various plant species are found in various forest types, open

grasslands, fallow lands, wastelands, agricultural fields, mined out areas and their

overburden dumps. There are various types of terrestrial ecosystems with diverse

vegetation all over the basin. The basin is rich in large number of plants have socio-

economic importance, besides their role in natural ecosystem functions. The floral

biodiversity of the river basin is also rich and is represented by 137 flowering plant

families and 853 species belonging to 535 genera. The poaceae is the dominant family

of the region with 148 species followed by leguminoseae, the second largest family

with 92 species. Since a local population resides around the forests, their daily

requirements of food, fodder, shelter are met by these natural plant resources. The

basin is also rich in medicinal plants and these species can become an important

resource for economic development of the local population.

Table 2: Constituents of the Damodar river basin

Sl. No. District Total area (Sq.km)

Area in the basin(Sq.km)

% Area of district in the basin

% Share in the basin

Jharkhand Sub-Region

1 Palamau 12677 736.84 5.01 3.15 2 Ranchi 18311 910.33 4.97 3.90 3 Hazaribag 11152 6631.56 59.47 28.38 4 Giridi 6908 5376.81 77.83 23.01 5 Dhanbad 2996.80 2996.80 100.00 12.82 6 Santhal

Parganas 14129 571.05 4.04 2.44

Sub total -- -- 17223.39 -- 73.70

West Bengal Sub-Region

1 Purulia 6259 1383.28 22.10 5.92 2 Bankura 6881 1564.67 22.74 6.69 3 Burdwan 7028 2113.61 30.07 9.04 4 Hooghly 3145 359.87 11.44 1.54 5 Howrah 1474 726.16 49.29 3.11 Sub total -- 6147.59 -- 26.30 Grand total -- 23370.98 -- 100.00


[21]

Table 3. Stratigraphic succession of Gondwana sediments in Damodar valley (Raja Rao 1987).

Age Group Formation

LowerCretaceous

Upper Gondwana

Lamprophyre and

Dolerite Intrusive Jurassic Upper Middle Non-deposition Lower Triassic Upper Rhaetic Suptra-Panchet

Formation Middle Noric Infra-Norian

erosional surface Lower Carnic Panchet Formation Permian Upper Lower

GondwanaRaniganj Formation

Barren Measure Formation

Barakar Formation Kaharbari Formation Talchir Formation Precambrian Gneissic Basement of Chhotanagpur Granite Gneiss Complex

REVIEW OF LITERATURE

[22]

3.0 REVIEW OF LITRATURE

series of geochemical work have been published on the river system in India

and abroad. The works related to the present study are included in the review.

3.1 Weathering and geochemical processes controlling river water/sediment

chemistry

The detailed study of the geochemical evolution of river water and water quality

assessment can enhance understanding of the hydrochemical system, promoting

sustainable development and effective management of river water resources.

Weathering and geochemical processes controlling solute acquisition in Ganga

Headwater–Bhagirathi river, Garhwal Himalaya, India was studied by Pandey et al.

1999. Water and suspended sediment samples were collected along a longitudinal

transect of the Bhagirathi – a headwater stream of the river Ganga, during the

premonsoon and postmonsoon seasons, in order to assess the solute acquisition

processes and sediment transfer in a high elevation river basin. Study results show that

surface waters were dominated by HCO3� and SO4

2� in anionic abundance and Ca2+ in

cationic concentrations. A high concentration of sulphate in the source region indicates

oxidative weathering of sulphide bearing minerals in the drainage basin. The

combination of high concentrations of calcium, bicarbonate and sulphate in river water

suggests that coupled reaction involving sulphide oxidation and carbonate dissolution

are mainly controlling the solute acquisition processes in the drainage basin. The

sediment transfer reveals that glacial weathering and erosion is the major influence on

sediment production and transfer. The seasonal and spatial variation in ionic

concentration, in general, is related to discharge and lithology. The sediment

mineralogy and water mineral equilibrium indicate that water composition is in

equilibrium with kaolinite.

The Llobregat and Ter rivers, typical Mediterranean catchments in Northeast

Spain, supply water to more than 4.5 million inhabitants residing in the metropolitan

area of Barcelona. The objective of the research work is to study the factors that

influence the surface water quality of Llobregat Catchment (Fernández-turiel 2003). As

such, spatial and temporal variations of more 50 water chemical parameters were

monitored in 10 sampling sites for a period that extended from July 1996 to December

A


[23]

2000. The temperature, pH and conductivity were measured at sites, whereas metals

were analysed using ICPOES and ICP-MS instrumental techniques. The head waters of

the Llobregat river catchment flow through detrital Mesozoic–Cenozoic sedimentary

rocks resulting in calcium bicarbonate-type water with low mineral content. The high

water quality of the waterhead is deteriorated in the upper-middle part of the catchment

due to: occurrence of evaporite-bearing geological formations, and the mining and

industrial activities related to potash exploitation. As a result, an obvious increase in

Na+, K+, Mg2+, Cl�, Br, Rb, and Sr concentrations is reported leading to a sodium

(potassium) chloride water type. This saline hydrochemical fingerprint persists

downstream. This important feature renders the low water quality of the Llobregat river

to be adequate for drinking supply purposes. In addition, the industrial and residential

activities, specially at the lower part of the catchment, increases P, B, Mn, Fe, Pb, Al,

Cr, Co, Ni, Cu, Zn, As and Sb water concentrations.

Major and trace element geochemistry in upper Ganga river in the Himalayas,

India also studied by Chakrapani 2005. In this study for the first time, temporal and

spatial sampling for a 1 year period (monthly intervals) was carried out and analyzed

for dissolved major elements and trace elements. Amounts of physical and chemical

loads show large seasonal variations and the overall physical load dominates over

chemical load by a factor of more than three. The dominant physical weathering is also

reflected in high quartz and illite/ mica contents in suspended sediments. Large

seasonal variations also occur in major elemental concentrations. The water type is

categorized as HCO3�– SO4

2� –Ca2+ dominant, which constitute >60% of the total water

composition. On an average, only about 5–12% of HCO3� is derived from silicate

lithology, indicating the predominance of carbonate lithology in water chemistry in the

head waters of the Ganga river. More than 80% Na+ and K+ are derived from silicate

lithology.

Ingri et al. 2005 worked on geochemistry of major elements in a Pristine Boreal

river System. Once or twice weekly, water sampling was undertaken for a two and a

half year period in the Kalix river, northern Sweden. Soil water, groundwater, water in

tributaries and mire water were also sampled at several occasions. Samples were

filtered and analysed for major dissolved elements and TOC. Although only 5% of the

bedrock in the Kalix river drainage basin is situated in the Caledonian mountains


[24]

(mostly schist, with some outcrops of dolomite and limestone), the chemical

composition of the river, at the river mouth, is clearly influenced by water from the

mountain areas. High dissolved Ca2+/Mg2+ ratios in June and July indicate a large

influence of water from the mountain areas during summer. The dissolved Si/Mg2+

ratio increases when water from the woodland (bedrock consisting of Precambrian

granitoids) predominates during snowmelt in May, but the ratio is low during summer

when water from the mountains is increased. However, the low Si concentrations in the

mountain areas are probably not primarily the result of the different rocks but more a

reflection of the less intense weathering of silicate minerals in the mountains. High

Si/Mg2+ ratios are closely related to high TOC. All the major dissolved elements,

except TOC, are diluted by snowmelt in May. However, the dilution varies for different

elements. Based on the interpretations of major element ratios the melt water discharge

in May reflects two major compartments in the woodland; peatland areas and the upper

section of the soil. During summer and autumn storm events in the woodland most of

the storm water originated from peatland. High K+/Mg2+ ratios in the river in May are

related to water discharge from the upper section of the till. Low S/Mg ratios in the

river indicate an influence of mire water from the woodland both during melt water

discharge in May and during increased water discharge in autumn. The Ca2+/Mg2+

ratios in tributaries in the woodland are consistently lower during melt water discharge

compared with values in August. The lower Ca2+ / Mg2+ ratio in May probably reflects

water that has been in contact with the B-horizon in the till during spring flood. Data

show that the TOC discharged during spring flood originates from two major

compartments in the landscape, the upper soil profile and peatland. Storm discharge of

TOC during the rest of the year originates mostly from peatland.

Similar kind of geochemical investigation was carried out on Song stream, a

headwater tributary of the South Han river, South Korea (Ryu et al. 2007). To

investigate the geochemical characteristics stream samples were collected from in

summer 2003. The stream water samples of the study area were divided into three

water types, among which dissolved ion concentrations differed considerably. The

results strongly indicate that the chemical composition of Song stream is controlled by

silicate and carbonate weathering, as well as anthropogenic contamination, and

variations in major dissolved ions anthropogenic contamination of river water.


[25]

Apart from river water chemistry river sediment geochemistry is the major

reflector of chemical weathering. Sediments constitute a pollutant trap and have proven

to be an efficient tool to identify environmental impacts. Sediments are considered a

very important means to assess the level of contamination of water bodies because of

their ability to accumulate metals and organic. Some of the important research works

related to sediment geochemistry have been highlighted here.

Hongbing et al. 2012 carried out research work on geochemical constraints on

seasonal recharge of water and major dissolved solutes in the Huangshui river, China.

The Huangshui river, an important tributary in the upper reaches of the Yellow river,

has been regarded as a mother river which gestates Qinghai civilization in China. Water

chemistry shows that the processes affecting dissolved solutes in the Huangshui river

are also different between summer and winter. In summer, major ions in the river water

are dominantly derived from carbonate and evaporate dissolution and anthropogenic

inputs. In winter, carbonate dissolution decreases greatly while anthropogenic inputs

play a much more important role for dissolved solutes in the river. Hence, further

measures should be taken to lay stress on the winter Huangshui river water in order to

protect the environment of the Huangshui river and reduce effects of dissolved solutes

on, or prevent their pollution toward the upper Yellow river.

Recently intensity of chemical weathering in the catchment of large rives of

Tibetan Plateau and Himalayan region was estimated by calculating chemical indices of

alteration (CIA) of sediments and comparing them with bedrocks which indicates

relatively weak chemical weathering intensity (Wu et al. 2012). Results indicate that

lithology, climate, and topography affect the chemical weathering intensity in these

river catchments.

3.2 Influence on river water/sediment chemistry due to anthropogenic

activities

Besides some natural activities, anthropogenic inputs have a much more

important effect on the concentrations of dissolved solutes in the river water. Several

industries viz, chemical, paper, electrical and light engineering and other ancillary

industries have led to a continuous influx of settlement on the river banks with a

consequence to the deterioration and damage of the water and sediment quality of the


[26]

river and streams. The geochemical characteristics of aquatic sediments in different

parts of the world have been worked out in detail.

3.2.1 Mining activities: Luis et al. 2011 worked out on surface water and stream

sediment contamination near to the abandoned mine, Lousal mine. The mine is closed

at present, but the heavy metal enriched tailings remain at the surface in oxidizing

conditions. Surface water and stream sediments revealed much higher concentrations

than the local geochemical background values, which the “Contaminated Sediment

Standing Team” classifies as very toxic. High concentrations of Cu, Pb, Zn, As, Cd and

Hg occurred within the stream sediments downstream of the tailings sites (up to: 817

mg/kg As, 6.7 mg/kg Cd, 1568 mg/kg Cu, 1059 mg/kg Pb, 82.4 mg/kg Sb, 4373 mg/kg

Zn). The AMD waters showed values of pH ranging from 1.9 to 2.9 and concentrations

of 9249 to 20,700 mg/l SO42�, 959 to 4830 mg/l Fe and 136 to 624 mg/l Al. Meanwhile,

the acid effluents and mixed stream waters also carried high contents of SO42�, Fe, Al,

Cu, Pb, Zn, Cd, and As, generally exceeding the fresh water aquatic life acute criteria.

3.2.2 Treated and untreated discharge of municipal and industrial discharges:

Chabukdhara and Nema 2012 worked out on the level of heavy metals (Cd, Cr, Cu, Fe,

Mn, Ni, Pb, and Zn) in the surface sediments of the Hindon river, India that receives

both treated and untreated municipal and industrial discharges generated in and around

Ghaziabad, India. Mean metals concentrations (mg/kg) were in the range of; Cu:

21.70–280.33, Cd: 0.29–6.29, Fe: 4151.75–17318.75, Zn: 22.22.50–288.29, Ni: 13.90–

57.66, Mn: 49.55–516.97, Cr: 17.48–33.70 and Pb: 27.56–313.57 respectively.

Chemometric analysis was applied to identify contribution sources by heavy metals

while geochemical approaches (enrichment factor and geo-accumulation index) were

exploited for the assessment of the enrichment and contamination level of heavy metals

in the river sediments. Chemometric analysis suggested anthropogenic origin of Cu,

Cd, Pb, Zn, and Ni while Fe showed lithogenic origin. Mn and Cr was associated and

controlled by mixed origin. Geochemical approach confirms the anthropogenic

influence of heavy metal pollution in the river sediments. The study suggests that a

complementary approach that integrates chemometric analysis, sediment quality

criteria, and geochemical investigation should be considered in order to provide a more

accurate appraisal of the heavy metal pollution in river sediments. Consequently, it may


[27]

serve to undertake and design effective strategies and remedial measures to prevent

further deterioration of the river ecosystem in future.

Similar kind of impact was also studied by Suthar et al. 2009 river Hindon is a

major source of water to the highly populated and predominantly rural population of

western Uttar Pradesh, India. For this, river water samples were collected from six

different sites all along the route of Hindon main streamline and its branch and were

analyzed for pH, turbidity, electrical conductivity (EC), total dissolved solids (TDS),

total alkalinity (TA), total hardness (TH) and calcium hardness (Ca2+-H), chemical

oxygen (COD) demand, biochemical oxygen demand (BOD), dissolved oxygen (D.O.),

sulphate (as SO42�), nitrate (as NO3

�) and chloride (Cl�) levels. There were drastic

variations for EC (0.83–5.04 ms), turbidity (28.7– 109.3 NTU), TDS (222.2–2426.3

mg/l), SO42� (36.4–162.4 mg/l), NO3

� (106–245 mg/l), TA (347.0–596.3 mg/l), TH

(235.1–459.9 mg/l), and COD (85.0–337.4 mg/l) levels at different sites. Water

pollution indicating parameters were manifold higher than the prescribed limit by the

National Pollution Control Agency, i.e. CPCB. This is the first study on itself and the

interrelationship of human activities and river water quality makes the study significant

and interesting to assess the pollution load discharges in catchments of Hindon at

Ghaziabad. Overall, the water quality of Hindon was relatively poor with respect to its

use for domestic purposes.Ca2+–H (64.5–402.2 mg/l), BOD (27–51 mg/l).

Rather 2010 also carried out impact of urban waste of Srinagar City on the

quality of water of river Jehlum. The present study is an attempt to make an assessment

of the change in the quality of water of river Jehlum by the addition of urban waste in

comparison to water quality standards of CPB and the impact of the same on the health

of the people downstream of Srinagar. This work also provides a planning strategy for

maintaining the quality of water of river Jehlum during its course through the city

which will be very helpful not only in maintaining the ecology of the river but also in

the control of water borne diseases in the areas downstream of Srinagar city. The river

Jehlum after originating from a spring at Verinag and joined by 17 tributaries during its

course of about 175 kilometres through the whole length of Kashmir Valley constitutes

the main drainage network of Kashmir Valley. Srinagar city, the summer capital of

Jammu and Kashmir State and the largest urban centre with a population of 13 lakhs

constituting 71 percent of the total urban population and 18.70% of the total population


[28]

of Kashmir Valley is located on both sides of river Jehlum for a length of about 23

kilometres. During the course of river Jehlum through the heart of Srinagar City, the

quality of water of the river gets deteriorated due to the direct discharge of urban waste

including both domestic and human excreta through 65 drains and 134 latrines on both

sides of the river. The sample villages downstream of Srinagar city where the same

water is used not only for washing but also for drinking purposes have been reported

having higher incidence of water borne diseases. Change in the quality of water of the

river have been noted in almost all the parameters like dissolved oxygen, TDS (both

dissolved and suspended), alkalinity, pH, phosphorus, nitrogen (nitrite and nitrate),

bicarbonate, chloride, ammonia, conductivity, hardness and biological indicators. Near

about 30% of b the total patients of the 5 sample villages downstream of Srinagar city

who attended the nearest health care facilities have been reported suffering from water

borne diseases like typhoid (11.39%), dysentery (8.30%), gastroenteritis (7.07%) and

infectious hepatitis (3.69%).

3.2.3 Dam construction: Effect of sediment geochemistry due to dam construction on

river course was studied by Papastergios et al. 2009. For this study fourteen sediment

samples from the banks of river Nestos, Northern Greece, were collected, extracted

with HNO3 and analyzed for their content in 10 major and 32 trace elements. The

analytical methods used were ICPOES and ICP-MS. The results indicate that the

sediments in the northern Greek part of the river have the highest elemental

concentrations partly because of human activities, but mainly due to natural processes.

The two dams that have been constructed in the middle course play a buffering role on

the elemental content, for all the elements analyzed, of the river sediments, decreasing

downstream concentrations and sediment load. An increase of concentrations is newly

observed in the low course and delta because of the mobilization of fine sediments by

natural processes and agricultural practices. The comparison of the river sediment

contents with contaminated land guidelines has not revealed any potentially dangerous

concentrations for the elements analyzed.

3.2.4 Influence of sandbar-regulated hydrodynamic on river hydrochemistry:

Influences of river hydrodynamic behaviours on hydrochemistry (salinity, pH,

dissolved oxygen saturations and dissolved phosphorus) were evaluated through high

spatial and temporal resolution study of a sandbar-regulated coastal river (Koh et al.


[29]

2012). River hydrodynamic during sandbar-closed event was characterized by minor

dependency on tidal fluctuations, very gradual increase of water level and continual

low flow velocity. These hydrodynamic behaviours established a hydrochemistry

equilibrium, in which water properties generally were characterized by virtual absence

of horizontal gradients while vertical stratifications were significant. In addition, the

river was in high trophic status as algae blooms were visible. Conversely, river

hydrodynamic in sand bar opened event was tidal-controlled and showed higher flow

velocity. Horizontal gradients of water properties became significant while vertically

more homogenised and with lower trophic status. In essence, this study reveals that

estuarine sandbar directly regulates river hydrodynamic behaviours which in turn

influences river hydrochemistry.

3.2.5 Effects of land use: The Nandong Underground River System (NURS) is located

in Southeast Yunnan Province, China. Groundwater in NURS plays a critical role in

socio-economical development of the region. However, with the rapid increase of

population in recent years, water quality has degraded greatly. Jiang and Yan, 2010

carried out a research work in which 36 water samples collected from springs in both

rain and dry seasons to show significant spatial disparities and slight seasonal variations

of major element concentrations in the water. In addition, results from factor analysis

indicate that NO3�, Cl�, SO4

2�, Na+, K+, and EC in the groundwater are mainly from the

sources related to human activities while Ca2+, Mg2+, HCO3�, and pH are primarily

controlled by water–rock interactions in karst system with Ca2+ and HCO3� somewhat

from anthropogenic inputs. With the increased anthropogenic contaminations, the water

chemistry changes widely. Concentrations of NO3�, Cl�, SO4

2�, Na+ and K+ generally

show an indistinct grouping with respect to land use types, with very high

concentrations observed in the water from residential and agricultural areas. This

suggests that those ions are mainly derived from sewage effluents and fertilizers. No

specific land use control on the Mg2+ ion distribution is observed, suggesting Mg2+ is

originated from natural dissolution of carbonate rocks. The distribution of Ca2+ and

HCO3� does not show any distinct land use control either, except for the samples from

residential zones, suggesting the Ca2+ and HCO3� mainly come from both natural

dissolution of carbonate rocks and sewage effluents.


[30]

3.3 Spatio-temporal distribution of heavy metals in river bottom sediments

Preliminary study of heavy metals in sediments from the Paraguay river was

studied by Facettia et al. 1998. The first results ever obtained on heavy metal

concentrations Fe, Mn, Cr, Cu, Zn and Pb. in the Paraguay river surface sediments are

presented. Samples collected at 11 locations, along a distance of 570 km, between the

cities of Bahia Negra and Alberdi in Paraguay, for six different periods between

November 1991 and 1993, were analyzed. The Paraguay sediments appeared to have

features of an unpolluted river even though significant amounts of domestic and

industrial effluents are discharged near the river channel. The relative heavy metal

enrichment in sediments between Bella Vista and Asunci´on, caused by local domestic

sewage and industrial outfalls, is less than for the shale standard values. The heavy

metal content of the sediments exhibited seasonal variations. Enhanced organic matter

content and biochemical oxygen demand of the river load in winter caused most likely

a retainment of the heavy metals in a dissolved state. Consequently, the sediments

deposited in the winter were relatively depleted in these elements.

Soares et al. 1999 studied those sediments as monitors of heavy metal

contamination in the Ave river basin (Portugal): multivariate analysis of data. The

concentrations of heavy metals (Cd, Cr, Cu, Ni, Pb, Zn) were determined in river

sediments collected at the Ave river basin (Portugal) to obtain a general classification

scenery of the pollution in this highly polluted region. Multivariate data analysis

techniques of clustering, principal components and eigenvector projections were used

in this classification. Five general areas with different polluting characteristics were

detected and several individual heavy metal concentration abnormalities were detected

in restricted areas. A good correlation between the overall metal contamination

determined by multivariate analysis and metal pollution indexes for all sampling

stations was obtained. Some preliminary experiments showed that the metal

concentrations normalised to the volatile matter content in the sediment fraction with

grain size <63 mm seems to be an adequate method for assessing metal pollution.

Another study on the assessment of heavy metal cations in sediments of Shing

Mun river, Hong Kong was carried out by Sina et al. 2001. The extent of heavy metal

cation contamination in the Shing Mun river has been assessed. Sediment samples were

taken at eight strategic locations along the river system. The highest concentrations of


[31]

copper (Cu, 1.66 mg/g), lead (Pb, 0.354 mg/g), zinc (Zn, 2.2 mg/g) and chromium (Cr,

0.047 mg/g) were found in the Fo Tan Nullah, a major tributary of the Shing Mun river.

The highest concentrations of aluminum (114 mg/g) and cadmium (Cd, 0.047 mg/g)

were found in the Shing Mun Main river Channel. These contaminated sediments,

accumulated over the years on the river bed, could act as secondary sources of pollution

to the overlying water column in the river.

Ouyang et al. 2002 worked out on characterization and spatial distribution of

heavy metals in sediment from Cedar and Ortega rivers sub-basin. The Cedar and

Ortega rivers sub-basin is a complex environment where both natural and

anthropogenic processes influence the characteristics and distributions of sediments and

contaminants, which in turn is of importance for maintenance, dredging and pollution

control. This study investigated the characteristics and spatial distribution of heavy

metals, including lead (Pb), copper (Cu), zinc (Zn) and cadmium (Cd), from sediments

in the sub-basin using field measurements and three-dimensional kriging estimates.

Sediment samples collected from three sampling depth intervals (i.e., 0–0.10, 0.11–0.56

and 0.57–1.88 m) in 58 locations showed that concentrations of Pb ranged from 4.47 to

420.00 mg/kg dry weight, Cu from 2.30 to 107.00 mg/kg dry weight, Zn from 9.75 to

2,050.00 mg/kg dry weight and Cd from 0.07 to 3.83 mg/kg dry weight. Kriging

estimates showed that Pb, Cu and Cd concentrations decreased significantly from the

sediment depth of 0.10 to 1.5 m, whereas Zn concentrations were still enriched at 1.5

m. It further revealed that the Cedar river area was a potential source area since it was

more contaminated than the rest of the sub-basin. Comparison of aluminum (Al)-

normalized metal concentrations indicated that most of the metals within the top two

intervals (0–0.56 m) had concentrations exceeding the background levels by factors of

2–10. A three-dimensional view of the metal contamination plumes showed that all of

the heavy metals, with concentrations exceeding the threshold effect level (TEL).

A hydrochemical study on a 630 km stretch of river Gomti, a tributary of the

river Ganges examined the distribution of heavy metals in sediments and the

partitioning of their chemical species between five geochemical phases (exchangeable

fraction, carbonate fraction, Fe/Mn oxide fraction, and organic fraction) using Tessier’s

analytical sequential extraction technique (Singh et al. 2005). Most fractions in the

sediments associated with the carbonate and the exchangeable fractions were between


[32]

11 and 30% except in a few cases where it was more than 50%. According to the Risk

Assessment Code (RAC), the sediments having 11–30% carbonate and exchangeable

fractions are at medium risk. The concentrations of cadmium and lead at mid Lucknow,

Pipraghat, Sultanpur U/S and Sulthanpur D/S are between 31 and 50%. They thus pose

a high risk to the environment. Since the concentrations of cadmium and lead at

Neemsar (Cd 56.79%; Pb 51%) are higher than 50%, the RAC as very high. In most

cases, the average metal concentrations were lower than the standard shale values.

Various physicochemical parameters such as pH, total solids, total dissolved solids,

total suspended solids, COD, BOD, DO, conductivity, chloride, sulphate, phosphate,

fluoride, total alkalinity, total hardness, etc. were also reported

Status of heavy metals in water and bed sediments of river Gomti – a tributary

of the Ganga river, India was focused by Sing et al, 2005. The concentrations of

cadmium, chromium, copper, iron, lead, manganese, nickel, and zinc in water and bed

sediments of river Gomti have been studied in a fairly long stretch of 500 km from

Neemsar to Jaunpur. Grab samples of water (October 2002–March 2003) and bed

sediments (December 2002 and March 2003) were collected from 10 different locations

following the standard methods. The river water and sediment samples were processed

and analyzed for heavy metals viz., Cd, Cr, Cu, Fe, Pb, Mn, Ni, and Zn , and using ICP-

AES. The heavy metals found in the river water were in the range: Cd (0.0001–0.0005

mg/l); Cr (0.0015–0.0688 mg/l); Cu (0.0013–0.0.0043 mg/l); Fe (0.0791–0.3190 mg/l);

Mn (0.0038–0.0.0973 mg/l); Ni (0.0066–0.011 mg/l); Pb (0.0158– 0.0276 mg/l); and

Zn (0.0144–0.0298 mg/l) respectively. In the sediments the same were found in the

range: Cd (0.70–7.90 µg/g); Cr (6.105–20.595 µg/g); Cu (3.735–35.68 µg/g); Fe

(5051.485– 8291.485 µg/g); Mn (134.915–320.45 µg/g); Ni (13.905–37.370 µg/g); Pb

(21.25–92.15 µg/g); and Zn (15.72–99.35 µg/g) of dry weight respectively. Some

physico-chemical parameters viz., pH, total solids, total dissolved solids, total

suspended solids, dissolved oxygen, biological oxygen demand, chemical oxygen

demand, hardness etc. were estimated as these have direct or indirect influence on the

incidence, transport and speciation of the heavy metals. Based on the geoaccumulation

indices, the Gomti river sediments from Neemsar to Jaunpur are considered to be

unpolluted with respect to Cr, Cu, Fe, Mn, and Zn. It is unpolluted to moderately

polluted with Pb. In case of Cd it varies from moderately polluted to highly polluted.


[33]

As far as Ni is concerned the sediment is very highly polluted at Barabanki and Jaunpur

D/s. No correlation was found between enrichment factor and geoaccumulation index.

The Almendares river watershed covers a large portion of Havana, Cuba and is

centrally important to both recreational and other activities in the region. In order to

assess current water quality conditions prior to planned remediation efforts, the spatial

distribution of six heavy metals and other compounds were determined in river

sediments at fifteen sampling stations in the watershed (Olivares-Rieumonta 2005).

Metal concentrations in sediments ranged from 86.1 to 708.8 for Zn,39.3 to 189.0 for

Pb, 71.6 to 420.8 for Cu,84.4 to 209.7 Cr, 1.5 to 23.4 for Co, and 1.0 to 4.3 for Cd mg/g

dry weight sediment. Calculated enrichment factors (EF; measured metal versus

background mineral conditions) were almost always greater than 1.0, suggesting

significant anthropogenic impact on metal levels in the River. The highest EF values

were seen immediately below Cotorro (EF 410 for Pb, Cu and Cd), a suburban town

that has an active secondary smelter, and below the largest municipal landfill in Havana

(EF 410 for Pb, Cu, Cd, and Zn). Further, three sampling stations had multiple metals at

concentrations higher than probable effects concentrations (PEC), implying possible

local ecotoxicological impacts. Finally, sequential extractions of the ediments indicated

that heavy metals were largely associated with the organic fraction, and it was

estimated that up to 62% of metals in the sediments would be susceptible to release

back into the water column if hydraulic or other changes occurred in the river. These

data are being used to prioritize decisions related to the remediation of the river system.

Characteristics of heavy metals and their evaluation in sediments from middle

and lower reaches of the Huaihe river was studied by Jia-ping et al. 2007. They have

collected 18 samples corresponding to 18 locations in the middle and lower reaches of

the Huaihe river. The sediment samples were tested for their pH level, percentage of

solids, organic matter and five heavy metals (Cr, Cu, Zn, Cd and Pb). The average

concentrations of Cr, Cu, Zn, Cd and Pb of the 18 sampling locations were respectively

56.1, 22.2, 70.0, 0.17 and 20.4 µg/g. Compared with their background values, the

average concentrations of Zn and Cu in sediment samples from the Huaihe river were

slightly higher, while the average concentrations of Cr and Pb were slightly lower. The

concentration of Cd in all sediment samples was higher than its background value,

while the average concentration of Cd in all sediment samples was about twice the


[34]

amount of the background value. The concentration of the five heavy metals was lower

than that of the Yangtze river. A correlation analysis revealed that heavy metals have

similar geochemical feautures. The geo-accumulation index (Igeo) was used to evaluate

the degree of pollution of the Huaihe river sediments. The index reveals that the

sediment samples are largely ranked from zero pollution to no to medium pollution.

Distribution of heavy metals in water, particulate matter and sediments of Gediz

river (Eastern Aegean) was studied by Kucuksezgin et al. 2008. The present paper is

the first document of heavy metal levels in surficial sediment, water and particulate

matter of the Gediz river collected from five different sites in August, October 1998,

February, June 1999. The present work attempts to establish the status of distribution

and environmental implications of metals in the sediment, water and particulate matter

and their possible sources of derivation. The concentrations of mercury ranged 0.037–

0.81, 120–430; lead 0.59–1.5, 190–8,100; copper 0.24–1.6, 30–180; zinc 0.19–2.9, 10–

80; manganese 30–170, 20–490; nickel 0.39–9.0, 100–510; iron 1.3–687, 100–6,200

µg/l in water and particulate matter, respectively. The maximum values in water were

generally obtained in summer periods due to industrial and agricultural activities at

Muradiye. The particulate metal concentrations also generally showed increased levels

from the upper Gediz to the mouth of the River. Calculation of metal partition

coefficients shows that the relative importance of the particulate and the water phases

varies in response to water hydrochemistry and suspended solid content, but that most

elements achieve a conditional equilibrium in the Gediz river. The metals ranged

between Hg: 0.25–0.49, Cr: 59– 814, Pb: 38–198, Cu: 15–148, Zn: 34–196, Mn: 235–

1,371, Ni: 35–175, and Fe: 10,629–72,387 mg/kg in sediment. The significant increase

of metals found in Muradiye suggested a pollution effect, related to anthropogenic

wastes. Also, relatively high concentrations of Ni and Mn occurred in sampling site

upstream, due to geochemical composition of the sediments. Maximum values of

contamination factor for metals were noticed for sediment of Muradiye. The sampling

stations have very high degree of contamination indicating serious anthropogenic

pollution.

Heavy metal contamination of River Yamuna, Haryana, India was studied by

Kaushik et al. 2009 and the assessment was done by Metal Enrichment Factor of the

Sediments. Concentration of Heavy Metals (Cd, Cr, Fe, Ni) in water, plants and


[35]

sediments of river Yamuna flowing in Haryana through Delhi are reported here

selecting 14 stations covering the upstream and downstream sites of major industrial

complexes of the State. Some important characteristics of river water and sediments

(pH, EC, Cl�, SO42�, and PO4

3� in water and sediments, COD of water and organic

matter content of sediments) were also analysed and inter-relationships of all these

parameters with heavy metal concentration in different compartments were examined.

The sediments of the river show significant enrichment with Cd and Ni indicating

inputs from industrial sources. Concentrations of Cr are moderate and show high

enrichment values only at a few sites. Enrichment factor for Fe is found to be <1,

showing insignificant effect of anthropogenic flux. Concentrations of these metals in

river water are generally high exceeding the standard maximum permissible limits

prescribed for drinking water, particularly in the downstream sites. The aquatic plants

show maximum accumulation of Fe. The other heavy metals Cd, Cr and Ni, though less

in concentration, show some accumulation in the plants growing in contaminated sites.

Interrelationships of metal concentration with important characteristics of water and

sediment have been analysed. Analysis of heavy metals in water, sediments and littoral

flora in the stretch of river Yamuna is first study of itself and interrelationship of metal

concentration and other important characteristics make the study significant and

interesting in analysing the pollution load at different points of the river body.

Studies on source and distribution of trace metals and nutrients in Narmada and

Tapti river basins, India was carried out by Sharma and Subramanian 2010. The study

was designed to establish the distributions of trace metals, dissolved organic carbon,

and inorganic nutrients as well as to assess the extent of anthropogenic inputs into the

Narmada and Tapti rivers. Water and sediment qualities are variable in the rivers, and

there are major pollution problems at certain locations, mainly associated with urban

and industrial centres. The metal concentrations of samples of the aquatic

compartments investigated were close to the maximum permissible concentration for

the survival of aquatic life, except for higher values of Cu, Pb, Zn and Cr and for

drinking water except for elevated concentrations of metals such as Pb, Fe, Cr and Ni.

In general, the concentrations of trace metals in the rivers vary downstream which may

affect the ‘‘health’’ of the aquatic ecosystem and may also affect the health of the rural

community that depends on the untreated river water directly for domestic use. The


[36]

assessment of EF, Igeo, and PLI in the sediments reveals overall moderate pollution in

the river basins.

Seasonal and spatial distribution of trace elements in the water and sediments of

the Tsurumi river in Japan was focused by Mohiuddin et al. 2012. River has a

significant metal loading originating from urban environment. Water and sediment

samples were collected from 20 sites in winter and summer, 2009 and were analyzed to

determine and compare the extent of different trace element enrichment. A widely used

five-step sequential extraction procedure was also employed for the fractionation of the

trace elements. Concentrations of zinc, copper, lead, chromium, and cadmium were

three to four times higher than that of reference values and downstream sediments are

much more polluted than the upstream sites. Geochemical partitioning results suggest

that the potential trace metal mobility in aquatic environment was in the order of:

cadmium > zinc > lead > copper > cobalt > chromium > molybdenum > nickel. About

80.2% zinc, 77.9% molybdenum, 75.3% cobalt, 63.7% lead, 60.9% copper, 55.1%

chromium, and 39.8% nickel in the sediment were contributed anthropogenically.

According to intensity of pollution, Tsurumi river sediments are moderately to heavily

contaminate by zinc, lead, and cobalt. Enrichment factor values demonstrated that zinc,

lead, and molybdenum have minor enrichment in both the season. The pollution load

index (PLI) has been used to access the pollution load of different sampling sites. The

area load index and average PLI values of the river were 7.77 and 4.93 in winter and

7.72 and 4.89 in summer, respectively. If the magnitude of pollution with trace metal in

the river system increases continuously, it may have a severe impact on the river’s

aquatic ecology.

Geochemical variations in stream sediments (n = 54) from the Mahaweli river

of Sri Lanka have been evaluated from the viewpoints of lithological control, sorting,

heavy mineral concentration, influence of climatic zonation (wet, intermediate, and dry

zones), weathering, and downstream transport (Young et al. 2012). Com-positions of

soils (n = 22) and basement rocks (n = 38) of the catchment and fractions of the stream

sediments were also examined. The sediments, fractions, soils and basement rocks were

analyzed by X-ray fluorescence to determine their As, Pb, Zn, Cu, Ni, Cr, V, Sr, Y, Nb,

Zr, Th, Sc, Fe2O3, TiO2, MnO, CaO, P2O5 and total sulfur contents. The chemistry of


[37]

the sediments, rocks and the soils in the Mahaweli river are thus mainly controlled by

source lithotype, weathering, sorting, and heavy mineral accumulation.

Assessment of heavy metal contamination in sediments of the Tigris river

(Turkey) using pollution indices and multivariate statistical techniques was studied by

Varol 2011. Heavy metal concentrations in sediment samples from the Tigris river were

determined to evaluate the level of contamination. The highest concentrations of metals

were found at the first site due to metallic wastewater discharges from copper mine

plant. Sediment pollution assessment was carried out using contamination factor (CF),

pollution load index (PLI), geoaccumulation index (Igeo) and enrichment factor (EF).

The CF values for Co, Cu and Zn were >6 in sediments of the first site, which denotes a

very high contamination by these metals. The PLIs indicated that all sites except the

first site were moderately polluted. Cu, Co, Zn and Pb had the highest Igeo values,

respectively. The mean EF values for all metals studied except Cr and Mn were >1.5 in

the sediments of the Tigris river, suggesting anthropogenic impact on the metal levels

in the river. The concentrations of Cr, Cu, Ni and Pb are likely to result in harmful

effects on sediment-dwelling organisms which are expected to occur frequently based

on the comparison with sediment quality guidelines. PCA/FA and cluster analysis

suggest that As, Cd, Co, Cr, Cu, Mn, Ni and Zn are derived from the anthropogenic

sources, particularly metallic discharges of the copper mine plant.

Major ion concentrations of river, lake and snow waters were measured to better

understand the water quality, hydrochemical processes and solute sources of surface

waters within the Tarim river Basin in the extreme arid region (Xiao et al. 2012).

Surface waters are slightly alkaline and are characterized by high total dissolved solids

(TDS). TDS values vary over two orders of magnitude from fresh (76%) to brackish

(24%) with a mean value of 1000 mg/l, higher than the global river average and river

waters draining the Himalayas and the southeastern Tibetan Plateau. Most of the

samples were Ca2+-Mg2+- HCO3� type and suited for drinking and irrigation. Water

quality of Aksu river (AK), Hotan river (HT) and Northern rivers (NR) is better than

the others. Rock weathering, ion exchange and precipitation are the major

hydrogeochemical processes responsible for the solutes in rivers waters. Anthropogenic

input to the water chemistry is minor and human activities accelerate increase of river

TDS. The quantitative solute sources are first calculated using a forward model in this


[38]

area. The results show that evaporite dissolution, carbonate weathering, atmospheric

input, and silicate weathering contributed 58.3%, 25.7%, 8.7%, and 8.2% of the total

dissolved cations for the whole basin. Evaporite dissolution dominated in Lake Waters

(LW), HT, Yarkant river (YK), Tarim river (TR), and Southern rivers (SR),

contributing 73.5%, 53.4%, 56.7%, 77%, and 74.2% of the total dissolved cations,

respectively. Carbonate weathering dominated in AK and NR, contributing 48% and

44.4% of the total dissolved cations, respectively. The TDS flux of HT, TR, AK, YK

was 66.0, 118.6, 134.9, and 170.4 t/km2/yr, respectively, higher than most of the rivers

in the world. Knowledge of our research can promote effective management of water

resources in this desert environment and add new data to global river database.

3.4 Ecological risk due to heavy metal

The concentrations of heavy metals (Cu, Zn, and Pb) in the water, sediment, algae,

crustacean and rotifer were investigated in the on the Le An river polluted by acid mine

drainage (He et al. 1997). Integration and comparison of metal contamination from acid

mine drainage (AMD) and an assessmet of the potential for ecological impact was

conducted in the aquatic ecosystems of the Le An river. The results of this study

indicated low acidity, high levels of suspended solids containing a high content of

copper in river water and sediment in the upstream region of the Le An river due to the

pollution from the Dexing copper mine, and high concentrations of zinc and copper in

surface water and sediment. The pollution from acid mine drainage in the Le An river

potentially effects an ecological impact on the aquatic ecosystem. By integration and

comparison of several years’ data, the results clearly showed that the discharges from

the Dexing copper mine and mines along Jishui river has resulted in a significant

increase in the concentration of Cu, Zn and Pb in water and sediments along the Le An

river.

3.5 River water quality

Water quality assessment of river Nile from Idfo to Cairo was studid by

Abdelsatar 2005. Water quality of the river Nile from Idfo to Cairo and trace elements

of the Nile water were seasonally investigated from autumn 2000 to summer 2001.

Eleven sites were selected along the main channel of the river Nile. In addition, six

stations in front of some shore-line activities were also sampled to study the man's

impact on the water quality of the Nile. The distribution of major cations and anions


[39]

possessed the highest values in cold seasons and the lowest during the hot high-flow

period. In addition, EC, TS, TDS, COD, NH4+, orthophosphate, total phosphorus, Fe,

Mn and Cu showed a steady increase from south to north. Point and non-point sources

of pollution exerted negative local effects on the water quality of the receiving waters.

The multiple correlation analysis showed a pattern of interrelationships between

physical and chemical parameters.

Spatial –temporal variation and comparative assessment of water qualities of

urban river system: a case study of the river Bagmati (Nepal) by Kannel et al 2007. The

study presents the assessment of variation of water qualities, classification of

monitoring networks and detection of pollution sources along the Bagmati river and its

tributaries in the Kathmandu valley of Nepal. Seventeen stations, monitored for 23

physical and chemical parameters in pre-monsoon, monsoon, post-monsoon and winter

seasons, during the period 1999–2003, were selected for the purpose of this study. The

study revealed that the upstream river water qualities in the rural areas were

increasingly affected from human sewage and chemical fertilizers. In downstream

urban areas, the river was heavily polluted with untreated municipal sewage. The

contribution of industries to pollute the river was minimal. The higher ratio of COD to

BOD (3.74 in the rural and 2.06 in the urban) confirmed the increased industrial

activities in the rural areas. An increasing trend of nitrate was found in the rural areas.

In the urban areas, increasing trend of phosphorus was detected. The water quality

measurement in the study period showed that DO was below 4 mg/l and BOD, COD,

TIN, TP and TSS above 39.1, 59.2, 10.1, 0.84 and 199 mg/l, respectively, in the urban

areas. In the rural areas, DO was above 6.2 mg/l and BOD, COD, TIN, TP and TSS

below 15.9, 31, 5.24, 0.41 and 134.5 mg/l, respectively. The analysis for data from

1988 to 2003 at a key station in the river revealed that BOD was increasing at a rate of

1.8 mg/l in the Bagmati river. A comparative study for the water quality variables in the

urban areas showed that the main river and its tributaries were equally polluted. The

other comparison showed the urban water qualities were significantly poor as compared

with rural. The cluster analysis detected three distinct monitoring groups: (1) low water

pollution region, (2) medium water pollution region, (3) heavy water pollution region.

For rapid assessment of water qualities using the representative sites could serve to

optimize cost and time without loosing any significance of the outcome. The factor


[40]

analysis revealed distinct groups of sources and pollutions (organics, nutrients, solutes

and physicochemical).

3.6 Assessment of natural and anthropogenic sources of chemical element in

the river water/sediment through multivariate statistical methods and pollution

indices

The river water contains minerals carried in solution, the type and concentration

of which depends upon several factors like soluble products of rock weathering and

decomposition in addition to external polluting sources and changes in space and time.

Multivariate statistical techniques were used by various workers to explore the data,

following appropriate data transformation, to understand the data structure, investigate

underlying processes controlling spatial geochemical variability and identify element

associations.

The concentrations of heavy metals (Cd, Cr, Cu, Ni, Pb, Zn) were determined in

river sediments collected at the Ave river basin (Portugal) to obtain a general

classification scenery of the pollution in this highly polluted region Soares 1999.

Multivariate data analysis techniques of clustering, principal components and

eigenvector projections were used in this classification. Five general areas with

different polluting characteristics were detected and several individual heavy metal

concentration abnormalities were detected in restricted areas. A good correlation

between the overall metal contamination determined by multivariate analysis and metal

pollution indexes for all sampling stations was obtained. Some preliminary experiments

showed that the metal concentrations normalised to the volatile matter content in the

sediment fraction with grain size <63 mm seems to be an adequate method for

assessing metal pollution.

The degree of contamination in the sediments of the Dikrong river, for the

metals Al, Fe, Ti, Mn, Zn, Cu, Cr, Ni and Pb, has been evaluated using Enrichment

ratio (ER), Pollution load index (PLI) and Geo-accumulation index (Igeo) (Chakravarty

and Patgiri 2009). The sediments have been found to be contaminated with Cu and Pb

which has been attributed mainly to dispersion from the mineralized zone of the upper

catchment area since no major industrial establishments are present in the area.

In another survey heavy metal contamination of river Yamuna, Haryana was

assessed by metal enrichment factor of the sediments (Kaushik et al. 2009).


[41]

Concentration of Heavy Metals (Cd, Cr, Fe, Ni) in water, plants and sediments of river

Yamuna flowing in Haryana through Delhi are reported here selecting 14 stations

covering the upstream and downstream sites of major industrial complexes of the State.

Some important characteristics of river water and sediments (pH, EC, Cl�, SO42� and

PO43� in water and sediments, COD of water and organic matter content of sediments)

were also analysed and inter-relationships of all these parameters with heavy metal

concentration in different compartments were examined. The sediments of the river

show significant enrichment with Cd and Ni indicating inputs from industrial sources.

Concentrations of Cr are moderate and show high enrichment values only at a few sites.

Enrichment factor for Fe is found to be <1, showing insignificant effect of

anthropogenic flux. Concentrations of these metals in river water are generally high

exceeding he standard maximum permissible limits prescribed for drinking water,

particularly in the downstream sites. The aquatic plants show maximum accumulation

of Fe. The other heavy metals Cd, Cr and Ni, though less in concentration, show some

accumulation in the plants growing in contaminated sites. Interrelationships of metal

concentration with important characteristics of water and sediment have been analysed.

Analysis of heavy metals in water, sediments and littoral flora in the stretch of river

Yamuna is first study of itself and interrelationship of metal concentration and other

important characteristics make the study significant and interesting in analysing the

pollution load at different points of the river body.

Heavy metal concentrations in sediment samples from the Tigris river were

determined to evaluate the level of contamination. The highest concentrations of metals

were found at the first site due to metallic wastewater discharges from copper mine

plant. Sediment pollution assessment was carried out using contamination factor (CF),

pollution load index (PLI), geoaccumulation index (Igeo) and enrichment factor (EF)

(Varol 2011). The CF values for Co, Cu and Zn were >6 in sediments of the first site,

which denotes a very high contamination by these metals. The PLIs indicated that all

sites except the first site were moderately polluted. Cu, Co, Zn and Pb had the highest

Igeo values, respectively. The mean EF values for all metals studied except Cr and Mn

were >1.5 in the sediments of the Tigris river, suggesting anthropogenic impact on the

metal levels in the river. The concentrations of Cr, Cu, Ni and Pb are likely to result in

harmful effects on sediment-dwelling organisms which are expected to occur frequently


[42]

based on the comparison with sediment quality guidelines. PCA/FA and cluster

analysis suggest that As, Cd, Co, Cr, Cu, Mn, Ni and Zn are derived from the

anthropogenic sources, particularly metallic discharges of the copper mine plant.

Similar kind of research work but on different river was carried out by Singh et

al. 2005. This study explores the extent and possible sources of heavy metal (Cd, Cr,

Cu, Fe, Mn, Pb, Zn and Ni) contamination in the bed sediments of the Gomti river

performing principal component analysis on the five years (Jan. 1994–Dec. 1998) data

set obtained through continuous monitoring of the river water and bed sediments at

eight selected sites and water/wastewater of its tributaries/drains. Influence of

anthropogenic activities on metal contamination of the bed sediments was evaluated

through computing the geoaccumulation index for various metals at studied sites. PCA

performed on combined (river bed sediment, water, suspended solids, water/wastewater

from tributaries/drains) data set extracted two significant factors explaining more than

58% of total variance. Factor loadings suggested the presence of both natural as well as

anthropogenic sources for all these metals in the river bed sediments. Among all the

sites, the sites 4 and 5 are more contaminated with Cd, Cu, Cr and Pb, which was

supported by the geoaccumulation indices computed for metals. Factor scores revealed

presence of seasonal (monsoon-related) differences in metals profiles for river water

and suspended solids and absence of seasonal differences for bed sediment and

wastewater. Further, the metal contamination of the bed sediment was also evaluated

using biological thresholds. Results suggested that the river bed sediments are

contaminated with heavy metals, which may contribute to sediment toxicity to the

freshwater ecosystem of the Gomti river.

Pollution indices are also applied for the assessment of heavy metal (Fe, Mn,

Zn, Cu, Ni, Pb, and Cd) concentrations and their chemical speciations in bed sediments

of Bharali river, a major tributary of the Brahmaputra river of the Eastern Himalayas

(Hoque et al. 2011). Levels of Fe, Mn, Pb, and Cd in the bed sediments were much

below the average Indian rivers; however, Cu and Zn exhibit levels on the higher side.

Enrichment factors (EF) of all metals was greater than 1 and a higher trend of EF was

seen in the abandoned channel for most metals. Pb showed maximum EF of 32 at site

near an urban center. The geoaccumulation indices indicate that Bharali river is

moderately polluted. The metals speciations, done by a sequential extraction regime,


[43]

show that Cd, Cu, and Pb exhibit considerable presence in the exchangeable and

carbonate fraction, thereby showing higher mobility and bioavailability. On the other

hand, Ni, Mn, and Fe exhibit greater presence in the residual fraction and Zn was

dominant in the Fe–Mn oxide phase. Inter-species correlations at three sites did not

show similar trends for metal pairs indicating potential variations in the contributing

sources.

Luo et al. 2010 also applied pollution indices to investigate on Metal Pollution

in the Sediment of Chongqing Segment of Yangtse river. Experimental dada collected

from 1995 to 2007 at Chongqing segment of Yangtse river, the pollution and the

potential toxic effect of sediment were depicted and characterized by using the Index of

geoaccumulation (Igeo) method and the logistic regression model respectively. Results

showed that the sediment had been slightly polluted by metals and had possible adverse

effect on aquatic life. According to the Igeo, the order of the analyzed metals, arranging

from highest to lowest pollution degree, was Cd>Hg>Pb>Cu>Zn>As. Meanwhile,

sediment contamination level had been obviously decreasing before the storing water of

Three Gorge Reservoir.

Factor analysis applied to a geochemical study of suspended sediments from the

Ggediz river, western Turkey (Bakac 2000). Suspended sediment particles collected

from 33 sampling points at a site located close to industrial and geological areas in

Gediz river, western Turkey were analysed for 15 elements by Energy Dispersive X-ray

Fluorescence Spectroscopy (XRF), Gamma Spectroscopy (GS), and Collector Chamber

Method (CCM). Both varimax and oblimin factor rotations were applied to the data, but

varimax rotated factor analysis was used for source identification of suspended

sediments. Three factors were extracted from the suspended data, which account for

about 70% of the total data variance. These factors are interpreted as economy, mine

and mine/agriculture.�

MATERIALS AND METHODS

[44]

4.0 MATERIALS AND METHODS

his chapter includes the various techniques and methods involved in the study of

the river water and sediment characteristics along with detailed description of the

materials required and equipments used for analytical tools.

4.1 Collection of the river water Samples

In order to obtain the research objective, samples were collected from twenty

seven locations from the river Damodar – on seasonal basis (premonsoon, monsoon and

postmonsoon season). The collected samples were stored in acid-cleaned, wide-mouth

high-density polyethylene (HDPE) bottles (1000 ml), which were rinsed with the river

water before use. The pH and electrical conductivity (EC) were measured at the site

immediately after the collection, and other physico-chemical analysis was performed in

the laboratory. The river water samples were filtered through 0.45 µm millipore

membrane filters to separate suspended sediments. For estimation of metals, the river

water samples were acidified to prevent the precipitation of metals, and stored in

refrigerator for further analysis.

4.2 Quality Control Assurance

Quality control measures were taken to assess contamination and reliability of

the analyzed data. For quality control purposes, care has been taken for sample

collection and preservation during every experimental procedure and for the analytical

precision, each (water and sediments) samples were performed for three replicates. A

blank was also run at the same time during experiment and no detectable contamination

was found when aliquots of reagents were processed and analyzed with the samples.

Double distilled deionized water was used throughout the experiment. E-mark (AR

grade) standards were used for the preparation of standard curve during analysis of

samples. For FTIR analysis KBr (spectroscopic grade) was used for the preparation of

pellets. For further enhancement of experimental results, the mean values for each

parameter along with standard deviation and coefficient of variance (CV) were

considered.

T


[45]

4.3 Physico-chemical analysis of the river water samples:

4.3.1 Determination of pH [Standard Methods (APHA 1998)]:

Principle: The pH of a solution is defined as negative logarithm of hydrogen ion

activity to the base 10 i.e. pH = - log10 aH+

When the solution is very dilute aH+ = CH+, i.e., pH = - log10 cH+

Requisition:

1. pH meter (Orion Thermo).

2. Standard pH buffer solutions (Orion).

Procedure: At first the digital pH meter was calibrated by standard pH solutions (pH 4,

pH 7 and pH 10). After calibration, the pH of water samples was measured at a room

temperature (27ºC).

In case of field sampling, onsite samples pH was measured by using hand analyzer

(HANNA-HI 98121).

4.3.2 Electrical Conductivity [EC] [Standard Methods (APHA 1998)]

Principle: Electrical conductivity (EC), also called specific conductance is a measure

of the ability of water samples to convey electrical current, and it is related to the total

concentration ionized substances in water. Organic compounds have little influence on

the conductivity. The water conductivity increases with temperature owing to a

decrease in viscosity and increasing dissociation. Normally EC value of distilled water

range from 1 to 5 µmho or µS conveniently measured at 25ºC.

Requisition:

1. Standard KCl solution (0.01 M) which has a conductivity of 1412 µS/cm at 25ºC.

2. Conductivity meter (Eutech CON 510).

Procedure: Digital conductivity meter was calibrated with standard KCl solutions (1N,

0.1N and 0.01N) and the EC values shows on the digital display of conductivity meter,

then conductivity of river water samples are analyzed.

During field study, onsite samples EC was measured by using hand analyzer (HI-

98301).


[46]

4.3.3 Total Dissolved Solids [TDS] [Standard Methods (APHA 1998)]

Principle: The filtrate of well mixed river water samples filtered for total suspended

solid, through a standard glass fibre filter is evaporated to dryness in weighed dish and

dried at 180oC. The increase in weight over that of the empty dish represents the total

dissolved solids.

Requisition:

1. Hot water bath.

2. Beaker, Glassgoods

Procedure: An evaporating dish of appropriate size is heated in an oven at 180 oC for

1hr, cooled in a dessicator and weighed. 100 ml of accurately measured well mixed

water samples is filtered with slight suction. The filtrate is then transferred to a pre-

weighed evaporating dish and evaporated to dryness. It is dried for at least 1hr at 180 oC, cooled in room temperature and weighed.

Calculation:

��

Where Sample taken (ml) = V, Weight of empty beaker (mg) = X

Weight of beaker with water (mg) = Y, Weight of the dissolved solid (mg) = (Y-X)

4.3.4 Estimation of Bicarbonate [Titrimetric Method (APHA 1998)]

Principle: Alkalinity is the quantitative capacity of an aqueous medium to react with

hydrogen ions to pH 8.3 and then to pH 3.7. The equation in its simplest form is as

follows:

CO32-+H+=HCO3

- (pH 8.3)

From pH 8.3 - 3.7, the following reaction may occur.

HCO3-+H+ = H2CO3

Reagents:

A. Sulfuric acid (0.02N)

B. Phenolphthalein indicator


[47]

C. Methyl Orange indicator

Procedure: Samples were analyzed in the laboratory after collection. 10 ml of sample

were taken in a flask and add 2-3 drops of phenolphthalein indicator. If a slight pink

colour appears, phenolphthalein alkalinity is present. Solution was titrated against

sulphuric acid until the solution becomes colour less (end point). The reading was

noted, after that, 2-3 drops of methyl orange indicator was added in the same flask and

continue to titrate against sulfuric acid until yellow colour of solution tern orange (end

point). The reading was noted as t which is the volume of titrant used for both the

titrations.

Calculations:

Phenolphthalein alkalinity as CaCO3, mg/l = ��

�

Total alkalinity as CaCO3, mg/l��where, p = Volume of titrant used against phenolphthalein indicator (ml); s = Volume

of sample (ml); and t = Total volume of titrant used for the two titrations (ml)

The value of different forms of alkalinities (carbonate, and bicarbonate) in terms of

CaCO3 (mg/l) can be computed using following table:

Values of carbonate and bicarbonate alkalinities:

P = Phenolphthalein alkalinity; T= Total alkalinity

Value of alkalinity expressed in CaCO3

Result Carbonate Bicarbonate

P = 0 0 T P < ½ T 2P T – 2P P = ½ T 2P 0 P > ½ T 2(T – P) 0 P = T 0 0

To compute the concentration of carbonate (CO32–), and bicarbonate (HCO3

–) ions the

following calculations are employed:

CO32– (mg/l) = Carbonate alkalinity x 0.60 (in CaCO3,)


[48]

HCO3– (mg/l) = Bicarbonate alkalinity x 1.2 (in CaCO3,)

4.3.5 Estimation of Calcium [Titrimetric Method (APHA 1998)]

Principle: In a solution containing both calcium and magnesium, calcium can be

determined directly with EDTA when the pH is made sufficiently high (12 - 13) so that

the magnesium is largely precipitated as the hydroxide and an indicator is used which

combines, only with calcium.

Reagents:

A. Sodium hydroxide solution (8%)

B. Mureoxide indicator

C. EDTA solution (0.01M)

Procedure: 50 ml of the sample was taken in an Erlenmiyer flask and 1 ml of sodium

hydroxide solution and a pinch of murexide indicator were added. Titrate against

EDTA solution until the pink colour turns into purple (end point).

Calculation:

Calcium (mg/l)�� where, T= Volume of titrant (ml); and V= Volume of sample (ml)

To determine the calcium hardness to be expressed in as CaCO3 employ following

formula is used.

Calcium hardness (mg/l, as CaCO3) = ��

�where, T= Volume of titrant (ml); and V= Volume of sample (ml)

4.3.6 Estimation of Magnesium (Titrimetric Method (APHA 1998)]

Method and calculation: Total hardness and calcium hardness of water as CaCO3 are

determined. From these values magnesium content in calculated as given below:

Magnesium (mg/l) = (T – C) x 0.244


[49]

Where, T = Total hardness (as CaCO3); and C = Calcium hardness (as CaCO3)

4.3.7 Estimation of Sodium [Flame photometric Method (APHA 1998)]

Principle: Trace amounts of sodium can be determined by flame emission photometry

at 589 nm. Sample is nebulised into a gas flame under carefully controlled,

reproducible excitation conditions. The sodium resonant spectral line at 589 nm is

isolated by interference filters or by light- dispersing devices such as prisms or gratings.

Emission light intensity is measured by a phototube, photomultiplier, or photodiode.

The light intensity at 589 nm is approximately proportional to the sodium

concentration. The appropriate wavelength setting, which may slightly more or less

than 589 nm, can be determined from the maximum emission when aspirating a sodium

standard solution, and then used for emission measurements.

Reagents

A. Double distilled water

B. Standard stock sodium solution (1000 mg/l)

C. Intermediate standard sodium solution (100 mg/l).

Procedure: Different standard sodium solution of following concentrations (for

calibration curve) was prepared from intermediate standard sodium solution (100 mg/l)

such as 2, 4, 6, 8, 10 mg/l. A blank solution was also prepared. The intensity of the

different standard solutions was measured with a flame photometer (Systronics-128)

using a Na-filter. The intensity of the sodium in the unknown sample was measured in a

similar manner by taking 5 ml sample water in 50 ml volumetric flasks and then diluted

it up to the mark.

4.3.8 Estimation of Potassium [Flame photometric Method (APHA 1998)]

Principle: Trace amounts of potassium can be determined in either a direct reading or

internal standard type of flame photometer at a wavelength of 766.5 nm. Because

much of the information pertaining to sodium applies equally to the potassium

determination, carefully study the entire discussion dealing with the flame photometric

determination of sodium before making a potassium determination.


[50]

Reagents:

A. Double distilled water

B. Standard stock potassium solution

C. Intermediate standard potassium solution (100 mg/l)

Procedure: Different standard potassium solutions (for calibration curve) of following

strength (2, 4, 6, 8, and 10 mg/l) were prepared from the intermediate standard

potassium solution. A blank solution was also prepared. Intensity of the different

standard solutions was measured with a flame photometer (Systronics-128) with a K-

filter. The sample water was analyzed in the same procedure.

4.3.9 Estimation of Chloride (Titrimetric Method (APHA 1998)]

Principle: In a natural or slightly alkaline medium K2CrO4 can indicate the end point in

chloride titration. AgCl is precipitated quantitatively before red silver chromate is

formed.

Reagents:

A. 0.0141 (N) AgNO3 (Silver nitrate)

B. K2CrO4 (Potassium Chromate) indicator

Procedure: 5 ml. samples was taken in a conical flask, then 2-3 drops of K2CrO4

indicator was added to it and solution was titrated against 0.0141 (N) AgNO3. The end

point was marked by a brick red precipitate. The titrant volume was noted and the

chloride content was calculated.

Calculation:

(mg) Cl–/l = ��

!Where, V = Volume of titrate, ml; N = Normality of titrant, ml; S = Volume of Sample,

ml

4.3.10 Estimation of Sulfate [Turbidimetric Method (APHA 1998)]

Principle: Sulphate ion (SO42-) is precipitated in an acetic acid medium with barium

chloride (BaCl2) so as to form barium sulphate (BaSO4) crystals of uniform size. Light


[51]

absorbance of the BaSO4 suspension is measured by a photometer and the SO42-

concentration is determined by comparison of the reading with a standard curve.

Reagents:

A. Conditioning reagent

B. Barium chloride

C. Standard sulphate solution

Procedure: Take 100 ml of clear sample (not containing more than 40 of SO42–) or a

suitable aliquot diluted to 100 ml in a 250 ml conical flask. Add 5.0 ml of conditioning

reagent to it. Care should be taken not to add the conditioning reagent in all the samples

simultaneously. This is to be added to each sample just prior to the further processing.

Stir the sample on a magnetic stirrer and during stirring; add a spoonful of BaCl2

crystals. Stir only for 1 minute after addition of BaCl2. After the stirring is over, take

the optical density reading on a spectrophotometer at 420nm, exactly after 4 minutes.

Find out the concentration of sulphate from the standard curve was found out. Standard

curve was prepared employing the same procedure described above, for the sample

from 0.0 to 40.0 at the interval of 5. Calculation of sample concentration was made

from the equation Y= 85.985x

4.3.11 Estimation of Phosphate [Spectrophotometric Method (APHA 1998)]

Principle: Molybdophosphoric acid is formed and reduced by stannous chloride to

intensely coloured molybdenum blue .This method is more sensitive than others and

makes feasible measurements down to 7 µg P/l by use of increased light path length.

Below 100 µg P/l an extraction step may increase reliability and lessen interference.

Regents:

A. Stannous chloride solution (2.5%)

B. Ammonium molybdate solution (2.5%)

C. Conc. H2SO4 (Sulfuric acid)

D. Standard Phosphate Solution (10 mg/l)


[52]

Procedure: Different standard solutions of following strength were prepared (for

calibration carves) from the standard phosphate solution (10): 0.2, 0.4, 0.6, 0.8, and 1.0.

A blank solution was also prepared. To each volumetric flask, 4 ml ammonium

molybdate solution and 2 - 4 drops of stannous chloride solution were added, a blue

colour appeared, volume diluted up to the mark with distilled water and absorbance was

measured at 690 nm in spectrophotometer (Systronics-169).Sample water was also

analyzed in the same way and concentration was made from the equation Y= 29.021x

4.3.12 Estimation of Nitrate Nitrogen [Spectrophotometric Method (APHA 1998)]

Principle: Brucine is a naturally occurring complex organic compound (hepatocyclic

alkaloid). It reacts with nitrates under acidic conditions at an elevated temperature to

produce a yellow colour. Such solution obey the Beer’s law only at low nitrate nitrogen

concentration of 0.1-1 mg/l. the intencity of the colour developed is a function of both

time and temperature, therefore these two factors must be carefully fixed during

estimation to obtain corrected results. The presence of chloride in water does not

interfere in this method.

Regents:

A. Standard Nitrate Solution (10 mg/l)

B. Brucine Sulfanilic acid

C. H2SO4 acid reagent

Procedure: Different standard solutions of following strength were prepared (for

calibration curve) from the standard nitrate solution (10 mg/l): 0.5, 1.5, 2.5, 3.5, 5.0,

7.5, and 10.0 mg/l. 2ml of each standard solution was taken in corresponding beaker,

1ml. of brucine sulfanilic acid and 10 ml of H2SO4 acid was taken in another beaker

and then the contents of both the beaker was mixed for about 4-5 times. The beakers

were then kept in cold dark place for 10 min. The 10 ml of distilled water was added to

each beaker and again were kept in the dark for 20-30 minutes. The absorbance was

measured at 410 nm (Systronics, 169). Calculation of sample concentration was made

from the equation Y= 10.506x


[53]

4.3.13 Estimation of Silica [Spectrophotometric Method (APHA 1998)]

Principle: Ammonium molybdate at low pH reacts with silica and any phosphate

present to produce hetropoly acids giving a yellow colour. Oxalic acid is added to

destroy the molybdo phosphoric acid. The intensity of the colour can be measured at

410 nm.

Reagents:

A. Ammonium molybdate regent.

B. Oxalic acid solution.

C. Hydrochloric acid (HCl: Water) =1:1

D. Standard Silica solution.

Procedure: Take 100 ml of clear sample or a suitable aliquot diluted to 100 ml in a 250

ml conical flask. Add 1.0 ml of 1:1 HCl and 2 ml ammonium molybdate solution to it

and shake well. 5-10 minutes 2 ml Oxalic acid was added and mixes thoroughly. Then

the O.D. reading was taken after 2 minutes but before 20 minutes in spectrophotometer

at 410 nm. (Systronics, 169). Standard curve was prepared employing the same

procedure described above, for the sample from 0.0 to 10.0 mg/l at the interval of 2

minutes. Calculation of sample concentration was made from the equation Y=45.273x

4.4 Collection, preparation and analysis of sediment samples

The river bottom sediment samples were collected from the fifteen different

sites (S1- Dishergarh, S3- Ramghat, S4- Chinakuri, S6- Dihika, S9- Narayankuri, S10-

Mejhiaghat, S11- Madanpur, S12- Baska, S14- Ashishnagar, S17- Majhermana, S18-

Dhobighat, S19- Silampur, S22- Gohogram, S25-Sadarghat, and S27-Palla Road) to

measure the metal contamination in the river Damodar. The sediment samples were

collected using stainless steel dagger and were immediately kept in air tight plastic

bags. In laboratory conditions, the sediment samples were air dried, crushed and sieved

through 10 mm mesh for further analysis. The sediment samples were collected from

(0-15 cm depth) and were kept immediately into plastic bags. In laboratory conditions,

the sediment samples were air dried, crushed and sieved through 2 mm mesh for further

analysis. The wetted sediment samples were spread out on the large sheets of brown

paper to become air dry, the large lumps were broken up for quick results. When air


[54]

dried, the main samples were grinded well by a mortar pestle to crush the aggregate

particles of air dried sediments and then the sediment samples were sieved and kept for

further metal analysis.

4.4.1 Metal Speciation in BCR Sequential Extraction Process

Metal fractions were estimated by sequential extraction process as per BCR

(Community Bureau of Referance) optimized three step sequential extraction procedure

(modified by Rauret et al. 1990). Extraction protocol is summarized in Table 4. The

extractant and digested solutions were diluted with double distilled water to the desired

dilution factor. Metal concentrations in extract and digests were determined by atomic

absorption spectrophotometer (GBC – Avanta).

Table 4: Extraction protocol (BCR)

No Extrantant used Fraction Nominal target phase

Experimentalcondition(s)

1 40 ml of 0.11 mol/l acetic acid solution

Exchangeable and soluble

Soil solution, exchangeable cations, carbonates

Room temperature, 12 hr constant shaking.

2

40ml of 0.5 mol/l hydroxylamine hydrochloridesolution at pH 2

Reducible Iron and manganese oxyhydroxide

Room temperature, 12 hr constant shaking.

3

10 ml of 30% w/v H2O250 ml of 1 mol/l of ammonium acetate at pH 2

Oxidisable Organic matter and sulfides

Room temperature 1 h, occasional agitation + evaporation at 85 º C, reduce to moist residue.Room temperature, 12 hr constant shaking.

4Aqua regia (1:3 v/v of Conc. HNO3 + HCl).

Residual Unextractable phase Digested in microwave (8 min in 600 Watt).


[55]

4.4.2 Estimation of Heavy Metals

Principle: The water sample was digested for determining the heavy metals. After

digestion the digested sample was measured by Atomic Absorbance Spectrophotometer

(AAS - GBC, Avanta).

Requisitions:

1. Perchloric acid (HClO4)

2. Nitric acid (HNO3)

Procedure: Heavy metals effluents/water samples were determined in atomic

adsorption spectrophotometer (AAS). 500 ml of water samples were taken in a conical

flask, and was placed on hot oven to reduce the volume, almost evaporates to dryness.

Then 10 ml of distilled water was added into the conical flask to transferred the

solution in closed tephlon containers, and digested in micro-oven with a mixture (4:1)

of concentrated HNO3 and HClO4 (Buchaure 1973) for 8 min at 600W . After the

containers were cooled, double distilled water was added into the mixture. The

suspension was filtered with Whatman 42 filter paper and the filtrate volume was

making up to 50 ml.

The filtered solution obtained after digestion were analysed for Iron (Fe), Cadmium

(Cd), Lead (Pb) and Manganese.

4.4.3 Infrared spectroscopic analysis of Bottom Sediments: The Fourier transform

infrared (FTIR) spectra of river sediment were recorded with Fourier transform infrared

spectrophotometer (PERKIN-ELMER, Model-RX1, spectrometer, USA). The KBr

pressed-disc technique is used in this study for preparing a solid sample for routine

scanning of the spectra in the in the range of 400-4000 cm_1.

4.5 Statistical analysis

4.5.1 Descriptive statistical analysis: Parametric statistical methods were used to

compute the central tendency (arithmetic mean; Eq. 1), dispersion (standard deviation;

Eq. 2) and coefficient of variation (Eq. 3) for 17 physicochemical parameters (pH, EC,

TDS, Ca2+, Mg2+, Na+, K+, HCO3-,Cl-,SO4

2-,NO3-, PO4

3-, Fe, Cd, Mn, Pb and H4SiO4) of

27 river water samples, using XL Stat (Version 11.0). This reflects a significant

influence towards the hydrogeochemical conditions.


[56]

"#$%&�'%$(��')*��+,��-.�/ � (1)

where (+,) random variable, n is total number of observations.

�%)*0)#0�0'1$)%$2*��3��45 6.�7.,8²9: ��;��

where <= is degree of freedom.

>2'??$($'*%�2?�1)#$)%$2*��>�� = @., (3)

>2##')%$2*�2?�(2'??$($'*%��#� = A6.�7.86B�7BC�8

/�@��@� (4)

Where D� is other random variable, E� is standard deviation of +� and E� is standard

deviation of DC�.4.5.2 Pearson Correlation coefficient analysis: A correlation analysis is a bivariate

method applied to describe the degree of relation between two hydrochemical

parameters. The result of the correlation analysis is considered in the subsequent

interpretation. A high correlation coefficient (near + 1 or -1) means a good relationship

between two variables and its value around zero means no relationship between them at

a significant level of p < 0.05. More precisely, it can be said that parameters showing r

> 0.7 are considered to be strongly correlated whereas r between 0.5 and 0.7 shows

moderate correlation.

4.5.3 Multivariate statistical analysis: The obtained matrix of hydrogeochemical data

was subjected to multivariate analytical technique. Factor analysis (FA) also known as

principle component analysis (PCA) is an efficient ways of displaying complex

relationships among many variables and their roles (Dalton and Upchurch 1978). Such

analyses were performed using the Exel Stat software package (Version 11.0). The data

have been standardized and presented using the standard statistical procedures (Usunoff

and Guzman 1989). Factor analysis (FA) based on a varimax rotation technique is used

for this study as a statistical method of discussing variables and identifying the

pollution sources by extracting minimum acceptable eigenvalue greater than 1. With

the help of linear combinations, an originally large number of variables are reduced to a


[57]

few factors. The factors can be interpreted in terms of new variables. Factor analysis

also aims to explain observed relation between numerous variables in term of simpler

relations. It is also a way to classifying manifestation of variables.

The factor model used is expressed as:

�F GH)I#?#�I�

JK�where fr is the rth common factor, p is the specified number of factors, ‘‘j’’ is the

random variation unique to the original variable Xj, aji is the loading of the Jth variate

on the rth factor. It corresponds to the loading or weights on principal components.

Principal component approach was started by extracting eigenvalues and eigenvectors

of the correlation matrix and then discarding the less important of these (Davis 1986).

The eigenvectors are then transformed to the factors of the data set. The number of

variables retained in the factors or communalities is obtained by squaring the elements

in the factor matrix and summing the total within each variable. The magnitude of

communalities is dependent upon the number of factors retained.

4.6 GIS Methodology

4.6.1 Supervised classification: IRS-P6 LISS-IV satellite image of January 7, 2011 was

taken as a base image for the classification. A standard technique is adopted for

georeferencing the image using PCI Geomatica V10.1 software. Then georeferenced

image was reprojected to UTM projection. UTM projection was done to minimize the

map distortion and to activate the grid option. After Subsetting and clipping of the

Damodar river supervised classification was run by using Maximum likelihood with

null class algorithm. Post Classification Analysis is done by merging classes and by

masking and unmasking methods after each field survey.

4.6.2 Digital Elevation Model (DEM): DEM is generated on the basis of sampling

points, stored as a point layer along with attributes of physicochemical parameters.

DEM is generated by using VEDIMINT algorithm in the Geomatica V.10.1 software.

The output DEM is represented as a zonation map of the said parameter. The algorithm

consist of three major steps plus and optical step for processing 2D features. In the first

step, input vector points (concentration with respect to different locations) are re-


[58]

projected to the raster coordinates and burned into the raster buffer, with the elevations

generated due to different concentration of the said parameter interpolated linearly

between vector nodes. 2D layers are ignored in this stage. If multiple elevation values

are scanned into a single pixel, the maximum value is assigned the pixel, and the pixel

is marked as a cliff. In the second step, the elevation at each DEM pixel is interpolated

from the source elevation data. The interpolation process is based on an algorithm

called Distance Transform. Interpolation is made between the source elevations and

elevations at equal-distance points from source locations. If 2D vector layers are

present, they are scan converted into a flag buffer during the optional step. The 2D

features are also initialized to prepare for use in the smoothing stage. In step 3, a finite

difference method is used to iteratively smooth the DEM grid. The algorithm uses over

relaxation technique to accelerate the convergence. During the iterations, the source

elevation values are never changed, while the interpolated values are updated based on

the neighborhood values.

RESULTS AND DISCUSSION

[59]

5.0 RESULTS AND DISCUSSION

n this section, geochemical characteristics of the Damodar river water and the

major controls that lead to evolution of hydrogeochemistry are discussed. Apart

from this, river water samples are compared with WHO (2006) standards, FAO

irrigation standards (Pescod 1992) and Indian standard for irrigation (IS 11624: 1986)

in order to assess drinking water and irrigation water suitability respectively.

Secondly, river bottom sediment geochemistries in the tune of spatio-temporal

variation of heavy metals, their partitioning and distribution coefficient along with

various risk assessment indices are discussed in detail. Lastly a spatial modeling has

been undertaken out in order to demarcate various probabilistic

uncontaminated/contaminated zones along the studied stretch of river course on the

basis of geoaccumulation index and pollution load index. Spatio-temporal variation of

hydrogeochemistry and sediment geochemistry has been discussed on the basis of

calculated average database on consecutive three years i.e. 2007, 2008 and 2009

analytical results.

5.1 Computation of ion balance and analytical precision

Total dissolved cation (TZ+) and anion (TZ�) charges in the Damodar river

waters varied from 14.725 to 37.616 meq/l and 15.718 to 37.458 meq/l respectively.

Deviations from electro neutrality are within 5% deviation for 92.593% of the

samples and within 10% deviation for 7.407% of the samples. This indicates that the

reliability of the data is sufficient to study the main regional hydrochemical processes

and water types. Most of the river water samples showed good charge balance with

±5.0% error, which is generally considered acceptable because it is very difficult to

analyze all cations and anions (Berner-Kay and Berner 1987; Edmond et al. 1995;

Huh et al. 1998). Most of the river water samples showed a charge balance mainly

with positive-charge excess except for a few samples with some negative-charge

deficit. The overall study of ion balance shows that low values of charge balance

errors of the analytical data demonstrate that the accuracy of the analysis is within the

acceptable range.

5.2 Spatio-temporal variations in hydrochemistry

I


[60]

Rivers and streams have highly heterogeneous spatial variability along with

temporal changes in hydrogeochemistry. In this section distribution of various cations

and anions along the studied stretch of the river is taken into consideration.

Descriptive statistical analysis of physico-chemical parameters is represented in Table

5.1. Year wise and season-wise concentrations of each physico-chemical parameters

are represented in Annexure I to XVII.

pH is an important parameter which determines the suitability of water for

various purposes. The pH of water is affected not only by the reaction of carbon

dioxide but also by organic and inorganic solutes present. Any type alteration in water

pH is accompanied by the change in other physicochemical parameters. High pH of

the river water may result in the reduction of heavy metal toxicity (Liu et al. 2003).

Water having pH beyond the normal range may cause a nutritional imbalance. The

measured values of pH in the river Damodar ranged from 7.00 to 8.94 during

premonsoon season; 7.00 to 8.71 during monsoon season and 7.00 to 8.73 during

postmonsoon season with a mean of 8.02±0.21, 7.81±0.26 and 7.90±0.23

respectively.

So the pH value of the river water in the study area is neutral to alkaline in

nature. The increase of pH in the agriculture dominated downstream area may be due

to the contribution from agricultural run-off. Small local differences were observed

with no clear seasonal variations at all the sites of the study area.

Electrical conductivity (EC) is directly related to the concentration of ionized

substance in the water and may also be related to problems of excessive hardness

and/or other mineral contamination. The high EC indicates a larger quantity of

dissolved mineral salts and making it unsuitable for drinking (Srivastava et al. 1996).

The hydro-chemical study (Benerjee and Gupta 2010) of EC indicates an increase in

concentration of major ions in the non-monsoon seasons.

The measured values of EC in the river Damodar ranged from 180.0 to 650.00

µS/cm during premonsoon season with a mean of 312.22±142.43 µS/cm (CV%

45.619); 110.00 to 450.00 µS/cm during monsoon season with a mean of

214.81±74.54 µS/cm (CV% 34.701) and 180.00 to 710.00 µS/cm during postmonsoon

season with a mean of 285.19±120.24 µS/cm (CV% 42.161) in 2007. In 2008 the


[61]

value ranged from 210.00 to 690.00 µS/cm during premonsoon season with a mean of

340.74±137.75 µS/cm (CV% 40.428); 100.00 to 540.00mg/l during monsoon season

with a mean of 186.30±85.22 µS/cm (CV% 45.745) and 140.00 to 650.00 µS/cm

during postmonsoon season with a mean of 237.78±105.00 µS/cm (CV% 44.160).

The EC value in 2009 ranged from 200.00 to 710.00 µS/cm during premonsoon

season with a mean of 305.190±125.65 µS/cm (CV% 41.171); 100.00 to 520.00

µS/cm during monsoon season with a mean of 223.70±91.91 µS/cm (CV% 41.085)

and 180.00 to 590.00 µS/cm during postmonsoon season with a mean of

288.89±105.48 µS/cm (CV% 36.512).

Spatial distribution of EC shows that higher concentrations are mainly located

at S3 (Ramghat), S6 (Dihika), S9 (Narayankuri) and S17 (Majher mana). This high

EC value may be corroborated to the discharge of effluents from thermal power plant

industries and coal mining activities; iron and steel industries and confluence of

Tamla nala into the river Damodar at the four sampling location respectively.

Total dissolved solid (TDS) can be attributed as mainly due to addition of ions

by weathering and leaching of non-resistant minerals from rocks (geogenic), although

the influence of anthropogenic component prevailing in the study area . According to

Carrol (1962) there are four classes of water such as fresh (<1,000 mg/l), brackish

(1,000–10,000 mg/l), saline (10,000–100,000 mg/l) and brine (>100,000 mg/l) based

on TDS. According to this classification all the collected river water samples are of

fresh category. The measured values of TDS in the river Damodar ranged from

119.75 to 482.75 mg/l during premonsoon season; 68.52 to 363.48 mg/l during

monsoon season and 95.63 to 482.64 mg/l during postmonsoon season with a mean of

210.492±81.214 mg/l, 138.949±48.365 mg/l and 179.663±62.234 mg/l respectively.

Spatial distribution of TDS follows the same trend like EC. This large

variation in TDS values may be attributed to the variation in geological formations,

hydrological processes and prevailing mining conditions in the region. The higher

values for EC and TDS in the Damodar river water reveal its ionic

strength/concentrations. The average total dissolved solid (TDS) of the present study

(176.37 mg/l) is comparable to the Indian average (159 mg/l) (Subramanian 1983)


[62]

and higher to global average values (115 mg/l) for an aquatic system (Sarin and

Krishnawamy 1984).

The calcium (Ca2+) and magnesium (Mg2+) are an essential nutritional

elements for humans and the optimum concentration of Ca2+ is required to prevent

cardiac disorders and for proper functioning of metabolic processes. Calcium (Ca2+)

as such has no adverse effect on human health and it is one of the important nutrients

required by all organisms. Calcium occurs in water naturally, the main reason for the

abundance of calcium in water is its natural occurrence in the earth's crust.

Magnesium (Mg2+), an essential nutrient for living organisms, is weathered from

minerals like dolomite, magnesite, etc. and subsequently ends up in water, being also

responsible for water hardness. The average calcium concentration in the analysed

river water samples is higher than the magnesium concentration. Calcium and

magnesium in the studied river ranged from 7.452 to 48.942 mg/l and 3.233 to 28.513

mg/l respectively.

Calcium and magnesium were higher both in premonsoon and postmonsoon

season indicating the weathering from primary mineral sources. The higher

contribution of Mg2+ in some areas compared to that of Ca2+ is due to the effect of

ferromagnesium minerals, ion exchange (between Na+ and Ca2+) and precipitation of

CaCO3. Spatial distribution of Ca2+ and Mg2+ showed that maximum concentration of

both the cations are found at S3 (Ramghat), S6 (Dihika), S9 (Narayankuri) and S17

(Majher mana). So apart from geological input anthropogenic input in the form of

coal mining and industrial discharge also plays a dominant role in controlling the

concentration of the said cations in the Damodar river. The average concentration of

calcium (19.620 mg/l) is lower than the Indian average (30 mg/l) (Subramanian 1983)

and comparable to global average values (16 mg/l) for an aquatic system (Sarin and

Krishnawamy 1984).

The sodium (Na+) and potassium (K+) in the aquatic system is generally

derived from the atmospheric deposition, evaporite dissolution and silicate

weathering. In natural water the weathering of Na+ and K+ silicate minerals like albite,

anorthite, orthoclase and microcline are the major possible sources of Na+ and K+. A

higher sodium intake may cause hypertension, congenial heart diseases and kidney


[63]

problems. According to Kelley 1951 and Tijani 1994 sodium concentration in surface

water is important since increase of sodium concentration in waters effects

deterioration of the soil properties reducing the permeability.

Sodium (Na+) and potassium (K+) in the studied river ranged from 4.28 to

50.46 mg/l and 1.21 to 24.882 mg/l respectively. The average sodium concentration

(15.585 mg/l) is comparable to the Indian average (12 mg/l) (Subramanian 1983) and

higher to global average values (4.4 mg/l) for an aquatic system (Sarin and

Krishnawamy 1984).

Sarin et al. 1989 and Singh et al. 2005 reported that the dissolution of Na+/K+

salts developed in the drainage basin due to cycling wetting and drying phases during

high and low flow regimes of the Damodar River. Maximum concentration is located

at S16 (Shyampur) and S17 (Majher mana).

Chloride (Cl–) is present in lower concentrations in common rock types than

any of the other major constituents of natural water. However, high concentration of

Cl– was observed in some areas may result from anthropogenic sources including

agricultural runoff, domestic and industrial wastes. Generally, the chloride

concentration can be used as an indicator for contamination, because chloride in

inland areas essentially originates from surface sources, such as domestic

wastewaters, irrigation runoff flow and fertilizers (Andreasen and Fleck 1997;

Lowrance et al. 1997). Anthropogenic activities contributes high quantities of

chloride, therefore, it indicate sewage contamination.

The chloride value in the studied river ranged from 1.298 to 72.643 mg/l.

Higher concentration of chloride was observed at S16 (Shyampur) and S17 (Majher

mana). Both these sites have densely populated urbanized area. Therefore, municipal

and domestic sewage are the major contributor of Cl– along with industrial discharge.

The overall study represents that the upstream concentration is slightly higher than

downstream area. The average chloride concentration (13.709 mg/l) is comparable to

the Indian average (15 mg/l) (Subramanian 1983) and higher to global average values

(4 mg/l) for an aquatic system (Sarin and Krishnawamy 1984).

Nitrogen in water and wastewater occurs in various forms like nitrates, nitrites

ammonia and organic nitrogen etc. Nitrate nitrogen (NO3–N) is one of the most


[64]

important indicators of pollution and represents the highest oxidized form of nitrogen.

Nitrate contamination of water resources is becoming a serious environmental

problem worldwide (Sakakibara et al. 1994; Fan et al. 1996; Hu et al. 1999).

Nitrate is one of the most important indicators of pollution of water and its

value ranged from 0.035 to 2.833 mg/l during premonsoon season with a mean of

0.824±0.786 mg/l (CV% 2.833); BDL to 3.956 mg/l during monsoon season with a

mean of 0.922±0.818 mg/l (CV% 3.956) and BDL to 2.982 mg/l during postmonsoon

season with a mean of 0.764±0.575 mg/l (CV% 2.982) in 2007. In 2008 the value

ranged from 0.184 to 4.119 mg/l during premonsoon season with a mean of

0.841±0.786 mg/l (CV% 4.119); BDL to 3.846 mg/l during monsoon season with a

mean of 0.751±0.853 mg/l (CV% 3.846) and BDL to 2.445 mg/l during postmonsoon

season with a mean of 0.643±0.566 mg/l (CV% 2.445). The nitrate in 2009 ranged

from 0.068 to 2.742 mg/l during premonsoon season with a mean of 0.646±0.470

mg/l (CV% 2.742); 0.154 to 2.099 mg/l during monsoon season with a mean of

0.860±0.535 mg/l (CV% 2.099) and 0.059 to 2.816 mg/l during postmonsoon season

with a mean of 0.754±0.686 mg/l (CV% 2.816). The sites S2 (Purbanchal), S3

(Ramghat), S6 (Dihika) S9 (Narayankuri) being mix representation of residential and

industrial area and S17 (Majher mana), agriculturally dominated area, are the major

contributor of NO3� in the river water.

Phosphate (PO43�P) is an essential and often limiting nutrient in freshwater

ecosystems; it plays a significant role in many environments due to its role in

eutrophication (Thomas 1973). Several studies related to eutrophication reports the

deteriorating quality of surface waters due to pollution (Bukit 1995; Ekholm et al.

2000). Phosphates in water are obtained from the rocks converting them into its

soluble forms and may also occur, in agricultural runoff, industrial wastes, municipal

sewage. Elevated concentration of inorganic phosphate to the lakes, rivers, bays and

other surface water causes eutrophication. Further, it was found that the PO43–

concentrations were higher in the downstream than that of the upstream of the study

area due to agricultural runoff.

The concentration of phosphate in Damodar river water ranged from 0.015 to

1.155 mg/l during premonsoon season with a mean of 0.229±0.308 mg/l (CV%


[65]

134.2); 0.015 to 1.024 mg/l during monsoon season with a mean of 0.237±0.291 mg/l

(CV% 122.6) and 0.012 to 1.058 mg/l during postmonsoon season with a mean of

0.134±0.237 mg/l (CV% 176.8) in 2007. In 2008 the value ranged from 0.010 to

0.350 mg/l during premonsoon season with a mean of 0.099±0.09 mg/l (CV% 91.7);

0.028 to 1.382 mg/l during monsoon season with a mean of 0.310±0.424 mg/l (CV%

136.8) and 0.017 to 0.424 mg/l during postmonsoon season with a mean of

0.135±0.120 mg/l (CV% 88.8). The phosphate in 2009 ranged from 0.020 to 0.880

mg/l during premonsoon season with a mean of 0.175±0.252 mg/l (CV% 144.0);

0.034 to 1.250 mg/l during monsoon season with a mean of 0.236±0.357 mg/l (CV%

151.7) and 0.010 to 1.090 mg/l during postmonsoon season with a mean of

1.152±0.275 mg/l (CV% 180.7).

Spatial distribution indicated a gradual increase in concentration from

upstream area, S1 (Dishergarh) to S13 (Pursa), thereafter it increased rapidly at S16

(Shyampur) to S19 (Silampur). Beyond this location a gradual decreasing trend was

noticed. Densely populated urbanized area due to presence industrial complexes in the

upstream side is the major contributor of PO43– in the river system by means of

discharging domestic and municipal waste. Rapid shooting up of PO43– concentration

within Shyampur to Silampurpur might be corroborated to the contribution from

agricultural runoff.

Higher concentration of sulphate (SO42–) in drinking water may cause the

respiratory problems. The anthropogenic source of sulphate in water is the discharge

of industrial effluent. Acid mine drainage is another source of sulphate ions to water

environment. A number of crops show sensitivity to very high concentrations of

sulfates in irrigation water.

Sulphates showed remarkable seasonal variation at the most sampling sites

with higher concentrations being recorded during premonsoon season. The sulphate

value in the studied river ranged from 5.352 to 84.049 mg/l. The observed high values

of sulphate in the river water near the coal mine area S3 (Ramghat) to S6 (Dihika)

may be attributed to the oxidative weathering of pyrites. Majher mana (S17) also

showed higher concentration of sulphate. This may be due to contribution from

industrial effluent.


[66]

The measured values of dissolved silica (H4SiO4) in the river water ranged

from 7.368 to 27.511 mg/l during premonsoon season with a mean of 14.194±4.263

mg/l; 1.363 to 17.540 mg/l during monsoon season with a mean of 9.268±4.292 mg/l

and 7.354 to 16.751 mg/l during postmonsoon season with a mean of 11.432±2.731

mg/l in 2007. In 2008 the value ranged from 4.087 to 27.434 mg/l during

premonsoon season with a mean of 14.028±5.025 mg/l, 1.174 to 17.936 mg/l during

monsoon season with a mean of 9.635±4.362 mg/l and 3.665 to 20.529 mg/l during

postmonsoon season with a mean of 10.537±4.621 mg/l. The dissolved silica in 2009

ranged from 7.309 to 28.452 mg/l during premonsoon season with a mean of

16.028±6.032 mg/l; 1.030 to 23.4498 mg/l during monsoon season with a mean of

10.160±6.038 mg/l and 4.640 to 25.945 mg/l during postmonsoon season with a mean

of 12.720±4.133 mg/l.

The average concentration of dissolved silica (12.00 mg/l) is higher than the

Indian average (7.00 mg/l) and comparable to global average values (12.00 mg/l) for

an aquatic system (Subramanian 1979). Silica showed a general baseline trend with a

local fluctuation at S8 (Burnpur river side). The study reveals that at some of the

sampling stations concentrations of dissolved silica are higher than chloride and

sulphate.

5.3 Spatio-temporal distribution of heavy metals in the river water

Heavy metals are widespread pollutants of great environmental concern as

they are nondegradable, toxic and persistent in nature (Chopra et al. 2009) and have

toxic effects on living organisms, when they exceed the certain concentrations (Chen

et al. 2007). However, heavy metal concentrations in surface water which are not very

high, dilute and undetectable quantities, their recalcitrance and consequent persistence

in water bodies exhibit toxic characteristics (Atkinson et al. 1998). The cadmium and

lead represent a coherent group of metals both from the metallogenic point of view,

and as contaminants of the environment (Thornton and Webb 1981). Lead is a toxic

heavy metal accumulate in aquatic biomass, they are concentrated and passed up the

food chain to human consumers. Cadmium is of even greater concern because of its

harmful effects on plants, animal and man also. Spatial variation of heavy metals


[67]

along with descriptive statistics on three year (2007, 2008 and 2009) average value is

represented in Table 5.1.

The mean value of lead (Pb) in the river water reached their maximum value

during the premonsoon season (133.73±589.54 µg/l), minimum during the monsoon

season (0.999±1.36 µg/l) while the postmonsoon season is characterised by

intermediate values (22.312±101.226 µg/l). High concentration of Pb was found at

S16 (Shyampur) and S17 (Majher mana) site.

The measured values of manganese (Mn) in the river Damodar ranged from

BDL to 41.69 µg/l during premonsoon season with a mean of 3.467±8.569 µg/l (CV%

247.2); BDL to 34.25 µg/l during monsoon season with a mean of 3.228±8.071 µg/l

(CV% 250.0) and BDL to 7.524 µg/l during postmonsoon season with a mean of

0.910±1.704 µg/l (CV% 187.2) in 2007. In 2008 the value ranged from BDL to 47.52

µg/l during premonsoon season with a mean of 4.383±10.61 µg/l (CV% 242.1); BDL

to 4.961 µg/l during monsoon season with a mean of 1.129±1.484 µg/l (CV% 131.4)

and BDL to 8.410 µg/l during postmonsoon season with a mean of 0.757±1.872 µg/l

(CV% 247.3). The manganese in 2009 ranged from BDL to 9.654 µg/l during

premonsoon season with a mean of 1.164±2.146 µg/l (CV% 184.3); BDL to 15.75

µg/l during monsoon season with a mean of 1.389±3.075 µg/l (CV% 221.4) and BDL

to 6.321 µg/l during postmonsoon season with a mean of 0.667±1.323 µg/l (CV%

198.2). S3 (Ramghat), S4 (Chinakuri) and S6 (Dihika) showed higher concentration

of Mn due to contribution from coal fired thermal power plant, mining industries and

iron and steel industries respectively. S11 (Madanpur), S12 (Baska) and S16

(Shyampur) also contributed higher amount of Mn due to downstream effect and

Tamla nala discharge.

The cadmium (Cd) in the river water reached their maximum value during

premonsoon season (0.800±0.998 µg/l), minimum during the monsoon season

(0.157±0.217 µg/l) while the postmonsoon season is characterised by intermediate

values (0.337±0.432 µg/l). Gradual increment in concentration was noticed at S3

(Ramghat), S6 (Dihika), S9 (Narayankuri) and S12 (Baska). Thereafter it increased

suddenly at S16 (Shyampur) and reached to base level at downstream.


[68]

The measured values of iron (Fe) in the river Damodar ranged from 0.120 to



(CV 95.43) and 0.034 to 2.475 mg/l during postmonsoon season with a mean of

0.533±0.548 mg/l (CV% 102.70) in 2007. In 2008 the value ranged from 0.042 to


100.68); 0.041 to 0.690 mg/l during monsoon season with a mean of 0.334±0.192

mg/l (CV% 57.65) and 0.068 to 3.554 mg/l during postmonsoon season with a mean

of 0.647±0.723 mg/l (CV% 111.73). The iron (Fe) in 2009 ranged from 0.052 to



(CV% 65.60) and 0.148 to 1.987 mg/l during postmonsoon season with a mean of

0.527±0.399 mg/l (CV% 75.74). Pronounced effect of industrial effluent arising out

from iron and steel industries was noticed at S6 (Dihika) location.

So in summary it can be said that the highest concentrations of most of the

heavy metals (Fe, Cd and Pb) in river Damodar may be due to the discharge of heavy

metal loaded industrial wastewater. The results of the present study indicate a

remarkable increase in pollution along with heavy metals concentration at Chinakuri

of river Damodar due to the increased loading of the indiscriminate and long term

disposal of effluents from thermal power plant and mining activities. Sampling

covered both monsoon and non-monsoon seasons and it was observed that generally

the water quality in monsoon season was slightly better than that in non-monsoon

seasons due to flushing effect. The mean values of metal concentrations can be

arranged in the order Fe > Mn > Pb > Cd. The values for most of the metals in the

river water of the downstream region were found to be much lower than those of the

upstream region.

Higher concentrations of certain physicochemical parameters in the water at the

discharge points in river Damodar in the upper stretch is largely due to the

untreated and/or partially treated waste inputs of municipal and industrial effluents.

The distribution patterns of heavy metals in the river indicate that the

continuous discharge of sewage and industrial effluents into the river will continue to


[69]

increase the magnitude of metal pollution in the river to intolerable limits, and this

may have severe impact on aquatic plants and other organisms in the rivers. Human

settlement and urbanization along the banks of the river are also increased rapidly

demanding more and more water for their activities. High concentration of metals in

rainy season in some of the study areas in the water of the river Damodar could be

due to runoff coming from areas like contaminated sites, open dumping waste sites,

agricultural field and city drains/industrial discharge.

5.4 Statistical analysis

5.4.1 Descriptive data analysis: Descriptive data analysis (mean, standard deviation

(SD), standard error mean (SEM), coefficient of variance (CV%), maximum and

minimum concentrations of the river water including element concentrations was

applied and accompanied by correlation analysis to determine relationships among

different physiochemical parameters. For the purpose of comparison between the

degrees of variability of each component along the study area, CV% was calculated.

The coefficient of variation of electrical conductivity (EC) (CV%) and total dissolved

solids (TDS) (CV%) shows much fluctuation in the samples of the analysed river, and

the higher values indicate that the analysed river in this study area is extremely

variable due to the flow of the river through the variable topography and geology

along with effluent discharge. Among the heavy metals Pb (average value CV%

307.77) shows much fluctuation in the samples of the analysed river, and the higher

values indicate that the river in this study area is extremely variable due to the

wastewater discharged from industrial activities. The variability (mean value CV%)

of heavy metals in the river water are in the order of Pb (307.77) > Mn (212.12) > Cd

(182.97) > Fe (89.12). Results have shown that phosphate content of river water have

the highest degree of variation (CV% 136.37) among other constituents. This pointed

out that phosphate content is one of the most subjected to variations along the study

area. Although none of the sampling sites of was consistent in terms of coefficient of

variation except pH of the river water.

5.4.2 Pearson correlation coefficient: Correlation analysis was done between heavy

metal and various physicochemical properties in river water samples to assess

possible similar sources. Study also shows that EC and TDS (r= 0.999) bears a


[70]

significant (P<0.05) positive correlation in samples because conductivity increases

with the concentration of all dissolved constituents. There was no positive correlation

observed in the Cd (r= �0.231), Pb (r= �0.283), and Fe (r= �0.288) concentrations

with the pH of the water. Chloride ion bears significant (P<0.05) positive correlation

with EC (r = 0.817), TDS (r= 0.821) Pb (r =0.934), Cd (r = 0.840) and Fe (r =0.504)

inferred common source like sewage and industrial discharge. Fe showed positive

correlation with Cl� (r= 0.504) in the analysed river water, indicating leaching of steel

and alloys from an anthropogenic source. The matrix shows the sulphate (SO42–) bears

positive correlation with EC (r= 0.840) which may be explained by similar pattern of

distribution due to their interdependence/influence on each other. The correlation

coefficients between the major ions in the river water showed positive correlation

between Na+–Mg2+ (r= 0.553), K+–Na+ (r= 0.825), Cl–– SO42– (r= 0.775), K+–Cl– (r=

0.713), Mg2+–Cl– (r= 0.627), Mg2+–SO42– (r= 0.564), Mg2+–NO3

– (r= 0.683), and

Na+–SO42– (r= 0.714), indicating the predominance of chemical weathering along

with leaching of secondary salts. The correlation between Cl– and Na+ confirmed by

the correlation coefficient [significant (P<0.05)] Na+ – Cl– (r = 0.860) showing a

strong geochemical link between these two elements.

HCO3� exhibited a positive correlation with Mn (r= 0.436) which could

indicate the same or similar sources i.e. mainly geogenic sources suggesting influx of

these ions by the dissolution from rocks. EC has positive significant (P<0.05)

correlation with Ca2+ (r= 0.834), Mg2+ (r= 0.757), Na+ (r= 0.735), K+ (r= 0.826). The

study suggested that HCO3�, SO4

2–, Ca2+ and Mg2+ have positive correlations with

each other, and their significant contributions to the hydrochemistry are shown by

their correlations with EC. Metals have positive correlation [significant (P<0.05] with

electrical conductivity [EC�Pb (r= 0.691), EC�Cd (r= 0.860) and EC�Fe (r= 0.816)].

It can be deduced that metal concentrations influences electrical conductivity. Almost

all analyzed metals showed good correlation with conductivity because conductivity

increases with dissolution of metals through ion exchange or oxidation-reduction

reaction in the water system. Study reveals that positive correlations exist between

elemental pairs Pb�Cd (r= 0.729), Cd�Fe (r= 0.728), Pb�Fe (r= 0.386) could indicate

the same or similar source input likely resulting from industrial waste discharges. It is

can, thus, be inferred that Pb, Fe, Cd were introduced into the water column from a


[71]

common source. The results of the correlation matrix analysis demonstrate that the

metals in the studied river exhibit different degree of correlation. Understanding such

relationships may help to clarify sources and transport of individual metals within the

riverine environment.

5.5 Multivariate statistical analysis

Factor analysis (PCA extraction) was applied to identify different sources of

controlling hydrogeochemistry of the Damodar river. Eigenvalue gives a measure of

the significance of the factor, and the factors with the highest eigenvalues are the most

significant. According to Liu et al. 2003, factor loading is classified as “strong”,

“moderate”, and “weak”, corresponding to absolute loading values of >0.75, 0.75–

0.50, and 0.50–0.30, respectively. Average component loadings of principal

components for all the seasons in all the studied year has been represented in Table

5.2. The results of factor analysis performed on heavy metals and some

physicochemcal parameters suggested three factors (eigenvalue >1) controlling their

variability in waters of river Damodar. Factor 1 represents strong positive loading of

EC, TDS, Na+, K+, NO3�, SO4

2� and Cl� along with moderate loading of Ca2+ and

Mg2+. Factor 1 also indicates the strong loading of heavy metals such as Fe, Pb and

Cd. Factor 1 accounts for 54.776% of the total variance may be treated as a major

geogenic factor, suggesting influx of these ions by the dissolution from rocks of

granites and granitic gneisses with a secondary contribution from agricultural and

industrial sources. Among geogenic sources it may be attributed to the weathering of

Ca2+–Mg2+–Na+ silicates and cation exchange processes at water-rock interface (Guo

and Wang 2004). The high loading of SO42–, Cl– and NO3

– may be attributed to the

application of fertilizers to agricultural field and anthropogenic input from domestic

and industrial discharge in the study area. Sulphate, Cl– along with corresponding

cations Ca2+, Mg2+ and Na+ are, to a large extent, responsible for the conductivity of

the river water. K+ is the least dominant cation in the analyzed river water samples.

Factor 2 accounts for 10.812% variance (with a cumulative variance of 65.588%) in

the data matrix and has strong positive loading of variable HCO3– along with

moderate loading of Mn and weak loading of PO43–. The pH value depends on the

CO2–CO32––HCO3

– equilibrium and the pH of the water indicates the form in which

CO2 is present. The presence of carbonic acid is indicated when pH is 4.5, bicarbonate


[72]

is present when the pH is in the range 4.5–8.2 and carbonate exist at pH 8.2. The pH

range (7.24–8.25) of the analysed river water samples indicates the influence of

bicarbonate. High loadings on HCO3– can be attributed to the dissolution of

carbonates and/or silicate minerals by carbonic acid. Factor 3 is less significant,

accounts for only 7.957% of the total variance and mainly represented by the negative

loading of pH and positive loading of H4SiO4. Silica concentrations may reflect some

silicate mineral weathering in the river catchments. The silica concentration is notably

higher in all the seasons (premonsoon, monsoon and postmonsoon season) indicates

that silica minerals within the rock bearing minerals are susceptible to weathering

condition. The overall high pH throughout the season also supports the high rate of

silicate weathering in the studied river.

5.6 Hydrochemistry of the river Damodar – role of weathering and

anthropogenic input on dissolved load

The major ion chemistry of the river water is a cumulative reflection of

catchment geology, weathering the river erosional processes as well as anthropogenic

inputs. The higher values for EC and TDS in the river water reveal its ionic

strength/concentrations. The relative importance of each chemical weathering process

in natural water varies with the weathering materials and the conditions of the

weathering environment. Present investigation demonstrates that rock weathering as

major process for liberating ions in the river and also responsible for controlling water

chemistry along with contribution from anthropogenic sources (i.e. industrial effluents

discharge).

The cation chemistry of the river water is dominated by Ca2+ and Mg2+

comprising 38.672% and 30.024% of total cation balance in their equivalent weight.

Na+ and K+ concentrations represent on an average to 26.060%, and 5.244% of the

total cations (TZ+), respectively, and the order of abundance is Ca2+ > Mg2+ >

Na+ > K+. On an equivalent basis, HCO3– accounts for 67.759% of the total anions.

HCO3– is followed by SO4

2–, and Cl– which accounts for 17.903% and 14.518% of the

total anions respectively. The high concentration of HCO3– in river water indicates

that intense chemical weathering takes place in the catchment area.


[73]

5.6.1 Ionic ratio – an indicative of weathering and ion exchange input: Ionic ratio

discussed in this section is represented in Table 5.3. Higher value of

Ca2++Mg2+/Na++K+ (>1) in all the seasons can be corresponded with weathering of

Ca2+–Mg2+ silicates chiefly from Ca2+–plagioclase, amphiboles, pyroxenes and biotite

present in parent rocks and sediment materials. For rivers with prevailing carbonate

weathering in their basins, a characteristic feature is the predominance of Ca2+ and

Mg2+ cations and high (Ca2++Mg2+)/(Na++K+) ratios. Most of the world’s rivers and

the major Indian rivers have high (Ca2++Mg2+)/(Na++K+) ratios, suggesting the

weathering of carbonate rocks in the catchment area (Subramanian 1979). The

average milliequivalent ratio of (Ca2++Mg2+)/(Na++K+) for the Damodar River is

nearly equal to the Indian average (2.5) and suggests that the chemical composition of

the Damodar river water is controlled by silicate weathering but carbonate weathering

also. Further, the average (HCO3–)C/ (HCO3

–)Si equivalent ratio of 1.5 reflects the

combined influence of weathering of carbonates and silicate rocks. The Ca2+/Mg2+

ratio of 1 indicated dissolution of dolomite and of >2 reflected an effect of silicate

minerals on the water chemistry; it also suggested calcite dissolution for Ca2+–Mg2+

concentration in water (May and Loucks 1995). Majority of the river water samples

have Ca2+/Mg2+ ratio between 1 and <2, indicating dolomite dissolution responsible

for Ca2+–Mg2+ contribution. The river water in some areas has >2 Ca2+/Mg2+ ratio

where calcite dissolution and effect of silicate minerals were evident for the Ca2+–

Mg2+ content. High Ca2+/SO42– ratio (>1) indicating that H2SO4 does not replace

H2CO3 as source of protons require for rock weathering (Stallard and Edmond 1983).

Except some of the sites majority of the river water shows high Ca2+/SO42– ratio (>1).

High Ca2+/SO42– ratio (>1) and low HCO3

–/HCO3–+SO4

2– ratio indicates oxidative

weathering of minerals such as pyrite (FeS2), gypsum (CaSO4), and anhydrite

(CaSO4) that occur in sandstone and shale overlying the coal seams and excavated

overburden materials in the upstream of the study area. Na+/Cl� equivalent ratio will

be 1 if halite dissolution is responsible for sodium dominance in water and >1 if Na+

is released from silicate weathering process (Meybeck 1987). The Na+/Cl� ratio is >1

in some samples indicating that silicate weathering was the primary process

responsible for the release of Na+ into the river water (Stallard and Edmond 1983;

Pophare and Dewalkar 2007). Some other samples of the river water have the Na�/Cl�


[74]

ratio is <1 where the ion exchange and/or evaporation were dominant process

resulting in the addition of Cl� in the river water.

5.6.2 Ionic ratio- an indicative of anthropogenic input: The study reveals that the

dominance of weak acids (HCO3–) over strong acids (SO4

2– and Cl–) in the river

water. But the studied river shows the reverse situation in some areas suggesting the

dominance of anthropogenic influences (urban and industrial effluents discharge) over

natural phenomena. Taken together these arrays of weathering indicate that the

Damodar is a chemically active river with a dominance of continental weathering and

secondary inputs of anthropogenic and atmospheric sources.

5.7 Scatter diagram representing chemical weathering and ion exchange

processes of the Damodar river

The relationships between the measured hydrochemical parameters may help

to identify the main processes contributing to the river water chemistry.

Hydrochemical characteristics of ions in the river water were studied using 1:1

equiline diagrams.

5.7.1 Ionic relationship between (Ca2++Mg2+) versus (HCO3–+SO4

2–): The plot of

(Ca2++Mg2+) versus (HCO3–+SO4

2–) in equivalent units shows that most of the

(Ca2++Mg2+) data points lies below the 1 : 1 trend line, although some points approach

the theoretical 1 : 1 trend, reflecting the requirement of cations from weathering of

silicate rocks. In Ca2++Mg2+ versus SO42–+HCO3

– scatter diagram, the points falling

along the 1 : 1 trend line (Ca2++Mg2+ = SO42–+HCO3

–) suggest that these ions have

resulted from weathering of carbonates and silicates (Datta et al. 1996; Datta and

Tyagi 1996; Rajmohan and Elango 2004). The points of the diagram, which are

placed in the Ca2++Mg2+ over SO42–+HCO3

– side, indicate that carbonate weathering

is the dominant hydro-geochemical process, while those placed below the 1:1 line are

indicative of silicate weathering. Most of the points in this study fall in the SO42– +

HCO3– side (but not far below this 1:1 line), reflecting the requirement of cations

from weathering of silicate rocks suggesting that silicate weathering is the major

hydrogeochemical process operating in this part of river Damodar, irrespective of the

season with minor contribution of carbonate weathering (Fig. 3.1 a–c) also. The study

in general reveals that excess of (HCO3– + SO4

2–) over (Ca2++Mg2+) suggests


[75]

significant contribution from non-carbonate source and demanding the required

portion of the (HCO3–+ SO4

2–) to be balanced by the alkalis (Na+ + K+).

5.7.2 Ionic relationship between (Ca2++Mg2+)/HCO3–: The plot of (Ca2++Mg2+)

versus HCO3– marks the upper limit of HCO3

– input from weathering of carbonates

(Stallard and Edmond 1983). This plot for Damodar samples shows that except

monsoon both in pre and postmonsoon most of the samples have (Ca2++Mg2+) points

fall above the 1:1 trend. This can be produced by an extra source of Ca2+ and Mg2+

and is balanced by the anions SO42– and Cl– (Fig. 3. 1 d–f).

5.7.3 Ionic relationship between Ca2++Mg2+ versus TZ+: The scatter plot of

(Ca2++Mg2+) versus total cations (TZ+) variation plot shows that the plotted points of

river water samples fall much below the equiline and the departure being more

pronounced at higher concentration, reflecting an increasing contribution of Na+ and

K+ with increasing dissolved solids (Fig. 3.2 a – c).

5.7.4 Ionic relationship between Na+ versus Cl–: According to Mayback 1987;

Deutsch 1997 the 1:1 relationship between Na+ and Cl– implies halite dissolution,

whereas increased concentration of Na+ than Cl– is typically interpreted as Na+

released from silicate weathering. The Na+ vs Cl– plot of the Damodar river samples

indicates a majority of samples fall along or above the equiline (Fig. 3.2 d–f),

reflecting silicate weathering. Few samples, occupying along and below equiline,

could be due to the fact that halite dissolution was responsible for high Cl– (Elango

and Kannan 2007).

5.7.5 Ionic relationship between Na+ versus Ca2+: The Na+ vs Ca2+ scatter diagram

shows that the sample points are above and below the 1:1 equiline. The samples

below the equiline indicate ion exchange process. Those above the line show silicate

weathering. The Na+ vs Ca2+ scatter diagram of the river Damodar water (Fig. 3.3 a –

c) shows that the data points are remain in both above and below the 1:1 equiline. In

the study area considerable amount of river water samples falling below the 1:

1equiline indicate ion exchange process and those above the1:1 trend line show the

process of silicate weathering.

5.7.6 Ionic relationship between Na++K+ versus TZ+: Weathering of silicate may

play a vital role in controlling the major ions chemistry of the water (Mackenzie and


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Garrells 1965; Rajmohan and Elango 2004) and it can be understood by estimating

the ratio between Na+ + K+ and total cations (TZ+). The cation contribution to river

water by silicate weathering can also be estimated by the (Na++K+)/Total cations

index. The plot for (Na++K+) vs TZ+ of three seasons (Fig. 3.4 d–f). shows relatively

high ionic ratio of (Na++K+)/ TZ+ (0.30 – 0.32) and all samples fall far below the 1:

1equiline, suggesting that the cations in river water might have been derived from

weathering of aluminosilicates (Stallard and Edmond 1983).

5.8 Ternary diagram – an index of weathering

Ternary diagrams, relating Si, alkalinity and SO42– plus Cl–, is one of the

pictorial representation through which relationship between chemistry and geology

can be evaluated (Stallard and Edmond 1983). This ternary plot for Damodar (Fig.

4.1-4.3) shows that most of the plotted points cluster towards the alkalinity apex with

secondary trends towards (SO42–+Cl–) and SiO2. Some samples contain nearly equal

amounts of HCO3– and (SO4

2–+Cl–) indicating inputs from the weathering of pyrites.

Other ternary diagram relating Na++K+, Ca2++Mg2+ and SiO2 can also be used

as an index of weathering of igneous and metamorphic terrain. Fig. 4.4-4.6 shows that

Ca2++Mg2+ and SiO2 make significant contributions towards the cationic balance in

most of the samples, indicating that Ca2++Mg2+ and SiO2 in the water of this

catchment are mainly supplied by chemical weathering of highly weathered gneiss

and granites rich in orthoclase, plagioclase, hornblende, augite, biotite and muscovite.

This observation is in contrary to the earlier observation by Singh and Hasnain 1998.

A general reaction for the weathering of silicate rocks with carbonic acid can

be written as: (Ca2+, Mg2+, Na+, K+) Silicate + H2CO3 = H4SiO4 + HCO3– + Na+ + K+

+ Ca2+ + Mg2+ + Solid product

Taken together these arrays indicate that the Damodar is a chemically active

river with a dominance of continental weathering and secondary inputs of

anthropogenic and atmospheric sources.

5.9 Geochemical relationship and hydrogeochemical facies

The geochemical nature and relationship between dissolved ions in water may

also be evaluated by plotting the analytical value on Piper (Piper 1953) trilinear


[77]

diagram. It has been constructed to provide a summary of cation data (left triangle),

anion data (right triangle) and a composite diamond-shaped field (centre) to visualize

water of different chemistries and origin. The plot of hydrochemical data on diamond-

shaped field of trilinear Piper diagram (Piper 1953) reveals that that the plotted points

of majority of the water samples in all the three years and seasons fall in the field of 1,

2, 3, 4, 5, 7 and 9 (Fig. 5.1-5.3). The piper diagram clearly shows that the river water

is rich in Ca2+, Mg2+ and HCO3�. Except some sites plotted points, of premonsoon,

momsoon and postmonsoon season of the river water samples (97.531%); alkaline

earth (Ca2+ + Mg2+) exceeds alkalis (Na+ + K+) and the plotted points fall in the field

1. The plotted points for 2.469% water samples are falling in the field 2, indicating

dominance of alkalis over alkaline earth. Water samples of the Damodar river

(91.770%) exhibit dominance of weak acids (HCO3�) over strong acids (SO4

2– + Cl– )

and plotted points fall in the field 3. Only 8.230% water samples fall in the field 4

indicating dominance of strong acids (SO42– + Cl– > HCO3

–) over weak acids. The

plotted points of 89.712% water samples fall in the field 5, signify carbonate hardness

(secondary salinity) that exceeds 50%. Only 0.412% water samples fall in the field 7

signifying non-carbonate alkali (primary salinity) exceeds 50%. No water samples

fall in the field 6 and 8. About 9.877% water samples fall in the field 9, indicating

water of an intermediate (mixed) chemical character having no one cation– anion pair

that exceeds 50%. The trilinear diagram reveals that Ca2+–Mg2+– HCO3– (field 5) is

the dominant hydrogeochemical facies in the river water samples. There is no

significant change in the hydrochemical facies noticed during the study period, which

indicates that most of the major ions are natural in origin.

5.10 Mechanisms controlling the river water chemistry

The functional sources of dissolved ions in the Damodar river water is

assessed by plotting the samples according to the Gibb’s plot. Gibbs (1970) has

suggested a diagram in which ratios of dominant anions and cations are plotted

against the values of total dissolved solids. Gibbs diagrams, representing the ratio for

cations [Na+/ (Na++Ca2+)] (Fig. 6.1-6.3) and Cl–/(Cl–+ HCO3–)] (Fig. 6.4-6.6) as a

function of TDS are widely employed to assess the functional sources of dissolved

chemical constituents, such as precipitation-dominance, rock-dominance and

evaporation dominance. Where the sodium and calcium concentration is expressed in


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milliequivalent per liter and total dissolved solid in milligram per liter. The rock–

water interaction dominance field indicates the interaction between rock chemistry

and the chemistry of the river waters. The results from the water analysis were used as

a tool to identify the process and mechanisms affecting the chemistry of river water

from the study area. Present investigation for all the seasons and in all the three years

shows that rock weathering as major process for liberating ions in the river and also

responsible for controlling water chemistry.

5.11 Suitability for drinking, domestic and livestock uses

To assess the suitability for drinking and public health purposes, the

hydro-chemical parameters of the river water of the study area were compared

with the prescribed limit of WHO (2006). Besides the chemical analysis microbial

analysis is very important for the drinking water suitability assessment. Hence, there

is need for routine (physicochemical and biological) monitoring of the river water. In

this study only chemical quality was carried out from 27 sites along the stretch of the

river Damodar from Asansol to Pallaroad to assess the drinking water suitability of

the river water.

Excessive nitrate in drinking water can cause a number of disorders including

gastric cancer, goiter, methaemoglobinaemia in infants, birth malformations and

hypertensions (Majumdar and Gupta 2000). Concentration of NO3– is found to be

lower than the recommended level of 50 mg/l in all the river water samples. High

value of nitrate in some of the study area is attributed to decaying organic

matter and sewage water in the urban region. The downstream increase in

concentration indicates the anthropogenic contribution.

Some heavy metals are extremely essential to humans, for example, cobalt,

copper, etc., but some metals may cause physiological disorders. The cadmium

and lead are highly toxic to humans even in low concentrations. The contamination of

water by heavy metals has received great significance due to their toxicity and

accumulative behaviour. At the upstream site viz. S3, S4, S6, S9 and S14 of the study

area marginally exceeds the WHO norms (0.01 mg/l). High concentration (mean

value) of Pb was found at S16 (Shyampur; 342.164 µg/l) and S17 (Majher mana;

1033.67 µg/l) site. At the upstream site viz. S3, S6 and S12 of the study area


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marginally exceeds the WHO norms (0.003 mg/l). High concentration of Cd was

found at S16 (Shyampur; 3.965 µg/l) and S17 (Majher mana; 4.257 µg/l) near

Durgapur industrial area. The study in general reveals that the Cd concentration in

the entire study area was found to be well below the WHO norms (0.003 mg/l)

for drinking water (WHO 2006). The high Pb and Cd content at S16 and S17 due

an industrially polluted water stream joins into the river and influences this

zone as a result of which the water is not suitable for drinking purpose.

Long-term application of contaminated water can enrich heavy metal to

phototoxic levels and resulted in reduced plant growth and/or enhanced metal

concentration in plants which has an ultimate detrimental effect on the livestock. The

study shows that due to the discharge from coal mine and other industrial effluents

some of the sites in the analyzed area are not suitable for direct use in drinking and

domestic purposes and need treatment before utilization. Water to maintain

livestock should be of pure and high quality to prevent livestock diseases, salt

imbalance, or poisoning by toxic constituents. The Damodar river water serves as

drinking water source for livestock at many places in its course. According to Ayers

and Wascot 1985 and Shuval et al. 1986 the water having salinity <1500 mg/l and Mg

<250 mg/l is suitable for drinking by most livestock. Most of the river water in the

study area meet these standards and can be used for livestock, a preliminary treatment

and filtration is necessary in some areas. Water quality parameters were compared

with the prevalent water quality standards indicates that, with few exceptions, the

Damodar river water in the study area is fit for drinking and livestock uses.

5.12 Suitability of the river water for irrigation use

The suitability of the river water for irrigation depends upon the effects of its

mineral constituents. Irrigation water can create saline and/or alkaline soil depending

on the quality and types of the salt dissolved in the water. Excessive amount of

dissolved ions such as sodium, bicarbonate, and carbonate in irrigation water affects

plants and agricultural soil physically and chemically, thus reducing the plant

development, disrupt plant metabolism which ultimately affects the productivity. The

physical effects of these ions in irrigation water are to lower the osmotic pressure in

the plant structural cells, thus preventing water from reaching the branches and leaves


[80]

where as the chemical effects disrupt plant metabolism. So the monitoring of the river

water quality for irrigation is an important criterion in managing plant health.

Water for irrigation, to maintain sustainable agriculture, should satisfy the

needs of soil and the crop as the liquid phase in soil water plant growth and crop

production. Irrigation water quality is depending upon both the type and the quantity

of the dissolved salts originates from natural and anthropological sources. The sodium

through the process of base exchange may reduce calcium in the soil and thereby may

reduce the permeability of the soil to the water and adverse effect on plant growth

occurred over a long period of time. Electrical conductivity and Na+ play a vital role

in suitability of water for irrigation. Higher electrical conductivity in water creates a

saline soil where as high salt content in irrigation water causes an increase in soil

solution osmotic pressure. According to Subba Rao 2006 the salts affects the growth

of plants, soil structure, permeability and aeration, which indirectly affect plant

growth.

5.12.1 Suitability on the basis of pH, electrical conductivity, bicarbonate, sodium,

chloride, sulphate and nitrate: Irrigation water having pH outside the normal range

may cause a nutritional imbalance or may contain a toxic ion. pH in the water (7.00 –

8.94) samples exceeds the FAO Standards (6.5 - 8) for agricultural application but

within the recommended IS irrigation standards (5.5 - 9.0). Electrical conductivity

(EC) is an important parameter in determining the suitability of water for irrigation

use. The primary effect of high EC of water on crop productivity is the inability of

plant to compete with ions in the soil solution for water. Electrical conductivity is also

a good measure of salinity hazard Langenegger 1990 and is an indicator of the

potential problems in plant growth associated with increasing quantities of salt. TDS

is a significant parameter of irrigation water suitability which refers to any minerals,

salts, metals, cations or anions dissolved in water. On the basis of EC (100–710

µS/cm) and TDS (68.52 – 482.75 mg/l), water samples of the study area come under

the excellent to good category for irrigation purposes (Ayers and Westcot 1994).

Comparing with FAO irrigation standard guidelines, irrespective of the seasons, the

values of these parameters of the analysed water within the tolerance limit [EC (750 –

2000 µS/cm) and TDS (2000 mg/l)] for irrigation. Since the bicarbonate

concentration in the Damodar river water samples lie in the range from 44.00–52.00


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mg/l so it is within the tolerance limit for irrigation purposes (FAO irrigation

standard; 600 mg/l). Chloride ion commonly found in irrigation water and is essential

to crops at low concentrations; it can cause toxicity to sensitive crops at higher levels.

Chloride is not adsorbed or held back by soils, therefore it moves readily with the

soil-water, is taken up by the crop and accumulates in the leaves. When chloride

concentration in the leaves exceeds the tolerance of the crop, injury symptoms

develop such as leaf burn or drying of leaf tissue. According to Ayers and Westcot

1994 the excessive necrosis is often accompanied by early leaf drop or defoliation.

The chloride content of the Damodar river water is within the tolerance limit for

irrigation purposes (IS standards for Irrigation; 600 mg/l and FAO irrigation standard;

1100 mg/l).

The sodium toxicity symptoms are leaf burn, scorch and dead tissue along the

outside edges of leaves in contrast to symptoms of the chloride toxicity which

normally occur initially at the extreme leaf tip. Sodium toxicity symptoms appear first

on the older leaves, starting at the outer edges and, as the toxicity increases, move

progressively inward between the veins toward the leaf centre. Excess sodium in

irrigation water produces the undesirable effects of changing soil characteristics and

reducing soil permeability (Kelly 1951) and leads to development of an alkaline soil.

High concentrations of sulphates in the irrigation water causes sensitivity to crops but

it is likely that this sensitivity is related to the tendency of high sulphates

concentrations to limit the uptake of calcium by plants. Since the sulphate

concentration in the river water samples lie in the range from 5.352 to 84.0549 mg/l is

within the tolerance limit for irrigation purposes (IS standards for Irrigation and FAO

irrigation standard; 1,000 mg/l). Nitrogen in the irrigation water has much the same

effect as soil-applied fertilizer nitrogen and an excess will cause problems to the

crops. Generally sensitive crops may be affected by nitrogen concentrations above 5

mg/l whereas most other crops are relatively unaffected until nitrogen exceeds 30

mg/l (Ayers and Westcot 1994). Nitrate content in the river Damodar are low which is

much lower than the limit of 18 mg/l prescribed by the IS standards for irrigation.

5.12.2 Sodium adsorption ratio (SAR): Sodium adsorption ratio (SAR) is an

important parameter for determining the suitability for agricultural purpose and is an

indicator of the sodium hazard of water (Filintas 2005). The high SAR values have a


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negative impact on the soil structure due to the dispersion of clay particles. Sodium

concentration is very important parameter for irrigation water quality because high

level of sodium concentration in irrigation water produces an alkaline soil.

Todd 1980 also describes that SAR is an important parameter for the

determination of the suitability of irrigation water because it is responsible for

the sodium hazard. According to Kelly (1951) high level of sodium in water causes

the undesirable effects of changing soil properties and reducing soil permeability.

SAR value of irrigation water quantifies the relative proportions of sodium (Na+) to

calcium (Ca2+) and magnesium (Mg2+) and is a measure of alkali/sodium hazard to

crop. Calculation of SAR for irrigation water provides a useful index of the sodium

hazard of that water for soils and crops. According to Richards (1954), based on

SAR values, irrigation water is classified into four groups: low (SAR<10),

medium (SAR, 10–18), high (SAR, 18–26), and very high (SAR>26). High

level of sodium in irrigation waters may change the soil properties and reduce

its fertility due to salinization and alkalization processes (Dehayer et al. 1997).

Calculation of SAR for irrigation water provides a useful index of the sodium hazard

of that water for soils and crops. A low SAR (2 to 10) indicates little danger from

sodium; medium hazards are indicated between 10 to 18; high hazards between 18 to

26 and very high hazards more than that.

The calculated values of SAR (Table 5.4) in the river Damodar ranged from

0.404 to 2.649 during premonsoon season with a mean of 0.978±0.528 (CV%

54.021); 0.234 to 2.997 during monsoon season with a mean of 0.706±0.642 (CV%

90.847) and 0.285 to 2.252 during postmonsoon season with a mean of 0.774±0.498

(CV% 64.403) in 2007. The values of SAR in 2008 ranged from 0.327 to 2.811

during premonsoon season with a mean of 1.001±0.624 (CV% 62.356); 0.316 to

2.480 during monsoon season with a mean of 0.732±0.442 (CV% 60.387) and 0.484

to 1.345 during postmonsoon season with a mean of 0.864±0.264 (CV% 30.582). In

2009, the values of SAR ranged from 0.359 to 3.017 during premonsoon season with

a mean of 1.016±0.619 (CV% 60.932); 0.287 to 1.347 during monsoon season with a

mean of 0.775± 0.311 (CV% 40.108) and 0.302 to 2.222 during postmonsoon season

with a mean of 0.814± 0.381 (CV% 46.853).


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Therefore with respect to SAR, all river water samples are suitable for

irrigation and can be used for all soil types.

5.12.3 Sodium percentage Na%: Sodium concentration in irrigation water is of

utmost importance while considering the suitability for agricultural purposes.

According to Tijani 1994 the sodium concentration plays a significant role in

evaluating the water quality for irrigation because high sodium content makes the soil

hard as well as reduces its permeability. When water is used for irrigation with high

Na+ content and low in Ca2+ content, the ion exchange complex may become

saturated with Na+, which destroys soil structure, because of dispersion of clay

particles where sodium ions may tend to be absorbed by clay particles, displacing

magnesium, and calcium ions.

The calculated values of Na% (Table 5.4) in the river Damodar ranged from

11.843 to 61.490 during premonsoon season with a mean of 32.676± 11.106 (CV%

33.987); 15.986 to 60.461 during monsoon season with a mean of 30.165± 10.283

(CV% 34.088) and 15.427 to 43.539 during postmonsoon season with a mean of

28.936± 7.919 (CV% 27.369) in 2007. The values of Na% in 2008 ranged from


33.985); 17.510 to 56.333 during monsoon season with a mean of 31.667± 9.873


33.074±9.863 (CV% 29.822). In 2009, the values of Na% ranged from 17.349 to

48.917 during premonsoon season with a mean of 32.828± 8.660 (CV% 26.380);

14.236 to 47.974 during monsoon season with a mean of 31.694±9.888 (CV%

31.198) and 13.478 to 46.779 during postmonsoon season with a mean of

29.875±7.879 (CV% 26.372).

5.12.4 Permeability index (PI): Permeability index (PI) is a significant parameter for

the suitability of irrigation water and it indicates that the soil permeability is

affected by long-term use of irrigation water as influenced by Na+, Ca2+, Mg2+,

and HCO3– contents of the soil. Sodium, calcium, magnesium and bicarbonate

content of the agricultural soil influence the soil permeability. Long-term use of

irrigation water is influences the soil permeability. Waters can be classified as Class I,

Class II, and Class III. Class I and Class II waters are categorized as good for


[84]

irrigation with 50–75% or more of maximum permeability. Class III waters are

unsuitable with 25% of maximum permeability.

The calculated values of PI (Table 5.4) in the river Damodar ranged from


22.284); 62.776 to 140.46 during monsoon season with a mean of 92.831±21.03


81.436±18.053 (CV% 22.168) in 2007. The values of PI in 2008 ranged from 49.453

to 107.999 during premonsoon season with a mean of 75.789±14.849 (CV% 19.592);



83.327±17.512 (CV% 21.016). In 2009, the values of PI ranged from 51.866 to

109.58 during premonsoon season with a mean of 82.018±15.366 (CV% 18.735);



83.401±15.566 (CV% 18.664). Accordingly, all the samples fall into the Class I and II

category of Doneen’s chart.

5.12.5 Magnesium hazard (MH): Magnesium ions are essential for the plant

growth and its deficiency in plants causes late season yellowing between leaf

veins, especially in older leaves. Excess Mg in the water will adversely affect crop

yields. Magnesium ratio when exceeds more than 50 is considered to be harmful and

unsuitable for irrigation use irrigation (Sreedevi 2004) and this would adversely

affect the crop yield, as soils become more alkaline.

The calculated values of MH (Table 5.4) in the river Damodar ranged from


16.668); 34.012 to 50.305 during monsoon season with a mean of 45.192±4.216


41.334±7.523 (CV% 18.20) in 2007. The values of MH in 2008 ranged from 35.896

to 52.099 during premonsoon season with a mean of 44.814±4.469 (CV% 9.973);

33.980 to 50.791 during monsoon season with a mean of 40.860±4.069 (CV% 9.958)

and 25.435 to 54.912 during postmonsoon season with a mean of 45.119±6.635

(CV% 14.706). In 2009, the values of MH ranged from 28.248 to 50.592 during


[85]

premonsoon season with a mean of 40.989±5.375 (CV% 13.114); 34.378 to 57.622

during monsoon season with a mean of 46.034±4.580 (CV% 9.950) and 36.012 to

54.859 during postmonsoon season with a mean of 44.863±4.192 (CV% 9.343).

The analyzed water samples indicate that most of the river water samples are

not exceeding the magnesium ratio of 50.

5.12.6 Residual Sodium Carbonate (RSC): The excess quantity of sodium

bicarbonate and carbonate is considered to be detrimental to the physical properties of

soils as it causes dissolution of organic matter in the soil, which in turn leaves a black

stain on the soil surface on drying and this excess is denoted by Residual Sodium

Carbonate (RSC). In irrigation water having high concentration of HCO3–, there is a

tendency for Ca2+ and Mg2+ to precipitate as CO32–. The effect of CO3

2– and HCO3–

ion on quality of water was expressed in terms of the Residual Sodium

Carbonate (RSC) Eaton (1950). Carbonate levels when exceed the total amount of

calcium and magnesium, the water may be poor in quality. This excess of carbonate is

denoted by ‘residual sodium carbonate’ (RSC) and continued usage of high residual

sodium carbonate waters ultimately affects crop yields. According to Tiwari and

Manzoor 1988 the sites with negative RSC value indicating that there is no complete

precipitation of calcium and magnesium. High value of residual sodium carbonate

(RSC) in water value leads to an increase in the adsorption of sodium on soil

(Eaton 1950) and also causes the soil structure to deteriorate, as it restricts the water

and air movement through soil. According to the US Salinity Laboratory (1954), an

RSC value less than 1.25 meq/l is safe for irrigation, a value between 1.25 and 2.5

meq/l is of marginal quality and a value more than 2.5 meq/l is unsuitable for

irrigation.

The calculated values of RSC (Table 5.4) in the river Damodar ranged from -

2.133 to 1.758 during premonsoon season with a mean of -0.229±0.804; -0.878 to

1.278 during monsoon season with a mean of -0.009±0.483 and -1.477 to 1.379

during postmonsoon season with a mean of -0.171±0.593 in 2007. The values of RSC

in 2008 ranged from -1.946 to 1.478 during premonsoon season with a mean of -

0.252±0.687; -1.139 to 1.118 during monsoon season with a mean of 0.056±0.495 and

-1.483 to 0.754 during postmonsoon season with a mean of -0.092±0.526. In 2009,


[86]

the values of RSC ranged from -1.811 to 0.968 during premonsoon season with a

mean of -0.075±0.728; -0.749 to 1.539 during monsoon season with a mean of

0.128±0.553 and -2.493 to 1.212 during postmonsoon season with a mean of

0.001±0.725.

All the samples in the study area (except some areas) have RSC values much

less than 1.25 meq/l (safe for irrigation), which revealed that all samples are of safe

quality categories for irrigation.

5.12.7 Suitability on the basis of metal content: Metals may accumulate in different

parts of vegetables depending upon the plant species, soil condition, and types of

heavy metal (Fazeli et al. 1991; Boon and Soltanpour 1992; Rao et al. 1993).

Manganese and iron content in the studied river water was found to be well below the

Indian standards (2.0 mg/l and 3.0 mg/l respectively) for irrigation (IS 11624: 1986)

for all the analysed samples. Comparing with FAO irrigation standard guidelines,

irrespective of the seasons, the values of these parameters were well within the

tolerance limit [Mn (0.2 mg/l) and Fe (5.0 mg/l)] for irrigation. Only at Majher Mana

location the concentration of Pb (5.0 mg/l) was at par with the FAO irrigation

standards (5.0 mg/l) (Pescod 1992). The concentration of Cd was found to be well

below the tolerance limit for irrigation purposes [FAO irrigation standard guidelines

(0.01 mg/l) for all the samples.

5.12.8 US Salinity Laboratory Diagram (USSL 1954): The United States Salinity

Laboratory of the Department of Agriculture (1954) recommends the sodium

adsorption ratio (SAR) as a measure to assess the adsorption of sodium by agricultural

soil. Water point in the US Salinity diagram have been divided into C1, C2, C3 and

C4 types on the basis of salinity hazard and S1, S2, S3 and S4 types on the basis of

sodium hazard. The significance and interpretations of quality ratings on the USSL

diagram for irrigation water can be summarized as follows: (1) Low salinity water

(C1) can be used for irrigation with most crops on most soils. Some leaching is

required, but this occurs under normal irrigation practices, except in soils of extremely

low permeability. (2) Medium salinity water (C2) can be used if a moderate amount

of leaching occurs. Plants with moderate salt tolerance can be grown in most instances

without special practices of salinity control. (3) High salinity water (C3) is


[87]

satisfactory for plants having moderate salt tolerance, on soils of moderate

permeability with leaching. (4) Very High salinity water (C4 and C5) cannot be used

on agricultural soils with restricted drainage.

The plot of data on the US salinity diagram, in which the EC is taken as

salinity hazard and SAR as alkalinity hazard. The plotted points of 55.556% in 2007,

40.741% in 2008 and 48.148% in 2009 of premonsoon season water samples fall into

C1S1 (low salinity with low sodium) category. In the monsoon season, the plotted

points of 81.481%, 88.889% and 81.481% lie on the C1S1 criterion in the three

respective years of study. The postmonsoon season represents the plotted points of

62.963%, 81.481% and 51.852% lie on the in this category in the study periods of

2007, 2008 and 2009 respectively. The overall study of salinity hazard revealed that

these river water samples can be used to irrigate all soils for semi-tolerant and tolerant

as well as sensitive crops. The plotted points of 44.444% in 2007, 49.259% in 2008

and 51.852% in 2009 of premonsoon season water samples fall into C2S1 (low

salinity with low sodium) category. In the monsoon season, the plotted points of

18.519%, 11.111% and 18.519% lie on the C2S1 criterion in the three respective

years of study. The plotted points of 37.037%, 18.519% and 48.148% in the three

respective seasons of premonsoon of the water samples fall in the category C2S1,

indicating medium salinity and low alkali water, which can be used for irrigation in

most soil and crops with little danger of development of exchangeable sodium and

salinity (Fig. 7.1-7.3).

5.12.9 Wilcox diagram (Wilcox 1955): The suitability of irrigation water is judged by

measurement of electrical conductivity (expressing total dissolved solids) and sodium

content reported as percent sodium. Sodium percentage calculated for Damodar

river water in the study area is plotted against electrical conductance in Wilcox

diagram. Wilcox diagram shows that all of river water samples are excellent to good

for irrigation. All sampling points on the Wilcox diagram are shown in Fig. 8.1-8.3.

5.13 Sediment geochemistry

5.13.1 Distribution of heavy metals in the river bottom sediments: Sediments act as

both source and sinks for contaminants in aquatic environments. Generally, the heavy

metals are distributed between the aqueous phase and the suspended sediments during


[88]

their transport (Karbassi et al. 2007). Monitoring of riverine sediment with respect to

heavy metal contamination is an important aspect for assessment of the ecological

status. Metals represent a threat to the aquatic organisms because of their toxicity,

persistence and bioaccumulation. Hence, monitoring of riverine sediment can provide

important information on various pollution events. Sediment analysis to study the

overall water quality has an immense importance which is often included in

environmental assessment studies (Horsfall and Spiff 2002; Li et al. 2006; Adekola

and Eletta 2007; Jain et al. 2008). Heavy metals and different contaminants in the

aquatic system can lead to elevated sediment concentrations which ultimately cause

potential toxicity to aquatic biota (Heyvart et al. 2000; Yang and Rose 2003), and

residues may enter the human food chain (Cook et al. 1990; Deniseger et al. 1990).

Distribution of total heavy metals in surface sediments of river Damodar is

represented in Table 5.5–5.8.

The mean value of lead (Pb) in the river sediment reached their maximum

value during the premonsoon season (45.277±51.027 µg/g), minimum during the

monsoon season (23.206±21.189 µg/g) while the postmonsoon season is characterised

by intermediate values (32.494±34.526 µg/g).

Maximum Pb concentration is found in Dihika and Majher mana of the study

area and this may be due to presence of Steel and Iron industries and confluence of

Tamla Nala in both the sites respectively.

The measured values of manganese (Mn) in the Damodar river sediment

ranged from 53.485 to 256.472 µg/g during premonsoon season with a mean of

125.262±59.121 µg/g (CV% 47.198); 48.374 to 325.358 µg/g during monsoon season

with a mean of 148.789±87.279 µg/g (CV% 58.660) and 105.245 to 506.657 µg/g

during postmonsoon season with a mean of 245.353±114.696 µg/g (CV% 46.747) in

2007. In 2008 the value ranged from 86.347 to 245.698 µg/g during premonsoon

season with a mean of 161.866±49.129 µg/g (CV% 30.352); 74.215 to 452.145 µg/g

during monsoon season with a mean of 231.417±103.942 µg/g (CV% 44.915) and

46.347 to 275.354 µg/g during postmonsoon season with a mean of 130.380±67.998

µg/g (CV% 52.154). The manganese (Mn) in 2009 ranged from 69.354 to 469.785

µg/g during premonsoon season with a mean of 189.187±98.563 µg/g (CV% 52.098);


[89]

84.270 to 635.450 µg/g during monsoon season with a mean of 251.045±146.611

µg/g (CV% 58.400) and 45.452 to 269.463 µg/g during postmonsoon season with a

mean of 146.398±71.960 µg/g (CV% 49.153).

Sporadic higher concentration of Mn is found in Dihika and Mejhia in the

upstream stretch of the study area. Then after Durgapur Barrage Mn concentration

suddenly shoots up with the maximum recorded level at Majher Mana area. This may

be due to industrial wastewater discharge through Tamla nala.

The cadmium (Cd) in the river sediment reached their maximum value during

premonsoon season (1.102±1.082 µg/g), minimum during the monsoon season

(0.212±0.244 µg/g) while the postmonsoon season is characterised by intermediate

values (0.424±0.422 µg/g). Spatial distribution of Cd shows elevated concentration in

the stretch of Chinakuri-Dihika-Narayankuri-Mejhia and like Pd maximum

concentration is found at Majher mana to Dhobighat area.

The measured values of iron (Fe) in the Damodar river sediment ranged from

786 to 12547 µg/g during premonsoon season with a mean of 5350±3949 µg/g (CV%

74); 196 to 7363 µg/g during monsoon season with a mean of 2768±2664 µg/g (CV%

96) and 968 to 18635 µg/g during postmonsoon season with a mean of 4649±4676

µg/g (CV% 101) in 2007. In 2008 the value ranged from 358 to 19632 µg/g during

premonsoon season with a mean of 3908±4674 µg/g (CV% 120); 589 to 6699 µg/g

during monsoon season with a mean of 2437±1700 µg/g (CV% 70) and 976 to 11596

µg/g during postmonsoon season with a mean of 3974±2840 µg/g (CV% 71). The

iron (Fe) in 2009 ranged from 1037 to 12569 µg/g during premonsoon season with a

mean of 5504±3390 µg/g (CV% 62); 453 to 9134 µg/g during monsoon season with a

mean of 3191±2753 µg/g (CV% 86) and 800 to 15007 µg/g during postmonsoon

season with a mean of 4852±3988 µg/g (CV% 82).

Unlike Pd, Cd and Mn, Fe concentration is found higher in the upstream area

Chinakuri-Dihika. Thereafter there is gradual drop down in concentration with the

elevated concentration at Baska and Majher mana-Dhobighat area.

So in summary it can be concluded that appearance of relatively high

concentration Fe and Mn concentrations in the upstream area may be due to partially

controlled by geological formation and substantially controlled by the industrial


[90]

effluents arising out of coal fired iron and steel industries and power plants. The

concentration of heavy metals (Pb and Cd) in sediments seems to be related to the

corresponding concentration in the aquatic phase. Due to alkaline nature of the river

water, most of the heavy metals have precipitated and may settle as carbonates,

oxides, and hydroxides.

5.13.2 Metal speciation and its retention in bottom sediments: The toxicity and fate

of the metal contamination in sediment is dependents on its chemical forms therefore,

the study of solid phase chemical speciation and quantification of different species of

heavy metals is very important toll to assess the sediment quality. Metals

accumulation in sediment phases are known to occurs in major forms; exchangeable

(water soluble and extractable), reducible (oxy-hydroxides of Fe and Mn), oxidisable

(organic matter and sulfide bound) and residual (alumino silicates and strongly

bound). Generally, heavy metals in the water/acid soluble and exchangeable fractions

are considered readily and potentially mobile, while the reducible and oxidizable

fractions are relatively stable under normal sediment condition and the residual

fraction are entrapped within the crystal structure of the minerals and, thus, represent

the least mobile fraction.

The speciation of metals in the river sediments is analyzed by the BCR sequential

extraction process and given in Fig. 9. Iron (Fe) is the most abundant metal in all

analysed sediments because it is one of the most common elements in the earth’s

crust. Fractionation profile of iron in bottom sediments of the river Damodar indicates

that major portion (41.107%) is associated with residual fraction characterizing stable

compounds in sediments. The metal associated with this fraction cannot be

remobilized under normal environmental conditions encountered in the nature. A

substantial amount (29.20%) of the fraction is associated with reducible fraction and

to a lesser extent (17.381%) with oxidisable fraction. The percentage of iron in these

two phases is quite variable and may be attributed due to competition between iron

organic complexes and hydrous iron oxide forms. Relatively low amount (12.312%)

of iron was also found in exchangeable fraction. The residual fraction is the dominant

iron host in all the samples of the total concentration.


[91]

The fractionation profile of cadmium (Cd) indicates that major portion of

cadmium is associated with residual fraction (54.237%) followed by reducible

(23.164%) fraction and oxidisable fraction (19.435%). Toxic nature of cadmium and

its association with exchangeable (3.164%) fraction may cause deleterious effects to

aquatic life. A significant amount of the cadmium was associated in the first three

fractions and may be easily remobilized by changes in environmental conditions.

Manganese (Mn), which is also abundant in nature, behaves in a different way

in aquatic ecosystem. The distribution of various manganese fractions shows that the

greatest amounts are found in the residual fraction (37.237%), followed by the

reducible fractions (33.280%) and oxidizable fractions (18.364%) where as the

smallest amounts of manganese are associated with the exchangeable fraction

(11.119%). The fractionation profile of manganese also indicates that it is mostly

bound to residual fractions. However, the fraction of the manganese associated with

the residual fraction cannot be remobilized under normal conditions encountered in

the nature.

Fractionation profile of lead (Pb) in bottom sediments of river Damodar

indicates that major portion (48.124%) is associated with residual fraction

characterizing stable compounds in sediments. The lead associated with this fraction

cannot be remobilized under normal environmental conditions encountered in the

nature. A substantial amount (25.525%) of the fraction is associated with reducible

fraction and to a lesser extent (22.988%) with oxidisable fraction. Relatively low

amount (3.364%) of lead was also found in exchangeable fraction. More than 50% of

the Pb was associated with first three fractions i.e., exchangeable, oxidisable,

reducible fractions and may pose risk to aquatic life under changing environmental

conditions. Exchangeable fraction in BCR extraction, consist of water soluble and

bound to carbonate metal fractions of sediment where the metals are held by weak

electrostatic force of adsorption. Change of ionic strength and pH of the water can

change the process of adsorption-desorption resulting in metal uptake and release at

the water/sediment interface.

So in summary it can be concluded that iron shows the highest exchangeable

fraction amongst all the metal followed by Mn, Pb and Cd. The reducible fraction of


[92]

metal which is the fraction bound to Fe-Mn oxides and can release into dissolved

form in the reducing aquatic environments. Mn shows the highest reducible fraction

followed by Fe. The reducible fraction is the dominant phase of these two redox metal

because of their slow oxidation process in aquatic environment. Mn/Fe bounded in

exchangeable and/or reducible fraction by relatively weak electrostatic interactions,

and may release by ion exchange processes and dissociation of sulphide/carbonate

phase (Caplat et al. 2005) with the changing environmental condition. Oxidisable

fraction is the fraction where metals are bound to organic matter and sulfides and can

be degraded into soluble form during oxidisable environment. Pb shows the highest

oxidisable fraction amongst all the metal followed by Cd.

Residual fraction consists of lithogenic fraction i.e aluminosilicate forms of

metal which forms very stable crystals. This metal fraction is the highest in the river

sediment. Cd and Pb show their highest concentration in this form. Fe and Mn show

their second highest concentration in this fraction. Chalcophilic and lithophilic nature

of Pb and Cd accounts for the higher concentration in residual form, consistent with

earlier findings of metal fractionation (Jain et al. 2004). The overall percentage of

metal content in different BCR fractions is in the sequence of residual > reducible >

oxidisable > exchangeable and the order of metals in each fractions are as follows

Exchangeable: Fe > Mn > Pb > Cd, Oxidisable: Pb > Cd > Mn > Fe, Reducible: Mn

> Fe > Pb > Cd, Residual: Cd > Pb > Fe > Mn. The studies have shows that the

geochemical properties of the river sediments are critical in affecting the metal

bioavailability. The study also shows the cadmium and lead remain with

exchangeable fraction indicate dominance of the anthropogenic sources through

industrial wastes and municipal sewage.

5.13.3 Partitioning co-efficient (Kd) of heavy metals: The partitioning co-efficient

(Kd) is used to represent the distribution of metals between solid and dissolved form

in environmental risk and fate models. It is ratio between sorbed metal in

solids/sediment with dissolved metals in equilibrium condition and provides a

measure of the relative changes in affinity of heavy metals. Kd is important for the

evaluation of potential adsorption of dissolved contaminants in contact with sediment

surface (Knox et al. 2006).


[93]

The partitioning coefficient (Kd) of dissolved metals in pit pond water and adsorbed to

shallow sediments of pit pond is calculated as

L0G MN�OP�QRSQNS�JO�TRS�TS�!RPTU��VO�N�6WXYX8MN�OP�QRSQNS�JO�TRS�TS�UT��RPZNU��VO�N�6WXP8�

Geochemical investigation requires the detail understanding of distribution and

interaction of metals in solids-solution phases. According to Anderson and

Christensen 1988 high partitioning co-efficient (Kd) values indicate that the metal has

been preferentially retained by the sediment, while low values suggest that the metal

mostly remains in water where it is available for transport and biological uptake. The

Kd value of the analysed metals was ranged from 0.633 to 2.719 for Cd, 0.452 to

2.513 for Mn, 3.388 to 4.347 for Fe and 0.960 to 4.391 for Pb. The relatively higher

Kd values observed for Fe, Pb and Cd indicate their preferential association and

enrichment in sediments and suggest that they are characterized by a low geochemical

mobility in water. Relatively lower Kd values for Mn indicate that they are less likely

to be associated with sediments.

5.13.4 Recalcitrant Factor (RF): The immobile metal fractions in river sediments can

be determined by of recalcitrant factor (RF), which determines the extent of virtual

irreversible retention of metal in sediment (Knox et al. 2006). Retention of metals in

the sediments is based on the strength of bounded metals in different geochemical

fractions. The sequential extraction is often used to determine how strongly metals

were bound to the sediments. Recalcitrant Factor (RF) was introduced (Knox et al.

2006) in this study to estimate the percentage of contaminants in the river sediment

that was resistant to remobilization, and is estimated as

RF=� [\]^_`_abcdef\gea_`hbd�ij\e^klbmnbcde�f�\ge`hk_cde�f�\]^_`_abcdef\gea_`hbd�oX100

According to Knox et al. 2006 the recalcitrance fractions of metals consist of

crystalline oxides, sulfides or silicates, and aluminosilicates, which are very strongly

bound in the sediment, therefore indicates the virtually irreversible retention of metals

by the solid phase. Recalcitrant Factor (RF) is the ratio of strongly bound fractions to

total metal concentration in the sediments/solids. Recalcitrant Factor (RF) value of


[94]

monitored metals in the river sediments ranged from 55.601 (Mn) to 73.672 (Cd)

indicating variability in effective retention of individual metals. The recalcitrant factor

(RF) value of Pb and Fe is 71.112 and 58.488 respectively in the monitored river

sediments The ranking of metals with respect to RF value is in the order of Cd > Pb >

Fe > Mn. Higher RF value of Cd and Pb can be explained because of chalcophilic and

lithophilic nature of these elements, therefore indicating poor possibility of

mobilization into the aqueous system.

5.13.5 Infrared spectroscopic evaluation of the bottom sediments: Infrared analyses

(FT-IR) were carried out to study the distribution of functional groups in the Damodar

river sediments. The FTIR spectrum (Fig. 10.1-10.27) of river sediment displays a

number of absorption peaks indicating the presence of different types of functional

groups. The observed wave numbers with corresponding functional group are

presented in Table 5.9. Analysis of FTIR spectrum of river sediment exhibits the

functional group like –OH, -N-H, C-O, C=O, C-Cl , S-H, CO32-, OH2, CN, C-Br. The

FTIR spectra of the above mentioned studied area shows the presence of negatively

charged functional groups like –OH, CO32-, -Cl, -Br, N-H, C-O etc. Hence the

sediments of the studied area have pronounced affinity towards the heavy metal ion

binding. The study reveals that the peaks for the C-H bond region are excellent

indicators of the presence of anthropogenic contaminants. The FTIR spectra recorded

from the river sediment in the present experiment displayed characteristic absorption

band patterns in the frequency range of 4000–400 cm–1 indicating the presence of

more or less similar functional groups.

5.14 Geo-chemical assessment of the river sediments in relation to metal

contamination

The concentrations of metals in sediments can be sensitive indicators of

contaminants in hydrological systems. To assess the degree of contamination of heavy

metals in the sediments the enrichment factor (EF), geoaccumulation index (Igeo) and

pollution load index (PLI) is applied for the study.

5.14.1 Enrichment factor (EF): In order to assess the enrichment of metals in

sediments and to quantify the industrial input, the geochemical normalization

approach is applied, and it calculated according to the following equation.


[95]

EF= (M/X) sample/ (M/X) background

where M is the measured concentration of the element in the sediment, X is the

selected normalizer (reference metal) and (M/X) sample and (M/X) background are the

ratios of target metal and the normalizer in the interest and background sediments,

respectively. Enrichment factor (EF) is a method to estimate the anthropogenic impact

on sediments is to calculate a normalized enrichment factor (EF) for metal

concentrations above uncontaminated background levels (Dickinson et al. 1996). Inert

elements Al and Fe are less anthropogenic contamination in aquatic sediment and

were used as the normalizer most frequently (Liaghati et al. 2003). In this study iron

is used as normalizer. A five-category ranking system is used to express the degree of

anthropogenic contamination. EF <2 is deficiency to minimal contamination, EF = 2–

5 moderate contamination, EF = 5–20 significant contamination, EF = 20–40 very

high contamination, and EF > 40 extremely high contamination (Sutherland 2000;

Kartal et al. 2006). Spatio-temporal distribution along with descriptive statistics of EF

of Mn, Cd, Fe and Pb at different sampling locations is represented in Table 5.10–

5.13.

The calculated values of EF of manganese (Mn) (Table 5.10) in the river

sediment (mean value) reached their maximum value during premonsoon season

(0.187±0.052), minimum during the monsoon season (0.248±0.107) while the

postmonsoon season is characterised by intermediate values (0.205±0.66).

The calculated values of EF of cadmium (Cd) (Table 5.11) in the river

Damodar ranged from 0.152 to 14.787 during premonsoon season with a mean of

3.923±3.810 (CV% 97.14); 0.00 to 4.413 during monsoon season with a mean of

1.076±1.214 (CV% 112.81) and 0.018 to 5.827 during postmonsoon season with a

mean of 1.394±1.454 (CV% 104.26) in 2007. The values of EF in 2008 ranged from


110.22); 0.00 to 2.287 during monsoon season with a mean of 0.494±0.697 (CV%

141.21) and 0.008 to 3.083 during postmonsoon season with a mean of 1.182±0.954

(CV% 80.76). In 2009, the values of EF ranged from 0.009 to 18.120 during

premonsoon season with a mean of 3.970±4.746 (CV% 119.55); 0.00 to 2.783 during


[96]

monsoon season with a mean of 0.546±0.840 (CV% 153.97) and 0.015 to 7.653

during postmonsoon season with a mean of 1.669±2.199 (CV% 131.77).

The calculated values of EF of lead (Pb) (Table 5.12) in the river Damodar

ranged from 0.536 to 9.912 during premonsoon season with a mean of 1.952±2.320

(CV% 118.84); 0.049 to 2.643 during monsoon season with a mean of 1.062±0.768


1.481±1.791 (CV% 120.95) in 2007. The values of EF in 2008 ranged from 0.589 to

10.224 during premonsoon season with a mean of 2.078±2.386 (CV% 114.83); 0.043

to 2.624 during monsoon season with a mean of 1.008±0.775 (CV% 76.88) and 0.362


2009, the values of EF ranged from 0.591 to 12.827 during premonsoon season with a

mean of 2.762±2.989 (CV% 108.24); 0.288 to 7.877 during monsoon season with a

mean of 1.410± 1.870 (CV% 132.61) and 0.489 to 10.412 during postmonsoon season

with a mean of 2.051± 2.446 (CV% 119.26).

The iron (Fe) (Table 5.13) in the river sediment (mean value) reached their

maximum value during premonsoon season (0.104±0.070), minimum during the

monsoon season (0.059±0.047) while the postmonsoon season is characterised by

intermediate values (0.095±0.077).

The EF values for all the metals ranged from 0.053 to 0.748 for Mn, 0.00 to

18.12 for Cd, 0.004 to 0.416 for Fe and 0.043 to 12.827 for Pb. The EF values for all

the metals were in the range of 0.00–12.827, indicating a range from deficiency to

significant contamination within the study area. The average EF values for all

sediment decreased in the order Cd (1.931) > Pb (1.68) > Mn (0.213) > Fe (0.086).

The EF values were >2 for Pb and Cd, indicating anthropogenic impact on metal

concentration in the sediments and <2 for Mn and Fe, which fell in the unenriched

group of elements in the study area. The EF of Cd reached in premonsoon 2009 at

very high level (18.120) at site Majher mana, which was the most enriched element in

the sediment of the study area. The sediments from the Majher mana are heavily

polluted because of industrial wastes discharged from thermal power plant, chemical

plant, steel plant and chlor-alkali industry. Even though the EF values are less than the

pollution limit of 2 in the river sediment, the human and industrial activities along the


[97]

river catchment area if not properly monitored and managed, will cause a significant

rise in the enrichment level with its attendant environmental problems in future.

5.14.2 Index of geoaccumulation (Igeo): Index of geoaccumulation (Igeo) is an

assessment tool to assess the contamination by comparing the current and

preindustrial concentrations originally used with bottom sediments (Muller 1969). It

can also be applied to the assessment of soil and sediment contamination. Igeo is

calculated according to the following equation:

Igeo = log 2 Cn/1:5 Bn

where Cn is the measured concentration of the element in the sediment and Bn is the

geochemical background value in sediment (“average shale”). The constant 1.5 is

allowed to minimize the effect of possible variations in the background values which

may be attributed to lithologic variations in the sediments (Stoffers et al. 1986).

Geoaccumulation index consists of seven grades (0–6), indicating various

degrees of enrichment above the background values ranging from unpolluted to very

highly polluted sediment quality. Average shale concentration given by Turekian and

Wedepohl (1961) is one of the world-wide standards used as reference for this study.

Following descriptive classification for geoaccumulation is given by Muller (1969):

<0 = uncontaminated, 0–1 = uncontaminated to moderately contaminated, 1–2 =

moderately contaminated, 2–3 = moderately to heavily contaminated, 3–4 = heavily

contaminated, 4–5 = heavily to extremely contaminated, and >5 extremely

contaminated. A calculated value of geoaccumulation index along with descriptive

statistics for studied heavy metals is represented in Table 5.14_ 5.17.

The Igeo of lead (Pb) (Table 5.14) in the river sediment (mean value) reached

their maximum value during premonsoon season (0.097±1.082), minimum during the

monsoon season (-1.005±1.431) while the postmonsoon season is characterised by

intermediate values (-0.355±1.031).

The Igeo of cadmium (Cd) (Table 5.15) in the river sediment (mean value)

reached their maximum value during premonsoon season (0.0212±2.076), minimum

during the monsoon season (-2.528±1.965) while the postmonsoon season is


[98]

characterised by intermediate values (-1.260±2.151). The Igeo class for the

river sediments in river Damodar varies metal to metal and place to place.

The calculated values of Igeo of manganese (Mn) (Table 5.16) in the Damodar

river sediment ranged from -4.575 to -2.314 during premonsoon season with a mean

of -3.489±0.657 (SEM 0.170); -4.720 to -1.970 during monsoon season with a mean

of -3.335±0.866 (SEM 0.224) and -3.599 to -1.331 during postmonsoon season with a

mean of -2.513±0.641 (SEM 0.166) in 2007. The values of Igeo in 2008 ranged from

-3.884 to -2.376 during premonsoon season with a mean of -3.041±0.450 (SEM

0.116); -4.103 to -1.496 during monsoon season with a mean of -2.620±0.741 (SEM

0.191) and -4.782 to -2.211 during postmonsoon season with a mean of -3.462±0.725

(SEM 0.187). In 2009, the values of Igeo ranged from -4.20 to -1.440 during

premonsoon season with a mean of -2.905±0.670 (SEM 0.173); -3.919 to -1.005

during monsoon season with a mean of -2.561± 0.825 (SEM 0.213) and -4.810 to -

2.242 during postmonsoon season with a mean of -3.303±0.778 (SEM 0.201).

The calculated values of Igeo of iron (Fe) (Table 5.17) in the Damodar river

sediment ranged from -6.493 to -2.496 during premonsoon season with a mean of -

4.220±1.352 (SEM 0.349); -8.497 to -3.265 during monsoon season with a mean of -

5.436±1.646 (SEM 0.425) and -6.193 to -1.926 during postmonsoon season with a

mean of -4.471±1.271 (SEM 0.328) in 2007. The values of Igeo in 2008 ranged from

-7.628 to -1.851 during premonsoon season with a mean of -4.798±1.360 (SEM

0.351); -6.909 to -3.402 during monsoon season with a mean of -5.20±1.052 (SEM

0.272) and -6.181 to -2.610 during postmonsoon season with a mean of -4.493±1.051

(SEM 0.271). In 2009, the values of Igeo ranged from -6.093 to -2.494 during

premonsoon season with a mean of -3.994±1.056 (SEM 0.273); -7.288 to -2.954

during monsoon season with a mean of -5.053±1.420 (SEM 0.367) and -6.468 to -

2.238 during postmonsoon season with a mean of -4.40±1.377 (SEM 0.355).

Very high level of Igeo values for Pb (3.096) was observed at Majher mana,

indicating that sediments are heavily contaminated with this metal. The Igeo values

for Mn and Fe in the study area ranged from �4.81 to �1.005 and �8.497 to �1.85,

respectively. The Igeo value showed much fluctuation in the sediment of the study


[99]

area and the lower values of Igeo for Mn and Fe imply no appreciable input from

anthropogenic sources.

The study reveals that except certain areas the Igeo values for Cd, Pb, Fe, and

Mn in the river sediment fall in class “0” in indicating that there is no pollution from

these metals in the riverine sediment. The negative Igeo value of Mn and Fe in the

river suggested that there is no pollution from these metals in the sediments of the

study area. The background geogenic factors like chemical weathering of rock as well

as sediment, chemical compositions of catchment and even of the upper continental

crust may influence the water quality of the study area.

5.14.2.1 Spatial interpolation of Geoaccumulation Index in a GIS environment:

Krigging processed was applied in order to predict the probable uncontaminated

/contaminated areas of river Damodar with respect to Igeo of Pd, Cd, Fe and Mn.

Interpolation outputs are represented in Fig. 11. With respect to Igeo of Fe and Mn

entire stretch (study area portion) of Damodar river falls under the uncontaminated

category (<0) but in case of Cd some portion of stretches between Dihika to Mejhia

and Durgapur Barrage to Shyampur fall under the uncontaminated to moderately

contaminated category (0-1) whereas part of the stretch between Majhermana to

Dhobighat falls under the moderately contaminated category (1-2). In case of Pb,

larger portion of stretches between Chinakuri and Mejhia and Durgapur Barriage to

Dhobi ghat fall in the uncontaminated to moderately contaminated category. Only a

small patch in between Shyampur to Majher Mana falls under the category of

moderately contaminated category.

5.14.3 Pollution load index (PLI): Pollution load index (PLI), has been calculated for

a particular site following the method proposed by Tomlison et al (1980). PLI is

represented as geometric mean of Cf value of n number of metals estimated at each

site

PLI = CF1 X CF2 X CF3 X. . . . . . . . . X CFn) 1/n

where n is the number of metals and CF is the contamination factor. The

contamination factor can be calculated from the following relation:

Cf = Hc/Hb


[100]

where Hc is the metal concentration at the contaminated site and Hb is

maximum permissible limits/ background value of metals. PLI provides a simple,

comparative means for assessing the level of heavy metal pollution and is then

classified as no pollution (PLI <1), moderate pollution (1< PLI <2), heavy pollution

(2< PLI <3), and extremely heavy pollution (3< PLI).

The calculated values of PLI (Table 5.18) in the Damodar river sediment

ranged from 0.156 to 1.224 during premonsoon season with a mean of 0.502±0.294

(CV% 58.423); 0.00 to 0.88 during monsoon season with a mean of 0.253±0.212


0.431±0.310 (CV% 71.818) in 2007. The values of PLI in 2008 ranged from 0.090 to

1.312 during premonsoon season with a mean of 0.448±0.292 (CV% 65.292); 0.00 to

0.661 during monsoon season with a mean of 0.220±0.179 (CV% 81.232) and 0.069


2009, the values of calculated PLI ranged from 0.077 to 2.418 during premonsoon

season with a mean of 0.663±0.570 (CV% 85.929); 0.00 to 1.166 during monsoon

season with a mean of 0.270±0.287 (CV% 106.1) and 0.140 to 1.532 during

postmonsoon season with a mean of 0.550±0.350 (CV% 63.7).

The overall low PLI values were observed in the river sediments, though

relatively higher values were observed at the Majher mana (1.236±0.515) which

indicates that the site is moderately polluted. The trend of PLI values in the sediments

indicates that the discharge of effluents from the Durgapur industrial complex is the

main source of contamination in the study area.

5.14.3.1 Spatial interpolation of Pollution Load Index in a GIS environment:

Spatial interpolation by krigging process reveals that except some portion of the

stretch between Shyampur - Majher Mana – Dhobi ghat (1<PLI>2) remaining portion

of the study area stretch of river Damodar falls under the no pollution (PLI<1)

category (Fig. 12). With respect to areal coverage out of 663.10 sq.km studied stretch

of river Damodar only 10.88 sq.km falls under the moderate pollution category.

5.14.4 Eco-toxicological assessment of the river sediments in relation to metal

contamination: It is observed from the results of the fractionation study that the

metals in the sediments are bound to different fractions with different strengths. The


[101]

strength values of different fractions can, therefore, give a clear indication of sediment

reactivity, which in turn assess the risk connected with the presence of metals in an

aquatic riverine environment. According to Perin et al. 1985 risk Assessment Code

(RAC) is a classification based on the percentage of metal in exchangeable fractions.

RAC assessed the availability of metals in sediments by applying a scale to the

percentage of loosely bound mobile fractions (Jain et al. 2008). This was important

because exchangeable, and carbonate bound fractions, which were weakly bonded

metals could equilibrate with the aqueous phase and thus became more rapidly

bioavailable. According to the scale exchangeable form of < 1% shows no risk, 1-

10% falls in the low risk zone, 11-30% indicates medium risk to biota, whereas 31-

50% is in the high risk zone.

The risk assessment code as applied to the present study reveals that 12.312%

of iron, 11.119% of manganese, 3.364% of lead and 3.164% of cadmium exist in

exchangeable fraction and therefore, comes under low to medium risk category and

may enter into food chain. The association of these metals with exchangeable fraction

may cause deleterious effects to aquatic life. Though a significant amount of the

metals was associated in the first three fractions i.e., exchangeable, oxidisable,

reducible that can be easily remobilized by changes in environmental conditions such

as pH, redox -potential, salinity, etc.

5.14.5 Evaluation of the environmental significance of metals in the river sediment

by comparison with sediment quality guideline (SQGs): The heavy metal

concentrations at each of the sediment sampling site were compared with the

consensus-based sediment quality guideline (SQGs) values referred to as the threshold

effect concentration (TEC) and the probable effect concentration (PEC) proposed by

MacDonald et al. 2000. The study of MacDonald et al. 2000 suggested that these

guidelines have been selected for comparison because various evaluations have

demonstrated that the consensus-based SQGs provide a unifying synthesis of the

existing SQGs, and reflect causal rather than correlative effects. The primary purposes

of sediment quality guidelines (SQGs) are to protect the aquatic biota from the

deleterious effects associated with sediment-bound contaminants, to rank and/or

prioritize contaminated areas or chemicals of concern for further investigation.


[102]

The present study addressed the assessment of the ecological relevance of

heavy metal pollution in the river Damodar. Calculated TEC value of Cd varied from

0.995 to 5.436 with a mean value of 1.984 showing the exceedance limit (0.99)

proposed by Mac-Donald et al. (2000). The high value of observed PEC of Cd in the

year 2009 premonsoon season, at Majher mana was 5.436. In case of Pb the overall

value of threshold effect concentration (TEC) varied from 36.254 to 256.53 with a

mean value 72.351. The value of observed PEC of Pb was high at Majher mana,

ranged from 155.249 to 256.53 with a mean value 156.72. Comparing the heavy metal

concentrations with the consensus-based TEC and PEC values developed by Mac

Donald et al. 2000, revealed that over 26.667% of Pb and 17.037% of Cd

concentration of the river bottom sediment samples exceeded the TEC, with most

sample concentrations falling below the PEC (except 4.450% of Pb and 0.741% of

Cd). The site Majher mana receives industrial waste water from various steel plants,

thermal power plants, chloralkalies, sponge iron and chemical industries and high

PEC of Cd and Pb may exerts harmful effects on sediment-dwelling organisms.

RE

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[103

]

Tab

le 5

.1: D

escr

iptiv

e st

atis

tical

ana

lysi

s of p

hysi

co-c

hem

ical

par

amet

ers

20

07

2008

20

09

A

B

C

A

B

C

A

B

C

pH

Min

7.

35

7.00

7.00

7.00

7.17

7.

007.

277.

407.

17

Max

8.

94

8.71

8.62

8.84

8.44

8.

488.

928.

538.

73

Ave

8.

08

7.76

7.98

8.01

7.83

7.

827.

977.

847.

90

SD

0.42

0.

410.

400.

460.

35

0.38

0.44

0.29

0.39

SE

M

0.08

1 0.

079

0.07

70.

089

0.06

7 0.

073

0.08

50.

056

0.07

5 C

V%

5.

198

5.28

45.

013

5.74

34.

470

4.85

95.

521

3.69

94.

937

Electrical Conductivity

Min

18

0.00

11

0.00

180.

0021

0.00

100.

00

140.

0020

0.00

100.

0018

0.00

M

ax

650.

00

450.

0071

0.00

690.

0054

0.00

65

0.00

710.

0052

0.00

590.

00

Ave

31

2.22

21

4.81

285.

1934

0.74

186.

30

237.

7830

5.19

223.

7028

8.89

SD

14

2.43

74

.54

120.

2413

7.75

85.2

2 10

5.00

125.

6591

.91

105.

48

SEM

27

.411

14

.346

23.1

3926

.511

16.4

01

20.2

0824

.181

17.6

8820

.299

C

V%

45

.619

34

.701

42.1

6140

.428

45.7

45

44.1

6041

.171

41.0

8536

.512

Total Dissolved Solid

Min

11

9.75

78

.99

127.

5514

1.55

71.2

4 95

.63

129.

4268

.52

108.

42

Max

43

6.71

28

8.74

482.

6448

2.18

363.

48

451.

3948

2.75

342.

9639

3.65

A

ve

204.

30

142.

8619

3.89

228.

4512

7.76

15

8.34

198.

7314

6.22

186.

76

SD

95.4

71

50.9

5484

.058

95.6

0157

.503

73

.139

83.8

7761

.138

70.7

42

SEM

18

.373

9.

806

16.1

7718

.399

11.0

67

14.0

7616

.142

11.7

6613

.614

C

V%

46

.731

35

.666

43.3

5341

.848

45.0

08

46.1

9042

.207

41.8

1237

.880

Calcium

Min

15

.569

7.

452

12.4

6012

.340

11.3

54

10.3

4614

.246

9.34

012

.345

M

ax

44.1

65

23.6

2528

.560

36.3

5231

.992

28

.650

48.9

5425

.162

36.7

83

Ave

23

.179

14

.815

20.8

6422

.795

17.8

43

18.8

3822

.889

15.6

4719

.708

SD

6.

123

3.88

84.

440

5.67

54.

663

4.56

57.

334

4.61

04.

641

SEM

1.

178

0.74

80.

854

1.09

20.

897

0.87

81.

411

0.88

70.

893

CV

%

26.4

16

26.2

4521

.280

24.8

9626

.133

24

.232

32.0

4029

.462

23.5

47

A_ Pr

emon

soon

, B_

Mon

soon

, C_

Post

mon

soon

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USS

ION

[104

]

Tab

le 5

.1: C

ontin

ued

20

07

2008

20

09

A

B

C

A

B

C

A

B

C

Magnesium

Min

3.

733

3.23

33.

340

5.43

93.

548

3.25

44.

186

3.25

65.

478

Max

28

.513

13

.265

19.4

0017

.469

15.4

84

17.3

5416

.456

15.3

5527

.117

A

ve

10.6

87

7.68

49.

438

11.3

867.

775

9.85

49.

847

8.22

99.

972

SD

4.96

1 2.

834

3.97

13.

520

3.15

9 3.

853

3.53

02.

852

3.88

3 SE

M

0.95

5 0.

545

0.76

40.

677

0.60

8 0.

742

0.67

90.

549

0.74

7 C

V%

46

.422

36

.880

42.0

8030

.918

40.6

28

39.1

0635

.846

34.6

5938

.943

Sodium

Min

8.

200

4.28

06.

140

6.18

06.

520

8.63

07.

350

5.35

05.

350

Max

44

.350

50

.460

35.7

0049

.420

45.6

20

28.3

5048

.470

24.3

0039

.540

A

ve

18.4

07

12.3

7514

.398

17.8

5814

.196

15

.718

19.3

2313

.367

14.6

21

SD

8.84

2 10

.600

7.51

310

.521

8.05

5 5.

517

10.4

634.

723

6.61

8 SE

M

1.70

2 2.

040

1.44

62.

025

1.55

0 1.

062

2.01

40.

909

1.27

4 C

V%

48

.037

85

.658

52.1

7958

.918

56.7

45

35.0

9954

.150

35.3

3045

.263

Potassium

Min

2.

513

1.32

41.

271

1.25

31.

254

1.26

42.

161

1.24

51.

210

Max

23

.580

9.

186

10.3

5224

.882

12.3

64

22.4

4522

.542

7.53

610

.375

A

ve

6.80

8 3.

860

5.21

08.

112

4.28

1 6.

253

6.02

32.

920

4.99

0 SD

4.

331

2.12

62.

810

5.53

32.

676

4.78

24.

232

1.84

02.

289

SEM

0.

834

0.40

90.

541

1.06

50.

515

0.92

00.

814

0.35

40.

441

CV

%

63.6

20

55.0

7253

.942

68.2

0962

.495

76

.477

70.2

6463

.015

45.8

73

Bicarbonate

Min

52

.00

44.0

056

.00

52.0

072

.00

52.0

044

.00

52.0

052

.00

Max

20

4.0

164.

019

2.0

188.

019

2.0

184.

017

6.0

192.

015

2.0

Ave

11

0.2

83.1

100.

411

1.2

96.7

10

1.2

114.

696

.711

0.1

SD

37.7

90

24.7

1626

.378

35.2

8524

.129

30

.439

31.9

7224

.907

25.8

04

SEM

7.

273

4.75

75.

076

6.79

14.

644

5.85

86.

153

4.79

34.

966

CV

%

34.2

86

29.7

3826

.261

31.7

3524

.942

30

.083

27.9

0925

.746

23.4

35

A_ Pr

emon

soon

, B_

Mon

soon

, C_

Post

mon

soon

RE

SUL

TS

AN

D D

ISC

USS

ION

[105

]

Tab

le 5

.1: C

ontin

ued

20

07

2008

20

09

A

B

C

A

B

C

A

B

C

Sulphate

Min

9.

479

5.63

47.

445

10.4

697.

353

10.2

257.

650

5.35

28.

629

Max

81

.376

67

.142

68.6

8284

.049

42.4

57

41.6

5278

.514

39.3

5457

.652

A

ve

29.8

52

18.4

7925

.982

31.1

4116

.225

20

.833

27.1

7114

.986

19.3

32

SD

20.2

80

12.9

6515

.419

20.1

109.

279

8.84

920

.254

7.60

911

.687

SE

M

3.90

3 2.

495

2.96

73.

870

1.78

6 1.

703

3.89

81.

464

2.24

9 C

V%

67

.934

70

.164

59.3

4564

.578

57.1

86

42.4

7874

.544

50.7

7360

.456

Chloride

Min

2.

688

1.29

84.

572

8.24

12.

594

7.34

52.

935

4.32

56.

325

Max

72

.285

24

.249

56.8

2956

.476

32.2

10

50.4

5172

.643

38.4

2654

.255

A

ve

18.3

36

7.13

312

.999

16.7

999.

520

14.4

3318

.228

10.7

3115

.202

SD

13

.186

5.

526

11.5

259.

839

5.54

9 8.

588

15.9

917.

198

10.6

97

SEM

2.

538

1.06

32.

218

1.89

31.

068

1.65

33.

078

1.38

52.

059

CV

%

40.3

53

18.3

1939

.830

31.9

1817

.521

25

.966

48.7

1322

.711

35.8

06

H4SiO4

Min

7.

368

1.36

37.

354

4.08

71.

174

3.66

57.

309

1.03

04.

640

Max

27

.511

17

.540

16.7

5127

.434

17.9

36

20.5

2928

.452

23.4

4925

.945

A

ve

14.1

94

9.26

811

.432

14.0

289.

635

10.5

3716

.028

10.1

6012

.720

SD

4.

263

4.29

22.

731

5.02

54.

362

4.62

16.

032

6.03

84.

133

SEM

0.

820

0.82

60.

526

0.96

70.

839

0.88

91.

161

1.16

20.

795

CV

%

30.0

35

46.3

1723

.893

35.8

2145

.269

43

.852

37.6

3759

.426

32.4

90

Nitrate

Min

0.

035

0.00

00.

000

0.18

40.

000

0.00

00.

068

0.15

40.

059

Max

2.

833

3.95

62.

982

4.11

93.

846

2.44

52.

742

2.09

92.

816

Ave

0.

824

0.92

20.

764

0.84

10.

751

0.64

30.

646

0.86

00.

754

SD

0.78

6 0.

818

0.57

50.

785

0.85

3 0.

566

0.47

00.

535

0.68

6 SE

M

0.03

5 0.

000

0.00

00.

184

0.00

0 0.

000

0.06

80.

154

0.05

9 C

V%

2.

833

3.95

62.

982

4.11

93.

846

2.44

52.

742

2.09

92.

816

A_ Pr

emon

soon

, B_

Mon

soon

, C_

Post

mon

soon


[106]

Table 5.1: Continued

2007 2008 2009 A B C A B C A B C

Phos

phat

e

Min 0.015 0.015 0.012 0.010 0.028 0.017 0.020 0.034 0.010Max 1.155 1.024 1.058 0.350 1.382 0.424 0.880 1.250 1.090Ave 0.229 0.237 0.134 0.099 0.310 0.135 0.175 0.236 0.152SD 0.308 0.291 0.237 0.090 0.424 0.120 0.252 0.357 0.275SEM 0.059 0.056 0.046 0.017 0.082 0.023 0.049 0.069 0.053CV% 134.2 122.6 176.8 91.7 136.8 88.8 144.0 151.7 180.7

Lead

Min 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000Max 17.92 9.487 14.25 3469 7.583 1562 5649 9.547 62.50Ave 4.186 1.218 1.709 131.4 0.789 61.50 265.6 0.992 3.726SD 6.363 2.506 3.260 667.1 1.948 300.4 1112 2.347 12.03SEM 1.225 0.482 0.627 128.4 0.375 57.80 213.9 0.452 2.315CV% 152.0 205.8 190.8 507.9 246.9 488.4 418.7 236.7 322.8

Cad

miu

m


Man

gane

se


Iron


A_ Premonsoon, B_ Monsoon, C_ Postmonsoon


[107]

Table 5.2: Factor pattern (after varimax rotation)

variables F1 F2 F3 pH -0.251 0.085 -0.791EC 0.927 0.246 0.007 TDS 0.927 0.263 0.023 NO3

– 0.775 -0.090 -0.264 PO4

3– 0.057 0.489 0.424 Ca2+ 0.747 0.495 -0.165 Mg2+ 0.675 0.522 -0.258 Na+ 0.752 0.384 0.338 K+ 0.787 0.399 0.150 HCO3

– 0.154 0.884 -0.217 SO4

2– 0.897 0.115 -0.055 Cl– 0.893 0.123 0.110 H4SiO4 -0.232 -0.171 0.559Pb 0.821 -0.004 0.309 Cd 0.923 0.203 -0.023 Fe 0.763 0.132 -0.074 Mn 0.142 0.648 -0.068 Eigenvalue 9.312 1.838 1.353 % variance 54.776 10.812 7.957 Cumulative % 54.776 65.588 73.546 *Values in bold indicates significant loading


[108]

Table 5.3: Average ionic ratio of three years (2007, 2008 and 2009) and in three

seasons

SITES Ionic ratio (meq/l)

Ca2+/SO42� Ca2+/Mg2+ Na+/Cl� Ca2++Mg2+/Na+

+K+

S1 3.215±1.066 1.316±0.195 2.463±0.960 3.355±0.983 S2 3.258±1.532 1.243±0.315 1.810±0.851 2.986±0.941 S3 1.812±0.856 1.147±0.212 1.055±0.392 4.246±1.509 S4 2.305±1.257 1.510±0.551 1.778±1.867 2.561±1.301 S5 1.490±0.665 1.474±0.472 3.293±2.182 1.687±0.603 S6 1.863±1.018 1.296±0.146 1.617±0.818 2.844±1.082 S7 2.394±1.124 1.539±0.491 2.661±1.612 2.322±1.126 S8 2.808±1.313 1.570±0.716 2.461±1.715 2.446±0.950 S9 3.376±1.712 1.143±0.155 1.584±0.999 3.039±1.720 S10 2.733±0.868 1.324±0.332 2.292±0.754 2.648±0.982 S11 2.694±1.269 1.249±0.296 2.936±1.702 2.372±0.676 S12 3.083±1.191 1.202±0.125 2.228±0.624 2.255±1.228 S13 2.287±0.891 1.267±0.308 2.132±0.678 2.230±0.838 S14 2.209±1.208 1.343±0.308 2.392±2.200 2.205±0.694 S15 2.653±1.097 1.493±0.251 3.050±1.418 1.823±0.497 S16 2.078±1.306 1.198±0.331 1.761±1.166 2.207±1.678 S17 1.620±0.770 1.363±0.631 1.159±0.939 2.110±1.793 S18 4.476±2.966 1.160±0.273 3.272±2.770 2.798±1.322 S19 3.361±0.959 1.372±0.470 2.776±2.279 2.641±0.990 S20 3.328±1.613 1.293±0.253 2.647±2.380 3.294±1.430 S21 3.047±1.379 1.316±0.267 3.252±2.250 1.996±0.626 S22 2.227±1.034 1.640±0.473 2.024±1.232 2.354±1.620 S23 1.600±0.694 1.594±0.346 2.149±0.899 1.833±0.913 S24 2.827±1.197 1.332±0.175 2.301±1.064 2.351±1.033 S25 3.053±1.027 1.332±0.175 2.716±1.271 2.535±0.777 S26 3.707±1.566 1.235±0.331 2.097±1.072 2.666±0.870 S27 4.009±1.816 1.390±0.284 2.077±0.763 2.554±0.863

RE

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AN

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ISC

USS

ION

[109

]

Tab

le 5

.4: D

escr

iptiv

e st

atis

tical

ana

lysi

s of i

rrig

atio

n w

ater

qua

lity

para

met

ers

2007

20

08

2009

A

B

C

A

B

C

A

B

C

Sodium adsorption ration

Min

0.

404

0.23

4 0.

285

0.32

7 0.

316

0.48

4 0.

359

0.28

7 0.

302

Max

2.

649

2.99

7 2.

252

2.81

1 2.

480

1.34

5 3.

017

1.34

7 2.

222

Ave

0.

978

0.70

6 0.

774

1.00

1 0.

732

0.86

4 1.

016

0.77

5 0.

814

SD

0.52

8 0.

642

0.49

8 0.

624

0.44

2 0.

264

0.61

9 0.

311

0.38

1 SE

M

0.10

2 0.

124

0.09

6 0.

120

0.08

5 0.

051

0.11

9 0.

060

0.07

3 C

V

54.0

21

90.8

47

64.4

03

62.3

56

60.3

87

30.5

82

60.9

32

40.1

08

46.8

53

Sodium percentage

Min

11

.843

15

.986

15

.427

13

.623

17

.510

16

.533

17

.349

14

.236

13

.478

M

ax

61.4

90

60.4

61

43.5

39

61.1

59

56.3

33

55.2

69

48.9

17

47.9

74

46.7

79

Ave

32

.676

30

.165

28

.936

30

.825

31

.667

33

.074

32

.828

31

.694

29

.875

SD

11

.106

10

.283

7.

919

10.4

76

9.87

3 9.

863

8.66

0 9.

888

7.87

9 SE

M

2.13

7 1.

979

1.52

4 2.

016

1.90

0 1.

898

1.66

7 1.

903

1.51

6 C

V

33.9

87

34.0

88

27.3

69

33.9

85

31.1

78

29.8

22

26.3

80

31.1

98

26.3

72

Permeability index

Min

39

.607

62

.776

57

.894

49

.453

60

.590

48

.700

51

.866

61

.810

40

.521

M

ax

106.

904

140.

458

130.

677

107.

999

117.

343

124.

515

109.

577

141.

736

116.

414

Ave

77

.069

92

.831

81

.436

75

.789

90

.515

83

.327

82

.018

93

.657

83

.401

SD

17

.174

21

.030

18

.053

14

.849

15

.756

17

.512

15

.366

20

.598

15

.566

SE

M

3.30

5 4.

047

3.47

4 2.

858

3.03

2 3.

370

2.95

7 3.

964

2.99

6 C

V

22.2

84

22.6

54

22.1

68

19.5

92

17.4

07

21.0

16

18.7

35

21.9

92

18.6

64

Magnesium hazard

Min

28

.268

34

.012

22

.753

35

.896

33

.980

25

.435

28

.248

34

.378

36

.012

M

ax

63.9

45

50.3

05

52.9

92

52.0

99

50.7

91

54.9

12

50.5

92

57.6

22

54.8

59

Ave

41

.939

45

.192

41

.334

44

.814

40

.860

45

.119

40

.989

46

.034

44

.863

SD

6.

991

4.21

6 7.

523

4.46

9 4.

069

6.63

5 5.

375

4.58

0 4.

192

SEM

1.

345

0.81

1 1.

448

0.86

0 0.

783

1.27

7 1.

034

0.88

1 0.

807

CV

16

.668

9.

329

18.2

00

9.97

3 9.

958

14.7

06

13.1

14

9.95

0 9.

343

Residual sodium carbonate

Min

-2

.133

-0

.878

-1

.477

-1

.946

-1

.139

-1

.483

-1

.811

-0

.749

-2

.493

M

ax

1.75

8 1.

278

1.37

9 1.

478

1.11

8 0.

754

0.96

8 1.

539

1.21

2 A

ve

-0.2

29

-0.0

09

-0.1

71

-0.2

52

0.05

6 -0

.092

-0

.075

0.

128

0.00

1 SD

0.

804

0.48

3 0.

593

0.68

7 0.

495

0.52

6 0.

728

0.55

3 0.

725

SEM

0.

155

0.09

3 0.

114

0.13

2 0.

095

0.10

1 0.

140

0.10

6 0.

139

RE

SUL

TS

AN

D D

ISC

USS

ION

[110

]

Tab

le 5

.5: S

patio

-tem

pora

l dis

trib

utio

n of

man

gane

se (M

n) (µ

g/g)

in th

e D

amod

ar r

iver

bot

tom

sedi

men

ts

20

0720

08

2009

Site

s Pr

emon

soon

m

onso

on

Post

mon

soon

Pr

emon

soon

m

onso

on

Post

mon

soon

Pr

emon

soon

m

onso

on

Post

mon

soon

S1

14

3.34

6 23

6.25

924

5.14

715

5.36

578

.547

12

4.25

816

9.34

926

5.45

896

.352

S3

69

.485

48

.374

122.

470

232.

589

357.

152

191.

450

142.

126

245.

259

93.4

52

S4

204.

852

94.2

5717

5.39

717

9.48

774

.215

14

2.86

918

6.47

584

.270

145.

987

S6

79.8

55

56.3

3425

8.49

013

5.32

828

7.36

5 19

6.98

627

5.32

534

7.91

017

4.35

0 S9

53

.485

64

.259

435.

658

97.3

2914

7.34

9 87

.374

152.

224

257.

463

45.4

52S1

0 12

4.25

0 73

.468

354.

177

134.

520

248.

320

122.

425

69.3

5414

7.23

513

8.25

4 S1

1 25

6.47

2 98

.247

175.

462

164.

257

258.

239

275.

354

275.

439

176.

429

168.

753

S12

207.

432

109.

762

197.

445

155.

979

156.

963

97.2

4517

4.24

992

.451

104.

194

S14

76.2

41

178.

256

236.

327

175.

254

258.

142

63.2

5726

3.32

945

3.78

525

6.34

5 S1

7 76

.258

32

5.35

850

6.65

724

5.69

834

9.96

5 24

8.39

946

9.78

563

5.45

025

8.52

5 S1

8 12

5.45

2 27

5.67

332

6.34

586

.347

452.

145

76.3

7415

0.34

827

6.32

976

.324

S1

9 15

3.75

4 25

7.24

314

9.78

910

7.23

124

3.85

5 98

.786

108.

756

275.

324

176.

752

S22

96.4

32

146.

348

205.

345

243.

325

165.

435

46.3

4710

6.24

812

2.32

556

.342

S2

5 86

.140

14

2.52

518

6.34

516

7.96

320

7.31

4 88

.326

147.

320

258.

347

269.

463

S27

125.

475

125.

475

105.

245

147.

324

186.

248

96.2

4714

7.48

012

7.63

413

5.42

5 M

in

53.4

85

48.3

7410

5.24

586

.347

74.2

15

46.3

4769

.354

84.2

7045

.452

M

ax

256.

472

325.

358

506.

657

245.

698

452.

145

275.

354

469.

785

635.

450

269.

463

Ave

12

5.26

2 14

8.78

924

5.35

316

1.86

623

1.41

7 13

0.38

018

9.18

725

1.04

514

6.39

8 SD

59

.121

87

.279

114.

696

49.1

2910

3.94

2 67

.998

98.5

6314

6.61

171

.960

SE

M

15.2

65

22.5

3529

.614

12.6

8526

.838

17

.557

25.4

4937

.855

18.5

80

CV

%

47.1

98

58.6

6046

.747

30.3

5244

.915

52

.154

52.0

9858

.400

49.1

53

RE

SUL

TS

AN

D D

ISC

USS

ION

[111

]

Tab

le 5

.6: S

patio

-tem

pora

l dis

trib

utio

n of

cad

miu

m (C

d) (µ

g/g)

in th

e D

amod

ar r

iver

bot

tom

sedi

men

ts

20

07

2008

20

09Si

tes

Prem

onso

on

mon

soon

Po

stm

onso

on

Prem

onso

on

mon

soon

Po

stm

onso

on

Prem

onso

on

mon

soon

Po

stm

onso

on

S1

0.46

9 0.

076

0.54

20.

345

0.01

4 0.

005

0.42

50.

000

0.24

5 S3

0.

995

0.59

5 0.

153

0.32

40.

046

0.54

20.

534

0.03

50.

475

S4

0.53

4 0.

347

0.27

51.

247

0.54

7 0.

634

0.88

50.

248

0.63

5 S6

1.

823

0.53

2 0.

147

0.36

40.

249

0.76

22.

657

0.05

40.

475

S9

2.45

2 0.

568

0.78

62.

635

0.22

5 0.

425

1.49

60.

428

1.75

4 S1

0 1.

865

0.45

2 0.

587

2.75

40.

147

0.54

81.

247

0.00

50.

326

S11

1.34

9 0.

016

0.24

20.

176

0.00

3 0.

263

2.45

60.

016

0.04

7 S1

2 0.

574

0.07

5 0.

228

0.27

50.

021

0.27

41.

149

0.05

70.

035

S14

0.58

7 0.

527

0.53

41.

250

0.08

6 0.

276

0.59

30.

175

0.24

7 S1

7 4.

436

1.32

4 1.

748

2.92

70.

686

0.92

55.

436

0.83

52.

296

S18

1.42

5 0.

009

0.53

90.

542

0.00

4 0.

365

0.17

20.

557

0.55

8 S1

9 0.

586

0.30

6 0.

406

1.05

80.

025

0.25

40.

086

0.02

50.

324

S22

0.45

3 0.

000

0.04

60.

058

0.16

5 0.

027

0.39

80.

008

0.05

3 S2

5 0.

058

0.00

9 0.

005

0.00

50.

000

0.00

20.

003

0.00

50.

035

S27

0.04

6 0.

008

0.03

50.

095

0.00

5 0.

015

0.32

80.

009

0.00

4 M

in

0.04

6 0.

000

0.00

50.

005

0.00

0 0.

002

0.00

30.

000

0.00

4 M

ax

4.43

6 1.

324

1.74

82.

927

0.68

6 0.

925

5.43

60.

835

2.29

6 A

ve

1.17

7 0.

323

0.41

80.

937

0.14

8 0.

354

1.19

10.

164

0.50

1 SD

1.

143

0.36

4 0.

436

1.03

30.

209

0.28

61.

424

0.25

20.

660

SEM

0.

295

0.09

4 0.

113

0.26

70.

054

0.07

40.

368

0.06

50.

170

CV

%

97.1

4 11

2.81

10

4.26

110.

2214

1.21

80

.76

119.

5515

3.97

131.

77

RE

SUL

TS

AN

D D

ISC

USS

ION

[112

]

Tab

le 5

.7: S

patio

-tem

pora

l dis

trib

utio

n of

iron

(Fe)

(µg/

g) in

the

Dam

odar

riv

er b

otto

m se

dim

ents

2007

2008

20

09Si

tes

Prem

onso

on

mon

soon

Po

stm

onso

on

Prem

onso

on

mon

soon

Po

stm

onso

on

Prem

onso

on

mon

soon

Po

stm

onso

on

S1

1524

96

524

5724

5826

35

4853

4632

2587

3856

S3

56

24

4632

3654

2673

3586

34

5846

3526

8932

48

S4

2426

10

5852

3646

3536

58

4265

3452

2348

5348

S6

12

547

7363

1863

519

632

6699

11

596

1047

491

3415

007

S9

6324

23

5472

5614

5212

25

3754

4325

2152

3986

S1

0 45

23

1452

2415

1475

762

2563

2452

634

1452

S1

1 93

65

7254

1025

463

2442

58

7634

6345

7634

8342

S1

2 85

9 78

696

889

258

9 97

678

5489

610

27

S14

786

963

3426

358

865

1245

2453

453

864

S17

8063

63

5256

3248

5236

58

5968

1256

953

2686

34

S18

1024

5 56

3246

2835

4825

48

4632

9435

5632

7563

S1

9 12

95

1237

1475

4725

1542

12

7463

4952

8576

54

S22

3652

28

714

7525

6323

54

3785

5296

1463

3852

S2

5 25

64

196

1053

1742

1196

23

5412

4553

680

0 S2

7 10

457

984

1175

1295

982

1246

1037

1098

1147

M

in

786

196

968

358

589

976

1037

453

800

Max

12

547

7363

1863

519

632

6699

11

596

1256

991

3415

007

Ave

53

50

2768

4649

3908

2437

39

7455

0431

9148

52

SD

3949

26

6446

7646

7417

00

2840

3390

2753

3988

SE

M

1020

68

812

0712

0743

9 73

387

571

110

30

CV

%

74

9610

112

070

71

6286

82

RE

SUL

TS

AN

D D

ISC

USS

ION

[113

]

Tab

le 5

.8: S

patio

-tem

pora

l dis

trib

utio

n of

lead

(Pb)

(µg/

g) in

the

Dam

odar

riv

er b

otto

m se

dim

ents

2007

20

08

2009

Site

s Pr

emon

soon

m

onso

on

Post

mon

soon

Pr

emon

soon

m

onso

on

Post

mon

soon

Pr

emon

soon

m

onso

on

Post

mon

soon

S1

10

.842

5.

421

9.24

719

.752

5.42

1 7.

245

11.8

217.

425

10.4

54

S3

29.9

24

24.4

62

27.1

7527

.259

22.6

32

19.4

3631

.249

15.1

4729

.364

S4

28

.254

30

.358

17

.398

22.9

7312

.989

19

.931

52.4

7524

.254

45.7

54

S6

67.3

18

45.3

26

38.2

5872

.649

48.7

79

59.8

5474

.743

39.9

3458

.758

S9

37

.425

31

.348

28

.148

49.5

2431

.472

36

.429

77.9

5236

.254

38.4

15

S10

37.4

82

26.2

58

24.7

4838

.416

24.6

54

28.4

1564

.974

29.3

2249

.242

S1

1 23

.570

12

.247

25

.235

25.3

6514

.298

14

.854

52.4

7320

.471

44.3

69

S12

25.7

53

24.5

72

19.3

5925

.324

20.4

78

30.2

2546

.258

24.9

5732

.125

S1

4 25

.942

11

.754

14

.634

23.3

1916

.732

24

.696

35.5

3624

.154

22.2

47

S17

198.

247

52.8

65

155.

249

204.

472

52.4

77

86.3

4825

6.53

115

7.53

420

8.24

7 S1

8 38

.316

29

.547

32

.423

42.7

6429

.964

27

.149

50.1

4815

.422

30.4

75

S19

28.3

47

12.7

49

18.7

5828

.695

9.72

8 16

.234

30.4

728.

695

12.7

58

S22

12.2

49

8.34

1 11

.346

15.1

470.

964

8.54

214

.694

6.42

911

.852

S2

5 11

.247

0.

987

7.86

011

.786

0.85

7 10

.875

16.4

517.

374

9.78

4 S2

7 10

.725

2.

475

14.4

3215

.863

10.9

68

12.4

7712

.754

5.76

511

.425

M

in

10.7

25

0.98

7 7.

860

11.7

860.

857

7.24

511

.821

5.76

59.

784

Max

19

8.24

7 52

.865

15

5.24

920

4.47

252

.477

86

.348

256.

531

157.

534

208.

247

Ave

39

.043

21

.247

29

.618

41.5

5420

.161

26

.847

55.2

3528

.209

41.0

18

SD

46.3

99

15.3

60

35.8

2447

.716

15.4

99

21.1

8059

.790

37.4

0748

.918

SE

M

11.9

80

3.96

6 9.

250

12.3

204.

002

5.46

915

.438

9.65

812

.631

C

V%

11

8.84

2 72

.293

12

0.95

211

4.83

076

.877

78

.891

108.

245

132.

606

119.

260


[114]

Table: 5.9: Assignment of the principle descriptive IR absorption bands

Sites Frequency (Cm–1)

Assignment

S1 Dishergarh 3624.64 -OH stretching of alcohol and phenol 3407.00 -N-H stretching of amine 2364.64 C=O group 1627.97 Fingerprint region of C=O, C-O and–OH

group 1033.53 C-O stretching of ether 774.82 C-Cl stretching of alkyl halide 685.31 C-Cl stretching of alkyl halide

S3

Purbanchal 3756.98 Asymmetric stretching of water 3696.45 –O-H stretching free 3624.20 –O-H stretching H-bonded 3426.52 -N-H stretching of amine 2367.82 S-H group 2339.30 CN stretching 1634.26 Presence of carbonate group 1383.71 C-F stretching 1034.47 C-O stretching of ether 779.86 C-Cl stretching of alkyl halide 690.37 C-Cl stretching of alkyl halide 465.98 O-P-O bending vibration of phosphate group

S3 Ramghat 3623.92 -OH stretching of alcohol and phenol 3429.89 -N-H stretching of amine 2364.31 C=O group 1634.62 Fingerprint region of C=O, C-O and–OH

group 1034.00 C-O stretching of ether 688.11 C-Cl stretching

S4 Chinakuri 3696.92 –O-H stretching free 3624.20 –O-H stretching H-bonded 3427.86 -N-H stretching of amine 2364.94 S-H group 1631.38 Presence of carbonate group 1032.00 C-O stretching of ether 777.85 C-Cl stretching of alkyl halide 690.37 C-Cl stretching of alkyl halide 466.00 O-P-O bending vibration of phosphate group

S5 Damodar Railway Station

3407.80 -N-H stretching of primary amine 2364.90 S-H group 1033.00 C-O stretching of ether 777.90 C-Cl stretching of alkyl halide


[115]

Table: 5.9: continued Sites Frequency

(Cm–1)Assignment

S6 Dihika 3630.25 –O-H stretching H-bonded 2429.00 P-H stretching of phosphine 2364.14 S-H group 1627.97 Presence of carbonate group 688.11 C-Cl stretching of alkyl halide

S7

Madan Dihi 3405.80 -N-H stretching of primary amine 2926.70 C-H stretching of alkane 2365.59 S-H group 1657.51 C=O stretching of amide 1036.32 C-O stretching of ether 780.99 C-Cl stretching of alkyl halide 464.89 O-P-O bending vibration of phosphate group

S8 Burnpur River Side

3630.25 –O-H stretching H-bonded 3422.96 -N-H stretching of primary amine 2929.97 C-H stretching of alkane 2330.53 S-H group 1897.72 Transition metal carbonyls 1630.76 Presence of carbonate group 623.77 C-Cl stretching of alkyl halide

S9 Narayankuri 3695.696 –O-H stretching free 3622.04 -OH stretching of alcohol and phenol 3426.23 -N-H stretching of primary amine 2366.66 S-H group 1634.12 Presence of carbonate group 1030.66 C-O stretching of ether 789.29 C-Cl stretching of alkyl halide 688.11 C-Cl stretching of alkyl halide 535.87 C-Br stretching alkyl halide

S10 Mejhiaghat 3428.26 -N-H stretching of primary amine 2364.72 S-H group 1631.71 Presence of carbonate group 1034.38 C-O stretching of ether

S11 Madanpur 3425.89 -N-H stretching of primary amine 2935.57 C-H stretching of alkane 2369.74 S-H group 1633.56 Presence of carbonate group 1045.63 C-O stretching of ether 777.62 C-Cl stretching of alkyl halide


[116]


(Cm–1)Assignment

S12 Baska 3697.47 –O-H stretching free 3416.00 N-H stretching of primary amine 2364.14 S-H group 1879.72 Transition metal carbonyls 1630.76 Presence of carbonate group 690.90 C-Cl stretching of alkyl halide

450.34 O-P-O bending vibration of phosphate group

S13 Pursa 3411.76 N-H stretching of primary amine 2380.95 S-H group

2016.80 Cyanide ion, thiocyanate ion, and related ion

1630.76 Presence of carbonate group 1432.16 Aromatic C=C stretching 1009.79 C-O stretching of ether 791.60 C-Cl stretching of alkyl halide


S14 Ashishnagar 3635.85 –O-H stretching H-bonded 3484.59 N-H stretching of amide 3400.56 -N-H stretching of primary amine 2941.17 C-H stretching of alkane 2369.74 S-H group 1874.12 Transition metal carbonyls 1630.76 Presence of carbonate group 906.29 Aromatic phosphate (P-O-C) stretching 749.65 C-Cl stretching of alkyl halide 537.06 C-Br stretching alkyl halide

S15 Durgapur Barrage

3624.20 –O-H stretching H-bonded 2375.35 S-H group 1630.76 Presence of carbonate group 1000.00 C-O stretching of ether 783.21 C-Cl stretching of alkyl halide 693.70 C-Cl stretching of alkyl halide

S16 Shyampur 3630.25 –O-H stretching H-bonded 2929.97 C-H stretching of alkane 2352.94 S-H group 1633.56 Presence of carbonate group



[117]


(Cm–1)Assignment

S17 Majher mana

3495.79 -N-H stretching of primary amine 2347.33 S-H group 1874.12 C=O stretching of acid chloride 18.04.19 C=O stretching of anhydride 1588.81 Aromatic C=C stretching 1521.67 -N-O stretching of nitro group 1244.75 C-O stretching of acid 1037.76 C-O stretching of anhydride

S18 Dhobighat 3623.76 –O-H stretching H-bonded 3426.66 -N-H stretching of primary amine 1029.00 C-O stretching of ether 776.89 C-Cl stretching

S19 Silampur 3429.77 -N-H stretching of primary amine 2365.13 S-H group 1037.00 C-O stretching of ether 780.19 C-Cl stretching of alkyl halide

S20 Randiha 3624.64 –O-H stretching H-bonded 3428.42 -N-H stretching of primary amine 2364.14 S-H group 1625.17 Presence of carbonate group 1040.00 C-O stretching of ether 778.47 C-Cl stretching of alkyl halide 688.11 C-Cl stretching of alkyl halide

S21 Sillaghat 3630.25 O-H stretching of alcohol and phenol 3429.00 -N-H stretching of amine 2364.14 S-H group 1879.72 C=O stretching of acid chloride 1627.97 CO32_ group 688.00 C-Cl stretching of alkyl halide

S22 Gohogram 3756.87 Water contaminant 3630.25 –O-H stretching of alcohol and phenol 3427.34 -N-H stretching of amine 2365.51 C=O group 1627.97 Fingerprint region of C=O, C-O and–OH 1035.28 C-O stretching of ether 778.42 C-Cl stretching of alkyl halide 690.90 C-Cl stretching of alkyl halide


[118]


(Cm–1)Assignment

S23 Sikarpur

3635.85 –O-H stretching H-bonded 3408.05 -N-H stretching of primary amine 2364.93 S-H group 1625.17 Presence of carbonate group 1036.74 C-O stretching of ether 778.21 C-Cl stretching of alkyl halide 537.06 C-Br stretching of alkyl halide

S24 Sadarghat 3417.00 -N-H stretching of primary amine 2366.17 S-H group 1030.35 C-O stretching of ether 777.23 C-Cl stretching of alkyl halide 465.33 O-P-O bending vibration of phosphate group

S25 Pala Srirampur

3630.25 –O-H stretching H-bonded 3422.96 -N-H stretching of primary amine 2005.60 Cyanide ion, thiocyanate ion, and related ion 1630.76 Presence of carbonate group 1443.35 Aromatic C=C 772.02 C-Cl stretching of alkyl halide 537.06 C-Br stretching of alkyl halide

S26 Barsul 3703.08 O-H stretching (free) 3450.33 N-H stretching of amine 2336.13 CN stretching 1876.92 C=O stretching of acid chloride 520.27 C-Br stretching of alkyl halide

S27 Pallaroad 3906.01 OH2 stretching 3753.85 N-H stretching of amine 3624.71 -OH stretching 3426.88 N-H stretching of amide 2371.51 C=O group 1633.27 Carbonate group 1031.71 C-O stretching of anhydride 776.91 C-Cl stretching of alkyl halide 690.72 C-Cl stretching alkyl halide 532.45 C-Br stretching alkyl halide

RE

SUL

TS

AN

D D

ISC

USS

ION

[119

]

Tab

le 5

.10:

Spa

tio-t

empo

ral v

aria

tion

of E

nric

hmen

t Fac

tor

of m

anga

nese

in th

e D

amod

ar r

iver

bot

tom

sedi

men

ts

20

07

2008

20

09Si

tes

Prem

onso

on

Mon

soon

Po

stm

onso

on

Prem

onso

on

Mon

soon

Po

stm

onso

on

Prem

onso

on

Mon

soon

Po

stm

onso

on

S1

0.16

9 0.

278

0.28

80.

183

0.09

2 0.

146

0.19

90.

312

0.11

3 S3

0.

082

0.05

7 0.

144

0.27

40.

420

0.22

50.

167

0.28

90.

110

S4

0.24

1 0.

111

0.20

60.

211

0.08

7 0.

168

0.21

90.

099

0.17

2 S6

0.

094

0.06

6 0.

304

0.15

90.

338

0.23

20.

324

0.40

90.

205

S9

0.06

3 0.

076

0.51

30.

115

0.17

3 0.

103

0.17

90.

303

0.05

3 S1

0 0.

146

0.08

6 0.

417

0.15

80.

292

0.14

40.

082

0.17

30.

163

S11

0.30

2 0.

116

0.20

60.

193

0.30

4 0.

324

0.32

40.

208

0.19

9 S1

2 0.

244

0.12

9 0.

232

0.18

40.

185

0.11

40.

205

0.10

90.

123

S14

0.09

0 0.

210

0.27

80.

206

0.30

4 0.

074

0.31

00.

534

0.30

2 S1

7 0.

090

0.38

3 0.

596

0.28

90.

412

0.29

20.

553

0.74

80.

304

S18

0.14

8 0.

324

0.38

40.

102

0.53

2 0.

090

0.17

70.

325

0.09

0 S1

9 0.

181

0.30

3 0.

176

0.12

60.

287

0.11

60.

128

0.32

40.

208

S22

0.11

3 0.

172

0.24

20.

286

0.19

5 0.

055

0.12

50.

144

0.06

6 S2

5 0.

101

0.16

8 0.

219

0.19

80.

244

0.10

40.

173

0.30

40.

317

S27

0.14

8 0.

148

0.12

40.

173

0.21

9 0.

113

0.17

40.

150

0.15

9 M

in

0.06

3 0.

057

0.12

40.

102

0.08

7 0.

055

0.08

20.

099

0.05

3 M

ax

0.30

2 0.

383

0.59

60.

289

0.53

2 0.

324

0.55

30.

748

0.31

7 A

ve

0.14

7 0.

175

0.28

90.

190

0.27

2 0.

153

0.22

30.

295

0.17

2 SD

0.

070

0.10

3 0.

135

0.05

80.

122

0.08

00.

116

0.17

20.

085

SEM

0.

018

0.02

7 0.

035

0.01

50.

032

0.02

10.

030

0.04

50.

022

CV

%

47.1

98

58.6

60

46.7

4730

.352

44.9

15

52.1

5452

.098

58.4

0049

.153

RE

SUL

TS

AN

D D

ISC

USS

ION

[120

]

Tab

le 5

.11:

Spa

tio-t

empo

ral v

aria

tion

of E

nric

hmen

t Fac

tor

of c

adm

ium

in th

e D

amod

ar r

iver

bot

tom

sedi

men

ts

20

07

2008

20

09Si

tes

Prem

onso

on

Mon

soon

Po

stm

onso

on

Prem

onso

on

Mon

soon

Po

stm

onso

on

Prem

onso

on

Mon

soon

Po

stm

onso

on

S1

1.56

3 0.

253

1.80

71.

150

0.04

7 0.

017

1.41

70.

000

0.81

7 S3

3.

317

1.98

3 0.

511

1.08

00.

152

1.80

71.

780

0.11

61.

584

S4

1.78

0 1.

157

0.91

74.

157

1.82

3 2.

113

2.95

00.

827

2.11

7 S6

6.

077

1.77

3 0.

490

1.21

30.

830

2.54

08.

857

0.18

01.

583

S9

8.17

3 1.

893

2.62

08.

783

0.74

9 1.

417

4.98

71.

427

5.84

7 S1

0 6.

217

1.50

7 1.

957

9.18

00.

491

1.82

74.

157

0.01

61.

087

S11

4.49

7 0.

052

0.80

70.

587

0.01

0 0.

877

8.18

70.

053

0.15

7 S1

2 1.

913

0.25

0 0.

760

0.91

70.

070

0.91

33.

830

0.19

00.

116

S14

1.95

7 1.

757

1.78

04.

167

0.28

7 0.

920

1.97

80.

583

0.82

3 S1

7 14

.787

4.

413

5.82

79.

757

2.28

7 3.

083

18.1

202.

783

7.65

3 S1

8 4.

750

0.02

8 1.

797

1.80

70.

013

1.21

70.

573

1.85

71.

860

S19

1.95

4 1.

019

1.35

23.

527

0.08

4 0.

847

0.28

50.

082

1.08

0 S2

2 1.

510

0.00

0 0.

154

0.19

30.

550

0.08

91.

327

0.02

70.

178

S25

0.19

2 0.

030

0.01

80.

015

0.00

0 0.

008

0.00

90.

016

0.11

8 S2

7 0.

152

0.02

7 0.

118

0.31

70.

015

0.04

91.

093

0.02

90.

015

Min

0.

152

0.00

0 0.

018

0.01

50.

000

0.00

80.

009

0.00

00.

015

Max

14

.787

4.

413

5.82

79.

757

2.28

7 3.

083

18.1

202.

783

7.65

3 A

ve

3.92

3 1.

076

1.39

43.

123

0.49

4 1.

182

3.97

00.

546

1.66

9 SD

3.

810

1.21

4 1.

454

3.44

30.

697

0.95

44.

746

0.84

02.

199

SEM

0.

984

0.31

3 0.

375

0.88

90.

180

0.24

61.

225

0.21

70.

568

CV

%

97.1

4 11

2.81

10

4.26

110.

2214

1.21

80

.76

119.

5515

3.97

131.

77

RE

SUL

TS

AN

D D

ISC

USS

ION

[121

]

Tab

le 5

.12:

Spa

tio-t

empo

ral v

aria

tion

of E

nric

hmen

t Fac

tor

of ir

on in

the

Dam

odar

riv

er b

otto

m se

dim

ents

2007

20

08

2009

Site

s Pr

emon

soon

M

onso

on

Post

mon

soon

Pr

emon

soon

M

onso

on

Post

mon

soon

Pr

emon

soon

M

onso

on

Post

mon

soon

S1

0.

032

0.02

0 0.

052

0.05

20.

056

0.10

30.

098

0.05

50.

082

S3

0.11

9 0.

098

0.07

70.

057

0.07

6 0.

073

0.09

80.

057

0.06

9 S4

0.

051

0.02

2 0.

111

0.09

80.

078

0.09

00.

073

0.05

00.

113

S6

0.26

6 0.

156

0.39

50.

416

0.14

2 0.

246

0.22

20.

194

0.31

8 S9

0.

134

0.05

0 0.

154

0.03

10.

026

0.08

00.

092

0.04

60.

084

S10

0.09

6 0.

031

0.05

10.

031

0.01

6 0.

054

0.05

20.

013

0.03

1 S1

1 0.

198

0.15

4 0.

217

0.13

40.

090

0.16

20.

134

0.16

20.

177

S12

0.01

8 0.

017

0.02

10.

019

0.01

2 0.

021

0.16

60.

019

0.02

2 S1

4 0.

017

0.02

0 0.

073

0.00

80.

018

0.02

60.

052

0.01

00.

018

S17

0.17

1 0.

135

0.11

90.

103

0.07

8 0.

126

0.26

60.

113

0.18

3 S1

8 0.

217

0.11

9 0.

098

0.07

50.

054

0.09

80.

200

0.11

90.

160

S19

0.02

7 0.

026

0.03

10.

100

0.03

3 0.

027

0.13

50.

112

0.16

2 S2

2 0.

077

0.00

6 0.

031

0.05

40.

050

0.08

00.

112

0.03

10.

082

S25

0.05

4 0.

004

0.02

20.

037

0.02

5 0.

050

0.02

60.

011

0.01

7 S2

7 0.

222

0.02

1 0.

025

0.02

70.

021

0.02

60.

022

0.02

30.

024

Min

0.

017

0.00

4 0.

021

0.00

80.

012

0.02

10.

022

0.01

00.

017

Max

0.

266

0.15

6 0.

395

0.41

60.

142

0.24

60.

266

0.19

40.

318

Ave

0.

113

0.05

9 0.

099

0.08

30.

052

0.08

40.

117

0.06

80.

103

SD

0.08

4 0.

056

0.09

90.

099

0.03

6 0.

060

0.07

20.

058

0.08

4 SE

M

0.02

2 0.

015

0.02

60.

026

0.00

9 0.

016

0.01

90.

015

0.02

2 C

V%

73

.81

96.2

4 10

0.58

119.

6069

.75

71.4

661

.60

86.2

682

.18

RE

SUL

TS

AN

D D

ISC

USS

ION

[122

]

Tab

le 5

.13:

Spa

tio-t

empo

ral v

aria

tion

of E

nric

hmen

t Fac

tor

of le

ad in

the

Dam

odar

riv

er b

otto

m se

dim

ents

20

07

2008

20

09Si

tes

Prem

onso

on

Mon

soon

Po

stm

onso

on

Prem

onso

on

Mon

soon

Po

stm

onso

on

Prem

onso

on

Mon

soon

Po

stm

onso

on

S1

0.54

2 0.

271

0.46

20.

988

0.27

1 0.

362

0.59

10.

371

0.52

3 S3

1.

496

1.22

3 1.

359

1.36

31.

132

0.97

21.

562

0.75

71.

468

S4

1.41

3 1.

518

0.87

01.

149

0.64

9 0.

997

2.62

41.

213

2.28

8 S6

3.

366

2.26

6 1.

913

3.63

22.

439

2.99

33.

737

1.99

72.

938

S9

1.87

1 1.

567

1.40

72.

476

1.57

4 1.

821

3.89

81.

813

1.92

1 S1

0 1.

874

1.31

3 1.

237

1.92

11.

233

1.42

13.

249

1.46

62.

462

S11

1.17

9 0.

612

1.26

21.

268

0.71

5 0.

743

2.62

41.

024

2.21

8 S1

2 1.

288

1.22

9 0.

968

1.26

61.

024

1.51

12.

313

1.24

81.

606

S14

1.29

7 0.

588

0.73

21.

166

0.83

7 1.

235

1.77

71.

208

1.11

2 S1

7 9.

912

2.64

3 7.

762

10.2

242.

624

4.31

712

.827

7.87

710

.412

S1

8 1.

916

1.47

7 1.

621

2.13

81.

498

1.35

72.

507

0.77

11.

524

S19

1.41

7 0.

637

0.93

81.

435

0.48

6 0.

812

1.52

40.

435

0.63

8 S2

2 0.

612

0.41

7 0.

567

0.75

70.

048

0.42

70.

735

0.32

10.

593

S25

0.56

2 0.

049

0.39

30.

589

0.04

3 0.

544

0.82

30.

369

0.48

9 S2

7 0.

536

0.12

4 0.

722

0.79

30.

548

0.62

40.

638

0.28

80.

571

Min

0.

536

0.04

9 0.

393

0.58

90.

043

0.36

20.

591

0.28

80.

489

Max

9.

912

2.64

3 7.

762

10.2

242.

624

4.31

712

.827

7.87

710

.412

A

ve

1.95

2 1.

062

1.48

12.

078

1.00

8 1.

342

2.76

21.

410

2.05

1 SD

2.

320

0.76

8 1.

791

2.38

60.

775

1.05

92.

989

1.87

02.

446

SEM

0.

599

0.19

8 0.

462

0.61

60.

200

0.27

30.

772

0.48

30.

632

CV

%

118.

84

72.2

9 12

0.95

114.

8376

.88

78.8

910

8.24

132.

6111

9.26

RE

SUL

TS

AN

D D

ISC

USS

ION

[123

]

Tab

le 5

.14:

Spa

tio-t

empo

ral v

aria

tion

of Ig

eo o

f lea

d (P

b) in

the

Dam

odar

riv

er b

otto

m se

dim

ents

2007

20

08

2009

Site

s Pr

emon

soon

M

onso

on

Post

mon

soon

Pr

emon

soon

M

onso

on

Post

mon

soon

Pr

emon

soon

M

onso

on

Post

mon

soon

S1

-1

.468

-2

.468

-1

.698

-0.6

03-2

.468

-2

.050

-1.3

44-2

.014

-1.5

21

S3

-0.0

04

-0.2

94

-0.1

43-0

.138

-0.4

07

-0.6

260.

059

-0.9

86-0

.031

S4

-0

.087

0.

017

-0.7

86-0

.385

-1.2

08

-0.5

900.

807

-0.3

070.

609

S6

1.16

6 0.

595

0.35

11.

276

0.70

1 0.

996

1.31

70.

413

0.97

0 S9

0.

319

0.06

3 -0

.092

0.72

30.

069

0.28

01.

378

0.27

30.

357

S10

0.32

1 -0

.192

-0

.278

0.35

7-0

.283

-0

.078

1.11

5-0

.033

0.71

5 S1

1 -0

.348

-1

.293

-0

.250

-0.2

42-1

.069

-1

.014

0.80

7-0

.551

0.56

5 S1

2 -0

.220

-0

.288

-0

.632

-0.2

44-0

.551

0.

011

0.62

5-0

.266

0.09

9 S1

4 -0

.210

-1

.352

-1

.036

-0.3

63-0

.842

-0

.281

0.24

4-0

.313

-0.4

31

S17

2.72

4 0.

817

2.37

22.

769

0.80

7 1.

525

3.09

62.

393

2.79

5 S1

8 0.

353

-0.0

22

0.11

20.

511

-0.0

02

-0.1

440.

741

-0.9

600.

023

S19

-0.0

82

-1.2

35

-0.6

77-0

.064

-1.6

25

-0.8

860.

023

-1.7

87-1

.234

S2

2 -1

.292

-1

.847

-1

.403

-0.9

86-4

.960

-1

.812

-1.0

30-2

.222

-1.3

40

S25

-1.4

15

-4.9

26

-1.9

32-1

.348

-5.1

30

-1.4

64-0

.867

-2.0

24-1

.616

S2

7 -1

.484

-3

.599

-1

.056

-0.9

19-1

.452

-1

.266

-1.2

34-2

.380

-1.3

93

Min

-1

.484

-4

.926

-1

.932

-1.3

48-5

.130

-2

.050

-1.3

44-2

.380

-1.6

16

Max

2.

724

0.81

7 2.

372

2.76

90.

807

1.52

53.

096

2.39

32.

795

Ave

-0

.115

-1

.068

-0

.476

0.02

3-1

.228

-0

.493

0.38

2-0

.718

-0.0

96

SD

1.10

7 1.

601

1.02

71.

022

1.77

6 0.

987

1.18

71.

272

1.20

5 SE

M

0.28

6 0.

413

0.26

50.

264

0.45

8 0.

255

0.30

60.

328

0.31

1

RE

SUL

TS

AN

D D

ISC

USS

ION

[124

]

Tab

le 5

.15:

Spa

tio-t

empo

ral v

aria

tion

of Ig

eo o

f cad

miiu

m (C

d) in

the

Dam

odar

riv

er b

otto

m se

dim

ents

2007

20

08

2009

Site

s Pr

emon

soon

M

onso

on

Post

mon

soon

Pr

emon

soon

M

onso

on

Post

mon

soon

Pr

emon

soon

M

onso

on

Post

mon

soon

S1

0.

060

-2.5

66

0.26

8-0

.383

-5.0

06

-6.4

92-0

.082

0.00

0-0

.877

S3

1.

145

0.40

3 -1

.553

-0.4

74-3

.300

0.

268

0.24

7-3

.697

0.07

9 S4

0.

247

-0.3

75

-0.7

101.

470

0.28

2 0.

495

0.97

6-0

.860

0.49

7 S6

2.

018

0.24

2 -1

.614

-0.3

06-0

.854

0.

760

2.56

2-3

.059

0.07

8 S9

2.

446

0.33

6 0.

805

2.55

0-1

.002

-0

.082

1.73

3-0

.072

1.96

3 S1

0 2.

051

0.00

6 0.

383

2.61

4-1

.612

0.

284

1.47

0-6

.533

-0.4

65

S11

1.58

4 -4

.860

-0

.895

-1.3

54-7

.238

-0

.775

2.44

8-4

.832

-3.2

59

S12

0.35

1 -2

.585

-0

.981

-0.7

10-4

.415

-0

.716

1.35

2-2

.981

-3.6

97

S14

0.38

3 0.

228

0.24

71.

474

-2.3

88

-0.7

050.

399

-1.3

63-0

.865

S1

7 3.

301

1.55

7 1.

958

2.70

10.

608

1.04

03.

595

0.89

22.

351

S18

1.66

3 -5

.723

0.

260

0.26

8-6

.814

-0

.302

-1.3

880.

308

0.31

0 S1

9 0.

381

-0.5

58

-0.1

501.

233

-4.1

64

-0.8

25-2

.394

-4.1

99-0

.474

S2

2 0.

010

0.00

0 -3

.284

-2.9

56-1

.447

-4

.075

-0.1

77-5

.814

-3.0

75

S25

-2.9

66

-5.6

44

-6.3

89-6

.644

0.00

0 -7

.521

-7.3

54-6

.521

-3.6

68

S27

-3.3

00

-5.8

14

-3.6

68-2

.241

-6.6

37

-4.9

26-0

.456

-5.7

09-6

.693

M

in

-3.3

00

-5.8

14

-6.3

89-6

.644

-7.2

38

-7.5

21-7

.354

-6.5

33-6

.693

M

ax

3.30

1 1.

557

1.95

82.

701

0.60

8 1.

040

3.59

50.

892

2.35

1 A

ve

0.62

5 -1

.690

-1

.021

-0.1

84-2

.932

-1

.572

0.19

5-2

.963

-1.1

86

SD

1.81

4 2.

615

2.09

72.

489

2.66

2 2.

764

2.60

62.

623

2.42

9 SE

M

0.46

8 0.

675

0.54

10.

643

0.68

7 0.

714

0.67

30.

677

0.62

7

RE

SUL

TS

AN

D D

ISC

USS

ION

[125

]

Tab

le 5

.16:

Spa

tio-t

empo

ral v

aria

tion

of Ig

eo o

f man

gane

se (M

n) in

the

Dam

odar

riv

er b

otto

m se

dim

ents

2007

20

08

2009

Site

s Pr

emon

soon

M

onso

on

Post

mon

soon

Pr

emon

soon

M

onso

on

Post

mon

soon

Pr

emon

soon

M

onso

on

Post

mon

soon

S1

-3

.153

-2

.432

-2

.379

-3.0

37-4

.021

-3

.359

-2.9

12-2

.264

-3.7

26

S3

-4.1

98

-4.7

20

-3.3

80-2

.455

-1.8

36

-2.7

35-3

.165

-2.3

78-3

.770

S4

-2

.638

-3

.758

-2

.862

-2.8

29-4

.103

-3

.158

-2.7

73-3

.919

-3.1

27

S6

-3.9

97

-4.5

00

-2.3

02-3

.236

-2.1

50

-2.6

94-2

.211

-1.8

74-2

.870

S9

-4

.575

-4

.310

-1

.549

-3.7

11-3

.113

-3

.867

-3.0

66-2

.308

-4.8

10

S10

-3.3

59

-4.1

17

-1.8

48-3

.245

-2.3

60

-3.3

81-4

.200

-3.1

14-3

.205

S1

1 -2

.314

-3

.698

-2

.861

-2.9

56-2

.304

-2

.211

-2.2

11-2

.853

-2.9

18

S12

-2.6

20

-3.5

38

-2.6

91-3

.031

-3.0

22

-3.7

13-2

.871

-3.7

86-3

.613

S1

4 -4

.064

-2

.838

-2

.432

-2.8

63-2

.304

-4

.333

-2.2

76-1

.490

-2.3

14

S17

-4.0

63

-1.9

70

-1.3

31-2

.376

-1.8

65

-2.3

60-1

.440

-1.0

05-2

.302

S1

8 -3

.345

-2

.209

-1

.966

-3.8

84-1

.496

-4

.061

-3.0

84-2

.206

-4.0

62

S19

-3.0

52

-2.3

09

-3.0

89-3

.572

-2.3

86

-3.6

90-3

.551

-2.2

11-2

.851

S2

2 -3

.725

-3

.123

-2

.634

-2.3

90-2

.946

-4

.782

-3.5

85-3

.382

-4.5

00

S25

-3.8

88

-3.1

61

-2.7

74-2

.924

-2.6

21

-3.8

52-3

.113

-2.3

03-2

.242

S2

7 -3

.345

-3

.345

-3

.599

-3.1

13-2

.775

-3

.728

-3.1

12-3

.320

-3.2

35

Min

-4

.575

-4

.720

-3

.599

-3.8

84-4

.103

-4

.782

-4.2

00-3

.919

-4.8

10

Max

-2

.314

-1

.970

-1

.331

-2.3

76-1

.496

-2

.211

-1.4

40-1

.005

-2.2

42

Ave

-3

.489

-3

.335

-2

.513

-3.0

41-2

.620

-3

.462

-2.9

05-2

.561

-3.3

03

SD

0.65

7 0.

866

0.64

10.

450

0.74

1 0.

725

0.67

00.

825

0.77

8 SE

M

0.17

0 0.

224

0.16

60.

116

0.19

1 0.

187

0.17

30.

213

0.20

1

RE

SUL

TS

AN

D D

ISC

USS

ION

[126

]

Tab

le 5

.17:

Spa

tio-t

empo

ral v

aria

tion

of Ig

eo o

f iro

n (F

e) in

the

Dam

odar

riv

er b

otto

m se

dim

ents

2007

20

08

2009

Site

s Pr

emon

soon

M

onso

on

Post

mon

soon

Pr

emon

soon

M

onso

on

Post

mon

soon

Pr

emon

soon

M

onso

on

Post

mon

soon

S1

-5

.538

-6

.197

-4

.849

-4.8

48-4

.748

-3

.867

-3.9

34-4

.774

-4.1

99

S3

-3.6

54

-3.9

34

-4.2

76-4

.727

-4.3

03

-4.3

56-3

.933

-4.7

19-4

.446

S4

-4

.867

-6

.064

-3

.757

-3.9

33-4

.275

-4

.053

-4.3

58-4

.914

-3.7

27

S6

-2.4

96

-3.2

65

-1.9

26-1

.851

-3.4

02

-2.6

10-2

.757

-2.9

54-2

.238

S9

-3

.485

-4

.911

-3

.287

-5.6

08-5

.853

-4

.237

-4.0

33-5

.040

-4.1

51

S10

-3.9

68

-5.6

08

-4.8

74-5

.585

-6.5

38

-4.7

88-4

.852

-6.8

03-5

.608

S1

1 -2

.918

-3

.287

-2

.788

-3.4

85-4

.056

-3

.213

-3.4

80-3

.213

-3.0

85

S12

-6.3

65

-6.4

93

-6.1

93-6

.311

-6.9

09

-6.1

81-3

.172

-6.3

04-6

.107

S1

4 -6

.493

-6

.200

-4

.369

-7.6

28-6

.355

-5

.830

-4.8

51-7

.288

-6.3

57

S17

-3.1

34

-3.4

78

-3.6

52-3

.867

-4.2

75

-3.5

68-2

.494

-3.7

33-3

.036

S1

8 -2

.789

-3

.652

-3

.935

-4.3

19-4

.796

-3

.934

-2.9

08-3

.652

-3.2

27

S19

-5.7

73

-5.8

39

-5.5

85-3

.905

-5.5

21

-5.7

96-3

.479

-3.7

44-3

.209

S2

2 -4

.277

-7

.947

-5

.585

-4.7

88-4

.911

-4

.225

-3.7

41-5

.597

-4.2

00

S25

-4.7

87

-8.4

97

-6.0

71-5

.345

-5.8

87

-4.9

11-5

.830

-7.0

45-6

.468

S2

7 -2

.759

-6

.169

-5

.913

-5.7

73-6

.172

-5

.828

-6.0

93-6

.011

-5.9

48

Min

-6

.493

-8

.497

-6

.193

-7.6

28-6

.909

-6

.181

-6.0

93-7

.288

-6.4

68

Max

-2

.496

-3

.265

-1

.926

-1.8

51-3

.402

-2

.610

-2.4

94-2

.954

-2.2

38

Ave

-4

.220

-5

.436

-4

.471

-4.7

98-5

.200

-4

.493

-3.9

94-5

.053

-4.4

00

SD

1.35

2 1.

646

1.27

11.

360

1.05

2 1.

051

1.05

61.

420

1.37

7 SE

M

0.34

9 0.

425

0.32

80.

351

0.27

2 0.

271

0.27

30.

367

0.35

5

RE

SUL

TS

AN

D D

ISC

USS

ION

[127

]

Tab

le 5

.18:

Spa

tio-t

empo

ral v

aria

tion

of P

ollu

tion

Loa

d In

dex

(PL

I) in

the

Dam

odar

riv

er b

otto

m se

dim

ents

2007

20

08

2009

Site

s Pr

emon

soon

M

onso

on

Post

mon

soon

Pr

emon

soon

M

onso

on

Post

mon

soon

Pr

emon

soon

M

onso

on

Post

mon

soon

S1

0.

261

0.14

1 0.

335

0.32

20.

090

0.09

80.

358

0.00

00.

437

S3

0.46

9 0.

341

0.29

70.

389

0.27

2 0.

413

0.46

20.

195

0.48

6 S4

0.

420

0.25

7 0.

368

0.56

10.

299

0.42

30.

594

0.26

50.

740

S6

0.84

5 0.

451

0.57

90.

735

0.55

8 0.

811

1.24

20.

411

0.97

4 S9

0.

599

0.32

5 0.

734

0.52

60.

270

0.38

10.

751

0.43

50.

495

S10

0.63

6 0.

269

0.47

70.

543

0.23

1 0.

377

0.48

90.

086

0.44

0 S1

1 0.

751

0.15

4 0.

462

0.37

30.

118

0.43

00.

984

0.20

60.

464

S12

0.32

3 0.

160

0.24

30.

252

0.11

3 0.

239

0.74

10.

149

0.20

0 S1

4 0.

248

0.25

8 0.

403

0.29

50.

191

0.21

70.

488

0.24

50.

491

S17

1.22

4 0.

880

1.33

91.

312

0.66

1 0.

837

2.41

81.

166

1.53

2S1

8 0.

735

0.20

1 0.

575

0.41

40.

155

0.34

70.

475

0.48

50.

565

S19

0.34

2 0.

268

0.28

90.

503

0.14

0 0.

215

0.29

40.

189

0.75

3 S2

2 0.

300

0.00

0 0.

160

0.21

80.

127

0.11

40.

342

0.07

90.

228

S25

0.15

6 0.

032

0.07

70.

090

0.00

0 0.

069

0.07

70.

068

0.30

4 S2

7 0.

227

0.05

6 0.

127

0.18

60.

078

0.09

80.

227

0.07

30.

140

Min

0.

156

0.00

0 0.

077

0.09

00.

000

0.06

90.

077

0.00

00.

140

Max

1.

224

0.88

0 1.

339

1.31

20.

661

0.83

72.

418

1.16

61.

532

Ave

0.

502

0.25

3 0.

431

0.44

80.

220

0.33

80.

663

0.27

00.

550

SD

0.29

4 0.

212

0.31

00.

292

0.17

9 0.

235

0.57

00.

287

0.35

0 SE

M

0.07

6 0.

055

0.08

00.

076

0.04

6 0.

061

0.14

70.

074

0.09

C

V%

58

.423

83

.991

71

.818

65.2

9281

.232

69

.63

85.9

2910

6.1

63.7


[128]

3.1 a: (premonsoon season) 3.1 b: (monsoon season)

3.1 c: (postmonsoon season) 3.1 d: (premonsoon season)

3.1 e: (monsoon season) 3.1 f: (postmonsoon season)

Figure 3.1: a – c scatter diagram representing (Ca2++Mg2+) vs (HCO3–+SO4

2–) and

d – f representing (Ca2++Mg2+) vs HCO3–

0

1

2

3

4

5

0 1 2 3 4 5

Ca2+

+Mg2+

(meq

/l)

HCO3–+SO4

2– (meq/l)

0

2

4

6

0 1 2 3

Ca2+

+Mg2+

(meq

/l)

HCO3–+SO4

2– (meq/l)

0

1

2

3

4

5

0 1 2 3 4 5

Ca2+

+Mg2+

(meq

/l)

HCO3–+SO4

2– (meq/l)

0

1

2

3

4

0 2 4

Ca2+

+Mg2+

(meq

/l)

HCO3– (meq/l)

0

1

2

3

0 1 2 3

Ca2+

+Mg2+

(meq

/l)

HCO3– (meq/l)

0

1

2

3

0 1 2 3

Ca2+

+Mg2+

(meq

/l)

HCO3– (meq/l)


[129]




Figure 3.2: a – c scatter diagram representing Ca2++Mg2+ vs TZ+ and d – f

representing Na+ vs Cl–

0

1

2

3

4

5

0 2 4 6

Ca2+

+Mg2+

(meq

/l)

TZ+

0

1

2

3

0 2 4 6

Ca2+

+Mg2+

(meq

/l)

TZ+

0

1

2

3

4

0 2 4 6

Ca2+

+Mg2+

(meq

/l)

TZ+

0

1

2

3

0 1 2

Na+

(meq

/l)

Cl– (meq/l)

0

1

2

3

0 0.5 1

Na+

(meq

/l)

Cl– (meq/l)

0

1

2

0 1 2

Na+

(meq

/l)

Cl– (meq/l)


[130]




Figure 3.3: a – c scatter diagram representing Na+ vs Ca2+ and d – f representing Na++K+ vs TZ+

0

1

2

3

0 1 2

Na+

(meq

/l)

Ca2+ (meq/l)

0

1

2

0 1 2 3

Na+

(meq

/l)

Ca2+ (meq/l)

0

1

2

3

0 0.5 1 1.5

Na+

(meq

/l)

Ca2+ (meq/l)

0

1

2

3

0 1 2 3 4 5

Na+ +

K+(m

eq/l

)

TZ+

0

1

2

3

0 1 2 3 4 5

Na+ +

K+(m

eq/l

)

TZ+

0

1

2

0 1 2 3 4 5

Na+ +

K+(m

eq/l

)

TZ+


[131]

(a) (b)

(c) (d)

Figure 4.1: Ternary diagram showing relationship among (SO42–+Cl–)- HCO3

– -

SiO2 in 2007 a – premonsoon, b – monsoon, c – postmonsoon and d – all seasons


[132]

(a) (b)

(c) (d)

Figure 4.2: Ternary diagram showing relationship among (SO42–+Cl–)- HCO3

– -

SiO2 in 2008 a– premonsoon, b – monsoon, c – postmonsoon and d – all seasons


[133]

(a) (b)

(c) (d)

Figure 4.3: Ternary diagram showing relationship among (SO42–+Cl–)-HCO3

– -

SiO2 in 2009 a– premonsoon, b – monsoon, c – postmonsoon and d – all seasons


[134]

(a) (b)

(c) (d)

Figure 4.4: Ternary diagram showing relationship among (Na++K+) –

(Ca2++Mg2+)-SiO2 in 2007 a – premonsoon, b – monsoon, c – postmonsoon and d –

all seasons


[135]

(a) (b)

(c) (d)

Figure 4.5: Ternary diagram showing relationship among (Na++K+)– (

Ca2++Mg2+)-SiO2 in 2008 a– premonsoon, b – monsoon, c – postmonsoon and d –

all seasons


[136]

(a) (b)

(c) (d)

Figure 4.6: Ternary diagram showing relationship among (Na++K+) –

(Ca2++Mg2+) - SiO2 in 2009 a– premonsoon, b – monsoon, c – postmonsoon and d

– all seasons


[137]

(a) (b)

(c) (d)

Figure 5.1: Hydrochemical classification (Piper 1953) of the Damodar river water

in 2007 a – premonsoon, b – monsoon, c – postmonsoon and d – all seasons


[138]

(a) (b)

(c) (d)


in 2008 a – premonsoon, b – monsoon, c – postmonsoon and d – all seasons


[139]

(a) (b)

(c) (d)


in 2009 a– premonsoon, b – monsoon, c – postmonsoon and d – all seasons


[140]

(a) (b)

(c) (d)

Figure 6.1: Mechanism controlling river water chemistry (Gibbs 1970) [Na+/

(Na++Ca2+)] in 2007 a – premonsoon, b – monsoon, c – postmonsoon and d – all

seasons


[141]

(a) (b)

(c) (d)



seasons


[142]

(a) (b)

(c) (d)



seasons


[143]

(a) (b)

(c) (d)

Figure 6.4: Mechanism controlling river water chemistry (Gibbs 1970) Cl–/(Cl–+

HCO3–)] in 2007a – premonsoon, b – monsoon, c – postmonsoon and d – all

seasons


[144]

(a) (b)

(c) (d)


HCO3–)] in 2008 a – premonsoon, b – monsoon, c – postmonsoon and d – all

seasons


[145]

(a) (b)

(c) (d)


HCO3–)] in 2009 a – premonsoon, b – monsoon, c – postmonsoon and d – all

seasons


[146]

(a) (b)

(c) (d)

Figure 7.1: Diagram for classification of irrigation water (after U.S. Salinity

Laboratory Stuff 1954) in 2007 a– premonsoon, b – monsoon, c – postmonsoon

and d – all seasons


[147]

(a) (b)

(c) (d)





[148]

(a) (b)

(c) (d)





[149]

(a) (b)

(c) (d)

Figure 8.1: Classification of irrigation water (after Wilcox 1953) in 2007 a–

premonsoon, b – monsoon, c – postmonsoon and d – all seasons


[150]

(a) (b)

(c) (d)

Figure 8.2: Classification of irrigation water (after Wilcox 1953) in 2008

a– premonsoon, b – monsoon, c – postmonsoon and d – all seasons


[151]

(a) (b)

(c) (d)

Figure 8.3: Classification of irrigation water (after Wilcox 1953) in 2009 a–

premonsoon, b – monsoon, c – postmonsoon and d – all seasons


[152]

Figure 9: Speciation of metals in the bottom sediment (after BCR extraction)

0%

20%

40%

60%

80%

100%

Mn Pb Cd Fe

Residual

Reducible

Oxidisable

Exchangeable


[153]

Figure 10.1: FTIR spectrum of Damodar river sediment at Dishergarh (S1)

Figure 10.2: FTIR spectrum of Damodar river sediment at Purbanchal (S2)

4000.0 3600 3200 2800 2400 2000 1800 1600 1400 1200 1000 800 600 400.00.00

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

5.0

5.5

6.0

6.5

7.0

7.5

8.0

8.5

9.0

9.5

10.00

cm-1

%T

3407.00

2364.64

1033.53

3624.64

774.82

1627.97

685.31

4000.0 3600 3200 2800 2400 2000 1800 1600 1400 1200 1000 800 600 400.00.0

2

4

6

8

10

12

14

16

18

20

22

24

26

28

30.0

cm-1

%T

3756.98

3696.45

3624.20

3426.52

2367.822339.30

1634.26

1383.71

1034.47

779.86690.37

465.98


[154]

Figure 10.3: FTIR spectrum of Damodar river sediment at Ramghat (S3)

Figure 10.4: FTIR spectrum of Damodar river sediment at Chinakuri (S4)

4000.0 3600 3200 2800 2400 2000 1800 1600 1400 1200 1000 800 600 400.00.0

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20.0

cm-1

%T

3623.923429.89

2364.31

1634.62

1034.00

688.11

4000.0 3600 3200 2800 2400 2000 1800 1600 1400 1200 1000 800 600 400.00.0

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20.0

cm-1

%T

3775.85

3696.92

3624.203427.86

2364.94

1631.38

1032.00

777.85690.37

466.00


[155]

Figure 10.5: FTIR spectrum of Damodar river sediment at Damodar Railway Station (S5)

Figure 10.6: FTIR spectrum of Damodar river sediment at Dihika (S6)

4000.0 3600 3200 2800 2400 2000 1800 1600 1400 1200 1000 800 600 400.00.0

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15.0

cm-1

%T

3777.35

3407.80

2364.90

1033.00777.90

4000.0 3600 3200 2800 2400 2000 1800 1600 1400 1200 1000 800 600 400.00.00

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

2.2

2.4

2.50

cm-1

%T

3429.00

1627.97

2364.14

688.11

3630.25


[156]

Figure 10.7: FTIR spectrum of Damodar river sediment at Madandihi (S7)

Figure 10.8: FTIR spectrum of Damodar river sediment at Burnpur (S8)

4000.0 3600 3200 2800 2400 2000 1800 1600 1400 1200 1000 800 600 400.00.0

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20.0

cm-1

%T

3776.89

3405.80

2926.70

2365.59

1657.51

1036.32

780.99

464.89

4000.0 3600 3200 2800 2400 2000 1800 1600 1400 1200 1000 800 600 400.00.047

0.10

0.15

0.20

0.25

0.30

0.35

0.40

0.45

0.50

0.55

0.60

0.65

0.70

0.75

0.80

0.85

0.896

cm-1

%T

2929.97

3630.25

3422.96

1879.72

2330.53

1630.76

623.77


[157]

Figure 10.9: FTIR spectrum of Damodar river sediment at Narayankuri (S9)

Figure 10.10: FTIR spectrum of Damodar river sediment at Mejhiaghat (S10)

4000.0 3600 3200 2800 2400 2000 1800 1600 1400 1200 1000 800 600 400.00.0

2

4

6

8

10

12

14

16

18

20

22

24

25.0

cm-1

%T

3695.693622.04

3426.23

2366.66

1634.12

1030.66

789.29

535.87

688.11

4000.0 3600 3200 2800 2400 2000 1800 1600 1400 1200 1000 800 600 400.00.0

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20.0

cm-1

%T

3777.00

3428.26

2364.72

1631.71

1034.38


[158]

Figure 10.11: FTIR spectrum of Damodar river sediment at Madanpur (S11)

Figure 10.12: FTIR spectrum of Damodar river sediment at Baska (S12)

4000.0 3600 3200 2800 2400 2000 1800 1600 1400 1200 1000 800 600 400.00.0

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15.0

cm-1

%T

3425.891045.63

2935.57

2369.74

777.621633.56

4000.0 3600 3200 2800 2400 2000 1800 1600 1400 1200 1000 800 600 400.00.03

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

2.2

2.4

2.6

2.8

3.0

3.2

3.4

3.6

3.8

4.0

4.29

cm-1

%T

3416.00

2364.14

690.90

1630.76

1879.72

3697.47

450.34


[159]

Figure 10.13: FTIR spectrum of Damodar river sediment at Pursa (S13)

Figure 10.14: FTIR spectrum of Damodar river sediment at Ashishnagar (S14)

4000.0 3600 3200 2800 2400 2000 1800 1600 1400 1200 1000 800 600 400.00.30

0.4

0.5

0.6

0.7

0.8

0.9

1.0

1.1

1.2

1.3

1.4

1.5

1.6

1.70

cm-1

%T 3411.76

1630.76

2380.95

436.36

791.60

1009.79

1432.16

2016.80

4000.0 3600 3200 2800 2400 2000 1800 1600 1400 1200 1000 800 600 400.00.033

0.10

0.15

0.20

0.25

0.30

0.35

0.40

0.45

0.50

0.55

0.60

0.65

0.70

0.740

cm-1

%T

1630.763400.56

3484.59

3635.85

537.06749.65

906.29

1874.12

2369.74

2941.17

3703.08


[160]

Figure 10.15: FTIR spectrum of Damodar river sediment at Durgapur Brrage (S15)

Figure 10.16: FTIR spectrum of Damodar river sediment at Shyampur (S16)

4000.0 3600 3200 2800 2400 2000 1800 1600 1400 1200 1000 800 600 400.00.00

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

2.2

2.4

2.6

2.8

3.0

3.2

3.4

3.6

3.8

4.00

cm-1

%T

3624.20

1000.00

783.21693.70

1630.76

2375.35

4000.0 3600 3200 2800 2400 2000 1800 1600 1400 1200 1000 800 600 400.00.00

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

1.1

1.2

1.3

1.4

1.5

1.6

1.7

1.8

1.9

2.00

cm-1

%T

3630.25

1633.56

413.98

2352.942929.97


[161]

Figure 10.17: FTIR spectrum of Damodar river sediment at Majhermana (S17)

Figure 10.18: FTIR spectrum of Damodar river sediment at Dhobighat (S18)

4000.0 3600 3200 2800 2400 2000 1800 1600 1400 1200 1000 800 600.00.040

0.05

0.06

0.07

0.08

0.09

0.10

0.11

0.12

0.13

0.14

0.15

0.16

0.17

0.18

0.19

0.20

0.21

0.22

0.23

0.24

0.250

cm-1

%T 2347.33

1874.12

1244.75

1588.81

1804.19

1037.76

3495.79

1521.67

4000.0 3600 3200 2800 2400 2000 1800 1600 1400 1200 1000 800 600 400.00.0

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15.0

cm-1

%T

3623.76

3426.66

1029.00 776.89


[162]

Figure 10.19: FTIR spectrum of Damodar river sediment at Silampur (S19)

Figure 10.20: FTIR spectrum of Damodar river sediment at Randiha (S20)

4000.0 3600 3200 2800 2400 2000 1800 1600 1400 1200 1000 800 600 400.00.0

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15.0

cm-1

%T

3429.77

2365.13

1037.00 780.19

4000.0 3600 3200 2800 2400 2000 1800 1600 1400 1200 1000 800 600 400.00.00

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

5.0

5.5

6.0

6.5

7.0

7.5

8.00

cm-1

%T

3428.42

1040.00778.47

2364.14

1625.17

688.11

3624.64


[163]

Figure 10.21: FTIR spectrum of Damodar river sediment at Sillaghat (S21)

Figure 10.22: FTIR spectrum of Damodar river sediment at Gohogram (S22)

4000.0 3600 3200 2800 2400 2000 1800 1600 1400 1200 1000 800 600 400.00.03

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

5.0

5.52

cm-1

%T

3429.00

2364.14

1627.97

1879.72

688.11

3630.25

4000.0 3600 3200 2800 2400 2000 1800 1600 1400 1200 1000 800 600 400.00.0

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20.0

cm-1

%T

3756.87

3427.34

2365.51

1035.28

778.42

462.50

690.90

1627.97

3630.25


[164]

Figure 10.23: FTIR spectrum of Damodar river sediment at Sikarpur (S23)

Figure 10.24: FTIR spectrum of Damodar river sediment at Sadarghat (S24)

4000.0 3600 3200 2800 2400 2000 1800 1600 1400 1200 1000 800 600 400.00.0

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15.0

cm-1

%T

3777.73

3408.05

2364.93

1036.74

778.21

537.06

1625.17

3635.85

4000.0 3600 3200 2800 2400 2000 1800 1600 1400 1200 1000 800 600 400.00.0

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15.0

cm-1

%T

3777.50

3417.00

2366.17

1030.35

777.23465.33


[165]

Figure 10.25: FTIR spectrum of Damodar river sediment at Palasriampur (S25)

Figure 10.26: FTIR spectrum of Damodar river sediment at Barsul (S26)

4000.0 3600 3200 2800 2400 2000 1800 1600 1400 1200 1000 800 600 400.01.13

1.2

1.3

1.4

1.5

1.6

1.7

1.8

1.9

2.0

2.1

2.2

2.3

2.4

2.5

2.6

2.7

2.8

2.9

3.0

3.10

cm-1

%T 2005.60

1630.76

772.02

537.06

3422.96

3630.25

1443.35

4000.0 3600 3200 2800 2400 2000 1800 1600 1400 1200 1000 800 600 400.00.0

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17.0

cm-1

%T

3623.97

3428.00

2368.38

1632.27

1036.00

777.43

1876.92

696.50

3697.47


[166]

Figure 10.27: FTIR spectrum of Damodar rive+r sediment at Pallaroad (S27)

Figure 11.1: Spatial interpolation of Igeo of Cd

4000.0 3600 3200 2800 2400 2000 1800 1600 1400 1200 1000 800 600 400.00.0

2

4

6

8

10

12

14

16

18

20

22

24

26.0

cm-1

%T

3906.01

3753.85

3624.713426.88

2371.51

1633.27

1031.71

776.91690.72

532.45

466.43


[167]

Figure 11.2: Spatial interpolation of Igeo of Fe

Figure 11.3: Spatial interpolation of Igeo of Mn


[168]

Figure 11.4: Spatial interpolation of Igeo of Pb

Figure 12: Thematic zonation of study area with respect to PLI�

CONCLUSION

[169]

6.0 CONCLUSION

his section focuses major highlights of the factors controlling

hydrogeochemistry and river bottom sediment geochemistry of the river

Damodar.

The measured values of pH in the river Damodar indicating the river water is

neutral to alkaline in nature. Small local differences were observed with no clear

seasonal variations at all the sites of the study area. Electrical conductivity of the

water samples depicting a wide range of fluctuations at different locations and can be

explained by high concentrations of dissolved solids and ions in it. Seasonal variation

in EC indicates an increase in concentration of major ions in the non-monsoon

seasons. This increase in the ionic strength of the Damodar river water during

premonsoon and postmonsoon season is probably due to evaporation during periods

with low water levels, aided by elevated temperatures in this region. The higher

values for EC and TDS at some discharge points in the Damodar river water reveal its

ionic strength/concentrations. Spatial distribution of TDS follows the same trend like

EC. The average total dissolved solid (TDS) of the present study (176.37 mg/l) is

comparable to the Indian average (159 mg/l) and higher to global average values (115

mg/l) for an aquatic system. This large variation in TDS values may be attributed to

the variation in geological formations, hydrological processes and prevailing mining

and industrial conditions in the region. The results show that total dissolved solid

(TDS) concentration in a particular season is similar, but varies in different seasons.

Regarding solute abundance the cation of the river water is dominated by Ca2+

and Mg2+ comprising 38.672% and 30.024% of total cation balance in their equivalent

weight. Na+ and K+ concentrations represent on an average to 26.060% and

5.244% of the total cations (TZ+), respectively, and the order of abundance is

Ca2+>Mg2+> Na+>K+. The average concentration of calcium (19.620 mg/l) is lower

than the Indian average (30 mg/l) and comparable to global average values (16 mg/l)

for an aquatic system. The average sodium concentration (15.585 mg/l) is comparable

to the Indian average (12 mg/l) and higher to global average values (4.4 mg/l) for an

aquatic system. On an equivalent basis, HCO3– accounts for 67.759% of the total

anions. HCO3– is followed by SO4

2–, and Cl– which accounts for 17.903% and

14.518% of the total anions respectively. The high concentration of HCO3– in river

T

CONCLUSION

[170]

water indicates that intense chemical weathering takes place in the drainage basin.

Higher concentration of chloride was observed at Shyampur and Majher mana. The

average chloride concentration (13.709 mg/l) is comparable to the Indian average (15

mg/l) and higher to global average values (4 mg/l) for an aquatic system. The average

concentration of dissolved silica (12.00 mg/l) is higher than the Indian average (7

mg/l) and comparable to global average values (12.00 mg/l) for an aquatic system.

Nitrate and phosphate in the studied river ranged from BDL to 4.119 mg/l and 0.010

to 1.382 mg/l respectively. The nitrate concentration in the river water reached their

maximum value during monsoon seasoon, minimum during the postmonsoon season

the premonsoon season is characterised by intermediate values. Seasonal distribution

of phosphate follows the same trend like nitrate.

Among the heavy metals Pb (average value CV% 307.77) shows much

fluctuation in the samples of the analysed river, and the higher values indicate that the

analysed river in this study area is extremely variable due to the wastewater

discharged from industrial activities. The variability (CV%) of heavy metals in the

river water are in the order of Pb (307.77) > Mn (212.12) > Cd (182.97) > Fe (89.12).

Although none of the sampling sites of effluent channel was consistent in terms of

coefficient of variation. Highest concentrations of most of the heavy metals (Fe, Cd

and Pb) in river Damodar may be due to the discharge of heavy metal loaded

industrial wastewater. The results of the present study indicate a remarkable increase

in pollution along with heavy metals concentration at Chinakuri of river Damodar due

to the increased loading of the indiscriminate and long term disposal of effluents from

thermal power plant and mining areas. The values for most of the metals in the river

water in the downstream region were found to be much lower than those of the

upstream region.

Ionic ratio of (Ca2++Mg2+)/(Na++K+) and scatter diagram of (Ca2++Mg2+)

versus (HCO3–+SO4

2–) suggest that both silicate and carbonate weathering are the

major hydrogeochemical processes operating in the river Damodar. Ternary plot also

reveals that Ca2++Mg2+ and SiO2 make significant contributions towards the cationic

balance in most of the samples, indicating that Ca2++Mg2+ and SiO2 in the water of

this catchment are mainly supplied by chemical weathering of highly weathered

gneiss and granites rich in orthoclase, plagioclase, hornblende, augite, biotite and

CONCLUSION

[171]

muscovite. The geochemical nature and relationship between dissolved ions in water

may also be evaluated by plotting the analytical value on Piper (1953) trilinear

diagram. The trilinear diagram reveals that Ca2+–Mg2+– HCO3– is the dominant

hydrogeochemical facies in the river water samples. There is no significant change in

the hydrochemical facies noticed during the study period, which indicates that most of

the major ions are natural in origin. The Gibbs diagram suggests that rock weathering

as major process for liberating ions in the river and also responsible for controlling

water chemistry.

Overall there is a dominance of weak acids (HCO3–) over strong acids (SO4

2–

and Cl–) in the Damodar river water. But some areas occurrence of reverse condition

suggests the dominance of anthropogenic influences (urban and industrial effluents

discharge) over natural phenomena. Taken together these arrays of weathering

indicate that the Damodar is a chemically active river with a dominance of continental

weathering and secondary inputs of anthropogenic and atmospheric sources. This

phenomena is also supported my multivariate statistical analysis.

From the factor analysis it was observed that the geogenic sources, industrial

discharges and natural factors strongly influence the water quality of the study area.

Considering the drinking water suitability all the cations and anions are well within

the recommended limit prescribed by (WHO 2006) except at certain locations viz.

Ramghat, chinakuri, Dihika, Shyampur, Majher mana and Narayankuri where heavy

metals like Pb and Cd exceed their permissible limit. With respect to irrigation water

suitability all the samples are within the tolerance limit for irrigation and is free from

alkali and salinity hazards. All the samples in the study area (except some areas) have

RSC values much less than 1.25 meq/l (safe for irrigation), which revealed that all

samples are of safe quality categories for irrigation. The analyzed water samples

indicate that most of the river water samples are not exceeding the magnesium ratio of

50. The high RSC content and Na% were recorded at Shyampur due an

industrially polluted water stream which joins into the river and influence this zone as

a result of which the water is not suitable for irrigation use. The plot of data on

the US salinity diagram, in which the EC is taken as salinity hazard and SAR as

alkalinity hazard revealed that maximum number of the water samples fall into C1S1

(low salinity with low sodium) category. The overall study of salinity hazard revealed

CONCLUSION

[172]

that these river water samples can be used to irrigate all soils for semi-tolerant and

tolerant as well as sensitive crops. Therefore, all river water samples are suitable for

irrigation and can be used for all soil types. Sodium percentage calculated for

Damodar river water in the study area is plotted against electrical conductance in

Wilcox diagram shows that all of river water samples are excellent to good for

irrigation.

FTIR spectrum of sediment represents a number negative functional groups

like –OH, C=O, C=C, C-Cl, NH, C-H, P-OH, Ca5-(PO4)3(OH) which can effectively

bind with the metal ions. The study reveals that the peaks for the C-H bond region are

excellent indicators of the presence of anthropogenic contaminants. Among the

studied heavy metals Fe and Mn are the most dominant elements, followed by Pb and

Cd. The FTIR spectra have exhibited more or less similar spectral features indicating

the presence of similar functional groups present in the riverine sediments. The

present study reveals that the sediment of the above mentioned area of the river

Damodar contains a number negatively charged functional groups which can

effectively bind with the metal ions.

The relatively higher Kd values observed for Fe, Pb and Cd indicate their

preferential association and enrichment in sediments and suggest that they are

characterized by a low geochemical mobility in water. Relatively lower Kd values for

Mn indicate that they are less likely to be associated with sediments. The overall

percentage of metal content in different BCR fractions is in the sequence of residual >

reducible > oxidisable > exchangeable and the order of metals in each fractions are as

follows Exchangeable: Fe > Mn > Pb > Cd, Oxidisable: Pb > Cd > Mn > Fe,

Reducible: Mn > Fe > Pb > Cd, Residual: Cd > Pb > Fe > Mn. The risk assessment

code as applied to the present study reveals that 12.312% of iron, 11.119% of

manganese, 3.364% of lead and 3.164% of cadmium exist in exchangeable fraction

and therefore, comes under low to medium risk category and may enter into food

chain. The association of these metals with exchangeable fraction may cause

deleterious effects to aquatic life.

Recalcitrant Factor (RF) value of monitored metals in the river sediments

ranged from 55.601 (Mn) to 73.672 (Cd) indicating variability in effective retention of

individual metals. The recalcitrant factor (RF) value of Pb and Fe is 71.112 and

CONCLUSION

[173]

58.488 respectively in the monitored river sediments The ranking of metals with

respect to RF value is in the order of Cd > Pb > Fe > Mn. Higher RF value of Cd and

Pb can be explained because of chalcophilic and lithophilic nature of these elements,

therefore indicating poor possibility of mobilization into the aqueous system.

Comparing the heavy metal concentrations with the consensus-based TEC and

PEC values , revealed that over 26.667% of Pb and 17.037% of Cd concentration of

the river bottom sediment samples exceeded the TEC, with most sample

concentrations falling below the PEC (except 4.450% of Pb and 0.741% of Cd). The

site Majher mana receives industrial waste water from various steel plants, thermal

power plants, chloralkalies, sponge iron and chemical industries and high PEC of Cd

and Pb may exerts harmful effects on sediment-dwelling organisms.

The study reveals that the Igeo values for Cd, Pb, Fe, and Mn in the river

sediment fall in class “0” in indicating that there is no pollution from these metals in

the downstream riverine sediment. The negative Igeo value of Mn and Fe in the river

suggested that there is no pollution from these metals in the sediments of the study

area. The overall low PLI values were observed in the river sediments, though

relatively higher values were observed at the sites Majher mana (1.224) which

indicates that the site is moderately polluted. The result of the various indices of the

sediment analysis reveals that except some discharge points the EF, Igeo and PLI of

typical pollutants from all sites were far lower than the limit values, which indicated

that the area was overall in good condition except certain stretches. This was also

supported by spatial interpolation by krigging process in GIS environment which

reveals that except some portion of the stretch between Shyampur - Majher mana –

Dhobighat (1<PLI>2) remaining portion of the study area stretch of river Damodar

falls under the no pollution (PLI<1) category.

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soon

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Ann

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e II

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l con

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(µS/

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s 20

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20

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20

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20

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0 A

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Ann

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l var

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lved

Sol

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mg/

l) in

the

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er w

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s 20

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20

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20

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20

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20

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20

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A

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emon

soon

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on, B

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onso

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ason

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stm

onso

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ason

viii

Ann

exur

e IV

: Spa

tio-t

empo

ral v

aria

tion

of B

icar

bona

te (m

g/l)

conc

entr

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n in

the

Dam

odar

riv

er w

ater

Site

s 20

07 A

20

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20

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20

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20

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20

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20

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20

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A_Pr

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Ann

exur

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tio-t

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tion

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l) co

ncen

trat

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s 20

07 A

20

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20

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20

08 A

20

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20

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20

09 A

20

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20

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20

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III:

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tio-t

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l) co

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20

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A_Pr

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soon

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: Spa

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A

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soon

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on, B

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on se

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stm

onso

on se

ason

xiv

Ann

exur

e X

: Spa

tio-t

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20

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20

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20

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20

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20

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20

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A

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soon

seas

on, B

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on se

ason

, C_Po

stm

onso

on se

ason

xv

Ann

exur

e X

I: S

patio

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pora

l var

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n of

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nesi

um (m

g/l)

conc

entr

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n in

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Dam

odar

riv

er w

ater

Site

s 20

07 A

20

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20

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20

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365

A_Pr

emon

soon

seas

on, B

_M

onso

on se

ason

, C_Po

stm

onso

on se

ason

xvi

Ann

exur

e X

II: S

patio

-tem

pora

l var

iatio

n of

Sod

ium

(mg/

l) co

ncen

trat

ion

in th

e D

amod

ar r

iver

wat

er

Site

s 20

07 A

20

07 B

20

07 C

20

08 A

20

08 B

20

08 C

20

09 A

20

09 B

20

09 C

S1

12

.54

7.60

6.14

6.30

8.45

10

.24

12.3

48.

2614

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12.0

0 8.

877.

356.

477.

27

9.42

11.8

416

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5 S3

14

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6.90

8.24

8.70

8.25

9.

3227

.43

5.35

12.3

5 S4

17

.89

8.51

7.33

6.18

9.32

22

.35

48.4

711

.50

10.2

4 S5

29

.39

9.35

10.9

510

.43

12.5

0 18

.20

35.7

317

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12.4

5 S6

10

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10

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38.

8039

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20

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12.5

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19.3

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9.54

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8.26

11.6

0 A

_Pr

emon

soon

seas

on, B

_M

onso

on se

ason

, C_Po

stm

onso

on se

ason

xvii

Ann

exur

e X

III:

Spa

tio-t

empo

ral v

aria

tion

of P

otas

sium

(mg/

l) co

ncen

trat

ion

in th

e D

amod

ar r

iver

wat

er

Site

s 20

07 A

20

07 B

20

07 C

20

08 A

20

08 B

20

08 C

20

09 A

20

09 B

20

09 C

S1

4.

322

2.17

53.

548

3.65

51.

950

2.91

43.

647

1.55

51.

210

S2

4.54

5 4.

554

2.58

09.

866

1.45

2 5.

654

2.56

41.

334

6.41

0 S3

2.

531

2.42

56.

882

2.22

02.

634

3.22

76.

475

2.22

96.

644

S4

5.61

8 4.

253

5.27

14.

220

4.56

2 4.

727

8.24

24.

564

9.96

0 S5

6.

160

4.32

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445

12.7

963.

350

5.69

82.

761

2.43

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462

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13.6

45

7.22

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274

14.3

314.

357

22.4

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1.25

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012

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9.85

6 1.

324

8.24

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6.34

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.138

2.47

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4.31

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.243

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845

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948

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364

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452

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3.32

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324

8.61

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2.35

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510

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264

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468

6.56

7 A

_Pr

emon

soon

seas

on, B

_M

onso

on se

ason

, C_Po

stm

onso

on se

ason

xviii

Ann

exur

e X

IV: S

patio

-tem

pora

l var

iatio

n of

Lea

d (µ

g/l)

conc

entr

atio

n in

the

Dam

odar

riv

er w

ater

Site

s 20

07 A

20

07 B

20

07 C

20

08 A

20

08 B

20

08 C

20

09 A

20

09 B

20

09 C

S1

0.

475

BD

L 0.

476

0.53

5 B

DL

0.86

5 0.

864

0.05

4 0.

148

S2

0.98

6 B

DL

BD

L B

DL

0.32

6 0.

549

0.45

9 B

DL

BD

L S3

17

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BD

L 0.

299

12.4

7 7.

583

0.37

0 5.

346

0.43

7 B

DL

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17.5

7 1.

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4.26

3 1.

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0.34

7 4.

157

14.2

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0.24

5 2.

837

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1.58

4 B

DL

0.29

5 1.

418

7.18

4 0.

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BD

L S6

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1.96

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6 0.

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7 12

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0.24

7 2.

365

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BD

L B

DL

0.98

7 B

DL

5.32

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DL

0.34

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DL

BD

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BD

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DL

0.25

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DL

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DL

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12.4

7 0.

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1.98

5 4.

801

BD

L 1.

735

6.57

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436

1.92

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BD

L B

DL

4.56

9 0.

029

BD

L 5.

248

BD

L B

DL

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BD

L 7.

536

BD

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BD

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636

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2 0.

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9.86

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DL

BD

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264

BD

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BD

L B

DL

BD

L 0.

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8 B

DL

BD

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BD

L 14

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10.4

1 1.

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BD

L B

DL

0.25

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BD

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DL

0.42

6 S1

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17.8

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6.71

2 34

69

0.25

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5649

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547

62.5

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782

BD

L B

DL

0.96

3 B

DL

0.23

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BD

L B

DL

S19

BD

L B

DL

BD

L B

DL

0.41

7 B

DL

BD

L 0.

463

BD

L S2

0 6.

544

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6 B

DL

0.45

8 B

DL

0.52

5 0.

775

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L 0.

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0.85

4 B

DL

0.42

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DL

0.14

4 B

DL

0.36

7 S2

2 0.

557

BD

L B

DL

0.24

9 B

DL

0.27

0 B

DL

BD

L B

DL

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BD

L 0.

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1.47

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DL

0.09

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DL

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7 0.

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0.98

5 S2

4 0.

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BD

L 0.

267

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DL

0.11

4 0.

425

BD

L B

DL

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L 0.

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BD

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L B

DL

BD

L 0.

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BD

L B

DL

S27

0.34

7 0.

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BD

L 0.

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BD

L 0.

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5 0.

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6.54

3 A

_Pr

emon

soon

seas

on, B

_M

onso

on se

ason

, C_Po

stm

onso

on se

ason

xix

Ann

exur

e X

V: S

patio

-tem

pora

l var

iatio

n of

Iron

(mg/

l) co

ncen

trat

ion

in th

e D

amod

ar in

the

Dam

odar

riv

er w

ater

Site

s 20

07 A

20

07 B

20

07 C

20

08 A

20

08 B

20

08 C

20

09 A

20

09 B

20

09 C

S1

0.

324

0.86

3 0.

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60.

127

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80.

196

0.37

50.

152

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0.42

5 0.

245

0.16

70.

614

0.28

2 0.

527

0.17

50.

655

0.60

5 S3

0.

365

1.44

1 0.

239

0.50

40.

453

1.04

10.

725

0.42

20.

815

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0.75

2 0.

452

1.47

21.

871

0.32

1 0.

142

0.94

20.

276

0.36

4 S5

0.

648

0.21

5 0.

641

0.54

30.

642

1.36

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710

0.65

20.

237

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1.46

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254

2.47

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652

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7 S7

0.

287

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631

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120

0.56

4 S9

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452

0.05

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325

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548

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0.45

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462

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311

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0.03

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1.87

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2.78

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5 0.

451

0.74

50.

421

0.84

8 S1

9 0.

479

0.02

4 0.

145

0.05

50.

265

0.23

50.

052

0.04

20.

194

S20

0.76

3 0.

310

0.03

40.

365

0.23

0 0.

442

0.74

20.

062

0.24

2 S2

1 0.

424

0.04

5 0.

365

0.19

50.

074

0.06

80.

198

0.04

20.

764

S22

0.32

5 0.

186

0.26

50.

294

0.04

1 0.

419

0.54

80.

056

0.23

5 S2

3 0.

752

0.20

6 0.

944

0.26

50.

245

0.34

00.

147

0.24

50.

274

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0.27

4 0.

245

0.14

70.

579

0.69

0 0.

245

0.74

60.

142

0.14

8 S2

5 0.

260

0.35

0 0.

132

0.65

20.

397

0.24

60.

360

0.45

00.

425

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0.32

1 0.

578

0.25

50.

345

0.65

4 0.

385

0.71

00.

652

0.23

7 S2

7 0.

120

0.34

5 0.

124

0.14

40.

092

0.31

60.

242

0.32

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374

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of M

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(µg/

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wat

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Site

s 20

07 A

20

07 B

20

07 C

20

08 A

20

08 B

20

08 C

20

09 A

20

09 B

20

09 C

S1

1.

325

0.24

31.

275

0.22

51.

250

BD

L1.

253

1.46

8B

DL

S2

BD

L B

DL

1.36

01.

995

3.65

0 8.

410

1.35

0B

DL

0.25

5 S3

2.

825

BD

LB

DL

BD

L0.

105

0.34

3B

DL

15.7

50B

DL

S4

1.78

5 9.

654

0.15

831

.200

1.47

7 0.

254

4.15

83.

754

BD

L S5

B

DL

BD

LB

DL

9.66

53.

740

BD

LB

DL

0.46

50.

147

S6

17.5

2 7.

750

BD

L0.

641

1.65

0 B

DL

5.15

2B

DL

2.47

5 S7

1.

466

0.23

80.

748

1.25

0B

DL

4.52

10.

633

0.48

5B

DL

S8

3.46

9 B

DL

BD

L1.

864

0.19

5 B

DL

0.25

02.

864

0.98

5 S9

0.

253

BD

L7.

524

1.47

01.

078

0.38

30.

143

2.40

3B

DL

S10

BD

L 0.

965

3.45

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DL

BD

L B

DL

BD

LB

DL

0.23

3 S1

1 41

.69

24.2

50.

540

3.15

83.

140

BD

L0.

799

BD

L0.

497

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2.54

7 34

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LB

DL

BD

L 0.

150

9.65

40.

779

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L S1

3 0.

217

BD

L2.

750

0.13

64.

961

BD

LB

DL

0.15

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DL

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0.46

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DL

BD

LB

DL

BD

L 0.

754

1.24

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0.32

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0.56

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2.49

60.

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050

0.46

02.

460

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1.46

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0.12

647

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3.46

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025

2.96

52.

450

6.32

1 S1

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400

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197

3.50

00.

259

0.02

50.

365

1.20

00.

549

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L 7.

645

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BD

L B

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0.46

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L S1

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L0.

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259

2.69

81.

413

1.69

0 0.

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L0.

257

BD

L S2

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2.42

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0.26

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DL

2.16

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DL

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2.65

1 0.

215

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LB

DL

BD

L B

DL

1.96

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DL

0.45

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0.11

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DL

0.35

50.

148

0.24

70.

215

2.45

00.

313

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0.18

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DL

0.25

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266

2.63

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DL

0.25

50.

165

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L S2

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350

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3.65

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440

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L 0.

187

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L8.

500

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L 0.

775

0.19

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L S2

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150

0.25

30.

214

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0.22

80.

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VII

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of C

adm

ium

(µg/

l) co

ncen

trat

ion

in th

e D

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Site

s 20

07 A

20

07 B

20

07 C

20

08 A

20

08 B

20

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20

09 A

20

09 B

20

09 C

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0.

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71.

526

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L B

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30.

570

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L B

DL

1.65

40.

145

1.14

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3.

147

0.43

01.

248

1.14

80.

413

1.19

82.

587

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DL

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2.15

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BD

LB

DL

BD

L 0.

313

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LB

DL

BD

L S5

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BD

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0.24

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700

1.40

01.

440

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1.90

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1.79

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1.10

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3.48

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1.43

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1.17

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352

0.96

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0.96

5 0.

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3.87

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215

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144

2.56

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3.65

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1.96

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143

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0.04

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425

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L 0.

429

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985

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Impact of industrial waste effluents on river Damodar adjacentto Durgapur industrial complex, West Bengal, India

U. S. Banerjee & S. Gupta

Received: 17 October 2011 /Accepted: 10 May 2012 /Published online: 25 May 2012# Springer Science+Business Media B.V. 2012

Abstract The present study deals with the character-ization of industrial effluents released from variousindustries and distribution of heavy metals in effluentdischarge channel and its impact on the river Damo-dar. The effluent of tamlanala, a natural storm waterchannel, is extensively used for irrigation for growingvegetables in and around the study area. The heavymetals in water of the study area are in the order ofFe > Mn > Pb >Cd and sediments follow similartrends too. The enrichment of heavy metals in thesediments are in the order of Cd (39.904) > Pb(33.156) > Mn (0.164) > Fe (0.013). The geoaccumu-lation index values reveal effluent channel is subjectedto moderate to high pollution with respect to Cd(4.733) and Pb (4.466). The analyzed data for enrich-ment factors and the pollution load index (1.305) showthat effluent channels have suffered from significantheavy metal contamination following industrializationand urbanization. Compared to baseline values, thesurface sediment layers show high enrichment acrossthe channel and at its discharge point. The factoranalysis reveals three factors—industrial sources, sur-face runoff inputs, and background lithogenic factorswhich clarify the observed variance of the environ-mental variables. Metal pollution assessment of

sediments suggests that pollution from the heavy met-als observed is high in the tamlanala which in turnaffects the downstream of the river system.

Keywords Damodar river . Industrial wastewater .

Metal pollution assessment . Enrichment factor .

Geoaccumulation index . Pollution load index

Introduction

Discharge of industrial effluents and toxic compoundsinto riverine systems represents an ongoing environ-mental problem and so poses a potential threat tohuman health. The present study deals with the qualityassessment of industrial effluent and its impact on thereceiving river. Metals in the environment have in-creased tremendously as a result of rapid anthropogen-ic activities (Salomons and Forstner 1984). Increasingindustrial activity has continuously introduced pollu-tants into the riverine environment, and manyresearchers have attempted to assess chemical behav-ior of metals and potentially toxic inorganic pollutants(Li and Thornton 2001; Silveira et al. 2006; Farkas etal. 2007; Verma and Khan 2007; Morillo et al. 2008;Widmeyer and Bendell-Young 2008; Mil-Homens etal. 2009). River sediment can act both as source andsink for the nutrients and other elements (Thornton etal. 1975; Förstner and Wittmann 1983) and is alsoimportant for the assessment of anthropogenic con-tamination in riverine environment. Surface sediment

Environ Monit Assess (2013) 185:2083–2094DOI 10.1007/s10661-012-2690-1

U. S. Banerjee (*) : S. GuptaDepartment of Environmental Science,The University of Burdwan,Golapbag 713104 West Bengal, Indiae-mail: [email protected]

act as a metal pool that can release metals to the over-laying water though natural and anthropogenic process-es and pose potential adverse health effects to theecosystems(Howarth and Nombela 2003; McCready etal. 2006).

Being a peninsular Indian river, the Damodartributaries are used to serve a variety of purposesincluding drinking, recreation, agriculture, and indus-try. Such an indispensable vital water course is affect-ed by the changing land use pattern, together with thedischarge of excessively huge volume of industrialeffluents and silt load from sand and coal miningactivities. Tamlanala is a natural water channel thatultimately drains into the river Damodar near Durga-pur industrial complex. Along its course, it receiveseffluents from various industries such as iron and steelplant, thermal power plant, chemical plant, etc., aswell as untreated sewage water from various settle-ments along it. Industrial effluent and wastewater areused for irrigation purposes for growing vegetablesbesides the Tamlanala. Several studies on the distribu-tion of heavy metals and toxic chemicals and theireffects on aquatic environment have been noted fromdifferent rivers (Downing 1971; Wang et al. 2008;Zheng et al. 2008; Gupta et al. 2010). The localcommunities around the effluent channel and the mainriver use the water for domestic, fishing, and agricul-tural purposes. Extensive use of industrial effluent forirrigation is a common practice in this area and so thestudy of this open channel and the main river is verysignificant. The objectives of the present study are: (1)to assess the chemical load of river Damodar withrespect to contamination of industrial effluents (2)metal distribution in water and sediment system and(3) contamination assessment of surface sediments byusing various indices.

Experimental methodology

Water and sediment samples collection and analysis

Surface water and sediment samples were collectedfrom six locations (three (S1, S2, and S3) from efflu-ent channel and three (S4, S5, and S6) from the re-ceiving river) near Durgapur industrial complex foranalysis. Each sampling station is further subdividedinto two sites (Sa and Sb). S1 and S2 are situated at theupstream of Durgapur Chemicals Limited (DCL) and

S3 is in close proximity to DCL, Burdwan; S4 isdesignated as discharge point of tamlanala on the riverDamodar at Majhermana (Burdwan); S5 and S6 aresituated at the mainstream of the river Damodar. Thewater samples were collected in 1lit high-densitypolyethylene bottles prewashed with nitric acid andrinsed three to four times with the water sample beforefilling them to the required capacity. The unfilteredeffluent and river water samples were preserved usingultra pure nitric acid to lower the pH to <2.0. Thesamples thus preserved, were brought to the laboratoryfor heavy metal analysis. electrical conductivity (EC)and pH of water samples were measured in the fieldimmediately after the collection of the samples usingpH and conductivity meters. Analysis of physico-chemical parameters like pH, EC, total dissolved sol-ids (TDS), lead (Pb), cadmium (Cd), manganese (Mn),iron (Fe), chloride (Cl−), phosphate (PO4

3−), nitrate(NO3

−), sulfate (SO42−), and bicarbonate (HCO3

−)was carried out as per APHA (1998) guidelines. Thesediment samples were air-dried, ground, and sievedwith 0.5-mm sieve and further analysis was carriedout. For the analysis of heavy metals, 1 g of the sievedsediment samples were digested with 5:1 mixture ofconcentrated HNO3 and HClO4 (Barman and Lal1994). The solution obtained after digestion was fil-tered (Whatman 42 filter paper), diluted to 25 ml andanalyzed through the atomic absorption spectropho-tometer (GBC Avanta).

Indexing approach for assessing metal contaminationin sediments

Enrichment factor of the metals

In order to compensate the influence of sedimentcharacters on metals concentrations and to quantifythe anthropogenic input, the geochemical normaliza-tion approach is applied in this study, and the normal-ized enrichment factor (EF) is computed, using thefollowing equation:

EF ¼ M X=ð Þ sample M X=ð Þ background=

where M is the measured concentration of theelement in the sediment, X is the selected normal-izer (reference metal) and (M/X) sample and (M/X)background are the ratios of target metal and thenormalizer in the interest and background

2084 Environ Monit Assess (2013) 185:2083–2094

sediments, respectively. To calculate the EF of themetals, the normalizer and the background levels ofthe metals should be determined. Aluminum (Al)and iron (Fe) were used as the normalizer byvarious workers (Çelo et al. 1999; Liaghati et al.2003; Reimann and de Caritat 2005) as these inertelements have less anthropogenic contamination inaquatic sediment. Fe was used as the referenceelement as 472,000 mg/kg, which is the shaleaverage value (Turekian and Wedepohl 1961). Afive-category ranking system (Sutherland 2000;Kartal et al. 2006) is applied in this study todenote the degree of anthropogenic contamination.EF <2 states deficiency to minimal contamination,EF02–5 moderate contamination, EF05–20 signif-icant contamination, EF020–40 very high contam-ination, and EF>40 extremely high contamination.

Index of geoaccumulation

Index of geoaccumulation (Igeo) is an assessment toolto assess the contamination by comparing the currentand preindustrial concentrations originally used withbottom sediments (Muller 1969). It can also be appliedto the assessment of soil and sediment contamination.Igeo is calculated according to the following equation:

Igeo ¼ log 2 Cn 1:5 Bn=

where Cn is the measured concentration of the elementin the sediment and Bn is the geochemical backgroundvalue in sediment (“average shale”). The constant 1.5is allowed to minimize the effect of possible variationsin the background values which may be attributed tolithologic variations in the sediments (Stoffers et al.1986).

Geoaccumulation index consists of seven grades(0–6), indicating various degrees of enrichmentabove the background values ranging from unpollut-ed to very highly polluted sediment quality. Averageshale concentration given by Turekian and Wedepohl(1961) is one of the world-wide standards used asreference for this study. Following descriptive clas-sification for geoaccumulation is given by Muller(1969): <0 0 uncontaminated, 0–1 0 uncontaminatedto moderately contaminated, 1–2 0 moderately con-taminated, 2–3 0 moderately to heavily contaminat-ed, 3–4 0 heavily contaminated, 4–5 0 heavilyto extremely contaminated, and >5 extremelycontaminated.

Pollution load index

Pollution load index (PLI), has been calculated for aparticular site following the method proposed byTomlison et al (1980). PLI is represented as geometricmean of Cf value of n number of metals estimated ateach site

PLI ¼ CF1 � CF2 � CF3 � . . . . . . . . . :: � CFnð Þ1 n=

where n is the number of metals and CF is the con-tamination factor. The contamination factor can becalculated from the following relation:

Cf ¼ Hc Hb=

where Hc is the metal concentration at the contami-nated site and Hb is maximum permissible limits/background value of metals. PLI provides a simple,comparative means for assessing the level of heavymetal pollution and is then classified as no pollution(PLI <1), moderate pollution (1< PLI <2), heavy pol-lution (2< PLI <3), and extremely heavy pollution(3< PLI).

Statistical analysis

Descriptive statistics and correlation analysis weredone between heavy metals and various physicochem-ical properties to assess possible similar sources. Fac-tor analysis (FA) based on a varimax rotationtechnique is used for this study as a statistical methodof discussing variables and identifying the pollutionsources by extracting minimum acceptable eigenvaluegreater than 1. Statistical calculations are carried outfor this study at significance level 0.05 by XL Stat(version 11).

Quality control assurance

Quality control measures were taken to assess contam-ination and reliability of the analyzed data. For qualitycontrol purposes, care has been taken for sample col-lection and preservation during every experimentalprocedure and for the analytical precision, each (waterand sediments) samples were performed for three rep-licates. Double distilled deionized water was usedthroughout the experiment. Glassware was properlycleaned, and E-mark (AR grade) standards were usedfor the preparation of standard curve during analysis

Environ Monit Assess (2013) 185:2083–2094 2085

of samples. For further enhancement of experimentalresults, the mean values for each parameter along withstandard deviation and coefficient of variance (CV)were considered.

Results and discussion

Characterization of industrial effluents

A summary of the analytical and statistical data for thewastewater and river water samples are presented inTable 1. The wastewater exhibited acidic to alkalinepH in the range of 6.5–8.5 with an overall mean of7.62. The values of conductivity ranged from 240 to690 μS/cm with an overall mean of 428.88 μS/cm. Thelarge variation of electrical conductivity may be mainlyattributed to industrial processes prevailing in thisregion. In the study area, the TDS concentration inwastewater ranges from 152.4 to 451.5 mg/L with amean of 275.81 mg/L. Mean value of phosphate, chlo-ride, nitrate sulfate and bicarbonate is 0.31, 14.45, 0.60,22.94 and 95.33 mg/L, respectively. Durgapur industrialcomplex expels wastewater through a commonly unitedopen channel without any proper treatment which ulti-mately joins into the river Damodar. Widespread use ofheavy metals in industries as well as intensive agricul-ture has resulted in a variety of heavy metals beingreleased into the environment with concentrations inexcess of the natural background levels (De Groot etal. 1976; Dryssen and Wedborg 1980). The heavy met-als viz. Pb, Cd, Mn, and Fe in wastewater samples are inthe range of 6.25–108.75, 0.1–4, 0.1–31, and 52–2198 μg/L with an overall mean of 46.22, 1.85, 7.49,and 592.55 μg/L, respectively. The high mean value ofPb (108.75 μg/L) and Cd (1.85 μg/L) are found at S3,because this site is in close proximity to a chlor-alkaliindustry. The concentration level of Pb and Cd along theentire reach of effluent channel is also high. The siteslike S1 and S2 are in close proximity to each other andthese in the vicinity of Durgapur industrial complexmight have influenced heavy metal contents in the raweffluents. The channel contains raw effluents from var-ious industries like metal processing and chemicalindustries where the heavy metals are used as raw mate-rials or as process catalysts. High levels of variousphysicochemical parameters in the effluent channel in-dicate an increase in concentration of major ions due tothe impact of industrial discharge (Singh et al. 2005).

Characterization of river water

The pH value of river water in the study area rangesfrom 7.3 to 8.9 (mean 7.93), indicating an alkaline typeof river water. The values of conductivity ranged from190 to 640 with an overall mean of 384.44 μS/cm. Thehigh level of electrical conductivity at the dischargepoint is mainly attributed to industrial waste dis-charge in this region. TDS in the study area variesin the range of 125.35–453.4 mg/L with an overallmean value of 253.86 mg/L. Mean values of phos-phate, chloride, nitrate sulfate, and bicarbonate is0.29, 8.18, 0.98, 26.84, and 109.33 mg/L, respec-tively. The four heavy metals viz. Pb, Cd, Mn, andFe were detected in most of the samples in the rangeof 0.00–71.5, 0.00–0.502, 0.1–24.25, and 2.0–1520 μg/L with an overall mean of 11.21, 0.156,6.25, and 300.13 μg/L, respectively. The mean val-ues of metal concentrations can be arranged in theorder Fe > Mn > Pb > Cd. The concentration levelof Pb and Cd at S5 and S6 along the downstreamside of river is remaining low. The values for mostof the parameters in the river water were found to bemuch lower than those of the raw effluent as pollut-ant may break down or become diluted due to self-purification or natural processes (Saha and Konar1985; Truu et al. Truu et al. 2002). However, therecalcitrance and consequent persistence of heavymetal concentrations in surface water which are not veryhigh, in dilute and undetectable quantities, exhibitedtoxic characteristics (Atkinson et al. 1998).

Distribution of heavy metals in sediments

Sediments act as both source and sinks for contam-inants in aquatic environments. Generally, the heavymetals are distributed between the aqueous phaseand the suspended sediments during their transport(Karbassi et al. 2007). Sediment analysis to studythe overall water quality has an immense importancewhich is often included in environmental assessmentstudies (Horsfall and Spiff 2002; Jain et al. 2005; Liet al. 2006; Adekola and Eletta 2007). Heavy metalsand different contaminants in the aquatic system canlead to elevated sediment concentrations which ulti-mately cause potential toxicity to aquatic biota(Heyvart et al. 2000; Yang and Rose 2003), andresidues may enter the human food chain (Cook etal. 1990; Deniseger et al. 1990). Distribution of


Tab

le1

Physicochem

ical

characteristicsof

raw

effluent

andDam

odar

riverwater

Wastewater

Riverwater

S1

S2

S3

S4

S5

S6

S1a

S1b

S2a

S2b

S3a

S3b

S4a

S4b

S5a

S5b

S6a

S6b

pHRange

6.5–

7.9

7.2–

8.2

6.9–

8.3

6.7–8

6.9–

8.5

6.9–

8.4

7.62

–87.7–

7.8

7.3–

7.7

7.45

–7.704

7.9–

8.5

8.8–

8.9

Ave

7.23

7.70

7.70

7.49

7.87

7.73

7.81

7.77

7.50

7.55

8.13

8.87

SD

0.70

0.50

0.72

0.70

0.85

0.76

0.19

0.06

0.20

0.13

0.32

0.06

CV

9.71

6.49

9.37

9.29

10.81

9.88

2.43

0.74

2.67

1.78

3.95

0.65

EC

Range

280–

450

260–

610

310–

510

240–480

240–

610

260–

690

310–

710

270–

690

310–

460

390–

410

200–

310

190–

270

Ave

376.67

463.33

403.33

386.67

453.33

490.00

530.00

533.33

363.33

396.67

246.67

236.67

SD

87.37

181.75

100.66

128.58

191.40

216.56

202.98

229.42

83.86

11.55

56.86

41.63

CV

23.20

39.23

24.96

33.25

42.22

44.20

38.30

43.02

23.08

2.91

23.05

17.59

TDS

Range

153.5–

292.5

152.4–

389.2

210.2–

335.6

160.3–308

156–

409.2

159.3–

451.5

196.6–

453.4

175.5–

439.5

199.5–

356.8

229.1–250.1

125.35

–222.6

129.99

–199.55

Ave

231.67

294.40

265.10

247.47

298.90

317.33

339.50

349.77

253.60

240.13

169.22

170.97

SD

71.10

125.26

64.14

77.37

129.71

147.55

130.83

150.94

89.41

10.54

49.32

36.40

CV

30.69

42.55

24.19

31.26

43.40

46.50

38.54

43.15

35.26

4.39

29.15

21.29

Pb

Range

23.5–6

8.75

18–1

02.25

6.3–

98.7

9.75–5

0.2

6.25

–108.75

18.5–8

6.75

7.75

–71.5

7.75

–38.75

3.025–

6.7

5.08

–5.725

0–0

6.5–

11.75

Ave

49.05

74.06

39.08

23.90

41.50

49.75

29.00

19.58

4.53

5.41

0.00

8.75

SD

23.19

48.55

51.72

22.80

58.26

34.49

36.81

16.75

1.92

0.32

0.00

2.70

CV

47.27

65.55

132.32

95.39

140.39

69.32

126.92

85.54

42.44

5.97

0.00

30.90

Cd

Range

0.825–

2.25

0.75

–40.25

–0.8

0.25–0

.75

0.1–

0.325

0.225–

3.5

0.15

–0.502

0.175–

0.5

0.05

–0.225

0–0.2

0–0

0–0

Ave

1.325

1.833

0.517

0.467

0.208

1.925

0.384

0.315

0.142

0.10

0.0

0.0

SD

0.80

1.88

0.28

0.26

0.11

1.64

0.20

0.17

0.09

0.10

0.0

0.0

CV

60.52

102.35

53.30

54.98

54.11

85.25

52.77

53.05

61.97

100.00

0.0

0.0

Mn

Range

0.85

–2.3

0.1–

8.5

2.5–

27.75

0.245–23.75

1.5–

7.75

2.45

–31

0.1–

6.75

7.5–

90.9–

2.375

2.075–8.7

0.4–

24.25

0.7–

7.5

Ave

1.35

3.20

12.67

8.25

5.33

14.15

2.75

8.33

1.59

4.73

15.13

4.98

SD

0.82

4.61

13.32

13.43

3.36

14.96

3.52

0.76

0.74

3.50

12.88

3.73

CV

60.97

144.12

105.19

162.79

62.95

105.69

128.17

9.17

46.60

73.96

85.10

74.82

Fe

Range

175–

1052

98–2

198

120–

196

254–1422

52–4

21352–

985

421–

1520

2–23.7

62.1–7

6.3

443.2–469

21–4

0152

–316

Ave

557.67

915.67

152.67

975.33

298.00

656.00

857.67

11.90

67.17

454.73

206.67

202.67

SD

449.04

1124.47

39.11

630.58

213.04

317.24

583.22

10.97

7.93

13.12

190.15

135.90

CV

80.52

122.80

25.62

64.65

71.49

48.36

68.00

92.22

11.80

2.88

92.01

67.06

SO4

Range

16.55–

17.12

12.42–

17.77

11.74–

17.71

35.54–51.35

9.23

–27.25

22.77 –

32.95

42.47–

61.83

18.34–

31.57

28.55–

43.55

18.45–36.325

12.39–

13.63

8.165–

18.285

Ave

16.81

15.69

15.03

43.01

20.21

26.90

49.30

23.52

35.40

27.67

12.90

12.26

SD

0.29

2.87

3.03

7.94

9.63

5.36

10.87

7.06

7.58

8.95

0.65

5.33

CV

1.71

18.28

20.17

18.47

47.67

19.94

22.05

30.03

21.42

32.35

5.065

43.49


Tab

le1

(contin

ued)

Wastewater

Riverwater

S1

S2

S3

S4

S5

S6

S1a

S1b

S2a

S2b

S3a

S3b

S4a

S4b

S5a

S5b

S6a

S6b

NO3

Range

0.27

–0.79

0.4–

1.17

0.08

–2.49

0.15–0

.97

0.11–0.67

0.28

–0.58

0.56

–2.8

0.76

–1.4

0.19

–1.14

0.35

–1.5

0.21

–1.24

0.25

–2.77

Ave

0.55

0.68

1.09

0.48

0.38

0.45

1.33

1.12

0.73

0.77

0.74

1.23

SD

0.26

0.43

1.25

0.43

0.28

0.16

1.27

0.33

0.49

0.64

0.52

1.35

CV

47.85

63.29

115.31

90.16

74.59

34.27

95.31

29.22

66.87

83.09

69.96

109.30

Cl

Range

9.27

–14.24

20.23–

48.15

6.2–

9.2

9.37–11.34

9.53

–15.3

9.42

–21.2

9.35

–12.32

9.2–

9.5

2.26

–14.32

6.28

–6.6

5.29

–8.24

6.12

–8.51

Ave

11.27

31.18

7.64

10.42

11.84

14.37

10.66

9.30

8.37

6.46

6.71

7.61

SD

2.62

14.90

1.50

0.99

3.05

6.11

1.51

0.17

6.03

0.16

1.48

1.30

CV

23.25

47.80

19.68

9.52

25.75

42.50

14.20

1.86

72.03

2.53

22.03

17.10

PO4

Range

0.42

–0.52

0.61

–1.21

0–0.018

0.02–0

.12

0–0.12

0.02

–0.56

0–0.05

0–0.07

0.04

–0.12

0–0.12

0.5–

1.6

0.52

–0.82

Ave

0.47

0.93

0.01

0.07

0.06

0.34

0.03

0.04

0.07

0.04

0.87

0.70

SD

0.05

0.30

0.01

0.05

0.06

0.28

0.03

0.04

0.04

0.07

0.64

0.16

CV

10.79

32.43

96.63

75.50

106.37

83.71

88.19

87.37

56.77

173.21

73.28

22.68

HCO3

Range

68–8

852

–148

92–1

3284

–100

76–1

0064

–148

88–1

3272

–136

76–1

00112 –

168

116–

172

72–1

04

Ave

76.00

100.00

112.00

92.00

88.00

104.00

104.00

104.00

88.00

132.00

140.00

88.00

SD

10.58

48.00

20.00

8.00

12.00

42.14

24.33

32.00

12.00

31.24

28.84

16.00

CV

13.93

48.00

17.86

8.70

13.64

40.52

23.40

30.77

13.64

23.67

20.60

18.18

Units:ECin

μS/cm;Pb,

Cd,

Mn,

andFein

μg/L;otherphysicochemical

parametersarein

mg/L


heavy metals in surface sediments of river Damodaris represented in Table 2. Concentrations of Cd(16.494 mg/kg), Pb (671.11 mg/kg), and Fe(689.84 mg/kg) in the sediment of effluent channelwere clearly higher at the sampling site S3 as thissite is in close proximity with DCL. In the mainriver, the concentrations of Cd (2.29 mg/kg) and Pb(203.51 mg/kg) were clearly higher at the samplingsite S4, i.e., the discharge point of tamlanala on river,while highest concentrations of Fe (455.34 mg/kg) andMn (93.56 mg/kg) occurred at the sampling sites S5 andS6, respectively. Appearance of relatively high Fe andMn concentrations, observed at the main river sediment,may be due to its geological composition of surroundingrocks. The concentration of heavy metals (Pb and Cd) insediments seems to be related to the correspondingconcentration in the aquatic phase. Due to alkaline na-ture of the river water, most of the heavy metals haveprecipitated and may settle as carbonates, oxides, andhydroxides. The occurrence of heavy metals in riverwater and sediments is due to discharge of industrialeffluents from various industries and agricultural runoff.

Metal pollution assessment by using enrichment factorgeoaccumulation index and pollution load index

The EFs were calculated to evaluate actual level ofcontamination for all the elements, using the shalevalue as reference matrix. The EF values calculatedfor Pb, Cd, Mn, and Fe are represented in Table 3. TheEF values for all the metals were in the range of0.004–39.904, indicating a range from deficiency tovery high enrichment within the study area. The aver-age EF values for all sediment decreased in the orderCd (9.749)> Pb (8.053)> Mn (0.074)> Fe (0.007). TheEF values were >2 for Pb and Cd, indicating anthro-pogenic impact on metal concentration in the sedi-ments of effluent channel and <2 for Mn and Fe,which fell in the unenriched group of elements in thestudy area. The EF of Cd reached 39.904 at site S3,which was the most enriched element in the sedimentof the study area. Maximum value (39.904) of enrich-ment factor for cadmium was noticed for sediment ofS3 at effluent channel while the minimum wasrecorded at S6 (1.212) along the downstream stretchof the river Damodar (Table 3). The sediments fromthe S3 are heavily polluted because of industrialwastes discharged from a chlor-alkali industry. Eventhough the EF values are less than the pollution limit T

able

2Elementalcompositio

nof

effluent

channelandDam

odar

riversurfacesediment

Param

eters

Pb(m

g/kg)

Cd(m

g/kg)

Mn(m

g/kg)

Fe(m

g/kg)

Sites

Min

Max

Ave

SD

CV

Min

Max

Ave

SD

CV

Min

Max

Ave

SD

CV

Min

Max

Ave

SD

CV

S1

S1a

148.39

167.63

156.31

10.1

6.44

0.997

1.966

1.4953

0.49

32.4

136.47

146.47

141.14

5.03

3.57

226.32

314.56

261.4

46.82

17.91

S1b

65.82

72.92

69.52

3.56

5.12

0.296

0.53

0.4416

0.13

28.7

79.71

92.7

87.35

6.79

7.77

245.63

312.56

281.17

33.66

11.97

S2

S2a

26.31

32.41

29.32

3.05

10.4

2.962

3.827

3.4803

0.46

13.1

59.34

70.52

65.12

5.6

8.6

215.34

258.36

243.02

24.02

9.88

S2b

248.42

261.32

255.36

6.51

2.55

2.716

4.243

3.3697

0.79

23.4

38.96

47.96

42.96

4.58

10.7

165.45

210.34

185.07

22.97

12.41

S3

S3a

649.13

671.11

663.12

12.2

1.83

7.425

16.49

11.971

4.53

37.9

131.47

144.47

139.47

75.02

586.34

689.84

631.53

52.98

8.39

S3b

384.71

411.52

398.62

13.4

3.37

3.488

12.96

9.5237

5.24

55.1

62.52

78.23

72.35

8.57

11.8

189.63

253.35

222.8

31.94

14.34

S4

S4a

55.81

68.41

61.35

6.44

10.5

1.26

2.219

1.6917

0.49

28.8

18.6

32.59

26.89

7.35

27.3

312.65

362.3

343.97

27.26

7.92

S4b

175.73

203.51

189.52

13.9

7.33

0.966

2.295

1.55

0.68

43.8

22.65

37.65

32.65

8.66

26.5

368.14

455.65

417.12

44.68

10.71

S5

S5a

19.21

41.28

29.32

11.2

380.292

0.417

0.3457

0.06

18.6

6.12

14.12

11.12

4.36

39.2

389.63

455.34

425.77

33.35

7.83

S5b

21.84

33.46

27.65

5.81

210.286

0.601

0.4378

0.16

3613.35

23.35

19.35

5.29

27.3

236.34

267.45

251.15

15.61

6.22

S6

S6a

22.15

38.41

29.99

8.15

27.2

0.236

0.513

0.3637

0.14

38.4

51.63

60.63

55.63

4.58

8.24

197.63

341.54

271.93

72.07

26.50

S6b

16.63

28.56

22.59

5.97

26.4

0.315

0.532

0.4259

0.11

25.5

79.2

93.56

87.56

7.47

8.53

215.39

251.35

233.83

18.0

7.70


of 2 in the river sediment, the human and industrialactivities along the river catchment area if not properlymonitored and managed, will cause a significant risein the enrichment level with its attendant environmen-tal problems in future.

The calculated Igeo values, based on the averageshale value, are presented in Table 3. The Igeo valuesfor Cd ranged from −0.380 to 4.733 in the study area.Very high level of Igeo values for Cd (4.733), accord-ing to the Muller’s classification, was observed at S3,while most of the investigated stations in effluentchannel recorded a moderate contamination for thismetal. The geoaccumulation index values for Pb

ranged from 0.033 to 4.466 and corresponded withclass 0 (background) and class 5 (highly polluted)values. Very high level of Igeo values for Pb (4.46)was also observed at S3, indicating that sediments arevery strongly polluted with this metal. The Igeo valuesfor Mn and Fe in the study area range from −6.841to −3.192 and −8.579 to −6.808, respectively. The Igeovalue shows much fluctuation in the sediment of thestudy area and the lower values of Igeo for Mn and Feimply no appreciable input from anthropogenic sour-ces. The study reveals that the Igeo values for Cd, Pb,Fe, and Mn in the river sediment fall in class “0” inindicating that there is no pollution from these metals

Table 3 Enrichment factors,geoaccumulation index, andPollution load index values forthe sediment samples

Sites EF Igeo PLI

Pb Mn Cd Fe Pb Mn Cd Fe

S1 S1a 7.816 0.166 4.984 0.006 2.381 −3.175 1.732 −8.081 0.435

S1b 3.476 0.103 1.472 0.006 1.212 −3.868 −0.027 −7.976 0.237

S2 S2a 1.466 0.077 11.601 0.005 −0.033 −4.291 2.951 −8.187 0.286

S2b 12.768 0.051 11.232 0.004 3.089 −4.891 2.905 −8.580 0.411

S3 S3a 33.156 0.164 39.904 0.013 4.466 −3.192 4.734 −6.809 1.305

S3b 19.931 0.085 31.746 0.005 3.732 −4.139 4.404 −8.312 0.710

S4 S4a 3.068 0.032 5.639 0.007 1.032 −5.567 1.910 −7.685 0.251

S4b 9.476 0.038 5.167 0.009 2.659 −5.287 1.784 −7.407 0.359

S5 S5a 1.466 0.013 1.152 0.009 −0.033 −6.841 −0.381 −7.378 0.119

S5b 1.383 0.023 1.459 0.005 −0.118 −6.042 −0.040 −8.139 0.125

S6 S6a 1.500 0.065 1.212 0.006 0.000 −4.518 −0.307 −8.024 0.162

S6b 1.130 0.103 1.420 0.005 −0.409 −3.864 −0.079 −8.242 0.169

Table 4 Pearson correlation coefficient matrix of analyze variables

Variables pH EC TDS Pb Mn Cd Fe NO3 Cl SO4 PO4 HCO3

pH 1

EC −0.480 1

TDS −0.398 0.986 1

Pb −0.330 0.523 0.481 1

Mn 0.215 −0.123 −0.097 −0.106 1

Cd −0.375 0.414 0.362 0.856 0.032 1

Fe −0.311 0.322 0.230 0.532 −0.188 0.554 1

NO3 0.470 0.009 0.055 −0.324 −0.083 −0.402 −0.274 1

Cl −0.160 0.375 0.350 0.809 −0.223 0.751 0.565 −0.271 1

SO4 −0.378 0.449 0.445 −0.163 −0.265 −0.115 0.461 0.067 −0.128 1

PO4 0.428 −0.494 −0.502 0.198 0.096 0.330 0.130 −0.081 0.478 −0.606 1

HCO3 0.124 −0.129 −0.165 −0.386 0.580 −0.258 −0.139 0.172 −0.220 −0.112 0.104 1

Values in bold are significant at P00.05


in the downstream riverine sediment. The negative Igeovalue of Mn and Fe both in the effluent channel and inthe main river suggested that there is no pollution fromthese metals in the sediments of the study area. Thebackground geogenic factors like chemical weatheringof rock as well as sediment, chemical and isotopiccompositions of drainage and even of the upper con-tinental crust may influence the water quality of thestudy area.

The PLI values of sediments at the various sam-pling points are presented in Table 3. The PLI valuefor all sediments varies with a wide range of fluctua-tion and ranges from 0.118 to1.305. The overall lowPLI values were observed in the river sediments,though relatively higher values were observed at theeffluent channels sites S3 (1.305) which indicates thatthe site is extremely polluted. The trend of PLI valuesin the sediments indicates that the discharge of efflu-ents from the Durgapur industrial complex is the mainsource of contamination in the study area.

Pearson correlation and coefficient of variance

Correlation analysis was done between heavy metaland various physicochemical properties in river watersamples to assess possible similar sources, and theresults are presented in Table 4. There was no positivecorrelation observed in the Cd (r0−0.375), Pb(r0−0.330), and Fe (r0−0.311) concentrations withthe pH of water. EC has positive correlation with Pb(r00.523), Cd (r0414), and Fe (r0322). It can bededuced that EC depends upon these metal concen-trations. Study shows that EC and TDS (0.986) show apositive correlation in samples because conductivityincreases with the concentration of all dissolved con-stituents. Chloride ion bears significant positive corre-lation with EC (r00.375), TDS (r00.350), Pb(r00.809), Cd (r00.751), and Fe (r00.565). Cd andPb exhibited a positive correlation with conductivity,while Mn indicated a negative correlation with con-ductivity (Table 4). HCO3

− exhibited a positive corre-lation with Mn (r00.580) which could indicate thesame or similar source. Positive correlations wereobserved between the contaminants of Cd and Pb(r00.856), Cd and Fe (r00.554), and Fe and Pb(r00.532); this may indicate the same or similar sourceinput resulting from industrial waste discharges.

For the purpose of comparison between the degreesof variability of each component along the study area,

CV was calculated (Tables 1 and 2). The coefficientof variation shows much fluctuation in the samplesof the effluent channel, and the higher values of Pb(CV0140.39) indicate that the site (S3) is extremelyvariable due to the wastewater discharged from in-dustrial activities. Results have shown that phos-phate content of river water have the highestdegree of variation (CV0173.21) among other con-stituents. This pointed out that phosphate content isthe one most subjected to variations (S5b) along thestudy area. Although none of the sampling sites of

Factor loadings (axes F1 and F2: 55.69 %)

pH

ECTDS

Pb

M n

Cd

Fe

NO3

Cl

SO4

PO4

HCO3

-1

-0.5

0

0.5

1

-1.5 -1 -0.5 0 0.5 1 1.5

F1 (34.40 %)

F2

(21.

28 %

)

Fig. 1 The ordination of the physicochemical parameters

Table 5 Factor loading matrix, eigenvalues and variances

Variables F1 F2 F3

pH −0.571 0.290 0.024

EC 0.798 −0.480 0.267

TDS 0.742 −0.495 0.270

Pb 0.844 0.393 0.054

Mn −0.283 0.177 0.929

Cd 0.783 0.472 0.145

Fe 0.591 0.127 −0.097NO3 −0.329 −0.260 −0.011Cl− 0.731 0.524 −0.058SO4

2+ 0.281 −0.629 −0.131PO4

3− −0.113 0.912 −0.048HCO3

− −0.352 −0.001 0.464

Eigenvalue 4.128 2.554 1.295

Variability (%) 34.401 21.285 10.790

Cumulative % 34.401 55.685 66.476

Values in bold set indicates significant loading


effluent channel was consistent in terms of coeffi-cient of variation. Among the studied metals in thesediments of the effluent channel, the coefficient ofvariation of Cd (CV055.06) shows much fluctuationat the site S3, which indicates that the site is notconsistent in nature due to the discharge of industrialeffluents.

Factor analysis (PCA extraction)

FAwas applied to study the water quality status and toidentify different pollution sources of river Damodar.Eigenvalue gives a measure of the significance of thefactor, and the factors with the highest eigenvalues arethe most significant. According to Liu et al. (2003),factor loading is classified as “strong”, “moderate”,and “weak”, corresponding to absolute loading valuesof >0.75, 0.75–0.50, and 0.50–0.30, respectively.Component loadings of principal components for eachseason are presented in Fig. 1. The results of factoranalysis performed on heavy metals and some physi-cochemical parameters suggested three factors (eigen-value >1) controlling their variability in waters of riverDamodar. Factor loading matrix, eigenvalues, and var-iances are represented in Table 5. Factor 1 which waspositively loaded with EC, TDS, Pb, Cd, Cl (strong),Fe (moderate), and negatively loaded with pH andNO3

− seemed to be related to the discharge of indus-trial effluents, attributed to anthropogenic activities.Factor 2 which was positively loaded with PO4(strong) and negatively loaded with SO4

2− attributedto surface runoff inputs and factor 3 was positivelyloaded with Mn (strong) and HCO3

− (weak), whichattributed to geogenic sources. The study reveals thatthe industrial discharge, surface runoff inputs, andbackground geogenic factors strongly influence thewater quality of the study area.

Conclusion

The high degree of metal pollution has occurred inwater-sediment system and shows a negative impactof the discharged effluent on the receiving river. Theincreased level of EF, Igeo, and PLI value in the studyarea near Durgapur industrial complex is considerablyhigh due to direct discharge of industrial wastes intothe river. Metal pollution assessment of sedimentssuggests that heavy metal pollution observed in the

main river is not high but it is significantly high in thetamlanala. From the factor analysis, it was observedthat the industrial discharges, surface runoff inputs,and background geogenic factors strongly influencethe water quality of the study area. Elevated levels ofthese metals in surface sediments suggest for higherexposure risk to the aquatic flora and fauna of theriver. Therefore, detailed investigations on metal dis-tribution in water and sediment at different sites fromupstream to downstream of tamlanala and its dischargepoint of river Damodar are essential. It is thereforeimportant to continuously carry out environmentalmonitoring in order to evaluate the effects of industrialeffluent discharge in the riverine environment.

Acknowledgments The authors wish to thank Prof. J.K.Datta, Prof A.R. Ghosh, and Dr N.K. Mondal, Dept. of Envi-ronmental Science, The University of Burdwan, West Bengalfor their valuable suggestions and cooperation throughout thisresearch work. USB thankfully acknowledges Prof. H Lahiri,Dept of English, The University of Burdwan and Mr. J Mondal,Asst. teacher, Mohanpur High School, Burdwan, West Bengal,India for their suggestions to improve the manuscript. The com-ments of the reviewers are highly appreciated.

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