SPATIAL AND TEMPORAL VARIATIONS OF PHYSICOCHEMICAL CHARACTERISTICS OF SURFACE WATER AND SEDIMENT OF OSUN RIVER IN SOUTHWESTERN NIGERIA BY OLAYIWOLA, OLAJUMOKE ABIDEMI MATRIC NO: 55403 B.Sc. (Chemistry), M.Sc. (Analytical Chemistry), University of Ibadan A THESIS IN THE DEPARTMENT OF CHEMISTRY, SUBMITTED TO THE FACULTY OF SCIENCE, IN PARTI L FULFILMENT OF THE REQUIREMENT FOR THE AWARD OF THE DEGREE OF DOCTOR OF PHILOSOPHY OF THE UNIVERSITY OF IBADAN, IBADAN, NIGERIA. SEPTEMBER, 2012 i UNIVERSITY OF IBADAN LIBRARY DEDICATION I dedicate this research work to:  Almighty ALLAH. The Most Gracious, Most Merciful, Originator of Heavens and Earth, Full of Majesty and Honour,  My loving husband, Engineer Rahman Tunde,  My children of destiny: Aishat, Waliyat, Aminat and Rilwanulahi for their persevearance, endurance, cooperation and encouragement, and  My parents, late Engineer Alade Oladipo and Mrs Olalonpe Oladipo, for making me what I am today. ii UNIVERSITY OF IBADAN LIBRARY ABSTRACT Osun River is important for domestic, recreational and other activities. It flows along a channel that may be polluted by inputs from industrial, agricultural and other anthropogenic activities thereby limiting its normal use for drinking, fishing, recreation and other purposes. Available literature on the river quality is limited in scope, frequency of sampling and duration of studies. Therefore, a study of the river and its tributaries was carried out to determine the spatial and temporal variations of physicochemical characteristics of its water and sediment. Surface water and sediments were sampled bimonthly from July 2006 to May 2008 at upstream and downstream points of the main river course and 31 tributaries. Sampling was by compositing at each point of 90 locations for surface water and 63 identified locations for sediment, where possible. Water samples were analysed for alkalinity, hardness, ammonia, anions, Dissolved Oxygen (DO), Biochemical Oxygen Demand (BOD), Chemical Oxygen Demand (COD), heavy metals and turbidity. Sediment samples were analysed for organic carbon, particle size and selected heavy metals using APHA methods. Location-based and overall data obtained were fitted into a time series model using a number cruncher statistical system, and applied to predict contaminant concentrations up to year 2018. The Pratti model was applied to determine locational pollution classes (Class 1-5) based on gross organic pollutants and ammonia. Statistical evaluation of data involved use of principal component analysis, analysis of variance and Student’s t-test at p = 0.05. The concentrations (mg/L) of alkalinity, hardness, ammonia, nitrate, phosphate and chloride were 93±130, 116±120, 4.2±6.6, 1.8±1.5, 0.15±0.23 and 54±110 respectively. Those of DO, BOD, COD, lead, copper, cadmium and zinc were 7.9±3.0, 6.9±7.5, 135±120, 0.003±0.004, 0.003±0.004, 0.002±0.003, 0.07±0.10 mg/L respectively and turbidity, 34±43 FTU. Values of parameters for upstream locations did not differ significantly from downstream points, indicating randomness of contaminant inputs. Turbidity, sulphate and DO were higher during the wet seasons while phosphate, nitrate and BOD were higher in the dry seasons. Metal levels correlated positively between water and sediment, with coefficients ranging between 0.75 for Cu and 0.99 for Co. Highest concentration factors in sediment were 233 (Pb) and 171 (Zn). Inter-element association in sediment was high only for Pb/Cu (r =+0.72). Two locations fitted into Class 4 (grossly polluted) of the Pratti scale, while thirty-one were Class 3 (slightly polluted) which was iii UNIVERSITY OF IBADAN LIBRARY indicative of pollution derived from mild industrial and agricultural impacts. Fifty-three locations were acceptable (Class 2), and four excellent (Class 1). Time series modelling fitted well for 2 2 2 nitrate (R = 0.79), phosphate (R = 0.84) and BOD (R = 0.71) data and gave their 2018 predicted values of 19.2, 18.1 and 21.9 mg/L respectively. Comparison with WHO guidelines indicated that 37.0% of sampling points for surface water were unfit for drinking mostly due to high turbidities, but suitable for irrigation. Metal levels in sediment were within international limits. Osun River and its tributaries have been adversely impacted upon by non-point pollutant inputs. Further deterioration in the near future was predicted, and heavy metal pollution is not yet a significant problem in the river basin. Keywords: Osun River, Gross organic pollution, Modelling, Spatial variation, Water quality. Word count: 500 iv UNIVERSITY OF IBADAN LIBRARY ACKNOWLEDGEMENTS I appreciate the effort of my supervisor Professor P. C. Onianwa for his fatherly love, criticism, teaching, words of advice and encouragement which eventually made this work successful. I acknowledge the support and encouragement of the following members of staff of the Department of Chemistry: Professor R. A. Oderinde (Head of Department), Prof. O. Osibanjo, Prof. J. A. O. Woods, Prof. K. O. Adebowale, Prof. B. B. Adeleke, Dr. (Mrs) Onocha, Dr. (Mrs) O. O. Ayelagbe, Dr. (Mrs) M. Ogundiran, Dr. Oladosu, Dr. Adewuyi, Dr. Obi Egbedi, Dr. (Mrs) Osowole, Dr. Babalola, Dr. Ipeayeda, Dr. Etim, Dr. (Mrs) Alabi, and especially Dr. (Mrs) Yinka Aboaba and Dr. (Mrs) K. Oloyede. I appreciate the effort of some of my colleagues in the Department: Ms Tessy Onwordi, Mr. Jude Emurotu and Mrs Bolaji for their words of encouragement. I am grateful to the Department of Chemistry, University of Ibadan for making available all necessary working tools for the successful completion of this work. Special thanks are due to the following laboratory staff: Mr. Ajayi, Mrs Fadekemi, Alhaja Abdul-Waheed, Mr Chris Ehilebo, Mr. Dimeji, Mr Charles Nwabueze, Pastor Ayodele, Mrs Opeisetan, Mr. Dada and Alhaja Adeagbo. I appreciate members of the Department of Applied Sciences, Osun State Polytechnic, Iree, for their words of encouragement. My profound gratitude goes to my Rector, Dr. Agboola, and my late H.O.D, Mr. Gbenjo, for his fatherly love. I am grateful to my loving and caring husband, Engineer Rahman Tunde Olayiwola, for his support and words of encouragement, financial contribution to the successful completion of this programme. You are a responsible husband and father that all women and children should pray to have. Many thanks to my children of destiny: Aishat, Waliyat, Aminat and little Rilwanulahi Tunji. May the Almighty ALLAH guide you through the right path (Amen). Special appreciation to my late father, Engr. Alade Oladipo for making me what I am today, and to my sweet mother, Mrs Olalonpe Oladipo - I love you. I appreciate the support of my siblings - Mr. Dayo Oladipo, Mrs Bola Oguntunde, Mrs Sumbo Akanmu, and my in-laws - Mr. Titus Oguntunde, Mr. Biola Akanmu, and Mrs Oladipo. Special thanks are due to my sweet father and mother in-law - Alhaji v UNIVERSITY OF IBADAN LIBRARY and Alhaja Olayiwola, and my brother-in-laws - Taofeeq Olayiwola, Taofeeq Dauda and Barrister Adejare. I appreciate the friendliness and encouragement of my neighbours and family friends at Oroki Housing Estate Osogbo, late Rev. and Deaconess Eniola Fapojuwo, Mrs Adetuyi, Mrs Agboola, Mr and Mrs Aderinto, Alhaji and Alhaja Taofeeq Alimi and Mr and Mrs Omotosho. vi UNIVERSITY OF IBADAN LIBRARY CERTIFICATION I certify that this research work was carried out under my supervision by OLAJUMOKE ABIDEMI OLAYIWOLA, in the Department of Chemistry, University of Ibadan, Ibadan Nigeria. Supervisor P. C. Onianwa B.Sc., M.Sc., Ph.D. (Ibadan) Professor of Analytical/Environmental Chemistry Department of Chemistry, University of Ibadan, Nigeria vii UNIVERSITY OF IBADAN LIBRARY TABLE OF CONTENTS Page Title Page i Dedication ii Abstract iii Acknowledgements v Certification vii Table of Contents viii List of Tables xiii List of Figures xvi List of Appendices xviii Lists of xx Acronyms/Abbreviation CHAPTER ONE: INTRODUCTION 1.1 Environmental Pollution 1 1.1.1 Different Types of Pollution 2 1.2 Water Pollution 5 1.3 Classification of the Causes of Water Pollution 7 1.4 Water Quality Indicators 9 1.4.1 Biological Indicators 9 1.4.2 Chemical Assessment 10 1.4.3 Physical Assessment 10 1.5 Water Quality and its Beneficial Uses 11 1.6 The Osun River Channel 13 1.7 Previous Studies on Osun River 16 1.7.1 The Limitation of Previous Studies 17 1.8 Aim/Scope 18 CHAPTER TWO: LITERATURE REVIEW 2.1 General Surface Water Qualities 20 viii UNIVERSITY OF IBADAN LIBRARY 2.1.1 Assessment of Water Quality and Toxicity of Polluted Rivers 20 2.1.2 Significance of Oxygen-Demanding Substances and Nutrients on Surface Water Quality 21 2.1.3 Seasonal Dynamics of Physicochemical Properties in Surface Water 23 2.2 Impacts of Human Activities on Surface Water Quality 25 2.2.1 Impact of Industrial Activities on Water Quality of Rivers. 26 2.2.2 Impact of Agricultural Runoff on Surface Water Quality. 29 2.2.3 Effect of Domestic Sewage Discharge on Water Quality of Surface Water. 30 2.3 Surface Water Quality Standards 31 2.4 Water Quality Index 32 2.5 River Water Quality Models 51 2.5.1 Model Classification 53 2.5.2 Selection Factors for Model Selection 55 2.5.3 Diffuse Pollution 57 2.5.4 Trend Analysis 58 2.5.4.1 Time series analysis 58 2.5.4.2 Seasonality 59 2.5.4.3 Smoothing techniques 60 2.6 Self-Purification and its Relationship to Pollution Abatement 62 2.7 Sediment and Nutrient Discharge Characteristics of Rivers. 66 2.7.1 Characteristics of Eroded Sediments 66 2.7.2 Sediment Related Problems 68 2.7.2.1 Fisheries/Aquatic Habitat 68 2.7.2.2 Bioavailabity and Toxicity of Metals in Sediment 69 2.7.2.3 Speciation of Heavy Metals in Bottom Sediments 71 2.7.2.4 Remediation of Contaminated Sediment 72 2.8 Heavy Metals Absorption in Vegetation 75 2.8.1 Accumulation and Toxicity of Heavy Metals in Vegetation 75 CHAPTER THREE: METHODOLOGY 3.1 Design of the Study 80 ix UNIVERSITY OF IBADAN LIBRARY 3.2 Description of Study Area/Sampling Points 81 3.2.1 Map of River Osun Showing Sampling Locations 81 3.3 Sampling 81 3.3.1 Sampling of Surface Water 89 3.3.2 Sampling of River Sediment 89 3.3.3 Sampling of Plant 89 3.3.4 Summary of Sample Distribution by Location 90 3.4 Analysis of Surface Waters 90 3.4.1 Determination of pH 90 3.4.2 Determination of Electrical Conductivity 90 3.4.3 Determination of Temperature 90 3.4.4 Determination of Total Solids 93 3.4.5 Determination of Total Dissolved Solids 93 3.4.6 Determination of Total Suspended Solids 93 3.4.7 Determination of Turbidity 93 3.4.8 Determination of Alkalinity 94 3.4.9 Determination of Total Hardness 94 3.4.10 Determination of Calcium 95 3.4.11 Determination of Magnesium 95 3.4.12 Determination of Nitrate 96 3.4.13 Determination of Chloride 96 3.4.14 Determination of Ammonia 97 3.4.15 Determination of Sulphate 98 3.4.16 Determination of Phosphate 99 3.4.17 Determination of Dissolved Oxygen 100 3.4.18 Determination of Biochemical Oxygen Demand 100 3.4.19 Determination of Chemical Oxygen Demand 102 3.4.20 Determination of Heavy Metals 103 3.5 Analysis of Sediment for Quality Parameters 103 3.5.1 Determination of Effective Cation Exchange Capacity (CECe) 103 3.5.2 Determination of Heavy Metals in Sediment 105 x UNIVERSITY OF IBADAN LIBRARY 3.5.3 Total Organic Carbon 105 3.5.4 Sediment Mechanical Properties 106 3.6 Analysis of Plant Samples 107 3.7 Quality Assurance of the Analytical System 108 3.7.1 Recovery Study of the Analytical Process for Metal Analysis of Sediment and Plant Samples 108 3.7.2 Analysis of Standard Reference Material 109 3.7.3 Quality Assurance for BOD Determination 110 3.7.4 General Quality Control 110 3.8 Statistical Analysis of Data 110 3.9 Application of Modeling to Prediction of Surface Water and Sediments Quality 115 CHAPTER FOUR: RESULTS AND DISCUSSION 4.1 Average Characteristics (Overall) of Surface Water 117 4.2 Average Characteristics of Surface Water of the Various Tributaries 125 4.2.1 Pratti Scale Classification of River Waters 132 4.3 Spatial Variations of Physicochemical Characteristics and Heavy Metals in Surface Water of Osun River 137 4.4 Seasonal Variations of the Physicochemical Characteristics and Heavy Metals in Surface Water 151 4.5 Application of Model to Prediction of Future Water Qualities 155 4.5.1 Time Series Analysis of Parameters of Osun River Surface Water 155 4.6 Principal Component Analysis of Heavy Metals in Surface Water 158 4.7 Pearson Correlation Matrix for Parameters in Surface Water 158 4.8 Comparison of the Overall Characteristics of the Osun River Surface Water Drinking Water Quality Guidelines. 161 4.9 Comparison of Surface Water Characteristics of the Tributaries with Water Quality Guidelines for Various Categories of Water Usage. 162 4.10 Comparison of the Physicochemical Characteristics of Surface Water in this Study with Studies Elsewhere. 164 xi UNIVERSITY OF IBADAN LIBRARY 4.11 Average Physicochemical Characteristics (Overall) of the Sediment 169 4.12 Average Physicochemical Characteristics of Sediments of Osun River and its Tributaries 172 4.13 Spatial Variation of the Physicochemical Characteristics of the Sediment 178 4.14 Seasonal Variation of the Physicochemical Characteristics and Heavy Metals in Sediment 178 4.15 Application of Models to Prediction of Future Sediment Qualities 179 4.16 Principal Component Analysis of Heavy Metals in Sediment 187 4.17 Correlation of Metals Concentrations between Water and Sediment 189 4.18 Accumulation Factors between Metals in Sediment and Water for the Study 189 4.19 Pearson Matrix for Heavy Metals in Sediment 189 4.20 Calculation of Enrichment Factor and Index of Geo-accumulation 193 4.21 Comparison of Sediment Metal Concentrations with Sediment Quality Guidelines 194 4.22 Comparison of Heavy Metals in Sediment in this Study with Studies Elsewhere 194 4.23 Average Metal Concentration (Overall) in Plant 199 4.24 Average Concentrations of Heavy Metals in Plants around the Tributaries 201 4.25 Spatial Variation of Heavy Metals Levels in Plants 204 4.26 Seasonal Variation of Heavy Metals Levels in Plants 204 4.27 Pearson Correlation Matrix of Heavy Metals in Plants 210 4.28 Principal Component Analysis of Heavy Metals in Plants 210 4.29 Comparison of the Heavy Metals in this Study with Studies Elsewhere. 211 CHAPTER FIVE: SUMMARY, CONCLUSION AND RECOMMENDATION 5.1 Summary and Conclusion 216 5.2 Recommendation 217 REFERENCES 218 APPENDICES 252 xii UNIVERSITY OF IBADAN LIBRARY LIST OF TABLES Table Title Page No. 2.1 Some industrial pollutants and their effect on the environment (FMHE, 1982) 27 2.2 Drinking water quality guidelines 33 2.3 Water quality tolerance for certain industrial applications (mg/L) except as indicated 34 2.4 Water quality guideline for pulp and industry 35 2.5 Water quality guidelines for the iron and steel industry 36 2.6 Water quality guidelines for the petroleum industry 37 2.7 Water quality guidelines for power generating stations 38 2.8 Water quality guidelines for the food and beverage industry 39 2.9 FAO maximum concentrations of trace elements in irrigation water 40 2.10 Guides for evaluating the quality of water for aquatic life 41 2.11 Recreational water quality 42 2.12 Water quality for livestock 43 2.13 Water quality criteria for livestock (II) 44 2.14 Some parameters (indicative of gross organic pollution) used in the classification of surface water quality 45 2.15 Sediment quality guidelines values of ERL and ERM for each metal and 46 contamination level ranking 2.16 Canadian and Ontario sediment quality guidelines 47 2.17 Concensus based sediment quality guidelines of Wisconsin (CBSQG) 48 3.1 Features of the sampling locations 83 3.2 List of plants sampled for heavy metal in the river Osun 91 3.3 Summary of sample distribution by location 92 3.4 Operating conditions for the atomic absorption spectrophotometer 104 3.5 Recovery (%) of Pb in sediment 111 3.6 Average recoveries of heavy metals in sediment and plants 112 3.7 Results of analysis of standard reference material 113 3.8 Results of BOD check study 114 xiii UNIVERSITY OF IBADAN LIBRARY Table Title Page No. 4.1 Average concentrations of water quality characteristics of Osun River 118 4.2 Average characteristics of the tributaries of Osun River 126 4.3 Pratti scale classification for each location of river Osun surface water samples for each location 133 4.4 Average dry and wet season characteristics of the Osun River water 152 4.5 Time-series analysis results of selected parameters at selected tributaries 156 4.6 Time series analysis of overall concentrations of surface water parameters in 157 Osun River 4.7 Pearson correlation matrix for the physicochemical characteristics of surface water 160 4.8 Tributaries with water quality meeting specific industrial and other requirements 163 4.9 Comparison of the physicochemical parameters in surface water in this study with studies elsewhere 165 4.10 Average physicochemical characteristics of sediment 170 4.11 Average characteristics of sediments of Osun River (for all tributaries) 173 4.12 Seasonal variation of the physicochemical characteristics and heavy metals in 185 sediment 4.13 Time-series analysis results of selected parameters of sediment from selected 187 tributaries 4.14 Sediment-Water correlations of metal concentrations 191 4.15 Sediment/water factors of accumulation of metal concentrations for Osun River 191 4.16 Correlation matrix for heavy metals in sediment 192 4.17 Calculation of enrichment factor and index of geo-accumulation in sediment 194 4.18 Number of locations (tributaries) with sediment metal concentration 196 exceeding the guideline limit 4.19 Comparison of heavy metals in sediment in this studies with studies elsewhere (µg/g) 197 4.20 Average (overall) concentration of metals in plants 200 4.21 Average concentrations (µg/g) of heavy metals in plants in all tributaries 202 xiv UNIVERSITY OF IBADAN LIBRARY Table Title Page No. 4.22 Seasonal variation of heavy metals levels in plants (µg/g) 209 4.23 Pearson correlation of heavy metals in plants 211 4.24 Comparison of heavy metals concentrations in plants from this study with studies elsewhere (µg/g) 214 xv UNIVERSITY OF IBADAN LIBRARY LISTS OF FIGURES Figure Title Page No. 1 Map of Osun River showing various industrial activities 14 3.1 River Osun showing sampling locations 82 4.1 Spatial variation of electrical conductivity in surface water 138 4.2 Spatial variation of pH in surface water 138 4.3 Spatial variation of total solids in surface water 139 4.4 Spatial variation of total dissolved solids in surface 139 4.5 Spatial variation of total suspended solids in surface water 140 4.6 Spatial variation of turbidity is surface water 140 4.7 Spatial variation of alkalinity in surface water 141 4.8 Spatial variation of hardness in surface water 141 4.9 Spatial variation of temperature in surface water 142 4.10 Spatial variation of nitrate in surface water 142 4.11 Spatial variation of sulphate in surface water 143 4.12 Spatial variation of phosphate in sulphate water 143 4.13 Spatial variation of chloride in surface water 144 4.14 Spatial variation of ammonia in surface water 144 4.15 Spatial variation of dissolved oxygen of surface water 145 4.16 Spatial variation of biochemical oxygen demand in surface water 145 4.17 Spatial variation of chemical oxygen demand in surface water 146 4.18 Spatial variation of lead in surface water 146 4.19 Spatial variation of copper in surface water 147 4.20 Spatial variation of cadmium in surface water 147 4.21 Spatial variation of cobalt in surface water 148 4.22 Spatial variation of chromium in surface water 148 4.23 Spatial variation of nickel in surface water 149 4.24 Spatial variation of zinc in surface water 149 4.25 Spatial variation of calcium in surface water 150 4.26 Spatial variation of magnesium in surface water 150 xvi UNIVERSITY OF IBADAN LIBRARY Figure Title Page No. 4.27 Principal component analysis of heavy metals in surface water 159 4.28 Spatial variation of organic carbon in sediment 179 4.29 Spatial variation of sand in sediment 179 4.30 Spatial variation of clay in sediment 180 4.31 Spatial variation of silt in sediment 180 4.32 Spatial variation of cation exchange capacity in sediment 181 4.33 Spatial variation of Pb in sediment 181 4.34 Spatial variation of Cu in sediment 182 4.35 Spatial variation of Cd in sediment 182 4.36 Spatial variation of Co in sediment 183 4.37 Spatial variation of Cr in sediment 183 4.38 Spatial variation of Ni in sediment 184 4.39 Spatial variation of Zn in sediment 184 4.40 Principal component analyses of heavy metals in sediment 190 4.41 Spatial variation of Pb in plants 205 4.42 Spatial variation of Cu in plants 205 4.43 Spatial variation of Cd in plants 206 4.44 Spatial variation of Co in plants 206 4.45 Spatial variation of Cr in plants 207 4.46 Spatial variation of Ni in plants 207 4.47 Spatial variation of Zn in plants 208 4.48 Principal component analyses of heavy metals in plants 212 xvii UNIVERSITY OF IBADAN LIBRARY LISTS OF APPENDICES Appendix Title Page No. 1 Concentrations of electrical conductivity (µS/cm) surface water for all 252 locations 2 pH in surface water for all locations 253 3 Concentration of TS (mg/L) in surface water for all the locations 254 4 Concentrations of TDS (mg/L) in surface water for all the locations 255 5 Concentrations of TSS (mg/L) in surface water for all the locations 256 6 Turbidity (FTU) in surface water for all the locations 257 7 Alkalinity (mg/L) in surface water for all the locations 258 8 Hardness (mg/L) of surface water for all the locations 259 9 Temperature (OC) of surface water for all the locations 260 10 Concentrations of nitrate (mg/L) in surface water for all the locations 261 11 Concentrations of sulphate (mg/L) in surface water for all the locations 262 12 Concentrations of phosphate (mg/L) in surface water for all the locations 263 13 Concentrations of chloride (mg/L) in surface water for all the locations 264 14 Concentrations of ammonia (mg/L) in surface water for all the locations 265 15 Concentrations of dissolved oxygen (mg/L) in surface water for all the locations 266 16 Biochemical oxygen demand (mg/L) in surface water for all the locations 267 17 Chemical oxygen demand (mg/L) in surface water for all the locations 268 18 Concentrations of lead (mg/L) in surface water for all the locations 269 19 Concentrations of copper (mg/L) in surface water for all the locations 270 20 Concentrations of cadmium (mg/L) in surface water for all the locations 271 21 Concentrations of cobalt (mg/L) in surface water for all the locations 272 22 Concentrations of chromium (mg/L) surface water for all the locations 273 23 Concentrations of nickel (mg/L) in surface water for all the locations 274 24 Concentrations of zinc (mg/L) in surface water for all the locations 275 25 Concentrations of Calcium (mg/L) in surface water for all the locations 276 26 Concentrations of Magnesium (mg/L) in surface water for all the locations 277 27 Concentrations of organic carbon (%) in sediment for all the locations 278 xviii UNIVERSITY OF IBADAN LIBRARY Appendix Title Page No. 28 % Sand in sediment for all the locations 279 29 % Clay in sediment for all the locations 280 30 % Silt in sediment for all the locations 281 31 Cation exchange capacity (meq/100g) in sediment for all the locations 282 32 Concentrations of lead (µg/g) in sediment for all the locations 283 33 Concentrations of copper (µg/g) in sediment for all the locations 284 34 Concentrations of cadmium (µg/g) in sediment for all the locations 285 35 Concentrations of cobalt (µg/g) in sediment for all the locations 286 36 Concentrations of chromium (µg/g) in sediment for all the locations 287 37 Concentrations of nickel in (µg/g) sediment for all the locations 287 38 Concentrations of zinc (µg/g) in sediment for all the locations 289 39 Concentrations of lead (µg/g) in plant for all the locations 290 40 Concentrations of copper (µg/g) in plant for all the locations 291 41 Concentrations of cadmium (µg/g) in plant for all the locations 292 42 Concentrations of cobalt (µg/g) in plant for all the locations 293 43 Concentrations of chromium (µg/g) in plant for all the locations 294 44 Concentrations of nickel (µg/g) in plant for all the locations 295 45 Concentrations of zinc (µg/g) in plant for all the locations 296 46 Physicochemical properties of surface water obtained from control sites (Egun) 297 47 Concentration of heavy metals (mg/L) in surface water of control sites 298 48 Physicochemical characteristics and heavy metals in sediments of control 299 sites 49 Concentrations of heavy metals (µg/g) in plants of control sites 300 50 Description of sampling locations at Adeti, Oyika and Anne 301 51 Description of sampling locations at Ojutu, Gbodofon and Olumirin 302 52 Description of sampling locations at Osun, Yeyekare and Olumirin 303 53 Description of sampling locations at Asejire 304 xix UNIVERSITY OF IBADAN LIBRARY CHAPTER ONE 1.0 INTRODUCTION 1.1 ENVIRONMENTAL POLLUTION Virtually all types of pollution are harmful to the health of humans and animals. Pollution may not damage ones health immediately but can be harmful after long term exposure. Pollution is the introduction of contaminants into an environment, of whatever predetermined or agreed upon proportions or frame of reference. These contaminants cause instability, disorder, harm, and discomfort to the physical systems or living organisms therein. Pollutants, the elements of pollution, can be foreign substances or energies, or naturally occurring. When naturally occurring, they are considered contaminants when they exceed natural threshold levels. Pollution in general is the activity of disturbing the natural system and balance of an environment. The increase in pollution over the years by man has caused severe damage to the earth‟s ecosystem. It is responsible for global warming which is leading to the end of all lives on earth. Over the years has been extreme increase in the rate of human diseases, death rate and extinction of various animals and plants on earth, and that is all because of the pollution caused by man himself. Until relatively recently in humanity‟s history, where ever pollution had existed, it has been primarily a local problem. The industrialization of society, the introduction of motorized vehicles, and the explosion of human population, however, have caused an exponential growth in the production of goods and services. Coupled with this growth has been a tremendous increase in waste by-products. The indiscriminate discharge of untreated industrial and domestic wastes into water ways, the spewing of thousands of tons of particulates and airborne gases into the atmosphere, the “throwaway” attitude toward solid wastes, and the use of newly developed chemicals without considering potential consequences have resulted in major environmental disasters. Technology has begun to solve some pollution problems, and public awareness of the extent of pollution 1 UNIVERSITY OF IBADAN LIBRARY will eventually force governments to undertake more effective environmental planning and adopt more effective antipollution measures. 1.1.1 Types of Pollution of Environmental Media (a) Water pollution About 75% of the earth‟s surface is covered with water and more than half of the total population of earth‟s species resides in water. Moreover, our life greatly depends on water and life without water is impossible. Water is essential for the development and maintenance of the dynamics of every ramification of society (Herschy, 1999; UNSCD, 2000). According to Gore (1993), human beings are made up of water, in roughly the same percentage as water is in the surface of the Earth. Our tissues and membranes, brains, and hearts, our sweat and tears- all reflect the same recipe for life, in which efficient use is made of those ingredients available on the surface of the earth. Water pollution not only affects the fish and animals living in the water but also affects the whole food chain by also transferring the contaminants to the consumers depending on these animals. Water used from a polluted lake directly contaminates its user. Many water creatures are on the verge of extinction due to the dramatic increase in water pollution. Conventional pollutants: Conventional or classical pollutants are generally associated with the direct input (mainly by human) of waste products. Rapid urbanization and rapid population increases have produced sewage problems because treatment facilities have not kept pace with need. Untreated and partially treated sewage from municipal wastewater systems and septic tanks in unsewered areas contribute significant quantities of nutrients, suspended solids, dissolved solids, oil, metals (arsenic, mercury, chromium, lead, iron and manganese), and biodegradable organic carbon to the water environment. Conventional pollutants may cause a myriad of water pollution problems. Excess suspended solids block out energy from the sun and thus affect the carbon dioxide-oxygen conversion process, which is vital to the maintenance of the biological food chain. Also, high concentration of suspended solids silt up rivers and navigational channels, necessitating frequent dredging. Excess dissolved solids make the water undesirable for drinking and for crop irrigation. 2 UNIVERSITY OF IBADAN LIBRARY The Rhine River of Europe, once the most scenic river, is now called the longest sewer line in Europe (Shaheen and Chantarason, 1971). Although essential to the aquatic habitat, nutrients such as nitrogen and phosphorus may also cause over fertilization and accelerate the natural aging process (eutrophication) of lakes. This acceleration in turn produces an overgrowth of aquatic vegetation, massive algal blooms and an overall shift in the biologic community- from low productivity with large numbers of a few species of a less desirable nature. Nonconventional pollutants: The nonconventional pollutants include dissolved and particulate forms of metals, both toxic and nontoxic and degradable and persistent organic carbon compounds discharged into water as a by-product of industry or as an integral part of marketable products. Nonconventional pollutants vary from biologically inert materials such as clay and iron residues to the most toxic and insidious materials such as halogenated hydrocarbons (DDT, kepone, mirex and polychlorinated biphenyls-PCB). The latter group may produce damage ranging from acute biological effects to chronic sub lethal effects that may go undetected for years. The chronic low-level pollutants are proving to be the most difficult to correct and abate because of their ubiquitous nature and chemical stability. (b) Soil pollution Land pollution is the degradation of the Earth‟s land surface through misuse of the soil by poor agricultural practices, mineral exploitation, industrial waste dumping, and indiscriminate disposal of urban wastes (EPA, 1998). The most common and convenient method of disposing of municipal solid wastes is in the sanitary landfill continuous urban development and large solid waste pose as major environmental risks because of the difficulties in disposal. Landfills and other solid wastes disposal sites are major targets of pollution because rainfall and groundwater leach these highly contaminated substances into rivers, streams and waterways (surface water) which are inadvertently used by people residing in such areas. The open dump, once a common eyesore in towns across the United States, that attracted populations of rodents and other 3 UNIVERSITY OF IBADAN LIBRARY pests and often emitted hideous odours is now illegal. Recycling of materials is practical to some extent for much municipal and some industrial wastes, and a small but growing proportion of solid wastes are being recycled. When wastes are commingled, however, recovery becomes difficult and expensive. Crucial issues in recycling are devising better processing methods, inventing new products for the recycled materials and finding new markets for them. Incineration is another method for disposing solid wastes. Advanced incinerations use solid wastes as fuel, burning quantities of refuse and utilizing the resultant heat to make steam for electricity generation. Wastes must be burned at very high temperatures, and incinerator exhausts must be equipped with sophisticated scrubbers and other devices for removing dioxins and other pollutants. Problems remain, however: that since incinerator ash contains high ratios of heavy metals thus, becoming a hazardous waste itself; the high- efficiency incinerators may discourage the use of recycling and other waste-reduction methods. Compositing is increasingly used to treat some agricultural wastes, as well as such municipal wastes as leaves and bush. Compositing systems can produce usable soil conditioners, or humus, within a few months. (c) Air pollution Air pollution is the accumulation in the atmosphere of substances that, in sufficient concentrations, endanger human health or produce other measured effects on living matter and other measured effects on living matter and other materials. Among the major sources of pollution are power and heat generation, the burning of solid wastes, industrial process, and, especially, transportation. The six major types of pollutants are carbon monoxide, hydrocarbon, nitrogen oxides, particulates, sulfur dioxide, and photochemical oxidants. Smog has seriously affected more persons than any other type of air pollution. It can be loosely defined as multisource, widespread air pollution that occurs in the air of cities. Smog, a concentration of the world‟s smoke and fog, has been caused throughout recorded history by water condensing on smoke particles, usually from burning coal. As a coal economy has gradually been replaced by a petroleum economy, photochemical smog has 4 UNIVERSITY OF IBADAN LIBRARY become predominant in many cities. Its unpleasant properties result from the irradiation by sunlight of hydrocarbons and other pollutants in the air. Air pollution on a regional scale is in part the result of local air pollution including that produced by individual sources, such as automobiles- that have spread out to encompass areas of many thousands of square kilometers. Oxides of sulfur and nitrogen carried long distances by the atmosphere and then precipitated in solution as acid rain, can cause serious damage to vegetation, waterways and buildings. Humans also pollute the atmosphere on a global scale, although until the early 1970s little attention was paid to the possible deleterious effects of such pollution. Global warming is the biggest threat towards mankind since the Black Death, claiming millions of lives in the process. The effects of global warming are playing havoc everywhere-like higher temperatures, hurricanes, heavy rains, rise in the sea level; flooding and droughts have now become more frequent and severe in intensity. Certain pollutants decrease the concentration of ozone layer occurring naturally in the stratosphere, which in turn increases the amount of ultraviolet radiation reaching the Earth‟s surface. Such radiation may damage vegetation and increase the incidence of skin cancer. Examples of stratospheric contaminants include nitrogen oxides emitted by supersonic aircraft and chlorofluorocarbons used as refrigerants and aerosol-can propellants. The chlorofluorocarbons reach the stratosphere by upward mixing from the lower parts of the atmosphere. It is believed that these chemicals are responsible for the noticeable loss of ozone over the Polar Regions that have occurred in the 1980s. 1.2 WATER POLLUTION Water Pollution is defined as the degradation of the natural quality of water due to the addition of harmful wastes in excessive concentrations (Knapp, 1970). The causes of water pollution may be due to direct and indirect contaminant sources. The former are effluent outputs from refineries, factories and waste treatment plants. Fluids of differing qualities are emitted to the urban water supplies. In the United States and some other countries, these methods are controlled (Abhay, 2007). Still, pollutants can be found 5 UNIVERSITY OF IBADAN LIBRARY in the water bodies. Indirect contaminant sources are the water supply from soils and ground water systems that have fertilizers, pesticides and industrial wastes. Also, they include those from atmosphere such as factory emissions and automobile discharge. Continuous urban development and large solid waste pose major environmental risks because of the difficulties in disposal. Landfills and other solid wastes disposal sites are major sources of pollution because rainfall and groundwater leach these highly contaminated substances into rivers, streams and waterways (surface water) which are inadvertently used by people residing in such areas. Stranberg (1971) include sewage and other oxygen demanding wastes, infectious agents, organic chemicals, other chemicals and mineral substances, sediments (turbidity), radioactive substances and thermal pollution. Human activities that may result to water pollution include the following, agriculture, irrigation, urbanization, mining, fire and industrialization (Goudie, 1990). These activities have been documented to have impacted negatively on some specified Nigerian surface water (Olajire et al., 2003). Of particular interests are the heavy metals because of their very toxic nature. The modern era of industrialization has increased the spread of environmental contamination by heavy metals. Heavy metals toxicity dates back to ancient period. The heavy metals of interest include lead, chromium, cadmium, iron, zinc, manganese, mercury, arsenic, copper etc. According to Freedman (1989) the chemical form of toxic elements dissolved in water is generally relatively available to biota and even seemingly small aqueous concentrations may exert a powerful toxic effect. Though these trace elements are usually present in the environment, they are potentially extremely toxic and not only would they affect the biota at a water soluble concentration, at less than 1ppm, humans can be grossly affected. The supply of water in several cities is limited, and in many cases, water supply is chronically insufficient for the inhabitants. Despite the inadequacy of water supply, the management and conservation of the available water bodies is generally poor. Industrial growth is fast increasing globally, and so is the water demand for industrial productions or processes. This has put more pressure on the limited available water resources. Water bodies are also constantly used as receptacles for untreated waste water. Poorly treated effluents accruing from industrial activities have rendered many water bodies unsuitable 6 UNIVERSITY OF IBADAN LIBRARY for both primary and secondary usage. River water quality is often a useful indicator of the state of community health in underdeveloped countries, where, because of inadequate supply of treated tap water, river water serves as a direct source of drinking water, in addition to its normal uses for irrigation, recreation and fishing. The impairment of river in urban settlements of such communities has been shown to be the primary source of health hazards in some cases (Adesina, 1986). Besides the modifying influence on river water quality by natural processes such as weathering of bedrock minerals, leaching of soil nutrients and evapotranspiration, anthropogenic activities which significantly impact on urban river water quality in under-developed communities include the discharge of industrial and domestic wastes into river courses. 1.3 CLASSIFICATION OF THE CAUSES OF WATER POLLUTION The major sources of water pollution can be classified as municipal, industrial, agricultural and oil spills. (a) Municipal Water Pollution Municipal waste is a source of water pollution especially where combined sewers are used to empty both storm water and sanitary wastes into the same water body, usually without being treated. This consists of wastewater from homes and commercial establishments. The contents of municipal water include suspended solids, oxygen-demanding materials, dissolved inorganic compounds and harmful bacteria. Detergent waste containing alkylbenzene sulfonate for instance is not biodegradable and causes foaming problems. This can get into water body from homes and it is at times retained in filtered municipal water supplies, causing odour and taste problems. (b) Industrial Water Pollution: The characteristics of industrial wastewaters can differ considerably both within and among industries. The impact of industrial discharges depends not only on their collective characteristics, such as biochemical oxygen demand and the amount of suspended solids, but also on their content of specific inorganic and 7 UNIVERSITY OF IBADAN LIBRARY organic substances. Waste from industries cover the wide gamut of pollutants which come from huge number of chemicals that are made in the world today. When these chemicals are dumped into water bodies, they cause serious harm due to their chemical toxicity. The chemicals from industrial wastes cover a wide spectrum including sulfuric acid waste, hydrocarbons, pulp and paper sulfates, nitric acid, etc. Industries contribute a lot to the pollution of the environment. Most industries in Nigeria do not treat their wastes before disposal. (c) Agricultural Water Pollution These are derived from commercial livestock and poultry farming, and are the sources of many organic and inorganic pollutants in surface waters (David and Brad, 2006). Agricultural crops are prevented from being attacked by pest and insects through application of chemicals such as DDT, and a series of chlorinated hydrocarbons have been used in varying degrees. These chlorinated hydrocarbons enter the river from treated areas through water runoff or by natural percolation through the soil. Chlorinated hydrocarbons are biologically non-degradable and are persistent, and this affects organisms in water and humans that eat these organisms. Aquatic ecosystem has the ability to assimilate certain amount of waste and maintain near normal function. However, when these wastes are discharged excessively, the natural cleaning process of a river ceases to exist and this causes damage and death to organisms. Drainage from feed lots (cattle pens constructed for the purpose of fattening up the cattle before slaughter) for example has an extremely high pollution strength (Vesilind and Pierce, 1982). (d) Oil Spills Oil is the life-line of the modern industrial revolution. The industrialized nations have developed a great thirst for oil which is shipped from foreign sources under proper controls. Oil is a good source of energy. Oil spills and contribution from routine operations are another source of water pollution. Sources of oil pollution include losses which arise from careless handling at small factories. Oil pollution of water bodies may arise from spillage of damaged pipeline, 8 UNIVERSITY OF IBADAN LIBRARY leakage at drilling rigs, disposal of used oil or lubricant, gas flaring and usage of chemicals. Any of these situations may lead to the destruction or extinction of wildlife habitat, plants birds and marine life and contamination of drinking water (Smith, 1968). Major oil disasters are Exxon oil spill, in 2010, Ogbodo oil spill, 2001 in Nigeria (Vidal, 2010), Torrey Canyon, in 1967, the Ocean Eagle, in 1968, Puerto Rico URS Research Company, 1970 and Santa Barbara, in 1969 (California University, 1971). 1.4 WATER QUALITY INDICATORS 1.4.1 Biological Indicators Biological indicators are numerical values derived from actual measurements, have known statistical properties, and convey useful information for environmental decision making. They can be measures, indices of measures, or model that characterize ecosystems or one of their critical components. The primary uses of indicators are to characterize current status and to track or predict significant change. Biological attributes of waterway can be important indicators of water quality. Biological attributes refer to the number and types of organisms that inhabit a waterway. The poorer the quality of water the fewer the number and types of organisms that can live in it (Chris and Edward, 1998). When assessing water quality, it is important to look at the quality of organisms that live in a waterway. Some species are more sensitive to chemical and physical changes in their habitat than other species. If species that tend to be sensitive to pollution are present in a water way, then that water may mostly likely have good water quality. Examples of biological indicators are fish, algae or protozoas and benthic macro invertebrates like crustaceans, mollusks, worms, and many species of insect larva such as mayflies, caddis flies and beetles (Vannote, et al., 1980). For example, Ayas et al. (2007) described the accumulation of some heavy metals Pb, Cd, Cu and Ni in fish samples (Albumus escherichil, Cuprinus carplo and Silurus glaniso) and were biologically magnified in the tissues. Chris and Edward (1998) described biological criteria as the broader concept of water resource integrity which supplements the roles of chemical and toxicological approaches and reduces the weelhood of making overly optimistic estimates of aquatic life conditions. 9 UNIVERSITY OF IBADAN LIBRARY 1.4.2 Chemical Assessment Chemical attributes of waterway are important indicators of water quality. Chemical attributes can affect aesthetic qualities such as how water looks, smells, and tastes. It can also affect its toxicity and whether or not it is safe to use. Chemical quality of water is important to the health of humans as well as the plants and animals that live in and around rivers (Schlesinger, 1991). Assessment of water quality by its chemistry includes measures of many elements and molecules dissolved or suspended in the water. Chemical measures can be used to directly detect pollutants such as lead or mercury. Inbalances within the ecosystem may indicate the presence of certain pollutants. Commonly measured chemical parameters include pH, alkalinity, hardness, nitrates, nitrites and ammonia, ortho and total phosphates, and dissolved oxygen and biochemical oxygen demand. Some chemical measurements actually indicate the physical presence of pollutants in water. Examples are conductivity and density. The use of chemical assessment by many workers abounds Chemical properties of water such as BOD, DO, COD, pH nitrate, and phosphate were used to examine the water quality of rivers. (Onianwa et al., 2001; Adeniyi and Imevbore, 2008; Ajibade et al., 2008). 1.4.3 Physical Assessment Physical assessments are important indicators of water quality. The most basic physical attribute of a river or stream is the path along which it flows. Most streams classified as “meandering‟‟ streams have many bends. The bends are characterized by deep pools of cold water along the outside banks where faster-moving water scours the bank. Meandering streams also have riffles (i.e. portion of a stream characterized by fast-moving turbulent water) along the straight stretches between pools. The S-shaped path of meandering streams prevents water from moving too quickly and flooding downstream ecosystems. The deep, cold pools of water provide ideal habitat for many species of fish even when overall stream flow is reduced. The riffles help to hold water upstream during times of low stream flow. Also, turbulence in the riffles mixes oxygen into the water. Natural stream channel patterns, with their bends, pools and riffles, are essential to decreasing flooding as well as providing a suitable habitat for certain plants and animals. 10 UNIVERSITY OF IBADAN LIBRARY This makes it important to assess the physical attributes of a stream when examining its water quality. Measurements of a streams physical attributes are used to describe the structure of a sampling site. This allows for the comparison of the biota and chemistry of similarly-structured streams at different locations. Measurements of a stream physical attributes can also serve to indicate the presence of certain effluents, while changes in stream width, depth, and velocity, turbidity, and rock size may indicate dredging in the area. Other physical characteristics measured in a stream include elevation and catchment area, stream order, forest canopy and total solids (Vannote, 1980). 1.5 WATER QUALITY AND ITS BENEFICIAL USES Water is essential to human life and the health of the environment. As a valuable natural resource, it comprises marine, estuarine, freshwater (river and lakes) and groundwater environment that stretch across coastal and inland areas. Water quality in a body of water influences the way in which communities use the water for activities such as drinking, swimming or commercial purposes. Most people believe good water quality means the water is pure and clean. However, fish and wildlife have lots of other requirements. Fish must get all of their oxygen and food from water, and therefore need water that has enough oxygen and nutrients. Thus, good water quality implies that harmful substances (pollutants) are absent from the water, and needed substances (oxygen, nutrients) are present. Generally, the water quality of rivers is best in the headwaters, where rainfall is often abundant. Water quality frequently declines as rivers flow through regions where land and water use are intense and pollution from intensive agriculture, large towns, and industry and recreation areas increases. There are exceptions to the rule and water quality may improve downstream, behind dams and weirs, at points where tributaries enter the main stream, and in wetlands. Rivers frequently act as conduits for pollutants by collecting and carrying wastewater from catchments and, ultimately, discharging it into the ocean. Storm water which can also carry heavy loads of nutrients, organic matter and pollutants finds its way into rivers and oceans, mostly via the storm water drain network. 11 UNIVERSITY OF IBADAN LIBRARY Water quality is the ability of a water body to support all appropriate beneficial uses. Beneficial uses are the ways in which water is used by humans and wildlife; drinking water and fish habitat are two good examples. Beneficial use designations describe existing or potential uses of water body. If water supports a beneficial use, water quality is said to be good or unimpaired. If water does not support a beneficial use, water quality is said to be poor or impaired (Carlson, 1977). Different beneficial uses have different needs. The following beneficial uses have been recognized:  Water supply for domestic, industrial, agricultural, and stock watering.  Fishing.  Wildlife habitat.  Recreation-primary contact (swimming) and secondary contact.  Navigation All rivers, streams, estuaries, and lakes are assigned to a class based on the beneficial uses they could support if they had good water quality. Based on these beneficial uses, they following classes have been defined: Class AA: Has all beneficial uses to a high degree. Class A: Has all beneficial uses, but not as well as Class AA. Class B: Has all beneficial uses except domestic water supply, salmon spawning, fish harvesting, and primary contact recreation. Class C: Has only a set of beneficial uses, including industrial water supply, fish migration, wildlife habitat, secondary contact recreation, and navigation. Lake: Lake has all beneficial uses, but has hydraulic and water quality characteristics that are different from those of rivers, streams, and estuaries. Class AA waters have the highest water quality, and Class C waters have the lowest. It is important that a waters‟ class defines water quality goals and standard, not actual water quality. Class AA water does not necessarily have quality than class B water; it just has higher standards to meet because it could support more beneficial uses. There are many global water quality issues, and a number of priority issues of concern. One of these is safeguarding human drinking water supplies. The protection of source water quality for 12 UNIVERSITY OF IBADAN LIBRARY domestic use (drinking water, abstraction, etc) was identified by the experts‟ group as a priority for assessment. It was selected because of its significance to human health; could be conducted on a global scale; and the approach for assessment would be user-based and involve application of common guidelines such as those from the World Health Organization across multiple water quality monitoring stations (WHO, 2004). 1.6 THE OSUN RIVER CHANNEL o o o The Osun River lies within latitude 08 20′N and 6 30′N and longitude 05 10′E and o 03 25′E in the forest zone of Nigeria (Figure 1). It flows southwards through central Yoruba-land in South western Nigeria into the Lagos Lagoon and the Atlantic Gulf of Guinea. Osun River is perennial and its volume fluctuates with seasons. It flows through a narrow valley throughout its course across basement complex rocks and incised to the bedrock along many reaches (Tahal, 1976). The river originates from Igede Ekiti (Ekiti State) and flows through many agricultural plains and cities of about 267 km. The drainage system of Osun River rises in Oke-Mesi ridge, about 5 km North of Effon Alaiye on the border between Oyo and Ondo States of o Nigeria. It flows North through the Itawure gap to latitude 7 53′ before winding its way Westwards through Osogbo and Ede and Southwards to enter Lagos Lagoon about 8 km east of Epe. It is underlain by metamorphic rocks of the basement complex, which outcrop over many parts. Rocks of the basement complex found here are schists, associated with quartzite ridges of the type found in Ilesha area. The metamorphic rocks are largely undifferentiated, but specific rock groups are identified. The first group consists of the migmatite complex, including bandid magmatic and auguen gnesses and pegmatites which outcrop in Ilesha and Ife areas. Metasediments consisting of sciests and quartzites, calsilicates, metaconglomerates, amphibolites and metamorphic iron beds make up the second group. They are found in two Ikire areas (Tahal, 1976). The major occupation of the people living along the drainage area of the river is farming, but there are also some industries located near the river and its tributaries. More than 75% of those living along the river course are farmers; consequently crops such as cocoa and palm produce that are produced in commercial quantities serve as raw materials for those industries that 13 UNIVERSITY OF IBADAN LIBRARY Figure 1: Map of Osun River showing various industrial activities 14 UNIVERSITY OF IBADAN LIBRARY manufacture cocoa and chocolate-based products. Due to the large concentration of palm trees along the banks of some of the tributaries, it makes it a viable zone for vegetable oil processing industry. River Osun flows through the forest zone in Nigeria, with the abundance of forest; it makes it a haven for any wood- based industry. The farmers living along the bank of the river and its tributaries produces commercial quantities of food crops such as yam, cassava, millet, rice as well as a variety of fruits. In this vein, these foods serve as raw materials for fruit and rice processing industries. Cocoa and kolanut are produced in large quantities. There are many poultry farms, fish ponds located along the banks of this river and its tributaries. Wastes from these farms are used as manure for crops, feed for fish or dumped into the bush and when it rains these get carried away into Osun River and its tributaries and impair the water quality. With regards to mineral resources, there have been discoveries of various mineral deposits at different locations along the course of the river and its tributaries. The most prominent of these is gold found in commercial quantities around Ilesha. This mineral is already being exploited. Other important minerals found along the bank of the river and its tributaries include nickel, found mostly in various parts of Ile-Ife and Ilesha. Talc, a raw material in the manufacture of fillers in paints, ceramics, cosmetics, paper, textile, plastic and rubber is also found in Ife. Potash occurs in Osogbo. The river also flows through tributaries that have dolomite, limestone and clay. Various industrial activities along Osun River are as shown in Figure 1. The presence of steel rolling mills and machine tools industry at Osogbo and Ikirun facilitated the establishment of many industries such as the International Breweries and Supreme oil at Ilesha, Adeniran Steel and Wire Flexible Packages in Ilesha, Adeyera Industrial Company (metal fabrication) at Ejigbo, They discharge their effluents into the popular Adeti and Aro rivers, which are tributaries of River Osun. Cottage industries are scattered all over the drainage area of the river in Osun State. There are more than fifty soap making units (including black native soap) operated by rural women in places such as Ede, Osogbo, Okuku and Awo; seven textile units, thirteen tie and dye units, more than 200,111 weaving industries, fifty-three raffia works units, seven 15 UNIVERSITY OF IBADAN LIBRARY cane works, twelve foundry works, 157 goldsmith and brick moulding, several cream making units, leather work units and processing of local gin (Osun State, 2003). Domestic human wastes such as human excreta, urine and associated sludge (collectively known as black water) and waste generated through bathing and kitchen (collectively known as grey water) are not left out in this pollution saga. Sewage from large and small towns is discharged either into a water body or discharged on land for irrigation. Wastes entering these water bodies are both solid and liquid forms. They are mostly derived from industrial, agricultural and domestic activities. Contributing to the menace of indiscriminate discharges of industrial effluents in some banks of River Osun and its tributaries is the improper disposal of domestic wastes particularly in urban areas. This practice contributes significantly to environmental degradation caused by incessant flooding. Most domestic wastes now contain modern environmental health hazard substances thus posing additional risk to public health. The contributions of various industrial activities in the Osun River area are as shown in Figure 1. The control site for the study was Egun River, in Iperindo. This site was chosen because it has the same geology as the studied river, Osun and it is less exposed to environmental pollution compared to the Osun River. Periodical monitoring of chemical and physical water quality indicators is therefore essential for assessing and/or protecting the integrity of River Osun and its tributaries. It has the advantages of identifying changes in water quality, early discovery of emerging water quality problems, the evaluation of pollution control measures, the effectiveness of compliance and how to respond in an emergency situation and the ability to project the future concentrations of these indicators to guide against or reduce future pollution of the water body. 1.7 PREVIOUS STUDIES ON OSUN RIVER Very little research work has been carried out on the physicochemical characteristics of the river water, sediments and vegetation. For example, Olajire et al. (2001) carried out 16 UNIVERSITY OF IBADAN LIBRARY water quality assessment of Osun River by studying inorganic nutrients of surface water + 2+ + - - - 3- and ground water for Na , Ca , NH4 , Cl , NO3 , CN , PO4 , pH, temperature, EC, TDS, total hardness and total carbon (IV) oxide at five tributaries of River Osun namely River Asoba, River Ogboagba, River Okoko, River Ajibu, and River Elekunkun. The study was conducted in May, June and August, 1998. Their investigation revealed impairment of the water body by these ions. Adeboye and Alatise (2009) investigated the annual maximum discharges at Apoje station in order to estimate the flood of River Osun at Apoje sub- basin. The stream flow gauge height data for 18 years (1982-1999) were used for the study. Joshua et al. (2010) looked into the grain sizes of sediments, types of minerals present in the sediment of River Osun. A total of 106 samples were collected between February and March, 2006 across South Western Nigeria at Ekiti, Osun, Oyo, Lagos and Ogun States. Their investigation revealed that heavy mineral assemblages indicated the presence of opaque and non-opaque mineral. Their investigation showed that heavy mineral in Osun River sediment is mineralogically immature. Tijani and Onodera (2009) assessed the contamination of thirty-eight bottom sediment samples and corresponding water samples in Osogbo for levels of Mn, Cu, Pb, Zn, Ni, As, Cr and Co in clay fractions using ICP-AES method and X-ray fluorescence. The study highlighted the influence of anthropogenic activities due to lack of proper waste disposal and management practices. Impact of market effluent on chemical quality of BOD, alkalinity, heavy metals and anions of receiving Opa reservoir in Ile-Ife, Osun State was investigated by Eludoyin et al. (2004). They observed seasonal variation in most of these variables with high values in dry seasons and low values in the rainy seasons. Comparison of the reservoir water with international limitation standards for drinking water supply showed that it was high and needs treatments, implying that the effluent from the markets and other tributaries of River Osun significantly impacts the chemical quality of the reservoir water. 1.7.1 The Limitation of Previous Studies The researchers in the previous studies failed to carry out sufficiently detailed work on the physicochemical characteristics of River Osun with regards to the types of parameters 17 UNIVERSITY OF IBADAN LIBRARY studied, duration of the study and the number of sampling points used. For example, Olajire et al. (2001) in their study did not investigate levels of heavy metals in surface water at the five tributaries studied. No data was obtained on the level of gross organic pollutants. The investigation was only carried out in the rainy season and for only three consecutive months of only one year. Adeboye and Alatise (2009) carried out their investigation on the estimate of flooding at Apoje station alone for only two consecutive months using data previously collected. The study did not carry out comparison with other sub-stations to know where flooding will occur later. Joshua et al. (2010) investigated opaque and non-opaque minerals in sediments of River Osun. They did not investigate these minerals at various tributaries of River Osun and did not discuss the impact these minerals will have on the quality of the river. The duration of the study also did not allow for comparison of these mineral types in rainy season and dry season periods. Tijani and Onodera (2009) did not study other physical properties of sediments such as CEC, organic carbon content and particle size of those sediment samples analyzed. The duration and frequency of sample collection were short and did not create avenue to study how frequently this river sediments were being impaired by anthropogenic activities occurring along the river bank. No detailed investigations on the pollution of plants found along the river bank of River Osun and its tributaries were found in the literature. The reports obtained from the literature show that the following information are not yet available: 1) Detailed physicochemical properties of surface water, sediment and vegetation. 2) Data for many of the tributaries of Osun River. 3) Data for a longer duration. 4) Comparison of physicochemical properties of surface water in the wet and dry seasons. 5) Predicted water qualities for future dates. 1.8 AIM/SCOPE The goal of this study was to carry out a more detailed investigation of the quality of the Osun River and its tributaries, taking into account the knowledge gaps in the literature on 18 UNIVERSITY OF IBADAN LIBRARY the subject. The specific objectives in this regards were as follows: 1) To determine physicochemical characteristics and heavy metals contents of the surface water of Osun River and its tributaries bimonthly, over a period of twenty four months. 2) To determine physicochemical characteristics and heavy metals contents of the sediment of Osun River and its tributaries. 3) To determine heavy metals contents of plants along the banks of the river. 4) To classify the river water quality according to standard indices, using the analysis results. 5) To apply the current water/sediment quality data to a predictive model, for the purpose of determining future water/sediment qualities of the river. 19 UNIVERSITY OF IBADAN LIBRARY CHAPTER TWO 2.0 LITERATURE REVIEW 2.1 SURFACE WATER QUALITIES 2.1.1 Assessment of Water Quality and Toxicity of Polluted Rivers The quality of river water is a key issue in water management. In the world today, relatively few rivers remain in an un-impacted or pristine state. Most rivers are affected by a number of in-stream, riparian and catchment medications or practices. This often results in their being less biologically functional and of lower ecological value than their original states. Important river stresses include nutrient enrichment, increasing salinity, pesticides, water extraction, flow controls, loss of riparian vegetation and effluent discharge. The water quality of rivers varies greatly with different waters and their uses as well as with geography, climate, populations and living standards. From the physical point of view, rivers are essentially agents of erosion and of transportation, they remove water and ground sediments from the land surface and carry it to the sea. The hydrological regime, together with the quality and the quantity of sediments, are determined by basin physiography, the climate, the geology and the land use (Knighton, 1998). From a biological point of view, rivers are a typical example of open ecosystems. This is because their metabolism is characterized by continuous energy exchanges (in the shape of biological stuff), between the ground areas and the low stream waters. Assessment of the physicochemical characteristics of rivers is necessary in order to determine the water quality status of a river. For example, Ajibade et al. (2008) examined the physical and chemical properties of the major rivers in Kainji Lake National Park for gross pollutants, anions and metals. The presence of these pollutants greatly affected the water quality of the studied rivers. Mahmood et al. (2000) assessed the River Ravi (Pakistan) for the physic-chemistry and heavy metals toxicity and discovered that electrical conductivity of the water was elevated due to the input of large amount of salts and other nutrients in the tributary. 20 UNIVERSITY OF IBADAN LIBRARY Different parameters have been used to assess the state of water quality of rivers. These parameters include sulphate, phosphate, nitrate, chloride, ammonia, turbidity, alkalinity, hardness, parameters of gross organic pollutants such as DO, BOD and COD, solids, temperature, pH, etc. Onianwa et al. (2001) studied the water quality of urban rivers of Ibadan, Nigeria using all the above mentioned parameters and assessed the rivers to be of fairly poor quality. Several other studies on Nigerian surface waters have been conducted (Imevbore, 1967; Adebisi, 1981; Oyeike et al., 2002). The characteristics investigated by these authors included plankton, physicochemical properties such as transparency, pH, conductivity, inorganic ions, heavy metals, petroleum hydrocarbons, etc. Heavy metals are among the very toxic elements. The modern era of industrialization has increased the spread of environmental contamination by heavy metals. Heavy metal toxicity dates back to the ancient period. According to Freedman (1989), the chemical form of toxic elements dissolved in water, generally relatively available to biota even in seemingly small aqueous concentrations, may exert a powerful toxic effect. Asonye et al. (2007) examined heavy metals profiles of seventy two rivers, streams and waterways in southwestern Nigeria and found out that most of the metals studied were above the WHO and EEC guide limit, implying that some of the rivers are toxic and might pose serious risks to the health of communities residing around and using these surface waters for domestic, commercial and socio-cultural purposes. Heavy metals cannot be destroyed biologically but may be transformed from one oxidation state or organic complex to another (Gisbert et al., 2003; Garbisu and Alkorla, 2009). Heavy metals therefore poss a potential threat to the environment and human health. Among the major disasters caused by heavy metals are the Minamata Syndrome in 1952 and the ltai-itai Byo in 1955 in Japan (Friedman, 1972). 2.1.2 Significance of Oxygen-Demanding Substances and Nutrients to Surface Water Quality. Oxygen is one of several dissolved gases important to aquatic life. It is a primary and comprehensive indicator of water quality in surface water. Dissolved oxygen declines 21 UNIVERSITY OF IBADAN LIBRARY have serious implications for the health of aquatic systems. These declines are often attributed to changes in organic or nutrient loading but are generally attributed to species invasions (Caraco et al., 2000). The concentration of DO in natural water reduces as a result of biodegradation of carbonaceous and nitrogenous wastes discharged into water bodies and deposited in sediment, and from the point or non-point input of plant limiting nutrients which leads to eutrophication. Oxygen buck can be induced through over fertilization of water plants by run-off from fields and sewage containing phosphates and nitrates. Under this condition, the number and size of plants increase a great deal. When plants die they become food for bacteria, which in turn multiply and use large amounts of oxygen. An example is the study carried out by Sridhar and Sharma (1985) where low levels of dissolved oxygen was attributed to the incoming organically rich water, coverage of the surface by Pistia plants, and to the decomposition of these plants which resulted in the formation of sludge at the bottom of a lake. An increase in water temperature can have a number of effects on physical and biological processes that take place in receiving waters. As the temperature increases the solubility of oxygen decreases, which invariably increases the metabolic rate of organisms, resulting in increasing consumption of oxygen (Buren et al., 2000). Ecological integrity of aquatic ecosystems is threatened when significant organic pollution exists that exceed self- purification capacity of the water body. For any surface water to sustain aquatic life there should be balanced physicochemical and biological interactions. Abnormally too high or too low of each factor may lead to deleterious ecosystem disturbance such as that discussed by Tesfaye et al. (1989) in the discharge of untreated waste into water courses of river systems flowing through Addis Ababa. Freshwater ecosystems have been used for the investigation of factors controlling the distribution and abundance of aquatic organisms (Bagenal, 1978). Under conditions of nitrate and phosphorus availability, the green algae (chlorophyte) are known to proliferate and form noxious bloom in freshwater environment (Ayoade, 2000). 22 UNIVERSITY OF IBADAN LIBRARY According to Oputa (2002) hydrobiological changes emanate from adequate solar radiation and nutrient enrichment of a lake, which results in eutrophication. Importantly, blooming of algae species have dentrimental effects on domestic and recreational uses of water, and in many cases have acted as a direct motivation for restoration measures (Bryant, 1994). High phosphate levels could add to the growth of blue-green algae, which could release toxic substances (cyanotoxins) into water. Cyanotoxins are known to have caused the death of farm livestock (Holdworth, 1991). Studies on oxygen demanding substances of rivers in literature abound (Ajayi and Osibanjo, 1981; Fried, 1991; Bich and Anyata, 1999; Izonfuo and Bariweni, 2001; Morrison et al., 2002; Islam et al., 2007 and Olele and Ekelemu, 2007). As organic pollution increases, the ecologically stable and complex relationships present in water containing a high diversity of organisms is replaced by a low diversity of pollution-tolerant organisms. The greater the decomposable matter present, the greater the oxygen demand, and the greater the BOD value (Ademoroti, 1996). 2.1.3 Seasonal Dynamics of Physicochemical Properties in Surface Water Rainfall and solar radiations are the major climatic factors that influence most physicochemical hydrology of water bodies (Odun, 1992; Kadiri, 2000). Seasonal variations in the intensity of rainfall cause both the quality and the quantity of flow of rivers to vary widely. During wet seasons, the storm run-off conveys both suspended and dissolved matter into the rivers. In the dry season, many rivers and streams either dry up completely or have very little flow. Studies have been carried out on some important characteristics of selected rivers and streams in Nigeria. In this region, there are two main seasons, the wet season which is from April to October and the dry seasons from November to March. During the wet season the rains are very heavy, and often the annual o rainfall reaches 200cm in several places. The average air temperature is 27 C with a range o of less than 2 C (Oluwande, 1978). There is sunlight daily, although its intensity is reduced considerably during a few days in the wet season. In the dry season there is little or no rain. For example Odede and Ekelemu (2007) reported monthly variation in water temperature in dry season, especially in April, compared to the situation in December. The 23 UNIVERSITY OF IBADAN LIBRARY high temperature recorded during March was attributed to the peak of dry season when isolation was at its highest. The minimum and maximum temperatures (25.0 – 35.5 respectively) are normal for tropical waters and are required for the normal growth of aquatic organisms. The abundant sunlight encourages prolific algae blooms in polluted tropical rivers during the dry season. As stated by Strahler and Strahler (1973), all rainfall, wherever it occurs, carries with it a variety of ions, some introduced into the atmosphere from the sea surface, some from land surfaces undisturbed by man, and some from man-made sources. The ions and other substances carried into rivers or streams via rainfall may result to pollution. For instance, - 3- - lower levels of some phyisicochemical parameters like SO4, Cl , PO4 , NO3 and NH3 have been reported in the dry season than in the wet season (Devito and Hill, 1998; Izonfuo and Bariweni, 2001), suggesting that runoff water contributes to their levels in rivers. The characteristics of any water body may indicate its level of pollution. According to Chov (1964), a great deal of information on river water quality may be evaluated from the climatic or geological conditions in the river basin. These two factors generally play a role in the quality of water available for different purposes. In most rivers, the normal dry weather flow is made up primarily of water which seeps from the ground. However, most of the flow of a river is contributed during the high runoff or flood period. During the period of high runoff, most rivers exhibit their most favourable chemical characteristics. Chov (1964), suggests that although the river may contain extremely large amounts of suspended matter, the concentration of dissolved substances are usually low, often only a fraction of that present during dry weather. However, there are some instances where high runoff may cause deterioration in water quality. For instance, if rain falls selectively on the watershed of a tributary which contribute poor-quality water to a comparatively good- quality river system, the water contributed may cause a transitory deterioration of the water quality in the system. For example, Izonfuo and Beriweni (2001) reported higher mean levels of pH, conductivity, TDS, BODS, alkalinity, hardness, Ca, Mg, K, Na, and temperature for the Epie Creek, Niger Delta in the dry season than in the rainy season, and 24 UNIVERSITY OF IBADAN LIBRARY attributed the lower values in the rainy season obtained to run off water which only contributes to dilution of the parameters in the rainy season. They reported a dominance ++ + ++ pattern of major cations based on the mean value as Ca > K > Na > Mg in the wet season. This was found to be consistent with the dominance pattern of some African rivers ++ where Ca was found to be the dominant cation (Imevbore, 1970). DO recorded in wet season was found to be higher than in dry season. The seasonal fluctuation may be due to the effect of temperature on the solubility of oxygen in water. At high temperature, the solubility of oxygen decreases while at lower temperature, it increases (Plimmer, 1978). As DO increases, BOD decreases, and vice versa. Higher BOD in the dry season is perhaps due to the lower volume of water in rivers during the dry season. 2.2 IMPACTS OF HUMAN ACTIVITIES ON SURFACE WATER QUALITY The industrial revolution of the early 19th century gave great impetus to the factors that brought dramatic changes in the Earth‟s waters. Ever since then, human impact on Earth‟s waters has increasingly been detrimental to human survival. Though water pollution is an old phenomenon, the rate of industrialization, and consequently urbanization, has exacerbated its effect on the environment. This is because, the process of urbanization has considerable hydrological impact both in terms of controlling rate of erosion, delivery of pollutants to rivers, and in terms of influencing the nature of runoff and other hydrological characteristics (Goudie, 1990). Two types of pollutants exist: point source and non-point source. Point source pollution occurs when harmful substances are emitted directly into a body of water. A non-point source delivers pollutants indirectly through environmental changes. The technology exists for point sources of pollution to be monitored and regulated. Non-point sources are much more difficult to control. Pollution arising from non-point sources accounts for a majority of the contaminant in streams, rivers and lakes. Point source pollutants, in contrast to nonpoint pollutants, are associated, as a point location such as toxic-waste spill site. As such, point source pollutants are compared to nonpoint source pollutants, characteristically (i) easier to control, (ii) more readily identifiable and measurable, and (iii) generally more toxic. Nonpoint sources of pollution are the consequences of 25 UNIVERSITY OF IBADAN LIBRARY agricultural activities (e.g. irrigation and drainage, applications of pesticides and fertilizers, runoff and erosion); urban and industrial runoff; erosion associated with construction; mining and forest harvesting activities; pesticides and fertilizer application. Point source includes hazardous spills, underground storage tanks, and storage piles of chemicals, industrial or municipal waste runoff. Compared to point source pollution, nonpoint source pollution is more difficult, related to monitoring and enforcement of mitigating controls due to heterogeneity of soil and water systems at large scales. Characteristically, nonpoint source pollutants (i) are difficult or impossible to trace to a source, (ii) enter the environment over an extensive area and sporadic timeframe, (iii) are related (at least in part) to certain uncontrollable meteorological events and existing geographic/geomorphologic conditions, (iv) have the potential for maintaining a relative long active presence on the global ecosystem, and (v) may result in long-term, chronic (and endocrine) effects on human health and soil-aquatic degradation. 2.2.1 Impact of Industrial Activities on Water Quality of Rivers. Industrial growth is fast increasing globally, just as the water demand for industrial production processes. This has put more pressure on the limited available water resources. Water bodies are also constantly used as receptacles for untreated waste water or poorly treated effluents accrued from industrial activities. This has rendered many water bodies unsuitable for both primary and secondary usage. Generally, industrial effluents discharged into water bodies are toxic, and discharge of such untreated or poorly treated effluent could have serious consequences on aquatic organisms (Fisher et al., 1998 and Morseenko, 1999). Different types of industries exist and most of these, especially in Nigeria, do not treat their waste before being disposed. For example, Fakayode (2005) reported that the water qualities of Alaro River in Ibadan, Nigeria were adversely affected and impaired by the discharge of industrial effluent. The quality of the industrial effluent discharged into Alaro River was poor and did not meet the minimum requirement for discharged into surface water. Table 2.1 describes some types of industries, the pollutants they produce and their effect on river water quality. 26 UNIVERSITY OF IBADAN LIBRARY Table 2.1: Some industrial pollutants and their effect on the environment (FMHE, 1982) INDUSTRY POLLUTANTS EFFECTS Sugar Refinery High settleable solids such as bagasse, High BOD and COD, low molasses and press cake biodegradable sugars, pH, colouration of the water. Trace metals, Fe, Zn, Cu, Mn, heat from cooling of boilers. Oil and Margarine Factory Acids, alkalis, fatty materials, glycerol, High BOD, coating of water suspended particles. surface. Textile Mills Biodegradable materials: starch and related High pH, BOD and COD, compounds, non-biodegradable organics: oils reduction in photosynthetic waxes, greases, dyes and pigments. rate. Toxic substances: Trace metals (Cu, Cr), inorganic anions (sulphides, cyanides), organics (phenol, pesticides, used in moth – High colouration of water proofing) acids bases, chlorides and suspended bodies due to dyes. solids. Abattoirs Organic wastes, blood, fats, bones, hair. Extremely high BOD, high pH, eutrophication of water. Cement industry Kiln dust and other particulates. Trace metals: Indirect effects because most Fe, Cu, Mn. pollutants are airborne. High pH, turbidity. Breweries Solid wastes; spent grains and hops, paper Turbidity due to high total labels, rejected crown corks and cartons, solids high conductivity and broken bottles. Dissolved and suspended BOD. solids; sugar, yeast, kieselghur (diatomaceous earth for improving beer colour). Inorganic chemicals: caustic soda, hypochlorites, soaps and detergents, peroxides, silicate oxide. Leather and tannery industries Organic wastes: Flashings, hair, fats High BOD, COD, and Inorganic chemicals: sulphides, carbonates, conductivity of water, acids, alkalis, detergent. Tanning agents: possible accumulation of phenols, acids, chromium, sulphate, alum, toxic metals in organisms. dyes, pigments, oils and waxes. Petroleum industries Oil and grease, organic and inorganic High total solids, total substances, nutrients, heavy metals, suspended solids, DO, BOD, organochlorine compounds. COD, total nitrogen, total phosphate, lead. 27 UNIVERSITY OF IBADAN LIBRARY The pollution of rivers and streams with chemical contaminants became one of the most crucial environmental problems within the 20th century. Waterborne chemical pollution entering rivers and streams causes tremendous amounts of destruction. Drinking such water has led to outbreaks of epidemics such as cholera and other water related diseases on several occasions (Adesina, 1986; USFDA, 1993; Frontiers, 1996). Reports of impact of industrial effluent on the physicohemical properties of river abounds (Edltruada, 1996; Adeyeye and Adejuyo, 2002). Unacceptably high levels of the assayed parameters such as pH, temperature, EC, salinity, turbidity, TDS, DO, COD, nitrite and orthophosphate were observed in many, implying that there was an adverse impact on the physicochemical characteristics of the receiving river and stream as a result of the discharge of untreated effluents. This poses health risk to several rural communities which rely on such receiving water bodies as their sources of domestic water. The petroleum industry impairs the water quality of rivers, especially in the Niger Delta. According to Egborge (1994), rapid urbanization, industrialization and high population gave impetus to the pollution potentials of rivers and streams in the Western and Eastern Niger Delta. The report showed that the petroleum industry is a formidable source of pollutants due to activities such as drilling, production and refining of crude and the production of petrochemicals such as carbon black, marketing, utilization and disposal of spent petroleum products and the events of oil spills. Metals are unique industrial pollutants in that they are found naturally distributed in all the phases of the environment (Ackerfor, 1977). Through industrial processes, metals are concentrated and transformed into various products. These often lead to much higher concentrations of different chemicals than those naturally present in the environment. Metal wastes are produced by a variety of industries, including mines, tanneries, electroplating, paint, metal pipe and ammunition. These containments are leached into the groundwater through the soil and may finally be transported from there to surface water (Okonkwo and Ablaut, 1999). 28 UNIVERSITY OF IBADAN LIBRARY Investigation of the effect of brewery industries at different locations in Benin, Ibadan, and Lagos on surface water was reported by Folasegun and Kolawole (2008). They reported that the physicochemical conditions of the streams located at Agidingbi, Alaka, Ona and Ossioma have been adversely influenced by the pollutants. Ipeaiyeda and Onianwa (2009) also reported significant levels of chloride, nitrate, ammonia, dissolved solid, turbidity and BOD and reduced pH and DO downstream the river network of Olosun River in Ibadan, Nigeria as a result of brewery effluents discharged into this river. 2.2.2 Impact of Agricultural Runoff on Surface Water Quality. Agricultural runoff is excess water from rainfall and other precipitation that runs off agricultural land. When uncontrolled, agricultural runoff removes topsoil, nutrients, fertilizers, pesticides, and organic materials and carries them to water bodies where they become pollutants. Erosion is the detachment of soil particles from clods and the soil surface. Most of this occurs during rain storms which can detach or loosen up to 100 tons of soil per acre in a severe storm. The detached soil can be transported by agricultural runoff which dislodges additional soil particles as it flows across unprotected soil surfaces. Heavier soil particles may settle out in bottomlands and on foot slopes before reaching a body of water. Nutrients and pesticides that may be present in agricultural runoff also cause serious problems. The direct effect on the producer is the economic losses connected with removing these materials. In addition, nutrients derived from soil, commercial fertilizers or animal manure may cause excessive algal growths in ponds and lakes. These growths filter out and absorb sunlight, and release offensive odours and toxicants. The effects of pesticides on water quality can be dramatic in terms of aquatic life. These substances are as toxic in the water as they are on the field and may affect a wide variety of aquatic organisms. If contacted or ingested in sufficient quantities, pesticides pose a health hazard to all forms of life. Agriculture involves commercial livestock and poultry farming and it is the source of many organic and inorganic pollutants in surface water. Rain and irrigation water drains 29 UNIVERSITY OF IBADAN LIBRARY off cultivated land that has been fertilized and treated with pesticides. The excess nitrogen and poisons are mixed with it into the water supply. Fertilizers in the absence of natural fertilizer enhance the growth of bacteria that are in water and increase the concentration of bacteria to hazardous levels. Chemical fertilizers contain high levels of nitrogen and phosphorus which are harmful because they flow from crop land to water. Both of these elements are known to cause algal blooms. Algal blooms and erosion present a problem to fish because both contribute to an increase of particles in the water which can occasionally clog the fish‟s gills, suffocating them (Nanagia et al., 2008). For example, Gyles and David (2001) revealed that subsurface tile drainage from row-crop agricultural production has been identified as a major source of nitrate entering surface waters in Mississippi River Basin. Non-controllable factors such as precipitation and mineralization of soil organic matter have a tremendous effect on drainage losses, nitrate concentrations, and nitrate loadings in subsurface drainage water. Cooper (1993) reported agricultural activities to have been identified as major contributors to environmental stress, which affects all ecosystem components. Agricultural contaminants are most noticeable when they produce immediate, dramatic toxic effects on aquatic life, although more subtle, sub lethal chronic effects may be just as damaging over long periods. 2.2.3 Effect of Domestic Sewage Discharge on Quality of Surface Water. Sewage discharges are major components of water pollution, contributing to oxygen demand and nutrient loading of the water bodies. Organic matter is the important polluting constituent of sewage in respect of its effect on receiving water bodies. Thousands of organic compounds enter water bodies as a result of human activities. Organic matter is mainly composed of proteins, carbohydrates and fats, and measured in terms of BOD and COD. If untreated sewage is discharged into natural water bodies‟ biological stabilization of organic matter leads to depletion of oxygen in such water bodies. If sufficient oxygen is not available to aquatic life, the ecosystem will be adversely affected. Nitrogen and phosphorus are also very important polluting constituents of sewage because of their role in algal growth and eutrophication of water bodies. Nitrogen is present in fresh domestic sewage in the form of proteinaceous matter urea (i.e. organic nitrogen). The presence of these nutrients, nitrogen and phosphorus, can result in eutrophication which can occur 30 UNIVERSITY OF IBADAN LIBRARY at both microscopic levels in form of algal or macroscopic level in form of larger aquatic weeds. 2.3 SURFACE WATER QUALITY STANDARDS Water quality standard defines the water quality goals of a water body. Water quality standard regulations establishes the use or uses to be made of a water body, set criteria necessary to protect the uses, and establish policies to maintain and protect water quality. Appropriate uses are identified by taking into consideration the use and values of the water body for public supply, for protection of fish, shellfish, and wildlife, and for recreation, agricultural, industries, and navigational purposes. There are different types of water quality standards that describe guideline values for constituents of water or indicators of water quality. Table 2.2 describes drinking water quality standards for some countries/organization. Setting water quality standards is a complex task that requires input by experts in water quality and aquatic ecology. The scientific information usually underlying water quality guideline can be used to develop standards, but it is important to understand the difference between guidelines and standards. Guideline is often a benchmark that should be followed, but technically, is not lawfully required to be followed. Standards are a rule dealing with details or procedures, a rule or order issued by an executive authority or regulatory agency of a government (David, 2006). A standard is similar to a guideline, in that, benchmarks are established, but in contrast to a guideline, standards are enforceable by law. Standards, in the context of drinking water, are clearly preferred, because any deviation from the benchmark can result in legal contravention against the negligent body; thus, ensuring safe drinking water. Guidelines are usually based on scientific information about the effects of contaminants on the conditions of a water body, or on the organisms that live in that water body. Standards actually define threshold for the point that is deemed to be acceptable for a given situation. Guidelines for other categories of water usage also exist. These include guidelines for industries such as pulp and paper, iron and steel, petroleum, power generating stations, food and beverage, FAO for trace elements in irrigation water, quality for aquatic life, recreational water quality, and water quality criteria for livestock. These 31 UNIVERSITY OF IBADAN LIBRARY Table 2.2: Drinking water quality guidelines Parameter USEPA Canada EEC Japan WHO SON (2006) ( 1 9 9 6) (1980) NEMAMNR (2004) (2007) (1997) Electrical - - - - - 1000 conductivity pH 6.5-8.5 6.5-8.5 6.5-9.5 5.8-8.6 6.5-8.5 7.0-8.0 Total dissolved 500 500 - 500 1000 500 solids Turbidity 0.5-5 1 4 1-2 5 5 Hardness - - 50 300 - 150 Temperature - - - - - Ambient Nitrate 10.0 10.0 50 10.0 50 10 Sulphate 250 500 250 - - 100 Phosphate - - 5 - - - Chloride 250 250 250 200 250 250 Ammonia - - - - - 0.05 Lead 0.015 0.01 0.01 0.05 0.01 0.01 Copper 1.3 1.0 2.0 1.0 1-2 1.0 Cadmium 0.005 0.005 0.005 0.01 0.003 0.003 Chromium 0.1 0.05 0.05 0.05 0.05 - Zinc 5.0 5.0 - 1.0 3.0 5.0 Magnesium - - - - - 0.20 o All units in mg/L except electrical conductivity (µS/cm), Turbidity (FTU), Temperature ( C) and pH with no unit. EEC (1980), Canada (1996), NEMAMNR (1997), WHO (2004), USEPA (2006), SON (2007). 32 UNIVERSITY OF IBADAN LIBRARY guidelines are used for comparison purpose to check for compliance. They are as shown in Tables 2.3 – 2.13. 2.4 WATER QUALITY INDICES The most challenging problem of modern theoretical and applied hydroecology is to understand the fundamental principles of ecology for its application in effective management of water resources for both hydrological availability and water quality. Quantification of water quality aims at describing the condition of a water body with reference to human needs. The water quality index is a single number that expresses the quality of water by integrating the water quality variables. Its purpose is to provide a simple and concise method for expressing the water quality for different usage. Any number of water quality measurements can serve, and have already been used, as indicators of water quality. However, there is no single measure that can describe overall water quality for any one body of water, let alone at a global level. As such, a composite index that quantifies the extent to which a number of water quality measures deviate from normal, expected of „ideal‟ concentrations, may be more appropriate for summarizing water quality conditions across a range of inland water types and over time. Although there is no globally accepted composite index of water quality, some countries and regions have used, or are using, aggregated water quality data in development of water quality indices. Most water quality indices rely on normalizing, or standardizing, data, parameter by parameter, according to expected concentrations and some interpretation of „good‟ versus „bad‟ concentrations. Parameters are often then weighted according to their perceived importance to overall water quality and the index is calculated as the weighted average of all observations of interest (Stambuk-Giljanovic, 1999; Pesce and Wunderlin, 2000; Sargaonkar and Deshpande, 2003; Liou et al., 2004; Tsegaye et al., 2006). Pesce and Wunderlin (2000) compared the performance of three water quality indices on Suquia River in Argentina. All three indices were calculated using observations for twenty different parameters that were normalized to a common scale according to observed 33 UNIVERSITY OF IBADAN LIBRARY Table 2.3: Water quality tolerance for certain industrial applications (mg/L) except as indicated Industry Dissolved Turbidity** Hardness Alkalinity pH*** Total Solids Chloride Indication* General Oxygen 2 - 250 125 - 850 250 C Beverages Baking 10 - D - - - - A, B Food (general) 10 - 10 - 250 30 250 - 850 - C Brewing Light 10 - - 75 6.5- 7.0 500 100 C, D, G Dark 10 - - 150 7.0+ 1000 100 C, D, H Laundering - - 50 60 6.0 – 6.8 - - - Paper & Pulp Ground wood 50 - 200 150 - 500 75 E Tanning 20 - 50 – 135 135 6.0 – 8.0 100 - - Textiles 5 - 20 - - - 100 - General Dyeing 5 - 20 - - - - - Boiler feed (0 – 20 2 50 - 8.0+ 1000 - 3000 - 150 pound per square inch) * A- no corrosiveness, B- no slime formation, C – conformity with federal drinking water standards necessary, D – NaCl, 275 ppm, E – free CO2 less than 10 mg/L, G- calcium 100 – 200 mg/L, H – calcium 200- 500 mg/L ** FTU *** No unit 34 UNIVERSITY OF IBADAN LIBRARY Table 2.4: Water quality guideline for pulp and paper industry Kraft Chemical Pulp & Paper Ground Parameter Fine Paper Wood Bleached Unbleached Bleached Unbleached pH - 6 - 8 - - 6 – 8 6 – 8 Turbidity (NTU) <10 <20 <40 <100 <10 <20 Calcium <20 <20 - - <20 <20 Magnesium <12 <12 - - <12 <12 Chloride - 25-75 <200 <200 <200 <200 Hardness <100 <100 <100 <100 <100 <100 Alkalinity 40-75 <150 <75 <159 - - Dissolved solids <200 <250 <300 <500 <200 <250 Suspended solids <10 - - - <10 <10 Temperature - - - - <36 - Source: Canadian Council of Resource and Environmental Ministers, Canadian Water Quality Guideline (1987) 35 UNIVERSITY OF IBADAN LIBRARY Table 2.5: Water quality guidelines for the iron and steel industry Hot-Rolling, Rinse Water Parameter* Quenching, Gas Demineralised Steel Manufacturing Cold-Rolling Softened Cleaning pH 5.0 – 9.0 5.0 – 9.0 6.0 – 9.0 - 6.8 – 7.0 a Suspended Solids <25 <10 ND ND - Dissolved solids <1000 <1000 ND ND - Dissolved oxygen ---------------------------------------------(minimum for aerobic conditions)-------------------- Temperature <38 <38 <38 <38 <38 b,c b Hardness NS NS <100 <0.1 <50 c c c Alkalinity NS NS NS <0.5 - Sulphate <200 <200 <200 - <175 Chloride <150 <150 <150 ND <150 o * units in mg/L, except temperature ( C), and pH Source: Canadian Council of Resource and Environmental Ministers, Canadian Water Quality Guidelines (1987), U.S Environmental Protection Agency (1973) ND – not detected b - controlled by other treatment NS - not specified; c - the parameter has never been a problem at concentrations encountered 36 UNIVERSITY OF IBADAN LIBRARY Table 2.6: Water quality guidelines for the petroleum industry Parameter Concentration pH 6.0 – 9.0 Calcium (mg/L) <75 Magnesium (mg/L) <25 Sulphate (mg/L) NS Chloride (mg/L) <200 Nitrate (mg/L) NS Hardness (as CaCO3) (mg/L) <350 Dissolved Solids (mg/L) <750 Suspended Solids <10 NS- not specified Source: Canadian Water Quality Guidelines (1987); Ontario Ministry of the Environment (1974) Federal Water Pollution Control Administration (1968). 37 UNIVERSITY OF IBADAN LIBRARY Table 2.7: Water quality guidelines for power generating stations Cooling Once-Through Boiler Feedwater Miscellaneous Parameters Fresh Brackish (10.35 – 34.48 Mpa) Uses Calcium <200 <420 <0.01 - Magnesium NS NS <0.01 - Ammonia NS NS <0.07 - Sulphate <680 <2700 NSc - Chloride <600 <19000 NSc - Dissolved Solids <1000 <35000 <0.5 <1000 Copper NS NS <0.01 - Hardness <850 <6250 <0.07 - Zinc NS NS <0.01 - Alkalinity (CaCO3) <500 <115 <1.00 - pH units 5.0 – 8.3 6.0 - 8.3 8.8 - 9.4 5.0 – 9.0 Chemical Oxygen Demand <75 <75 <1.00 - Dissolved Oxygen - - <0.007 - Suspended Solids <5000 <2500 <0.05 <5.00 c – controlled by treatment for other constituents NS- Not specified Sources: Canadian Council of Resources and Environment Ministers, Canadian Water Quality Guidelines (1987), Krisher (1978) Chemical Engineering (1978). McGraw-Hill, Inc. 38 UNIVERSITY OF IBADAN LIBRARY Table 2.8: Water quality guidelines for the food and beverage industry Food Canning, Carbonate Confec- Freezing, Food Process Sugar Parameter* Baking Brewing Diary Beverage tionary Dried, Frozen General Manufacturing Fruits, Vegetables Turbidity <10 <10 1 -2 - - <5 <5 - 10 - Suspended Solids - - - 50 – 100 <500 <10 - ND Dissolved Solids - <800 <850 50 -100 <500 <500 <850 - b, c Calcium NS <100 - - - <100 - <20 Magnesium - <30 - - - - - <10 Copper - - - - ND - - - Sulphate - <100 <200 - <60 <250 - <20 Chloride - 20 – 60 <250 <250 <30 <250 - <20 Nitrate - <10 - - <20 <10 - - b Hardness NS <70 200 -250 - <180 <250 1-250 <100 * all units in mg/L except turbidity (FTU) ND- Not detected b – some required for yeast action; excess retards fermentation c NS- Not specified Source: Canadian Council of Resource and Environmental Ministers, Canadian Water Quality Guidelines (1987). 39 UNIVERSITY OF IBADAN LIBRARY Table 2.9: FAO maximum concentrations of trace elements in irrigation water Element Recommended Remarks Maximum Concentration (mg/L) Cadmium 0.01 Toxic to beans, beets and turnips at concentrations as low as 0.1mg/L in nutrient solution Cobalt 0.05 Toxic to tomato plants at 0.1mg/L in nutrient solution Chromium 0.10 Not generally recognized as essential growth element Copper 0.20 Toxic to a number of plants at 0.1 mg/L in nutrient solutions Nickel 0.20 Toxic to a number of plants at 0.5 mg/L to 1.0 mg/L Zinc 2.00 Toxic to many plants at widely varying concentration 3 The maximum concentration is based on a water application on water is consistent with good irrigation practices (10,000m /ha/yr). If the water application rate greatly exceeds this, the maximum concentrations should be adjusted downwards accordingly. The values given are for water used on a continuous basis at one site. Source: Food and Agricultural Organization of the United Nations (1985). 40 UNIVERSITY OF IBADAN LIBRARY Table 2.10: Guides for evaluating the quality of water for aquatic life Determination Threshold Concentration* Freshwater Saltwater Total dissolved solids (TDS) mg/L 2000† - o Electrical conductivity, µmhos/cm@25 C 3000† - o Temperature, maximum C 34 34 Range pH 6.5 – 8.5 6.5 – 9.0 Dissolved oxygen (DO) minimum, mg/L 5.0‡ 5.0‡ Ammonia (free) mg/L 0.5† - Cadmium, mg/L 0.01† - Chromium, hexavalent, mg/L 0.05† 0.05† Copper, mg/L 0.02† 0.02† Lead, mg/L 0.1† 0.1† Nickel, mg/L 0.05† - Zinc, mg/L 0.1† - * Threshold concentration is value that normally might not be deleterious to fish life. Waters that do not exceed these values should be suitable habitat for mixed fauna and flora. † Values no to be exceeded more than 20 percent of any 20 consecutive samples, nor in any 3 consecutive samples should other values never be exceeded. ‡ Dissolved oxygen concentrations should not fall below 5.0 mg/L data indicate that rate of change of oxygen tension is an important factor. Source: California State Water Quality Board (1963). 41 UNIVERSITY OF IBADAN LIBRARY Table 2.11: Recreational water quality Water Contact Boating and Aesthetic Parameter Noticeable Noticeable Limiting Threshold Limiting Threshold Threshold Threshold Suspended Solids (mg/L) 20* 100 20* 100 Turbidity (FTU) 10* 50 20* ‡ Range of pH 6.5 – 9.0 6.0 – 10.0 6.5 - 9.0 6.0 – 10.0 o Temperature, maximum ( C) 30 50 30 50 * Value not to be exceeded † No limiting concentration can be specified in the basis of epidemiological evidence, provided no fecal pollution is evident ‡ No concentration likely to be found in surface waters would immediate use Source: California State Water Quality Control Board (1963). 42 UNIVERSITY OF IBADAN LIBRARY Table 2.12: Water quality for livestock Threshold Quality Factor Limiting Concentration† Concentration* Total dissolved solids (TDS) mg/L 2500 5000 Cadmium mg/L 5 1000 Calcium mg/L 500 500‡ Magnesium, mg/L 1500 3000 Nitrate, mg/L 200 400 Sulphate, mg/L 500 1000‡ Range of pH 6.0 - 8.5 5.6 – 9.0 * Threshold values represent concentrations at which poultry or sensitive animals might show slight effects from prolong use of such water ‡ Total magnesium compounds plus sodium sulphate should not exceed 50 percent of the total dissolved solids. Source: California State Water Quality Control Board (1963) 43 UNIVERSITY OF IBADAN LIBRARY Table 2.13: Water quality criteria for livestock (II) Quality Factor Limiting Threshold (mg/L) Cadmium 0.05 Chromium 1 Cobalt 1 Copper 0.5 Lead 0.1 Nickel 1 Zinc 25 Source: Ontario Ministry of the Environment (1984) 44 UNIVERSITY OF IBADAN LIBRARY Table 2.14: Some parameters (indicative of gross organic pollution) used in the classification of surface water quality Analytical Parameter CLASS 1 CLASS 2 CLASS 3 CLASS 4 CLASS 5 pH 6.5 – 8.0 6.0 – 8.4 5.0 -9.0 3.9 – 10.1 3.9 – 10.1 Dissolved Oxygen (mg/L) 7.8 6.2 6.0 1.8 <1.8 BOD (mg O2/L) 1.5 3.0 6.0 12.0 >12.1 NH3 (mg/L) 0.1 0.3 0.9 2.7 >2.7 COD (mg O2/L) 10 20 40 80 80 Suspended Solids (mg/L) 20 40 100 278 >268 N.B Class 1 = Excellent Class 2 = Acceptable quality Class 3 = Slightly polluted Class 4 = Polluted Class 5 = Heavily Polluted Source: Pratti et al. (1971). 45 UNIVERSITY OF IBADAN LIBRARY Table 2.15: Sediment quality guidelines values of ERL and ERM for each metal and contamination level ranking (µg/g) Guidelines Parameter ERL ERM Cadmium 1.2 70 Chromium 81 9.6 Copper 34 270 Lead 46.7 218 Nickel 20.9 51.6 Zinc 150 410 46 UNIVERSITY OF IBADAN LIBRARY Table 2.16: Canadian and Ontario sediment quality guielines Canadian Sediment Quality Ontario Sediment quality Heavy metal Guidelines Guidelines Lead (mg/kg) 35.0 31.9 Copper (mg/kg) 35.7 16.0 Cadmium (mg/kg) 0.60 0.60 Chromium (mg/kg) 37.3 26.0 Nickel (mg/kg) - 16.0 Zinc (mg/kg) 123 120 47 UNIVERSITY OF IBADAN LIBRARY Table 2.17: Concensus Based Sediment Quality Guidelines of Wisconsin (CBSQG) Metal Concensus Based Sediment Cadmium (µg/g) 0.99 Chromium (µg/g) 43.0 Copper (µg/g) 32.0 Lead (µg/g) 36.0 Nickel (µg/g) 23.0 Zinc (µg/g) 120 Ref: Wisconsin Department of Natural Resources (2003) 48 UNIVERSITY OF IBADAN LIBRARY concentrations and expected ranges. The „objective‟ and „subjective‟ indices were then calculated as a function of the normalized values, the relative weight assigned to each parameter, and, in the case of the subjective index, a constant that represented the visual impression of the contamination level of a monitoring station. A third index, „the minimal‟ index, was calculated as the average of the normalized values for only three parameters (dissolved oxygen, conductivity, and turbidity). The study reported that the minimal index was well correlated to the objective index, and that both water quality indices were generally correlated to the measured concentrations of different parameters. In a study similar to the Argentinean one, Stambul-Giljanov (2003) compared the performance of several water quality indices for Croatian waters. All indices were similar to the objective index used in Argentinan in that field measurements were normalized, or scored, on a parameter by parameter basis according to their observed concentrations, and then a weighted average index was calculated from normalized values. The indices were tested with data for nine water quality parameters collected monthly over one year at 50 sites in Croatia. Examination of the different water quality indices found that two modified arithmetic indices were best suited for discriminating sites according to water quality condition (good versus poor). Liou et al. (2004) developed an index of river water quality in Taiwan that is a multiplicative aggregate function of standardized scores for temperature, pH, toxic substances, organics (dissolved oxygen, BOD, ammonia), particulate (suspended solids, turbidity), and microorganisms (faecal coliforms). The standardized scores for each water quality parameter were based on predetermined rating curves, such that a score of 100 indicates excellent water quality and a score of 0 indicates poor water quality. The index relies on the geometric means of the standardized scores. The development of various indices based on different parameters in the river has been reported in literature: Tsegaye et al. (2006) developed a chemical water quality index on data from 18 streams in one Lake basin in Northern Alabama that summed the concentration of seven water quality parameters (total nitrogen, dissolved lead, dissolved oxygen, pH, and total particulate and dissolved phosphorus) after standardizing each 49 UNIVERSITY OF IBADAN LIBRARY observation to the maximum concentration for each parameter. Pratti et al. (1971) suggested an index that included 13 parameters, while the water quality was evaluated by assessing scores from 0 to 14. Sargaonnkar and Deshpande (2003) developed the Overall Index of Pollution (OIP) for Indian rivers based on measurements and subsequent classification of pH, turbidity, dissolved oxygen, BOD, hardness, total dissolved solids, total coliforms, arsenic, and fluoride. Each water quality observation was scored as excellent, acceptable, slightly polluted, polluted, and heavily polluted, according to Indian standards and/or other acceptable guidelines and standards such as World Health Organization and European Community Standards. Once categorized, each observation was assigned a pollution index value and the OIP was calculated as the average of each index value. Kim and Carone (2005) developed a water quality index that evaluates changes in water quality over time and space. The Scatter Score Index identifies increases or decreases in any water quality parameter over time and/or space. It does not rely on water quality standards or guidelines and can include an unlimited number of parameters. It was developed primarily to detect positive or negative changes in water quality around mining sites in the United States, but could be applied to non-impacted sites as well. The Well-being Assessment (Prescott-Allen, 2001) calculates a number of indices to assess global human and environmental condition. The indices were developed under two main categories: (1) Human well-being, including indices for health and population, which assesses both health life expectancy and total fertility rate, and indices for wealth which assesses average household and national wealth; and (2) Ecosystem well-being, which includes assessment of both air, such as greenhouse gases and ozone depleting substances, and water (such as inland water quality, river conversion and water withdrawal). To establish an overall Well-being Index, the human and ecosystem indices are combined. The method yields a score for each country, with the top scores translating into a high 50 UNIVERSITY OF IBADAN LIBRARY quality of life for a low environmental price, and the lower scores translating into a low quality of life for a high environmental price. The Environmental Performance Index of Esty et al. (2006) is composed of 16 indicators that represent various policy-relevant objectives on a global scale. There are six policy categories: environmental health, air quality, water resources, biodiversity and habitat, productive natural resources and sustainable energy. These six categories are placed into two broad objectives, Environmental Health and Ecosystem Vitality, which are then combined to give the overall Environmental Performance Index (EPI). To calculate the EPI, each of the 16 indicators are converted to a proximity-to-target measure and placed onto 0 to 100 scale (100 is the target and zero is the worst observed value). Principal components analysis is then conducted with all the indicators to distinguish weights for each indicator and groupings into specific objectives and/or policy categories. Those without a clear designation on the PCA are placed into their policy categories after literature review and expert consultation. The EPI score is calculated on a country by country basis that results in a global ranking of countries. The development of a global index of water quality will not only allow assessment of changes in water quality over time and space but also evaluate successes and shortcomings of domestic policy and international treaties designed to protect aquatic resources. For example, a global index will be one tool for tracking progress towards meeting the Millennium Development Goals and the Plan Implementation of the World Summit on Sustainable Development, as well as other internationally agreed goals and targets. 2.5 RIVER WATER QUALITY MODELS Water quality concept has been evaluated in the last years owing to greater understanding of water mineralization process and greater concern about its origin. Water quality shows water-rock interaction and indicates residence time and recharge zone confirmation (Cronin et al., 2005). Water quality models are very useful in describing the ecological state of a river system and to predict the change in this state when certain boundary or initial conditions are altered. Such changes may be due to morphological modifications of the water body, such as straightening, and discharge regulations using control structures 51 UNIVERSITY OF IBADAN LIBRARY (weirs, dams, etc), changes in the (point or non-point) amount and location of pollutant loading into the system , and changes in meteorological inputs due to changing trends in climate. The degree of complexity in describing the ecological state varies from model to model. Ecological effects of chemical on ecosystems are the results of direct effects of the chemical, determined in single-species toxicity testing and indirect effects due to ecological interaction between species (Laender et al., 2007). In order to determine the impacts of a particular discharge in ambient water quality, it is usually necessary to model the diffusion and dispersion of the discharge in the relevant water body. The approach applies both to new discharges and to the upgrading of existing sources. Models are a set of mathematical expressions (partial or ordinary differential or algebraic equations) describing the physical, biological, chemical, and economic processes which take place in a system (e.g. river). The challenge of using mathematical modeling, as a support tool to evaluate remediation options in developing countries is well documented (Ongley and Booty, 1999). However, modeling is expensive, requires substantial investments in reliable data, development of scientific capacity and a relatively sophisticated management culture that are often not found in developing countries. Nevertheless, new developments in water quality management policies and strategies require prediction of the fate of in stream pollutants, as well as estimates of the likely effects that the resultant water quality may have on recognized water uses. The complex relationships between waste load inputs, and the resulting water quality responses in receiving water bodies are best described with mathematical models. Studying complex hydrologic problems and synthesizing different kinds of information have been made possible using models. The watershed models are of different types and are intended to serve different purposes. Hydrologists classify models into data driven models and mechanistic models. Data-driven models, sometimes called black-box models are usually inferred from the raw or processed data and the formulation may not be conceptually supported by the mechanism of phenomenon under consideration. Regression models, linear time series models, non linear time series analysis and artificial neutral networks have been used for variety of purposes, such as forecasting, rainfall runoff modeling, estimation of missing hydrologic data, and modeling of water quality parameters in 52 UNIVERSITY OF IBADAN LIBRARY streams (Ongley and Booty, 1999). A modeling approach with at least seven specific characteristics is needed. These characteristics are:  The watershed or any hydrologic system, are to be described and simulated in a simple fashion.  The model should start simple, relying on the available data.  The model should be adequately dynamic to cope with the nature of hydrologic systems.  The model should have the ability to simulate both linear and nonlinear processes.  The model needs to provide a way to represent the feedback mechanism in order to handle counter intuitive processes.  The model should have ability to model human intervention and any shocks that might be encountered in the system and;  The model should have the ability to test different policy or management scenarios for better decision making. 2.5.1 Model Classification Water quality models are usually classified according to model complexity, type of receiving water and the water quality parameters (dissolved oxygen, nutrients, etc) that the model can predict. The more complex the model is the more difficult and expensive will be its application to a given situation (Thomann and John, 1987). Model complexity is a function of four factors:  The number and type of water quality indicators: in general, the more indicators that are included, the more complex, the model will be. Some indicators are more complicated to predict than others.  The level of spatial detail: as the number of pollution sources and water quality monitoring increase, so do the data required and the size of the model.  The level of temporal detail: it is much easier to predict long-term static averages than short term. Dynamic changes in water quality point estimates of water quality 53 UNIVERSITY OF IBADAN LIBRARY parameters are usually simpler than stochastic predictions of the probability distributions of those parameters.  The complexity of the water body under analysis: small lakes that “mix” completely are less complex than moderate-size Rivers which are less complex than large rivers, estuaries, and coastal zones. The level of details required can vary tremendously across different management applications. Models can cover only a limited number of pollutants. In selecting parameters for the model, care should be taken to choose pollutants that are a concern in them and are also representative of the broader set of substances which cannot all be modeled in detail. Simulation models are used to predict a system response to a given design configuration with great accuracy and detail, and to identify the probable costs, benefits and impacts of a project. That is, the simulation model predicts the outcome of a single, specified set of design or policy variables. However, the space of possible design and policy variable values is in general, infinite. Separate simulation model runs are required for each design or policy alternative considered. In many situations the number of alternative designs is sufficiently large to preclude simulating each alternative designs and some other method is normally used to narrow the field of search. Optimization models provide a means of reducing the number of alternatives which need to be simulated in detail, i.e. screening them. Those models search the space of possible design variable values and identity an optimal design and/or operating policy for a given system design objective and sets of constraints. These models include relationships which describe the state variables and costs or benefits or each alternative as a function of the decision variables. The assessment of long-term water quality changes is also a challenging problem. During the last decades, there has been an increasing demand for monitoring water quality of many rivers by regular measurements of various water quality variables. The result has 54 UNIVERSITY OF IBADAN LIBRARY been the gradual accumulation of reliable long-term water quality records and the examination of these data for long-term trends (Hirsch et al., 1991). Computer systems now offer the possibility of handling and manipulating very large databases in ways which were not previously a practical option. Littlewood et al. (1998) have used such databases for estimation of UK river mass loads of pollutants. Miller and Hirst (1998) used the hydrochemical databases from an upland catchment in Scotland for a period of five years to assess the annual variation in amounts and concentration of solutes and to examine the variation in stream water quality due to changes in flow, season and long time trend. Ferrier et al. (2001) analyzed in details databases for Scotland and identified temporal changes in water quality over the last 20 years. 2.5.2 Selection Factors for Model Selection As one might expect the data requirement for different models increase with the complexity and scope of application. Static, determistic models require point estimates of these data and often use worst case „„design flow” estimates to capture the behavior of pollutants under the worst plausible circumstances. Selection of an appropriate water quality model depends on the site conditions of the watershed of concern. The selection of appropriate model can be based on the following screening factors:  Combined point and non point sources: an important screening factor is how the model handles the loadings from point and nonpoint sources. Models based on water quality data implicitly take the point and non point sources into account, whereas models that use continuous simulation of the water quality directly account for the source. Typically, the sources are part of the input parameters.  Dominant mixing and transport processes: the water body type dictates the dominant mixing and the transport process of a pollutant. In river and streams the dominant processes are advection and dispersion. In estuaries these processes are influenced by tidal cycles and flows. Water body size and net freshwater flow are also important in determining the dominant processes. For discharges in the ocean 55 UNIVERSITY OF IBADAN LIBRARY surf zone, dominant dispersion processes include mixture due to breaking waves and transport from near shore currents.  Ability to provide time-relevant analysis, decision making, and guideline establishment. Timely or time-relevant analysis is needed for an effective advisory. Models applied to predict water quality conditions can be used as a basis for decision making and as management tools.  Ease of use: the level of user experience; simple methods require only a conceptual understanding of the processes and results can be readily obtained.  Input data requirement: input data requirement are a function of a model‟s complexity. In general, complex models require more specific and complex input data than simple models. Some of these data might not be readily available and acquiring such data might require expending resources. Therefore, the objective of the model application can be very important in this step.  Calibration requirements: Decision making and management alternatives based on modeling results require that the model outcome be acceptable and reliable. Not all models can be calibrated. Models that simulate water quality conditions are calibrated against in-stream monitoring stations. In recent decades, multivariate statistical methods have been employed to extract significant information from hydrochemical datasets in compound systems. Different techniques have been used in attempt to evaluate water quality, essentially based on chemical ions correlation and some ions rapports to predict the origin of the mineralization (Tim et al., 1992; Laroche et al., 1996; Leon et al., 2001; Engelmann et al., 2003; Sharma et al., 2008; Asien et al., 2010). There is the need to provide a cost effective alternative which can help to protect the water quality of lakes. Modeling provides such an alternative to interpret data with prediction for the future. Wang et al. (2003a, b) developed a model to study the phosphorus dynamics in aquatic sediment and to conduct dynamics predictions of phosphorus release across a sediment-water interface. The model focuses on the sediment active layer below the sediment-water interface and was based on primary mechanisms regulating phosphorus behavior in sediments. 56 UNIVERSITY OF IBADAN LIBRARY Petterson (2001) studied the proportions of phosphorus forms as well as total phosphorus content in suspended and settling particles during spring, summer and autumn in order to improve the understanding of particle composition and mineralization process in moderately eutrophic, temperature lakes with summer stratification. However, vast amount of data requirement (rainfall, soil type, land use, etc) places several constraints on the application of these models. Statistical models are developed for estimation of concentration of different water quality constituents using routinely-monitored water quality parameters. The best subset modeling procedure enables comparison between full 2 models (containing all the independent variables). Best subset procedure based on R and F values can be used in model dissemination (Sharma and Jain, 2005). Correlation and regression analysis have been described for the study of land use and non point source impacts on water quality (Rajendra et al., 2009). 2.5.3. Diffuse Pollution Modelling Diffuse pollution refers to the pollution arising from land use activities that are generally dispersed across a catchment, and this form of pollution frequently causes, or contributes towards, water quality problems. Point source pollution input loads are normally determined based on field measured data of flow and water quality indicator. In comparison with point source pollution, diffuse source pollution is more difficult to quantify as it is distributed over large areas and is difficult to measure directly. The modeling of non-point source pollution, however complex is very essential for any water quality management programme (Sekhar and Raj, 1995). Diffuse pollution is hard to analyze, control and manage by its nature. It is usually temporally and spatially uncertain, and thus hard to analyze. In many cases, the discretion diffuse source of pollution into individual point sources can ease diffuse pollution modelling and analysis, and therefore reduce high uncertainty especially, in the spatial distribution of pollution loads (Erturk et al., 2007). In recent years GIS (Geographical Information System) software has been increasingly used as a tool in land-use studies for modeling the input of pollution loads from diffuse sources. The use of GIS for water quality modeling abounds in literature (Dimov, 2002; Enrique and Richards, 2005; Yuan et al., 2007; Richard et al., 2008). For example, Jeuness et al. (2002) studied the variation in phosphorus loads of point and non- 57 UNIVERSITY OF IBADAN LIBRARY point sources in the Thau catchment located adjacent to the Mediterranean seas. Wischmeier and Smith (1978), Renard et al. (1991), and Silvertum and Prange (2003) developed a land use model based on the widely used universal soil loss equation. The indirect approaches to assess the non point pollution utilize water quality measurements in streams, rivers, or lakes to infer the importance of pollution sources. The alternative approach focuses on the non-point sources and attempts to mathematically describe the transport of pollutants to the water body (Haith and Dougherty, 1976). The indirect approach utilizes water quality data (immission data) from streams, rivers or lakes, and infers the importance of non-point source pollution from these in stream observations. The indirect inference approach can be extended beyond simple loading factors by the use of regression models which have land use characteristics as independent variables, and in- stream water quality parameters as dependent variables. 2.5.4 Trend Analysis 2.5.4.1 Time series analysis Long-term trend of water quality in natural systems reveal information about chemical and biological changes and variations due to manmade and/or seasonal interventions. The success of such trend analysis depends largely on the initial exploratory analysis of the data and in identifying the appropriate model orders to predict trend (Hipel, 1985). Time series is that collection of quantitative observations that are evenly spaced in time and measured successively. Time series are analyzed in order to understand the underlying structure and function that produce the observations. It is assumed that a time series data set has at least one systematic pattern. The most common patterns are trends and seasonality. Trends are generally linear or quadratic. Seasonality is a trend that repeats itself systematically over-time. A second assumption is that the data exhibits enough of a random process so that it is hard to identify the systematic patterns within the data. Time series anaylysis techniques often employ some type of filter to the data in other to dampen the error. Other potential patterns have to do with lingering effects of earlier observations or earlier random errors. 58 UNIVERSITY OF IBADAN LIBRARY There are numerous software programs that will analyze time-series; such as NCSS, SPSS, JMP and SAS/ETS. Observations over-time can be either discrete or continous. Both types of observations can be equally spaced, unequally spaced, or have missing data. Discrete measurements can be recorded at any time interval, but are most often taken at evenly spaced intervals. Continous measurements can be spaced randomly in time. Time series are very complex because each observation is somewhat dependent upon the previous observation, and often is influenced by more than one previous observation. Random error is also influential from one observation to another. These influences are called autocorrelation-dependent relationships between successive observations of the same variable. The challenge of time series analysis is to extract the autocorrelation elements of the data, either to understand the trend itself or to model the underlying mechanisms. 2.5.4.2 Seasonality Seasonal variation is a component of a time series which is defined as the repetitive and predictive movement around the trend line in one year or less, i.e. the frequency of time- series data to exhibit behavior that repeats itself. Seasonal variation is studied for reasons such as better understanding of the impact the component has upon a particular series and to project the past patterns of the future trends. Seasonality is often the major exogenous effect that must be compensated for to discern trends in water quality. Qian et al. (2007) evaluated selected water quality constituents from 1979 to 2004 at three monitoring stations in southern Florida for seasonality. The seasonal patterns of flow-weighted and log-transformed concentrations were identified by applying side-by-side box plots and Wilcoxon signed-rank test (p< 0.05). They observed that major water quality indicators (specific conductivity, turbidity, colour and chloride) exhibited significant seasonal patterns. Almost all nutrients species (NO2-N, NH4-N, total Kjeldah N, PO4-P and total P) had an identical seasonal pattern of concentrations significantly greater in the wet than in the dry season. Some water quality constituents were observed to exhibit significant annual or seasonal trends. In some cases, the overall annual trend was insignificant while opposing trends were present in different seasons. 59 UNIVERSITY OF IBADAN LIBRARY 2.5.4.3 Smoothing techniques Smoothing techniques are used to reduce irregularities (random fluctuations) in time series data. They provide a clearer view of the true underlying behavior of the series. Exponential smoothing is a very popular scheme to produce a smoothed time series. It assigns exponentially decreasing weights as the observation gets older. Exponential smoothing is a widely used method in forecasting based on the time series itself. Unlike regression models, exponential smoothing does not impose any deterministic model to fit the series other than what is inherent in the time series itself. Types of exponential smoothing Single exponential smoothing It is also known as simple exponential smoothing. It is used for short range forecasting, usually just one month into the future. The model assumes that the data fluctuates around a reasonably stable mean (no trend or constituent pattern of growth). The specific formula for simple exponential smoothing is: St is exponential smoothing factor α is alpha value Xt is observed value at time t When applied recursively to each successive observation in the series, each new smoothed value (forecast) is computed as the weighted average of the current observation and the previous smoothed observation; the previous smoothed observation was computed in turn from the previous observed value and the smoothed value before the previous observation, and so on. Double exponential smoothing This method is used when data shows a trend. Exponential smoothing with a trend works much like simple smoothing except that two components must be updated each period- level and trend. The level is a smoothed estimate of the value of the data at the end of each 60 UNIVERSITY OF IBADAN LIBRARY period. The trend is a smoothed estimate of average growth at the end of each period. The specific formula for simple exponential smoothing is: St = yt + (1- + bt-1) 0 < < 1 bt = γ is gamma St is exponential smoothing factor Triple exponential smoothing This method is used when data shows trend and seasonality. To handle seasonality, one has to add a third parameter. A third equation will be introduced to take care of seasonality. The resulting set of equation is called the „Holt-Winters‟ (HW) method after the names of the inventors. There are two main HW models, depending on the type of seasonality. Multiplicative seasonal model This model is used when the data exhibits multiplicative seasonality. The time series is represented by the model Where, Yt is the data value observed at time t b1 is the base signal also called the permanent component. b2 is a linear trend component. St is a multiplicative seasonal factor. is the random error component Let the length of the season be L period. The seasonal factors are defined so that they sum up to the length of the season, i.e. The trend components b2 if deemed unnecessary may be deleted from the model. The multiplicative seasonal models appropriate for a time series in which the amplitude of the 61 UNIVERSITY OF IBADAN LIBRARY seasonal pattern is proportional to the average level of the series, i.e. a time series displaying multiplicative seasonality. Additive seasonal model This model is used when the data exhibits additive seasonality. This model assumes that the time series is represented by the model Where, b1 is the base signal also called the permanent component b2 is a linear trend component St is an additive seasonal factor Random error component Let the length of the season be L periods. The seasonal factors are defined so that they sum to the length of the season i.e. The trend components b2 if deemed unnecessary may be deleted from the model. The additive seasonal model is appropriate for a time series in which the amplitude of the seasonal pattern is independent of the average level of the series i.e. a time series displaying additive seasonality. 2.6 SELF-PURIFICATION AND ITS RELATIONSHIP TO POLLUTION ABATEMENT Running water is capable of purifying itself with distances through a process known as self-purification. This is the ability of rivers to purify itself of sewage or other wastes naturally. It is produced by certain processes which work as rivers move downstream. The mechanisms can be inform of dilution of polluted water with influx of surface water and groundwater or through certain complex hydrological, biological and chemical processes 62 UNIVERSITY OF IBADAN LIBRARY such as sedimentation, coagulation, volatilization, precipitation of colloids and its subsequent settlement at the base of channel or lastly due to biological uptake of pollutants. Certain streams however are capable of adding-up more materials as they flow downstream from riparian inputs (Ongley, 1987; 1991). Self-purification of rivers primarily involves chemical oxidation, biodegradation of organic material, volatilization of volatile organic compounds, and deposition of solid or particulate materials into the sediment and dilution of the contaminants by water. Self- purification of river involves complex mechanism and depends on several factors such as the flow rate, time, and temperature, presence of microorganisms, pH and dissolved oxygen content of the water. The nature of the contaminants also plays significant roles on the river recovery capacity. Hence, some rivers quickly recover from pollution stress than others depending on the prevailing factors. Rivers are individualistic and natural purification capacities vary from stream to stream and reach to reach along a river course. A river is not static: it is living, dynamic, constantly responsive to the laws of biologic change and the vagaries that apply to living things. Further, each river is sensitive and/in response to hydrological fluctuations which follow the laws of chance and probability. The purpose of sewage and waste treatment is to protect the condition of streams consistent with reasonable uses of the water heritage. In sewage and waste disposal it is not economically feasible to provide such a high degree of treatment as to return streams to virgin conditions. On the other hand, neither can our water resources be allowed to degenerate to points where they are useful only as sewers. Approached from either of these extremes, the problem of stream pollution is frequently over simplified, as both ignore completely natural self-purification capacities. If pollution control and stream improvement are to be promoted on a sound and economical basis, it is essential to take into consideration natural self-purification. It is well known that the purifying action of a river-water polluted with sewage is very considerable, as a few miles below the outfall or point of pollution a river may show little or no sign of pollution at all. Purification is 63 UNIVERSITY OF IBADAN LIBRARY effected by sedimentation of the suspended solids, and oxidation of the soluble material. The process of oxidation gives rise to deoxygenation depending on the strength of the sewage, the degree of dilution afforded by admixture with the river water and the velocity of the river. If the concentration of oxidisable material is excessive, the river-water will suffer considerable or complete deoxygenation, and a problem will result owing to the septic condition caused by aerobic decomposition of the organic matter. On the other hand, if there be sufficient dilution the organic matter can be oxidized and thus destroyed without depriving the river-water of oxygen to any appreciable degree. Recovery from pollution or self purification as it is termed depends on the conditions within the particular river. Ordinarily, towns, situated on the same river are sufficiently separated to give time for the river to recover from the effects of the upper pollution before it is subjected to the next. On the other hand, if towns be close together, a nuisance may result, and the river may become unfit to receive a further volume of sewage lower down, until a considerable length of time and dilution from tributaries enable purification to be effected. Evidence of self- purification of some rivers in literature abounds (Lueck et al., 1957; Ifabiyi, 2008; Aisien et al., 2010). The mere knowledge that the water is polluted tells little more than the bare fact that the immediate capacity of that body of water for self-purification has been exceeded (Lueck et al., 1957). Unfortunately, it is seldom possible to immediately isolate and completely eliminate the sources of overload which may be producing pollution conditions in large rivers and other natural water bodies. Usually it becomes desirable, if not imperative, to learn as much as possible about the working capacity of the water body for self-purification and also of the degree of overload. For example, Cazelles et al. (1991) emphasized the role of transport mechanisms (convention and longitudinal dispersion) and the role of benthic bio-film which accounts for 35- 40 % of the organic load removal of a small organically polluted stream of the Albenche River (Savoie, France). Ifabiyi (2008) in his work determined evidence of the process of self purification and identified which of the water chemistry variables were affected by the processes and examined the portability of the water with a view to making recommendations for the purpose of water resources management in Ile-Ife. 64 UNIVERSITY OF IBADAN LIBRARY Seasonal variation is another factor in the self purification of rivers. Owing to the increased rate of oxidation of organic matter due to greater bacterial activity, and the removal of ammonia due to plant development, the process of purification will operate more rapidly during the warmer months. On the other hand, if the river-water be overcharged with sewage, the nuisance may be greater in the summer months than in the winter, as the increased rate of oxidation may lead to deoxygenation of the water. Bakari (2004) reported that the structure of the bottom substrate mainly influences the degree of self-purification capacity of River Kizinga (Dar es Salaam, Tanzania) in connection with the discharge and flow velocity. He observed that, generally, the river-water purified itself faster during dry season than in rainy season, with turbulence physical state accelerating the purification process. Islam et al. (2010) found the treatment efficiency of the Surma River (Bangladesh) by itself purification capacity due to the pollution through discharges of choras (small canal), industrial wastewater discharges and human excreta disposal. Monayari et al. (2006) reported the enhancement of self-purification of streams using stepped aeration. He reported that the presence of organic matter in waste affects the amount of oxygen that could be dissolved during cascade aeration which invariable means that the concentration of organic matter should be considered in the equations present in the literature for cascade aeration. Chen et al. (2007) reported that seasonal variations of self-purification for the pollutants not only resulted from riverine hydrological and ecological conditions, but was also affected by the pollution loading. Natural self purification is not a fixed quantity, but rather a range in variability of capacity associated with biological and hydrological changes. Failure to recognize this dynamic character of natural purification results in rigid stream standards which cannot possibly be maintained under a normal pattern of stream variability. Streams cannot be generalized: hence stream standards cannot be applied wholesale to all rivers without regard to their widely varying natural self-purification capacities. River or stream has natural recovery capacity or self purification ability in which the pollutants are removed, redistributed, decomposed or transformed to harmless substances. Self-purification capacity of water is a good indicator to evaluate the ecological status of a water body (Ernewstova and Semenova, 1994). 65 UNIVERSITY OF IBADAN LIBRARY 2.7 SEDIMENT AND NUTRIENT DISCHARGE CHARACTERISTICS OF RIVERS Water quality impairment is related to flow and sediment from soil erosion. Non-point source pollutants come from a number of sources and are washed into our waterways by surface runoff. When land disturbing activities occur, soil particles are transported by surface water movement, and are often deposited in streams, rivers, lakes and wetlands. This soil material is called sediment, and is the largest single non-point source pollutant and the primary factor in the deterioration of surface water quality. Sediment can also come from the decomposition of plants and animals, while wind, water, and ice can help carry these particles to rivers, lakes and streams. Sediment is the loose sand, clay, silt and other particles that settle at the bottom of bodies of water. The erosion of bedrock and soils leads to accumulation of sediments of past or on-going natural and anthropogenic processes and components. Data from sediments can provide information on the impact of distant human activity on the wider ecosystem. 2.7.1 Characteristics of Eroded Sediments River bottom sediment plays a role in the study of pollution, and can be used to ascertain the quality of surface waters (Oyeyiola et al., 2006). An attempt to understand the dynamics of sediment movement through the river system must take account of the potential contrast between the ultimate and effective particle size distribution of suspended sediment in response to aggregation. Walling and Moorehead (1989) reported that in rivers with relatively low solute concentrations, an order of magnitude difference exists between the medium particle size associated with the ultimate and effective grain size distributions. Cation exchange capacity (CEC) of sediment is a good indicator of water quality. Cation exchange sites are found primarily on clay and organic matter. It is based on surface area + of sediment grain particles available for binding cations such as hydrogen (H ) and free 2+ metal ions (e.g. Mn ). Sediments with a high percentage of grains, such as silt and clay, have high surface-to-volume ratios and can absorb more heavy metals than sediments composed of large grains, such as sand (Liber et al., 1992). Organic matter content of sediment can contribute to the total CEC, since it binds sediment particles together into stable aggregates which are necessary for stability and adsorption of cations such as 66 UNIVERSITY OF IBADAN LIBRARY calcium, magnesium, sodium and others. This can significantly influence water holding capacity especially if the sediment is more sandy. Sediment is the ultimate sink of contaminants in the aquatic systems. Accumulation of contaminated sediment in rivers and lakes is becoming a problem of some concern especially in more industrial and urban environment. Heavy metals in sediments are more dangerous because they bioaccumulate. For example, Kuashik et al. (2009) found that sediments of River Yamuna in India showed a significant enrichment with Cd and Al indicating inputs from industrial sources. Accumulation of heavy metals like Pb, Cd, Cu, and Ni have been reported to be very high in sediment samples collected in Nallihan Bird Paradise (Aladag Creek, Kirmir Creek and Sakarya River) in Turkey (Ayas et al., 2007) and invariably can affect the existence of organisms in the water body. Toxic chemicals can be attached or adsorbed to sediment particles. These pollutants characteristics can be ascertained by studying the quantity, quality and characteristic of sediment in rivers; this allows one to determine sources and evaluate the impact on aquatic environments. All countries have been affected, though the area and severity of pollution vary enormously. According to government statistics, coal mine has contaminated more than 19,000 km of US streams and rivers with heavy metals, acid mine drainage and polluted sediments. Studies on heavy metals pollution in sediment abound. Adefemi et al. (2007) studied the seasonal variation in heavy metal distribution in the sediment of major dams in Ekiti State, Nigeria. The concentration of most of the metals appeared higher in the dry season than those recorded for the wet season. Adekola et al. (2002) determined levels of some heavy metals in urban run-off sediments in Ilorin and Lagos, Nigeria. Zn, Fe and Cd were found in very high concentrations in the urban sediments from the cities while Pb had the lowest level. One of the most distinguishing features of metals from other toxic pollutants is that they are not biodegradable. Sediments can incorporate and accumulate many metals added to a body of natural water. The favourable physicochemical conditions of sediment can remobilize and release the metals to the water column. It has been stated that specific local sources such as discharge from smelters (Cu, Pb, Ni), metal-based industries (e.g. Zn, Cr, and Cd from electroplating), paint and dye formulators (Cd, Cr, Cu, Pb, Hg, Se and Zn), 67 UNIVERSITY OF IBADAN LIBRARY petroleum refineries (As and Pb), as well as effluents from chemical manufacturing plants may lead to metal accumulation in sediments (Bonnevie et al., 1994; Al-Masri et al., 2002). Thus, there is the need for controlling both point and non-point discharges of heavy metals from industries (Bakan and Ozkoc, 2007). Ying et al. 2012 reported that sediment of Honghu Lake (East Central China) showed a decreasing trend while Cd presented an increasing trend. The analysis of ecological risk assessment based on sediment quality guidelines suggested that heavy metals in most sediment from the Hohghu Lake had moderate toxicity, with Cr being the highest priority pollutant. Hongbin et al. 2011 studied the distribution, sources and ecological risk of heavy metals in surface sediments from Lake Taihu. Their results showed that the measured heavy metals had varied spatial distribution patterns, indicating that they had complex origins and controlling patterns, indicating that they had complex origins and controlling factors. 2.7.2 Sediment Related Problems 2.7.2.1 Fisheries/aquatic habitat Excess sediment can change a river or stream from one with a clean gravel land to one with a muddy bottom. With this change many of the mature fish and animals will disappear. Stream-born sediment directly affects fish populations in several ways:  Suspended sediment decreases the penetration of light into the water. This affects fish feeding and can lead to reduced survival.  Suspended sediment in high concentration irritates the gills of fish, and can cause death.  Sediment can destroy the protective mucous covering the eyes and scales of fish, making them more susceptible to infection and disease.  Sediment particles absorb warmth from the sun and thus increase water temperature. This can stress some species of fish.  Suspended sediments in high concentrations can dislodge plants, invertebrates, and insects in the stream bed. This affects the food source of fish, and can result in smaller and fewer fish.  Settling sediment can bury and suffocate fish eggs. 68 UNIVERSITY OF IBADAN LIBRARY  Sediment particles can carry toxic agricultural and industrial compounds. If these are released into the habitat they can cause abnormalities or death in the fish. 2.7.2.2 Bioavailability and toxicity of metals in sediment Sediments act as a natural sink for many metals moving from the terrestrial environment to the marine environment. Mobilized toxic metals from geogenic or anthropogenic sources can be scavenged from the aqueous environment via sediment/organic matter sorption, or they are biogeochemically cycled by a variety of different microorganisms. When the physicochemical characteristics of the environment such as pH, redox conditions, and dissolved oxygen are changed, metal solubility will increase, resulting in remobilisation. Free toxic metals ions, formerly sequestered in the aforementioned solids, will become more concentrated and bioavailable to both microorganisms alike within that ecosystem. Natural wetlands, salt marshes, estuaries, and inevitably the associated marine ecosystem, will become disrupted. The bioavailability of heavy metals in sediment in literature abound. Besser et al. (2008) studied the bioavailability and toxicity of copper, zinc, arsenic, cadmium, and lead in sediments from Lake Roosevelt, Columbia River in Washington. They characterized chronic sediment toxicity, metal bioaccumulation, and metal concentrations in sediment and pore water from eight study sites. Their results indicate that metals in sediments from both riverine and reservoir habitats of Lake Roosevelt are available to benthic invertebrates. Vicente-Martorell (2009) measured concentrations of heavy metals (Cu, Zn, Cd, Pb and As) in sediment from the estuary of Tinto and Odei Rivers in Huelva (Spain), one of the most metallic polluted estuaries in Europe. High pollution of Zn, Pb, As and Cu were found in sediments. Availability of metals was established as following the ranking: Cd>Zn>Cu>Pb in sediment. Lin et al. (2008a) studied the geochemical behavior of major trace elements (As, Cd, Co, Cr, Cu, Hg, Zn, Ni, Pb, Sb, Sc, V, Mn, Ti, Al, Fe, Mg, Ca, Na and K) in 39 bottom sediment samples collected from Songhua River. Results indicated that the concentrations of As, Cd, Co, Cr, Cu, Hg, Zn, Ni, Pb, and V in the sediments, were 2.7-11.5, 0.05-1.38, 4.8-14.7, 15.9-78.9, 2.4-75.4, 0.01-1.27, 21.8-403.1, 6.2-35.8, 12.6 - 24.4, and 22.1-108.0 mg/kg, respectively. Due to the input of anthropogenic 69 UNIVERSITY OF IBADAN LIBRARY sources, temporal and spatial variations of Cd, Cu, Hg, Zn, and Pb contents in the sediment were higher than that of major elements. They discovered that generally, sediment contamination of the Songhua River by trace metals was less than that of the Zhujiang river and the Changjiang River, and similar to that of the Huabghe River. Lin et al. (2008 a,b) investigated trace metal contamination in the sediment of the second Songhua River in China after being subjected to large amounts of raw effluent from chemical industries in Jilin City in 1960s to 1970s, which resulted in serious mercury pollution. Total concentrations of Al, Fe, Mg, Ca, K, Na, Ti, Mn, V, Sc, Co, Cu, Cr, Ni, Pb and Zn in the sediment samples were measured by ICP-MS, following digestion with various acids. Results showed that concentrations of Co, Cr, and Ni in sediment were generally only slightly higher than or equal to their background values, while concentrations of Co, Pb, and Zn in some sediment samples were significantly higher than their background values. The sediment at Jilin City was moderately contaminated with Cu, and the sediment of second Songhua River was moderately contaminated with Pb and Zn. This buried contamination of trace metals poses a potential risk to water column under disturbance of sediment. Liu et al. (2012) evaluated the anthropogenic proportion and potential sources of trace metals in surface sediments of Chaohu Lake, China based on the executive geochemical data. Their analysis showed that concentrations of major and trace metals displayed significant spatial diversity and almost all elements were over the pre- industrial background value, which should be related to the variations of sediment composition partially. They highlighted the contribution of anthropogenic contamination to the elevated potential biological effects of trace metals. Though there had been no obvious human contamination of Cr and Ni in Chaohu Lake, concentrations were all over the threshold effect concentrations, which may be due to higher background levels in the parent materials of soils and bedrocks in Chaohu Lake catchment. Babale et al. (2011) conducted a field study to determine the level of Cd and Cr bioavailability of the sediment of Challawa River in Kano, Nigeria. The study revealed that pH and organic matter favours bioavailability of Cd and Cr. The potential risk to river water contamination was highest downstream for Cd based on the calculated contamination factor. Cd posed the highest risk to Challawa river water contamination. 70 UNIVERSITY OF IBADAN LIBRARY Eggleton and Thomas (2004) reviewed that sediment disturbance can lead to changes in the chemical properties of sediment that stimulate the mobilization of contaminants. Research shows that changes in both redox potential and pH can accelerate desorption, partitioning, bacteria degradation and the oxidation of organic contaminants. However, these processes are both sediment and compound-specific. By affecting the affinity of contaminants to sediments, disturbance events in turn can have a significant effect on their bioavailability. The following factors were considered when assessing the release of contaminants from sediments: the fate of contaminants in undisturbed sediments and those that are not subjected to major disturbances, the kinetic processes that regulate metal release during changes in redox potential, the release of organometallic compounds from sediments re-suspension, the bioavailability of organic and organometallic compounds and the process affecting contaminant release. 2.7.2.3 Speciation of heavy metals in bottom sediments Speciation studies of heavy metals in bottom sediments of surface water reservoirs are usually conducted in areas subjected to enhanced anthropogenic factors since an increased level of heavy metal presence is a consequence of man‟s activity. Metals enter the aquatic environment from a variety of sources, including those naturally occurring through biogeochemical cycles and those through anthropogenic sources. The migration behavior and bioavailability of metals is controlled by the way in which metal ions are distributed between the aqueous and particulate phases. As a result, sediments act not only as transport media of contaminants, but also as potential secondary sources of contamination of an aquatic system (Salomons and Forstner, 1984). The advantage of sequential chemical metal speciation over total metal extraction includes: (1) Assessment of the source of a particular metal (i.e. natural or anthropogenic). (2) Determination of the relative toxicities to aquatic biota, and (3) A better understanding of metal-sediment interactions. On the other hand, since the mobility of a metal and its bioavailability also depend on its speciation, considerable attention has been paid to this aspect in lacustrine systems. For example, Fityanos et al. (2004) employed a five-step sequential metal extraction procedure 71 UNIVERSITY OF IBADAN LIBRARY to evaluate pollution by six metals (Cd, Cr, Pb, Mn, Zn, and Cu) of two lakes of northern Greece, Volvi and Koronia. Their findings demonstrated that Cd, Cr, Pb and Cu were bound to oxidizable and residual fractions, and to a lesser extent, to carbonate fractions thus posing a low pollution risk to the lakes. In contrast, Dollar et al. (2001) employed the sequential extraction scheme of Tessier et al. (1979) to separate the portion of a metal bound to different geochemical groups at Indiana Dunes National Lakeshore. Their results showed a potential risk of pollution by release of Cd, Pb, and Cr present in the exchangeable and carbonate fractions (attributed to anthropogenic sources). In the case of the Mexican Lerma-Chapala Watershed, previous studies using total metal concentrations demonstrated that suspended sediments acted as transport vectors of six metals (Cd, Cr, Ni, Cu, Pb, and Zn) along the Lerma River, with Lake Chapala as the final destination of these pollutants (Juan et al., 2010). Babale et al. (2011) assessed the sediment quality of River Challawa in Kano, Nigeria. Their study revealed the distribution of Cr in different fractions in the order; residual > carbonate bound > Fe-Mn oxide bound > organic bound > exchangeable in the study and in the control areas. Chromium was associated mainly (65-93.3%) with the residual fraction in all the samples and relatively small amount of chromium occur in the non- residual fractions. The Cd association with different sediment fraction followed the order: residual bound > exchangeable > carbonate bound > Fe-Mn oxide bound > organic bound. Despite the high levels of Cr, the very low percentage of the metal in the non-residual fraction indicates their limited environmental mobility. Cd is associated more with exchangeable and carbonate fractions, an indication of potential bioavailability of the metals. 2.7.2.4 Remediation of contaminated sediment Contaminated sediment has been identified as a source of ecological impacts in marine and freshwater systems throughout the world, and the importance of the contaminated sediment management issue continues to increase in all industrialized countries. In many areas, dredging or removal of sediments contaminated with nutrients, metals, oxygen- demanding substances, and persistent toxic organic chemicals has been employed as a 72 UNIVERSITY OF IBADAN LIBRARY form of environmental remediation. In most situations, however, the documentation of the sediment problem has not been quantitatively coupled to ecological impairments. In addition, the lack of long-term, post activity research and monitoring for most projects has impeded a better understanding of the ecological significance of sediment contamination. The lack of information coupling contaminated sediment to specific ecological impairment has, in many instances, precluded a clear estimate of how much sediment requires action to be taken, why, and what improvements can be expected to existing impairments over time. A clear understanding of ecological links not only provides adequate justification for cleanup program but also represents a principal consideration in the adoption of nonintervention, alternative strategies. Timely and effective remediation of contaminated sediments is essential for protecting human health and the environment and restoring beneficial uses to water ways. A number of site operational conditions influence the effect of environmental dredging of contaminated sediment on aquatic systems. Site experience shows that re-suspension of contaminated sediment and release of contaminants occurs during dredging and contaminated residuals remains after operations. It is understood that these processes affect the magnitude, distribution, and bioavailability of contaminants, and hence the exposure and risk to receptors of concern (Bridges et al., 2010). For example, Wang and Feng (2007) conducted a 5-year field monitoring study to assess the effectiveness of the environmental dredging in South Lake, China. They determined the concentrations of total nitrogen, total phosphorus, and heavy metals (Zn, Pb, Cd, Cu, Cr, Ni, Hg, and As), before and after dredging in sediment and compared the results. Multiple ecological risk indices were employed to assess the contamination of heavy metals before and after dredging. Their results showed that the total phosphorus level reduced 42% after dredging. Similar changes for Hg, Zn, As, Pb, Cd, Cu, Cr, and Ni were observed, with reduction percentages of 97.0, 93.1, 826, 63.9, 52.7, 50.1, 32.0, and 23.6, respectively, and the quality of sediment improved based on the criterion of Sediment Quality Guidelines by USEPA. Contamination degree values decreased significantly. Unexpectedly, the total nitrogen increased by 49% after dredging, compared to before dredging. Their finding was that environmental dredging was an effective mechanism for removal of total phosphorus and 73 UNIVERSITY OF IBADAN LIBRARY heavy metals from South Lake. But, the dredging was ineffective in removing nitrogen from sediment. The increase in total nitrogen level was likely due to ammonia release from the sediment, impairing the effectiveness of the dredging. Disposal of polluted dredged sediments on land may lead to certain risks. Currently, contaminated dredged sediments are often not vaporizable due to their high contents of contaminants and their consequent hazardous properties. It is generally admitted that treatment and re-use of heavily contaminated dredged sediments is not a cost-effective alternative to confined disposal. Reports on plants growing in polluted stands without being seriously harmed indicate that it should be possible to detoxify contaminants using agricultural and biotechnological approaches. Several types of phytoremediation can be defined according to Switzguebel (2000) as:  Phytoextraction: The use of pollutant-accumulating plants to remove pollutants like metals or organics from soil by concentrating them in harvestable plants parts.  Phytotransformation: The degradation of complex organic molecules or the incorporation of these molecules into plant tissues.  Phytostimulation: Plant-assisted bioremediation, the stimulation of microbial and fungal degradation by release of exudates/enzymes into the root zone (rhizosphore).  Phytovolatilization: The use of plants to volatilize pollutants or metabolites.  Rhizofiltration: The use of plant roots to absorb or adsorb pollutants, mainly metals, but also organic pollutants, from water and aqueous waste streams.  Pump and tress (Dendroremediation): The use of trees to evaporate water and thus to extract pollutants from the soil.  Phytostabilsation: The use of plants to reduce the mobility and bioavailability of pollutants in the environment, thus preventing their migration to groundwater or their entry into the food chain.  Hydraulic control: the control of the water table and the soil field capacity by plant canopies. 74 UNIVERSITY OF IBADAN LIBRARY Phytoremediation is given as a management option for semi-terrestrial and terrestrial ecosystems affected by polluted sediments, and the process affecting pollutant bioavailablity in the sediments. The status of sediments, i.e reduced or oxidized, highly influences contaminant mobility, its (eco) toxicity and success of phytoremediation. Bert et al. (2009) reviewed that a large variety of plants and trees are able to colonize or develop on contaminated dredged sediments in particular conditions or events (e.g. high level of organic matter, clay and moisture content, flooding, seasonal hydrological variations). Trees, high-biomass crop species and graminaceous species could be used to dredge organic pollutants, to extract to stabillise inorganic pollutants. Choice of plants is particularly crucial for phytoremediation success on contaminated sediments. Research reports on phytoremediation of contaminated sediments in literature abound (Vervaeke et al., 2003; Erakhrumen, 2007; Ndimele and Jimoh, 2011; Veronica et al., 2011). 2.8 HEAVY METALS ABSORPTION IN VEGETATION 2.8.1 Accumulation and Toxicity of Heavy Metals in Vegetation Water pollution of most rivers is due to millions of litre of sewage, domestic waste, industrial and agricultural effluents containing substances such as heavy metals such as lead, cadmium; mercury e.t.c. Heavy metals in the environment is increasing following their ever increasing utilization in modern technology. Substantial efforts have been made in identifying plant species and their mechanisms of hyperaccumulation of heavy metals. Variations exist for hyperaccumulation of different metals among various plant species and within populations (Pollard et al., 2002). Plants absorb a number of elements from soil, some of which have known biological functions and some are known to be toxic at low concentrations. Their availability in a soil-plant system depends on a number of factors which include pH of soil, soil organic matter content, cationic exchange capacity as well as plant species, stage of development, and others (Farago, 1994). It is known that the availability of some heavy metals decreases with rising pH of the soil, organic matter and clay content. Our environment has always been under natural stresses but its degradation was not as severe as it is today. The effect of environmental pollution on vegetation is astounding. Plants can face a number of foreign (xenobiotics) compounds during their life. These xenobiotics include ions of heavy metals such as lead, cadmium or copper. Study of 75 UNIVERSITY OF IBADAN LIBRARY plant response to chemical pollution is important for the management of healthy ecosystems. Many studies have indicated that the accumulation of heavy metals in sediment has had an adverse effect on the growth and development of a wide variety of plants species. Although, low concentrations of some heavy metals, such as copper and zinc, are necessary for the proper functioning of most plant systems. Higher concentrations of copper and zinc have been found to be responsible for metabolic disturbances and growth inhibition of some plants (Fernandes and Henriques, 1991). Other studies have demonstrated that the uptake of such metals as lead, nickel, and cadmium can damage the integrity of cell membranes in certain plants. For example, excess concentration of lead, cadmium, copper and zinc significantly affected the plant water status of sunflowers, causing water deficit and subsequent changes in the plant (Kastori et al., 1992). Although the uptake of heavy metals is antagonistic to a myriad number of plant systems, other studies have shown that some plants are able to absorb heavy metals, adapt to them, and thrive. Currently, those sites that contain the highest concentrations of heavy metals are situated near industrial sources such as smelters and steel refineries. Even in locations such as these, certain plant species have been able to adapt to heavy metal ions. Both the mechanisms that the plants use for adaptation and the specific effects of the metals on plant‟s bio-systems, however, remain unclear (Ernst et al., 1992). High level of metals in sediment does not necessarily reflect elevated doses in plants. The ability of some plants to absorb and accumulate xenobiotics makes them useful as indicators of environmental pollution. Plants absorb a number of elements from soil, some of which have no known biological function and some are known to be toxic at low concentrations. It is known that the availability of some metals decrease with rising pH of the soil, organic matter and clay content. The source of heavy metals in plant is the environment in which they grow and their growth medium (soil) from which heavy metals are taken up by roots or foliage of plants. Plants growing in polluted environment can accumulate heavy metals at high concentration causing risk to human health when consumed as food or for medicinal usage. 76 UNIVERSITY OF IBADAN LIBRARY The study of excessive concentrations of pollutants in biological matrices has been reported in numerous publications. Muhammed (2007) evaluated the levels of arsenic and heavy metals and their genetic effects on C. latifolia growing in the Mamut River (Sabah, East Malaysia) riparian zone. His study revealed significantly higher levels of As (45±20µg/kg), Cd (22±2µg/kg), Cu (3678±160 µg/kg) and Zn (3773±1710µg/kg) compared to the same plant specimens from control site at Kipungit with As (16.3±11µg/kg), Cd (22±2µg/kg), Cu (450±200µg/kg) and Zn (1770±640µg/kg) at p< 0.05. Ozdilek et al. (2007) evaluated the potential impacts that metals may have on vegetation and plant tissues in the vicinity of the Blackstone River, USA. The river had been subjected to metals load that included contributions from urban runoff, waste water discharges, contaminated sediments, and also re-suspension of contaminated sediments in the river-bed. All these effects lead to the elevated concentrations of metals such as lead, copper, zinc, chromium, cadmium and arsenic. Their results showed that the metals concentrations in vegetation were generally inversely related to the distance between the vegetation and the riverbank. Heavy metals enter the biological cycle through the roots and leaves of plants and are enriched in various plant organs. They can directly affect plant growth. The chemical composition of plants reflects the elemental composition of the sediment and the contamination of the plant surface indicates the presence of noxious environmental contaminants. Metal content will vary depending on which part of the plant is sampled. The extent of accumulation in different plant parts will vary with species and the nature of the element. Chemical composition varies not only with the age of the plant itself but with the age of the leaf/needle. For example, Thomas et al. (1991) studied the levels of Fe, Mn, Ni, Zn, Cr and Cu in roots, stems and leaves of aquatic plants collected at river Pimos in Central Greece in order to obtain information about the heavy metal contamination of the river. The selected species proved to be good indicators for the monitoring of heavy metals. Ahmet et al. (2005) investigated the heavy metal status of plants in Sultan Marsh, Turkey. Tissues of Phragmites australis accumulated heavy metals more than those of Ranunculus sphaerosphermus. They reported that heavy metal accumulation in different parts of plants followed the sequence: roots> stem > leaf. Both plants were found to be 77 UNIVERSITY OF IBADAN LIBRARY useful as biological indicators while determining environmental pressures; however, Phragmites australis proved more appropriate for such studies. For example, Agneta et al. (2002) discovered high level of Pb, Cd and Hg in spinach which served as recipients for domestic and other types of wastewater in Thailand. Reports on high accumulation of heavy metals in plants in literature abounds (Sawidis, 1995; Mustafa, 2003; Syed et al., 2008). Jordao et al. (1997) evaluated the chromium contaminations from tannery discharges into rivers in the states of Minas Gerails, Brazil using samples of vegetation. Chromium concentration in analysed samples was higher than those normally found. Steit and Strumm (1993) classified the exchange of chemicals between soil and plants. They divided the most common methods of assessing metal toxicity to plants from soil into three categories in a conditions closed system: 1. Monitoring of the presence or absence of specific plant ecotypes and/or plant species (indicator plant). 2. Measurements of metal concentration in tissues of selected species (accumulative bioindicators). 3. Recording of physiological and biochemical responses (biomarkers) in sensitive bioindicators. Content of essential elements in plants is conditional, the content being affected by geological characteristics of a soil, and by the ability of plants to selectively accumulate some of these elements. Bioavailability of the elements depends on the form of their bond with the constituents of a soil. Plants readily assimilate through the roots such compounds which dissolve in waters and occur in ionic forms. Additional sources of these elements for plants are rainfall; atmospheric dusts; plant protection agents; and fertilizers, which could be absorbed through the leaf blades (Lozak et al., 2001). The advantages of using plants, as indicators are summarized below: 1. Vegetation samples are more practicable to collect than sediment because they weigh less. 2. In thick (dense) vegetation, plant sampling is quicker. 78 UNIVERSITY OF IBADAN LIBRARY 3. Large plants exploit the equivalent of many kilograms of sediment and hence can be more representative. 4. Deep-rooted plants can reveal mineralized areas not accessible by surface sampling of sediment. 5. Chemical analysis of plant tissues is less complicated and quicker than sediment analysis. 6. Where the plant accumulates an element, this can produce a more sensitive method of detection than sediment sampling. The use of plants had been quite extensive and they are generally accepted as good and effective indicators of heavy metal pollution. 79 UNIVERSITY OF IBADAN LIBRARY CHAPTER THREE 3.0 METHODOLOGY 3.1 DESIGN OF THE STUDY In meeting the objectives, the following studies were carried out: (a) Study of quality characteristics of the surface waters:  Total study period: Surface water was studied for a period of twenty-four months from July, 2006 to May, 2008.  Frequency: Surface water samples were collected bimonthly for twenty-four months.  Total number of sampling points: Samples were collected from all the tributaries with a total of 1,080 sampling points. Surface water was studied for the following parameters: (i) General physicochemical parameters (pH, electrical conductivity, total solid, total dissolved solid, total suspended solid, turbidity, alkalinity, hardness, calcium and magnesium). (ii) Trace metals (Pb, Cu, Co, Cr, Cd, Ni and Zn). (iii) Gross organics (dissolved oxygen, biochemical oxygen demand, and chemical oxygen demand). (iv) Anions (phosphate, sulphate, nitrate, chloride, ammonia). (b) Study of the quality characteristics of the sediment  Total study period: River sediment was also studied for the same period as the surface water.  Frequency: Sediments were also collected bimonthly as for surface water.  Total number of sampling points: River sediments were collected at selected locations of surface water with a total of 756 sampling points. Sediments were studied for the following parameters: (i) Total organic carbon. (ii) Sediment mechanical properties (% sand, % clay and % silt). (iii) Cation exchange capacity. 80 UNIVERSITY OF IBADAN LIBRARY (iv) Trace metals (Pb, Cu, Cd, Co, Cr, Ni and Zn). (c) Study of the quality characteristics of plants along river banks  Total study period: plant samples were collected along the river banks.  Frequency: samples were collected bimonthly for the period of study.  Total number of sampling points: samples were collected at the same locations where sediments were sampled with a total of 756 sampling points. (d) Modeling of the quality characteristics of surface water and sediment (i) Pratti Scale Quality Classification: data obtained from all the ninety locations were fitted into Pratti scale for surface water classification (Class I - V). (ii) Data obtained for surface water and sediment were fitted into Time Series Modelling for trend analysis and seasonality prediction of some selected tributaries for future concentration of parameters. 3.2 DESCRIPTION OF STUDY AREA/SAMPLING POINTS Section 1.6 describes the activities along the river channel. Figure 1 describes the types of industries located along the river banks. The wastes generated from these various activities impair the water quality of the study area. 3.2.1 Map of River Osun showing sampling locations The sampling points used for the study were selected based on the activities occurring along the river channel as shown in Figure 1. The sampling points are as shown in Figure 3.1. Thirty-one tributaries of river Osun were used. The length of Osun River is 267 km. 3.2.2 Description of Features of Sampling Points The special features observed at the locations are as described in Table 3.1. Pictures of selected tributaries are as shown in appendices 50 to 53. The table illustrates some of the activities seen at each location. Some of these can increase the concentrations of these pollutants in the river. 81 UNIVERSITY OF IBADAN LIBRARY Figure 3.1: Location of sampling points on Osun River and tributaries 82 UNIVERSITY OF IBADAN LIBRARY Table 3.1: Features of the sampled rivers Code Local Name Near-by Features Settlement ADE 1 Adeti Ilesha Dirty surroundings, near foam and brewing industry ADE 2 Adeti Ilesha Near slaughter house ADE 3 Adeti Ilesha Dirty surroundings AHO 1 Ahoyaya Ikirun Turbulent flow; bathing; near palm oil mill AHO 2 Ahoyaya Ikirun Turbulent flow; bathing AHO 3 Ahoyaya Ikirun Shaded with trees ANN 1 Anne Okuku High flow rate; boating; suspended particles ANN 2 Anne Okuku Boating ANN 3 Anne Okuku Farming ANN 4 Anne Okuku High flow rate ANN 5 Anne Okuku Boating ANN 6 Anne Okuku Boating ARE 1 Arenounyun Otan Ayegbaju Farming along river bank ARE 2 Arenounyun Otan Ayegbaju Near palm oil mill ARE 3 Arenounyun Otan Ayegbaju Washing of clothes and materials used for palm oil production ARO 1 Aro Iragberi Farming; washing of clothes; cassava processing ARO 2 Aro Iragberi Cassava processing; near poultry farm 83 UNIVERSITY OF IBADAN LIBRARY Table 3.1 contd. Code Local Name Near-by Features Settlement ASJ 1 Asejire Ikire Fishing; beverage industry; near dam; boating ASJ 2 Asejire Ikire Washing of clothes; under a bridge ASJ 3 Asejire Ikire Boating ASS 1 Ashahsha Imesile Fast flow rate; shaded with tress ASS 2 Ashahsha Imesile Near palm oil mill AWE 1 Awesin Ifon Washing of clothes and vehicles; near palm oil mill AWE 2 Awesin Ifon Washing of motorcycles AWE 3 Awesin Ifon Beside a bridge EGU 1 Egun Iperindo Fast flowing; Trees along river bank EGU 2 Egun Iperindo Fetching of water for drinking EJI 1 Aro Ejigbo Washing of motorcycles; near soap industry EJI 2 Aro Ejigbo Near soap industry ENJ 1 Enja Igbajo Near palm oil mill; Farming ENJ 2 Enja Igbajo Deposit of palm oil waste ENJ 3 Enja Igbajo Washing of materials used for extracting palm oil ETI 1 Etioni Ifetedo Turbulent flow; bathing ETI 2 Etioni Ifetedo Turbulent flow; washing of clothes 84 UNIVERSITY OF IBADAN LIBRARY Table 3.1 contd. Code Local Name Near-by Features Settlement ETI 3 Etioni Ifetedo Turbulent flow; under a bridge GBD 1 Gbodofon Osogbo Turbulent flow; steel rolling mill; boating GBD 2 Gbodofon Osogbo Near a waste dumpsite GBD 3 Gbodofon Osogbo Under a bridge; boating GBD 4 Gbodofon Osogbo Turbulent flow IRE 1 Isin Iree Fast flowing; near palm oil mill, washing of vehicles IRE 2 Isin Iree Washing of vehicles IRE 3 Isin Iree Shaded with trees ISA 1 Ishasha Odeomu Fast flow rate; shaded with trees ISA 2 Ishasha Odeomu Fast flow rate; ISA 3 Ishasha Odeomu Near a bridge ISA 4 Ishasha Odeomu boating; turbulent flow KAN 1 Kankere Awo Farming; washing of clothes KAN 2 Kankere Awo Near poultry MOG 1 Moginmogin Apomu Dirty with bad odour; near soap industry MOG 2 Moginmogin Apomu Near a market MOG 3 Moginmogin Apomu Bathing 85 UNIVERSITY OF IBADAN LIBRARY Table 3.1 contd. Code Local Name Near-by Features Settlement OBB 1 Oba Iwo Fast flow rate; textile industry, OBB 2 Oba Iwo Dirty environment OBB 3 Oba Iwo Under a bridge; flow rate low here atimes OBB 4 Oba Iwo Fast flow rate; shaded with trees ODD 1 Odoiya Bode Osi Slow flow rate; washing of clothes; ODD 2 Odoiya Bode Osi Washing of clothes OJU 1 Ojutu Ilobu Fast flow rate; washing of clothes OJU 2 Ojutu Ilobu Fast flow rate; washing of clothes and vehicles OJU 3 Ojutu Ilobu Washing of various materials by spritualists OLO 1 Oloyo Ibokun Near palm oil mill OLO 2 Oloyo Ibokun Near a small bridge OLO 3 Oloyo Ibokun Washing of motorcycles OLU 1 Olumirin Erin Ijesha Tourist centre; Turbulent flow; Water coming out from the rock OLU 2 Olumirin Erin Ijesha Location of fast food joint around here for tourists OLU 3 Olumirin Erin Ijesha Near car pack for visitors; various anthropogenic activities ONN 1 Oni Ijebu Ijesha Washing of motorcycles and clothes; bathing ONN 2 Oni Ijebu Ijesha Beside a bridge 86 UNIVERSITY OF IBADAN LIBRARY Table 3.1 contd. Code Local Name Near-by Features Settlement ONN 3 Oni Ijebu Ijesha Washing of vehicles ONN 4 Oni Ijebu Ijesha Washing of clothes OPE 1 Ope Araromi Owu Foamy water, cassava processing OPE 2 Ope Araromi Owu Under the bridge; cassava processing OPE 3 Ope Araromi Owu Washing of motorcycles OPP 1 Opa Ile-Ife Fast flow rate; farming OPP 2 Opa Ile-Ife Shaded with trees; near soap industry ORU 1 Orufu Ilawo Washing of motor vehicles; soap industry ORU 2 Orufu Ilawo Slow flow rate; near palm oil mill; secretion of white liquid substance OSI 1 Osin Ila Orangun Farming; near palm oil mill OSI 2 Osin Ila Orangun Beside a bridge OSU 1 Osun Ede Turbulent flow; boating OSU 2 Osun Ede Automobile workshop OSU 3 Osun Ede Washing of clothes; vehicles and motorcycles OUN 1 Ounseku Oyan Foam seen in some part of the river OUN 2 Ounseku Oyan Slow flow rate OUN 3 Ounseku Oyan Soap industry 87 UNIVERSITY OF IBADAN LIBRARY Table 3.1 contd. Code Local Name Near-by Features Settlement OUN 4 Ounseku Oyan Bathing; Washing of Motorcycles OYI 1 Oyi Okeila High flow rate; trees along river bank OYI 2 Oyi Okeila Shaded with trees; log of wood inside river OYK 1 Oyika Ipetu Ijesha Washing of beans and motorcycles; bathing OYK 2 Oyika Ipetu Ijesha Beside a bridge; shaded with trees YEY 1 Yeyekare Esa Odo Turbulent flow; boating; washing of clothes YEY 2 Yeyekare Esa Odo Boating; bathing 88 UNIVERSITY OF IBADAN LIBRARY 3.3 SAMPLING 3.3.1 Sampling of Surface Water (a) Sampling for physicochemical parameters Methodology: Each sample was obtained as a composite. Subsamples at a given sampling location were obtained with the aid of a plastic cup at the middle and the bank of the stream/river. These were placed in a plastic bucket and mixed as the composite. Pre- cleaned plastic bottles were used to store the samples. However, preservation was by cooling in an ice chest. Samples were collected bimonthly, for twenty-four months. (b) Sampling for trace metals (Pb, Cu, Co, Cr, Cd, Ni and Zn) analysis. Methodology: Samples were also obtained as composites, and stored in plastic containers. However, preservation was carried out by acidifying each sample with 50% HNO3 (3.5 ml to 1L of surface water sample). Samples were also collected bimonthly, for twenty- four months. (c) Sampling for the determination of gross organics (DO, BOD, COD) Methodology: Samples were collected by gently dipping the bottle into the river without allowing air bubbles, and covering the bottle immediately after collection. White BOD glass bottles were used for sample collection, and samples were preserved by cooling to o 4 C. Samples were collected bimonthly, for twenty-four months. (d) Sampling for labile parameters Some parameters require in-situ analysis. These are pH, electrical conductivity, temperature, total dissolved solids and dissolved oxygen. Collection and sample analysis for these are described separately under the respective sections on parameter determination. 3.3.2 Sampling of River Sediment Methodology: Each river sediment sample was collected at several points around each sampling location, and then mixed together to form a composite sample. They were collected with a plastic hand trowel to avoid contamination, and then stored in polythene bags. Samples were preserved by storing in ice chest on the field. Air-dried, 89 UNIVERSITY OF IBADAN LIBRARY ground, sieved and then stored in the laboratory. Samples were collected bimonthly for twenty-four months. 3.3.3 Sampling of Plants Methodology: At each location, samples of leaves and stems of particular plants found along the river bank were collected and mixed together to form composite sample and then stored in polythene bags. They were preserved by placing the polythene bags in an ice chest on the field and then air-dried, ground, sieved and stored in the laboratory for further analysis. Samples were collected bimonthly for twenty-four months. The various types of plants species used for the study are as shown in Table 3.2. 3.3.4 Summary of Sample Distribution by Location The numbers of sampling points used for the study were chosen based on the length of each tributary as measured on the map (Fig. 3.1) and the accessibility of the river during sampling. Sediment and vegetation samples were not collected in areas where rocks were found during sampling. Table 3.3 summarizes the sampling distribution by location. 3.4 ANALYSIS OF SURFACE WATERS Physicochemical parameters in surface water samples were determined using standard methods (USEPA, 1979; APHA-AWWA-WPCF, 1998; Department of the Environment, 1972). Other methods used are as indicated under appropriate parameters. 3.4.1 Determination of pH pH was determined by directly dipping the battery operated pH meter (HANNAH HI96107) in a plastic container containing the surface water sample. Calibration was carried out using buffer solutions of pH 4.0 and pH 7.0. 3.4.2 Determination of Electrical Conductivity Electrical conductivity was determined by dipping the portable EC meter (COM-100 model) into plastic container containing 200ml of the surface water. Calibration was carried out using 0.001M KCl. 90 UNIVERSITY OF IBADAN LIBRARY Table 3.2: List of plants sampled for heavy metals along the banks of River Osun S/N Species* Family Locations 1 Paspalum auriculatum presl Cuperaceae Asejire 2 Syndrella nodiflora Asteraceae Osun 3 Cyprus iria Linn Cyperaceae Gbodofon 4 Pilea ceratomera Urticaceae Ahoyaya 5 Brachiara lata Poaceae Isin, Oni 6 Rhaphio stylis beninensis Icacinaceae Oyi 7 Aystacia gigantic Acantheceae Osin, Arenounyun, Esaodo, Moginmogin, Adeti 8 Culasia sexatillis Araceae Oloyo 9 Chromolena odorata Asteraceae Enja, Kankere, Aro, Adeti 10 Bambusa vulgaris Poaceae Ashasha 11 Calapogonium mucuniodis Papilonaceae Ounseku 12 Nelsonia canescens Acantheceae Aro 13 Cylcasia saxatilis Araceae Oba 14 Brachiaria villosa (lam) Poaceae Ope 15 Urena lobata Malvaceae Awesin 16 Axonopus compressus Poaceae Ojutu 17 Nymphaea lotus Nymphaeaceae Anne 18 Furirena umbrella potts Cyperaceae Odoiya 19 Commelina zambesia Commelinaceae Ishasha 20 Commelina nigritana Commelinaceae Etioni, Opa 21 Centhotheca cappace deso Poaceae Olumirin * All identifications were made by Forestry Research Institute of Nigeria, Ibadan 91 UNIVERSITY OF IBADAN LIBRARY Table 3.3: Summary of sample distribution by location Code River No of Total no. of Total no. Total no. of Length of tributary Sub-location Surface water of sediment of vegetation (km) ADE Adeti 3 36 24 24 11 AHO Ahoyaya 3 36 24 24 11 ANN Anne 6 72 36 36 18 ARE Arenounyun 3 36 24 24 13 ARO Aro 2 24 24 24 5 ASJ Asejire 3 36 24 24 25 ASS Ashasha 2 24 24 24 8 AWE Awesin 3 36 24 24 10 EJI Aro 2 24 24 24 6 ENJ Enja 3 36 24 24 10 ETI Etioni 3 36 24 24 15 GBD Gbodofon 4 48 24 24 12 IRE Isin 3 36 24 24 11 ISA Ishasha 4 48 24 24 30 KAN Kankere 2 24 24 24 12 MOG Moginmogin 3 36 24 24 12 OBB Oba 4 48 24 24 16 ODD Odoiya 2 24 24 24 5 OJU Ojutu 3 36 24 24 18 OLO Oloyo 3 36 24 24 11 OLU Olumirin 3 36 24 24 12 ONN Oni 4 48 24 24 11 OPE Ope 3 36 24 24 22 OPP Opa 2 24 24 24 20 ORU Orufu 2 24 24 24 5 OSI Osin 2 24 24 24 11 OSU Osun 3 36 24 24 18 OUN Ounseku 4 48 24 24 11 OYI Oyi 2 24 24 24 13 OYK Oyika 2 24 24 24 10 YEY Yeyekare 2 24 24 24 15 Total 90 1080 756 756 92 UNIVERSITY OF IBADAN LIBRARY 3.4.3 Determination of Temperature The temperature meter (COM-100 model) was dipped directly into the river and temperature was measured and recorded immediately. The meter was calibrated according to manufacturer‟s instruction. 3.4.4 Determination of Total Solids Water samples were analysed gravimetrically. A labeled evaporating dish was ignited, cooled, placed in a desiccator to cool, and weighed to constant weight. 100mL of the water sample was measured into the dish. It was evaporated to dryness on a steam bath, and dried at 105 to constant weight. Calculation: Where, X1 = weight (g) of empty dish X2 = weight (g) of dish + sample Y = volume (mL) of sample taken. 3.4.5 Determination of Total Dissolved Solids A portable TDS meter was dipped into a plastic container containing the river water, and the TDS value read immediately. Replicate determinations were made to estimate precision. The TDS meter (COM-100 model) was calibrated with 0.02M KCl and 0.20M KCl. 3.4.6 Determination of Total Suspended Solids Total suspended solids (TSS) was calculated as the difference between total solids and total dissolved solids. Total Suspended Solids ( ) = . 93 UNIVERSITY OF IBADAN LIBRARY 3.4.7 Determination of Turbidity Turbidity determination was carried out by visible molecular absorption spectrophotometry. Preparation of Stock Turbidity Suspension: 10.000g of hexamethylene tetraamine (CH2)6N4 was dissolved with distilled water in a 100mL volumetric flask, and diluted to mark. This was labelled as Solution 1. Hydrazine sulphate, (N2H2) H2SO4 (1.00g) was dissolved in distilled water and diluted to the mark in 100mL volumetric flask. It was labelled as Solution 2. 5.0mL of Solution 1 and 5.0mL of Solution 2 were added into a 100mL volumetric flask and mixed. The mixture was allowed to stand for 24 hours at 25±3 , and then diluted to the mark with distilled water and mixed. The turbidity of this prepared solution was 400 FTU. Procedure: Water sample for analysis was allowed to cool to room temperature. The turbidity of the water sample was measured immediately after vigorous shaking. The blank solution was also prepared without the addition of the sample. Calibration and sample reading: Working standard (0, 2, 10, 40, 80, 100 and 150 FTU respectively) solutions were prepared from 400 FTU stock solutions. These were used to 2 prepare calibration graph for turbidity (R = 0.953) and treated as for sample. The absorbance of the water sample and blank were measured at 580nm with a visible double beam spectrophotometer (Lambda 3B). The turbidity reading of the blank was subtracted from sample reading and result read from graph and recorded in FTU. 3.4.8 Determination of Alkalinity 50 mL of water sample was pipetted and two drops of methyl orange indicator was added. This was then titrated with 0.1M HCl to an orange end point. 94 UNIVERSITY OF IBADAN LIBRARY 3.4.9 Determination of Total Hardness 50mL of water sample was measured into a conical flask and placed on a tile. Water sample containing suspended materials was filtered before measuring. 1mL of buffer solution (ethanolamine) was then added. 1mL of 0.05% sodium sulphide was added as a masking agent. Two drops of 0.005% eriochrome black T was added as indicator. The colour of the resulting solution turned wine-red showing the presence of Ca and Mg ions. This was immediately titrated with standard 0.01M EDTA solution with continuous stirring to a light blue colour at the end point. 3.4. 10 Determination of Calcium 50mL of water sample was placed in a conical flask. 2.0mL of 0.05M HCl was added to the water sample, and it was then boiled for three minutes. This was cooled to room temperature. 1mL sodium sulphide inhibitor was then added for masking. 2.0mL 0.05M NaOH was added to this and mixed. The solution was placed on a white tile and 3.5mL of glyoxalbix-2-hydroxoanil indicator was added, stirred for one minute, and titrated with 0.01M EDTA until the colour of the solution changed from orange-red to lemon-yellow. 3.4.11 Determination of Magnesium The magnesium titre of the water sample was determined by subtracting titre value obtained for calcium determination from the titre value for hardness determination. 95 UNIVERSITY OF IBADAN LIBRARY Determination of Nitrate Nitrate content of water sample was determined using phenoldisulfonic acid method (Gary, 1994). Preparation of reagents: Phenoldisulfonic Acid: 25g phenol was dissolved in 150mL concentrated H2SO4. 75mL of fuming H2SO4 was added to this solution and stirred. The resulting solution was heated for 2 hours on a hot water bath. The solution was allowed to cool before use. Silver Sulphate Solution: 4.4g AgSO4 was dissolved in 1L distilled water Stock Nitrate Solution: 100mg/L of nitrogen was prepared by dissolving 0.722g anhydrous KNO3 in 1L distilled water. Procedure: 100mL sample was treated with 100mL Ag2SO4 solution in order to remove interference by chloride. This sample was neutralized to pH 7 with dilute NaOH and filtered. The water sample was transferred to a beaker and evaporated to dryness. The residue was mixed with 2.0mL phenoldisulfonic acid reagent using a glass rod to help dissolve the solids. The resulting mixture was diluted with 20mL distilled water and 6mL concentrated ammonia of Analar grade was added until maximum colour of deep yellow was developed. The clear solution was transferred into a 50mL volumetric flask and diluted to volume with distilled water. Blank was prepared by measuring 100mL of distilled and carrying out all the procedures for sample analyses. - Calibration and Sample Reading: Working standard (0, 0.05, 1, 2, 4, 6, 10 mg/L NO3 ) solutions were prepared by dilution of the stock and treated with appropriate reagents 2 as for sample and used to prepare calibration graph for nitrate (R = 0.987). Sample solution prepared above was measured at 410 nm wavelength and blank was read at the same wavelength with a double beam visible spectrophotometer (Lambda 3B). Blank - reading was subtracted from sample reading and result read from the graph. NO3 in the sample was recorded as mg/L. 3.4.13 Determination of Chloride Chloride in water sample was determined by mercurimetric titration. 96 UNIVERSITY OF IBADAN LIBRARY Preparation of reagents: 0.02M Mercuric Nitrate Solution: 5.04g of mercuric nitrate, Hg (NO3).H2O was dissolved in 50 mL distilled water containing 0.5mL nitric acid. It was diluted to 1L and filtered. This solution was standardized against standard sodium chloride. 0.02M Standard Solution of Sodium Chloride: 1.648g dried sodium chloride was dissolved in distilled water and diluted to 1L. Indicator: 0.5g of 5-diphenylcarbazone and 0.05g bromophenol blue was dissolved in 100mL alcohol. 0.05M HNO3: 3.2mL nitric acid was diluted to 1 L. Procedure: 50mL of water sample was measured into a 250mL conical flask and 5 drops of mixture of 5-diphenylcarbazone and bromophenol blue was added as indicator. This changed the colour of the solution to purple. 1mL 0.05M HNO3 was added drop wise until the colour changed to yellow. The solution was then titrated with 0.02M standard mercuric nitrate solution. The blank correction was determined by titrating 50mL distilled water by the same procedure. A = volume of mercuric nitrate used by sample (mL). B = volume of mercuric nitrate used by blank (mL). M = molarity of standardized mercuric nitrate (mL). 3.4.14 Determination of ammonia Ammonia in water sample was determined by direct nesslerization colorimetrically. Preparation of reagents: Nessler Reagent: 35g of potassium iodide and 12.5g mercuric chloride were dissolved in 700mL distilled water. A saturated solution of mercuric chloride was added and stirred 97 UNIVERSITY OF IBADAN LIBRARY to obtain a permanent red precipitate. This was then mixed with a solution of 120g sodium hydroxide in 150mL water. After cooling, it was transferred to a 1L volumetric flask and another 1 mL saturated mercuric chloride solution was added with shaking. The reagent was allowed to settle and the supernatant was stored in a bottle in the dark. A portion of the clear supernatant was transferred periodically into a small bottle when required. 0.5% EDTA Reagent: 50g disodium tetraacetate dehydrate was dissolved in 60mL water containing 10g NaOH. 1000ppm Ammonium Chloride Stock Solution: 3.819g pure ammonium chloride, dried at 105 was dissolved in ammonia free water and made up to 1L with ammonia free water. Procedure: 50.0mL of water sample was measured into 100mL volumetric flask. One drop of 0.5% EDTA solution was added to inhibit precipitation of residual calcium and magnesium ions. 2.0mL Nessler reagent was added and the reaction was left to proceed for 10 minutes for colour to develop. The blank solution was also prepared without the addition of the sample. Calibration and Sample Reading: Working standard (0.5, 2.0, 5.0, 10, 20, 40 and 60 mg/L) solutions prepared from the stock solution were treated with appropriate reagents as for sample and absorbance read. Readings were used to prepare the calibration graph 2 for ammonia (R = 0.988). The absorbance of the sample solution and blank was read at 410nm with a double beam visible spectrophotometer (Lambda 3B). Blank reading was subtracted from sample reading and result read from graph. Ammonia concentration in the sample was recorded as mg/L. 3.4.15 Determination of Sulphate Sulphate in water sample was determined turbidimetrically by absorption spectrophotometry. 98 UNIVERSITY OF IBADAN LIBRARY Preparation of reagents: Conditioning Reagent: 75g NaCl was dissolved in 300mL distilled water. 30 mL hydrochloric acid, 100mL 95% isopropyl alcohol and 50 mL glycerol were all added to this and mixed properly. Sulphate Standard Solutions: 1.486g anhydrous sodium sulphate was dissolved and diluted to 1L to give 1000ppm sulphate standard solution. Procedure: 50mL of water sample was measured into 250mL conical flask. 5.0mL of conditioning reagent was added and mixed with magnetic stirrer for one minute at a constant speed. The absorbance was measured after one minute. The blank solution was also prepared without the addition of the sample. Calibration and Sample Reading: Working standard (0, 5, 10, 30, 50, 100 and 250 mg/L) solutions were prepared from the sulphate stock solution and treated with appropriate reagents as for sample and absorbance read. These were used to prepare calibration 2 graph (R = 0.991). The sample and blank solutions were measured at 420nm with a double beam visible spectrophotometer (Lambda 3B). Blank reading was subtracted 2- from sample and the result was read from the calibration graph recorded as mg/L SO4 . 3.4.16 Determination of Phosphate Phosphate in water sample was determined by the ammonium molybdate spectrophotometric method. Preparation of reagents: Reducing Agent for Phosphate: 250mL 10M sulphuric acid and 75mL ammonium molybdate solutions were added to 150mL ascorbic acid solution prepared by adding 2.6g ascorbic acid in 150mL distilled water, and mixed. 25mL potassium antimonyl tartrate solution was added and the contents mixed again. 1000 ppm Phosphate Stock Solution and Working standards: 4.3937g potassium dihydrogen phosphate, KH2PO4 was dissolved in 1L of distilled water. One drop of toluene was added as preservative. 99 UNIVERSITY OF IBADAN LIBRARY 50 ppm Phosphate Solution: 5 mL of 1000ppm stock solution was added to 100ml flask to give a concentration of 50 ppm. Procedure: 20mL of the water sample was measured into a 50mL volumetric flask and diluted to 40mL mark with distilled water. 40mL of distilled water was placed in another flask as blank. To both flasks, 8mL of mixed reducing agent was added and diluted to 50mL with distilled water, and mixed. This was allowed to stand for 10 minutes. The absorbance of the sample and blank were measured at a wavelength of 880nm. Calibration and Sample Reading: Working standard (0, 0.05, 0.1, 0.5, 1.0 and 2.0 mg/L) solutions prepared from stock solution was treated with appropriate reagents as for 2 sample and absorbance was read. This was used to prepare calibration graph (R = 0.984). The amount of orthophosphate in the water sample and the blank were read at 880nm with a double beam visible spectrophotrometer (Lambda 3B). Blank reading was subtracted from sample reading and result read from the calibration graph. Sample 3- was recorded as mg/L PO4 . 3.4.17 Determination of Dissolved Oxygen Procedure: Dissolved oxygen (DO) was determined in-situ, using a battery operated HANNA H19142 dissolved oxygen meter. The probe of the meter was dipped directly and gently into the water body without disturbing the water body, and the dissolved oxygen content (mg/L) was read and recorded immediately on the field. Calibration of Dissolved Oxygen Meter: Zero calibration: A zero calibration was performed for maintenance purpose by dipping the probe in a HI 7040 zero oxygen solution prepared from sodium metabisulphite. The meter was adjusted to reduce to 0%. Air calibration: This was done by dipping the probe above in 100mL beaker containing distilled water and adjusting to 100%. The meter was always re-calibrated before use. 100 UNIVERSITY OF IBADAN LIBRARY 3.4.18 Determination of Biochemical Oxygen Demand Biochemical oxygen demand (BOD) was determined by measuring the dissolved oxygen content of the sample on a given day, then incubating the sample, and repeating the DO determination after incubation for five days. The difference is a measure of the BOD. Preparation of reagent: Ferric chloride: 0.125g of ferric chloride, FeCl3.6H2O was dissolved in 1L water. Calcium chloride solution: 27.5g of calcium chloride, CaCl2, was dissolved in 1L distilled water. Magnesium sulphate solution: 25g magnesium sulphate, MgSO4.7H2O was dissolved in 1L distilled water. Phosphate Buffer Stock Solution: 42.5g potassium dihydrogen phosphate, KH2PO4, was dissolved in 700mL distilled water and 8.8g NaOH was added to this and mixed. This gave a pH of 7.2. Then, 2.0g ammonium sulphate was added and diluted to 1L. Preparation of Dilution Water: 1mL of each reagent stock solution was added to 1000mL of freshly distilled water. The water was brought to incubation temperature at 20±1 . Aerated dilution water was then prepared by saturating this with oxygen, by bubbling air through it using a vacuum pump. Procedure: 200mL of saturated dilution water was added to 300mL BOD bottle. A measured volume of water sample was then added to this, and mixed with it. Initial dissolved oxygen content (DO1) was then measured, using a dissolved oxygen meter as described in section 3.4.17. The sample in the BOD bottle was then incubated for 5 days at a temperature of 20 and in the dark. After five days of incubation, the dissolved oxygen content (DO5) was again determined. 5 1 5 101 UNIVERSITY OF IBADAN LIBRARY 3.4.19 Determination of Chemical Oxygen Demand The chemically oxidisable organic matter content of a given sample is oxidized with known excess amount of acidified potassium dichromate. Unreacted dichromate is then determined by redox titration with ferrous sulphate. Preparation of reagents: Potassium dichromate, 0.0625M: 6.129g potassium dichromate was dissolved in 1L distilled water. Ferrous sulphate, 0.0625M: 34.75g ferrous sulphate, FeSO4.7H2O was dissolved in 100mL sulphuric acid, 25% by volume, and diluted to 1L. Indicator: Commercial ferrous phenanthroline complex was used as indicator. Procedure: 0.2g of mercuric sulphate was added to 5mL of sample in a round-bottom flask which was then shaken thoroughly. The flask was immersed in cold running water. 5mL of 0.0625M potassium dichromate solution, 10mL concentrated sulphuric acid and 1ml saturated silver sulphate solution was added, and the mixture mixed thoroughly and cooled in a container of cold water. The flask was fitted to a condenser, and few anti- bumping granules were added. One drop of ferrous 1:10 phenanthroline indicator was added and the residual dichromate was titrated with 0.0625M ferrous sulphate. Blank determination was also carried out, following procedure used for the sample. The concentration of COD in the water sample was calculated as follows. Where, A = volume (mL) of standard ferrous sulphate used for blank B = volume (mL) of standard sulphate used for sample M = molarity of standardized ferrous sulphate 102 UNIVERSITY OF IBADAN LIBRARY 3.4.20 Determination of Heavy Metals Heavy metals in water samples were determined by digesting the samples with nitric acid. The digests were then analysed by flame atomic absorption spectrophotometry. Procedure Digestion/Concentration process: 5mL of concentrated nitric acid was added to 250mL water sample in a beaker, and stirred. This was heated on a hot plate till the volume was reduced to about 20mL. This was then diluted to 50mL with distilled-deionised water and transferred to a labeled sample bottle. Preparation of 1000 ppm Stock Solution of Metals: Stock solutions were prepared by dissolving specific amount of salts of Pb, Cu, Cr, Co, Cd, Ni and Zn in 1L flask and made up to 1000mL. Instrumental analysis: Calibration and Sample Reading: Working standards (0, 2, 4, 6, 8 and 10 ppm) solutions were prepared from the stock solution and treated appropriately as for sample and absorbance read. The readings were used to prepare calibration graph. The sample and blank solutions were measured at wavelength for specific metal using atomic absorption spectrophotometer (AAS) (Buck Scientific, 200A). The absorbance of blank was subtracted from absorbance of sample. The result was read from the graph and concentration of heavy metal was reported in mg/L. Heavy metals were determined at the Federal University of Technology, Akure. Table 3.4 shows the instrumental characteristics of the element determined. 3.5 Analysis of Sediment for Quality Parameters 3.5.1 Determination of Effective Cation Exchange Capacity (CECe) Cation exchange capacity of sediment was determined by equilibrating air dried sediment sample with 1M ammonium chloride solution. The upper layer was decanted and extract used for direct measurement of CECe which involved measurement of Ca, Mg, and K by 103 UNIVERSITY OF IBADAN LIBRARY atomic absorption spectrophotometer (British Columbia, 2005). Table 3.4: Operating conditions for the atomic absorption spectrophotometer Metal Wavelength (nm) Slit width (nm) Pb 283.3 7 Cu 217.9 7 Co 240.7 2 Cd 228.8 2 Cr 357.9 7 Ni 232 7 Zn 213.9 7 Soure: Price (1983). 104 UNIVERSITY OF IBADAN LIBRARY Preparation of 1M NH4Cl: 53.6g of NH4Cl was dissolved in 1 L distilled water and made up to 1 L mark. Procedure: 2.00g of air-dried sieved sediment sample was transferred into a 30 mL centrifuge tube. 20mL of 1M NH4Cl was added, and the tube was then covered. This was centrifuged at 10,000 rpm and then left to settle. The upper layer was decanted carefully and the extract was used for direct analysis for CECe for Ca, Mg and K determination, using an atomic absorption spectrophotometer (Buck Scientific, 200 A). The concentration of Ca, Mg and K were determined from the calibration graph. 3.5.2 Determination of Heavy Metals in Sediment Procedure Digestion of Samples: 5.0 g of air-dried sediment sample was extracted with 50mL 2M HNO3 on a boiling water bath for two hours, shaking at intervals of 15 mins. The resulting extract was filtered and kept for analysis. Blanks were also prepared for every twenty samples determined (Anderson, 1976). Instrumental analysis: Calibrations on the AAS, and readings of samples and blanks were made as previously described (see section 3.4.20). Concentrations of the metals (3.4.20) in the sample extracts were then obtained by extrapolation. 3.5.3 Determination of Total Organic Carbon Total organic carbon in sediments was determined by the redox titrimetric method involving oxidation of sample with an excess of acidified potassium dichromate, and back- titration of unreacted oxidant (IITA, 1979; A.O.A.C., 2000). 105 UNIVERSITY OF IBADAN LIBRARY Preparation of reagents: Chromic acid, 0.2M solution: 58.8g oven dried potassium dichromate K2Cr2O7 was dissolved in 1L of 1:1 mixture of 96% H2SO4 and 85% phosphoric acid, H3PO4 and o was placed in a water bath at 95-100 C for 2 hours with occasional stirring. Ferrous ammonium sulphate, 0.1M: 39.2g of Fe(NH4SO4)2.6H2O was dissolved in 500mL distilled water containing 5mL 96% H2SO4. This was diluted to 1 L and stored in a stoppered bottle, away from light. Diphenylamine Indicator: 0.20g diphenylamine was dissolved in 100mL 96% H2SO4. This solution was stored in an amber-glass dropping bottle. Procedure: 0.3g of sieved sediment sample was weighed and transferred to a dry 25 x 200mm Pyrex test tube. 10mL of 0.2M Chromic acid was added to each test tube from the burette. It was heated for 3 minutes to 175 in an electric oven. After cooling, the content of the tube was rinsed into 250mL conical flasks with enough distilled water to make 100mL. 5 drops of diphenylamine was added to the flask and it was then titrated with 0.1M ferrous ammonium sulfate solution to sharp green end point. Where, B = volume (mL) of ferrous ammonium sulphate solution required for blank. U = volume (mL) of ferrous ammonium sulphate required for sample. D = volume (mL) of potassium dichromate used. M = Molarity of chromic acid. A = milliequivalent weight of C (0.003g). W = weight of sample used. 3.5.4 Sediment Mechanical Properties The percentages of sand, clay and silt in the sediments were determined by the hydrometer 106 UNIVERSITY OF IBADAN LIBRARY method (British Columbia, 2005). Preparation of Reagent: Calgon solution 5% w/v: 50g of sodium hexametaphosphate was dissolved in 1L distilled water. Procedure: 100g of air-dried sediment, which had been passed through a 2mm sieve, was weighed and transferred into a mill shake cup. 50mL of 50% sodium hexametaphosphate and 100mL of distilled water was added. It was mixed with a stirring rod and left to set for 30 minutes. The sediment suspension was stirred for 15 minutes. The suspension was later transferred to a glass cylinder. The top of the cylinder was covered with hand and inverted several times until sediment sample was in suspension. The cylinder was placed on a flat surface and the time was noted. The soil hydrometer was then placed into the suspension until floating. The first reading on the hydrometer was taken at 40seconds as H1, after the cylinder was set down. The hydrometer was removed and temperature of the suspension was recorded as T1 with a thermometer. After the first hydrometer reading, the suspension was allowed to stand for 3 hours and the second reading was taken as H2, and temperature was also taken as T2. The percentage of sand, clay and silt in the sediment was calculated as follows: % sand = 100-[H1 + 0.2 (T1- 68) – 2.0]2 % clay = [H2 + 0.2 (T2- 68) – 2.0]2 % silt = 100-(% sand + % clay) Where, H1 = Hydrometer reading at 40 seconds o T1 = Temperature ( C) at 40 seconds H2 = Hydrometer reading at 3 hours o T2 = Temperature ( C) reading at 3 hours 0.2(T-68) = Temperature correction to be added to hydrometer reading. -2.0 = Salt correction to be added to hydrometer reading. 3.6 ANALYSIS OF PLANT SAMPLES FOR HEAVY METALS 107 UNIVERSITY OF IBADAN LIBRARY The metals were determined by AAS. Procedure Solubilisation of sample by dry ashing: 5.0g of dried ground plant sample was placed in a porcelain crucible and ashed in a furnace at 500 2.0g of ashed sample was completely dissolved by treatment with 50mL of 2M concentrated nitric acid, and this was evaporated to near dryness on a hot plate. Distilled water was added, and the solution was filtered into a labeled sample bottle. A blank determination was made for every twenty samples analysed. Instrumental analysis: Calibrations on the AAS, and readings of samples and blanks were made as previously described (see section 3.4.20). Concentrations of the metals (3.4.20) in the sample extracts were then obtained by extrapolation. 3.7 QUALITY ASSURANCE OF THE ANALYTICAL SYSTEM The quality assurance carried out for the study involved:  Recovery study of the analytical process for some parameters  Analysis of standard reference material (sediment)  Validation of results for BOD determination  General quality control 3.7.1 Recovery Study of the Analytical Process for Metal Analysis of Sediment and Plant Samples Sample preparation steps, particularly with the involvement of heating, often lead to some kind of losses of the analyte. It may be due to volatilization, absorption, precipitation, etc. It is imperative to estimate the effectiveness of the digestion process so as to correctly determine the average accuracy of the final results after the instrumental analysis. Recovery is done to determine if there are any losses of analyte during analysis. 108 UNIVERSITY OF IBADAN LIBRARY This was done for the metals: lead, copper, chromium and nickel in five sediment and plant samples. The concentrations of the metals were first determined using atomic absorption spectrophotometer. 1000ppm of each metal was prepared and working standards for each metal were prepared depending on the initial concentration of the metal in each sediment and plant samples. 1.00g of selected sediment and plant samples already 3 dried were spiked with 1.00cm of 50µg/mL of heavy metals, and were taken through the process described for sediment and plant analysis using AAS. The percentage recovery was calculated as follows: The results are as given in Tables 3.5 - 3.6. The calculated average recoveries in sediment samples were: Pb 96.0+4.6%, Cu 91.8+3.3%, Cr 93.8+7.2% and Ni 97.3+4.3% and for plant samples: Pb 98.3+2.9%, Cu 94.9+5.3%, Cr 94.9.8+9.0% and Ni 91.7+11.1. These results represent very good recovery values, and indicate that the sample analysis results are reliable. 3.7.2 Analysis of standard reference material The quality of the interpretation of results is no better than the quality of the chemical analysis. For this reason, it is imperative to have good set of sediment standard reference materials (SRM) for use in the laboratory quality assurance program. The reference material used for this purpose was the CANMET-SO2 (soil reference of the Canada Centre for Mineral and Energy Technology). The reference sample was dried in an oven at o 105 C for 16 hours. 1.0g of the standard reference material was digested with 25mL aqua- regia and heated until the volume was reduced to less than 25mL. Digest was quantitatively transferred (with filtration) into a standard flask, and made up to 25mL mark and then determined by atomic absorption spectrophotometry (as in section 3.4.20). The results are given in Table 3.7. 109 UNIVERSITY OF IBADAN LIBRARY Statistical analysis t-test shows that there is no significant difference between the values obtained and the standard reference values. This implies that the results obtained are accurate. 110 UNIVERSITY OF IBADAN LIBRARY Table 3.5: Recovery (%) of Pb in sediment Sample Original Mass of Concentration Volume of Increased Concentration Result of Percentage No. Sample Sample of Standard Standard Concentration Expected by Re- Recoveries Conc. Spiked (g) Spiked Spiked Achieved Re-analysis analysis (%) (µg/g) (µg/ml) (ml) (µg/g) (µg/g) (µg/g) 1 12.1 1.0 50.0 1.0 24.8 36.9 33.9 88.0 2 0.41 1.0 50.0 1.0 0.90 1.30 1.28 97.9 3 0.10 1.0 50.0 1.0 0.20 0.31 0.31 100.0 4 0.19 1.0 50.0 1.0 0.40 0.58 0.57 96.8 5 0.19 1.0 50.0 1.0 0.38 0.58 0.57 97.4 Average 96.0±4.6 111 UNIVERSITY OF IBADAN LIBRARY Table 3.6: Average recoveries of heavy metals in sediment and plants Heavy Metals Recoveries (%) Pb (in sediment) 96.0±4.6 Cu (in sediment) 91.8±3.3 Cr (in sediment) 93.8±7.2 Ni (in sediment) 97.3±4.3 Pb (in plant) 98.3±2.9 Cu ( in plant) 94.9±5.3 Cr (in plant) 94.9±9.0 Ni (in plant) 91.7±11.1 112 UNIVERSITY OF IBADAN LIBRARY Table 3.7: Results of analysis of standard reference material Element Reference Values (µg/g) Results Obtained (µg/g) Pb 21 24 Cu 7 13 Co 9 11 Cr 16 21 Ni 8 11 Zn 124 112 113 UNIVERSITY OF IBADAN LIBRARY 3.7.3 Quality assurance for BOD determination The purpose of carrying out BOD5 check is to determine if the results obtained in the study are accurate. This was achieved by carrying out BOD determination on a standard solution of known BOD value. A standard solution of 1:1 glucose-glutamic acid (2%) is known to have a BOD value of 198mg/L. Glucose-glutamic acid solution was prepared by o drying reagent grade glutamic acid at 103 C for one hour. 150mg of glucose and 150mg glutamic acid were weighed and dissolved in 400ml distilled water in a beaker and diluted to mark in a 1 L flask. BOD5 of the prepared glucose-glutamic acid solution was determined as described in section 3.4.18. The determination was carried out five times and is as shown in Table 3.8. Statistical analysis t-test shows that there is no significant difference between the BOD values obtained and the reference values. This implies that the results obtained are accurate. 3.7.4 General quality control Quality of reagent: All reagents used for the analysis were of high purity. Reagents blanks were also prepared and utilized, where appropriate. Cleaning of apparatus: Sampling containers and general glassware were cleaned using appropriate methods. The plastic containers used for sampling metals and other parameters were cleaned by washing in non-ionic detergent, rinsed with tap water and pre-soaked in dilute HNO3 for 72 hours, and finally rinsed with distilled water. Sampling bottles for phosphate determination were washed with non-phosphate soaps. They were air-dried in a dust free environment, covered up and packed in cartons until time for sample collection. The container was then labeled on the field prior to sampling. A cleaned plastic bottle with a long rope tied to it was used for collecting water from the rivers. During sampling, sample bottles were rinsed with the water sample several times, before being filled. 3.8 STATISTICAL ANALYSIS OF DATA Statistical analysis of the data involved:  T-test was used to test for the difference between the means of parameters obtained during the wet and dry seasons. STATISTICA Release 7 software was used for this purpose. 114 UNIVERSITY OF IBADAN LIBRARY Table 3.8: Results of BOD check study S/No. BOD5 of Reference BOD5 Value Obtained (mg/L) (mg/L) 1 200 205 2 200 212 3 200 189 4 200 207 5 200 186 Mean±SD 200±12 115 UNIVERSITY OF IBADAN LIBRARY  One-Way analysis of variance was used to test for the differences between means of parameters obtained in all the tributaries. STATISTICAL Release 7 software was used for this purpose.  Multivariate analysis involving Pearson correlation and Principal Component Analysis (PCA). STATISTICA 7 software was used for this purpose.  Time series analysis was used to study trend and seasonality, using Number Cruncher Statistical System (NCSS 18335841) package. The method of expo- smoothing was used for trend and forecasting. 3.9 APPLICATION OF MODELING TO STUDY OF SURFACE WATER AND SEDIMENTS QUALITY The data obtained for surface water and sediment were subjected to time series analysis and forecasting (seasonality) using the Number Cruncher Statistical System package (Lynwood et al., 1990). This was used to determine the pattern of distribution of specific parameters and to project the concentrations up to year 2018. Time series used involved trend and seasonality analysis, with the use of exponential smoothing to observe the trend and seasonality. Smoothing in time series involves some form of local averaging of data such that the nonsystematic components of individual observations cancel each other out. This method filters out the noise and converts the data into a smooth curve that is relatively unbiased by outliers. Trend analysis Method used here was linear trend where least squares trend computes a straight-line trend equation through the data using standard least squares techniques in which the dependent variable is time series and the independent variable is the row (sequence number). The forecasting equation is This method is useful for those series that show a stable, long term trend. It places the largest weights in estimation on the two ends of the series, while the rows near the middle have an insignificant impact on the estimates. 116 UNIVERSITY OF IBADAN LIBRARY Seasonality Forecast method used was Winters with multiplicative seasonal adjustment. The season factor explains the month that the highest concentration of the parameter will be observed in that year. There are six season factors that explain the predicted values, each season correspond to months as follows: Season 1 (January and February), Season 2 (March and April), Season 3 (May and June), Season 4 (July and August), Season 5 (September and October) and Season 6 (November and December). 117 UNIVERSITY OF IBADAN LIBRARY CHAPTER FOUR 4.0 RESULTS AND DISCUSSION 4.1 AVERAGE CHARACTERISTICS (OVERALL) OF SURFACE WATER The physicochemical parameters determined included electrical conductivity, pH, total solids, total dissolved solids, total suspended solids, turbidity, alkalinity, hardness, temperature, nitrate, sulphate, phosphate, chloride, ammonia, dissolved oxygen, biochemical oxygen demand, chemical oxygen demand and heavy metals (Pb, Cu, Cd, Co, Cr, Ni, Zn, Ca and Mg). The overall average results obtained are as shown in Table 4.1. Average electrical conductivity was 216±380 µS/cm. The electrical conductivity was higher compared to the amount obtained at the control site as illustrated in Table 4.1. EC in the water body was most likely derived from limestone due to dissolution of carbonate minerals (Michaud, 1991). Also, build up of salts from domestic wastes can interfere with water re-use by municipalities, industries manufacturing food product, paper and agriculture for irrigation (Hammer, 1975). Waste water effluents often contain high amounts of dissolved salts from domestic sewage. Other sources of salts include windblown sea salt, municipal storm water drainage and industrial effluent discharges. Salts such as sodium chloride, and potassium sulphate pass through conventional water and wastewater-treatment plants unaffected. Urbanization, industrialization and agricultural activities along the river channel resulted in the electrical conductivity value of the river (Fried, 1991). However, it was found to be within the SON (2007) limit for drinking water quality guidelines (1000 µS/cm) and guides for evaluating the quality of water for aquatic life (3000 µS/cm). The overall pH obtained for the study is illustrated in Table 4.1. pH of the study area was 7.6±0.5. The pH was found to be comparable to what was obtained at the control site. pH is a general measure of the acidity or alkalinity of a water sample. This suggests that the overall pH of waters of Osun River tend to be alkaline and will be suitable for some species of fish and other aquatic organisms. High pH could alter the toxicity of other pollutants it can also alter recreational uses of water. In a river, the water‟s pH is affected by its age and the chemicals discharged by communities and industries. It was found to be within the range for all the drinking water quality. 118 UNIVERSITY OF IBADAN LIBRARY Table 4.1: Average concentrations of water quality characteristics of Osun River Parameter Concentration Control Area AF* Electrical Conductivity (µS/cm) 216±380 101±45 - pH 7.6±0.5 7.6±0.2 - o Temperature ( C) 25.3±2.6 27.5±2.0 - TS (mg/L) 546±570 235±75 2.32 TDS (mg/L) 111±200 62±24 1.79 TSS (mg/L) 435±500 187±69 2.33 Turbidity (FTU) 34±43 1.3±3.4 - Alkalinity (mg/L) 93±13 66±25 - Hardness (mg/L) 116±120 61±36 - Nitrate (mg/L) 1.80±1.50 0.70±0.30 2.57 Sulphate (mg/L) 39±30 35±82 1.11 Phosphate (mg/L) 0.20±0.20 0.05±0.03 4.00 Chloride (mg/L) 55±110 33±8 1.67 Ammonia (mg/L) 4.20±6.60 1.50±0.50 2.80 Dissolved Oxygen (mg/L) 7.90±3.00 7.00±1.00 - Biochemical Oxygen Demand (mg/L)6.90±7.50 4.00±2.00 - Chemical Oxygen Demand (mg/L) 135±120 101±32 - Ca (mg/L) 68±72 34±25 2.00 Mg (mg/L) 14±34 6.40±4.10 2.19 Pb (mg/L) 0.003±0.004 0.002±0.003 1.50 Cu (mg/L) 0.003±0.004 0.003±0.003 1.00 Cd (mg/L) 0.002±0.003 0.002±0.003 1.00 Co (mg/L) 0.004±0.004 0.004±0.004 1.00 Cr (mg/L) 0.01±0.01 0.003±0.004 3.33 Ni (mg/L) 0.004±0.010 0.004±0.010 1.00 Zn (mg/L) 0.10±0.10 0.10±0.10 1.00 * Accumulation Factor 119 UNIVERSITY OF IBADAN LIBRARY o Table 4.1 illustrates the temperature obtained (25.3±2.6 C). Temperature in the control site was found to be higher compared to what was obtained for the study. Temperature depends on the weather, sunlight and depth, and does not undergo changes during the year in the fluvaitile environment (Egborge, 1970; Gupta, 2006; Akinyemi and Nwankwo, 2006) as compared to lacustrine environment. Generally, most of the locations in the study area were shaded with trees. Temperature was found to comply with all the guidelines for all the industries and guides for evaluating the quality of water for aquatic life and recreational water quality. Total solids (TS) was 546±570 mg/L as shown in Table 4.1. Total solid was found to be higher in the study area compared to the control site. Osun River was found to be impaired with solids from various sources and it is as shown from the factor of accumulation obtained (2.32). TS was derived from the introduction of waste into Osun River. Dumping of solid wastes of various kinds along the river channel also leads to increased total solids content of the river. It was found to be within the limit of most of the industries but exceeds the threshold for light brewing industry (500 mg/L), pulp and paper industries (ground wood, 500mg/L) and tanning industries (100mg/L). Table 4.1 shows the amount of total dissolved solids obtained for the study (111±200 mg/L). The total dissolved solid (TDS) measures the amount of particles that are dissolved in the river water. It was found to be higher compared to the level derived from control site with an accumulation factor of 1.79. The value indicates that the quality of the river water was impaired as a result of dissolved particles in the river. This will hamper the usage of the water for some specific purpose. It was however noticed to be within the limits for all drinking water quality guidelines and within limits for some categories of industries except power generating industry for boiler feedwater (<0.5mg/L) and food and bevearage industry (Confectionary, 50 – 100mg/L). The total suspended solid for the study was 435±500 mg/L (Table 4.1). This signifies that the river contained some suspended particles in the river that cannot dissolve and thus impair the quality of the river and sometimes its aesthetic properties. Suspended solids are not desirable in water used for drinking and bathing. Solids are an important parameter to monitor in the control of biological and physical treatment processes and 120 UNIVERSITY OF IBADAN LIBRARY for assessing compliance with regulatory limitations. The value of total suspended solids obtained in this study has also been reported by a survey conducted by Ogunfowokan et al. (2005) where a high level of total suspended solids values -1 (200.00+3.11 - 500.00+7.10 mg L ) were reported for a University sewage treatment oxidation pond at Ile-Ife. This was as a result of wastewater water which entered the river which contains human excreta, urine and other semi solid wastes. High levels obtained can affect the health of the aquatic ecosystem and can also have a deleterious effect on the health of rural dwellers that use the water for domestic purposes without treatment. The overall turbidity for Osun River was 34±43 FTU. The flow rate of a water body is a primary factor influencing turbidity. Fast running water can carry more particles and larger-sized sediment. The turbidity of Osun River was found to be higher than what was obtained at the control site as shown in Table 4.1, signifying contamination of the river. Decaying plants and animals, bottom feeding fish, algae blooms, flooding and soil erosion can all contribute to turbidity. Slower moving streams usually contain finer particles. Turbidity is clearly related to total suspended solids. High TSS does not mean high turbidity and versa (Matthew and Michael, 2001). This anomaly may be explained by the fact that particles are effective at dispersing light and causing high turbidity readings, while not resulting in high TSS. On the other hand, large organic or inorganic particles can be less effective at dispersing light, yet their greater mass results in high TSS level. However, some authors such as Jason et al. (1997) reported high turbidity consistent with high levels of suspended solids. Turbidity of Osun River was found to be outside the range for most drinking water quality guidelines and some industries except for water quality tolerance for pulp and paper industry (Ground wood, 50 FTU and Kraft, <100 FTU). Alkalinity recorded in the river was 93±13 mg/L. This level is high compared to what was obtained at the control site as shown in Table 4.1. Measuring alkalinity is important in determining a stream‟s ability to neutralize acidic pollution. It refers to the ability of water to resist changes in pH. These buffering materials are primarily - bicarbonate (HCO3) and carbonate (CO3), and occasionally hydroxide (OH ), borates, silicates, phosphates, ammonium, sulfides, and organic ligands. Waters with low alkalinity are very susceptible to changes in pH. Waters with high alkalinity are able to 121 UNIVERSITY OF IBADAN LIBRARY resist major shifts in pH. Onianwa et al. (2001) reported that alkalinity in the waters of urban rivers and streams in Ibadan were derived primarily from bicarbonates as indicated by the pH values obtained. Alkalinity ranged from 59.0mg/L to 688 mg/L. If the alkalinity of water is too high, the water can be cloudy, which inhibits the growth of underwater plants. Too high alkalinity raises the pH level, which in turn harms or kills fish and other river organisms. A number of industries neutralize aqueous wastes to a pH of 7.0. These wastes are usually used by another industry or allowed to empty into streams, rivers and other waterways depending on the level of other pollutants. It was however noticed that alkalinity in Osun River does not exceed the range for certain industrial applications, but was found to be outside the range for power generation (boiler feedwater < 1.00mg/L). Hardness value was 116±120 mg/L. This value was higher compared to what was obtained at the control site. Hard waters usually have concentrations greater than 200mg/L while soft water are usually less than 75mg/L (Hunter, 1997). Thus, according to Hunter (1997), the water quality of river Osun was hard due to an abundance of minerals such as calcium, magnesium, carbonate and other ions. Hard water streams usually have a large amount of plant growth and life due to the amounts of nutrients that can be used for nourishment and growth. Soft water streams have very low mineral contents. Comparison with drinking water guidelines indicated that it falls within the limit except for the EEC guideline. It was found to exceed the limit for certain industrial applications such as textiles, general dyeing and boiler feed, pulp and paper, iron and steel, power generating stations ( boiler feed water, <0.07mg/L), food and beverage industry ( brewing, < 70 mg/L) and sugar manufacturing. Nitrate level was 1.8±1.5 mg/L. This concentration was higher compared to the level at the control site, with an accumulation factor of 2.57. This signifies some level of contamination of the Osun River. Nitrate is a major ingredient of farm fertilizer and is necessary for crop production. When it rains, nitrate may be washed from farmland into nearby waterways. Nitrates stimulate the growth of plankton and water weeds that provide food for fish and increase fish population. However, if algae grow too widely, oxygen levels will be reduced and fish will die. High nitrate levels in waste effluents could also contribute to eutrophication effects, particularly in freshwater (OECD, 122 UNIVERSITY OF IBADAN LIBRARY 1982; Fried, 1991; Morrison et al., 2001). The level of nitrate obtained compares well with low concentration reported by Kolo (1996) (0.12 – 2.24mg/L in Oguta Lake, and 0.06-14.8mg/L in Shiroro Lake). However, low nitrate level was attributed to sedimentation, which was responsible for the reduction of nitrate to nitrite. Nitrate in Osun River network was found to comply with all the drinking water guidelines and all the guidelines for different categories of industries. Sulphate level was 39±30 mg/L. This value was higher compared to what was obtained for the control site with an accumulation factor of 1.11. This result suggests that the water quality of Osun river system is affected and impaired by the discharge of domestic, agricultural and industrial wastes and other sulphur containing substances. The level however complied with all the guidelines on drinking water quality standards, all guidelines for various industries except sugar manufacturing where the value exceeds the recommended limit of <20 mg/L. The overall concentration of phosphate was 0.20±0.20 mg/L. This level was about four fold of the level obtained in control sample. The accumulation factor (4.00) suggests contamination of Osun River compared to the control site. The level of phosphate in the study area was derived from anthropogenic activities. Izonfuo and Bariweni (2001) also recorded low phosphate concentrations (0.19±0.07 mg/L – 0.33±0.17 mg/L) in Epie Creek in Niger Delta, which were lower than the limit for drinking water. These were higher than the range of 0.01 – 0.03mg/L for phosphorus normally found in uncontaminated streams (USDASCS, 1975). Olele and Ekelemu (2007) recorded a low concentration of phosphorous in Tigo Lake (0.03mg/L) and Victoria Lake (0.14mg/L). The low concentration of phosphorus was supported by the assertion that over 90% of the element was obtained in bottom sediment (Ovie et al., 2000). The level thus complies with the drinking water guidelines. The concentration of chloride was 55±110 mg/L. This level was higher compared to the level at the control site, with accumulation factor of 1.67. Chloride is formed naturally when hydrochloric acid reacts with any metal in water. The presence of chloride where it does not occur naturally indicates possible water pollution. The rock salt (NaCl) applied to roads, parking lots and sidewalks to lower ice melting point, often ends up in rivers and streams. Chloride in water might be derived from 123 UNIVERSITY OF IBADAN LIBRARY anthropogenic activities. Chloride was found to comply with all the drinking water guidelines but outside the range for diary and sugar manufacturing industries. The level of ammonia in river Osun was 4.20±6.60 mg/L. In nature, ammonia is formed by the action of bacteria on proteins and urea at high pH values (pH >8.5) and is extremely toxic to fish and other aquatic life at high concentrations (>2.0mg/L N). Realization of this undesirable consequence has led many local and national authorities to set up stringent guidelines for control of ammonia in surface water. The level of ammonia in this study was higher than the value obtained at the control site, with an accumulation factor of 2.80. This factor suggests a contamination of the surface water quality. High ammonia concentration have been reported in various studies; 249.1, 173.5 and 107.7mg/L for high, medium and low density areas of an open drain area in Ibadan (Sridhar et al., 1981); 8.2mg/L in Estuarino, Medras, India (Sridhar, 1982); 6.3mg/L in a Ogunpa Lake, Ibadan (Sridhar and Bammeke, 1985). The level was higher than level stipulated by S. O. N. (2007) for drinking water, and outside the range for power generating stations (boiler feedwater) and guides for evaluating the quality of water for aquatic life. The dissolved oxygen level of river Osun was 7.9±3.0 mg/L. Dissolved oxygen level was higher than what was obtained at the control site. Which suggests that the study area was not deprived of oxygen and could be attributed to low organic enrichment. Sources of dissolved oxygen in the aquatic environment include the atmosphere and photosynthesis, and depend on its solubility while losses of oxygen include respiration, decay by aerobic bacteria and decomposition of dead decaying sediments (Gupta and Gupta, 2006). The value of DO in this study is in accordance with some findings in the literature; Oke (1998) reported that Owena reservoir had a dissolved oxygen concentration that varied from 0 to 5mg/L. Ikenweiwe and Otubusin (2005) recorded a mean value of 6.40±0.3mg/L in Oyan Lake, Abeokuta. Idowu and Ugwumba (2005) recorded dissolved oxygen concentrations ranging from 6.3-8.3mg/L in Eleiyele reservoir, Ibadan. Dissolved oxygen complied with limits set for quality for aquatic life but outside the range for power generating stations. The biochemical oxygen demand (BOD) for river Osun was 6.9±7.5 mg/L. This was higher compared to BOD at the control site. This suggests contamination of the Osun 124 UNIVERSITY OF IBADAN LIBRARY River because of the presence of organic material and many bacteria. Bacteria and other microorganisms decompose this organic material. In a healthy body of water, this process has only a slight impact on dissolved oxygen levels. It serves to release vital nutrients, such as nitrates and phosphates, which stimulate algae and aquatic plant growth. If the amount of decomposing organic material is too high, dissolved oxygen levels can be severely reduced. When aquatic plants die, they are fed upon by aerobic bacteria. The input of nutrients into river Osun, such as nitrates and phosphates, stimulates plant growth. Ogunfowokan et al. (2005) in their studies reported a BOD of range 98.85 – 31.64mg/L for University oxidation pond at Ile-Ife, the result indicates some purification and effectiveness of the oxidation pond in the removal of biochemical oxygen demanding substance from the effluent before they enter the stream. Generally, the BOD levels recorded in their study were higher than the EU guidelines of 3.0 – 6.0 mg/L for the protection of fisheries and aquatic life (Chapman, 1996). Average BOD values have been reported from various studies 3.5mg/L (Onianwa et al., 2001), 2.25 – 4.28 (Izonfuo and Bariweni, 2001). BOD is not one of the parameter in some of the guidelines for some industries. Chemical oxygen demand (COD) for Osun River was 135±120 mg/L. This was higher compared to the COD at control site. The factor explains contamination of the river water as a result of discharge of organic matter from various sources. The development of unplanned houses to accommodate the rapidly growing population leads to the proliferation of refuse (waste) dumps, which invariably pose disposal problems. This is usually a common problem with many rapidly developing towns. The high population growth, poor development plan, chronic unhygienic habits and lack of enforcement of regulation have served collectively as recipe for environmental pollution. Cases where COD of river were due to industrial discharge of effluent into river have been reported. Bich and Anyata (1999) reported 5987mg/L of COD in a Kano river. Although these rivers are being used extensively for water supply, irrigation and fishing, the quality of the water was found to be unsuitable for these purposes. Morrison et al. (2001) reported a COD value of 32 – 63 mg/L in river Kieskamma in Eastern Cape of South Africa due to the point–source discharge. They suggested an urgent monitoring and attention by the water authorities of the area. COD in Osun River was found to be outside the range stipulated by guidelines for power generating stations. 125 UNIVERSITY OF IBADAN LIBRARY Low levels of heavy metals were found in surface water especially for Pb (0.003±0.004 mg/L), Cu (0.003±0.004 mg/L), Cd (0.002±0.003 mg/L), Co (0.004±0.004 mg/L), Cr (0.01±0.01 mg/L), and Ni (0.004±0.010 mg/L) except for Zn (0.10±0.10 mg/L). Levels of calcium (68±72mg/L) and magnesium (14±34 mg/L) were significant in the study compared to other metals. These are as shown in Table 4.1. The levels of these heavy metals were found to be higher in the study site than control site especially for Pb and Cr with accumulation factors of 1.50 and 3.33 respectively. The levels were however found to be within the guidelines for drinking water and all the industries. Paired t-test was used to check if there were significant differences between the means obtained for the study area and control area. The result revealed that the means were found not to be statistically significant except for pH, turbidity, hardness, temperature, nitrate and ammonia. 4.2 AVERAGE CHARACTERISTICS OF SURFACE WATER OF THE VARIOUS TRIBUTARIES The average characteristics of surface water for the thirty-one tributaries of Osun River studied are as shown in Table 4.2. The levels of each parameter were compared with the guidelines set by various categories of water usage as shown in Tables 2.2 to 2.13. Electrical conductivity at Adeti was found to be higher than EC stipulated by S. O. N. (2007) for drinking water guidelines. This was due to the various discharges of all forms of waste into river Adeti. The physical appearance of the river looks so dirty. pH of all the tributaries was found to be higher than the level stipulated for certain industrial applications for light brewing and laundering, steel manufacturing in iron and steel industry. The pH of these tributaries was alkaline. All tributaries had total solids higher than the limit stipulated for certain industrial applications for tanning. Thirteen of the tributaries had values higher than limits for certain industrial applications for light brewing and pulp and paper industry (ground wood). Adeti had values higher than what was stipulated for brewing but was found to be within the limit for general dyeing boiler feed. Seven of the tributaries had total dissolved solids higher than the level stipulated for confectionary for food and beverage industry. Only two tributaries had values higher than the limit set for pulp 126 UNIVERSITY OF IBADAN LIBRARY Table 4.2: Average characteristics of the tributaries of Osun River Parameter Oyi Osin Ounseku Arenounyun Ashasha Isin Anne Ahoyaya Enja (OYI) (OSI) (OUN) (ARE) (ASS) (IRE) (ANN) (AHO) (ENJ) EC 71±32 179±84 99±46 229±150 242±160 136±120 128±49 320±390 91±51 pH 7.4±0.6 7.2±0.5 7.2±0.5 7.7±0.5 7.7±0.3 7.4±0.4 7.5±0.6 7.5±0.3 7.1±0.5 Temperature 23±2 24.6±2.3 22.9±2.1 26.0±2.1 25±2 26.5±1.7 26.0±2.2 26.7±2.7 24.3±2.0 TS 342±210 507±280 457±370 511±350 453±190 520±380 485±330 685±690 352±270 TDS 37±19 92±44 31±25 123±89 118±90 71±62 66±29 169±200 47±30 TSS 305±210 415±290 405±370 388±340 335±180 448±360 419±330 516±650 305±280 Turbidity 27±27 38±53 53±41 37±39 31±41 36±36 15±15 57±66 72±79 Alkalinity 41±9 80±24 58±23 89±17 85±34 68±63 64±15 109±98 49±20 Hardness 95±95 135±130 101±99 133±140 139±140 110±120 123±110 139±160 87±100 Nitrate 1.0±1.0 1.8±1.2 2.2±1.7 2.0±1.2 3.0±3.6 1.6±1.0 1.9±1.7 2.2±1.2 2.5±2.2 Sulphate 30±22 31±25 50±23 36±22 34±28 38±23 22±19 42±32 64±46 Phosphate 0.10±0.10 0.10±0.10 0.10±0.10 0.10±0.10 0.20±0.20 0.10±0.10 0.10±0.10 0.30±0.40 0.10±0.10 Chloride 17.7±9.7 32±13 17±12 45±23 41±28 28±21 23±17 501±39 25±15 Ammonia 3.2±2.6 2.9±1.4 4.5±3.3 3.9±3.0 3.2±2.7 4.4±4.2 3.6±2.9 5.3±4.4 4.6±2.6 DO 8.5±1.9 6.6±3.1 6.6±3.3 7.4±2.8 9.3±1.9 7.2±3.5 6.4±2.6 7.7±3.2 7.5±1.9 BOD 6.1±3.6 5.1±3.6 5.0±3.6 5.7±3.6 6.1±4.1 6.3±3.8 6.4±3.7 5.70±3.3 5.3±3.2 COD 177±150 143±130 183±170 131±120 163±120 128±140 173±160 141±110 177±190 Ca 48±42 73±80 51±42 74±71 81±71 68±76 66±58 64±69 45±41 Mg 7.10±8.30 12±13 8.40±9.90 7.40±7.50 7.90±8.80 4.80±5.10 9±11 15±17 5.70±6.50 Pb 0.003±0.100 0.003±0.004 0.003±0.003 0.003±0.003 0.002±0.003 0.002±0.003 0.003±0.004 0.003±30.004 0.003±0.003 Cu 0.003±0.003 0.003±0.003 0.003±0.003 0.004±0.009 0.004±0.003 0.002±0.003 0.003±0.003 0.003±0.003 0.003±0.003 Cd 0.002±0.003 0.002±0.003 0.002±0.003 0.002±0.003 0.002±0.003 0.002±0.003 0.002±0.003 0.002±0.003 0.002±0.003 Co 0.004±0.004 0.004±0.004 0.004±0.004 0.004±0.004 0.004±0.004 0.003±0.003 0.004±0.004 0.004±0.004 0.004±0.004 Cr 0.010±0.010 0.003±0.004 0.004±0.004 0.003±0.003 0.004±0.006 0.003±0.004 0.004±0.01 0.003±0.004 0.004±0.004 Ni 0.003±0.010 0.003±0.007 0.003±0.010 0.01±0.01 0.004±0.100 0.004±0.010 0.0040.010 0.003±0.010 0.01±0.02 Zn 0.10±0.10 0.10±0.10 0.10±0.10 0.10±0.10 0.10±0.10 0.10±0.10 0.01±0.10 0.10±0.10 0.10±0.20 o * units in mg/L, except EC- µS/cm, turbidity – FTU, temperature – C and pH ( no units). 127 UNIVERSITY OF IBADAN LIBRARY Table 4.2 (contd.) Parameter Oloyo Gbodofon Awesin Ojutu Osun Yeyekare Oni Olumirin Orufu (OLO) (GBD) (AWE) (OJU) (OSU) (YEY) (ONN) (OLU) (ORU) EC 80±53 173±100 243±170 113±45 139±81 139±77 113±69 39±39 55±11 pH 7.5±0.6 7.6±0.4 7.7±0.4 7.6±0.4 7.8±0.5 7.4±0.6 7.7±0.6 7.5±0.6 7.9±0.5 Temperature 23±2 26.2±2.1 26.7±1.5 26.9±1.8 25.1±1.9 24.0±1.7 23.0±1.9 23.4±2.0 26±1.4 TS 459±380 659±410 700±500 491±40 559±460 310±230 397±270 311±280 284±290 TDS 40±26 89±54 128±94 58±27 70±41 69±39 57±35 19±15 27.2±5.90 TSS 419±370 570±400 570±480 432±400 489±460 241±240 339±270 292±280 257±290 Turbidity 30±23 43±51 74±92 28±40 25±29 19±20 35±42 16±14 72±69 Alkalinity 52±24 70±26 101±138 58±15 65±29 71±20 59±19 28±11 53±27 Hardness 86±110 112±100 119±120 96±79 86±53 90±53 82±47 59±38 37±23 Nitrate 1.3±0.8 1.7±1.6 2.8±2.6 2.4±1.3 1.3±0.7 1.4±0.8 1.5±0.7 1.6±0.6 1.3±0.9 Sulphate 39±29 43±29 53±49 25±11 33±18 35±20 31±16 26±12 59±52 Phosphate 0.10±0.10 0.20±0.10 0.30±0.30 0.10±0.10 0.10±0.10 0.10±0.10 0.10±0.10 0.10±0.10 0.10±0.10 Chloride 27±14 38±24 53±28 25±11 31±14 27±12 30±15 26±12 18.0±9.5 Ammonia 3.4±2.0 3.1±1.8 4.8±4.8 2.4±1.3 2.9±2.5 2.7±1.2 2.8±1.5 2.6±0.8 5.5±3.3 DO 8.2±3.6 8.3±2.4 7.5±2.6 8.0±2.3 8.5±2.2 7.4±2.8 9.9±2.8 10.6±2.2 10.4±3.8 BOD 8.0±9.8 7.0±5.0 5.5±3.9 6.5±4.1 7.8±4.7 7.3±8.2 7.2±9.4 10±13 6.6±3.4 COD 120±82 147±140 151±110 153±110 135±110 162±130 192±180 153±140 88±52 Ca 48±46 64±56 58±47 63±52 59±45 82±100 58±38 40±36 23±19 Mg 25±69 24±60 8.40±9.40 5.90±6.10 13±24 15±32 14±28 10±20 3.30±0.10 Pb 0.002±0.003 0.004±0.010 0.002±0.003 0.003±0.004 0.003±0.004 0.003±0.010 0.003±0.003 0.003±0.003 0.003±0.003 Cu 0.003±0.003 0.003±0.003 0.004±0.010 0.003±0.004 0.003±0.003 0.004±0.004 0.004±0.010 0.004±0.004 0.003±0.003 Cd 0.002±0.003 0.002±0.003 0.002±0.003 0.002±0.002 0.002±0.003 0.002±0.003 0.002±0.003 0.002±0.003 0.003±0.004 Co 0.004±0.003 0.003±0.003 0.004±0.004 0.004±0.004 0.004±0.004 0.004±0.003 0.004±0.003 0.004±0.004 0.010±0.004 Cr 0.01±0.01 0.01±0.01 0.01±0.01 0.003±0.004 0.003±0.004 0.004±0.004 0.004±0.004 0.004±0.004 0.01±0.02 Ni 0.003±0.003 0.004±0.010 0.01±0.01 0.004±0.010 0.01±0.02 0.003±0.010 0.003±0.010 0.004±0.010 0.004±0.004 Zn 0.10±0.20 0.10±0.10 0.10±0.10 0.10±0.10 0.10±0.10 0.10±0.10 0.10±0.10 0.10±0.10 0.10±0.10 o * units in mg/L, except EC- µS/cm, turbidity – FTU, temperature – C and pH ( no units). 128 UNIVERSITY OF IBADAN LIBRARY Table 4.2 (contd.) Parameter Kankere Adeti Aro Aro Odoiya Oba Oyika Etioni Ishasha (KAN) (ADE) (EJI) (ARO) (ODD) (OBB) (OYK) (ETI) (ISA) EC 198±82 1760±1200 143±88 147±89 83±31 192±140 53±24 98±54 212±130 pH 7.4±0.5 7.8±0.4 7.6±0.4 7.6±0.5 7.5±0.6 7.7±0.5 7.4±0.5 7.5±0.4 7.8±0.5 Temperature 25.1±1.3 24.7±2.2 24.4±2.4 24.3±2.3 26.1±1.4 26.4±1.9 24.9±1.9 26.3±1.9 25.5±1.7 TS 538±320 2000±1800 412±300 385±330 443±360 493±370 285±190 409±280 612±440 TDS 102±45 893±610 71±45 73±45 41±16 95±71 26±13 49±27 107±69 TSS 436±330 1110±1800 341±290 312±330 401±360 398±340 258±190 360±280 506±480 Turbidity 27±22 37±37 15±13 35±53 32±23 34±30 19±21 22±17 28±22 Alkalinity 72±26 657±280 72±18 102±63 53±27 82±40 36.1±2.3 54±19 98±41 Hardness 107±61 401±210 94±75 96±110 37±23 106±89 61±42 70±40 129±120 Nitrate 1.5±0.8 2.8±2.7 1.3±0.8 1.5±1.0 1.3±0.90 1.90±1.80 1.40±0.80 1.80±1.70 1.40±0.80 Sulphate 46±21 91±50 33±25 41±22 59±52 37±24 24±13 31±26 38±21 Phosphate 0.30±0.30 0.80±0.60 0.01±0.01 0.20±0.20 0.10±0.10 0.10±0.10 0.10±0.10 0.10±0.10 0.10±0.10 Chloride 54±19 603±220 26±15 43±23 17±15 48±31 23±12 43±92 46±25 Ammonia 2.9±1.5 27±25 3.1±2.6 3.6±2.1 2.8±1.5 3.2±1.4 2.6±0.8 2.8±1.0 3.5±2.8 DO 5.4±3.4 5.3±3.2 8.1±2.8 7.1±4.1 7.7±2.5 7.2±3.2 8.5±2.8 9.4±2.4 9.1±2.9 BOD 7.1±4.4 13±25 6.3±4.2 6.4±4.9 6.1±4.3 6.8±4.3 6.8±8.1 7.4±8.9 8.3±8.7 COD 121±120 183±94 133±100 99±61 139±120 147±110 159±110 127±120 177±120 Ca 60±57 262±150 61±56 60±66 44±31 67±53 4.30±8.60 51±33 75±66 Mg 26±57 14±17 14±27 9±13 15±28 19±46 10±18 12±29 31±100 Pb 0.004±0.010 0.01±0.01 0.002±0.003 0.004±0.004 0.003±0.003 0.003±0.003 0.003±0.004 0.003±0.003 0.010±0.004 Cu 0.003±0.003 0.004±0.004 0.004±0.004 0.004±0.004 0.003±0.003 0.003±0.003 0.003±0.003 0.003±0.003 0.003±0.003 Cd 0.002±0.003 0.002±0.003 0.002±0.003 0.002±0.003 0.002±0.003 0.002±0.003 0.002±0.003 0.002±0.003 0.002±0.003 Co 0.004±0.003 0.010±0.004 0.004±0.003 0.010±0.004 0.003±0.003 0.004±0.004 0.004±0.004 0.004±0.004 0.010±0.004 Cr 0.003±0.003 0.01±0.01 0.004±0.004 0.003±0.004 0.01±0.01 0.01±0.01 0.004±0.004 0.004±0.010 0.01±0.01 Ni 0.01±0.01 0.01±0.01 0.01±0.01 0.01±0.01 0.003±0.010 0.004±0.010 0.01±0.01 0.004±0.010 0.004±0.004 Zn 0.10±0.10 0.10±0.10 0.10±0.10 0.04±0.10 0.10±0.10 0.01±0.10 0.10±0.10 0.10±0.10 0.10±0.10 o * units in mg/L, except EC- µS/cm, turbidity – FTU, temperature – C and pH ( no units). 129 UNIVERSITY OF IBADAN LIBRARY Table 4.2 (contd.): Parameter Opa Ope Moginmogin Asejire (OPP) (OPE) (MOG) (ASJ) EC 176±97 287±200 604±400 167±85 pH 7.6±0.5 7.6±0.5 7.7±0.4 7.7±0.4 Temperature 25.6±1.7 26.4±2.1 28.6±2.7 28.8±3.2 TS 460±350 722±570 760±590 560±580 TDS 87±49 154±120 330±270 70±41 TSS 373±360 571±500 435±430 489±460 Turbidity 35±38 42±69 26±26 25±29 Alkalinity 88±29 139±100 195±92 64±18 Hardness 99±50 170±190 222±190 89±58 Nitrate 1.6±0.9 1.7±1.0 2.7±1.6 1.5±0.6 Sulphate 39±27 42±24 43±20 47±44 Phosphate 0.10±0.10 0.30±0.50 0.30±0.20 0.10±0.10 Chloride 32±14 50±39 144±80 34±20 Ammonia 2.8±1.7 4.4±4.4 4.1±3.1 2.7±1.1 DO 8±12 7.5±3.0 7.3±2.5 8.8±2.5 BOD 7.4±8.0 6.7±4.6 6.6±4.2 7.4±5.1 COD 135±130 123±130 127±66 118±120 Ca 63±36 74±64 122±78 54±37 Mg 6.9±5.5 14±14 4±43 21±45 Pb 0.003±0.003 0.004±0.003 0.01±0.01 0.003±0.003 Cu 0.003±0.003 0.005±0.004 0.004±0.003 0.01±0.01 Cd 0.002±0.003 0.002±0.003 0.002±0.003 0.002±0.003 Co 0.004±0.003 0.004±0.004 0.010±0.004 0.004±0.003 Cr 0.010±0.004 0.010±0.004 0.01±0.01 0.003±0.003 Ni 0.01±0.01 0.01±0.01 0.01±0.01 0.004±0.010 Zn 0.10±0.10 0.10±0.10 0.10±0.10 0.10±0.10 o * units in mg/L, except EC- µS/cm, turbidity – FTU, temperature – C and pH ( no units). 130 UNIVERSITY OF IBADAN LIBRARY and paper industry (for bleached paper). Only one of the tributaries had TDS that exceeds the limit for drinking water quality guidelines and for use in the paper and pulp industry (Unbleached). Table 4.2 shows that all the tributaries had TSS greater than the limit set for the production of confectioneries in the food and beverage industries. Six out of the tributaries had their TSS values greater than the value recommended for diary production. All the thirty-one tributaries exceeded the limit stipulated for boating/aesthetic for recreational water quality. All the tribuatries had total suspended solids higher than value prescribed for food canning, freezing, dried, frozen fruits and vegetables. All the tributaries had turbidity values greater than the drinking water quality guidelines, signifying that they need pretreatment before they can be used for drinking. All had their turbidity greater than what is stipulated for the food and beverage industries. Twenty-four out of the thirty-one tributaries had turbidity greater than the limit set for noticeable threshold of water contact for recreational water quality. All the tributaries in Table 4.2 showed alkalinity greater than the limit set for the iron and steel industry. Only one of the tributaries had alkalinity exceeding the limit set for usage of water at the power generating stations. Four tributaries had alkalinity greater than what was prescribed for the pulp and paper industry (fine paper). Three tributaries were observed to exceed the limit stipulated for the power generating stations. Hardness for all tributaries studied is as shown in Table 4.2. Twenty-nine tributaries had their water hardness above the guideline stipulated by EEC, two tributaries were above the limit set by S.O.N. and one tributary exceeded the Japan water quality guidelines. One of the tributaries had its hardness greater than the limit set for the petroleum industries. Five of the tributaries had hardness above the limit set for the tanning industry; all the tributaries had hardness above what was stipulated for the textiles and general dyeing industries. Twenty tributaries had hardness that exceeded the guideline set for brewing industry; fifteen tributaries had hardness above the limit set for sugar manufacturing industries; and two exceeded limit for the diary industry. This implies a level of contamination in these tributaries and will require treatment before usage. 131 UNIVERSITY OF IBADAN LIBRARY None of the tributaries had nitrate concentration above the limit stipulated by the guidelines for various categories of water usage. All complied for the limit set for drinking water quality by all the guidelines for sulphate. All the tributaries showed higher sulphate concentration above the limit set for sugar manufacturing as shown in Table 4.2. Only one tributary had its sulphate concentration above the standard set for diary industry. The phosphate level of the thirty-one tributaries is illustrated in Table 4.2. All were within the limit set for drinking water quality guidelines. Two out of the tributaries had chloride levels above the limit set for drinking water quality by all the guidelines. Two of them were found to have chloride level above limits stipulated for beverage and brewing industries, iron and steel industry and petroleum industry. Three tributaries were outside the limit set for brewing, textiles and pulp and paper industries. One tributary was above the guideline set for power generating stations. Twenty of the tributaries did not comply with the threshold limit for diary industry and twenty-seven had chloride values above the limit set for the usage of water by the sugar manufacturing industry. All the tributaries had ammonia values higher than the level set by all the guidelines for various categories of water usage as shown in Table 4.2. Ammonia is formed only at high pH values (pH >8.5) and is extremely toxic to fish and other aquatic life at high concentrations (>2.0mg/L N). Ecological integrity of aquatic ecosystems is threatened when significant organic pollution exists that exceed self-purification capacity of the water bodies. Any surface water to sustain aquatic life there should be a balanced physicochemical and biological interaction. Abnormally too high or too low of each factor may lead to a deleterious ecosystem disturbance. The dissolved oxygen (DO) for all the tributaries indicated higher concentrations above the limit set for certain industrial applications, power generating stations and guide for evaluating the quality for aquatic life. Lewis (2000) opined that oxygen conservation is an important management principle for tropical lakes and was also supported by Adeniyi et al. (1997). Asa Lake (3.4 –7.3mg/L), Kainji Lake (3.0 –17.8mg/L) and Onah Lake (2.00 – 11.34mg/L) in Nigeria reported by Olele and Ekelemu (2007). However, low primary productivity caused by low transparency and low nutrient load was implicated for low oxygen content of some of the tributaries (Kankere and Adeti) 132 UNIVERSITY OF IBADAN LIBRARY in river Osun. Chemical oxygen demand in Table 4.2 for all the tributaries was higher than what was stipulated by the power generating station and can not be useful except if treated. One-way analysis of variance (ANOVA) shows that the means were statistically significant at p = 0.05. All the heavy metals showed compliance with all the guidelines. Which implies that the tributaries of Osun River were not contaminated with heavy metals. Four of the tributaries showed levels to be higher than the limit set for the petroleum industry and two tirbutaries with magnesium level higher than stipulated for petroleum industry. Two tributaries showed calcium level to exceed limit set for usage of water for food and beverage industry and one tributary had magnesium level greater than the limt prescribed for food and beverage industry. 4.2.1 Pratti Scale Classification of the River Waters The water samples collected for each of the 90 locations for the thirty-one tributaries in the study were used for this classification. The classification was based on the average values of the concentrations of the parameters according to Pratti et al. (1971). The interpretation of these various classes are: Class 1 – excellent Class 2 – acceptable Class 3 – slightly polluted Class 4 – polluted Class 5 – heavily polluted Based on Table 2.14, the locations used were classified. Table 4.3 shows the quality classes that each of the locations belongs to. The study reveals that four of the locations showed excellent surface water quality and these accounts for 5%. Fifty-three of the locations showed acceptable surface water quality and accounts for 59%. Thirty- one of the locations were slightly polluted, accounting for 34%; while two of them were polluted and accounted for 2%. Ishasha (ISA-1), Isin (IRE-2, IRE-3) and OYI-1 had excellent surface water quality. This suggests why some residents along these tributaries fetch and drink water from these sources. Fifty-three locations have acceptable surface water qualities, included those on the tributaries Osun, Gbodofon, 133 UNIVERSITY OF IBADAN LIBRARY Table 4.3: Pratti scale classification for each location of surface water samples Tributary Location Pratti Scale Interpretation Classification Asejire ASJ 1 3 slightly polluted ASJ 2 4 polluted ASJ 3 3 slightly polluted Osun OSU 1 2 acceptable OSU 2 2 acceptable OSU 3 2 acceptable Gbodofon GBD 1 2 acceptable GBD 2 2 acceptable GBD 3 2 acceptable GBD 4 2 acceptable Ahoyaya AHO 1 2 acceptable AHO 2 2 acceptable AHO 3 2 acceptable Isin IRE 1 2 acceptable IRE 2 1 excellent IRE 3 1 excellent Oyi OYI 1 2 acceptable OYI 2 1 excellent Osin OSI 1 2 acceptable OSI 2 3 slightly polluted Oloyo OLO 1 2 acceptable OLO 2 2 acceptatble OLO 3 3 slightly polluted Enja ENJ 1 2 acceptable ENJ 2 2 acceptable ENJ 3 2 acceptable Ashasha ASH 1 2 acceptable 134 UNIVERSITY OF IBADAN LIBRARY Table 4.3(contd.): Tributary Location Pratti Scale Interpretation Classification ASH 2 2 acceptable Ounseku OUN 1 2 acceptable OUN 2 2 acceptable OUN 3 3 slightly polluted OUN 4 2 acceptable Kankere KAN 1 3 slightly polluted KAN 2 3 slightly polluted Aro ARO 1 2 acceptable ARO 2 2 acceptable Arenounyun ARE 1 2 acceptable ARE 2 3 slightly polluted ARE 3 3 slightly polluted Oba OBB 1 2 acceptable OBB 2 2 acceptable OBB 3 2 acceptable OBB 4 3 acceptable Moginmogin MOG 1 3 slightly polluted MOG 2 4 polluted MOG 3 2 acceptable Ope OPE 1 2 acceptable OPE 2 2 acceptable OPE 3 3 slightly polluted Awesin AWE 1 3 slightly polluted AWE 2 3 slightly polluted AWE 3 2 acceptable Ojutu OJU 1 3 slightly polluted OJU 2 2 acceptable 135 UNIVERSITY OF IBADAN LIBRARY Table 4.3 (contd.): Tributary Location Pratti Scale Interpretation Classification OJU 3 3 slightly polluted Anne ANN 1 2 acceptable ANN 2 3 slightly polluted ANN 3 2 acceptable ANN 4 2 acceptable ANN 5 3 slightly polluted ANN 6 3 slightly polluted Aro EJI 1 2 acceptable EJI 2 2 acceptable Orufu ORU 1 2 acceptable ORU 2 3 slightly polluted Odoiya ODD 1 2 acceptable ODD 2 2 acceptable Ishasha ISA 1 1 excellent ISA 2 2 acceptable Ishasha ISA 3 2 acceptable ISA 4 2 acceptable Adeti ADE 1 3 slightly polluted ADE 2 3 slightly polluted ADE 3 3 slightly polluted Oni ONN 1 3 slightly polluted Oni ONN 1 3 slightly polluted ONN2 2 acceptable ONN 3 2 acceptable ONN 4 3 slightly polluted Etioni ETI 1 2 acceptable ETI 2 2 acceptable 136 UNIVERSITY OF IBADAN LIBRARY Table 4.3 (contd.): Tributary Location Pratti Scale Interpretation Classification ETI 3 2 acceptable Oyika OYK 1 3 slightly polluted OYK 2 2 acceptable Olumirin OLU 1 2 acceptable OLU 2 2 acceptable OLU 3 3 slightly polluted Yeyekare YEY 1 3 slightly polluted YEY 2 3 slightly polluted Opa OPP 1 3 slightly polluted OPP 2 3 slightly polluted 137 UNIVERSITY OF IBADAN LIBRARY Ahoyaya, Isin, Osin, Oloyo, Enja, Ashasha, Ounseku, Odoiya, part of Ishasha, Oni, Etioni, Olumirin, Aro, Oba, Ope, Anne and Aro. Olumirin is a tourist centre and pollution activities around this vicinity are minimal. But the downstream point however was exposed to visitors and anthropogenic activities accounted for the slightly polluted nature of this water body downstream. Thirty-one locations had slightly polluted surface water quality as a result of industrial, agricultural and municipal activities. These included those on Asejire, Osin, Oloyo, Ounseku, Kankere, Arenounyun, Oba, Moginmogin, Awesin, Ojutu, Anne, Orufu, Adeti, Oyika, Yeyekare and Opa. Two percent of these locations account for the polluted nature of surface water of Osun River and are located on Moginmogin (MOG-2) and Asejire (ASJ-2) as a result of the activities along this water course. 4.3 SPATIAL VARIATIONS OF PHYSICOCHEMICAL CHARACTERISTICS AND HEAVY METALS IN SURFACE WATER OF OSUN RIVER Figures 4.1 - 4.26 illustrate the spatial variations of each physicochemical characteristic observed for all the tributaries used in this study. It can be noticed that for each of the parameters for all the tributaries, the concentration of the parameters varies depending on the tributary. This is as result of different environmental factors of industrial, urbanization and agricultural activities. The figures depict a transition from more upstream portions (such as OYI, OSI, OUN, ARE, ASS, IRE, ANN, AHO, and ENJ) to down stream portions (such as ASJ, MOG, OPE, OPP and ISA). Figure 4.1 explains the spatial variation of electrical conductivity in surface water for all the tributaries. Changes in the electrical conductivity are not significant as the water flows from upstream to downstream except at ADE and MOG. This implies that these two tributaries contain more dissolved ions or salts among the tribuatries studied. Anthropogenic activities occurring along the river banks can increase the total dissolved ions in rivers. Some unusual spikes where noticed at some of the tributaries were as a result of random input from various sources. Spatial variation of other parameters in Figures 4.2 to 4.26 follow general pattern as for electrical conductivity. 138 UNIVERSITY OF IBADAN LIBRARY Figure 4.1: Spatial variation of electrical conductivity in surface water Figure 4.2: Spatial variation of pH in surface water 139 UNIVERSITY OF IBADAN LIBRARY Figure 4.3: Spatial variation of total solids (TS) in surface water Figure 4.4: Spatial variation of total dissolved solids (TDS) in surface 140 UNIVERSITY OF IBADAN LIBRARY Figure 4.5: Spatial Variation of total suspended solids (TSS) in surface water 141 UNIVERSITY OF IBADAN LIBRARY Figure 4.6: Spatial variations of turbidity in surface water Figure 4.7: Spatial variation of alkalinity in surface water Figure 4.8: Spatial variation of hardness in surface water 142 UNIVERSITY OF IBADAN LIBRARY Figure 4.9: Spatial variation of temperature of surface water 143 UNIVERSITY OF IBADAN LIBRARY Figure 4.10: Spatial variation of nitrate in surface water Figure 4.11: Spatial variation of sulphate in surface water 144 UNIVERSITY OF IBADAN LIBRARY Figure 4.12: Spatial variation of phosphate in sulphate water Figure 4.13: Spatial variation of chloride in surface water 145 UNIVERSITY OF IBADAN LIBRARY Figure 4.14: Spatial variation of ammonia in surface water Figure 4.15: Spatial variation of dissolved oxygen of surface water 146 UNIVERSITY OF IBADAN LIBRARY Figure 4.16: Spatial variation of biochemical oxygen demand in surface water Figure 4.17: Spatial variation of chemical oxygen demand in surface water 147 UNIVERSITY OF IBADAN LIBRARY Figure 4.18: Spatial variation of lead in surface water Figure 4.19: Spatial variation of copper in surface water 148 UNIVERSITY OF IBADAN LIBRARY Figure 4.20: Spatial variation of cadmium in surface water Figure 4.21: Spatial variation of cobalt in surface water 149 UNIVERSITY OF IBADAN LIBRARY Figure 4.22: Spatial variation of chromium in surface water Figure 4.23: Spatial variation of nickel in surface water 150 UNIVERSITY OF IBADAN LIBRARY Figure 4.24: Spatial variation of zinc in surface water 151 UNIVERSITY OF IBADAN LIBRARY Figure 4.25: Spatial variation of calcium in surface water Figure 4.26: Spatial variation of magnesium in surface water 152 UNIVERSITY OF IBADAN LIBRARY 4.4 SEASONAL VARIATIONS OF THE PHYSICOCHEMICAL CHARACTERISTICS AND HEAVY METALS IN SURFACE WATER. The seasonal variations in the physicochemical and heavy metals characteristics between wet and dry seasons are as shown in Table 4.4. This shows the variations in the physicochemical properties of Osun River in the wet and dry season. Parameters that showed no difference between dry and wet seasons No significant seasonal variation was recorded for temperature as shown in Table 4.4. Temperature depends on the climate, sunlight and depth and does not undergo changes during the year in the fluvaitile environment (Egborge, 1970; Akinyemi and Nwankwo 2006; Gupta, 2006) as compared to lacustrine environment. Generally, some tributaries used for the study were shaded with trees which explained why the average temperature in the wet seasons almost equal that in the dry seasons. Parameters that were higher during dry seasons Table 4.4 illustrates those parameters that were higher during dry seasons. These parameters included electrical conductivity, total solids, total dissolved solids, total suspended solids, alkalinity, hardness, phosphate, chloride, ammonia, nitrate, biochemical oxygen demand, chemical oxygen demand, zinc, lead, copper, cadmium, cobalt, chromium, calcium and magnesium. These parameters showed significant difference in their wet and dry season values. This was so because the volume of water in Osun River reduces in dry season because of low flow of water. In most rivers, the normal or dry weather flow is made up to exhibit their most favourable chemical water characteristics (Chov, 1964). During dry seasons much sediment at the bottom of most of these rivers are remobilized and become particulates in the river and then contributes to the pollution of the river. Chov (1964), suggests that although the river may contain extremely large amounts of suspended matter, the concentration of dissolved substances are usually low, often only a fraction of that present during dry weather. However, there are some instances where high runoff may cause deterioration in water quality. Ogunfowokan et al. (2005), also reported a high total solids (900±8 - 500±9 mg/L) and high suspended solids values of (500±7 - 200±3mg/L) in their study 153 UNIVERSITY OF IBADAN LIBRARY Table 4.4: Average dry and wet season characteristics of the Osun River Water Parameter Dry Season Wet Season EC (µS/cm) 238±444 195±316 pH 7.5±0.5 7.7±0.5 Temperature 25.2±3.3 25.5±1.8 TS 591±471 504±650 TDS 126±238 97.5±160 TSS 466±397 408±55 Turbidity 24±28 43±45 Alkalinity 98±126 88±123 Hardness 132±146 102±94.9 Nitrate 1.84±1.51 1.80±1.56 Sulphate 32.1±28.7 45.9±30.1 Phosphate 0.16±0.26 0.15±0.20 Chloride 56.3±127 53.4±97.7 Ammonia 4.32±8.55 4.11±4.05 DO 7.46±3.07 8.26±2.92 BOD 7.04±7.05 6.81±7.98 COD 167±150 106±82.1 Calcium 76.0±80.4 60.1±57.7 Magnesium 17.4±46.5 9.89±14.2 Lead 0.004±0.004 0.003±0.004 Copper 0.004±0.004 0.003±0.004 Cadmium 0.003±0.004 0.001±0.001 Cobalt 0.010±0.004 0.003±0.003 Chromium 0.010±0.004 0.003±0.003 Nickel 0.003±0.004 0.01±0.01 Zinc 0.08±0.12 0.05±0.07 o * units in mg/L, except EC- µS/cm, turbidity – FTU, temperature – C and pH (no units). 154 UNIVERSITY OF IBADAN LIBRARY and this was as a result of waste water which entered the river which contains human excreta, urine and other semi solid wastes. Strahler and Strahler (1973) stated that all rainfall wherever it occurs carries with it a variety of ions, some introduced into the atmosphere from the sea surface, some from land surfaces undisturbed by man and some from man-made sources. The ions and other substances carried into the streams or rivers via rainfall may result into pollution. These ions during dry season now get concentrated because there is no rain to wash them away. Also many activities were carried out in most of these tributaries in the dry seasons like washing of clothes, cars and motor bicycle using different types of detergents and through this, contaminants are introduced as pollutants which impair the quality of the water body. The increase in nitrate and phosphate level in the dry seasons could lead to eutrophication in the nearest future if not controlled. The inability of river to flow normally in dry seasons makes aerobic bacteria to consume the available dissolved oxygen in the river. This invariably means the depletion of available oxygen meant for organisms in the river. This will increase the nutrient present in the water and can result in high biochemical oxygen demand. Parameters that were higher during wet seasons Those parameters that showed higher concentrations during wet seasons include pH, turbidity, sulphate, dissolved oxygen and nickel. These parameters were derived mainly from anthropogenic sources. Urbanization, industrial and agricultural activities along the river channel contaminate the river. When it rains these contaminants are washed into the river and can undergo different chemical reactions which can have impact on the quality of the river water. In the wet seasons, there is a high leaching of surrounding bodies of water. This causes the carrying of large quantities of particles and pollutants into the tributaries of Osun River characterizing the contribution of diffuse sources. Fast running water can carry more particles and larger sized sediment. Decaying plants and animals, bottom feeding fish, algae blooms, flooding and soil erosion can all contribute to disturbance of the quality of rivers. Slower moving streams usually contain finer particles, but does not mean it does not contain some amount of pollutants. 155 UNIVERSITY OF IBADAN LIBRARY During wet seasons, there is always a rapid mixing of river water thereby increasingthe oxygen content and invariably increasing the dissolved oxygen of the river. An increase in DO during wet seasons was attributed to low organic enrichment. At alkaline pH values, photosynthetic activities result in high oxygen content. Significant differences that existed between wet and dry seasons at most of the tributaries include: a significant difference observed for total dissolved solids at Gbodofon, Isin and Osun. Turbidity was significant at Osun, Oyi, Oloyo, Enja, Ashasha, Ounseku, Aro, Arenounyun, Oba, Moginmogin, Awesin, Ojutu, Aro (Ejigbo), Odoiya, Oni and Etioni. Alkalinity was significant at Gbodofon, Oyi, Kankere, Aro, Oba, Moginmogin, Awesin, Ojutu, Anne, Orufu and Odoiya. Differences in hardness were only observed to be significant at four tributaries (Isin, Moginmogin, Anne and Odoiya). Differences in nitrate levels were significant at Isin and Adeti. Sulphate level differences were however found to be significant at many of the tributaries (Osun, Oyi, Osin, Oloyo, Enja, Oba, Moginmogin, Ope, Awesin, Ojutu, Orufu, Odoiya, Oni, Etioni, Olumirin, Yeyekare and Opa). Differences in phosphate level were significant at Awesin, Ojutu and Adeti and could be as a result of anthropogenic activities. The differences in chloride were significant at Isin, Osin, Kankere, Arenounyun, Awesin, Ojutu and Orufu. The differences in dissolved oxygen between the two seasons were found to be significant at Gbodofon, Isin, Oloyo, Oba, Ope, Awesin, Aro (Ejigbo) and Odoiya. BOD also shows significant differences between the wet and dry seasons at Oloyo, Odoiya and Oni, while COD showed significant difference at Isin, Osin, Ashasha, Ounseku, Arenounyun, Moginmogin, Ojutu and Odoiya. Turbidity, sulphate and alkalinity showed high significant differences among the parameters studied. Paired sample t-tests was used to see if there were any differences between the means obtained for wet and dry seasons. The results showed that there was no significant difference in some of the means obtained except turbidity, sulphate, dissolved oxygen and heavy metals that were statistically significant. 156 UNIVERSITY OF IBADAN LIBRARY 4.5 APPLICATION OF MODEL TO PREDICTION OF FUTURE WATER QUALITIES The time series analysis for selected parameters and selected tributaries are presented in Table 4.5. The model equations and the predicted concentrations up to year 2018 are 2 also indicated. The coefficient of determination (R ) gives an indication about the suitability of the model. In Asejire, the model explains effectively 56% variation of EC (with predicted value of 799 µS/cm for September 2018) and 59% variation of zinc (with predicted value of 0.25 mg/L for January 2018) in surface water. For other parameters in Asejire the model could not explain significant variations. For surface water in Osun, the model explains 69% variation of nitrate with concentration of 14.1 mg/L in September, 2018. The model could not explain significant variations for other parameters. In Gbodofon, the model obtained explains 31% variation of EC with predicted concentration of 1324 µS/cm for 2018, and 34% variations of TDS (with predicted concentration of 840 mg/L for March 2018). In Oloyo, the model used could not explain significant variations of parameters in surface water. In Oyi surface water, the model explained 70% variation EC with a forecast of 549 µS/cm for July 2018, 67% variation of total dissolved solids with a concentration of 173 mg/L for July 2018. Models obtained for Olumirin surface water explained 71% variation of nitrate with a forecast of 11.1mg/L for July 2018, 59% variation of ammonia with a forecast of 3.26mg/L for September 2018. Models obtained for surface water for some parameters in the selected tributaries, the models could not explain significant variations. This is possibly due to some reactions occurring in surface water and sediment. The predictions of the parameters using the obtained models are in good agreement with observed values. Some of the predicted values for 2018 where found to be above the limit stipulated for drinking water quality guidelines. These include Osun, Gbodofon, Olumirin and Ishasha, and strict monitoring of the water quality of the river is advisable. However, some were still within the standard stipulated for drinking water guidelines such as Oyi. 4.5.1 Time Series Analysis of Parameters of Osun River Surface Water Table 4.6 illustrates the overall time series analysis obtained for surface water in Osun River. This explained 79% variation of nitrate with predicted concentration of 19.0 157 UNIVERSITY OF IBADAN LIBRARY Table 4.5: Time-Series analysis results of selected parameters at selected tributaries 2 Tributary Parameter R Model equation Forecast for 2018 Asejire EC 0.56 63.3 +18.3b 799µS/cm, September. Zn 0.59 42.3-4.53b 0.25mg/L, January. Etioni TDS 0.39 18.1 + 4.74b 684 mg/L, March. Gbodofon EC 0.31 68.6 + 16.1b 1324 µS/cm, March. TDS 0.34 31.0 + 8.97b 840 mg/L, March. Ishasha Nitrate 0.60 0.28 + 0.18b 5.79 mg/L,November Moginmogin BOD 0.35 10.4 - 0.63b 3.13 mg/L, Novenber. - Oba pH 0.49 8.27-9.20x10 2b 4.9, November. Ojutu EC 0.61 50.7 + 9.80b 2923 µS/cm, July. TDS 0.49 23.7 + 5.32b 1620 mg/L, January. Cu 0.44 6.89 - 1.39b - Oloyo Nitrate 0.23 0.49 + 0.11b 19.9 mg/L, March. COD 0.27 156-9b 1300mg/L,September. Olumirin Nitrate 0.71 0.67 + 0.14b 11.1 mg/L, July. NH3 0.59 3.24 - 0.10b 3.26mg/L, September. - Cu 0.38 5.21x10 4b- 4.70 - Osun Nitrate 0.69 0.39 +0.15b 14.1mg/L, September. Oyi EC 0.70 26.1 + 7.63b 549 µS/cm, July. TDS 0.67 11.2 + 4.27b 173 mg/L, July. 158 UNIVERSITY OF IBADAN LIBRARY Table 4.6: Time series analysis of overall concentrations of surface water Parameters in Osun River 2 Parameter R Forecast for 2018 EC 0.27 2367µS/cm, January. TDS 0.33 1404 mg/L, January. Turbidity 0.06 46.9 FTU, September. Alkalinity 0.03 129 mg/L, March. Hardness 0.08 163 mg/L, March. Nitrate 0.79 19mg/L, July. Sulphate 0.03 54.7 mg/L, July. Phosphate 0.84 18.1 mg/L, March. BOD 0.71 21.9 mg/L, March. 159 UNIVERSITY OF IBADAN LIBRARY mg/L for March, 2018; 84% variations of phosphate with a predicted concentration of 18.1 mg/L for March 2018, and 71% variation of BOD with predicted concentration of 21.9 mg/L in March 2018. Parameters such as EC, pH, TDS, turbidity, alkalinity, hardness and sulphate, do not show significant variations. Nitrate, phosphate and BOD where predicted to be prevalent by March 2018. The predicted concentration of nitrate exceeded the limit stipulated by drinking water quality guidelines for SON and others except WHO guidelines and will impair the water quality of Osun River. It will not make the river water to be useful for the food and beverage industry. 4.6 PRINCIPAL COMPONENT ANALYSIS OF HEAVY METALS IN SURFACE WATER Figure 4.27 illustrates the principal component analysis for heavy metals in water. Noticeable amount of clustering was observed among the parameters studied here. Parameters placed close to each other influence the principal component model in similar ways, which indicates they are correlated. Pb, Cu, Co, Cr, Cd, Ni and Zn are clustered together as shown in the graph; this is even supported by the Pearson correlation obtained in Table 4.7. Ca and Mg are not correlated with the other metals. The further away a parameter is from the origin, the more influential the parameter is in determining the principal component analysis model. The first principal eigenvalues captures 77.8% of the total variance which was due to the clustered heavy metals (Pb, Cu, Cd, Co, Cr, Ni and Zn). They contributed significantly to water quality variations of Osun River through anthropogenic activities such as industrial waste discharge, agricultural and domestic waste discharge. The second principal component eigenvalues accounts for 14.6% of the total variance which was due to Calcium. This probably originated from natural source. The third accounts for 7.60% of the total variance was due to presence of magnesium in the water body. The existence of magnesium ions and its compounds led to high loading of the variable. 4.7 PEARSON CORRELATION MATRIX FOR PARAMETERS IN SURFACE WATER Table 4.7 shows the correlation matrix obtained for the physicochemical characteristics and heavy metals in surface water. Strong and positive correlations were obtained for 160 UNIVERSITY OF IBADAN LIBRARY Mg Ca CPZNUbodnri Figure 4.27: Principal component analysis of heavy metals in surface water 161 UNIVERSITY OF IBADAN LIBRARY Table 4.7: Pearson correlation matrix for the physicochemical characteristics of surface water - 2- 3- - Parameters EC TS TDS TSS Turbidity Alkalinity Hardness Temp. NO3 SO4 PO4 Cl NH3 DO BOD COD Pb Cu Cd Co Cr Ni Zn Ca Mg EC 1.000 TS 0.497 1.000 TDS 0.996 0.493 1.000 TSS 0.165 0.937 0.160 1.000 Turbidity 0.048 0.062 0.039 0.055 1.000 Alkalinity 0.860 0.488 0.843 0.217 0.055 1.000 Hardness 0.524 0.447 0.496 0.309 0.130 0.550 1.000 Temp. 0.140 0.085 0.133 0.043 -0.008 0.081 0.155 1.000 - NO3 0.142 -0.007 0.145 0.066 0.179 0.089 0.016 0.031 1.000 2- SO4 0.219 0.199 0.205 0.144 0.551 0.245 0.238 -0.017 0.138 1.000 3- PO4 0.540 0.345 0.526 0.181 0.379 0.543 0.449 0.081 0.220 0.394 1.000 - Cl 0.868 0.476 0.852 0.199 0.036 0.899 0.496 0.022 0.141 0.301 0.564 1.000 NH3 0.531 0.294 0.516 0.127 0.212 0.555 0.459 0.075 0.118 0.343 0.597 0.658 1.000 DO 0.176 0.196 0.171 -0.154 0.041 0.259 -0.292 -0.044 0.090 -0.045 0.082 0.178 0.169 1.000 BOD 0.050 0.111 0.042 0.109 0.104 0.103 0.275 0.124 0.060 0.201 0.195 0.132 0.276 -0.034 1.000 COD 0.120 0.070 0.112 0.035 0.134 0.090 0.203 0.069 0.053 0.016 0.090 0.046 0.063 0.024 0.034 1.000 Pb 0.036 -0.016 0.036 -0.024 -0.006 0.108 0.142 0.000 0.171 0.117 0.119 0.046 0.038 0.078 0.087 -0.002 1.000 Cu -0.048 0.074 -0.048 -0.070 0.040 0.027 -0.170 0.047 0.077 -0.046 0.134 -0.029 -0.016 0.022 0.100 0.100 0.417 1.000 Cd -0.005 -0.016 -0.005 -0.016 0.039 0.108 0.170 0.028 -0.189 0.141 0.146 0.003 -0.032 -0.052 -0.063 0.023 0.564 0.578 1.000 Co -0.049 -0.009 -0.048 -0.001 -0.088 0.037 -0.172 -0.012 0.141 0.161 -0.161 -0.057 -0.048 0.117 -0.056 -0.031 0.387 0.372 0.589 1.000 Cr -0.035 -0.022 -0.034 -0.017 -0.012 0.010 -0.228 0.046 0.104 0.075 -0.140 -0.047 -0.042 0.088 0.132 -0.014 0.438 0.273 0.313 0.413 1.000 Ni -0.005 -0.082 -0.005 0.087 -0.045 0.025 0.192 0.004 0.110 0.086 -0.169 0.012 -0.018 0.122 0.149 -0.023 0.368 0.397 0.201 0.112 0.285 1.000 Zn 0.075 -0.102 -0.074 - 0.095 0.003 -0.051 0.239 0.108 -0.070 -0.052 0.086 -0.062 -0.050 0.055 -0.086 0.076 0.275 0.311 0.518 0.241 0.103 0.005 1.000 Ca -0.025 0.054 -0.027 0.063 -0.032 -0.054 0.169 0.124 0.078 -0.024 0.028 -0.041 0.012 0.004 0.079 0.113 -0.061 0.188 -0.181 -0.014 -0.064 -0.193 -0.266 1.000 Mg -0.045 0.052 0.045 0.048 -0.031 0.032 0.192 -0.036 0.034 0.009 0.000 0.059 0.032 -0.010 0.030 0.062 0.109 0.107 0.089 0.172 -0.119 -0.100 -0.149 0.337 1.000 162 UNIVERSITY OF IBADAN LIBRARY the following pairs at p= 0.01: EC and TDS (r = 0.966), EC and alkalinity (r = 0.860), EC and chloride (r = 0.868), TS and TSS (r = 0.937), TDS and alkalinity (r = 0.843), TDS and chloride (r = 0.852), alkalinity and chloride (r = 0.899), chloride and ammonia (r = 0.658), meaning that there was a strong association between these pairs, and common sources for the pairs of polluting substances. Positive correlations were also observed for the following pairs of metals Pb and Cd (r = 0.564), Pb and Cr (r = 0.438), Cu and Cd (r = 0.578), Cd and Co (r = 0.589), Co and Cr(r = 0.413) at p< 0.01. This suggests that the metals might have originated from the same anthropogenic sources. The relationship here is not as strong as for other physicochemical properties. The probable sources of the pollutants varied widely and may include leachates from wastes waters generated municipally, domestically and industrially, and wastes from intensive agricultural practices. 4.8 COMPARISON OF THE OVERALL CHARACTERISTICS OF THE OSUN RIVER SURFACE WATER WITH DRINKING WATER QUALITY GUIDELINES. Water is an absolute necessity for the survival of life on earth and on it depends much on man‟s food. The various facets of the hydrological cycle serve beneficial uses to mankind. For example, surface water sources serve multipurpose functions for drinking, cooking, bathing, laundry, irrigation, farming, livestock watering and transportation. Population explosion, haphazard rapid urbanization, industrial and technological expansion, energy utilization and waste generation from domestic and industrial sources have rendered many surface water unwholesome and hazardous to man and other living resources. People living close to river Osun use the water for variety of purposes. Water quality data are one of the important tools in environmental management. It is therefore necessary to compare the overall results obtained for the physicochemical parameters and heavy metals in these rivers with the international drinking water guidelines. Table 2.2 illustrates some international drinking water guidelines. Comparison of pH in this study (7.6±0.5) with the USEPA, Canada, EEC, WHO, Japan and SON guidelines indicates that the pH of River Osun for the study is within the acceptable level of all the guidelines at that time of sampling, indicating that river Osun was not acidic and will be useful for some purposes that do not require acidic water for the production of 163 UNIVERSITY OF IBADAN LIBRARY its products. The total solid measured in this study was 111±200 mg/L. If compared to the values in Table 2.2, with USEPA (500 mg/L), Canada (500 mg/L), WHO (1000 mg/L) and Japan (500 mg/L), it would be noticed that the TDS in the study was within the acceptable limit of most of the guidelines, meaning that the TDS in River Osun is still within the normal range and cannot be said to be polluted. Turbidity obtained was 34±43 FTU and was far above all the guidelines stipulated in Table 2.2. This means that the river water exceeded the international drinking water guidelines, because normal drinkable water should not be turbid. The reason for the high turbidity was due to disturbance of the water body and presence of particles that disperse light effectively during wet season. Sulphate and chloride were all within the acceptable limit of all the guidelines in Table 2.2. In the present study, hardness (116±120 mg/L) was above the EEC guideline (50.0 mg/L) but within that of Japan, (300 mg/L) and Nigeria (150 mg/L). Heavy metal levels were generally within stipulated limits. 4.9 COMPARISON OF SURFACE WATER CHARACTERISTICS OF THE TRIBUTARIES WITH WATER QUALITY GUIDELINES FOR VARIOUS CATEGORIES OF WATER USAGE. Table 4.8 describes the comparison of surface water characteristics of the tributaries with water quality guidelines for some industries to determine if the water would be suitable for the classes of industries discussed in Tables 2.3 – 2.13. Table 4.8 explains the tributaries whose water could be used for specific industry. All the tributaries were found to contain more than 10mg/L suspended solids and made them not to comply for usage in pulp and paper, and petroleum industries. The alkalinity of the waters of these tributaries was above 0.5mg/L and could not be used in the iron and steel industry. The turbidity of most of the tirbutaries was found to be greater than 10 FTU and could not be utilized in the food and beverage industries. The ammonia content of these tributaries were above 0.5mg/L and thus rendered it unuseful in power generating stations and for aquatic life support. Five of the tributaries were found to comply with regualations for boating and aesthetic activities and about thirteen for water contact alone. All the tributaries complied with the stipulated limit for livestock rearing. The waters of these tributaries could become useful if given adequate treatment. The 164 UNIVERSITY OF IBADAN LIBRARY Table 4.8: Tributaries with water quality meeting specific industrial and other requirements* Industry Type Tributaries Pulp and Paper None, suspended solids> 10 mg/L Iron and Steel None, alkalinity > 0.5 mg/L Petroleum Industry None, suspended solids > 10 mg/L Power Generating Stations None, ammonia > 0.07 mg/L Food and Brewing None, turbidity > 10 mg/L Diary Production None, turbidity > 10 mg/L Irrigation All tributaries Aquatic Life Support None, ammonia > 0.5 mg/L Recreation Anne, Aro, Oyika, Olumirin, Yeyekare boating and aesthetic activities Livestock Rearing All tributaries * Requirements as in Table 2.4 – 2.13 165 UNIVERSITY OF IBADAN LIBRARY activities around them could be monitored so that level of treatment could be reduced. 4.10 COMPARISON OF THE PHYSICOCHEMICAL CHARACTERISTICS OF SURFACE WATER IN THIS STUDY WITH STUDIES ELSEWHERE. The physicochemical characteristics of surface water in this study were compared with those obtained from rivers elsewhere. Table 4.9 reveals the comparison of the physicochemical properties in surface water with studies elsewhere. The temperature o obtained in the study area (25.3±2.6 C) was somehow comparable with what was o o obtained in Nigeria (26-32 C, and 27.6±1.6 C) by Onianwa et al. (2001) in Ogunpa Lake and Courant et al. (2007) in Benin River, Nigeria respectively. It was below the o amount obtained in rivers in Lagos, Nigeria (30-40 C ) by Sangodyin (1995) but well o above the temperature obtained in Boye pond, Ethiopia ( 21.5-21.8 C ) by Teferi et al. (2005). The pH of the study area (7.6±0.5) compares well with what was reported in the following rivers; Ogun River, Nigeria (7.6±0.2) by Udousoro and Osibanjo (1997), Kieskamma River, South Africa (7.3±0.1) by Morrison et al. (2001), in Ogunpa River, Nigeria (6.9-7.6) by Ajayi and Adelaye (1977); Epie Lake, Nigeria (6.5-7.8) by Izonfuo and Bariweni (2001); in Alaro River, Nigeria (7.7) by Fakayode (2005). Other countries however showed an acidic pH level of the surface water than that of the study area such as in Tinto River, Spain (2.6±0.2) by Elba-Poulichet et al. (1999) and in Nigeria (4.5±9.5 and 5.9±1.1) by Courant et al. (1987) in Benin River and Sangodoyin (1995) in rivers in Lagos respectively. The total solids (546±570 mg/L) obtained for the study area falls between the values reported in Nigeria (329-1380 mg/L and 160-1480 mg/L) by Onianwa et al. (2001) in Ogunpa River and Fakayode (2005) in Alaro River respectively. The total suspended solids (434±500 mg/L) obtained in this studies was noticed to be higher than all the values obtained from other places according to Table 4.9. The total dissolved solids (111±200 mg/L) shows higher level than other places except in Nigeria (414 mg/L and 3300±7700 mg/L) by Ajayi and Adelaye (1977) in Ogunpa River and Courant et al. (1987) in Benin River. Dissolved oxygen in this study (7.9±3.0 mg/L) falls within the range obtained in 166 UNIVERSITY OF IBADAN LIBRARY Table 4.9: Comparison of the physicochemical parameters in surface water in this study with studies elsewhere Major activities in - Country River Temp. pH TS TSS TDS DO BOD COD Cl Alkalinity References the area Nigeria Osun 25.3±2.6 7.6±0.5 546±570 434±500 111±200 7.9±3.0 6.9±7.5 136±120 55±110 93±120 This study South Industrial Kieskamma - 7.3±0.1 - - - - - 47.6±15.5 - - Morrison et al. (2001) Africa area Nigeria Epie Urban Area 27.3-30.5 6.9-7.6 - - 33.0-62.0 1.38-9.06 0.31-6.77 - 1.65-4.62 15.3-37.3 Izonfuo and Bariweni (2001) Industrial Nigeria Alaro - 6.5-7.8 329-1380 - - 0.63-7.0 - - 475-1130 405-744 Fakayode (2005) area Ethiopia Boye Urban area 21.5-21.8 6.5-6.7 - - - 2.10-5.73 - 6.00-98.2 - - Teferi et al. (2005) Industrial Nigeria Lagos 30.0-40.0 4.5-9.5 - - - - - 44.0-6000 - - Sangodoyin (1995) area South Yamuna, Urban area - - - - - - 20.0-30.0 - - - Sunil and Hideki (2001) Asia Bagmati Nigeria Ogunpa Urban area - 6.9-7.0 - - - 3.5-5.7 4.2-13.2 26.0-44.0 - Sridhar and Bammeke (1985) South Rural Tyume - - - - - 4.15-11.2 - 7.5-249 - - Igbinosa and Okoh (2009) Africa community Nigeria Ogunpa Urban area 26-32 6.6-8.1 160-1480 10-270 0.1-5.9 0.2-8.3 13-560 11.9-224 70.5-688 1.76-28.0 Onianwa et al. (2001) Nigeria Ogunpa Urban area - 7.7 - - 414 2.5 16.4 - 508 25.6 Ajayi and Adelaye (1977) Mining, Crooked U.S.A milling and - 6.7±0.7 - 7.1±7.7 200±200 - - 10±16 - 2±11 Janneth and Foil (1979) creek smelting Industrial Spain Tinto - 2.6±0.2 - - - - - - 11200 - Elba-Poulichet et al. (1999) area Courant et al. (1987) Pet. Nigeria Benin 27.6±1.6 5.9±1.1 - 80.4±130 3300±7700 4.7±1.0 - - 1730±400 - Prospecting Asonye et al. (2007) Farming, urban and Udousoro and Osibanjo (1997) Nigeria Ogun - 7.6±0.2 - - - 6.6±1.5 1.9±1.2 44±80 165±654 48.4±7.70 industrial area * all units in mg/L except temperature (oC) 167 UNIVERSITY OF IBADAN LIBRARY Table 4.9 (contd.) Major Country River activities in the Pb Cu Cd Co Cr Ni Zn References area Nigeria Osun 0.003±0.004 0.003±0.004 0.002±0.003 0.004±0.004 0.01±0.01 0.004±0.010 0.07±0.10 This study Pet. Nigeria New Calabar 850 2080 560 - 50 - 65.9 Wegwu and Akininwor (2006) Prospecting Oghoro, Nigeria Oil Activities ND ND - - 100 - 700 Asonye et al. (2007) Ugheli Nigeria Ogunpa Urban area <10.0-86.0 <1.00-39.0 <1.00-23.0 - 2.00-19.0 <1.00-27.0 <1.00-35.0 Onianwa et al. (2001) U.S.A Mississipi Transportation - - - - - - - NRC (2003) Man-Made Nigeria Urban area 1.22±0.05 - - - - 1.61±0.14 - Adakole et al.(2008) Lake India Brahmani Industrial area 10.0 - 27.0 1.0 - 4.7 0.4 - 4.0 4.0 - 5.6 - 9.0 - 52 0.4 - 80.1 Reza and Singh (2010) India Damodar Industrial area - 3950 300 - 11550 - - Chatterjee et al. (2010) Iran Abbasa Urban area 1.33 6.95 2.62 3.28 - - 124 Sayyed et al. (2006) Rwanda Nyabugogo Agriculture 0.113 - - - 0.110 - - Nhapi et al. (2011) Harare Marimba Urban area 0.21 - 0.54 - - - - - 0.18 - 0.42 Mvungi et al. (2003) South Africa Thohoyan Urban area 0.010 - 0.012 - - - - - 0.002 - 0.003 Okonkwo and Mothiba (2005) Home Nigeria Warri 0.001 0.041 0.007 0.008 - 0.039 0.009 Wogu and Okaka (2011) Industries India Ganga Industrial area 120 10.0 5.00 - - 140 60.0 Aktar et al. ( 2010) Nigeria Ogun Urban area 0.04 0.12 0.01 - - - 0.19 Jaji et al.(2007) Nigeria New Calabar Urban area 0.14 0.07 0.05 - - 0.01 - Abu and Egenonu ( 2008) Farming, Nigeria Ogun urban and - 0.17±0.03 - - - - 0.47±0.57 Udousoro and Osibanjo (1997) industrial area ND –Not detected 168 UNIVERSITY OF IBADAN LIBRARY Nigeria (1.38-9.06 mg/L, 0.2-8.3 mg/L) by Izonfuo and Bariweni (2001) in Epie Lake; Onianwa et al. (2001) in Ogunpa River respectively and in Tyume River, South Africa (4.15-11.2 mg/L) by Igbinosa and Okoh (2009). Lower dissolved oxygen content of some of the rivers than the study area were also noticed such as in Boye pond, Ethiopia (2.10±5.73 mg/L) by Teferi et al. (2005) and in Nigeria (3.5 ±5.7 mg/L, 2.5 mg/L, 4.7±1.0 mg/L) by Ajayi and Adelaye (1977) in Ogunpa River, in Ogun River, Nigeria (6.6±1.5 mg/L) by Udousoro and Osibanjo (1997), Courant et al. (1987) in Benin River and in Boye pond, Ethiopia by Teferi et al. (2005). The biochemical oxygen demand (6.9±7.5 mg/L) obtained for the study was well above the BOD obtained in Epie Lake, Nigeria (0.31±6.77 mg/L) by Izonfuo and Bariweni (2001) and Ogun River, Nigeria (1.9±1.2 mg/L) by Udousoro and Osibanjo (1997) but lower to values obtained in other places such as in river Yamuna, South Asia (20.0-30.0 mg/L) by Sunil and Hideki (2001) and in Ogunpa River, Nigeria (13-560 mg/L and 16.4 mg/L) by Ajayi and Adelaye (1977) and Onianwa et al. (2001) respectively. The COD for the study area was (136±120 mg/L) and falls within the range obtained in Nigeria (44.0-6000 mg/L, 7.5-249 mg/L, 11.9-224 mg/L) as reported by Sangodoyin (1995) in Lagos River, Onianwa et al. (2001) in Ogunpa Lake and Igbinosa and Okoh, (2009) Tyume River, South Africa respectively. Lower value for COD were however reported in Kieskamma River by Morrison et al. 2001 (47.6±15.5 mg/L) in South Africa, Ogun River, Nigeria (44±80 mg/L) by Udousoro and Osibanjo (1997) and in Crooked Creek, U.S.A. (10±16 mg/L) by Jenneth and Foil (1979) in U.S.A. The chloride level obtained for the study area (55±110 mg/L) revealed a lower concentration when compared to what was obtained in Nigeria (475-1130 mg/L, 70.5- 688 mg/L, 508 mg/L, 1730±400 mg/L, 165±654 mg/L and 11200±6300 mg/L) by Ajayi and Adelaye (1977) in Ogunpa Lake; Onianwa et al. (2001); Fakayode, (2005) in Alaro River; Courant et al. (1987) in Benin River; Udousoro and Osibanjo (1997) and Elba-Poulichet et al. (1999) in Tinto River, Rea of Hueva, Southwest Spain respectively. The reason might be due to the discharge of untreated waste from some of these industries into the water bodies without considering the assimilative capacity of the river. 169 UNIVERSITY OF IBADAN LIBRARY However, a lower chloride value than in the study area was reported in Ogunpa Lake, Nigeria (1.65-4.62 mg/L; 26.0-44.0 mg/L) by Sridhar and Bammeke (1985); Izonfuo and Bariweni (2001) in Epie Lake. The level of alkalinity in the study area was 93±120 mg/L and lower to what was obtained in other countries except in Alaro River, Nigeria (405-744 mg/L) by Fakayode (2005). Sulphate (39±30 mg/L) level was lower compared to other countries whose values were used for comparison except what was reported by Sridhar and Bammeke (1985) in Ogunpa River (92.0-116 mg/L). Table 4.9 shows comparison of heavy metals in surface water with other studies. The concentration of lead (0.003±0.004 mg/L) was found to be lower compared to values reported in in Calabar River, Nigeria (850 mg/L, <10.0-86.0 mg/L) by Wegwu and Akininwor (2006) and in Ogunpa Lake, Nigeria by Oniawa et al. (2001) respectively. The reason for the high lead concentration in Calabar River was as a result of the nature of petroleum activities occurring in that area. However, lead was not detected in Benin River, Nigeria by Asonye et al. (2007) in another oil activity region. The level of copper obtained for the study was 0.003±0.004 mg/L and low to what was obtained in Calabar River, Nigeria (2080 mg/L) by Wegwu and Akininwor (2006) and also as reported in Ogunpa Lake, Nigeria by Onianwa et al. (2001) (<1.00-39.0 mg/L). Copper was not detected at all in Benin River, Nigeria by Asonye et al. (2007) and by the report submitted in Mississipi River, U.S.A. by NRC (2003). The cadmium content of River Osun was 0.002±0.003 mg/L and was low compared to the values obtained in Ogunpa Lake, Nigeria (560 mg/L and <1.00-23.0 mg/L) as reported by Oniawa et al. (2001); Wegwu and Akininwor (2006) in Calabar River, Nigeria. The concentration of cobalt obtained in River Osun was 0.004±0.004 mg/L and was not reported by any of the authors used in this study. Chromium (0.01±0.01 mg/L), Nickel (0.004±0.010 mg/L) and Zinc (0.07±0.10 mg/L) had low values compared to all other values obtained in other studies used in this work. The level of lead and zinc in Osun River was higher than what was obtained in Warri River, Nigeria (0.001mg/L and 0.009 mg/L respectively) by Wogu and Okaka (2011). The levels of heavy metals in Osun River was low compared to what were obtained in some rivers: in a man-made Lake, Nigeria by Adakole et al. (2010); in Marimba River, Harare by Mvubgi et al. (2008); in Ogun River, Nigeria by Jaji et al. (2007); and in New Calabar River, Nigeria by Abu and Egenonu (2008). These areas are urban 170 UNIVERSITY OF IBADAN LIBRARY settlement where most wastes from homes are discharged into the rivers without being treated. The concentration of heavy metals in industrial areas in other studies reveals a higher concentration because of industrial activities and the fact that most of the industries do not treat their wastes before discharging into rivers as reported in Brahmani River, India by Reza and Singh (2010); in Damoda River, India by Chartterjee et al. (2010); in Ganga River, India by Aktar et al. (2010). High level of lead (0.113 mg/L) and chromium (0.110 mg/L) were found in Nyabugogo River, Rwanda by Nhapi et al. (2011) compared to what was obtained in Rriver Osun in this study. 4. 11 AVERAGE PHYSICOCHEMICAL CHARACTERISTICS (OVERALL) OF THE SEDIMENT The physicochemical parameters determined for sediment quality included organic carbon, sediment mechanical properties (sand, clay and silt), cation exchange capacity (CEC) and the heavy metals (Pb, Cu, Cd, Co, Cr, Ni and Zn). The overall average results obtained are as shown in Table 4.10. The organic carbon content of Osun river sediment had an average value of 2.5%. This value was higher than the value obtained at the control site. It indicates that the study area was contaminated. Organic carbon gives an estimate of the amount of organic matter in sediment; and its concentration is largely determined by the addition of surface litter (fallen leaves, manure and dead organisms) and root material and the rate at which microbes break down organic compounds. Organic matter is a key component in assessing soil fertility, stability, and catchment health and sediment condition. The sediment contained 72.5±3.1% sand, 25.6±3.2% clay and 2.0±2.2% silt. The levels of these sediment particles at the control site were lower to the results obtained in Osun River. The results indicate that the river sediment was sandy in nature, signifying that the water holding capacity of the river sediment will be greatly influenced by draining more readily and will not hold ions for a long time. The clay content of the sediment was low compared to the sand and this can have effect on the absorbed cations and will reduce the upward movement of these cations. The silt content was not all that significant. 171 UNIVERSITY OF IBADAN LIBRARY Table 4.10 Average physicochemical characteristics of sediment Accumulation Parameters (Unit) Present Study Control Site Factor Organic Carbon (%) 2.5±7.3 0.7±0.4 3.57 Sand (%) 72.53.1 71.7±2.4 - Clay (%) 25.6±3.2 25.5±2.7 - 2.0±2.2 2.6±2.6 - Silt (%) 48±16 48±11 1.00 CEC (meq/100g) 0.70±5.80 0.10±0.20 7.00 Pb (µg/g) 0.40±1.90 0.10±0.10 4.00 Cu (µg/g) 0.03±0.10 0.01±0.01 3.00 Cd (µg/g) 0.10±0.30 0.10±0.10 1.00 Co (µg/g) 0.10±0.20 0.10±0.10 1.00 Cr (µg/g) 0.10±0.40 0.20±0.30 0.50 Ni (µg/g) Zn (µg/g) 12±31 8.6±9.6 1.40 172 UNIVERSITY OF IBADAN LIBRARY The sediment textures of many rivers have been reported. (Young, 1980; Zobeck and Fryrear, 1986; Davies and Abowie 2009). Davies and Abowei (2009) reported sediment consisting 57.86±2.65% sand, 17.4±1.68% silt and 24.67±1.33% clay and with significant spatial variation at p<0.005. Young (1980) described particle detachment and transport on particle size distribution, density and degree of the matrix sediment. Clay and silt aggregates in the 20 to 200 micrometers range are more erodible than larger or smaller particles. Very sandy sediments, not well aggregated erode as primary particles. Carolyn et al. (2004) obtained sediment composition of a wetland of Kerny Marsh to be 70-94% sand while the smaller particles of silt and clay ranged from 4% to 31%. The sediments at the bottom of waters play a role in the study of pollution in the rivers and can be used to ascertain the quality of the surface water (Oyeyiola et al., 2006). Walling and Moorehead (1989) reported that for rivers with relatively low solute concentrations, an order of magnitude difference exist between the medium particle size associated with the ultimate and effective grain size distributions. Carolyn et al. (2004) reported that Kearny Marsh grain size was dominated by sand typically at levels greater than 80%. Those sand levels were similar to those found in Massachusetts salts marshes, which averaged 80% (Hansen et al., 1996). The cation exchange capacity of the river sediment obtained was 48±16 meq/100g as shown Table 4.10. The cation exchange capacity (CEC) of sediment is simply a measure of the quantity of sites on sediment surfaces that can retain positively charged ions (cations) by electrostatic forces. Thus, CEC is important for maintaining adequate quantities of plant available calcium, magnesium, and potassium in sediments. It is a good indicator of sediment quality and productivity. Cation exchange sites are found primarily on clay and organic matter surfaces. Cation exchange capacity is based on the surface area of sediment + grain particles available for binding cations such as hydrogen (H ) and free metal ions 2+ (e.g. Mn ). Sediments with a high percentage of small grains, such as silt and clay, have high surface-to-volume ratios and can absorb more heavy metals than sediments composed of large grains, such as sand (Liber et al., 1996). Ali (1978) reported in his study that the level of exchangeable Ca, Mg and K as well as extractable Cu, Zn and Mn are highly influenced by the amount of organic matter present. Organic matter is largely confined to the surface horizons which invariably contain higher amounts of the nutrients than the one below. Ronald and Kennedy (1970) reported the average 173 UNIVERSITY OF IBADAN LIBRARY CEC of clay, silt, sand and gravel fractions of steam sediment from the Mattole River of Northern California as 37.6, 11.0, 9.7 and 7.2 meq/100g respectively. Organic matter contributed about 15 percent of the total CEC of the sediment. However the CEC of the sand and gravel fractions exclusive organic matter were 5.5 to 8.0 meq/100g. The results obtained for heavy metals in Osun River sediments as shown in Table 4.10 are 0.7±5.8 µg/g (Pb), 0.4±1.9 µg/g (Cu), 0.03±0.10 µg/g (Cd), 0.1±0.3 µg/g (Co), 0.1±0.2 µg/g (Cr), 0.1±0.4 µg/g (Ni) and 12±31 µg/g (Zn). These levels were found to be higher than what was obtained in the control site except for nickel. Lead was the most accumulated of the heavy metals in sediment with a factor of 7.00, and then copper, with factor of 4.00. The levels of heavy metals in this sediment were due to the effect of industrial, urbanization and agricultural activities along the Osun River channel. Data on levels of heavy metals in sediment of rivers abound in literature: Bonnevie et al. (1994) reported high metal concentrations in the sediment found in Hackensack River and Newark Bay, New Jersey to contain Cd (10±6mg/kg), Hg (2.1±2.6mg/kg), Ni (39± 49mg/kg) and Pb (421±403mg/kg). The levels of heavy metals in Shasha River sediment in Lagos Lagoon was reported by Oyeyiola et al. (2006) to contain 20.5 µg/g Pb, 25.3 µg/g Zn, 7.9 µg/g Cu, 30.8 µg/g Cr and 1.5 µg/g Cd. Akpan et al. (2002) reported concentrations in Calabar River to be below levels that are known to be harmful to aquatic biota: Pb (0.6-3.0ppm), Ni (1.2-22.5ppm), Cr (0.6-3.3ppm), Cu (0.3-48ppm), Zn (0.8-27ppm), Fe (0.2-2,880ppm) and V (0ppm). 4.12 AVERAGE PHYSICOCHEMICAL CHARACTERISTICS OF SEDIMENTS OF OSUN RIVER AND ITS TRIBUTARIES The overall average organic carbon obtained was 2.5±7.3 %. Table 4.11 shows that all the tributaries had higher percentage organic carbon than what was obtained in the control area. This implies that the sediment of Osun River is contaminated with different types of organic material ranging from fallen leaves, decay carcasses of some animals found along the river bank and faeces seen in some of the tributaries. All these can contribute to the organic matter in sediment. Twenty-five of the tributaries had sand as the most predominant particle in its sediment texture but higher compared with the control site. All the tributaries were found to contain more clay in its texture compared to the control site except in Anne. Six out of the tributaries contained silt 174 UNIVERSITY OF IBADAN LIBRARY Table 4.11: Average characteristics of sediments of Osun River (for all tributaries) Parameter Oyi Osin Ounseku Arenounyun Ashasha Isin Anne Ahoyaya (OYI) (OSI) (OUN) (ARE) (ASH) (IRE) (ANN) (AHO) Organic Carbon (%) 1.5±1.3 4.3±9.2 2.7±5.9 1.9±2.6 3.2±6.1 1.9±1.8 4.9±6.5 3.7±3.6 Sand (%) 72.7±3.5 73.8±3.5 73.6±3.6 73.1±3.2 72.3±3.7 72.2±3.5 74.6±4.0 72.5±2.2 Clay (%) 25.5±3.6 24.6±3.8 24.6±3.5 25.4±2.8 25.6±3.3 26.1±3.5 23.9±4.1 25.3±2.5 Silt (%) 1.9±1.4 1.6±1.1 1.8±1.6 1.5±1.1 2.2±1.3 1.7±1.3 1.5±1.2 2.3±1.4 CEC (meq/100g) 46±16 56±15 47±16 43±14 53±19 45.4±8.7 54±16 52±14 Pb (µg/g) 0.10±0.10 0.20±0.20 0.10±0.10 0.10±0.20 0.20±0.20 0.10±0.10 0.10±0.20 0.10±0.200 Cu (µg/g) 0.10±0.10 0.20±0.70 0.30±0.60 0.10±0.10 0.20±0.30 0.10±0.10 0.20±0.20 0.20±0.30 Cd (µg/g) 0.03±0.10 0.03±0.10 0.01±0.01 0.01 0.01±0.10 0.01 0.03±0.10 0.02±0.03 Co (µg/g) 0.10±0.20 0.10±0.40 0.20±0.40 0.10±0.10 0.11±0.10 0.10±0.20 0.10±0.20 0.10±0.10 Cr (µg/g) 0.03±0.03 0.10±0.10 0.12±0.20 0.10±0.10 0.10±0.20 0.10±0.10 0.10±0.10 0.10±0.10 Ni (µg/g) 0.10±0.10 0.10±0.20 0.10±0.20 0.10±0.20 0.10±0.10 0.10±0.20 0.10±0.20 0.20±0.60 Zn (µg/g) 7±10 15±19 5.30±5.20 13±21 17±25 10±14 14±20 22±33 175 UNIVERSITY OF IBADAN LIBRARY Table 4.11 (contd.): Parameter Enja Oloyo Gbodofon Awesin Ojutu Osun Yeyekare Oni (ENJ) (OLO) (GBD) (AWE) (OJU) (OSU) (YEY) (ONN) Organic Carbon (%) 1.8±2.8 2.1±2.6 1.5±1.4 1.5±1.9 2.5±8.6 1.3±1.2 1.3±1.7 3.0±3.4 Sand (%) 73.9±2.7 72.8±2.5 73.7±3.3 72.8±2.1 72.6±3.2 71.4±1.8 72.0±2.5 71.6±3.6 Clay (%) 25.3±2.1 25.3±2.1 24.9±3.8 26±3 25.9±3.7 26.5±3.0 25.9±2.5 26.1±2.0 Silt (%) 1.5±1.2 1.8±1.1 1.4±1.4 1.3±1.1 1.5±1.2 2.2±2.9 2.1±2.4 2.2±3.8 CEC (meq/100g) 45±12 47±13 54±16 48±14 54±16 48±16 45±14 44±18 Pb (µg/g) 0.10±0.10 0.10±0.10 0.10±0.10 0.10±0.20 0.10±0.10 0.10±0.10 0.10±0.10 0.10±0.10 Cu (µg/g) 0.10±0.10 0.20±0.50 0.50±0.90 0.10±0.30 0.10±0.10 0.10±0.20 0.23±0.40 0.20±0.40 Cd (µg/g) 0.04±0.10 0.03±0.10 0.10±0.20 0.02±0.10 0.03±0.10 0.03±0.10 0.02±0.04 0.03±0.10 Co (µg/g) 0.04±0.10 0.10±0.10 0.10±0.10 0.10±0.10 0.10±0.10 0.03±0.04 0.03±0.04 0.10±0.20 Cr (µg/g) 0.10±0.10 0.10±0.10 0.10±0.10 0.10±0.10 0.10±0.10 0.10±0.10 0.10±0.10 0.10±0.60 Ni (µg/g) 0.10±0.10 0.10±0.10 0.20±0.60 0.10±0.20 0.10±0.20 0.10±0.10 0.10±0.10 0.10±0.20 Zn (µg/g) 6±11 12±19 21±25 13±19 14±20 13±15 7.7±10.0 11±16 176 UNIVERSITY OF IBADAN LIBRARY Table 4.11(contd.): Parameter Olumirin Orufu Kankere Adeti Aro Aro Odoiya Oba (OLU) (ORU) (KAN) (ADE) (EJI) (ARO) (ODD) (OBB) Organic Carbon (%) 2.7±7.0 1.8±3.4 2.6±4.8 2.5±3.2 1.3±1.3 1.4±2.1 1.8±1.3 1.0±1.4 Sand (%) 72.1±2.1 71.2±5.4 72.4±2.5 71.5±3.4 72.4±1.9 71.9±2.1 72.5±2.6 72.2±2.8 Clay (%) 26.0±2.4 26.0±4.4 25.4±4.6 26.5±3.3 25.7±3.0 26.1±2.9 25.6±2.4 26.3±2.7 Silt (%) 1.9±1.8 2.8±4.4 2.3±4.3 2.0±2.4 1.9±1.6 1.9±2.0 1.9±1.5 1.5±1.1 CEC (meq/100g) 43±11 45±15 46±11 49±12 48±17 40±16 44±12 43±14 Pb (µg/g) 0.10±0.10 0.10±0.10 0.03±0.10 18±28 0.10±0.30 0.10±0.10 0.10±0.10 0.10±0.10 Cu (µg/g) 0.10±0.20 0.10±0.20 0.20±0.30 6.1±8.9 0.10±0.10 0.20±0.30 0.10±0.10 0.10±0.10 Cd (µg/g) 0.03±0.10 0.01 0.03±0.20 0.10±0.20 0.01 0.01±0.01 0.01 0.02±0.03 Co (µg/g) 0.10±0.10 0.04±0.10 0.10±0.10 0.40±1.10 0.10±0.10 0.20±0.50 0.10±0.10 0.20±0.30 Cr (µg/g) 0.10±0.10 0.04±0.03 0.20±0.50 0.10±0.10 0.10±0.10 0.10±0.10 0.10±0.04 0.10±0.30 Ni (µg/g) 0.10±0.10 0.10±0.20 0.10±0.20 0.20±0.30 0.10±0.10 0.10±0.20 0.10±0.10 0.10±0.20 Zn (µg/g) 5.00±6.20 6.80±9.50 12±19 60±140 5.6±11.0 7.6±11.0 9.4±19.0 9.3±17.0 177 UNIVERSITY OF IBADAN LIBRARY Table 4.11(contd.): Parameter Oyika Etioni Ishasha Opa Ope Moginmogin Asejire (OYK) (ETI) (ISA) (OPP) (OPE) (MOG) (ASJ) Organic Carbon (%) 2.0±1.8 2.4±3.6 2.5±3.2 2.5±2.7 2.9±3.0 1.7±1.7 1.6±2.3 Sand (%) 72.4±3.7 72.5±2.3 71.5±3.4 74.2±2.8 71.2±2.8 73.2±3.2 71.5±1.8 Clay (%) 25.1±3.6 25.8±2.3 26.5±2.4 24.5±2.8 26.4±3.0 25.2±3.1 25.9±2.8 Silt (%) 2.4±3.6 2.2±3.8 2.0±2.4 1.2±0.9 2.5±3.1 1.6±0.9 2.7±2.8 CEC (meq/100g) 42±20 44±18 49±12 62±14 51±18 52±17 44.1±9.2 Pb (µg/g) 0.10±0.20 0.10±0.10 0.10±0.20 0.10±0.20 0.20±0.30 0.10±0.10 0.10±0.10 Cu (µg/g) 0.20±0.80 0.10±0.10 0.20±0.50 0.30±0.50 0.80±1.60 0.30±0.70 0.10±0.30 Cd (µg/g) 0.02±0.70 0.01 0.02±0.10 0.01 0.10±0.10 0.03±0.10 0.10±0.10 Co (µg/g) 0.04±0.10 0.10±0.10 0.10±0.30 0.30±0.70 0.10±0.20 0.10±0.10 0.10±0.10 Cr (µg/g) 0.10±0.10 0.10±0.10 0.20±0.20 0.10±0.10 0.10±0.20 0.10±0.10 0.10±0.10 Ni (µg/g) 0.10±0.20 0.40±1.50 0.10±0.20 0.10±0.20 0.20±0.30 0.10±0.20 0.10±0.20 Zn (µg/g) 8±14 5.40±7.60 8.30±9.20 16±31 12±20 25±34 12±18 178 UNIVERSITY OF IBADAN LIBRARY particle higher than the control site at Egun. Cation exchange capacity was found to be very high in nine of the tributaries compared to the control site, suggesting that these tributaries will have more surfaces for exchange of cations. The levels of heavy metals in sediment of all the tributaries in Table 4.11 were found to be below the guidelines stipulated by the Canadian and Ontario sediment quality guidelines, the consensus based sediment quality guidelines of the Wisconsin and sediment quality guideline values of ERL and ERM. Three of the tributaries had their lead level higher than what was obtained at the control site; two had an accumulation factor of 2 (Osin and Ope) and one had a factor of 180 (Adeti). These tributaries accumulated high level of lead and might be due to anthropogenic activities around the bank of the river. Eight of the tributaries had more level chromium than level obtained in the control site. The tributaries and their accumulation factor are: Osin, Ashasha, Anne, Ahoyaya, Kankere and Aro with accumulation factor of 2. Adeti had the highest accumulation factor of 61 for copper, and it‟s the second most accumulated of the heavy metals. These could be due to various activities by man along the river bank and dumping of electronic wastes in the river. This metal can be leached from river into sediment. None of the tributaries had its level of cadmium greater than what was obtained in the control site. Table 4.11 shows that all of the tributaries had higher cobalt level greater than the amount in control site.Those with very high accumulation factors were Ounseku (20), Ashasha (11), Adeti (40), Aro (20), Oba (20) and Opa (30). Cobalt was found to be the third most accumulated of the heavy metals in the sediment and was found in Adeti sediment. Three out of the thrity-one tributaries had higher concentration than in control site with factors of accumulation to be 1.2 (Ounseku) and 0.2 (Kankere and Ishasha). None of the tributaries had its nickel value greater than that of the control sample, sediment at Etioni with a factor of 2. Nineteen of the the tributaries had higher zinc content compared with the control sediment. Those with high factor of accumulation are Gbodofon (2.44), Ahoyaya (2.55), Moginmogin (2.91) and Adeti (6.98). Zinc was the fourth most accumulated metal in the sediment. Contaminated sediments may be derived from inputs of suspended solids to which toxic substances are adsorbed, such as soil particles in surface water run-off from fields treated with chemicals. Alternatively, the natural suspended material in a watercourse as well as the river bed surface can adsorb chemicals from the water. When the 179 UNIVERSITY OF IBADAN LIBRARY material settles out, the toxic material forms a sink or reservoir. A one-way analysis of variance (ANOVA) revealed statistical significant differences in means of parameters in these tributaries. 4.13 SPATIAL VARIATION OF THE PHYSICOCHEMICAL CHARACTERISTICS OF SEDIMENT The spatial variation of the physicochemical characteristics and heavy metals in sediment for all tributaries are as shown in Figures 4.28 - 4.39. From Figure 4.28, one would notice variation of some physicochemical parameters from upstream to downstream point of the river network. The organic carbon of the tributaries reduced as the river flows downstream. The figures depict a transition from more upstream portions (such as OYI, OSI, OUN, ARE, ASS, IRE, ANN, AHO, and ENJ) to down stream portions (such as ASJ, MOG, OPE, OPP and ISA). Changes were observed not to be significant except at some tributaries such as ADE, MOG, OPE, GBD, ETI and ISA where there are some unusual increase due to random input from diffuse sources. Spatial variation of other parameters in Figures 4.29 to 4.39 follow general pattern as for organic carbon. 4.14 SEASONAL VARIATION OF THE PHYSICOCHEMICAL CHARACTERISTICS AND HEAVY METALS IN SEDIMENT The mean seasonal variations of the physicochemical characteristics and heavy metals in sediment are as shown in Table 4.12. The Physicochemical characteristic include organic carbon, sand, clay, silt, CEC, Pb, Cu, Cd, Co, Cr, Ni and Zn. Parameters that were higher in wet seasons The following parameters were observed to be higher in the wet seasons: Organic carbon, clay and copper. This was as a result of the high flow rate of water in the wet seasons. People dump all sorts of wastes into streams during wet seasons. These wastes contain a lot of contaminant that impair river sediment. These contaminants get absorbed by sediment and undergo various kinds of reactions. These absorption and accumulation may cause secondary pollution problem. These newly received deposits are carried into the river by storm water runoff from surrounding complex watershed which sometimes comprises of towns, villages, major roads and industries through 180 UNIVERSITY OF IBADAN LIBRARY Figure 4.28: Spatial variation of organic carbon in sediment Figure 4.29: Spatial variation of sand in sediment 181 UNIVERSITY OF IBADAN LIBRARY Figure 4.30: Spatial variation of clay in sediment Figure 4.31: Spatial variation of silt in sediment 182 UNIVERSITY OF IBADAN LIBRARY Figure 4.32: Spatial variation of cation exchange capacity in sediment Figure 4.33: Spatial variation of Pb in sediment 183 UNIVERSITY OF IBADAN LIBRARY Figure 4.34: Spatial variation of Cu in sediment Figure 4.35: Spatial variation of Cd in sediment 184 UNIVERSITY OF IBADAN LIBRARY Figure 4.36: Spatial variation of Co in sediment Figure 4.37: Spatial variation of Cr in sediment 185 UNIVERSITY OF IBADAN LIBRARY Figure 4.38: Spatial variation of Ni in sediment Figure 4.39: Spatial variation of Zn in sediment 186 UNIVERSITY OF IBADAN LIBRARY Table 4.12: Seasonal variation of the physicochemical characteristics and heavy metals in sediment Parameters Dry Season Wet Season Organic Carbon (%) 1.69± 8.17 3.39±6.41 Sand (%) 73.0±3.86 71.9±2.00 Clay (%) 24.7±3.80 26.5±1.96 Silt (%) 2.30±2.81 1.61±1.28 CEC (meq/100g) 49.7±17.2 47.0±13.8 Pb (µg/g) 0.93±7.51 0.42±3.41 Cu (µg/g) 0.30±2.17 0.46±1.65 Cd (µg/g) 0.03±0.08 0.02±0.08 Co (µg/g) 0.13±0.41 0.07±0.11 Cr (µg/g) 0.10±0.19 0.07±0.11 Ni (µg/g) 0.12±0.10 0.10±0.17 Zn (µg/g) 14.4±4.10 11.1±16.1 187 UNIVERSITY OF IBADAN LIBRARY which the river flows. Contaminants are generated in these environments. The values were high because of early flush pheneomenon. As the wet seasons progresses towards the dry seasons some of the deposits are carried further along the course of the river thus reducing the level of sediment. Parameters that were higher during dry seasons Sand, silt, CEC, lead, cadmium, cobalt, chromium, nickel and zinc were higher in the dry seasons. In the dry seasons, the flow rate and volume of Osun River was low and the contaminants increase in concentration, since there is no water to wash them away and dilute the river. During dry seasons all wastes from urbanization, industrial and agricultural activities settle in sediment and increase the contaminant bioavailablity. The presence of suspended matter such as leaves, sticks, carcasses of different fauna and other floating substances that can not sink are seen in these river sediments. Since the flow rate is low, deposited contaminants can not be washed away and so there is bioconcentration. Paired t-tests performed revealed that there were no statistical significant differences at p< 0.05 except for organic carbon, Pb, Cu, Co and Cr that were statistically significant. 4.15 APPLICATION OF MODELS TO PREDICTION OF FUTURE SEDIMENT QUALITIES The data obtained for sediment were subjected to modeling to determine the trend and seasonality up to year 2018. The results are as shown in Table 4.13. The model equations and the predicted concentrations up to year 2018 are also indicated. The coefficient of 2 determination (R ) gives an indication about the suitability of the model. In Asejire, the model explains effectively 60% variation of Zn (with predicted concentration of 324µg/g for September, 2018). For other parameters, the model could not explain significant variations. Comparison of the predicted Zn concentration in Adeti with guidelines showed a high Zn content greater than the limit stipulated by sediment quality guidelines for ERL (150 µg/g Zn) and ERM (410 µg/g Zn), Canadian and Ontario sediment quality guidelines (123µg/g and 120 µg/g respectively) and the consensus based 188 UNIVERSITY OF IBADAN LIBRARY Table 4.13: Time-series analysis results of selected parameters of sediment from selected tributaries 2 Tributary Parameter R Model equation Forecast for 2018 Adeti Zn 0.46 19.5 – 20.8b 1243µg/g, November. Asejire Zn 0.60 42.3-4.54b 324 µg/g, September. Etioni Zn 0.42 15.1 -1.38b 273 µg/g, January. -2 Gbodofon Co 0.53 0.19-1.95 x 10 b - Zn 0.69 57.0 – 5.59b 3202 µg/g, January. Ishasha Zn 0.45 21.3 – 1.82b 675 µg/g, January. Isin Zn 0.47 28.3 -2.62b 304 µg/g, November. Moginmogin Zn 0.68 72.3 -7.48b 1.32 µg/g, January. Oba Co 0.52 0.51 -5.03b - Ojutu Ni 0.47 19.5-1.68b - Oloyo Zn 0.43 36.9 – 3.46b 1853µg/g, November. Osun Zn 0.59 35.9-3.67b 3899µg/g, September. -2 Oyi Ni 0.41 0.21- 2.23 x 10 b - Zn 0.42 31.0 – 3.13b - 189 UNIVERSITY OF IBADAN LIBRARY sediment quality guidelines of Wisconsin (120 µg/g Zn). This indicates that the river sediment here was contaminated with this metal. The level of Zn in Etioni was predicted to be 273 µg/g with 42% variation and Ishasha 675 µg/g with 45% variation. The Zn level of this two tributaries were found to exceed the limit stipulated by all the guidelines except in Ishasha where Zn was lower compared with the sediment quality guidelines of the ERM which is 410 µg/g.This means the quality of the sediment will not be impaired except at Ishasha. The predicted level of Zn at Adeti (1243 µg/g), Oloyo (1853 µg/g), Gbodofon (3202 µg/g) and Osun (3899 µg/g) were found to be higher than the level stipulated by all the sediment quality guidelines. This implies that the sediment will be grossly polluted in 2018. The sediment will therefore need to be remediated by any appropriate remediation methods. This contaminated sediment will release this toxic metal into waterbody and will also contaminate the available water meant for living organisms in the water and the usage of water for all purposes. This is so because Zn will dissolve in water and its bioavailability will increase. Moginmogin showed predicted level of Zn (1.32 µg/g), with 68.0% variation. This level was found to be very low compared to the level of Zn in all the sediment quality guidelines. This shows that the level will not give a significant contamination but might still need to be monitored. 4.16 PRINCIPAL COMPONENT ANALYSIS OF HEAVY METALS IN SEDIMENT The principal component analysis of metals in sediment is as shown Fig. 4.40. It reveals clustering property for the heavy metals in sediment. Cd, Co, Cr and Ni were observed to cluster together indicating correlation between these metals and were also supported from the Pearson correlation moment obtained in Table 4.14. Zn, Cd, Co, Cr, Cu and Ni are far from the origin and these parameters have more influence in determining the principal component analysis model unlike Pb with short distance from the origin. The first 190 UNIVERSITY OF IBADAN LIBRARY Pb Cu CCNo dirZn Figure 4.40: Principal component analysis of heavy metals in sediment Fig. 4.40: Principal Component Analyses of Heavy Metals in Sediment 191 UNIVERSITY OF IBADAN LIBRARY principal eigenvalues captures 74.9% of the total variance which was due to the clustered heavy metals (Cu, Cr, Cr, Cd and Ni). These metals contributed to the contamination of the Osun River sediment and were due to both industrial and domestic waste discharges. The second factor explains 14.5% of the total variance for Zn. Zn might have originated from antropogenic activities of man such as leaching of zinc material that contaminate water body and get absorbed by river sediment. The third factor explains 9.28% of the total variance for lead. Pb originated from anthropogeneic activities due to land erosion, because car exhausts which get deposited on land can be washed through erosion into river Osun and get settled at the bottom. The Figure suggests different sources of Zn, Pb and other metals. 4.17 CORRELATION OF METALS CONCENTRATIONS BETWEEN WATER AND SEDIMENT Table 4.14 shows the correlation between metals in water and metals in sediment. It reveals a high correlation for lead (0.99), cobalt (0.99) and cadmium (0.99), signifying that the source of these metals in the two media are the same. Lower but also strong correlation was observed for copper with coefficient of correlation of 0.75. 4.18 ACCUMULATION FACTORS BETWEEN METALS IN SEDIMENT AND WATER FOR THE STUDY Table 4.15 reveals the accumulation factors between sediment and water to be highest for lead (233) followed by zinc (171). Copper (133) also had high accumulation factor compared to the other remaining metals. A very high accumulation factor for lead indicates that lead is the most prominent of heavy metals pollutants discharged into the water course. This was probably due to instream hygrological modifications, instream sludges accumulation and air pollution fallout. The accumulation of these metals in sediment can be traced to terrestrial 4.19 PEARSON MATRIX FOR HEAVY METALS IN SEDIMENT Table 4.16 reveals the correlation matrix obtained for heavy metals in sediment. High positive correlations were obtained for the following pairs in sediment sample analysed: 192 UNIVERSITY OF IBADAN LIBRARY Table 4.14: Sediment-Water correlations of metal concentrations Metal Pb Cu Cd Co Cr Ni Zn Correlation 0.99 0.78 0.99 0.99 0.92 0.89 0.97 coefficient Table 4.15: Sediment/water factors of accumulation of metal concentrations for Osun River Metal Accumulation Factor Lead 233 Copper 133 Cadmium 15 Cobalt 25 Chromium 10 Nickel 25 Zinc 171 193 UNIVERSITY OF IBADAN LIBRARY Table 4.16: Correlation matrix for heavy metals in sediment Pb Cu Cd Co Cr Ni Zn Pb 1.000 Cu 0.723** 1.000 Cd 0.253** 0.398** 1.000 Co 0.379** 0.531** 0.128** 1.000 Cr 0.020 0.031 0.096** 0.103** 1.000 Ni 0.155** 0.118** 0.065 0.107** 0.112** 1.000 Zn 0.159** 0.206** 0.378** 0.234** 0.212** 0.226** 1.000 ** Correlation is significant at the 0.01 level (2-tailed) * Correlation is significant at the 0.05 level (2-tailed) 194 UNIVERSITY OF IBADAN LIBRARY Cu/Pb (r = 0.723), Co/Cu (r = 0.532). Medium positive correlations was also observed for Cd/Cu (r = 0.378) and Zn /Cd (r = 0.378) at p= 0.01. This suggests a common source for the polluting substances such as industrialization, urbanization and agricultural activites. 4.20 CALCULATION OF ENRICHMENT FACTOR AND INDEX OF GEO- ACCUMULATION A common approach to estimating how much sediment is impacted (naturally and anthropogenically) with heavy metals is to calculate using enrichment factor (EF) (Huu et al., 2010). The evaluation of the degree of metal contamination or pollution in terrestrial, aquatic and marine environment was also calculated using the index of geo-accumulation (Mediolla et al., 2008; Asaah and Abimbola, 2005). Table 4.17 illustrates the enrichment factor and index of geo-accumulation obtained for the sediment. Where C metal and Cnormalizer = concentrations of heavy metals and normalizer in sediment and control sample. Enrichment factors were calculated from the mean concentrations of the heavy metals in the sampling points used for this study. The control sampling point was considered to be the unpolluted or background point. The normalizing element used was zinc according to Mendiola et al. (2008). Enrichment (EF) of the heavy metals in sediment showed that Zn (1), Cd (1.74), Co (0.97), Cr (0.83), and Ni (0.42) had no enrichment: Pb (3.81) had strong enrichment and Cu (2.12). Therefore, contamination of Osun River sediment by lead and copper originated from anthropogenic activities such as agriculture, urbanization, and industrialization. 195 UNIVERSITY OF IBADAN LIBRARY Table 4.17: Overall Enrichment Factor and Indices of Geo-accumulation in sediments Zn Pb Cu Cd Co Cr Ni Cmetal 12.8 0.67 0.38 0.03 0.10 0.09 0.11 Cnormalisation 12.8 12.8 12.8 12.8 12.8 12.8 12.8 Cmetal/control 8.55 0.12 0.12 0.01 0.07 0.07 0.19 Cnormalisation 8.56 8.56 8.56 8.56 8.56 8.56 8.56 EF 1.00 3.81 2.16 1.74 0.97 0.83 0.42 Igeo 0.08 -3.25 -7.68 -351 -31.5 -35.0 -11.1 EF< 2 is deficiency to mininmal enrichment EF< 2-5 is moderate encrichment EF< 5-20 is significant enrichment EF > 20-40 very high enrichment EF > 40 extremely high enrichment I geo <0 = unpolluted 0< = I geo < 1 unpolluted to moderately polluted 1< = I geo < 2 moderately polluted 2< = I geo < 3 moderately to strongly polluted 3< =I geo < 4 strongly polluted 196 UNIVERSITY OF IBADAN LIBRARY The results of the calculation of geo-accumulation index (I geo) in the sediment are as shown in Table 4.17. The negative values of Pb (-3.22), Cu (-7.68), Cd (-352), Co (- 31.5), Cr (-35.0) and Ni (-11.1) according to Huu et al. (2010) showed that sediment was moderately polluted by Pb and Cu. The Igeo suggests pollution of the sediment mostly with lead and copper. 4.21: COMPARISON OF SEDIMENT METAL CONCENTRATIONS WITH SEDIMENT QUALITY GUIDELINES Table 4.18 shows the number of tributaries with heavy metal values greater than what was stipulated by the sediment quality guidelines. The upper range of some of these tributaries had high heavy metal levels than some of the guidelines. Adeti had as its highest range for lead to be -70 mg/kg; copper - 24 mg/kg; cadmium - 0.5 mg/kg; cobalt - 3.0 mg/kg; and zinc - 397 mg/kg. The value for lead was greater than the limit set by the Canadian and Ontario sediment quality guidelines (35.0 mg/kg and 31.0 mg/kg Pb respectively), sediment quality guideline of ERL (46.7 mg/kg Pb) and the consensus based sediment quality guidelines of Wisconsin (36.0 mg/kg Pb). The level of copper in Adeti was found to be above the Ontario sediment quality guideline (16.0 mg/kg Cu). The level of cadmium was not as high as the limit set for the Canadian and Ontario sediment quality guidelines. The level of zinc in Adeti was higher compared to all the guidelines except for the sediment quality guidelines of ERM. Sediment is the ultimate depository of many chemical compounds including heavy metals from natural and anthropogenic sources. The reason for this accumulation of heavy metals in Adeti sediment must be as a result of discharge of waste from brewery industry located around this environment and runoff from diffuse pollution sources. Enja and Etioni had chromium and nickel concentration to be lower to other guidelines. 4.22 COMPARISON OF HEAVY METALS IN SEDIMENT IN THIS STUDY WITH STUDIES ELSEWHERE Table 4.19 reveals the comparison of heavy metal concentration in this study with studies elsewhere. The level of lead was observed to fall below the value obtained in selected Rivers in Polland (10-126 µg/g) by Solecki and Chiboeski (1999), Ikpoba River, Nigeria 197 UNIVERSITY OF IBADAN LIBRARY Table 4.18: Comparison of metal levels in sediment with various guideline limit Guide/Criteria Pb Cd Cr Cu Ni Zn Canadian 35.0 0.60 37.3 35.7 - 123 Ontario 31.9 0.60 26.0 16.0 16.0 120 ERM 218 70 9.6 270 51.6 410 ERL 46.7 1.2 81 34 20.9 150 CBSQG 36.0 0.99 43.0 32.0 23.0 120 No. of locations not exceeding 30 31 30 30 31 30 the guidelines No. of locations exceeding the 1 - 1 1 - 1 guidelines Ref: Wisconsin Department of Natural Resources (2003), Canadian Council of Ministers of the Environment (1999), Ontario Ministry of Environment and Energy (1993) 198 UNIVERSITY OF IBADAN LIBRARY Table 4.19: Comparison of heavy metals in sediment in this studies with studies elsewhere (µg/g) Major activities Country River Pb Cu Cd Co Cr Ni Zn References in the area Nigeria Osun 0.7±5.8 0.4±1.9 0.03±0.10 0.1±0.3 0.1±0.2 0.1±0.3 12±31 This Study Pakistan Ravi Industrial - 3.38±160 0.99±3.17 2.22±18.5 4.60±57.4 - - Rauf et al. (2009) Kosova Prishtina Industrial 20.3-115 6.90-81.2 0.22-4.45 - - - - Fatos et al. (2010) Albania Lana Urban area 0.21-0.34 - 0.10-0.20 - - 54.4-87.0 - Alma et al. (2010) Hungary Tisza Urban/aquatic - 53 - - - 75 - Szilard et al. (2008) Nigeria Ojora Urban/industrial - - 0.47±0.36 - - - - Adekoya et al. (2006) Nigeria Ibiekuma Urban area 0.11-0.92 1.05-1.97 0.01-0.36 0.08-0.40 0.02-0.35 44.9-55.9 Obasohan (2008) Polland Masluchowskie Urban area 10-126 - - - - 9-169 Solecki and Chibowski (1999) Nigeria Ikpoba Urban area 3.3 1.9 1.5 - 1.9 3.95 4.7 Oguzie (2002) Bangladesh Buriganga Industrial - - - - - 12.3-31.5 - Habibet et al. (2009) Australia Swan Industrial area 184 297 0.9 - - - - Andrew et al. (2004) Turkey Nalihan Urban area 0.49 1.12 - - - 0.77 - Ayas et al. (2007) Lebanon Jordan Urban area 8.1 - 0.63 - - - 20.3 Howani and Banat (2007) Lebanon Yarmouk Urban area 8.4 - 0.67 - - - 26.4 Howani and Banat (2007) Portugal Lima Urban area - 16.0-406 - - 24.0-84.0 3.0-27 58-398 Raquel et al. (2008) Bangladesh Buriganga Industrial 64.7 -77.1 21.8 -32.5 2.36 -4.25 - 119 -218 147 – 258 - Ahmad et al. (2010) 60.1 - Bangladesh Shitalakhya Industrial 54.2- 65.9 56.1 - 91.5 1.71 -2.17 - 121- 132 - 91.0 Ahmad et al. (2009) France Lot Industrial 523 97.7 125 - - - - Audry et al. (2004) France Lot Urban 105 30.7 20.4 - - - - Audry et al. (2004) Spain Tinto Industrial 870 846 6.2 - - - - Galan et al. (2003) Farming, urban Nigeria Ogun and industrial - 39.5±11.7 5.76±1.22 - 136±5 96.5±44.7 Udousoro and Osibanjo (1997) area 199 UNIVERSITY OF IBADAN LIBRARY (3.3 µg/g) by Oguzie (2002) and in Iber River, Kosova (20.3-115 µg/g) by Fatos et a.l (2010), Swan River (184 µg/g), Australia by Andrew et al. (2007) and Jordan and Yarmouk Rivers, Lebanon (8.1 and 8.4 µg/g) by Howani and Banat (2007). Lead level in this study was however found to be higher than levels obtained in Ibiekuma stream, Nigeria (0.11-0.92 µg/g) by Obasohan (2008) and in Lana River, Albania (0.21-0.34 µg/g) by Alma et al. (2010). The level of cadmium in the present study was found to be below the levels obtained in Ogun River, Nigeria (5.76±1.22 mg/L) by Udousoro and Osibanjo (1997); Ikpoba River, Nigeria (1.50 µg/g) by Oguzie (2002), in Igbede, Ojo and Ojora Rivers, Nigeria (0.47±0.36 µg/g) by Adekoya et al. (2006), in river Ravi, Pakistan (0.99±3.17 µg/g) by Rauf et al. (2009) and in Iber River, Kosova (0.22-4.45 µg/g) by Fatos et al. (2010). The study reveals lower heavy metal levels compared to most other studies, this might be as a result of the environment of the studied river. For instance, most of the rivers elsewhere where industries are cited have their sediment contaminated. These industries discharge their wastes into these rivers without being treated and impair the quality of the river sediment. Levels of heavy metals metals were reported to be generally higher than Osun River in Yarmouk River, Lebanon by Howani and Banat (2007); in Lima River, Portugal by Raquel et al. (2008); Buriganga River, Bangladesh by Ahmad et al. (2010); in Shitalakhya River, Bangladesh by Ahmad (2009); Ogun River, Nigeria by Udousoro and Osibanjo (1997) and in River Lot, France by Audry et al. (2004). Cobalt was noticed to be present in river Ravi, Pakistan (2.22±18.5 µg/g) by Rauf et al. (2009) and higher than the level obtained in Osun River sediment and was due to industrial waste discharge into the river. Chromium in Osun River was higher than the lowest value obtained in Ibeikuma River, Nigeria (0.08±0.40 µg/g) by Obasohan (2008). Though still within the range in this river. Nickel in Osun River sediment was found to be greater than the lower range value of sediment in Ibeikuma, Nigeria (0.02 – 0.35 µg/g) by Obasohan (2008) and lower to the value obtained in Ogun River (136±5 mg/L) by Udousoro and Osibanjo (1997). An implication that the sediment is still comparable to sediment from this river. Zinc in this 200 UNIVERSITY OF IBADAN LIBRARY study was however higher than the level reported in Ikpoba River, Nigeria (4.7 µg/g) by Oguzie (2002) and the lower range in Masluchowskie River, Polland (9 – 169 µg/g) by Solecki and Chibowski (1999) but lower compared to the value obtained in Ogun River (96.5±44.7 mg/L) by Udouosoro and Osibanjo (1997). Some of the metals were noticed to be below the range obtained in some other countries signifying that the pollution level of River Osun sediment is still within minimal as compared to what was obtained elsewhere. 4.23 AVERAGE METAL CONCENTRATION (OVERALL) IN PLANT The overall average characteristics of heavy metals in plant are as shown in Table 4.20. Pb was 0.9±1.1 µg/g and higher than Pb at the control site with an accumulation factor of 1.13. Cu had an average concentration of 1.4±3.3 µg/g and higher than what was obtained at the control site with an accumulation factor of 1.40. The overall average value of Co was 1.1±1.6 µg/g and was higher than the level of cobalt at the control site with a factor of 1.22. The overall average level of Cr was 0.9±1.2 µg/g and higher than the level at the control site with a factor of 1.13. Ni had an average value of 1.3±8.4 µg/g and higher the the level obtained at the control site with a factor of 1.86. The level of Zn obtained for the study was 32±63 µg/g and higher compared to what was obtained at the control site with an accumulation factor of 1.1. The accumulation factor showed that nickel was the most accumulated heavy metal in plants along Osun River bank and then copper. The sources of these metals could be as a result of man‟s activities such as industrial and urban activities. Atmospheric deposition of metals is also another source by which these metals can get deposited on plants. Nickel is mainly transported in the form of a precipitated coating on particles and in association with organic matter. Nickel may also be absorbed via uptake by plants from sediments. The results obtained in this study was not as high as the one reported by Onianwa and Fakoyode (2000) where they reported an average value of heavy metals in plant around a battery manufacturing factory: Zn (59.2 mg/kg), Cu (18.4 mg/kg), Ni (8.26 mg/kg), Cr 201 UNIVERSITY OF IBADAN LIBRARY Table 4.20: Average (overall) concentration of metals in Plants Concentration Control Site Accumulation Metal (µg/g) (µg/g) Factor Pb 0.9±1.1 0.8±0.5 1.13 Cu 1.4±3.3 1.0±1.2 1.40 Cd 0.5±0.5 0.5±0.6 1.00 Co 1.1±1.6 0.9±1.1 1.22 Cr 0.9±1.2 0.8±0.8 1.13 Ni 1.3±8.4 0.7±0.9 1.86 Zn 32±63 29±31 1.10 202 UNIVERSITY OF IBADAN LIBRARY (8.74 mg/kg), Cd (1.63 mg/kg) and Pb (1350 mg/kg). This signifies that vegetations along the river banks are not as polluted as what was obtained in the control site. Bioavailability of the elements depends on the form of their bond within the constituents of a soil. The level of heavy metals in plants in this study was as a result of anthropogenic activities around the river banks. Plants readily assimilate through the roots such compounds, which dissolve in waters and occur in ionic forms. Additional sources of these elements for plants are rainfall, atmospheric dusts, plant protection agents, and fertilizers, which could be absorbed through the leaf blades. (Lozak et al., 2001). 4.24 AVERAGE CONCENTRATIONS OF HEAVY METALS IN PLANTS AROUND THE TRIBUTARIES The average characteristics of plants around Osun River for thirty-one tributaries are as shown in Table 4.21. The levels of heavy metals were compared with concentration obtained at the control site. Fourteen of the thirty-one tributaries had higher lead values in plant than the level obtained in the control site. Isin and Adeti were found to be one of those thirteen tributaries that accumulated lead the most with accumulation factor of 1.44. Fourteen tributaries were found to have higher concentration of copper compared to the control sample and the tributary that accumulated copper the most was Asejire with accumulation factor of 2.43. This might be due to the discharge of waste from near-by source such as the Nigerian bottling company and air deposition. Only one tributary was found to have higher cadmium concentration compared to the level in the control sample, and this was Osin with an accumulation factor of 2.0. Thirteen of the tributaries had higher cobalt level greater than plant at the control site. Three of these tributaries were Ounseku, Awesin and Adeti with a factor of 1.44. The sources of this metal in these tributaries are due to man‟s activities around the river bank and atmospheric pollution. Six out of the thirty-one tributaries had higher nickel level compared to what was obtained at the control site. Those tributaries that accumulated nickel prominently were Arenounyun and Awesin with a factor of 1.46. Tweleve tributaries were found to have higher zinc concentration greater than the control site. Adeti was found to accumulate the highest amount of zinc, with a 203 UNIVERSITY OF IBADAN LIBRARY Table 4.21: Average concentrations (µg/g) of heavy metals in plant in all tributaries Parameter Oyi Osin Ounseku Arenounyun Ashasha Isin Anne Ahoyaya (OYI) (OSI) (OUN) (ARE) (ASS) (IRE) (ANN) (AHO) Pb 1.00±1.60 0.80±0.90 1.10±1.30 0.80±0.90 1.20±1.50 1.30±1.70 1.00±1.30 0.80±0.70 Cu 0.80±0.90 1.00±1.30 1.00±1.10 3.10±5.50 1.40±2.70 1.00±1.20 1.40±3.10 0.60±0.70 Cd 0.40±0.50 0.70±0.90 0.40±0.50 0.50±0.50 0.50±0.60 0.40±0.50 0.40±0.50 0.40±0.50 Co 0.80±0.60 1.60±2.80 1.30±2.10 1.20±1.60 1.30±2.30 1.00±1.30 1.00±1.00 0.60±0.70 Cr 0.90±1.30 0.90±1.40 1.30±1.80 0.60±0.70 0.50±0.40 1.00±1.40 0.90±0.80 0.60±0.70 Ni 0.70±1.50 0.80±1.50 0.90±1.50 1.60±3.40 1.90±3.4 0.40±0.80 0.90±1.50 0.40±0.50 Zn 20±28 32±45 18±23 46±100 22±44 37±69 24±33 18±44 Table 4.21 (contd.): Parameter Enja Oloyo Gbodofon Awesin Ojutu Osun Yeyekare Oni (ENJ) (OLO) (GBD) (AWE) (OJU) (OSU) (YEY) (ONN) Pb 1.10±1.50 0.80±0.70 0.90±0.80 0.80±0.70 0.90±0.90 1.20±1.20 1.20±1.20 0.80±0.50 Cu 1.90±3.80 0.70±1.20 2.10±7.00 1.00±0.80 0.80±0.80 0.80±0.80 3.20±7.90 0.80±1.00 Cd 0.50±0.50 0.40±0.50 0.40±0.50 0.40±0.50 0.40±0.50 0.40±0.50 0.40±0.50 0.40±0.50 Co 0.70±0.70 1.70±2.30 0.80±1.10 0.90±1.20 1.10±2.00 1.40±2.30 1.40±0.20 0.80±1.00 Cr 0.80±1.00 1.00±2.30 0.80±0.80 1.30±1.60 0.70±1.20 1.20±1.70 0.60±0.60 0.60±0.70 Ni 0.90±1.50 0.90±1.80 1.50±5.10 1.90±3.80 1.80±4.30 1.10±1.90 0.40±0.60 0.70±1.50 Zn 32±47 20±30 49±91 28±51 43±90 41±70 30±71 18±30 204 UNIVERSITY OF IBADAN LIBRARY Table 4.21 (contd.): Parameter Olumirin Orufu Kankere Adeti Aro Aro Odoiya Oba (OLU) (ORU) (KAN) (ADE) (EJI) (ARO) (ODD) (OBB) Pb 1.10±0.90 1.10±1.30 1.10±1.30 1.30±1.40 0.80±0.80 0.60±0.50 1.00±1.60 0.60±0.50 Cu 1.70±3.10 0.60±0.60 0.80±0.70 3.00±5.80 1.10±2.20 0.50±0.70 0.60±0.60 0.70±0.80 Cd 0.50±0.50 0.40±0.50 0.40±0.50 0.50±0.50 0.40±0.50 0.40±0.50 0.40±0.50 0.40±0.50 Co 1.20±1.70 0.60±0.60 0.70±0.70 1.20±1.50 1.30±2.20 0.90±1.10 0.80±1.20 1.00±1.30 Cr 0.80±0.80 0.50±0.70 0.60±1.10 1.30±1.70 1.00±1.40 0.90±1.50 0.60±0.90 0.80±1.20 Ni 1.40±3.50 0.80±1.50 0.90±1.50 0.80±1.50 0.70±1.00 0.70±1.20 0.90±1.60 1.50±2.60 Zn 27±48 13±17 26±32 57±92 53±100 30±51 10±16 41±85 Table 4.21(contd.): Parameter Oyika Etioni Ishasha Opa Ope Moginmogin Asejire (OYK) (ETI) (ISA) (OPP) (OPE) (MOG) (ASJ) Pb 0.90±0.90 0.60±0.50 1.00±0.90 1.20±0.90 0.60±0.50 0.70±0.50 0.80±0.80 Cu 2.30±4.00 1.70±3.10 0.90±1.40 3.20±7.90 1.70±3.20 1.50±2.80 3.40±6.10 Cd 0.50±0.70 0.40±0.50 0.40±0.50 0.40±0.50 0.40±0.50 0.50±0.50 0.50±0.60 Co 0.90±1.40 0.50±0.60 1.20±1.80 1.40±0.20 1.40±1.80 0.70±0.90 1.00±1.30 Cr 0.60±0.80 1.10±1.80 1.00±1.40 0.60±0.60 1.10±1.50 1.00±1.40 0.70±0.90 Ni 0.60±1.00 0.60±0.90 0.60±1.50 0.40±0.60 0.80±1.50 0.70±0.40 0.80±1.10 Zn 41±100 31±56 34±59 30±71 32±43 44±89 35±63 205 UNIVERSITY OF IBADAN LIBRARY factor of 1.78. This is due to the way people discharge waste containing various materials into this river. Zinc in plant can be absorbed through the river sediment and through atmospheric dust fallout. One-way analysis of variance showed statistical significant differences in the means of parameters obtained between the tributaries. 4.25 SPATIAL VARIATION OF HEAVY METALS IN PLANTS The spatial variations of heavy metals in plants for all the tributaries are as shown in Figures 4.41 to 4.47. Figure 4.41 depicts transition of lead in plants from upstream (such as OYI, OSI, OUN, ARE, ASS, IRE) to downstream point (such as OYK, ETI, ISA, OPP, OPE, MOG and ASJ). The level of lead in plant decreases from upstream to the dowmstream of the river network. The changes in lead concentration are not significant except at some tributaries where there is an unusual increase in the level of lead due to input from diffuse point source. The spatial distribution of other heavy metals in plants follow general pattern as for lead and this is as illustrated in Figures 4.42 to 4.47. 4.26 SEASONAL VARIATION OF HEAVY METALS LEVELS IN PLANTS The seasonal variations of heavy metals in plants are as shown in Table 4.22. The metals are discussed on the basis of the period when they are significant. Heavy metals that are higher during dry season Table 4.22 shows that copper, cadmium, nickel and zinc were higher in dry seasons. This can be attributed to aerial deposition of metals on leaves through chimney of some industries. The metals are absorbed and get concentrated because there is no rain to wash them away. Wind can also be a major factor in aerial deposition of heavy metals on plants. Plant can also accumulate metals from sediment. The chemical composition of plants reflects the elemental composition of the sediment and the contamination of the plant surface indicates the presence of noxious environmental contaminants. This suggests that plant uptake these metals in sediment through the waste discharged into the river by some anthropogenic activites such as industrialization, erosion and domestic activites. 206 UNIVERSITY OF IBADAN LIBRARY Figure 4.41: Spatial variation of Pb in plant Figure 4.42: Spatial variation of Cu in plant 207 UNIVERSITY OF IBADAN LIBRARY Figure 4.43: Spatial variation of Cd in plant Figure 4.44: Spatial variation of Co in plant 208 UNIVERSITY OF IBADAN LIBRARY Figure 4.45: Spatial variation of Cr in plant Figure 4.46: Spatial variation of Ni in plant 209 UNIVERSITY OF IBADAN LIBRARY Figure 4.47: Spatial variation of Zn in plant 210 UNIVERSITY OF IBADAN LIBRARY Table 4.22: Seasonal variation of heavy metals level in plants (µg/g) Parameters Dry Season Wet Season Pb 0.77±0.73 1.12±1.30 Cu 1.69±4.21 1.16±2.03 Cd 0.53±0.63 0.38±0.52 Co 0.57±0.63 1.56±2.07 Cr 0.66±0.94 1.07±1.40 Ni 1.70±11.8 0.82±1.54 Zn 55.9±81.4 7.82±10.1 211 UNIVERSITY OF IBADAN LIBRARY Heavy metals that are higher during wet seasons Lead, cobalt and chromium were observed to be higher in the wet seasons. When it rains, contaminants are carried into rivers and get absorbed by sediment. The concentrations of these metals absorbed by plant in the wet seasons implies that runoff water from industries and agricultural fields contained these metals which get into the river sediment and it was absorbed by plants. Rain water can react with fumes containing these metals from industries and get deposited on leaves of plants. Paired t-test shows statistical significant differences between dry and wet seasons means values of all the metals. This signifies that the metals probably originated from diffuse point sources such as rainfall and anthropogenic activities such as agriculture, industrialization and municipal wastewater discharge. 4.27 CORRELATION OF HEAVY METAL LEVELS IN PLANTS Table 4.23 illustrates Pearson correlation matrix obtained for heavy metals in plant. A medium positive correlation was obtained for the following pairs of heavy metals: Zn/Cu (r = 0.518), Cd/Pb (r = 0.375), Cr/Co (r = 0.339), Co/Cd (0.264), Cu/Cd (r = 0.244) and Zn/Cd (r = 0.206). All these positive correlations signify that the association among these pairs is linear but very weak. Negative correlations were however obtained between Zn/Co (r = - 0.074), Zn/Cr (r = - 0.128) and Ni/Cr (r = - 0.009). 4.28 PRINCIPAL COMPONENT ANALYSIS OF HEAVY METALS IN PLANTS Figure 4.48 shows the principal component analysis model of heavy metals in plants. The first principal eigenvalues captures 75.6% of the total variance which was due to the metals Cd, Co, Cr, Pb and Cu revealing a kind of correlation between each other and from the same source. The second principal eigenvalues captures 15.2% of the total variance for Ni and the third principal eigenvalues captures 9.21% of the total variance for Zn. However, Cu, Zn and Ni did not show any clustering meaning that the source of these metals as pollutant might be from diffuse sources. 212 UNIVERSITY OF IBADAN LIBRARY Table 4.23: Pearson correlation of heavy metals in plant Parameter Lead Copper Cadmium Cobalt Chromium Nickel Zinc Lead 1.000 Copper 0.159** 1.000 Cadmium 0.375** 0.244** 1.000 Cobalt 0.343** 0.128** 0.264** 1.000 Chromium 0.156** 0.046 0.090* 0.339** 1.000 Nickel 0.045 0.072* 0.102** 0.037 -0.009 1.000 Zinc 0.034 0.518** 0.206** -0.074* - 0.128** 0.083* 1.000 ** Correlation is significant at the 0.01 level (2-tailed) * Correlation is significant at the 0.05 level (2-tailed) 213 UNIVERSITY OF IBADAN LIBRARY Ni CPd Cu CC bor Zn Figure 4.48: Principal Component Analyses of Heavy Metals in Plants 214 UNIVERSITY OF IBADAN LIBRARY 4.29 COMPARISON OF THE HEAVY METALS IN THIS STUDY WITH STUDIES ELSEWHERE The comparison of heavy metals in plants in this study with levels in other places is as shown in Table 4.24. The level of lead in this study was found to be greater than levels reported in Lana River, Albani (0.01 – 0.26 µg/g) by Alma et al. (2010) an urban area; Msimbazi River, Tanzania (0.19 – 0.66 µg/g) by Bahamuka and Mubofu (1999); Calabar River, Nigeria (0.13 µg/g) by Edem et al. (2008) and in Karachi River, Pakistan (0.020±0.001 µg/g) by Syed and Muhammad (2008). The level of lead was however reported in some rivers to be greater than what obtained in this study. These include River Khetri, India (4.0 – 6.6 µg/g) by Maharia et al. (2010), a copper mine and in Gujarat River, India (1.9±0.4 µg/g) by Nirmal et al. (2009) an agricultural field. The copper content of plants in study area was higher than levels reported in Calabar River, Nigeria (0.88µg/g) by Edem et al. (2009); in Khetri River, Pakistan (0.040±0.004 µg/g) by Syed and Mohammad (2008) an industrial area; in Sabah River, Malasia (1.2±1.6 µg/g) by Yap et al. (2009). The level of copper in plants were found to be higher than levels in this study in Tisza River, Hungary (64.0 µg/g) by Szilard et al. (2008) and Khetri, India (32 -77 µg/g) by Maharia et al. (2010). The levels of cadmium in Khetri River, India (1.5 – 3.0 µg/g) by Maharia et al. (2010), Gujarat River, India (2.8±0.3 µg/g) by Nirmal et al. (2009) and Buriganga River, Bangladesh (0.56 – 1.40 µg/g) by Habibat et al. (2009) were reported to be higher than cadmium in plants of the study. All other studies showed lower cadmium levels compared to the study area. Cobalt in plants in this study was higher than the level reported in Gujarat River, India (0.2 - 0.3 µg/g) by Nirmal et al. (2009) an agricultural field but lower to cobalt level in Tisza River, Hungary reported by Szilard et al. (2008). Chromium in plants in this study was higher than the level reported by Edem et al. (2009) in Calabar River, Nigeria (0.08 µg/g) but lower compared to levels in Karachi River, 215 UNIVERSITY OF IBADAN LIBRARY Table 4.24: Comparison of heavy metals in vegetation in this study with studies elsewhere (µg/g) Major River activities in Pb Cu Cd Co Cr Ni Zn References Country the area Nigeria Osun 0.9±1.1 1.4±3.3 0.5±0.5 1.1±1.6 0.9±1.2 1.3±8.4 32±63 This study Albani Lana Urban area 0.01-0.26 - 0.02-0.05 - - 18.6-250 - Alma et al. (2010) Hungary Tisza Urban area - 64.0 15.0 - 2.20 16.8 Szilard et al. (2008) Nigeria Lagos Urban area 6.35-20.9 - - - - - - Adekunle et al. (2009) Tanzania Msimbazi Urban area 0.19-0.66 0.25-1.60 0.01-0.06 - - - 1.48-4.93 Bahemuka and Mubofu (1999) Bangladesh Buriganga Industrial - - 0.56-1.40 - - 1.27-5.37 - Habib et al. (2009) area Calabar Urban area 0.13 0.88 - - 0.08 - 0.03 Edem et al. (2009) Nigeria River Pakistan Karachi Industrial 0.020±0.001 0.040±0.004 - - 1.11±0.90 - 0.02±0.02 Syed and Muhammad (2008) Areas India Khetri Copper 4.0 -6.6 32 - 77 1.5-3.0 - 2.6-5.9 3.1-9.0 24-50 Maharia et al. (2010) mines Malasia Sabah Rice Field ND 1.2±1.6 0.20±0.02 - 1.0±0.1 - 1.2±0.6 Yap et al. (2009) India Gujarat Agricultural 1.9±0.4 1±55 2.8±0.3 0.2±0.3 - 0.4±0.2 3.1±0.8 Nirmal et al. (2009) Field ND- not detected 216 UNIVERSITY OF IBADAN LIBRARY Pakistan by Syed and Muhammad (2008); in Khetri River, India by Maharia et al. (2010) a copper mine; and Sabah River, Malasia as reported by Yap et al. (2009). The level of nickel as reported in Lana River, Albani by Alma et al. (2010); Tisza River, Hungary by Szilard et al. (2008), an urban area and in Khetri River, India by Maharia et al. (2010) were higher than level of heavy metals in plant in the study. Other studies showed lower nickel level in plants. Zinc in the study was higher than the levels reported in Tisza River, Hungary (16.8 µg/g) by Szilard et al. (2008); Msimbazi River, Tanzania (1.48 – 4.93 µg/g) by Bahamuka and Mubofu (1999); in Calabar River, Nigeria (0.03 µg/g) by Edem et al. (2009); Karachi River, Pakistan (0.02±0.02 µg/g) reported by Syed and Muhammad (2008); in Sabah River, Malasia (1.2±0.6 µg/g) by Yap et al. (2009) and Gujarat River, India (3.1±0.8 µg/g) by Nirmal et al. (2009). The levels of these metals in plants varied as a result of environmental and the activities carried out along the bank of each river. 217 UNIVERSITY OF IBADAN LIBRARY CHAPTER FIVE 5.0 SUMMARY, CONCLUSION AND RECOMMENDATION 5.1 SUMMARY AND CONCLUSION As the world is ushered into the modern era of civilization, water and its management will continue to be a major issue, which will definitely have profound impact on our lives and that of our planet Earth than ever before. Indeed water is life. Everyday water systems all over the world receive polluting runoffs of industrial processes, fertilizers, pesticides, sewage, and mining drainage. River Osun flows through Osun State along with several tributaries and some man-made canals. Along this channel, rapid industrialization is taking place day by day and so this most peaceful area is changing in industries and urbanization. Most drains of these industries carry effluents from factories and also from adjacent residential colonies with their sewage which is finally discharged into River Osun and its tributaries. The study was carried out to evaluate the impact of industrial, agricultural and urbanisational activities on River Osun by studying the physicochemical properties of surface water, sediments and vegetations for twenty four months. Water quality characteristics, which include physicochemical parameters and seven heavy metals were analysed in the thirty one tributaries and control area. Sediment were analysed for organic carbon, sediment mechanical properties, cation exchange capacities and seven heavy metals in the thirty one tributaries and control area. Seven heavy metals were analysed in 26 species of plants in thirty one tributaries of Osun River so as to capture the main point of river and its tributaries of the study area and in the control area. The investigations carried out have yielded the following conclusions: The overall mean concentrations obtained for surface water parameters indicate levels that were within the water quality guidelines with the exception of turbidity. However, some of the tributaries showed concentrations higher than the water quality guidelines. This was due to discharge of waste entering the river and its tributaries from homes and industries. The result revealed that this discharge could pose significant health and environmental risk to those who rely on River Osun as their source of domestic water without treatment. The tributaries most grossly affected include Adeti, Moginmogin, Kankere, Ope, and Awesin. Those pollutants most prominent include turbidity, nitrate, phosphate, chloride, total 218 UNIVERSITY OF IBADAN LIBRARY suspended solids and ammonia. A look at these rivers physically shows that the rivers are not healthy. Some locations were found to contain higher sediment heavy metal levels in comparison with sediment quality guidelines. Tributaries such as Adeti, Etioni, Ahoyaya, Asejire, Moginmogin and Ope, and Enja were mostly affected. Seasonally, River Osun shows more significant pollution during the dry seasons for most of the parameters studied, due to low flow rates and volumes of most of the rivers which helps to increase the concentration of contaminants. Modelling of the data obtained for surface water and sediment predicted higher levels of some parameters by 2018. 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Transaction of the ASAE TAAEAJ 29.4:1032–103. 253 UNIVERSITY OF IBADAN LIBRARY Appendix 1: Concentrations of Electrical conductivity (µS/cm) in surface water for all locations Sample July September November January March May July S eptember November January M arch May Code 2006 2006 2006 2007 2007 2007 2007 2007 2007 2008 2008 2008 OYI 58.5 47.0 47.2 45.5 44.0 72.0 60.0 53.0 58.5 110 136 126 OSI 173 124 120 136 198 129 139 130 97.5 212 331 366 OUN 99.8 43.3 58.7 68.8 153 90.0 87.0 58.0 55.8 121 178 176 ARE 158 91.0 146 250 229 231 112 94.7 100.0 490 546 302 ASS 159 78.0 111 168 302 174 287 141 90.0 347 465 589 IRE 43.0 40.7 67.6 159 273 118 37.7 31.3 43.7 239 395 186 ANN 108 98.2 101 103 107 108 100 91.0 83.0 200 215 217 AHO 109 73.0 96.0 320 * 345 62.7 75.7 72.0 515 1410 442 ENJ 75.0 48.0 52.6 63.3 * 89.0 72.0 49.0 54.0 163 202 134 OLO 61.0 43.3 43.4 53.7 219 112 67.7 63.0 42.0 52.0 * 126 GBD 108 100 94.3 90.0 339 163 99.8 89.8 91.0 254 344 299 AWE 205 100 107 163 * 214 267 174 118 441 * 636 OJU 97.3 79.7 72.4 77.3 85.3 84.5 149 91.3 70.3 176 176 194 OSU 104 95.0 94.6 92.0 115 150 113 98.7 96.0 115 377 221 YEY 96.0 97.0 96.0 108 136 144 98.0 87.0 97.0 112 338 260 ONN 102 73.8 52.1 78.0 105 113 101 51.3 71.0 92.0 269 249 OLU 33.0 26.0 23.8 37.0 29.7 31.7 28.0 19.7 21.0 24.7 126 66.1 ORU 44.0 66.0 61.1 * * * 36.0 62.0 60.0 * * * KAN 154 154 143 165 215 159 153 149 162 185 400 342 ADE 1400 1360 1200 1590 * 1430 1030 484 1240 1680 4740 3160 EJI 90.5 91.5 83.6 103 149 136 130 73.0 87.5 122 348 310 ARO 98.0 98.0 88.0 164 * 371 139 81.5 98.5 * * * ODD 77.5 72.0 58.0 61.0 78.0 68.5 74.0 82.5 64.5 59.5 156 141 OBB 88.3 77.8 87.8 172 228 250 88.8 74.5 105 185 546 400 OYK 46.0 47.5 36.0 39.5 47.5 47.0 45.0 41.0 37.5 34.5 110.0 99.1 ETI 74.7 72.7 60.2 78.7 99.3 81.0 70.0 71.0 69.3 76.7 236 188 ISA 147 122 120 163 357 213 11.0 124 124 163 556 347 OPP 171 12.0 113 144 * 129 114 116 125 145 396 348 OPE 182 153 160 619 * 301 133 118 166 599 * 443 MOG 421 261 366 249 * 86.9 620 304 365 1440 * 1150 ASJ 134 138 97.8 117 133 137 121 91 113 275 295 348 * = No water found 254 UNIVERSITY OF IBADAN LIBRARY Appendix 2: pH in surface water for all locations Sample July September November January March May July September November January March May Code 2006 2006 2006 2007 2007 2007 2007 2007 2007 2008 2008 2008 OYI 8.3 7.9 6.5 7.5 7.5 7.1 7.7 7.9 7.2 7.7 7.5 6.4 OSI 8.0 7.7 7.0 7.2 7.4 7.1 7.4 7.9 6.5 7.0 7.1 6.5 OUN 7.9 8.0 7.7 6.8 7.1 7.0 6.9 7.8 6.4 6.7 7.2 7.0 ARE 8.1 7.5 7.3 7.2 7.3 7.2 7.0 7.8 7.4 9.3 8.9 6.9 ASS 8.0 7.7 7.3 7.8 7.8 7.2 7.9 8.1 7.4 8 8 7.7 IRE 8.0 7.4 6.8 7.2 7.6 7.1 7.0 8.0 7.5 7.4 7.5 7.2 ANN 8.0 8.3 6.8 7.1 7.2 7.3 7.9 7.9 6.4 7.1 7.5 7.5 AHO 8.0 7.4 7.5 7.7 * 7.1 6.9 7.9 7.5 7.7 7.4 7.2 ENJ 7.9 7.7 0.9 6.6 * 7.1 6.9 7.9 7.1 7.0 6.8 6.6 OLO 8.0 8.2 7.5 7.2 7.0 6.9 7.2 7.1 8.6 7.7 * 6.8 GBD 8.0 7.5 6.9 7.8 7.6 7.4 7.9 7.8 8.1 7.9 7.1 7.5 AWE 8.2 7.5 7.7 7.5 * 7.2 7.9 7.8 7.5 8.4 * 7.1 OJU 8.4 7.5 7.5 7.7 7.2 7.2 7.9 7.9 7.6 8.0 7.3 7.4 OSU 8.3 8.1 8.4 7.1 7.2 7.2 7.6 7.6 8.4 7.6 8.0 7.7 YEY 8.1 8.3 7.7 7.0 7.0 7.0 7.4 7.1 7.8 8.1 7.4 6.3 ONN 8.2 8.4 7.5 7.3 7.6 7.4 8.5 6.7 7.6 8.1 7.6 7.1 OLU 8.3 7.9 7.8 7.7 7.0 7.2 7.4 7.5 7.5 8.0 7.6 6.2 ORU 8.1 8.0 8.6 * * * 7.6 7.9 7.3 * * * KAN 8.1 8.1 8.6 7.0 7.1 7.0 7.3 7.7 7.1 7.0 7.1 7.3 ADE 8.2 8.4 7.7 7.6 * 7.9 7.9 7.6 7.4 8.3 7.4 7.6 EJI 8.2 8.2 8.4 7.1 7.4 7.1 7.2 7.9 7.5 7.4 7.7 7.5 ARO 8.3 8.0 8.6 7.5 * 7.2 7.3 7.8 7.3 * * * ODD 8.2 8.1 8.7 7.2 7.1 6.9 7.4 7.8 7.0 7.4 6.6 7.7 OBB 8.3 8.1 8.7 7.4 7.6 7.1 7.7 7.7 7.6 7.4 7.2 7.3 OYK 8.1 8.2 7.7 7.6 7.0 7.2 7.4 7.3 7.5 7.5 7.4 6.1 ETI 8.2 8.1 7.9 7.5 7.1 7.4 7.2 7.6 7.3 7.9 7.4 6.8 ISA 8.1 7.6 8.0 7.8 7.8 7.8 8.0 7.8 7.6 8.5 7.9 6.7 OPP 8.0 8.1 7.8 7.5 * 7.4 8.2 7.3 7.7 8.2 7.5 6.6 OPE 8.3 7.7 6.9 7.8 * 7.2 7.1 7.9 7.3 8.1 * 7.5 MOG 8.4 8.0 7.0 7.7 * 7.3 8.1 7.9 7.3 7.3 * 7.9 ASJ 8.3 8.0 6.8 7.8 7.4 7.2 7.4 7.9 7.5 7.9 7.8 7.8 * = No water found 255 UNIVERSITY OF IBADAN LIBRARY Appendix 3: Concentrations of TS (mg/L) in surface water for all the locations Sample July September November January March May July September November January March May Code 2006 2006 2006 2007 2007 2007 2007 2007 2007 2008 2008 2008 OYI 260 169 620 460 511 237 404 97.0 235 261 412 441 OSI 370 195 165 650 799 503 955 410 710 810 283 231 OUN 205 225 441 931 718 824 202 456 260 430 360 428 ARE 307 692 168 739 1640 349 404 323 405 727 601 381 ASS 320 302 695 361 596 375 363 410 370 550 420 670 IRE 647 589 139 659 1270 476 337 240 320 534 513 521 ANN 323 462 326 664 883 808 335 263 420 497 410 428 AHO 287 359 213 127 * 1550 517 239 239 919 880 1070 ENJ 333 638 72.0 973 * 258 270 300 207 263 248 313 OLO 80 458 367 840 1330 415 269 113 299 401 * 473 GBD 225 333 788 880 1400 756 553 585 601 531 530 730 AWE 273 598 1140 867 * 673 603 507 270 1010 * 1030 OJU 60.0 200 773 1200 411 943 404 273 413 466 440 303 OSU 207 700 568 847 635 1350 471 171 216 433 579 531 YEY 143 515 88.0 229 747 505 303 130 388 180 244 250 ONN 170 608 197 656 715 554 253 271 233 364 331 410 OLU 86.0 156 134 541 461 723 335 101 306 263 206 419 ORU 108 640 531 * * * 202 84.0 140 * * * KAN 410 643 500 712 470 909 202 295 990 128 502 691 ADE 1200 1610 1000 1520 * 2340 6450 480 873 1180 3040 2340 EJI 105 236 561 517 390 707 405 102 305 349 315 950 ARO 330 920 220 530 * 505 304 182 350 * * * ODD 91.0 651 631 256 1370 303 303 139 280 395 590 311 OBB 190 326 521 1020 623 657 253 200 390 394 592 755 OYK 99.0 130 291 380 630 318 303 129 250 395 181 310 ETI 163 240 579 367 720 538 202 187 353 560 413 581 ISA 208 969 421 980 946 845 253 541 392 566 739 491 OPP 360 364 372 1310 * 606 606 406 234 171 330 300 OPE 427 453 741 1750 * 943 405 161 323 893 * 1130 MOG 353 233 437 937 * 1040 606 161 323 1830 * 1170 ASJ 207 1380 507 899 473 648 302 353 393 421 646 500 * = No water found 256 UNIVERSITY OF IBADAN LIBRARY Appendix 4: Concentrations of TDS (mg/L) in surface water for all the locations Sample July September November January March May July September November January March May Code 2006 2006 2006 2007 2007 2007 2007 2007 2007 2008 2008 2008 OYI 29.0 23.0 23.5 22.0 21.5 355 30.5 26.5 29.0 71.2 68.9 63.5 OSI 86.0 61.5 59.0 67.5 98.5 64.5 68.5 63.0 47.0 138 166 182 OUN 49.3 21.0 28.9 34.5 76.5 44.8 43.3 28.8 27.8 78.1 95.0 87.0 ARE 81.0 45.3 72.7 126 131 115 55.7 46.7 50.0 329 276 150 ASS 80.0 39.5 53.5 9.65 151 84.5 143 69.5 25.3 227 238 292 IRE 21.3 20.0 33.7 780 136 59.0 18.7 16.0 21.7 153 202 92.6 ANN 54.2 48.8 50.5 51.0 53.3 53.8 50.0 45.0 41.3 130 109 107 AHO 54.3 34.7 48.0 160 * 172 30.7 38.3 35.7 347 718 220 ENJ 37.3 23.3 26.3 31.0 * 37.0 34.3 24.3 26.3 106 102 67.0 OLO 30.3 21.7 21.3 27.0 109 55.7 33.3 31.3 21.0 25.7 * 63.3 GBD 54.0 49.8 13.2 44.8 169 81.0 50.0 45.0 47.3 165 171 148 AWE 103 48.7 53.0 82.0 * 107 133 86.0 58.3 295 * 318 OJU 48.3 39.9 36.0 38.3 42.3 42.7 74.3 46.3 35.0 115 85.1 96.7 OSU 51.7 47.0 47.0 46.0 57.0 74.7 56.3 49.0 49.0 57.3 190 111 YEY 47.0 48.0 47.5 53.5 68.0 71.5 49.0 43.0 47.5 56.0 170 131 ONN 51.0 35.3 26.0 39.0 52.3 56.3 50.3 25 35.3 45.8 136 126 OLU 16.0 73.0 11.3 18.7 14.3 15.7 13.7 9.67 11.0 12.3 64.6 32.7 ORU 21.5 33.0 30.5 * * * 17.5 30.5 30.0 * * * KAN 77.0 76.0 70.5 81.0 147 79.5 76.0 74.0 81.0 92.5 199 172 ADE 700 679 595 793 * 715 512 242 618 839 2500 1630 EJI 45.0 45.5 41.5 51.0 74.0 61.0 64.5 36.5 44.0 61.0 174 156 ARO 48.5 49.0 44.0 79.0 * 186 70.0 40.0 49.0 * * * ODD 38.5 36.0 29.0 30.5 38.5 34 36.5 41.0 32.5 29.5 79.4 71.3 OBB 44.0 39.0 43.8 87.3 113 125 44.0 36.8 52.0 92.0 273 195 OYK 23.0 24.0 17.5 19.0 23.5 23 22.5 20.0 19.0 17.0 56.4 50.4 ETI 37.0 36.3 30.0 39.0 49.3 40.3 34.7 35.0 34.4 38.3 116 96.3 ISA 72.8 60.5 60.0 81.0 178 107 55.5 61.3 61.8 80.8 285 17.7 OPP 85.5 63.5 57.0 71.5 * 63.5 55 57.5 62.0 71.5 200 175 OPE 90.7 76.0 79.7 306 * 150 65.7 59.0 83.0 407 * 219 MOG 210 130 180 124 * 434 310 59.0 83.0 1000 * 575 ASJ 67.0 68.3 49.0 58.0 66.3 68.0 60.7 45.3 56.3 186 142 176 * = No water found 257 UNIVERSITY OF IBADAN LIBRARY Appendix 5: Concentrations of TSS (mg/L) in surface water for all the locations Sample July September November January March May July September November January March May Code 2006 2006 2006 2007 2007 2007 2007 2007 2007 2008 2008 2008 OYI 231 146 599 438 490 202 374 71.0 206 110 344 378 OSI 284 134 106 583 701 439 887 347 663 673 117 49.0 OUN 156 204 412 897 641 779 159 427 232 352 265 341 ARE 226 648 613 95.0 910 233 348 277 356 398 325 230 ASS 240 263 642 352 446 291 220 341 345 324 183 379 IRE 626 551 106 580 1130 417 318 224 298 381 311 428 ANN 269 414 275 613 832 754 285 218 379 367 301 321 AHO 232 325 165 1110 * 1380 486 201 203 573 162 847 ENJ 296 614 45.0 943 * 221 236 276 181 157 146 246 OLO 50.0 436 346 812 1223 359 236 81.0 278 375 * 410 GBD 171 288 740 836 1230 675 504 539 554 366 359 581 AWE 171 549 1090 785 * 567 470 421 212 719 * 716 OJU 11.7 160 736 1160 369 900 330 227 378 351 355 207 OSU 155 652 520 800 578 1270 415 122 167 375 359 421 YEY 96.0 467 40.0 176 678 434 254 87.0 341 124 74.0 119 ONN 106 573 171 617 663 497 202 246 197 319 195 284 OLU 70.0 143 123 522 449 707 321 92.0 295 251 141 387 ORU 86.0 609 501 * * * 185 53.0 110 * * * KAN 333 567 430 631 323 830 126 221 909 35.5 303 520 ADE 500 929 405 728 * 1630 5940 238 255 342 531 717 EJI 60.0 191 520 466 316 647 341 66.0 261 288 141 795 ARO 282 871 176 451 * 320 234 142 301 * * * ODD 53.0 615 602 226 1325 269 266 98.0 248 366 511 240 OBB 146 287 477 932 510 532 208 163 338 302 319 560 OYK 76.0 106 274 361 607 295 281 109 231 378 125 260 ETI 126 202 549 328 670 498 167 152 319 522 297 485 ISA 133 909 361 900 767 739 197 479 330 486 454 316 OPP 275 301 315 1240 * 543 551 349 172 99 131 125 OPE 356 377 661 1440 * 793 339 102 240 488 * 907 MOG 188 103 258 814 * 608 296 102 240 823 * 592 ASJ 140 1300 458 841 406 580 241 308 337 235 502 324 * = No water found 258 UNIVERSITY OF IBADAN LIBRARY Appendix 6: Turbidity (FTU) in surface water for all the locations Sample July September November January March May July September November January March May Code 2006 2006 2006 2007 2007 2007 2007 2007 2007 2008 2008 2008 OYI 31.0 36.0 13.3 9.40 12.3 30.0 38.0 100 25.3 0.70 19.0 12.4 OSI 28.3 12.4 8.80 18.00 21.0 44.0 193 25.0 11.8 3.70 42.0 43.0 OUN 51.1 23.0 14.9 32.00 79.0 57.0 72.0 50.0 23.5 3.90 92.0 140 ARE 16.8 37.3 12.9 21.4 27.0 10.8 108 13.0 107 3.82 8.23 81.0 ASS 3.80 30.6 12.7 16.0 10.6 15.0 145 18.3 20.6 0.40 20.0 76.0 IRE 110 15.0 14.5 89.0 109 31.0 38.0 22.5 13.7 2.30 28.0 45.0 ANN 9.60 21.0 14.8 12.5 14.2 17.2 21.0 20.0 13.5 1.30 29.0 8.50 AHO 41.0 20.8 8.63 50.6 * 53.0 114 26.9 27.1 1.60 45.0 236 ENJ 43.0 29.4 13.1 76.0 * 82.0 157 45.9 24.7 5.40 259 59.2 OLO 14.9 50.0 16.1 11.0 40.0 53.0 53.0 41.0 24.1 2.40 * 26.7 GBD 19.0 47.0 13.2 149 27.0 24.0 88.0 76.0 14.4 1.80 20.0 34.1 AWE 38.0 25.0 20.0 65.0 * 262 191 98.0 14.0 9.90 * 20.8 OJU 11.8 46.0 15.1 19.0 16.1 7.60 122 52.0 16.3 1.50 11.8 11.6 OSU 14.5 65.0 19.0 20.2 8.60 17.0 63.0 42.3 18.0 0.80 6.50 24.5 YEY 4.40 24.0 15.6 5.00 15.3 15.6 40.0 59.0 18.0 1.60 17.7 11.2 ONN 14.7 128 17.0 8.8 35.0 36.0 38.0 76.0 37.1 0.90 11.0 21.0 OLU 6.30 13.5 9.00 17.0 9.60 17.0 43.0 33.7 19.2 0.80 17.5 10.0 ORU 22.0 156 11.8 * * * 149 65.9 26.8 * * * KAN 17.4 24.0 10.0 35.0 70.0 21.5 56.5 18.0 20.6 5.60 17.4 24.0 ADE 29.0 28.0 47.0 44.0 * 18.4 50.0 39.0 11.2 5.00 124 9.62 EJI 25.0 18.0 13.0 11.5 19.0 9.70 34.0 31.5 4.40 0.71 8.80 10.0 ARO 22.4 36.0 10.9 130 * 57.7 10.9 31.0 13.8 * * * ODD 19.4 66.0 13.3 21 45.0 9.70 38.0 50.0 25.0 1.50 48.0 45.0 OBB 46.0 64.0 21.5 12.5 21.0 25.0 75.0 53.0 48.0 0.80 13.4 22.5 OYK 5.60 14.0 17.0 7.40 11.8 12.7 47.0 56.0 19.1 1.40 17.4 22.0 ETI 17.1 20.2 11.0 12.00 20.0 34.9 56.0 33.9 21.4 1.90 12.2 20.4 ISA 14.6 54.0 18.1 7.70 23.0 17.4 51.0 63.0 50.6 1.00 16.0 17.7 OPP 12.6 32.0 20.0 8.50 * 36.8 132 49.0 63.0 5.10 12.0 16.5 OPE 6.70 93.0 12.0 138 * 26.1 63.5 57.0 10.8 2.40 * 6.70 MOG 67.0 56.0 14.3 10.0 * 12.3 25.0 57.0 10.8 1.60 * 5.90 ASJ 20.2 62.0 14.0 24.0 17.8 14.5 92.0 37.0 13.9 0.40 3.50 9.80 * = No water found 259 UNIVERSITY OF IBADAN LIBRARY Appendix 7: Alkalinity (mg/L) in surface water for all the locations Sample July September November January March May July September November January March May Code 2006 2006 2006 2007 2007 2007 2007 2007 2007 2008 2008 2008 OYI 55.3 39.8 42.0 39.9 42.4 44.9 44.9 36.0 22.8 35.0 43.0 47.0 OSI 120 73.0 81.8 79.8 112 62.0 83.3 49.0 54.0 77.8 67.0 106 OUN 74.0 46.4 45.3 42.0 80.5 91.0 50.2 38.0 36.2 51.0 64.0 80.0 ARE 104 54.6 78.1 150 109 106 52.7 44.0 59.0 120 114 81.0 ASS 104 48.7 61.9 84.6 121 62.0 109 49.0 45.6 107 98.0 133 IRE 66.3 38.0 22.1 185 147 55.6 25.6 21.0 32.0 77.0 93.0 56.3 ANN 87.0 72.2 66.3 59.0 72.3 68.7 60.5 51.0 43.0 61.0 58.0 63.9 AHO 84.0 44.2 48.6 143 * 145 35.6 33.0 52.0 121 372 120 ENJ 66.3 38.3 39.8 39.6 * 54.8 34.2 25.6 38.4 65.0 77.8 54.0 OLO 30.0 59.0 32.4 38.3 114 62.0 47.0 47.0 46.5 45.6 * 43.8 GBD 53.6 66.3 63.0 62.6 132 69.5 52.0 30.0 58.0 76.5 105 68.4 AWE 140 71.0 63.4 77.2 * 101 122 76.0 58.0 131 * 16.0 OJU 96.0 59.0 50.1 48.0 56.5 54.1 64.1 44.9 43.8 62.6 53.6 63.0 OSU 51.6 53.0 59.0 60.0 59.3 65.5 44.0 37.0 69.7 123 92.0 61.7 YEY 71.0 115 67.4 67.1 70.0 46.0 58.0 49.2 86.0 71.1 67.0 80.5 ONN 58.0 97.3 36.5 49.6 55.0 56.1 62.0 34.7 49.0 57.0 79.0 77.1 OLU 27.3 24.0 18.0 20.7 32.5 16.0 35.6 17.1 50.0 30.4 25.0 35.8 ORU 36.5 68.6 50.9 * * * 32.1 28.0 102 * * * KAN 56.4 55.3 68.5 55.0 113 68.4 44.9 49.2 72.0 123 99.0 63.1 ADE 621 1220 483 650 * 585 423 165 569 780 1060 668 EJI 57.5 66.4 59.7 65.2 84.8 89.8 76.9 34.0 77.0 92.5 72.0 92.6 ARO 53.1 142 61.9 102 * 231 109 47.0 45.6 * * * ODD 55.3 53.1 50.9 50.6 70.0 59.9 56.0 49.2 70.0 61.7 52.0 60.0 OBB 49.2 39.0 51.0 104 126 114 58.0 41.7 75.0 102 152 71.8 OYK 39.8 50.9 31.0 23.3 38.1 26.7 41.0 17.1 42.9 26.9 43.0 52.3 ETI 44.2 79.6 56.0 45.0 54.0 31.3 48.0 38.5 73.0 65.0 51.0 59.9 ISA 71.9 75.2 80.2 95.3 194 87.6 63.0 56.1 89.0 103 169 91.2 OPP 97.3 138 69.6 83.3 * 59.0 73.0 36.0 86.0 99.3 116 106 OPE 96.1 78.1 97.0 392 * 171 66.9 57.0 136 176 * 117 MOG 178 103 159 93.0 * 366 269 57.0 136 294 * 230 ASJ 68.5 72.0 61.9 54.1 53.7 78.3 83.0 26.0 71.5 44.0 81.4 73.3 * = No water found 260 UNIVERSITY OF IBADAN LIBRARY Appendix 8: Hardness (mg/L) of surface water for all the locations Sample July September November January March May July September November January March May Code 2006 2006 2006 2007 2007 2007 2007 2007 2007 2008 2008 2008 OYI 21.2 22.7 23.6 326 210 122 75.6 27.9 23.3 22.3 117 150 OSI 47.8 45.3 40.6 448 291 169 99.0 58.1 51.1 25.8 170 176 OUN 32.8 22.7 17.0 361 186 136 87.2 32.0 68.0 21.0 120 131 ARE 46.0 33.1 42.2 497 287 174 59.0 44.2 45.0 34.4 156 149 ASS 37.6 33.4 36.0 442 361 145 163 51.1 40.7 29.3 153 176 IRE 13.0 17.9 17.6 396 302 151 73.6 26.3 24.4 21.5 156 125 ANN 33.5 41.4 38.0 398 229 166 116 44.2 57.0 26.6 145 184 AHO 32.2 23.3 25.2 462 * 209 105 34.1 28.6 28.9 391 192 ENJ 31.0 14.1 23.0 365 * 108 89.0 36.0 24.0 21.1 129 119 OLO 20.1 16.9 21.4 85.0 358 147 97.0 40.0 26.0 26.5 * 105 GBD 30.6 39.3 32.0 295 235 186 105 43.0 47.5 27.5 179 122 AWE 54.4 38.1 26.0 407 * 175 128 72.9 53.0 32.0 * 207 OJU 32.8 32.8 29.0 279 147 143 108 43.0 35.0 21.9 129 145 OSU 25.6 41.4 38.4 101 104 167 124 44.5 58.0 29.7 176 121 YEY 29.2 36.3 47.2 114 170 157 117 46.5 37.0 57.5 153 111 ONN 42.4 32.6 22.2 92.0 115 162 62.0 34.7 49.0 57.0 79.0 77.1 OLU 22.0 30.0 27.7 88.0 76.0 95.0 85.0 20.9 19.4 23.4 113 105 ORU 14.1 31.7 26.4 * * * 81.0 30.2 38.1 * * * KAN 37.2 47.7 46.3 149 214 174 117 62.2 92.0 37.5 173 129 ADE 305 317 285 860 * 535 364 155 471 106 516 492 EJI 16.1 31.2 35.9 102 230 169 134 36.6 45.1 24.6 179 129 ARO 28.0 38.1 40.6 119 * 378 128 39.0 55.1 * * * ODD 21.3 23.7 25.5 74.5 95.0 169 93.0 23.7 31.1 24.6 153 97 OBB 25.8 25.8 32.6 137 280 195 96.0 36.0 67.0 32.9 214 133 OYK 21.6 20.0 24.0 62.9 79.2 11.0 87.2 29.7 314 21.1 117 132 ETI 22.0 24.9 30.8 112 98.0 114 109 37.2 32.5 43.3 113 107 ISA 43.2 47.6 47.0 141 482 181 102 47.0 59 70.0 200 131 OPP 58.1 47.3 42.5 154 * 163 117 54.6 58.1 85.6 153 152 OPE 54.8 57.8 52.0 660 * 266 147 60.4 209 41.4 * 155 MOG 120 79.4 103 166 * 457 329 60.4 204 61.0 * 501 ASJ 36.0 47.0 30.0 79.0 151 178 120 40.3 57.8 28.1 168 133 * = No water found 261 UNIVERSITY OF IBADAN LIBRARY o Appendix 9: Temperature ( C) of surface water for all the locations Sample July September November January March May July September November January March May Code 2006 2006 2006 2007 2007 2007 2007 2007 2007 2008 2008 2008 OYI 24.3 23.3 22.0 20.3 22.9 24.5 22.7 23.6 24.7 16.5 26.3 24.4 OSI 26.8 24.6 22.0 22.5 24.2 25.4 23.4 24.9 25.5 18.4 27.3 24.0 OUN 24.3 23.6 21.1 20.4 23.8 24.8 23.4 23 24.6 17.8 24.9 24.0 ARE 25.9 25.3 23.4 26.6 24.8 28.3 24.1 25.5 26.7 26.2 31.1 23.9 ASS 26.3 25.5 22.1 24.1 23.8 27.2 23.4 24.6 25.3 21.2 27.1 23.9 IRE 26.0 24.6 24.7 26.0 28.6 27.8 25 25.4 28.1 25.3 29.6 26.9 ANN 26.3 26.3 23.8 24.3 26.9 28.3 26.2 26.8 27.5 20.3 27.4 28.2 AHO 25.9 25.6 24.0 29.6 * 30.8 24.3 25.1 26.8 23.7 31.5 26.7 ENJ 24.7 24.7 19.6 23.4 * 27 23.3 25.1 25.3 22.6 27.3 24.0 OLO 24.2 23.5 24.3 18.1 24.0 22.9 23.9 23.5 24.1 18.3 * 24.6 GBD 24.2 25.4 25.3 22.9 29.0 26.8 25.2 24.2 26.2 26.9 28.9 29.2 AWE 26.8 25.3 25.3 28.4 * 29.2 24.4 26.3 27.0 27.2 * 27.5 OJU 26.8 25.4 25.8 25.5 26.9 28.7 25.2 26.9 26.9 25.0 30.7 28.6 OSU 25.0 24.1 27.1 20.3 25.8 26.0 23.4 24.2 25.8 23.4 26.4 27.4 YEY 24.8 23.9 25.3 20.1 24.4 24.1 25.7 24.1 24.7 21.2 23.8 26.3 ONN 24.4 23.1 24.9 19.1 22.9 22.3 24.8 23.3 24.1 19.7 22.9 24.9 OLU 24.0 23.1 25.2 20.0 22.9 22.7 23.3 23.1 24.2 19.7 26.4 25.9 ORU 25.0 24.4 27.9 * * * 25.7 24.3 27.1 * * * KAN 25.1 25.0 27.8 23.3 24.7 25.8 25.3 24.8 26.2 22.6 26.2 24.7 ADE 24.1 23.4 26.8 19.7 * 24.9 25.5 24.1 25.8 22.3 26.1 27.4 EJI 24.9 24.7 27.9 18.7 23.8 23.5 25.6 24.8 26.7 22.1 26.5 24.3 ARO 24.7 24.7 27.9 20.0 * 23.9 24.9 24.9 26.2 * * * ODD 24.5 23.8 27.2 19.2 23.8 24.7 24.5 23.7 26.6 22.9 27.0 24.4 OBB 24.9 24.6 29.0 22.7 27.0 28.4 26.1 25.2 27.3 25.7 29.0 27.0 OYK 24.0 24.1 26.7 21.4 24.5 24.7 24.6 25.5 25.8 21.5 26.5 27.8 ETI 25.9 25.2 27.1 23.3 27.7 27.5 26.3 24.9 26.2 24.9 30.0 28.1 ISA 24.5 25.1 26.5 23.4 26.4 25.0 25.2 24.4 25.6 23.1 28.1 28.5 OPP 26.3 25.2 26.7 24.4 * 25.3 24.7 25.2 25.4 23.0 28.3 27.9 OPE 24.7 24.2 26.5 24.9 * 30.7 25.5 25 27.2 27.9 * 27.4 MOG 26.0 26.7 32.4 28.2 * 32.7 27.1 25.5 27.2 26.0 * 29.6 ASJ 26.6 28.6 32.0 25.3 29.2 32.7 27.2 26.2 32.7 23.9 32.8 27.9 * = No water found 262 UNIVERSITY OF IBADAN LIBRARY Appendix 10: Concentrations of nitrate (mg/L) in surface water for all the locations Sample July September November January March May July September November January March May Code 2006 2006 2006 2007 2007 2007 2007 2007 2007 2008 2008 2008 OYI 3.70 0.03 2.10 0.60 0.80 1.20 1.60 2.40 2.10 1.80 1.80 1.70 OSI 4.00 0.10 0.90 0.80 1.20 0.80 2.20 2.20 1.90 2.00 2.70 2.60 OUN 3.90 0.40 3.30 0.70 2.10 1.00 4.40 2.30 2.00 1.70 2.40 2.20 ARE 3.80 0.80 2.60 0.90 0.70 0.70 3.00 2.10 2.50 2.20 2.40 2.40 ASS 3.60 0.70 1.60 0.80 6.40 0.40 9.90 2.30 2.20 2.70 2.30 3.50 IRE 3.90 0.20 0.60 0.70 1.50 0.70 1.60 1.80 1.70 2.00 1.80 2.10 ANN 2.90 0.30 1.50 2.20 1.10 0.90 3.70 2.20 1.90 2.20 2.00 2.10 AHO 3.20 1.00 3.00 0.70 * 1.10 1.40 2.20 2.60 2.10 2.80 3.20 ENJ 2.00 1.80 0.67 6.20 * 1.30 3.80 2.10 2.20 2.00 3.10 2.40 OLO 0.80 0.20 0.70 0.70 1.10 0.60 2.00 2.40 1.80 1.80 * 2.00 GBD 1.00 0.60 0.60 3.50 1.00 0.70 1.90 3.50 1.80 1.90 1.90 2.10 AWE 3.60 0.80 2.40 1.20 * 1.40 7.50 3.80 2.40 2.40 * 2.50 OJU 3.00 0.30 1.10 3.60 1.30 0.80 2.30 2.60 1.90 1.90 1.80 1.90 OSU 0.80 0.60 0.70 0.60 1.10 1.00 1.80 2.30 1.80 1.80 1.60 2.10 YEY 0.60 0.34 0.70 0.90 1.40 1.00 1.90 2.50 2.30 1.80 1.57 2.20 ONN 0.70 0.60 1.00 1.60 1.20 0.80 1.60 2.80 2.00 2.10 1.90 1.90 OLU 0.70 0.50 1.10 1.80 1.20 1.20 2.10 2.10 2.00 2.00 2.00 2.00 ORU 0.50 0.14 0.70 * * * 2.00 2.20 2.20 * * * KAN 0.50 0.80 0.70 0.80 1.58 0.80 1.60 2.40 2.30 2.60 2.00 2.10 ADE 0.40 0.40 10.0 1.40 * 0.42 1.80 3.80 2.60 4.20 2.70 3.20 EJI 0.40 0.24 0.60 0.60 1.10 0.50 2.20 2.20 2.00 1.80 1.80 1.90 ARO 0.50 0.20 0.80 1.60 * 0.69 2.70 2.26 2.08 * * * ODD 0.40 0.40 0.90 0.43 1.20 0.77 2.50 2.50 2.10 1.90 1.94 1.80 OBB 0.60 0.40 4.10 1.30 0.90 1.20 2.10 2.60 2.70 2.10 2.00 2.70 OYK 0.60 0.40 0.60 0.70 0.80 1.01 2.10 2.50 2.21 2.00 1.40 2.00 ETI 0.90 0.40 0.90 3.00 0.80 0.80 4.10 2.70 2.10 1.94 2.00 2.00 ISA 0.60 0.40 0.70 1.00 0.70 0.70 2.40 2.50 2.40 2.00 1.60 2.20 OPP 1.00 0.36 0.80 1.40 * 0.70 2.20 3.10 1.80 2.60 2.00 2.10 OPE 0.50 0.60 0.70 1.90 * 1.10 2.20 2.60 2.30 2.10 * 2.60 MOG 1.20 2.40 2.60 2.00 * 0.90 2.30 2.60 2.30 4.30 * 3.50 ASJ 0.90 1.60 1.30 0.80 1.40 0.70 1.70 2.20 1.80 1.60 1.80 2.20 * = No water found 263 UNIVERSITY OF IBADAN LIBRARY Appendix 11: Concentrations of sulphate (mg/L) in surface water for all the locations Sample July September November January March May July September November January March May Code 2006 2006 2006 2007 2007 2007 2007 2007 2007 2008 2008 2008 OYI 24.6 23.4 29.0 17.5 25.0 52.7 78.9 60.9 18.2 11.9 9.80 10.1 OSI 20.5 25.4 23.7 29.1 33.6 55.9 100 14.8 2.30 24.1 17.8 26.6 OUN 38.0 43.0 31.2 37.7 84.0 64.0 63.6 48.5 26.3 24.3 53.0 91.0 ARE 19.4 35.0 28.4 45.0 40.7 44.4 78.0 14.0 21.1 35.6 6.10 68.5 ASS 16.6 27.4 29.6 27.9 37.5 50.0 107 14.8 17.5 15.5 10.7 56.0 IRE 16.0 26.9 57.9 44.0 69.0 62.0 56.0 15.2 9.80 43.0 41.0 14.9 ANN 12.6 20.0 25.6 19.8 22.6 29.7 68.0 14.0 10.3 12.5 25.0 6.20 AHO 19.5 25.1 37.4 36.5 * 42.3 104 20.3 21.0 2.60 33.0 101 ENJ 30.2 28.7 32.8 58.3 * 94.2 112 37.1 54.0 41.7 166 43.8 OLO 53.7 22.4 30.7 32.0 30.5 62.0 105 29.8 24.4 19.9 * 13.0 GBD 25.5 36.5 25.4 90.0 76.6 46.9 77.0 52.0 19.6 19.4 14.5 37.2 AWE 19.1 32.6 32.6 38.0 * 142 142 42.0 12.7 45.0 * 19.1 OJU 14.6 34.3 35.4 22.5 28.9 27.4 70.0 26.5 20.2 16.7 14.6 9.30 OSU 55.5 24.7 30.5 22.0 27.5 43.1 77.0 33.2 17.2 21.0 13.8 31.8 YEY 48.8 25.5 32.3 20.5 43.3 77.4 49.4 53.0 20.9 15.6 26.0 11.1 ONN 50.0 24.5 28.8 25.7 24.9 41.8 46.0 58.0 29.7 12.7 10.1 17.2 OLU 49.9 27.8 26.2 20.2 28.7 37.5 39.4 23.3 13.8 13.0 21.3 12.0 ORU 41.0 35.9 26.0 * * * 166 61.8 26.4 * * * KAN 51.0 43.7 40.8 92.9 58.0 54.4 64.2 22.7 30.4 32.8 18.5 39.0 ADE 66.0 41.5 115 * 104 130 110 50.1 83.3 114 45.8 136 EJI 63.0 24.9 23.6 33.8 20.1 43.0 84.0 33.1 12.2 13.0 34.9 8.81 ARO 55.1 32.8 24.0 70.0 * 41.0 58.0 41.9 10.7 * * * ODD 32.1 40.0 70.6 20.6 30.9 30.9 68.5 34.8 16.9 11.8 24.0 18.4 OBB 61.0 32.8 32.8 34.0 21.4 51.0 85.0 56.5 33.0 12.1 8.50 36.2 OYK 35.0 21.1 33.1 17.7 28.7 39.4 40.0 23.4 12.4 13.4 11.1 11.8 ETI 29.4 25.9 32.0 16.0 25.5 47.0 101 30.0 19.2 11.5 16.2 21.2 ISA 34.7 36.0 34.0 47.0 30.5 54.2 78.8 46.9 38.7 12.3 10.5 31.0 OPP 44.0 29.3 30.5 29.9 * 56.0 96.0 68.0 34.0 20.0 11.1 11.3 OPE 47.0 48.8 30.2 52.0 * 43.8 89.4 40.7 16.7 22.9 * 33.3 MOG 71.0 39.0 40.0 65.6 * 44.2 49.0 40.7 16.7 20.5 * 45.0 ASJ 26.3 34.9 33.9 53.7 51.0 32.1 70.0 41.0 13.8 129 32.0 42.0 * = No water found 264 UNIVERSITY OF IBADAN LIBRARY Appendix 12: Concentrations of phosphate (mg/L) in surface water for all the locations Sample July September November January March May July September November January March May Code 2006 2006 2006 2007 2007 2007 2007 2007 2007 2008 2008 2008 OYI 0.01 0.01 0.03 0.02 0.02 0.09 0.14 0.15 0.20 0.03 0.03 0.10 OSI 0.02 0.01 0.03 0.10 0.14 0.20 0.32 0.12 0.20 0.10 0.04 0.20 OUN 0.04 0.01 0.1 0.10 0.20 0.20 0.20 0.20 0.10 0.09 0.08 0.27 ARE 0.02 0.01 0.04 0.20 0.20 0.13 0.10 0.10 0.40 0.10 0.04 0.30 ASS 0.02 0.02 0.10 0.11 0.22 0.11 0.60 0.11 0.22 0.06 0.06 0.52 IRE 0.02 0.01 0.04 0.30 0.40 0.11 0.10 0.15 0.10 0.05 0.10 0.14 ANN 0.01 0.02 0.04 0.10 0.04 0.10 0.10 0.09 0.10 0.10 0.07 0.10 AHO 0.05 0.01 0.10 0.30 * 0.90 0.20 0.13 0.10 0.06 1.03 0.80 ENJ 0.01 0.02 0.10 0.16 * 0.16 0.20 0.15 0.30 0.08 0.30 0.19 OLO 0.02 0.01 0.10 0.10 0.13 0.20 0.02 0.20 0.10 0.10 * 0.12 GBD 0.04 0.02 0.10 0.30 0.40 0.20 0.20 0.20 0.20 0.10 0.10 0.20 AWE 0.02 0.02 0.10 0.20 * 0.70 0.80 0.40 0.20 0.20 * 0.10 OJU 0.01 0.10 0.10 0.04 0.10 0.10 0.20 0.20 0.20 0.05 0.03 0.07 OSU 0.01 0.01 0.07 0.10 0.10 0.20 0.03 0.20 0.20 0.07 0.04 0.15 YEY 0.01 0.01 0.10 0.02 0.04 0.10 0.10 0.21 0.20 0.10 0.10 0.11 ONN 0.03 0.04 0.10 0.04 0.10 0.10 0.10 0.30 0.21 0.03 0.02 0.14 OLU 0.02 0.004 0.02 0.10 0.10 0.10 0.10 0.22 0.10 0.04 0.04 0.20 ORU 0.01 0.10 0.04 * * * 0.21 0.22 0.12 * * * KAN 0.02 0.50 0.13 0.20 0.90 0.54 0.02 0.21 0.50 0.62 0.20 0.38 ADE 0.40 0.13 0.20 1.50 * 0.60 0.50 0.80 0.60 1.60 1.30 0.55 EJI 0.03 0.01 0.04 0.10 0.03 0.10 0.11 0.12 0.10 0.02 0.03 0.06 ARO 0.02 0.01 0.04 0.40 * 0.30 0.10 0.11 0.20 * * * ODD 0.02 0.02 0.08 0.01 0.20 0.12 0.10 0.30 0.29 0.08 0.06 0.18 OBB 0.04 0.03 0.10 0.10 0.04 0.10 0.10 0.20 0.20 0.10 0.03 0.10 OYK 0.01 0.01 0.10 0.10 0.10 0.10 0.10 0.20 0.08 0.05 0.10 0.10 ETI 0.01 0.01 0.10 0.03 0.10 0.11 0.10 0.16 0.10 0.06 0.03 0.14 ISA 0.03 0.02 0.11 0.10 0.10 0.20 0.17 0.30 0.30 0.05 0.04 0.20 OPP 0.02 0.01 0.09 0.04 * 0.10 0.09 0.20 0.26 0.04 0.03 0.12 OPE 0.02 0.04 0.10 1.00 * 1.00 0.10 0.17 0.11 0.30 * 0.16 MOG 0.03 0.10 0.23 0.40 * 0.40 0.20 0.17 0.11 0.30 * 0.29 ASJ 0.01 0.04 0.04 0.10 0.10 0.10 0.10 0.51 0.18 0.04 0.02 0.10 * = No water found 265 UNIVERSITY OF IBADAN LIBRARY Appendix 13: Concentrations of chloride (mg/L) in surface water for all the locations Sample July September November January March May July September November January March May Code 2006 2006 2006 2007 2007 2007 2007 2007 2007 2008 2008 2008 OYI 33.0 12.2 10.1 18.3 20.3 18.0 14.4 23.4 14.4 27.0 14.4 7.20 OSI 53.0 20.3 16.0 28.5 32.5 39.6 47.0 32.4 25.2 28.8 25.0 34.2 OUN 37.0 9.13 11.2 21.3 24.0 18.0 14.0 13.5 7.20 20.0 8.10 14.4 ARE 49.0 24.2 19.0 19.0 79.9 70.0 50.4 34.0 28.0 24.0 62.4 58.8 ASS 38.6 12.2 16.0 33.0 102 32.4 29.0 28.8 18.0 47.0 54.0 79.0 IRE 33.0 10.8 6.77 79.8 46.0 24.0 9.60 12.0 18.0 32.4 33.6 27.6 ANN 43.0 12.2 11.5 29.8 27.1 19.2 10.8 35.0 21.0 35.0 18 16.8 AHO 45.6 12.2 14.9 7.00 * 79.0 16.8 24.0 15.6 107 116 54.0 ENJ 60.0 10.8 13.5 28.0 * 22.0 24.0 34.0 14.4 25.0 25.2 20.4 OLO 35.2 10.8 10.8 21.6 38.0 34.8 34.0 19.0 20.4 40.8 * 29.0 GBD 28.5 20.3 11.2 32.0 82.0 47.0 43.2 36.0 3.00 29.7 27.0 66.0 AWE 92.0 27.1 13.6 65.0 * 67.0 60.0 49.2 29.0 46.8 * 82.3 OJU 46.0 21.6 13.5 20.3 27.1 12.0 36.0 28.8 22.0 28.0 18.0 23.0 OSU 16.2 17.6 13.5 22.0 25.7 43.2 43.2 38.4 26.0 48.0 46.0 33.6 YEY 20.3 20.3 10.1 28.0 20.3 36.0 32.4 29.0 29.0 48.6 8.50 32.0 ONN 28.0 22.4 12.2 18.3 20.0 40.5 38.0 31.0 32.0 80.0 25.2 40.0 OLU 31.1 16.2 8.00 21.7 18.9 30.0 28.8 14.4 19.2 49.2 43.2 33.6 ORU 16.2 8.12 8.12 * * * 32.4 25.2 18.0 * * * KAN 44.7 52.8 20.3 44.7 56.8 65.0 68.0 36.0 43.0 84.6 50.4 78.5 ADE 633 598 474 736 * 594 408 162 546 812 985 682 EJI 16.2 12.2 10.2 10.2 32.5 28.8 43.2 32.4 21.6 59.0 18.0 25.0 ARO 20.3 16.2 8.12 31.0 * 39.6 50.0 29.0 29.0 * * * ODD 8.12 12.2 6.10 6.10 33.0 18.0 18.0 40.0 14.4 31.0 5.40 14.4 OBB 19.3 26.0 10.1 42.0 51.0 70.0 59.0 23.4 34.2 70.0 70.0 106 OYK 12.2 16.2 8.00 16.2 18.3 18.0 36.0 41.0 21.6 31.0 25.0 32.0 ETI 17.6 21.7 9.50 13.5 28.0 54.0 28.8 24.0 216 29.0 34.8 40.8 ISA 22.0 20.3 11.2 23.4 77.0 79.0 52.2 47.0 67.0 54.0 36.0 65.0 OPP 24.0 16.2 12.2 20.3 * 43.2 50.4 36.0 40.0 38.0 32.0 41.4 OPE 32.5 42.0 25.7 95.0 * 41.0 43.0 38.0 28.8 95.0 * 84.0 MOG 137 57.0 91.0 71.7 * 149 223 38.0 28.8 260 * 204 ASJ 27.1 27.0 14.9 30.0 16.2 38.4 36.0 33.6 52.8 52.0 22.8 61.0 * = No water found 266 UNIVERSITY OF IBADAN LIBRARY Appendix 14: Concentrations of ammonia (mg/L) in surface water for all the locations Sample July September November January March May July September November January March May Code 2006 2006 2006 2007 2007 2007 2007 2007 2007 2008 2008 2008 OYI 3.60 2.90 1.90 2.30 3.10 2.20 6.10 8.50 2.80 1.20 1.60 2.10 OSI 2.50 3.20 2.30 3.70 3.80 2.90 5.60 2.20 1.70 6.20 2.20 2.70 OUN 6.50 3.00 1.80 21.0 6.20 2.80 11.1 5.30 2.80 2.30 3.30 6.00 ARE 2.20 2.60 1.80 10.4 5.40 1.50 4.20 6.90 2.90 1.90 1.60 5.20 ASS 2.00 3.10 2.20 4.20 3.00 1.90 10.7 1.70 2.00 1.55 1.59 3.90 IRE 2.30 2.40 1.90 10.3 14.1 1.50 2.30 2.20 2.00 7.70 4.10 27.6 ANN 2.10 3.90 2.50 3.90 5.80 3.80 6.90 1.70 3.70 2.40 5.10 1.70 AHO 3.20 2.60 2.30 9.30 * 2.60 4.30 4.80 2.60 1.60 13.4 11.0 ENJ 4.10 3.44 1.70 3.70 * 6.30 8.90 5.30 2.80 3.10 7.80 3.80 OLO 2.30 3.40 2.70 2.80 4.40 5.00 4.70 4.60 2.70 1.58 * 2.60 GBD 2.80 3.70 2.70 4.10 3.00 1.70 4.50 5.70 2.70 2.00 1.60 2.90 AWE 2.90 3.20 2.10 8.40 * 4.00 15.3 2.70 2.90 2.90 * 3.60 OJU 2.20 3.00 1.90 2.60 2.90 1.80 4.90 2.20 2.90 1.20 1.70 1.70 OSU 2.70 3.60 2.80 2.80 2.30 1.70 7.40 3.40 2.60 1.50 2.00 2.10 YEY 2.83 2.60 2.90 3.70 2.22 2.20 5.20 3.30 2.50 1.10 1.90 2.30 ONN 2.50 5.30 2.70 2.40 2.70 1.90 4.40 4.30 3.30 0.90 1.50 2.00 OLU 3.10 3.20 3.00 2.80 3.00 2.30 1.90 3.30 2.30 2.20 2.40 1.90 ORU 3.50 7.10 2.20 * * * 11.0 5.80 3.40 * * * KAN 2.60 3.00 2.60 5.80 4.50 1.90 4.20 1.70 2.50 2.00 1.90 1.90 ADE 29.4 4.60 7.40 95.6 * 1.40 21.0 16.9 28.4 34.8 31.0 24.0 EJI 4.70 2.70 2.50 1.30 2.60 2.90 9.30 2.90 2.40 1.60 1.70 2.80 ARO 4.40 3.00 2.60 4.00 * 1.90 5.30 6.80 3.00 * * * ODD 3.40 4.70 2.00 0.80 2.60 1.80 5.90 3.60 2.50 1.30 2.31 2.60 OBB 4.50 4.50 2.70 5.40 2.80 2.20 3.60 4.00 3.40 1.40 1.50 2.20 OYK 3.70 3.30 2.90 2.11 3.10 2.00 2.80 2.30 2.50 1.40 2.50 2.90 ETI 3.60 3.60 2.90 1.80 2.30 2.20 4.30 2.60 2.70 1.10 2.80 3.20 ISA 2.80 3.20 3.00 3.00 3.40 7.30 5.80 3.50 3.10 1.60 2.70 2.60 OPP 2.20 2.50 2.60 2.60 * 2.00 6.10 5.20 2.80 1.60 2.00 1.80 OPE 2.70 5.10 2.60 12.0 * 2.80 6.10 2.90 2.60 4.80 * 2.40 MOG 3.00 3.00 3.30 9.30 * 1.70 2.90 3.00 2.60 10.6 * 20.4 ASJ 2.80 4.00 2.80 3.90 2.20 1.70 4.40 3.20 2.30 1.70 2.30 1.50 * = No water found 267 UNIVERSITY OF IBADAN LIBRARY Appendix 15: Concentrations of dissolved oxygen (mg/L) in surface water for all the locations Sample July September November January March May July September November January March May Code 2006 2006 2006 2007 2007 2007 2007 2007 2007 2008 2008 2008 OYI 7.40 8.70 6.40 7.90 5.80 9.80 10.3 11.5 10.0 8.00 8.70 7.90 OSI 1.40 2.70 4.40 6.00 4.90 11.0 6.60 12.1 8.80 7.90 6.20 7.50 OUN 1.00 6.00 2.70 3.70 3.30 8.20 10.00 9.70 10.0 8.20 9.40 6.80 ARE 5.90 6.70 6.40 5.00 0.80 8.00 10.6 10.3 11.3 8.30 7.90 7.60 ASS 7.10 9.70 7.30 9.70 7.60 8.10 9.60 11.5 11.3 9.60 12.3 7.50 IRE 7.20 8.20 5.50 1.90 1.20 7.40 9.70 12.6 11.8 8.10 4.60 7.90 ANN 4.00 4.20 5.10 2.50 4.80 7.60 7.90 8.60 8.30 8.60 7.90 7.50 AHO 3.10 6.70 6.90 4.30 * 5.10 11.8 12.7 10.4 8.70 7.10 8.00 ENJ 3.60 7.00 5.60 6.70 * 8.50 7.60 10.2 9.40 8.50 7.20 8.00 OLO 6.10 8.70 6.30 14.0 2.70 4.10 9.00 14.1 9.40 9.50 * 6.80 GBD 7.80 7.80 6.80 8.90 5.20 12.0 12.4 10.9 7.50 6.30 5.90 8.00 AWE 3.90 6.30 4.50 6.50 * 11.1 7.60 7.80 10.3 8.90 * 8.30 OJU 6.00 4.70 6.00 9.90 6.70 10.8 7.30 11.7 10.8 8.00 7.80 6.60 OSU 8.70 8.40 7.30 6.80 6.20 7.30 11.5 12.8 9.20 7.10 8.60 7.80 YEY 6.10 7.70 7.30 11.7 2.90 2.20 7.40 8.60 7.90 10.2 8.50 8.80 ONN 7.70 9.90 9.30 13.5 6.80 35.5 11.6 10.0 10.3 10.9 12.4 7.50 OLU 8.90 7.90 11.8 13.5 7.70 13.4 10.0 12.0 9.70 11.0 12.6 8.50 ORU 7.60 8.80 7.70 * * * 12.1 16.7 9.90 * * * KAN 0.30 5.48 2.30 1.00 0.80 6.90 9.90 7.80 7.70 5.60 8.60 8.70 ADE -0.20 2.10 6.10 1.30 * 5.50 5.30 10.6 6.50 6.80 6.60 7.50 EJI 8.40 7.00 7.70 4.70 2.30 9.40 12.2 11.1 10.4 7.30 8.40 8.50 ARO 5.20 8.20 8.40 -2.10 * 7.00 8.50 10.0 12.7 * * * ODD 6.30 7.60 6.50 4.00 3.20 11.2 9.80 9.10 10.3 7.20 9.10 7.90 OBB 6.50 7.40 7.60 1.80 0.90 7.60 11.5 10.1 10.7 7.50 6.60 7.90 OYK 6.10 5.10 9.80 11.0 4.17 6.80 10.1 11.2 9.1 9.30 11.7 7.54 ETI 8.10 9.10 10.2 10.4 6.10 8.70 10.3 14.0 10.3 10.4 7.80 7.20 ISA 8.60 9.20 11.3 10.7 2.50 9.50 11.2 13.4 9.10 10.3 5.80 8.30 OPP 3.20 9.25 10.0 10.2 * 8.50 9.30 6.10 10.00 9.60 5.70 7.60 OPE 6.00 7.20 7.30 4.00 * 6.00 10.9 12.5 8.20 6.10 * 6.50 MOG 5.20 4.70 6.10 4.60 * 8.70 9.50 12.5 8.20 6.40 * 7.10 ASJ 6.90 9.00 10.7 9.70 6.50 9.50 12.7 11.1 9.20 6.30 6.70 7.50 * = No water found 268 UNIVERSITY OF IBADAN LIBRARY Appendix 16: Biochemical oxygen demand (mg/L) in surface water for all the locations Sample July September November January March May July September November January March May Code 2006 2006 2006 2007 2007 2007 2007 2007 2007 2008 2008 2008 OYI 3.10 6.20 1.50 7.70 12.2 4.40 12.2 6.30 6.50 7.90 2.50 2.70 OSI 1.00 5.30 5.50 12.3 3.5 3.00 9.30 1.10 3.80 7.90 4.50 3.70 OUN 0.90 4.80 1.60 6.40 9.8 5.00 10.4 4.90 5.60 7.10 2.00 2.00 ARE 0.43 4.10 4.60 9.60 8 6.40 11.9 2.60 6.70 7.00 3.70 3.40 ASS 1.10 4.10 8.30 10.7 3.6 3.90 14.4 8.00 5.90 7.00 4.30 2.30 IRE 1.90 5.00 1.60 6.70 11.5 5.70 9.10 6.20 5.80 11.2 7.30 4.00 ANN 2.40 7.70 1.60 9.70 11.4 6.30 9.10 5.50 7.40 7.30 5.00 2.90 AHO 1.50 3.30 1.50 6.50 * 4.50 10.4 7.40 4.70 7.90 8.40 6.60 ENJ 2.00 4.90 2.10 9.50 * 5.00 10.5 4.90 4.80 6.70 5.10 3.00 OLO 2.70 2.40 2.10 6.50 7.80 35.7 10.3 13.3 3.50 2.30 * 1.10 GBD 7.30 3.10 10.0 17.1 11.5 6.40 4.90 9.90 3.90 2.70 4.20 3.40 AWE 1.70 3.40 2.80 10.9 * 1.20 7.10 7.60 7.30 6.50 * 6.80 OJU 1.10 4.60 2.40 12.7 9.60 6.00 10.8 9.40 6.90 7.60 3.60 3.60 OSU 3.33 11.7 9.80 14.0 14.3 7.70 8.10 9.60 4.40 3.10 3.30 4.00 YEY 1.00 1.70 2.20 5.70 13.1 28.0 6.90 12.5 4.20 1.70 6.50 3.50 ONN 1.50 1.70 1.50 7.40 35.5 7.40 7.40 91.0 6.50 12.1 3.80 2.20 OLU 6.10 2.50 0.90 27.0 14.7 30.6 11.6 12.7 2.80 2.70 5.50 1.70 ORU 3.90 5.00 11.2 * * * 6.00 9.00 4.50 * * * KAN 5.40 13.5 9.40 15.8 8.60 5.60 5.30 7.10 3.80 3.00 4.00 3.60 ADE 4.40 12.7 1.70 51.0 * 29.3 8.20 14.2 5.40 7.40 6.10 1.90 EJI 7.50 6.80 7.40 15.8 0.50 7.70 6.00 7.10 5.40 5.10 2.80 3.40 ARO 1.20 4.10 10.3 16.8 * 7.20 3.30 7.80 3.90 * * * ODD 4.60 6.30 9.40 15.3 5.40 5.60 3.30 9.00 4.70 2.50 5.00 1.77 OBB 6.30 10.9 10.1 14.3 6.80 4.80 7.70 7.40 4.40 2.30 5.60 0.60 OYK 1.80 3.00 2.40 6.30 11.0 29.7 201 13.2 4.10 1.10 4.20 1.00 ETI 4.10 1.80 2.90 5.90 11.9 33.4 5.20 12.3 3.20 1.80 4.90 1.20 ISA 4.10 4.80 2.30 5.90 10.7 32.5 14.7 13.1 4.10 1.90 4.60 0.70 OPP 1.00 3.30 2.40 8.30 * 26.0 11.4 15.8 3.50 2.10 5.50 1.80 OPE 2.00 7.90 9.90 11.7 * 9.90 6.30 8.70 4.10 1.30 * 4.80 MOG 2.40 9.90 9.90 12.5 * 6.20 6.50 8.70 4.10 2.90 * 1.70 ASJ 1.40 5.70 11.3 16.2 12.6 11.5 6.20 9.70 4.00 2.80 5.40 2.30 * = No water found 269 UNIVERSITY OF IBADAN LIBRARY Appendix 17: Chemical oxygen demand (mg/L) in surface water for all the locations Sample July September November January March May July September November January March May Code 2006 2006 2006 2007 2007 2007 2007 2007 2007 2008 2008 2008 OYI 40.0 181 721 171 101 70.0 392 158 363 75.0 256 192 OSI 60.0 101 101 186 61.0 50.0 202 18.9 192 75.0 480 192 OUN 40.0 91.0 96.0 214 136 126 106 103 374 91.0 523 294 ARE 47.0 127 57.0 151 168 60.0 134 63.0 327 57.0 306 78.0 ASS 91.0 20.1 121 251 201 61.0 261 69.0 182 214 363 128 IRE 30.0 101 67.0 164 148 47.0 87.0 29.0 313 28.0 448 78.0 ANN 125 205 147 199 94.0 131 134 78.0 168 110 491 194 AHO 101 134 43.6 201 * 54.0 114 23.0 228 124 334 199 ENJ 91.0 154 30.0 154 * 27.0 141 34.0 327 71.0 640 277 OLO 94.0 201 40.0 134 148 154 121 84.0 71.0 84.0 * 185 GBD 956 176 111 254 96.0 50.0 126 41.0 75.0 203 347 197 AWE 60.4 141 47.0 214 * 101 315 59.0 214 121 * 242 OJU 67.0 104 74.0 201 107 188 740 63.0 213 149 391 199 OSU 167 161 107 245 104 121 121 25.0 157 50.0 192 171 YEY 170 101 20.1 241 171 91.0 222 208 107 32.0 448 139 ONN 310 206 40.0 191 185 91.0 221 63.0 96.0 229 453 222 OLU 30.0 41.0 87.1 238 31.0 208 114 97.0 114 18.0 434 142 ORU 70.0 101 30.0 * * * 121 101 107 * * * KAN 55.0 61.0 91.0 131 55.3 30.0 231 69.0 150 64.0 256 256 ADE 127 248 121 208 * 228 249 130 128 135 223 217 EJI 40.0 161 81.0 171 81.0 61.0 141 114 171 32.0 331 213 ARO 81.0 40.0 91.0 141 * 151 171 25.0 121 * * * ODD 121 50.0 161 101 61.0 60.0 121 76.0 150 102 459 203 OBB 105 126 116 221 101 106 131 56.8 117 96 389 203 OYK 40.0 202 101 221 176 65.0 201 214 85.0 150 38.0 64.0 ETI 80.0 34.0 167 168 61.0 208 94.0 126 142 57.0 266 124 ISA 53.0 91.0 106 196 116 216 156 123 277 235 456 96.0 OPP 100 40.0 20.1 171 * 181 201 101 128 107 406 32.0 OPE 37.0 125 114 411 * 167 107 59.0 85.3 33.0 * 85.0 MOG 180 144 114 154 * 74.0 101 59.0 85.3 57.0 * 213 ASJ 60.3 178 74.0 204 47.0 101 174 21.0 57.0 57.0 277 164 * = No water found 270 UNIVERSITY OF IBADAN LIBRARY Appendix 18: Concentrations of lead (mg/L) in surface water for all the locations Sample July September November January March May July September November January March May Code 2006 2006 2006 2007 2007 2007 2007 2007 2007 2008 2008 2008 OYI 0.001 0.001 0.01 0.001 0.004 0.004 0.001 0.001 0.001 0.001 0.001 0.002 OSI 0.01 0.003 0.01 0.004 0.001 0.004 0.001 0.001 0.002 0.002 0.001 0.001 OUN 0.004 0.001 0.01 0.003 0.001 0.003 0.001 0.002 0.002 0.002 0.003 0.001 ARE 0.001 0.001 0.01 0.01 0.001 0.001 0.001 0.001 0.002 0.001 0.001 0.002 ASS 0.004 0.001 0.01 0.003 0.001 0.001 0.001 0.001 0.002 0.002 0.001 0.002 IRE 0.001 0.001 0.01 0.01 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 ANN 0.004 0.01 0.01 0.01 0.001 0.002 0.001 0.001 0.002 0.001 0.001 0.002 AHO 0.01 0.001 0.01 0.004 * 0.001 0.001 0.001 0.001 0.001 0.004 0.001 ENJ 0.004 0.001 0.01 0.003 0.001 0.001 0.001 0.001 0.004 0.002 0.002 0.003 OLO 0.004 0.001 0.01 0.001 * 0.001 0.001 0.001 0.002 0.003 * 0.002 GBD 0.02 0.002 0.01 0.003 0.001 0.003 0.001 0.001 0.003 0.001 0.003 0.001 AWE 0.004 0.002 0.01 0.001 0.001 0.001 0.001 0.001 0.002 0.002 * 0.002 OJU 0.01 0.001 0.01 0.003 0.001 0.001 0.001 0.002 0.002 0.001 0.003 0.003 OSU 0.01 0.002 0.01 0.003 0.001 0.002 0.001 0.001 0.002 0.002 0.001 0.002 YEY 0.01 0.001 0.01 0.001 0.001 0.003 0.001 0.001 0.003 0.001 0.002 0.001 ONN 0.01 0.001 0.01 0.003 0.001 0.004 0.001 0.004 0.004 0.002 0.002 0.003 OLU 0.01 0.001 0.01 0.003 0.001 0.001 0.001 0.004 0.002 0.001 0.001 0.003 ORU 0.001 0.001 0.01 * 0.001 * 0.001 0.003 0.002 * * * KAN 0.001 0.02 0.01 0.003 * 0.01 0.001 0.001 0.002 0.001 0.002 0.001 ADE 0.02 0.001 0.01 0.01 0.001 0.01 0.001 0.01 0.01 0.002 0.002 0.001 EJI 0.004 0.001 0.01 0.001 * 0.001 0.001 0.001 0.001 0.001 0.001 0.002 ARO 0.004 0.01 0.01 0.001 0.004 0.004 0.001 0.001 0.001 * * * ODD 0.004 0.001 0.01 0.003 0.004 0.004 0.001 0.001 0.002 0.001 0.001 0.002 OBB 0.01 0.001 0.01 0.001 0.001 0.01 0.001 0.002 0.003 0.002 0.001 0.003 OYK 0.01 0.001 0.01 0.001 0.001 0.001 0.001 0.003 0.002 0.003 0.001 0.001 ETI 0.01 0.002 0.01 0.003 0.001 0.001 0.001 0.001 0.003 0.003 0.001 0.002 ISA 0.01 0.01 0.01 0.003 * 0.004 0.001 0.002 0.01 0.001 0.003 0.002 OPP 0.01 0.001 0.01 0.001 * 0.003 0.001 0.002 0.001 0.003 0.002 0.001 OPE 0.01 0.01 0.01 0.003 0.001 0.004 0.001 0.001 0.002 0.002 * 0.002 MOG 0.02 0.001 0.01 0.01 0.001 0.004 0.001 0.002 0.002 0.002 * 0.004 ASJ 0.001 0.003 0.01 0.01 0.001 0.001 0.001 0.001 0.002 0.002 0.002 0.001 * = No water found 271 UNIVERSITY OF IBADAN LIBRARY Appendix 19: Concentrations of copper (mg/L) in surface water for all the locations Sample July September November January March May July September November January March May Code 2006 2006 2006 2007 2007 2007 2007 2007 2007 2008 2008 2008 OYI 0.002 0.001 0.01 0.001 0.001 0.001 0.001 0.002 0.01 0.002 0.001 0.003 OSI 0.004 0.003 0.01 0.001 0.002 0.001 0.002 0.003 0.01 0.001 0.002 0.002 OUN 0.004 0.001 0.01 0.001 0.001 0.002 0.002 0.001 0.01 0.001 0.002 0.003 ARE 0.02 0.001 0.01 0.001 0.001 0.001 0.001 0.002 0.002 0.01 0.001 0.002 ASS 0.002 0.003 0.01 0.001 0.002 0.002 0.01 0.004 0.01 0.01 0.002 0.01 IRE 0.01 0.001 0.01 0.001 0.002 0.001 0.002 0.002 0.003 0.001 0.001 0.002 ANN 0.002 0.002 0.01 0.001 0.001 0.001 0.001 0.001 0.004 0.002 0.004 0.004 AHO 0.002 0.001 0.01 0.001 * 0.004 0.003 0.002 0.002 0.002 0.002 0.002 ENJ 0.002 0.003 0.01 0.002 * 0.003 0.002 0.004 0.002 0.001 0.002 0.002 OLO 0.001 0.001 0.01 0.001 0.002 0.001 0.003 0.002 0.002 0.01 * 0.002 GBD 0.002 0.001 0.01 0.002 0.002 0.003 0.004 0.002 0.01 0.002 0.003 0.002 AWE 0.01 0.002 0.01 0.001 * 0.002 0.003 0.001 0.01 0.01 * 0.002 OJU 0.002 0.002 0.01 0.001 0.001 0.002 0.001 0.002 0.01 0.01 0.002 0.01 OSU 0.01 0.002 0.01 0.001 0.001 0.002 0.002 0.004 0.003 0.001 0.003 0.002 YEY 0.01 0.001 0.01 0.001 0.002 0.01 0.002 0.002 0.002 0.01 0.001 0.002 ONN 0.002 0.001 0.01 0.002 0.002 0.002 0.003 0.002 0.002 0.01 0.002 0.002 OLU 0.002 0.002 0.01 0.001 0.001 0.002 0.002 0.003 0.01 0.001 0.01 0.01 ORU 0.002 0.001 0.01 * * 0.003 0.001 0.001 0.002 * * * KAN 0.002 0.001 0.01 0.001 0.002 0.001 0.003 0.003 0.004 0.01 0.002 0.002 ADE 0.01 0.002 0.01 0.003 * 0.01 0.002 0.003 0.01 0.002 0.002 0.002 EJI 0.01 0.002 0.01 0.001 0.002 0.002 0.001 0.002 0.01 0.002 0.002 0.01 ARO 0.002 0.003 0.01 0.001 * 0.01 0.001 0.003 0.01 * * * ODD 0.002 0.002 0.01 0.001 0.001 0.002 0.001 0.002 0.002 0.001 0.002 0.01 OBB 0.002 0.001 0.01 0.001 0.001 0.002 0.002 0.003 0.01 0.002 0.003 0.002 OYK 0.01 0.001 0.01 0.001 0.002 0.001 0.001 0.002 0.01 0.002 0.001 0.002 ETI 0.001 0.002 0.01 0.001 0.001 0.001 0.001 0.003 0.01 0.001 0.001 0.002 ISA 0.004 0.003 0.01 0.001 0.001 0.002 0.002 0.002 0.003 0.002 0.001 0.001 OPP 0.001 0.001 0.01 0.001 * 0.001 0.002 0.002 0.01 0.001 0.001 0.001 OPE 0.004 0.002 0.01 0.002 * 0.004 0.001 0.01 0.01 * 0.01 MOG 0.002 0.01 0.01 0.001 * 0.003 0.003 0.004 0.002 0.01 * 0.002 ASJ 0.034 0.001 0.01 0.001 0.002 0.002 0.001 0.004 0.01 0.001 0.003 0.002 * = No water found 272 UNIVERSITY OF IBADAN LIBRARY Appendix 20: Concentrations of cadmium (mg/L) in surface water for all the locations Sample July September November January March May July September November January March May Code 2006 2006 2006 2007 2007 2007 2007 2007 2007 2008 2008 2008 OYI 0.001 0.001 0.01 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 OSI 0.001 0.001 0.01 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 OUN 0.001 0.001 0.01 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 ARE 0.001 0.001 0.01 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 ASS 0.001 0.001 0.01 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 IRE 0.001 0.001 0.01 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 ANN 0.001 0.001 0.01 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 AHO 0.001 0.001 0.01 0.001 * 0.001 0.001 0.001 0.001 0.001 0.001 0.001 ENJ 0.001 0.001 0.01 0.001 * 0.001 0.001 0.001 0.001 0.001 0.001 0.001 OLO 0.001 0.001 0.01 0.001 0.001 0.001 0.001 0.001 0.001 0.001 * 0.001 GBD 0.001 0.001 0.01 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 AWE 0.001 0.001 0.01 0.001 * 0.001 0.001 0.001 0.001 0.001 0.001 OJU 0.001 0.001 0.01 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 OSU 0.001 0.001 0.01 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 YEY 0.001 0.001 0.01 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 ONN 0.001 0.001 0.01 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 OLU 0.001 0.001 0.01 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 ORU 0.001 0.001 0.01 * * * 0.001 0.001 0.001 * * * KAN 0.001 0.001 0.01 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 ADE 0.001 0.001 0.01 0.001 * 0.001 0.001 0.001 0.001 0.001 0.001 0.001 EJI 0.001 0.001 0.01 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 ARO 0.001 0.001 0.01 0.001 * 0.001 0.001 0.001 0.001 * * * ODD 0.001 0.001 0.01 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 OBB 0.002 0.001 0.01 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 OYK 0.001 0.001 0.01 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 ETI 0.001 0.001 0.01 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 ISA 0.001 0.001 0.01 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 OPP 0.001 0.001 0.01 0.001 * 0.001 0.001 0.001 0.001 0.001 0.001 0.001 OPE 0.001 0.001 0.01 0.001 * 0.001 0.001 0.001 0.001 0.001 * 0.001 MOG 0.001 0.001 0.01 0.001 * 0.001 0.001 0.001 0.001 0.001 * 0.001 ASJ 0.001 0.001 0.01 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 * = No water found 273 UNIVERSITY OF IBADAN LIBRARY Appendix 21: Concentrations of cobalt (mg/L) in surface water for all the locations Sample July September November January March May July September November January March May Code 2006 2006 2006 2007 2007 2007 2007 2007 2007 2008 2008 2008 OYI 0.002 0.001 0.01 0.01 0.001 0.001 0.003 0.001 0.01 0.003 0.002 0.003 OSI 0.002 0.001 0.01 0.01 0.004 0.01 0.001 0.001 0.001 0.003 0.001 0.003 OUN 0.002 0.001 0.01 0.01 0.001 0.01 0.002 0.004 0.001 0.003 0.002 0.01 ARE 0.002 0.001 0.01 0.01 0.001 0.01 0.01 0.001 0.001 0.003 0.001 0.003 ASS 0.002 0.001 0.01 0.01 0.001 0.001 0.01 0.001 0.001 0.003 0.001 0.01 IRE 0.002 0.001 0.01 0.01 0.001 0.001 0.01 0.004 0.001 0.003 0.001 0.002 ANN 0.002 0.002 0.01 0.01 0.001 0.001 0.002 0.01 0.004 0.01 0.01 0.003 AHO 0.004 0.001 0.01 0.01 * 0.001 0.01 0.001 0.001 0.003 0.002 0.003 ENJ 0.002 0.001 0.01 0.01 * 0.001 0.001 0.001 0.001 0.003 0.002 0.003 OLO 0.002 0.001 0.01 0.01 0.001 0.001 0.001 0.001 0.01 0.003 * 0.003 GBD 0.02 0.001 0.01 0.01 0.001 0.001 0.001 0.002 0.003 0.003 0.001 0.003 AWE 0.002 0.002 0.01 0.01 * 0.003 0.003 0.001 0.001 0.003 0.003 0.003 OJU 0.002 0.002 0.01 0.01 0.001 0.001 0.01 0.003 0.003 0.003 0.002 0.01 OSU 0.002 0.003 0.01 0.01 0.001 0.003 0.01 0.001 0.003 0.003 0.001 0.003 YEY 0.01 0.001 0.01 0.01 0.001 0.003 0.003 0.004 0.003 0.003 0.001 0.003 ONN 0.002 0.002 0.01 0.01 0.001 0.003 0.001 0.002 0.01 0.003 0.002 0.003 OLU 0.002 0.01 0.01 0.01 0.001 0.003 0.001 0.002 0.01 0.003 0.002 0.003 ORU 0.002 0.001 0.01 * * * 0.01 0.001 0.003 * * * KAN 0.002 0.001 0.01 0.01 0.001 0.01 0.001 0.004 0.001 0.003 0.002 0.003 ADE 0.004 0.001 0.01 0.01 * 0.01 0.002 0.002 0.01 0.003 0.001 0.003 EJI 0.004 0.001 0.01 0.01 0.002 0.01 0.01 0.001 0.003 0.003 0.002 0.003 ARO 0.002 0.002 0.01 0.01 * 0.01 0.01 0.004 0.003 * * * ODD 0.002 0.001 0.01 0.01 0.001 0.001 0.003 0.001 0.001 0.003 0.001 0.003 OBB 0.002 0.001 0.01 0.01 0.001 0.001 0.003 0.001 0.01 0.003 0.002 0.003 OYK 0.004 0.002 0.01 0.01 0.001 0.001 0.001 0.001 0.01 0.003 0.001 0.003 ETI 0.014 0.002 0.01 0.01 0.001 0.001 0.003 0.001 0.001 0.003 0.002 0.003 ISA 0.004 0.001 0.01 0.01 0.001 0.01 0.01 0.002 0.003 0.003 0.001 0.01 OPP 0.002 0.001 0.01 0.01 * 0.001 0.003 0.001 0.003 0.003 0.001 0.003 OPE 0.002 0.002 0.01 0.01 * 0.01 0.01 0.002 0.001 0.003 * 0.003 MOG 0.002 0.002 0.01 0.01 * 0.01 0.01 0.002 0.001 0.003 * 0.003 ASJ 0.01 0.001 0.01 0.01 0.002 0.01 0.003 0.001 0.003 0.003 0.02 0.003 * = No water found 274 UNIVERSITY OF IBADAN LIBRARY Appendix 22: Concentrations of chromium (mg/L) in surface water for all the locations Sample July September November January March May July September November January March May Code 2006 2006 2006 2007 2007 2007 2007 2007 2007 2008 2008 2008 OYI 0.01 0.001 0.01 0.01 0.001 0.001 0.001 0.003 0.02 0.002 0.003 0.004 OSI 0.01 0.001 0.01 0.01 0.001 0.001 0.001 0.003 0.003 0.002 0.003 0.003 OUN 0.01 0.001 0.01 0.01 0.001 0.001 0.01 0.003 0.01 0.003 0.004 0.001 ARE 0.001 0.001 0.01 0.01 0.001 0.001 0.001 0.004 0.003 0.001 0.002 0.001 ASS 0.01 0.001 0.01 0.01 0.001 0.003 0.003 0.003 0.003 0.002 0.002 0.001 IRE 0.001 0.001 0.01 0.01 0.001 0.001 0.001 0.001 0.003 0.01 0.001 0.002 ANN 0.01 0.001 0.01 0.01 0.001 0.001 0.002 0.002 0.01 0.001 0.002 0.004 AHO 0.001 0.001 0.01 0.01 * 0.001 0.001 0.003 0.003 0.001 0.001 0.003 ENJ 0.01 0.001 0.01 0.01 * 0.001 0.001 0.001 0.01 0.01 0.001 0.001 OLO 0.03 0.001 0.01 0.01 0.001 0.001 0.003 0.001 0.002 0.002 * 0.001 GBD 0.02 0.001 0.01 0.02 0.001 0.001 0.003 0.002 0.003 0.01 0.001 0.003 AWE 0.01 0.01 0.01 0.01 * 0.001 0.001 0.01 0.004 0.002 * 0.01 OJU 0.001 0.001 0.01 0.01 0.001 0.001 0.001 0.003 0.004 0.001 0.01 0.001 OSU 0.001 0.001 0.01 0.01 0.001 0.001 0.001 0.002 0.001 0.003 0.01 0.001 YEY 0.01 0.001 0.01 0.01 0.001 0.001 0.001 0.003 0.003 0.001 0.01 0.003 ONN 0.001 0.01 0.01 0.01 0.01 0.001 0.002 0.004 0.01 0.002 0.01 0.002 OLU 0.01 0.001 0.01 0.01 0.002 0.001 0.001 0.002 0.01 0.002 0.002 0.004 ORU 0.04 0.001 0.01 * * * 0.001 0.001 0.01 * * * KAN 0.001 0.001 0.01 0.01 0.001 0.001 0.001 0.002 0.003 0.001 0.001 0.002 ADE 0.1 0.01 0.01 0.01 * 0.002 0.002 0.002 0.01 0.001 0.01 0.004 EJI 0.001 0.001 0.01 0.01 0.002 0.001 0.002 0.004 0.01 0.001 0.01 0.002 ARO 0.001 0.001 0.01 0.001 * 0.001 0.001 0.001 0.003 * * * ODD 0.03 0.001 0.01 0.01 0.001 0.001 0.001 0.001 0.004 0.001 0.02 0.001 OBB 0.02 0.001 0.01 0.01 0.001 0.001 0.001 0.004 0.01 0.003 0.002 0.01 OYK 0.001 0.001 0.01 0.01 0.001 0.001 0.001 0.001 0.001 0.002 0.01 0.003 ETI 0.01 0.01 0.01 0.01 0.001 0.001 0.001 0.001 0.001 0.003 0.01 0.003 ISA 0.03 0.001 0.01 0.01 0.002 0.001 0.002 0.003 0.003 0.001 0.01 0.002 OPP 0.01 0.001 0.01 0.01 * 0.003 0.01 0.001 0.01 0.002 0.001 0.001 OPE 0.001 0.001 0.01 0.01 * 0.002 0.01 0.004 0.01 0.002 * 0.002 MOG 0.03 0.001 0.01 0.01 * 0.001 0.01 0.003 0.001 0.003 * 0.01 ASJ 0.001 0.001 0.01 0.01 0.001 0.001 0.001 0.004 0.004 0.001 0.003 0.001 * = No water found 275 UNIVERSITY OF IBADAN LIBRARY Appendix 23: Concentrations of nickel (mg/L) in surface water for all the locations Sample July September November January March May July September November January March May Code 2006 2006 2006 2007 2007 2007 2007 2007 2007 2008 2008 2008 OYI 0.02 0.001 0.01 0.001 0.001 0.001 0.001 0.001 0.003 0.001 0.001 0.001 OSI 0.02 0.001 0.01 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.002 0.001 OUN 0.02 0.001 0.01 0.003 0.001 0.001 0.003 0.001 0.001 0.001 0.002 0.001 ARE 0.03 0.001 0.01 0.001 0.001 0.003 0.001 0.001 0.001 0.003 0.001 0.001 ASS 0.02 0.001 0.001 0.001 0.001 0.001 0.004 0.001 0.01 0.001 0.001 0.001 IRE 0.03 0.001 0.01 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 ANN 0.03 0.001 0.01 0.001 0.001 0.001 0.001 0.001 0.004 0.001 0.001 0.001 AHO 0.02 0.001 0.01 0.001 * 0.001 0.001 0.001 0.001 0.001 0.001 0.001 ENJ 0.06 0.001 0.01 0.001 * 0.001 0.001 0.001 0.003 0.01 0.001 0.001 OLO 0.01 0.001 0.01 0.001 0.002 0.001 0.001 0.001 0.001 0.001 * 0.001 GBD 0.02 0.001 0.01 0.004 0.001 0.002 0.003 0.001 0.001 0.001 0.001 0.001 AWE 0.02 0.001 0.01 0.003 * 0.001 0.001 0.003 0.01 0.001 * 0.001 OJU 0.02 0.001 0.01 0.001 0.001 0.001 0.001 0.002 0.001 0.003 0.001 0.001 OSU 0.04 0.001 0.01 0.003 0.001 0.01 0.001 0.001 0.001 0.001 0.001 0.001 YEY 0.02 0.001 0.01 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.003 0.001 ONN 0.02 0.001 0.01 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 OLU 0.03 0.01 0.01 0.001 0.001 0.001 0.002 0.002 0.003 0.001 0.001 0.001 ORU 0.01 0.001 0.01 * * * 0.002 0.001 0.003 * * * KAN 0.05 0.001 0.01 0.003 0.001 0.001 0.001 0.001 0.003 0.002 0.001 0.001 ADE 0.03 0.001 0.01 0.01 * 0.01 0.001 0.003 0.001 0.001 0.001 0.001 EJI 0.03 0.001 0.01 0.001 0.001 0.001 0.001 0.001 0.003 0.001 0.001 0.001 ARO 0.04 0.001 0.01 0.001 * 0.001 0.001 0.004 0.001 * * * ODD 0.02 0.001 0.01 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 OBB 0.03 0.001 0.01 0.01 0.001 0.001 0.001 0.004 0.001 0.001 0.001 0.001 OYK 0.03 0.001 0.01 0.001 0.001 0.001 0.001 0.001 0.01 0.002 0.002 0.001 ETI 0.02 0.001 0.01 0.001 0.001 0.001 0.001 0.001 0.003 0.01 0.001 0.001 ISA 0.03 0.001 0.01 0.001 0.001 0.002 0.001 0.003 0.001 0.001 0.001 0.001 OPP 0.04 0.001 0.01 0.001 * 0.003 0.002 0.001 0.001 0.001 0.001 0.001 OPE 0.03 0.001 0.01 0.001 * 0.001 0.002 0.001 0.004 0.001 * 0.001 MOG 0.03 0.001 0.01 0.001 * 0.001 0.001 0.001 0.001 0.002 * 0.001 ASJ 0.032 0.001 0.01 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 * = No water found 276 UNIVERSITY OF IBADAN LIBRARY Appendix 24: Concentrations of zinc (mg/L) in surface water for all the locations Sample July September November January March May July September November January March May Code 2006 2006 2006 2007 2007 2007 2007 2007 2007 2008 2008 2008 OYI 0.01 0.15 0.01 0.001 0.001 0.01 0.003 0.04 0.08 0.09 0.02 0.05 OSI 0.02 0.18 0.02 0.01 0.01 0.01 0.01 0.07 0.14 0.12 0.01 0.06 OUN 0.05 0.18 0.01 0.01 0.01 0.004 0.003 0.03 0.09 0.14 0.02 0.07 ARE 0.01 0.14 0.04 0.24 0.01 0.01 0.001 0.05 0.001 0.06 0.01 0.06 ASS 0.003 0.10 0.27 0.01 0.004 0.01 0.02 0.12 0.01 0.21 0.02 0.03 IRE 0.01 0.14 0.1 0.01 0.01 0.002 0.01 0.07 0.16 0.10 0.03 0.04 ANN 0.02 0.06 0.01 0.01 0.01 0.01 0.001 0.08 0.14 0.15 0.03 0.04 AHO 0.01 0.17 0.12 0.03 * 0.002 0.001 0.03 0.02 0.10 0.04 0.05 ENJ 0.01 0.24 0.53 0.01 * 0.01 0.01 0.09 0.06 0.20 0.02 0.01 OLO 0.02 0.06 0.52 0.01 0.01 0.01 0.01 0.08 0.06 0.16 * 0.05 GBD 0.02 0.22 0.44 0.06 0.01 0.04 0.04 0.15 0.02 0.09 0.04 0.03 AWE 0.03 0.21 0.12 0.03 * 0.01 0.01 0.10 0.08 0.15 * 0.03 OJU 0.02 0.22 0.08 0.01 0.01 0.003 0.01 0.06 0.08 0.07 0.03 0.04 OSU 0.01 0.29 0.20 0.01 0.004 0.001 0.02 0.04 0.06 0.17 0.02 0 YEY 0.02 0.02 0.49 0.03 0.01 0.01 0.01 0.04 0.12 0.09 0.01 0.04 ONN 0.01 0.01 0.25 0.01 0.01 0.01 0.01 0.05 0.10 0.15 0.11 0.03 OLU 0.02 0.01 0.31 0.01 0.001 0.01 0.01 0.06 0.14 0.11 0.01 0.07 ORU 0.01 0.28 0.02 * * * 0.01 0.05 0.05 * * * KAN 0.01 0.001 0.40 0.01 0.004 0.01 0.01 0.09 0.12 0.12 0.02 0.03 ADE 0.02 0.10 0.25 0.05 * 0.02 0.01 0.08 0.10 0.15 0.04 0.03 EJI 0.003 0.09 0.03 0.01 0.001 0.004 0.02 0.14 0.12 0.13 0.06 0.08 ARO 0.01 0.16 0.03 0.02 * 0.01 0.01 0.06 0.05 * * * ODD 0.01 0.09 0.34 0.01 0.01 0.002 0.01 0.02 0.06 0.04 0.02 0.01 OBB 0.02 0.20 0.47 0.01 0.01 0.003 0.001 0.09 0.16 0.11 0.01 0.09 OYK 0.01 0.02 0.43 0.01 0.01 0.01 0.003 0.03 0.03 0.13 0.01 0.02 ETI 0.02 0.01 0.37 0.01 0.01 0.01 0.003 0.06 0.05 0.05 0.01 0.05 ISA 0.02 0.12 0.22 0.01 0.01 0.01 0.02 0.05 0.11 0.07 0.03 0.02 OPP 0.02 0.06 0.49 0.01 * 0.002 0.04 0.07 0.15 0.14 0.03 0.01 OPE 0.01 0.13 0.32 0.02 * 0.01 0.04 0.19 0.05 0.12 * 0.03 MOG 0.03 0.28 0.28 0.02 * 0.01 0.02 0.07 0.02 0.16 * 0.03 ASJ 0.01 0.39 0.01 0.01 0.01 0.004 0.01 0.13 0.13 0.13 0.06 0.001 * = No water found 277 UNIVERSITY OF IBADAN LIBRARY Appendix 25: Concentrations of calcium (mg/L) in surface water for all the locations Sample July September November January March May July September November January March May Code 2006 2006 2006 2007 2007 2007 2007 2007 2007 2008 2008 2008 OYI 16.0 13.2 11.3 96.0 146 75.6 58.0 22.1 17.0 12.0 64.5 41.1 OSI 33.0 26.2 21.7 152 295 17.4 81.0 33.7 45.0 6.40 100 64.5 OUN 29.0 16.2 5.90 111 105 108 52.0 18.2 43.0 10.0 53.0 58.6 ARE 31.0 24.2 30.2 163 256 75.6 52.0 32.5 22.1 22.3 107 70.0 ASS 39.0 27.2 20.8 175 256 93.0 99.0 36.0 34.9 22.3 99.0 70.0 IRE 14.0 12.3 15.1 111 268 128 40.7 10.5 24.4 15.3 111 64.5 ANN 28.7 27.0 17.0 159 201 89.0 66.0 33.7 39.0 16.8 62.5 50.8 AHO 26.0 21.9 15.1 215 * 105 23.0 22.2 20.9 18.8 170 59.0 ENJ 23.5 7.90 15.0 138 * 81.0 58.0 17.4 15.1 11.7 50.0 82.0 OLO 13.0 10.2 14.2 26.0 146 105 81.0 19.8 17.5 16.5 * 79.1 GBD 25.0 24.2 18.0 65.0 204 111 64.0 30.2 28.1 15.3 105 73.0 AWE 38.0 24.6 10.4 152 * 76.0 55.0 41.8 33.7 20.6 * 123 OJU 28.8 21.3 10.4 152 144 128 64.0 29.1 19.8 12.9 88.0 52.8 OSU 21.0 16.4 19.9 58.2 123 139 64.0 24.4 28.2 20.0 117 76.0 YEY 24.5 27.2 33.1 75.7 396 117 58.0 30.2 23.3 29.3 94.0 82.0 ONN 33.0 23.3 11.3 64.0 111 105 70.0 19.0 27.9 34.0 111 82.0 OLU 9.00 12.0 8.97 28.0 92.9 87.0 52.0 10.5 12.8 12.9 76.0 82.0 ORU 10.0 13.5 19.9 * * * 64.0 16.3 18.7 * * * KAN 19.0 19.0 23.6 111 210 99.0 64.0 33.7 12.8 14.1 88.0 73.0 ADE 49.1 221 201 476 * 396 180 115 326 75.0 451 393 EJI 16.0 16.0 20.8 75.0 198 111 81.0 32.5 7.00 17.6 88.0 72.7 ARO 13.0 18.5 20.8 82.0 * 209 87.0 19.8 30.3 * * * ODD 14.0 15.6 15.1 40.0 82.0 93.0 58.0 12.8 17.6 15.3 65.0 67.0 OBB 18.0 17.0 22.7 98.0 163 145 52.0 23.2 29.3 29.3 21.1 88.0 OYK 16.0 10.1 16.1 30.3 128 99.0 64.0 20.9 16.3 14.7 76.0 41.1 ETI 11.0 22.3 24.6 43.1 99.1 99.0 70.0 19.0 31.4 26.4 76.0 84.0 ISA 30.0 27.3 20.8 76.8 251 122 76.0 23.2 30.2 34.0 123 82.0 OPP 35.0 25.6 31.2 86.1 * 134 87.2 34.9 40.7 44.6 88.0 88.0 OPE 31.0 27.4 32.1 210 * 169 76.0 37.0 64.5 21.1 * 76.2 MOG 90.1 58.1 79.3 127 * 296 151 87.0 117 41.0 * 173 ASJ 15.0 27.2 14.2 60.6 105 116 76.0 26.0 35.0 17.6 100 52.8 *No water found 278 UNIVERSITY OF IBADAN LIBRARY Appendix 26: Concentrations of magnesium (mg/L) in surface water for all the locations Sample July September November January March May July September November January March May Code 2006 2006 2006 2007 2007 2007 2007 2007 2007 2008 2008 2008 OYI 0.60 2.30 3.00 1.20 16.0 11.3 7.10 1.40 1.40 1.60 12.8 26.4 OSI 1.40 5.10 4.60 1.60 7.60 36.7 4.30 5.90 26.00 4.70 17.0 27.1 OUN 1.20 1.70 1.30 1.15 28.0 10.6 8.50 1.80 8.00 1.99 18.5 17.8 ARE 1.60 1.20 2.30 1.60 11.3 24.0 9.90 2.82 5.40 2.90 8.30 17.8 ASS 0.60 1.20 3.67 1.40 14.0 12.7 15.5 3.70 1.40 1.71 13.0 25.6 IRE 1.20 0.90 0.90 1.40 14.0 3.10 7.10 3.70 0.60 1.30 11.0 11.4 ANN 1.00 3.60 2.90 1.50 5.66 19.0 7.50 3.10 7.90 2.40 20.0 35.0 AHO 1.50 0.25 2.30 1.46 * 25.4 15.6 2.50 2.30 2.28 48.0 34.2 ENJ 1.00 0.97 2.80 0.99 * 7.10 11.3 5.10 1.10 2.00 20.7 9.30 OLO 2.20 1.70 2.30 0.32 237 12.7 4.20 3.39 2.10 2.28 * 6.40 GBD 2.20 24.0 1.60 1.60 19.0 17.0 13.0 4.52 5.90 3.10 17.1 11.4 AWE 4.50 3.20 2.30 1.70 * 21.0 16.0 7.60 5.10 2.70 * 20.0 OJU 0.40 3.40 5.10 0.90 4.50 4.60 7.10 1.13 3.70 2.30 10.0 22.8 OSU 2.00 6.10 4.60 0.30 86.0 7.10 13.0 4.90 8.10 4.60 12.8 10.0 YEY 2.80 1.70 3.40 0.20 111 9.90 14.1 4.00 3.40 6.80 14.2 7.10 ONN 2.40 2.20 1.80 0.10 100 12.7 14.1 8.00 3.10 5.70 3.00 11.4 OLU 3.40 1.50 4.50 0.40 71.0 4.80 5.65 2.00 2.30 2.30 10.0 7.10 ORU 0.49 5.60 1.60 * * * 4.20 3.40 4.60 * * * KAN 3.20 6.00 5.50 1.15 211 18.4 13.0 6.90 7.80 5.70 20.7 13.7 ADE 38.0 24.1 23.7 1.60 * 35.0 42.0 10.7 33.0 7.40 26.0 22.8 EJI 1.70 4.10 3.70 0.15 84.0 14.0 13.0 1.00 9.20 1.70 22.1 13.7 ARO 1.70 3.00 4.80 0.20 * 41.0 10.0 4.70 5.90 * * * ODD 14.0 1.46 2.50 0.18 96.0 18.0 8.47 4.50 3.20 2.30 21.4 7.10 OBB 1.90 17.0 1.80 0.30 145 14.1 11.3 2.30 8.80 3.40 18.5 12.8 OYK 2.20 1.90 1.80 0.20 65.1 3.30 5.70 2.10 3.70 1.60 10.0 23.5 ETI 1.00 0.49 1.60 0.40 103 2.82 9.90 4.50 0.90 4.00 7.10 5.90 ISA 3.41 4.20 7.80 0.40 290 1.00 5.65 4.20 6.40 6.60 20.0 10.7 OPP 3.70 5.10 2.75 0.40 * 7.10 7.10 4.80 4.20 10.0 15.7 15.7 OPE 5.84 8.40 3.70 2.40 * 25.4 19.8 5.10 44.0 5.13 * 20.0 MOG 6.60 5.10 6.90 0.30 * 41.0 42.0 4.50 25.2 3.70 * 109 ASJ 1.50 2.90 1.20 1.20 16.4 19.8 12.9 2.59 4.80 17.6 12.8 18.5 * = No water found 279 UNIVERSITY OF IBADAN LIBRARY Appendix 27: Concentrations of organic carbon (%) in sediment for all the locations Sample July September November January March May July September November January March May Code 2006 2006 2006 2007 2007 2007 2007 2007 2007 2008 2008 2008 OYI 0.50 1.30 2.50 1.00 1.70 1.40 1.00 1.10 1.20 5.30 0.70 0.60 OSI 1.60 1.10 0.40 0.38 2.00 1.40 3.10 2.40 0.80 33.9 1.90 1.40 OUN 0.30 0.70 0.40 0.24 2.00 0.70 0.70 1.20 1.20 21.6 1.03 1.09 ARE 0.20 1.30 0.50 1.10 2.10 0.50 2.20 1.70 1.20 9.00 0.70 1.30 ASS 0.30 2.80 0.60 1.50 1.00 1.50 2.80 2.50 1.40 23.0 0.50 0.70 IRE 0.36 0.37 1.20 0.50 2.53 0.50 2.80 1.60 1.00 6.10 1.20 2.70 ANN 1.00 3.00 1.30 2.50 5.60 0.60 2.10 3.70 4.20 24.1 3.80 4.30 AHO 1.49 1.57 1.00 2.60 3.10 8.40 2.54 2.80 2.10 12.5 3.00 1.70 ENJ 0.30 1.00 0.90 0.90 1.40 0.30 0.80 1.50 1.20 10.5 1.40 1.20 OLO 0.80 1.70 0.20 1.30 1.70 0.70 1.40 1.80 0.60 9.90 2.20 1.50 GBD 0.80 0.80 0.50 1.10 1.60 1.40 1.40 0.80 0.60 4.90 2.60 0.60 AWE 0.20 0.50 1.90 1.40 1.90 0.30 0.70 1.10 1.10 7.10 1.30 0.70 OJU 0.30 0.60 0.50 0.30 0.70 0.30 0.50 0.50 0.60 24.0 1.60 0.60 OSU 0.31 0.50 0.28 0.70 2.10 0.30 0.50 1.40 0.90 4.43 2.00 0.70 YEY 0.30 0.60 0.60 0.10 1.40 0.10 0.83 1.20 1.20 6.10 1.00 1.20 ONN 0.70 1.60 1.60 0.90 4.30 1.10 1.40 4.70 1.80 11.7 2.30 0.60 OLU 0.80 0.49 0.80 2.90 1.40 0.40 1.60 1.80 1.20 18.0 2.00 0.90 ORU 0.20 1.30 2.40 0.40 1.00 0.50 0.90 1.20 1.10 12.1 1.30 0.50 KAN 0.30 0.24 0.20 0.24 2.50 1.10 0.60 1.10 1.10 17.8 1.00 0.88 ADE 0.39 0.20 0.30 0.30 7.60 0.50 0.80 2.00 6.00 46.0 5.70 0.80 EJI 0.21 1.70 0.50 0.60 1.10 0.42 0.20 1.20 0.80 4.80 2.30 1.30 ARO 0.80 0.50 0.60 0.20 1.20 0.70 2.20 1.60 0.90 5.70 1.10 0.90 ODD 0.73 0.40 0.80 0.30 3.50 0.80 1.40 3.60 1.60 2.10 2.60 0.80 OBB 0.50 0.30 0.20 0.16 0.50 0.40 0.80 1.80 1.00 5.30 0.71 0.50 OYK 0.20 4.00 1.10 0.50 1.50 0.70 1.60 3.80 1.00 5.30 2.60 1.20 ETI 0.21 0.30 0.50 0.30 1.00 0.50 1.20 3.10 6.60 11.0 2.40 0.70 ISA 0.80 0.40 1.00 0.40 1.10 0.84 4.90 3.00 3.00 9.60 2.40 2.20 OPP 3.00 0.50 1.70 2.90 1.80 1.30 2.06 2.00 1.00 10.7 2.40 1.00 OPE 2.90 1.90 7.30 0.60 3.20 1.60 1.60 4.10 0.78 11.9 1.60 1.70 MOG 0.60 1.60 0.20 1.20 1.00 0.20 0.40 4.00 2.50 6.06 1.20 0.60 ASJ 0.40 0.59 0.39 0.40 1.00 0.10 0.60 6.40 0.30 4.70 2.60 0.60 280 UNIVERSITY OF IBADAN LIBRARY Appendix 28: % sand in sediment for all the locations Sample July September November January March May July September November January March May Code 2006 2006 2006 2007 2007 2007 2007 2007 2007 2008 2008 2008 OYI 78.9 78.1 71.9 75.1 71.6 71.5 70.2 70.7 71.3 72.1 71.9 69.1 OSI 78.4 81.3 71.7 75.1 72.0 74.2 73.2 75.1 69.2 72.6 72.6 70.3 OUN 80.2 79.1 71.7 71.4 71.6 71.1 74.2 75.3 71.0 73.4 72.4 71.8 ARE 79.1 78.8 68.8 74.3 70.8 71.9 73.2 70.3 70.3 71.9 74.5 72.1 ASS 78.7 80.0 73.1 72.2 71.2 70.2 70.9 68.2 69.9 72.5 73.3 69.5 IRE 79.8 77.0 71.9 74.3 73.2 71.2 68.9 69.2 69.3 71.6 69.6 69.5 ANN 78.8 79.7 72.2 78.4 72.7 75.7 73.9 69.5 70.6 74.3 70.6 77.7 AHO 77.0 72.7 72.4 72.6 72.9 73.5 71.9 70.4 70.2 74.9 71.1 69.9 ENJ 75.1 80.6 80.5 74.2 73.5 73.4 73.2 70.1 73.5 76.2 72.2 71.5 OLO 78.3 76.3 72.4 72.9 71.5 72.8 72.5 70.1 70.9 74.3 72.3 70.8 GBD 77.3 79.0 72.0 74.4 72.3 72.5 72.1 72.1 74.1 75.5 71.8 71.0 AWE 75.1 76.9 69.9 72.6 73.0 71.8 70.9 71.2 72.0 73.0 71.9 72.5 OJU 75.6 79.4 69.4 72.7 71.5 73.5 70.0 72.3 69.6 71.5 71.8 71.6 OSU 70.1 77.4 69.0 71.4 72.6 70.5 70.9 69.1 69.8 71.1 70.2 71.1 YEY 68.6 77.3 73.9 71.2 74.1 73.4 71.2 70.2 70.5 70.7 71.9 71.2 ONN 66.6 75.8 69.6 72.7 72.9 72.2 71.2 71.0 70.0 73.2 71.2 70.4 OLU 73.6 74.7 72.3 71.3 73.0 73.9 70.2 70.7 73.9 69.4 70.8 71.3 ORU 61.0 84.8 72.6 72.4 71.2 72.3 68.8 70.9 69.2 69.8 70.2 72.9 KAN 76.7 76.6 72.1 71.4 71.8 72.0 71.9 72.1 68.8 72.9 71.8 70.5 ADE 70.2 72.7 72.5 73.7 72.3 73.2 70.5 71.3 70.7 70.7 70.2 71.5 EJI 74.1 76.1 71.7 73.9 71.9 72.9 71.5 71.7 71.7 72.5 70.5 70.9 ARO 72.0 75.3 71.9 73.4 72.5 71.6 73.5 70.9 70.1 69.2 70.0 71.6 ODD 79.6 73.6 70.1 73.3 71.3 71.2 73.2 71.2 73.2 71.9 70.9 70.0 OBB 78.2 74.7 71.7 72.8 73.2 70.5 73.8 69.8 69.6 71.6 68.4 71.0 OYK 66.9 80.0 70.9 72.6 74.8 71.5 68.8 71.9 71.9 69.6 76.2 70.9 ETI 77.0 76.4 72.4 73.8 72.6 71.9 71.2 72.0 70.6 71.6 70.8 69.3 ISA 70.8 80.0 72.0 71.8 70.8 71.8 68.2 70.5 71.0 70.5 71.3 70.6 OPP 79.1 76.7 72.1 74.6 76.1 76.2 72.2 72.1 70.0 72.9 74.1 71.2 OPE 67.5 76.4 70.2 74.0 70.8 69.8 68.8 69.9 70.3 73.3 71.2 71.3 MOG 77.8 78.9 68.6 72.1 73.0 72.3 72.2 74.6 70.2 73.8 69.1 71.8 ASJ 70.5 74.7 72.0 72.3 72.3 69.2 70.9 70.2 71.9 72.7 69.2 71.9 281 UNIVERSITY OF IBADAN LIBRARY Appendix 29: % clay in sediment for all the locations Sample July September November January March May July September November January March May Code 2006 2006 2006 2007 2007 2007 2007 2007 2007 2008 2008 2008 OYI 17.5 19.5 27.2 24.5 26.9 27.2 26.8 27.6 27.9 26.6 27.1 27.6 OSI 20.7 23.0 26.1 24.2 25.4 25.8 25.1 29.2 27.8 23.3 26.7 27.6 OUN 18.5 18.3 26.5 27.9 24.7 26.8 24.8 22.8 28.4 25.6 27.0 26.1 ARE 20.1 20.8 25.9 23.0 26.7 27.5 26.0 27.8 28.2 26.4 25.9 26.2 ASS 20.3 18.1 25.1 24.3 27.4 29.2 25.5 28.3 27.1 26.1 25.8 27.4 IRE 18.6 20.5 25.2 25.0 25.7 27.4 28.2 30.1 29.5 26.4 28.3 28.2 ANN 18.7 18.9 25.0 20.8 26.4 22.4 24.6 28.7 27.8 23.9 26.5 23.8 AHO 21.3 21.9 25.8 25.7 24.8 24.5 26.9 26.9 28.5 22.4 27.2 27.5 ENJ 20.0 18.2 25.4 24.7 25.3 25.5 25.2 29.2 26.2 22.1 26.0 27.3 OLO 20.7 23.0 26.1 24.2 25.4 25.8 25.1 29.2 27.8 23.3 26.7 27.6 GBD 19.2 18.3 26.7 24.4 26.1 26.7 26.5 26.8 24.8 24.0 27.7 28.3 AWE 22.5 20.6 28.2 26.1 25.7 27.7 27.2 28.7 26.8 26.3 27.2 26.4 OJU 22.3 17.3 27.3 24.9 27.0 25.0 28.4 27.1 29.5 27.4 27.3 28.2 OSU 20.2 21.1 27.1 26.4 26.2 28.2 28.0 28.4 29.2 27.4 29.7 26.7 YEY 23.8 20.4 24.6 24.6 25.3 25.8 27.5 29.4 27.7 27.7 26.2 27.7 ONN 22.9 23.0 27.6 26.0 26.7 26.0 26.7 27.1 27.1 25.6 27.3 27.6 OLU 21.4 22.3 26.4 27.8 26.0 25.5 26.7 27.3 25.1 27.8 28.1 27.9 ORU 22.2 13.6 26.2 26.1 27.8 26.7 28.5 27.8 29.4 28.4 27.8 26.7 KAN 20.4 22.7 24.7 27.9 26.2 17.0 27.2 27.2 30.4 26.1 27.8 26.9 ADE 21.9 24.4 26.6 24.9 25.7 25.4 27.4 27.0 29.1 26.8 29.2 27.0 EJI 21.4 19.3 24.9 25.5 26.5 26.5 26.5 27.3 27.8 24.8 29.1 28.1 ARO 22.3 20.0 24.4 26.1 26.4 26.2 24.8 27.9 29.0 28.5 28.5 27.8 ODD 19.9 23.6 28.5 26.0 25.6 26.6 25.1 28.1 26.3 26.7 27.7 26.5 OBB 20.1 24.1 25.0 25.1 25.9 28.5 24.8 27.8 29.5 27.3 29.7 27.3 OYK 19.5 18.2 24.6 27.3 24.5 26.7 28.2 26.8 26.8 28.8 22.2 28.2 ETI 21.9 21.3 27.4 25.0 26.7 24.7 28.2 27.7 26.9 26.5 25.8 28.2 ISA 21.8 19.3 27.0 25.9 27.4 26.4 28.5 28.5 28.3 28.0 28.1 29 OPP 20.2 20.2 25.4 24.7 23.3 22.5 26.5 27.5 28.0 25.4 25.4 27.6 OPE 21.5 20.7 26.0 25.8 27.0 26.7 29.7 27.2 29.3 26.3 27.7 27.8 MOG 21.2 19.7 28.2 25.8 25.3 26.4 27.1 24.2 28.2 24.2 29.6 25.5 ASJ 18.5 22.5 26.8 26.8 25.8 27.5 27.2 28.1 27.2 26.3 27.4 27.6 282 UNIVERSITY OF IBADAN LIBRARY Appendix 30: % silt in sediment for all the locations Sample July September November January March May July September November January March May Code 2006 2006 2006 2007 2007 2007 2007 2007 2007 2008 2008 2008 OYI 3.60 2.50 1.00 0.50 1.60 1.30 3.10 1.70 0.90 1.40 1.00 3.30 OSI 1.00 4.00 1.40 1.50 2.10 1.44 1.40 0.90 1.10 1.00 0.80 2.30 OUN 1.30 2.70 1.90 0.80 3.70 2.20 1.10 1.90 0.70 1.10 0.60 2.20 ARE 0.90 0.50 5.30 2.80 2.60 0.70 0.80 1.90 1.60 0.70 0.70 1.70 ASS 1.10 2.00 1.90 3.60 1.50 0.72 3.70 3.50 3.10 1.50 1.00 3.10 IRE 1.70 2.60 3.00 0.80 1.20 1.40 3.00 0.72 1.30 2.10 2.20 2.40 ANN 2.40 1.30 2.80 0.80 0.90 1.90 1.50 1.80 1.60 1.90 1.00 0.50 AHO 1.70 5.50 1.80 1.70 2.40 1.10 1.30 2.70 1.40 2.80 1.70 2.70 ENJ 4.00 1.30 1.70 1.20 1.20 1.10 1.70 0.70 26.0 1.80 1.90 1.30 OLO 1.10 0.80 1.60 3.00 3.10 1.44 2.40 0.70 1.30 2.50 1.10 1.60 GBD 3.50 2.50 1.40 1.30 1.70 0.80 1.40 1.10 1.20 0.60 0.50 0.80 AWE 2.50 2.50 2.00 1.40 1.40 0.50 2.00 0.10 1.30 0.80 1.00 1.10 OJU 2.10 3.40 3.30 2.50 1.50 1.44 1.60 0.60 1.00 1.20 1.00 0.20 OSU 9.80 1.50 3.90 2.30 1.30 1.40 1.10 2.50 1.10 1.60 0.20 2.30 YEY 7.70 2.40 1.60 4.30 0.70 0.90 1.40 0.50 1.90 1.70 2.00 1.20 ONN 11.0 1.30 2.90 1.40 0.50 1.90 2.16 0.80 3.00 1.30 1.60 2.10 OLU 5.20 3.10 1.30 1.20 1.00 0.70 3.10 1.10 1.10 2.80 1.20 0.90 ORU 16.9 1.70 1.30 1.50 1.10 1.10 2.70 1.30 1.50 1.80 2.10 0.50 KAN 2.90 0.70 3.30 0.80 2.10 11.0 0.90 0.72 0.80 1.10 0.40 2.60 ADE 8.00 3.00 1.00 1.40 1.40 2.10 2.10 2.30 0.20 2.50 0.70 1.60 EJI 4.50 4.60 3.50 0.70 1.70 0.80 2.10 2.20 0.50 2.80 0.50 1.10 ARO 4.80 4.80 3.70 0.60 1.20 2.30 1.70 1.30 1.00 2.40 1.00 0.60 ODD 0.60 2.90 1.50 0.70 3.20 2.30 1.80 0.70 0.50 1.50 1.50 3.60 OBB 2.10 1.30 3.40 2.20 1.00 1.10 1.40 2.40 1.00 1.20 1.90 1.80 OYK 13.6 1.9 4.50 0.20 0.80 1.80 3.00 1.60 1.40 1.70 1.70 1.00 ETI 1.10 2.40 0.30 1.30 0.80 3.44 0.70 0.30 2.60 2.00 3.50 2.60 ISA 7.50 0.70 1.10 2.30 1.90 1.80 3.40 1.10 0.80 1.60 0.60 0.50 OPP 0.80 3.20 2.50 0.80 0.70 1.30 1.40 0.40 2.10 1.70 0.60 1.30 OPE 11.10 3.00 3.90 0.20 2.20 3.40 1.60 2.90 0.50 0.50 1.20 0.90 MOG 1.10 1.60 3.30 2.20 1.80 1.40 0.80 1.30 1.60 2.10 1.30 2.70 ASJ 11.1 2.90 1.30 1.00 2.00 3.20 1.90 1.70 1.00 1.10 3.50 0.60 283 UNIVERSITY OF IBADAN LIBRARY Appendix 31: Cation exchange capacity (meq/100g) in sediment for all the locations Sample July September November January March May July September November January March May Code 2006 2006 2006 2007 2007 2007 2007 2007 2007 2008 2008 2008 OYI 16.9 80.8 44.2 47.1 39.5 51.5 50.3 44.9 63 53.4 33.8 37.0 OSI 19.0 65.9 65.7 58.8 64.0 62.6 69.7 43.2 65.6 50.4 61.5 47.4 OUN 14.2 57.0 35.0 44.0 45.0 55.1 55.4 51.0 66.0 38.9 46.1 43.1 ARE 23.8 76.0 46.8 47.6 57.5 60.8 67.7 57.0 51.3 42.5 44.9 48.4 ASS 21.9 88.6 40.9 39.4 48.5 80.0 71.0 51.6 48.1 35.1 54.2 52.6 IRE 24.3 49.3 35.0 42.3 39.5 47.1 52.4 55.4 51.7 48.5 47.8 47.2 ANN 28.2 52.0 54.9 58.0 58.0 58.6 57.5 49.0 65.7 45.7 53.0 51.0 AHO 31.3 71.8 61.7 47.7 58.5 77.9 48.9 38.3 47.9 40.3 46.7 56.4 ENJ 18.0 55.3 31.3 46.5 39.5 58.5 56.2 53.4 50.8 36.8 50.0 39.5 OLO 20.7 58.1 34.4 35.0 33.0 60.9 56.1 54.9 57.4 42.0 51.5 55.8 GBD 28.4 82.0 48.3 40.4 62.5 67.3 62.0 47.4 57.7 49.0 39.0 51.0 AWE 24.5 44.2 67.0 53.0 48.5 0.30 51.7 48.0 57.1 52.7 37.9 56.0 OJU 18.5 77.0 18.6 54.4 34.0 60.6 51.4 50.0 51.6 51.8 41.0 50.2 OSU 18.9 60.0 32.7 33.0 42.5 56.5 50.0 58.4 75.1 36.0 52.0 57.0 YEY 19.1 52.0 37.3 56.0 37.0 48.4 51.6 45.5 73.1 41.8 37.9 40.0 ONN 16.0 81.0 54.8 43.0 49.0 63.0 49.4 56.5 68.0 49.7 36.3 52.7 OLU 23.0 49.0 23.5 40.0 42.0 55.0 53.9 31.1 54.9 41.3 44.3 44.8 ORU 13.4 59.8 34.2 43.7 38.0 65.8 45.9 29.5 55.4 45.6 59.9 45.7 KAN 17.5 50.9 26.2 44.0 50.0 57.3 49.8 50.2 40.2 41.9 41.3 44.1 ADE 38.0 43.0 44.0 61.0 70.0 63.3 58.6 54.0 62.2 47.4 60.4 41.0 EJI 17.2 77.5 35.8 47.0 37.0 54.3 52.5 44.5 51.1 51.2 64.8 51.8 ARO 26.9 53.3 49.4 18.8 30.5 52.8 54.1 60.5 69.0 37.5 32.9 23.7 ODD 19.5 31.7 16.1 35.1 55.0 54.6 48.4 28.6 53.0 48.6 40.2 52.5 OBB 14.9 54.0 30.2 37.0 37.0 57.8 58.0 43.0 46.9 37.0 46.5 44.7 OYK 15.7 89.0 50.0 17.3 28.0 47.8 57.8 38.9 36.3 51.3 42.3 57.8 ETI 12.9 70.0 21.5 30.7 22.0 55.2 42.6 52.2 57.5 57.2 47.6 58.4 ISA 26.5 56.7 50.7 35.0 47.5 60.6 63.0 56.2 52.5 44.4 43.0 52.4 OPP 30.0 52.3 65.7 77.9 66.0 73.0 75.6 58.8 77.5 53.5 63.8 53.2 OPE 47.9 96.4 76.3 22.1 43.0 61.5 55.6 49.9 49.3 47.0 42.0 50.0 MOG 32.0 76.6 38.0 61.0 31.5 61.0 49.2 58.9 67.0 45.4 51.1 61.0 ASJ 36.8 47.4 50.6 30.4 36.5 47.2 66.4 41.1 50.5 48.2 44.3 44.2 284 UNIVERSITY OF IBADAN LIBRARY Appendix 32: Concentrations of lead (µg/g) in sediment for all the locations Sample July September November January March May July September November January March May Code 2006 2006 2006 2007 2007 2007 2007 2007 2007 2008 2008 2008 OYI 0.10 0.01 0.20 0.10 0.01 0.01 0.01 0.01 0.10 0.10 0.01 0.01 OSI 0.60 0.01 0.20 0.10 0.32 0.23 0.01 0.10 0.10 0.10 0.10 0.01 OUN 0.20 0.10 0.21 0.30 0.20 0.01 0.20 0.01 0.10 0.09 0.10 0.01 ARE 0.01 0.01 0.30 0.20 0.32 0.30 0.01 0.01 0.10 0.01 0.10 0.01 ASS 0.10 0.18 0.21 0.01 0.01 0.80 0.01 0.10 0.10 0.10 0.10 0.01 IRE 0.30 0.01 0.20 0.01 0.32 0.01 0.21 0.08 0.10 0.01 0.01 0.01 ANN 0.20 0.01 0.40 0.10 0.10 0.20 0.01 0.10 0.10 0.10 0.10 0.01 AHO 0.80 0.10 0.30 0.10 0.01 0.20 0.01 0.11 0.10 0.01 0.01 0.01 ENJ 0.10 0.01 0.40 0.01 0.01 0.01 0.01 0.01 0.20 0.10 0.01 0.01 OLO 0.10 0.01 0.30 0.20 0.01 0.23 0.30 0.11 0.10 0.01 0.10 0.01 GBD 0.10 0.01 0.30 0.01 0.20 0.01 0.21 0.01 0.10 0.01 0.10 0.01 AWE 0.40 0.01 0.50 0.01 0.01 0.10 0.01 0.10 0.10 0.01 0.01 0.01 OJU 0.10 0.01 0.20 0.01 0.20 0.01 0.01 0.10 0.10 0.01 0.01 0.01 OSU 0.10 0.01 0.20 0.10 0.20 0.01 0.01 0.10 0.20 0.01 0.20 0.01 YEY 0.04 0.01 0.30 0.01 0.01 0.10 0.01 0.30 0.20 0.01 0.20 0.10 ONN 0.10 0.01 0.42 0.10 0.01 0.01 0.01 0.24 0.20 0.10 0.10 0.01 OLU 0.04 0.10 0.21 0.20 0.20 0.23 0.01 0.30 0.30 0.10 0.01 0.01 ORU 0.01 0.01 0.10 0.10 0.01 0.10 0.01 0.01 0.20 0.01 0.10 0.01 KAN 0.10 0.01 0.10 0.30 0.01 0.01 0.01 0.01 0.10 0.01 0.01 0.01 ADE 8.60 21.0 39.0 10.0 4.40 70.0 63.0 0.34 0.41 0.20 0.01 0.01 EJI 0.04 0.01 0.90 0.01 0.20 0.01 0.01 0.20 0.10 0.10 0.01 0.01 ARO 0.01 0.01 0.10 0.10 0.01 0.10 0.01 0.01 0.10 0.10 0.01 0.01 ODD 0.10 0.01 0.42 0.01 0.32 0.01 0.01 0.01 0.11 0.01 0.01 0.01 OBB 0.04 0.18 0.21 0.19 0.20 0.01 0.10 0.10 0.10 0.10 0.20 0.01 OYK 0.04 0.20 0.60 0.01 0.20 0.01 0.01 0.30 0.10 0.01 0.01 0.01 ETI 0.01 0.18 0.30 0.01 0.32 0.01 0.01 0.10 0.30 0.01 0.01 0.01 ISA 0.30 0.18 0.40 0.10 0.01 0.1 0.01 0.23 0.20 0.01 0.10 0.01 OPP 0.21 0.20 0.70 0.10 0.32 0.01 0.01 0.10 0.20 0.10 0.10 0.01 OPE 0.20 0.39 0.90 0.10 0.01 0.23 0.01 0.10 0.20 0.09 0.10 0.01 MOG 0.20 0.10 0.10 0.10 0.20 0.01 0.01 0.11 0.10 0.1 0.01 0.01 ASJ 0.40 0.10 0.20 0.01 0.32 0.01 0.01 0.11 0.10 0.01 0.01 0.01 285 UNIVERSITY OF IBADAN LIBRARY Appendix 33: Concentrations of copper (µg/g) in sediment for all the locations Sample July September November January March May July September November January March May Code 2006 2006 2006 2007 2007 2007 2007 2007 2007 2008 2008 2008 OYI 0.01 0.10 0.10 0.10 0.08 0.01 0.01 0.10 0.08 0.10 0.03 0.03 OSI 0.03 0.10 0.10 0.10 0.10 0.10 0.17 0.10 0.08 0.40 0.03 0.03 OUN 0.01 0.06 0.10 2.20 0.08 0.01 0.10 0.10 0.20 0.26 0.20 0.06 ARE 0.01 0.10 0.10 0.10 0.08 0.01 0.17 0.10 0.08 0.06 0.03 0.03 ASS 0.01 0.30 0.17 0.70 0.08 0.05 0.20 0.10 0.10 0.30 0.30 0.05 IRE 0.01 0.03 0.10 0.30 0.10 0.07 0.01 0.08 0.10 0.20 0.20 0.03 ANN 0.30 0.03 0.40 0.50 0.08 0.04 0.04 0.10 0.30 0.20 0.10 0.06 AHO 1.20 0.17 0.30 0.10 0.10 0.05 0.01 0.23 0.10 0.20 0.10 0.10 ENJ 0.02 0.03 0.10 0.03 0.08 0.10 0.09 0.08 0.20 0.20 0.10 0.03 OLO 0.02 0.10 1.80 0.40 0.01 0.05 0.01 0.10 0.03 0.03 0.10 0.03 GBD 0.01 0.40 1.00 1.90 1.60 0.03 0.10 0.20 0.20 0.09 0.20 0.01 AWE 0.01 0.08 0.25 0.70 0.01 0.01 0.01 0.25 0.17 0.10 0.03 0.06 OJU 0.01 0.08 0.08 0.20 0.01 0.01 0.01 0.01 0.08 0.20 0.10 0.06 OSU 0.01 0.03 0.13 0.40 0.01 0.03 0.01 0.01 0.25 0.06 0.40 0.03 YEY 0.01 0.08 1.30 0.40 0.01 0.01 0.01 0.50 0.25 0.10 0.10 0.03 ONN 0.10 0.08 1.20 0.10 0.01 0.05 0.01 0.20 0.30 0.20 0.40 0.20 OLU 0.01 0.03 0.10 0.60 0.10 0.05 0.10 0.10 0.30 0.10 0.30 0.03 ORU 0.01 0.03 0.20 0.50 0.01 0.03 0.01 0.08 0.30 0.10 0.20 0.03 KAN 0.01 0.30 0.30 2.20 0.01 0.05 0.01 0.01 0.10 0.10 0.03 0.10 ADE 3.80 5.00 11.6 12.0 8.60 6.40 24.0 0.10 0.10 0.80 0.10 0.10 EJI 0.01 0.03 0.30 0.03 0.10 0.10 0.01 0.08 0.25 0.20 0.12 0.03 ARO 0.01 0.10 0.90 0.20 0.10 0.01 0.09 0.08 0.30 0.20 0.03 0.03 ODD 0.01 0.03 0.20 0.03 0.10 0.05 0.01 0.01 0.20 0.10 0.03 0.10 OBB 0.01 0.10 0.20 0.03 0.10 0.01 0.10 0.20 0.10 0.06 0.12 0.10 OYK 0.01 0.08 0.10 1.90 0.01 0.01 0.01 0.01 0.30 0.10 0.10 0.03 ETI 0.01 0.03 0.10 0.10 0.01 0.01 0.10 0.01 0.50 0.10 0.10 0.03 ISA 0.02 0.10 1.30 0.50 0.01 0.03 0.10 0.40 0.20 0.10 0.12 0.04 OPP 0.70 0.60 1.60 0.20 0.08 0.05 0.01 0.01 0.17 0.30 0.10 0.03 OPE 0.10 2.70 5.40 0.50 0.01 0.05 0.10 0.20 0.30 0.18 0.03 0.10 MOG 0.10 1.60 0.30 1.20 0.01 0.10 0.01 0.10 0.10 0.10 0.10 0.10 ASJ 0.07 0.03 0.38 0.70 0.08 0.01 0.01 0.01 0.06 0.06 0.03 0.03 286 UNIVERSITY OF IBADAN LIBRARY Appendix 34: Concentrations of cadmium (µg/g) in sediment for all the locations Sample July September November January March May July September November January March May Code 2006 2006 2006 2007 2007 2007 2007 2007 2007 2008 2008 2008 OYI 0.20 0.01 0.1 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 OSI 0.30 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 OUN 0.04 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 ARE 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 ASS 0.03 0.03 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 IRE 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 ANN 0.20 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 AHO 0.10 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 ENJ 0.40 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 OLO 0.20 0.01 0.10 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 GBD 0.29 0.01 0.01 0.30 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 AWE 0.01 0.01 0.01 0.20 0.01 0.01 0.01 0.25 0.01 0.01 0.01 0.01 OJU 0.20 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 OSU 0.20 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 YEY 0.01 0.01 0.10 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 ONN 0.20 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 OLU 0.10 0.01 0.01 0.20 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 ORU 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 KAN 0.04 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 ADE 0.30 0.14 0.20 0.50 0.10 0.10 0.17 0.01 0.01 0.01 0.01 0.01 EJI 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 ARO 0.04 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 ODD 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 OBB 0.04 0.10 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 OYK 0.01 0.01 0.01 0.20 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 ETI 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 ISA 0.01 0.10 0.10 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 OPP 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 OPE 0.03 0.01 0.10 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 MOG 0.20 0.10 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 ASJ 0.01 0.01 0.01 0.20 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 287 UNIVERSITY OF IBADAN LIBRARY Appendix 35: Concentrations of cobalt (µg/g) in sediment for all the locations Sample July September November January March May July September November January March May Code 2006 2006 2006 2007 2007 2007 2007 2007 2007 2008 2008 2008 OYI 0.40 0.02 0.20 0.02 0.14 0.24 0.01 0.02 0.02 0.02 0.02 0.02 OSI 0.90 0.20 0.16 0.40 0.02 0.01 0.01 0.02 0.02 0.02 0.02 0.02 OUN 1.10 0.10 0.01 0.02 0.10 0.30 0.30 0.02 0.02 0.02 0.02 0.02 ARE 0.10 0.02 0.40 0.10 0.01 0.01 0.01 0.02 0.02 0.02 0.02 0.02 ASS 0.10 0.02 0.30 0.20 0.01 0.50 0.01 0.02 0.02 0.10 0.10 0.02 IRE 0.75 0.10 0.30 0.02 0.02 0.22 0.01 0.02 0.02 0.02 0.02 0.02 ANN 0.30 0.02 0.16 0.02 0.01 0.60 0.01 0.10 0.02 0.02 0.02 0.02 AHO 0.12 0.02 0.30 0.02 0.02 0.20 0.01 0.02 0.02 0.02 0.02 0.02 ENJ 0.10 0.02 0.10 0.02 0.20 0.01 0.01 0.02 0.02 0.02 0.02 0.02 OLO 0.02 0.02 0.30 0.02 0.02 0.22 0.01 0.02 0.02 0.02 0.02 0.02 GBD 0.29 0.01 0.01 0.30 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 AWE 0.10 0.02 0.30 0.02 0.01 0.10 0.01 0.10 0.02 0.02 0.02 0.02 OJU 0.10 0.10 0.20 0.02 0.10 0.01 0.01 0.10 0.02 0.02 0.02 0.02 OSU 0.02 0.02 0.10 0.02 0.02 0.01 0.01 0.10 0.02 0.02 0.02 0.02 YEY 0.02 0.01 0.02 0.10 0.02 0.01 0.01 0.02 0.10 0.02 0.02 0.02 ONN 0.10 0.02 0.30 0.10 0.01 0.40 0.01 0.02 0.02 0.02 0.02 0.02 OLU 0.10 0.10 0.30 0.02 0.10 0.22 0.01 0.10 0.02 0.02 0.20 0.02 ORU 0.02 0.02 0.10 0.02 0.01 0.10 0.01 0.02 0.10 0.02 0.02 0.02 KAN 0.19 0.02 0.30 0.02 0.01 0.22 0.01 0.02 0.02 0.02 0.02 0.02 ADE 0.50 0.50 0.16 0.10 0.50 0.30 3.00 0.02 0.02 0.10 0.14 0.02 EJI 0.30 0.02 0.01 0.02 0.01 0.01 0.01 0.02 0.02 0.02 0.20 0.02 ARO 0.20 0.20 0.30 0.02 0.14 1.80 0.01 0.02 0.02 0.02 0.02 0.02 ODD 0.10 0.02 0.40 0.02 0.14 0.20 0.01 0.02 0.02 0.02 0.02 0.02 OBB 0.90 0.20 0.10 0.10 0.01 0.22 0.10 0.02 0.02 0.10 0.02 0.02 OYK 0.10 0.02 0.30 0.02 0.01 0.01 0.01 0.02 0.02 0.02 0.02 0.02 ETI 0.02 0.02 0.16 0.02 0.01 0.22 0.01 0.02 0.11 0.02 0.20 0.02 ISA 0.60 0.02 0.30 0.20 0.01 0.01 0.01 0.02 0.10 0.02 0.02 0.02 OPP 2.50 0.30 0.01 0.02 0.01 0.01 0.01 0.02 0.10 0.10 0.10 0.02 OPE 0.02 0.02 0.10 0.02 0.01 0.4 0.10 0.10 0.02 0.02 0.02 0.02 MOG 0.20 0.20 0.30 0.02 0.01 0.10 0.01 0.02 0.02 0.10 0.02 0.02 ASJ 0.20 0.02 0.10 0.02 0.14 0.30 0.01 0.02 0.02 0.02 0.02 0.02 288 UNIVERSITY OF IBADAN LIBRARY Appendix 36: Concentrations of chromium (µg/g) in sediment for all the locations Sample July September November January March May July September November January March May Code 2006 2006 2006 2007 2007 2007 2007 2007 2007 2008 2008 2008 OYI 0.03 0.10 0.01 0.10 0.01 0.01 0.01 0.03 0.03 0.03 0.03 0.03 OSI 0.50 0.03 0.01 0.03 0.01 0.01 0.01 0.11 0.06 0.10 0.03 0.03 OUN 0.50 0.10 0.01 0.10 0.13 0.01 0.10 0.11 0.10 0.11 0.10 0.03 ARE 0.03 0.30 0.10 0.03 0.01 0.01 0.01 0.22 0.03 0.11 0.19 0.03 ASS 0.35 0.02 0.30 0.20 0.01 0.50 0.01 0.02 0.02 0.10 0.10 0.02 IRE 0.18 0.10 0.01 0.02 0.01 0.01 0.20 0.03 0.03 0.22 0.10 0.03 ANN 0.20 0.10 0.04 0.10 0.13 0.01 0.01 0.10 0.10 0.2 0.03 0.10 AHO 0.13 0.03 0.01 0.10 0.01 0.01 0.01 0.10 0.03 0.10 0.03 0.03 ENJ 0.34 0.03 0.01 0.03 0.20 0.01 0.01 0.20 0.10 0.10 0.03 0.03 OLO 0.20 0.03 0.01 0.10 0.10 0.01 0.01 0.03 0.08 0.11 0.03 0.03 GBD 0.03 0.10 0.10 0.10 0.20 0.01 0.01 0.22 0.10 0.03 0.10 0.03 AWE 0.32 0.10 0.01 0.10 0.01 0.01 0.01 0.03 0.08 0.10 0.10 0.03 OJU 0.03 0.03 0.01 0.03 0.10 0.01 0.01 0.10 0.10 0.10 0.10 0.30 OSU 0.32 0.03 0.01 0.03 0.01 0.01 0.01 0.11 0.03 0.30 0.27 0.03 YEY 0.32 0.20 0.10 0.03 0.01 0.01 0.01 0.20 0.08 0.20 0.10 0.30 ONN 0.03 0.10 0.10 0.03 0.01 0.01 0.01 0.03 0.08 0.22 0.10 0.03 OLU 0.32 0.03 0.01 0.10 0.01 0.01 0.01 0.10 0.10 0.11 0.10 0.20 ORU 0.03 0.03 0.01 0.03 0.01 0.01 0.01 0.10 0.08 0.10 0.10 0.03 KAN 1.80 0.03 0.01 0.10 0.01 0.15 0.01 0.21 0.03 0.10 0.10 0.03 ADE 0.40 0.20 0.10 0.03 0.01 0.01 0.10 0.20 0.10 0.20 0.10 0.03 EJI 0.03 0.03 0.01 0.03 0.10 0.01 0.01 0.30 0.10 0.10 0.03 0.03 ARO 0.20 0.40 0.01 0.03 0.01 0.01 0.01 0.03 0.08 0.10 0.03 0.03 ODD 0.03 0.03 0.01 0.03 0.13 0.01 0.01 0.11 0.10 0.03 0.03 0.03 OBB 0.70 0.30 0.01 0.10 0.01 0.01 0.01 0.10 0.20 0.10 0.03 0.15 OYK 0.03 0.10 0.01 0.04 0.01 0.01 0.01 0.03 0.10 0.22 0.03 0.10 ETI 0.19 0.03 0.01 0.03 0.01 0.01 0.01 0.20 0.10 0.10 0.03 0.03 ISA 0.60 0.10 0.01 0.03 0.01 0.01 0.01 0.20 0.10 0.30 0.30 0.20 OPP 0.20 0.30 0.01 0.03 0.13 0.01 0.01 0.20 0.10 0.13 0.03 0.03 OPE 0.03 0.50 0.01 0.10 0.01 0.10 0.10 0.03 0.08 0.20 0.20 0.10 MOG 0.03 0.10 0.10 0.03 0.01 0.01 0.01 0.11 0.10 0.11 0.10 0.03 ASJ 0.03 0.10 0.01 0.03 0.01 0.01 0.19 0.03 0.20 0.10 0.10 0.03 289 UNIVERSITY OF IBADAN LIBRARY Appendix 37: Concentrations of nickel in (µg/g) sediment for all the locations Sample July September November January March May July September November January March May Code 2006 2006 2006 2007 2007 2007 2007 2007 2007 2008 2008 2008 OYI 0.11 0.27 0.40 0.40 0.01 0.01 0.01 0.02 0.02 0.02 0.02 0.02 OSI 0.10 0.70 0.40 0.20 0.01 0.15 0.01 0.02 0.02 0.10 0.02 0.02 OUN 0.04 0.50 0.10 0.10 0.20 0.01 0.20 0.02 0.02 0.10 0.02 0.02 ARE 0.07 0.60 0.40 0.02 0.01 0.15 0.01 0.02 0.02 0.02 0.02 0.02 ASS 0.20 0.27 0.24 0.10 0.01 0.20 0.01 0.02 0.02 0.20 0.02 0.02 IRE 0.39 0.01 0.02 0.01 0.02 0.01 0.22 0.03 0.02 0.02 0.02 0.02 ANN 0.30 0.50 0.30 0.20 0.10 0.10 0.01 0.02 0.02 0.10 0.02 0.02 AHO 0.11 0.20 0.40 0.02 0.01 0.01 0.01 0.02 0.14 0.02 0.02 0.02 ENJ 0.10 0.48 0.10 0.02 0.01 0.01 0.15 0.02 0.02 0.02 0.02 0.02 OLO 0.09 0.10 0.20 0.02 0.01 0.15 0.01 0.02 0.02 0.10 0.02 0.02 GBD 0.20 1.60 0.40 0.10 0.10 0.15 0.01 0.02 0.20 0.02 0.02 0.02 AWE 0.01 0.10 0.48 0.20 0.01 0.10 0.01 0.02 0.10 0.02 0.02 0.02 OJU 0.01 0.01 0.40 0.02 0.01 0.20 0.01 0.02 0.02 0.02 0.02 0.02 OSU 0.02 0.30 0.24 0.02 0.01 0.01 0.01 0.02 0.02 0.20 0.02 0.02 YEY 0.49 0.10 0.02 0.02 0.01 0.01 0.01 0.02 0.02 0.02 0.02 0.02 ONN 0.04 0.10 0.60 0.10 0.01 0.10 0.01 0.02 0.10 0.10 0.02 0.02 OLU 0.10 0.02 0.02 0.40 0.01 0.01 0.01 0.02 0.02 0.14 0.02 0.02 ORU 0.01 0.20 0.50 0.10 0.01 0.01 0.01 0.02 0.02 0.10 0.02 0.02 KAN 0.40 0.20 0.60 0.10 0.01 0.15 0.01 0.02 0.02 0.10 0.02 0.02 ADE 0.50 0.20 0.60 0.02 0.20 0.40 0.50 0.02 0.10 0.02 0.02 0.02 EJI 0.07 0.40 0.24 0.02 0.10 0.01 0.01 0.02 0.10 0.02 0.02 0.02 ARO 0.25 0.02 0.80 0.10 0.01 0.01 0.01 0.01 0.10 0.10 0.02 0.02 ODD 0.07 0.10 0.24 0.02 0.01 0.01 0.01 0.10 0.02 0.02 0.02 0.02 OBB 0.20 0.30 0.50 0.02 0.01 0.01 0.01 0.02 0.10 0.02 0.10 0.02 OYK 0.10 0.15 0.01 0.02 0.01 0.01 0.01 0.02 0.02 0.02 0.02 0.02 ETI 3.70 0.02 0.20 0.02 0.01 0.01 0.01 0.02 0.10 0.02 0.10 0.02 ISA 0.50 0.10 0.10 0.02 0.01 0.01 0.01 0.02 0.02 0.02 0.10 0.02 OPP 0.18 0.02 0.80 0.10 0.01 0.01 0.01 0.02 0.02 0.02 0.02 0.02 OPE 0.20 0.80 0.60 0.10 0.01 0.20 0.01 0.02 0.10 0.10 0.02 0.02 MOG 0.01 0.50 0.40 0.10 0.01 0.01 0.01 0.02 0.02 0.20 0.02 0.02 ASJ 0.11 0.50 0.40 0.02 0.01 0.01 0.01 0.02 0.02 0.02 0.02 0.02 290 UNIVERSITY OF IBADAN LIBRARY Appendix 38: Concentrations of zinc (µg/g) in sediment for all the locations Sample July September November January March May July September November January March May Code 2006 2006 2006 2007 2007 2007 2007 2007 2007 2008 2008 2008 OYI 28.9 7.10 25.7 5.60 0.80 0.70 0.20 1.20 1.10 7.50 0.70 5.60 OSI 60.0 17.0 43.0 10.0 0.50 20.10 0.70 7.20 5.50 12.8 2.80 5.00 OUN 8.10 4.60 11.4 9.60 2.10 0.14 0.60 3.50 2.40 16.0 0.99 4.70 ARE 58.0 0.30 50.1 23.0 0.50 3.50 0.22 2.00 2.10 6.40 0.90 7.30 ASS 75.6 39.0 22.0 27.0 0.40 15.30 0.60 1.20 3.90 11.5 1.50 4.30 IRE 46.6 7.00 27.9 9.00 0.50 4.80 0.54 4.70 2.20 10.7 2.80 4.40 ANN 64.9 29.0 32.9 11.7 0.70 1.50 0.30 2.70 4.40 10.4 2.90 3.30 AHO 109 3.70 54.0 22.0 0.80 9.79 0.20 1.60 0.14 16.3 2.80 4.90 ENJ 41.5 5.80 5.30 0.30 0.60 0.80 1.20 0.70 2.10 9.24 3.80 2.90 OLO 32.0 178 62.0 8.00 2.50 0.50 0.11 0.90 4.20 13.7 2.10 5.05 GBD 66.0 40.0 54.0 49.0 7.10 8.90 0.55 2.00 6.10 7.70 4.00 1.80 AWE 17.0 19.3 62.0 26.0 0.40 0.40 0.11 3.00 5.00 8.70 2.00 8.90 OJU 15.2 24.0 26.3 9.90 1.40 6.00 0.30 1.70 4.60 9.50 1.20 3.30 OSU 39.0 26.9 36.0 30.0 0.20 0.10 0.20 2.20 7.70 9.20 0.80 6.10 YEY 35.8 10.0 13.4 5.30 0.43 0.20 0.11 5.70 6.60 9.60 1.20 3.90 ONN 31.0 10.0 48.0 17.0 0.20 5.50 0.22 3.10 2.90 13.3 0.55 1.30 OLU 14.9 1.20 13.4 13.0 0.40 0.80 0.40 0.50 3.90 9.60 0.80 1.20 ORU 19.1 10.0 29.6 4.60 0.20 0.14 0.11 1.10 3.40 9.20 0.90 3.30 KAN 64.5 8.00 28.9 9.60 0.20 11.8 0.20 0.70 1.60 15.6 0.70 3.40 ADE 397 90.0 91.0 62.0 10.0 28.0 8.30 3.00 16.3 16.3 1.30 3.40 EJI 28.0 8.00 7.30 4.30 0.29 0.60 0.40 2.30 2.60 6.60 0.90 6.10 ARO 14.0 11.0 33.7 12.0 0.20 0.40 0.40 2.50 2.60 11.0 1.20 1.90 ODD 66.2 2.10 26.0 0.80 0.80 0.01 0.34 0.30 1.90 9.40 1.30 3.30 OBB 61.0 11.5 14.0 7.40 0.40 0.40 0.50 4.70 4.70 3.00 4.80 3.55 OYK 14.1 28.8 32.6 8.00 0.40 0.40 0.10 0.65 1.70 5.50 1.20 2.80 ETI 26.6 6.20 11.1 3.40 0.20 0.40 0.36 1.60 3.40 8.20 0.50 3.10 ISA 17.0 15.4 23.1 15.0 0.40 1.40 0.30 3.50 5.60 12.4 2.20 3.80 OPP 114 13.0 32.3 9.00 1.00 1.50 0.30 0.70 6.70 8.70 2.60 3.30 OPE 33.0 29.0 50.6 4.70 0.50 5.10 0.70 3.20 5.30 8.80 0.70 6.90 MOG 79.0 88.0 56.4 56.0 0.60 0.10 0.11 3.30 3.90 11.8 1.90 1.50 ASJ 60.1 19.0 25.0 11.0 0.50 0.14 0.60 0.70 8.50 9.60 1.10 0.02 291 UNIVERSITY OF IBADAN LIBRARY Appendix 39: Concentrations of lead (µg/g) in plant for all the locations Sample July September November January March May July September November January March May Code 2006 2006 2006 2007 2007 2007 2007 2007 2007 2008 2008 2008 OYI 6.00 0.01 0.20 1.00 1.00 1.00 1.00 0.01 0.40 0.40 0.01 0.10 OSI 2.80 0.01 0.80 1.00 1.00 1.45 1.00 0.01 0.70 0.30 0.30 0.10 OUN 3.50 3.0 1.16 1.00 1.00 1.20 1.00 0.01 0.70 0.30 0.01 0.10 ARE 1.90 0.01 2.00 1.70 1.50 1.10 1.00 0.01 0.40 0.10 0.01 0.30 ASS 2.80 4.50 2.12 1.00 1.00 2.70 1.00 0.01 0.01 0.30 0.10 0.30 IRE 5.00 4.50 1.00 1.00 1.00 1.20 1.00 0.70 0.10 0.10 0.01 0.30 ANN 3.30 2.00 1.60 1.00 1.00 1.30 1.00 0.20 0.50 0.10 0.01 0.40 AHO 1.00 1.50 1.32 1.00 1.00 1.20 1.00 0.01 0.70 0.10 0.01 0.10 ENJ 2.70 0.01 1.00 1.00 1.00 4.10 1.00 0.70 1.60 0.10 0.30 0.10 OLO 2.09 0.01 1.00 1.10 1.50 1.20 1.00 0.70 0.10 0.32 0.86 0.10 GBD 1.29 1.50 2.00 1.18 1.00 1.20 1.00 0.01 0.70 0.10 0.01 0.34 AWE 1.20 0.01 1.00 1.00 1.50 2.00 1.00 0.40 0.10 0.10 0.30 0.57 OJU 2.10 3.00 0.80 1.00 1.00 1.20 1.00 0.40 0.40 0.10 0.01 0.30 OSU 2.88 1.50 1.20 1.10 1.00 1.45 1.20 0.01 0.10 0.40 2.70 0.30 YEY 2.10 0.01 3.30 1.00 1.00 3.3 1.00 1.10 0.65 0.60 0.90 0.10 ONN 1.29 0.01 1.00 1.18 1.00 1.20 1.00 1.41 0.40 0.10 0.30 0.30 OLU 1.80 1.50 1.20 1.00 1.00 2.91 1.00 0.40 1.00 0.80 0.01 0.10 ORU 1.20 4.50 2.00 1.00 1.00 1.20 1.00 0.01 0.10 0.10 0.60 0.90 KAN 1.00 4.50 1.00 1.00 1.00 2.60 1.00 0.01 0.10 0.30 0.30 0.10 ADE 3.10 3.00 2.00 1.70 1.00 2.20 1.00 0.70 0.70 0.30 0.01 0.10 EJI 2.30 0.01 1.00 1.10 1.00 1.20 1.00 0.40 0.10 0.10 0.80 0.10 ARO 1.20 0.01 1.00 1.00 1.00 1.00 1.00 0.01 0.01 0.60 0.01 0.30 ODD 1.20 1.50 1.32 1.00 4.50 0.90 1.00 0.01 0.40 0.10 0.01 0.10 OBB 1.20 0.01 1.00 1.00 1.00 1.00 1.00 0.40 0.65 0.30 0.01 0.10 OYK 1.90 0.01 2.00 1.00 1.00 2.00 1.00 1.10 0.10 0.10 0.30 0.10 ETI 1.00 0.01 1.00 1.10 1.00 1.00 1.00 0.01 0.40 0.10 0.01 0.30 ISA 1.90 1.50 1.32 1.10 1.00 2.00 1.00 1.10 1.00 0.10 0.30 0.10 OPP 1.20 3.00 1.32 1.00 1.00 2.60 1.00 0.01 0.65 0.30 2.27 0.30 OPE 1.20 0.01 1.32 1.00 1.00 1.00 1.00 0.01 0.40 0.10 0.01 0.57 MOG 1.29 0.01 1.20 1.10 1.00 1.00 1.00 0.70 0.70 0.30 0.30 0.10 ASJ 1.29 1.50 1.00 1.00 1.50 1.45 1.00 0.40 0.10 0.01 0.01 0.30 292 UNIVERSITY OF IBADAN LIBRARY Appendix 40: Concentrations of copper (µg/g) in for all the locations Sample July September November January March May July September November January March May Code 2006 2006 2006 2007 2007 2007 2007 2007 2007 2008 2008 2008 OYI 2.08 2.60 0.30 0.01 0.01 0.76 0.80 0.80 1.10 0.40 0.30 0.10 OSI 3.10 0.01 0.30 0.20 0.30 0.01 0.80 0.01 2.90 0.30 0.01 0.10 OUN 1.50 2.60 0.30 0.20 0.01 0.40 0.50 0.01 2.60 0.90 2.18 0.30 ARE 1.00 0.90 17.6 0.50 6.00 7.90 0.80 0.01 0.30 0.80 0.01 1.40 ASS 1.00 3.50 2.60 6.10 0.01 0.01 0.80 0.53 0.52 0.80 0.10 0.80 IRE 2.08 0.01 0.10 0.20 0.01 1.00 0.80 0.80 2.40 2.50 1.10 0.10 ANN 1.40 0.60 5.60 0.30 0.01 0.01 0.50 1.10 1.90 0.10 0.90 4.60 AHO 2.60 2.60 0.01 0.01 0.01 0.01 0.10 0.53 0.80 0.30 1.10 0.60 ENJ 3.15 0.01 13.4 0.01 0.01 0.01 2.80 0.30 2.10 0.30 0.30 0.80 OLO 2.08 1.10 2.60 0.20 0.01 0.80 0.01 0.01 1.30 0.10 0.01 0.30 GBD 2.08 0.01 18.0 0.50 0.50 0.40 0.80 0.50 1.10 0.10 1.60 0.30 AWE 2.08 1.80 0.01 1.20 0.30 1.10 0.51 1.05 0.52 0.30 1.40 1.40 OJU 1.50 2.60 0.80 0.20 0.01 0.40 0.80 0.53 1.30 0.30 0.80 0.55 OSU 1.50 0.90 0.50 0.01 0.30 0.80 1.50 0.53 1.80 0.10 1.10 0.80 YEY 2.08 1.20 20.8 0.20 0.01 0.01 0.51 1.30 0.80 0.50 0.30 0.10 ONN 1.00 0.90 0.30 0.20 0.30 0.01 0.51 1.80 1.30 2.30 0.30 0.55 OLU 1.70 0.30 8.00 0.20 0.01 0.76 2.10 1.10 4.20 0.73 0.80 0.60 ORU 1.00 0.01 1.30 0.01 0.40 0.40 1.03 0.01 1.80 0.80 0.01 0.30 KAN 1.00 0.01 0.80 0.20 0.30 0.76 1.03 0.53 1.60 1.40 1.60 0.30 ADE 13.0 6.80 1.10 0.20 0.50 9.00 0.50 0.50 2.40 0.50 0.01 0.60 EJI 1.50 1.75 7.90 0.20 0.01 0.40 0.30 0.80 0.30 0.60 0.70 0.10 ARO 2.08 0.01 0.30 0.01 0.01 0.01 0.01 0.30 1.80 0.40 0.01 0.80 ODD 1.50 0.01 0.80 0.20 0.01 0.40 0.60 0.53 1.10 0.10 0.30 0.30 OBB 2.08 0.90 0.80 0.01 0.01 0.01 0.01 0.53 1.80 0.10 1.40 0.30 OYK 7.08 2.60 12.9 0.50 0.30 0.40 0.90 0.53 1.10 0.60 0.40 0.30 ETI 1.00 2.60 10.5 0.20 0.30 0.40 0.01 0.01 1.60 2.50 0.50 0.60 ISA 2.60 0.90 0.80 0.20 0.01 0.01 0.30 1.10 3.70 0.30 0.80 0.30 OPP 1.50 3.50 25.0 0.20 0.30 1.40 1.30 1.80 1.30 0.30 0.80 0.10 OPE 1.50 4.00 11.3 0.01 0.30 0.01 1.00 0.30 1.30 0.30 0.30 0.30 MOG 5.10 0.90 6.00 1.20 0.01 0.01 0.30 0.50 2.40 0.90 0.01 0.30 ASJ 4.12 0.01 15.3 17.4 0.20 0.01 0.51 0.53 2.10 0.10 0.01 0.30 293 UNIVERSITY OF IBADAN LIBRARY Appendix 41: Concentrations of cadmium (µg/g) in plant for all the locations Sample July September November January March May July September November January March May Code 2006 2006 2006 2007 2007 200 2007 2007 2007 2008 2008 2008 OYI 1.00 0.01 1.00 1.00 1.00 1.00 0.01 0.01 0.10 0.01 0.01 0.10 OSI 1.00 3.10 1.10 1.00 1.00 1.00 0.01 0.01 0.10 0.01 0.01 0.10 OUN 1.00 0.01 1.00 1.00 1.00 1.00 0.01 0.01 0.10 0.01 0.01 0.10 ARE 1.00 0.01 1.00 1.00 1.30 1.00 0.01 0.01 0.10 0.01 0.01 0.10 ASS 1.00 0.01 1.00 1.50 1.00 1.00 0.01 0.01 0.10 0.01 0.01 0.10 IRE 1.00 0.01 1.00 1.00 1.00 1.00 0.01 0.01 0.10 0.01 0.01 0.10 ANN 1.00 0.01 1.00 1.00 1.00 1.00 0.01 0.01 0.10 0.01 0.01 0.10 AHO 1.00 0.01 1.00 1.00 1.00 1.00 0.01 0.01 0.10 0.10 0.01 0.10 ENJ 1.00 0.01 1.10 1.00 1.00 1.10 0.01 0.01 0.10 0.01 0.01 0.10 OLO 1.00 0.01 1.00 1.00 0.01 1.10 0.01 0.01 0.10 0.01 0.01 0.10 GBD 1.00 0.01 1.00 1.00 1.00 1.00 0.01 0.01 0.10 0.01 0.01 0.10 AWE 1.00 0.01 1.00 1.00 1.00 1.00 0.01 0.01 0.10 0.01 0.01 0.10 OJU 1.00 0.01 1.00 1.00 1.00 1.00 0.01 0.01 0.10 0.01 0.01 0.10 OSU 1.00 0.01 1.00 1.00 1.00 1.00 0.01 0.01 0.10 0.01 0.01 0.10 YEY 1.00 0.01 1.00 1.00 1.00 1.00 0.01 0.01 0.10 0.01 0.01 0.10 ONN 1.00 0.01 1.00 1.00 1.00 1.00 0.01 0.01 0.10 0.01 0.01 0.10 OLU 1.20 0.01 1.00 1.00 1.00 1.00 0.01 0.01 0.10 0.01 0.01 0.10 ORU 1.00 0.01 1.00 1.00 1.00 1.00 0.01 0.01 0.10 0.01 0.01 0.10 KAN 1.00 0.01 1.00 1.00 1.00 1.00 0.01 0.01 0.10 0.01 0.01 0.10 ADE 1.20 0.01 1.00 1.00 1.00 1.00 0.01 0.01 0.10 0.01 0.01 0.10 EJI 1.00 0.01 1.00 1.00 1.00 1.00 0.01 0.01 0.10 0.01 0.01 0.10 ARO 1.00 0.01 1.00 1.00 1.00 1.00 0.01 0.01 0.10 0.01 0.01 0.10 ODD 1.00 0.01 1.00 1.00 1.00 1.00 0.01 0.01 0.10 0.50 0.01 0.10 OBB 1.00 0.01 1.00 1.00 1.00 1.00 0.01 0.01 0.10 0.01 0.01 0.10 OYK 2.00 0.01 1.10 1.00 1.00 1.00 0.01 0.01 0.10 0.01 0.01 0.10 ETI 1.00 0.01 1.00 1.00 1.00 1.00 0.01 0.01 0.10 0.01 0.01 0.10 ISA 1.00 0.01 1.00 1.00 1.00 1.00 0.01 0.01 0.10 0.01 0.01 0.10 OPP 1.00 0.01 1.00 1.00 1.00 1.00 0.01 0.01 0.10 0.01 0.01 0.10 OPE 1.00 0.01 1.00 1.00 1.00 1.00 0.01 0.01 0.10 0.01 0.01 0.10 MOG 1.20 0.01 1.00 1.00 1.00 1.00 0.01 0.01 0.10 0.01 0.01 0.10 ASJ 1.00 0.01 1.00 1.50 1.00 1.00 0.01 0.01 0.10 0.01 0.01 0.10 294 UNIVERSITY OF IBADAN LIBRARY Appendix 42: Concentrations of cobalt (µg/g) in plant for all the locations Sample July September November January March May July September November January March May Code 2006 2006 2006 2007 2007 2007 2007 2007 2007 2008 2008 2008 OYI 1.00 0.01 1.00 1.00 1.00 1.35 1.54 1.30 0.69 0.10 0.01 0.10 OSI 3.10 9.92 0.01 1.00 1.02 2.20 1.54 0.01 0.10 0.10 0.01 0.10 OUN 1.50 7.40 1.02 1.00 1.50 1.00 1.00 0.50 0.10 0.10 0.01 0.10 ARE 5.20 0.01 2.60 1.00 2.10 2.00 1.00 0.01 0.10 0.40 0.01 0.10 ASS 1.00 7.40 0.01 1.20 1.00 2.50 2.30 0.01 0.10 0.10 0.01 0.10 IRE 4.20 0.01 0.50 1.00 1.00 1.86 2.30 0.01 0.40 0.10 0.01 0.10 ANN 1.70 1.70 1.70 1.00 1.00 1.20 1.70 0.30 0.30 0.10 0.01 0.60 AHO 2.08 7.40 0.01 1.00 1.00 1.20 1.00 0.01 0.10 0.10 0.01 0.10 ENJ 1.50 0.01 0.01 1.00 1.00 1.00 1.00 1.77 0.10 0.10 0.01 0.50 OLO 4.20 7.40 1.02 1.00 1.00 1.20 2.70 1.30 0.40 0.10 0.01 0.10 GBD 3.10 0.01 0.50 1.20 2.10 1.00 1.54 0.01 0.40 0.10 0.01 0.10 AWE 2.60 3.20 0.01 1.00 1.02 1.00 1.30 0.50 0.10 0.10 0.40 0.10 OJU 2.08 5.70 0.01 1.00 1.00 1.20 1.30 0.01 0.10 0.70 0.01 0.10 OSU 2.60 7.40 0.01 1.20 1.02 1.20 2.10 0.01 0.10 0.10 0.01 0.50 YEY 1.50 7.40 1.50 2.60 1.02 2.00 1.00 0.01 0.40 0.10 0.01 0.10 ONN 1.00 2.50 0.01 1.00 1.00 1.00 1.00 0.89 0.10 0.10 0.01 0.50 OLU 4.20 2.50 0.01 1.00 1.00 3.40 1.30 0.90 0.40 0.10 0.01 0.10 ORU 1.00 0.01 1.50 1.00 1.00 1.20 1.00 0.01 0.10 0.10 0.01 0.10 KAN 1.50 0.01 1.30 1.00 1.00 1.20 1.80 0.01 0.10 0.10 0.01 0.10 ADE 3.10 4.96 1.50 1.00 1.02 1.35 1.30 0.01 0.10 0.20 0.01 0.10 EJI 2.60 7.40 0.01 1.00 1.50 1.20 1.00 0.50 0.10 0.10 0.60 0.10 ARO 3.70 0.01 1.50 1.00 1.54 1.00 1.00 0.01 0.69 0.10 0.01 0.10 ODD 4.20 0.01 0.50 1.20 1.00 1.00 1.00 0.01 0.10 0.10 0.40 0.10 OBB 1.50 4.96 0.50 1.00 1.00 1.35 1.00 0.50 0.10 0.10 0.01 0.50 OYK 3.60 0.01 1.50 1.00 1.00 1.90 1.54 0.01 0.10 0.10 0.01 0.10 ETI 1.50 0.01 0.50 1.00 1.00 1.00 1.00 0.01 0.10 0.10 0.01 0.10 ISA 3.60 4.96 0.01 1.90 1.00 1.35 1.00 0.01 0.40 0.10 0.01 0.10 OPP 1.00 7.40 1.50 1.00 1.00 2.00 1.54 0.89 0.10 0.10 0.01 0.10 OPE 5.20 4.96 1.02 1.00 1.00 1.20 1.54 0.01 0.10 0.10 0.01 0.10 MOG 2.60 0.01 0.50 1.39 1.00 1.00 1.00 0.50 0.40 0.10 0.01 0.10 ASJ 4.20 0.01 1.02 1.00 1.00 1.10 2.90 0.01 0.10 0.10 0.01 0.01 295 UNIVERSITY OF IBADAN LIBRARY Appendix 43: Concentrations of chromium (µg/g) in plant for all the locations Sample July September November January March May July September November January March May Code 2006 2006 2006 2007 2007 2007 2007 2007 2007 2008 2008 2008 OYI 3.60 0.01 0.01 0.01 1.00 1.20 1.80 1.00 1.66 0.10 0.40 0.10 OSI 1.00 4.70 1.00 0.01 1.90 0.01 1.00 0.01 0.10 1.40 0.03 0.10 OUN 3.60 3.79 0.30 0.01 1.00 1.00 1.00 0.70 1.30 0.70 2.80 0.10 ARE 1.00 0.01 0.80 0.01 1.00 2.40 1.00 0.01 0.10 0.10 0.40 0.10 ASS 1.00 0.01 0.01 0.01 1.00 0.50 1.00 0.69 0.83 0.40 0.10 0.10 IRE 1.00 4.70 0.01 0.01 1.00 0.50 1.00 0.01 1.30 0.40 0.02 0.10 ANN 1.00 1.90 0.70 0.01 1.60 0.60 1.00 0.70 1.10 0.30 0.30 1.40 AHO 1.00 4.70 0.80 0.01 1.90 0.50 1.00 0.01 0.10 0.10 0.03 0.40 ENJ 1.00 2.80 0.01 0.40 1.00 0.01 2.10 0.01 1.77 1.40 0.40 0.40 OLO 1.00 4.70 0.53 0.01 1.00 0.01 1.00 1.39 0.83 0.40 1.40 0.10 GBD 1.00 1.89 0.01 1.10 1.90 0.50 1.20 0.40 0.50 1.10 0.03 0.40 AWE 5.20 1.00 0.01 0.80 2.72 0.50 1.00 0.01 0.50 0.70 1.40 1.40 OJU 3.60 0.01 0.01 0.01 1.00 0.50 1.00 0.01 0.83 0.10 0.01 1.10 OSU 3.60 1.90 0.80 0.01 3.20 0.50 1.00 0.01 0.83 0.10 1.90 1.10 YEY 1.00 1.89 0.80 0.01 0.01 1.00 1.00 0.01 1.25 0.30 0.03 0.10 ONN 6.25 1.89 0.01 0.01 1.90 0.01 1.00 0.69 1.30 0.10 0.10 0.10 OLU 1.10 1.89 0.30 0.01 1.90 1.40 1.42 0.40 0.83 0.10 0.03 0.10 ORU 1.00 0.01 1.30 0.01 1.00 1.50 1.00 0.01 0.10 0.10 0.03 0.40 KAN 1.00 0.01 0.01 0.01 3.20 0.01 1.00 0.40 1.30 0.40 0.03 0.10 ADE 3.60 1.00 0.01 0.01 1.90 0.50 1.20 0.01 0.40 0.50 0.40 0.10 EJI 3.60 0.01 0.30 0.01 1.90 0.96 1.00 0.40 2.10 1.40 0.50 0.40 ARO 3.60 0.01 0.01 0.01 1.00 0.01 1.00 1.39 1.30 0.30 2.10 0.40 ODD 1.00 2.80 0.01 0.40 1.00 0.50 1.00 0.01 0.10 0.10 0.03 0.40 OBB 1.00 1.00 0.53 0.01 1.00 0.01 1.00 0.40 0.50 0.40 2.80 1.05 OYK 1.00 0.01 0.80 0.01 1.90 0.50 2.10 0.40 0.50 0.10 0.20 0.10 ETI 6.25 1.89 0.01 0.01 1.90 0.01 1.00 1.30 1.30 0.10 0.10 0.10 ISA 3.60 2.80 0.01 0.40 1.00 0.50 1.00 0.70 0.50 0.40 0.70 0.40 OPP 1.00 1.89 0.01 0.01 1.00 0.96 1.00 0.01 0.10 0.10 1.10 0.10 OPE 3.60 2.80 0.30 0.01 1.00 0.01 1.00 0.40 0.83 0.10 2.40 0.80 MOG 3.60 2.80 0.30 0.40 1.00 0.01 1.00 0.40 0.90 0.40 0.70 0.10 ASJ 1.00 2.80 0.40 0.01 1.00 0.50 1.00 0.01 1.30 0.10 0.03 0.10 296 UNIVERSITY OF IBADAN LIBRARY Appendix 44: Concentrations of nickel (µg/g) in plant for all the locations Sample July September November January March May July September November January March May Code 2006 2006 2006 2007 2007 2007 2007 2007 2007 2008 2008 2008 OYI 1.46 0.01 5.20 1.00 0.01 0.01 0.01 0.01 0.10 0.10 0.40 0.10 OSI 1.20 5.00 1.20 1.00 0.01 0.01 0.01 0.01 0.10 0.10 0.70 0.10 OUN 1.20 5.00 1.20 1.00 0.50 0.01 0.01 0.40 0.50 0.50 0.02 0.10 ARE 1.00 3.40 3.70 9.00 0.01 0.95 1.40 0.01 0.50 0.10 0.02 0.10 ASS 3.00 5.00 7.40 6.50 0.01 0.50 0.01 0.01 0.10 0.10 0.02 0.10 IRE 2.00 0.01 1.00 1.70 0.01 0.01 0.01 0.01 0.10 0.10 0.01 0.10 ANN 1.80 3.36 3.10 1.00 0.3 0.01 0.60 0.01 0.10 0.10 0.02 0.30 AHO 1.00 5.00 0.50 1.00 0.01 0.01 0.01 0.87 0.50 0.10 0.02 0.10 ENJ 1.46 5.00 1.20 1.10 0.01 0.50 0.95 0.01 0.10 0.10 0.02 0.10 OLO 1.00 5.04 3.50 1.10 0.01 0.01 0.01 0.01 0.10 0.10 0.40 0.10 GBD 1.00 0.01 14.0 1.83 0.50 0.01 0.01 0.01 0.10 0.10 0.02 0.10 AWE 2.00 5.00 1.00 10.0 1.30 0.50 1.00 0.01 0.90 0.90 0.40 0.10 OJU 1.20 1.20 15.6 1.80 0.50 0.01 0.50 0.01 0.10 0.50 0.02 0.10 OSU 1.00 6.72 1.00 1.10 0.90 0.01 1.40 0.40 0.10 0.10 0.02 0.10 YEY 1.20 2.20 1.20 7.80 0.01 0.01 0.01 0.01 0.10 0.10 0.02 0.10 ONN 1.00 0.01 4.20 1.00 0.01 0.01 0.01 0.01 0.10 1.00 0.10 0.10 OLU 2.00 2.20 1.20 9.00 0.01 0.50 1.00 0.01 0.10 0.10 0.02 0.10 ORU 1.44 5.00 1.20 1.00 0.50 0.01 0.01 0.01 0.10 0.10 0.02 0.10 KAN 1.00 5.00 1.20 1.00 1.30 0.01 0.01 0.01 0.10 0.50 0.02 0.10 ADE 1.20 3.40 2.20 1.22 0.50 0.50 0.01 0.01 0.10 0.10 0.02 0.40 EJI 2.00 1.90 1.00 1.00 0.50 0.01 0.01 0.01 0.10 1.90 0.02 0.40 ARO 1.40 2.50 1.20 2.90 0.01 0.01 0.01 0.01 0.50 0.10 0.02 0.10 ODD 1.00 2.80 2.20 1.70 0.01 0.01 0.01 0.01 0.50 0.50 0.02 0.10 OBB 4.20 5.00 1.20 5.40 0.50 0.01 0.01 0.90 0.10 0.10 0.02 0.10 OYK 1.46 1.20 3.00 1.00 0.01 0.50 0.01 0.01 0.10 0.10 0.10 0.10 ETI 3.00 0.01 1.10 1.00 0.50 0.01 0.01 0.01 0.10 0.50 0.02 0.40 ISA 1.00 0.50 4.50 1.10 0.01 0.01 0.01 0.01 0.10 0.10 0.02 0.10 OPP 1.00 0.01 1.00 1.70 0.01 0.01 0.01 0.01 0.10 0.10 0.40 0.10 OPE 1.00 0.01 2.70 3.60 0.01 0.01 0.50 0.01 0.10 0.50 0.70 0.10 MOG 2.20 0.01 1.13 3.70 0.01 0.01 0.01 0.01 0.10 0.90 0.02 0.10 ASJ 1.20 0.01 2.20 3.40 0.90 0.01 0.95 0.10 0.10 0.02 0.02 0.10 297 UNIVERSITY OF IBADAN LIBRARY Appendix 45: Concentrations of zinc (µg/g) in plant for all the locations Sample July September November January March May July September November January March May Code 2006 2006 2006 2007 2007 2007 2007 2007 2007 2008 2008 2008 OYI 10.4 2.25 70.0 1.00 0.30 1.77 1.00 8.90 79.0 20.0 23.7 8.00 OSI 15.3 7.30 159 39.0 1.90 4.00 3.10 13.4 33.6 60.9 14.9 25.4 OUN 1.04 1.80 36.0 18.0 2.80 1.30 1.40 19.1 60.0 24.0 41.1 8.90 ARE 1.00 4.90 357 88.0 12.0 12.0 1.99 3.25 27.5 24.7 8.30 9.10 ASS 1.60 3.50 52.0 104 1.50 4.00 3.20 9.40 24.6 55.0 3.40 5.40 IRE 5.40 5.30 30.0 30.0 1.20 2.20 1.70 15.4 34.1 185 10.9 0.10 ANN 1.80 3.00 86.2 34.0 4.10 1.80 2.30 21.0 50.0 29.0 12.0 42.0 AHO 8.30 4.90 1.00 109 0.60 2.70 0.10 6.10 27.0 47.0 5.40 4.70 ENJ 32.7 2.30 140 59.0 0.30 0.89 4.60 11.8 66.0 59.6 3.40 6.00 OLO 15.6 4.90 2.70 64.0 0.90 0.89 4.50 30.9 56.0 49.3 4.30 5.70 GBD 11.0 2.30 240 162 3.07 0.89 1.70 18.3 47.0 34.0 48.0 15.0 AWE 11.7 5.08 14.3 165 3.70 1.80 3.10 8.10 43.0 36.0 10.3 32.8 OJU 1.90 5.10 322 87.0 2.20 1.30 3.10 9.40 53.0 17.0 6.00 8.00 OSU 9.30 6.30 163 151 3.70 1.80 5.80 26.0 66.0 48.0 6.50 7.70 YEY 8.20 2.82 211 81.0 0.60 2.20 1.70 15.0 103 31.0 11.7 6.00 ONN 2.50 4.60 3.60 56.0 2.80 1.80 1.70 3.30 33.0 79.0 17.0 5.10 OLU 2.90 2.80 103 46.0 1.50 3.10 6.60 14.0 81.0 21.0 30.0 15.0 ORU 1.05 4.00 5.40 6.40 0.90 1.30 0.01 28.4 36.0 18.0 8.80 47.0 KAN 1.60 5.30 65.2 18.0 4.60 2.20 4.46 8.10 62.4 77.0 2.60 23.4 ADE 64.0 7.80 277 207 8.90 24.0 7.54 13.8 26.0 15.7 12.0 14.8 EJI 1.20 0.90 353 83.0 1.90 0.89 3.10 20.0 73.0 88.0 1.70 15.0 ARO 1.04 4.90 170 58.0 0.90 0.89 1.37 7.70 4.00 49.8 11.7 4.00 ODD 5.40 1.10 4.50 35.0 0.01 0.89 3.10 3.30 33.0 51.0 4.00 12.0 OBB 11.1 2.10 209 137 19.0 0.89 0.69 15.0 69.0 25.3 19.0 13.1 OYK 4.90 2.90 365 19.0 3.40 1.30 0.40 3.70 21.0 46.2 2.00 19.7 ETI 2.00 3.50 146 26.0 3.40 0.89 0.40 15.0 31.0 134 2.20 6.90 ISA 3.10 2.90 35 200 1.84 1.77 2.10 19.0 77.0 40.0 2.00 17.1 OPP 4.50 2.33 258 1.20 0.60 5.80 10.0 6.10 13.0 22.8 15.4 0.60 OPE 31.9 3.90 140 16.0 2.80 1.80 2.06 13.4 67.0 81.0 12.0 14.9 MOG 22.0 3.00 184 197 0.90 1.80 2.40 16.0 54.0 31.0 5.10 10.9 ASJ 3.70 5.30 201 89.0 1.20 1.30 3.40 6.10 53.5 17.9 33.0 5.10 298 UNIVERSITY OF IBADAN LIBRARY Appendix 46: Physicochemical properties of surface water obtained from control sites (Egun) Parameter July September November January March May July September November January March May 2006 2006 2006 2007 2007 2007 2007 2007 2007 2008 2008 2008 EC (µS/cm) 67.8 87.5 139 181 177 10.6 69.1 48.2 92.1 131 143 62.2 pH 8.1 7.9 7.9 7.6 7.5 7.8 7.7 7.5 7.5 7.6 7.6 7.3 TS (mg/L) 150 196 230 233 246 291 280 133 334 252 178 299 TDS (mg/L) 33.5 73.5 91.0 99.5 56.0 82.5 63.2 51.5 61.5 37.7 61.6 28.2 TSS (mg/L) 117 122 139 133 190 209 217 81.0 274 214 280 271 Turbidity (FTU) 13.6 10.4 14.7 12.1 8.25 14.3 14.9 9.85 6.85 7.25 9.35 13.8 Alkalinity (mg/L) 55.3 85.4 91.7 94.5 91.7 57.1 56.2 27.4 50.9 99.6 53.8 31.5 Hardness (mg/L) 28.0 32.5 32.7 74.5 133 39.2 58.1 53.1 47.6 60.7 129 43.0 Temperature ( °C) 25.9 27.6 29.8 30.0 29.5 27.4 28.1 24.0 27.2 28.0 27.7 24.7 Nitrate (mg/L) 0.53 0.66 1.03 1.11 0.81 0.40 0.98 0.60 0.54 0.60 0.20 0.39 Sulphate (mg/L) 30.2 36.7 35.7 53.7 39.1 27.9 28.7 43.5 34.4 31.0 30.8 33.8 Phosphate (mg/L) 0.02 0.03 0.04 0.04 0.05 0.04 0.06 0.11 0.04 0.09 0.06 0.04 Chloride (mg/L) 32.4 30.4 37.8 38.2 32.7 42.0 41.7 30.2 28.5 36.1 29.7 18.2 Ammonia (mg/L) 1.12 1.12 1.62 1.21 2.23 1.41 2.17 1.94 1.29 0.96 1.77 1.25 DO (mg/L) 6.55 6.90 6.55 6.45 5.67 8.25 7.91 8.83 6.56 5.50 5.91 7.72 BOD (mg/L) 0.60 1.55 3.15 4.00 7.72 4.97 3.67 4.75 4.77 6.17 3.27 3.85 COD (mg/L) 94.6 111 106 122 86.2 70.4 65.0 155 127 138 69.8 66.6 299 UNIVERSITY OF IBADAN LIBRARY Appendix 47: Concentration of heavy metals (mg/L) in surface water of control sites Parameter July September November January March May July September November January March May 2006 2006 2006 2007 2007 2007 2007 2007 2007 2008 2008 2008 Pb 0.001 0.002 0.001 0.003 0.001 0.03 0.001 0.001 0.001 0.001 0.002 0.001 Cu 0.01 0.001 0.01 0.001 0.002 0.003 0.002 0.01 0.002 0.001 0.002 0.001 Cd 0.001 0.001 0.01 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 Co 0.002 0.001 0.01 0.01 0.001 0.01 0.001 0.001 0.01 0.003 0.001 0.003 Cr 0.01 0.001 0.01 0.01 0.001 0.001 0.002 0.003 0.003 0.002 0.001 0.001 Ni 0.03 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 Zn 0.02 0.21 0.09 0.02 0.02 0.003 0.01 0.11 0.14 0.18 0.07 0.07 Ca 19.0 16.8 12.3 36.3 97.1 26.6 44.0 23.3 23.4 16.6 70.3 27.8 Mg 2.23 2.83 4.96 9.29 8.76 3.06 3.43 7.23 5.88 10.7 14.3 3.71 300 UNIVERSITY OF IBADAN LIBRARY Appendix 48: Physicochemical characteristics and heavy metals in sediments of control sites Parameter July September November January March May July September November January March May 2006 2006 2006 2007 2007 2007 2007 2007 2007 2008 2008 2008 Org. Carbon % 0.21 0.21 0.40 0.46 0.46 0.30 1.19 1.15 0.96 0.96 0.96 1.00 Sand (%) 71.7 72.6 69.0 73.1 70.5 70.2 70.9 69.1 73.0 76.1 74.3 70.6 Clay (%) 22.2 21.0 26.2 25.4 25.6 27.7 27.3 30.3 24.4 23.3 25.0 27.8 Silt (%) 3.73 6.50 4.85 1.60 4.00 2.15 1.82 0.65 2.70 0.70 0.75 1.60 CEC (meq/100g) 44.1 37.3 34.4 44.2 40.9 38.1 60.5 50.9 63.9 44.1 65.6 50.4 Pb (µg/g) 0.16 0.10 0.16 0.17 0.32 0.07 0.11 0.11 0.17 0.05 0.01 0.01 Cu (µg/g) 0.21 0.10 0.21 0.12 0.08 0.03 0.09 0.13 0.17 0.18 0.13 0.01 Cd (µg/g) 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 Co (µg/g) 0.25 0.08 0.09 0.08 0.08 0.12 0.01 0.08 0.02 0.02 0.02 0.02 Cr (µg/g) 0.03 0.07 0.01 0.01 0.13 0.01 0.10 0.07 0.12 0.17 0.08 0.03 Ni (µg/g) 0.48 0.35 0.36 0.01 0.01 0.08 0.76 0.02 0.02 0.16 0.02 0.02 Zn (µg/g) 19.7 21.9 27.9 0.57 0.95 3.3 0.87 3.45 6.67 9.50 1.77 6.22 301 UNIVERSITY OF IBADAN LIBRARY Appendix 49: Concentrations of heavy metals (µg/g) in plants of control sites Parameter July September November January March May July September November January March May 2006 2006 2006 2007 2007 2007 2007 2007 2007 2008 2008 2008 Pb 1.15 0.01 1.16 1.00 1.00 1.23 1.00 1.00 1.00 0.77 0.29 0.01 Cu 2.60 2.63 0.53 0.48 0.01 0.39 0.77 0.79 2.62 0.33 0.87 0.33 Cd 1.54 0.01 1.00 1.00 1.00 1.38 0.01 0.01 0.01 0.01 0.01 0.10 Co 1.00 2.49 0.52 1.20 1.02 1.18 1.89 0.45 0.35 0.10 0.01 0.10 Cr 1.00 1.89 0.14 1.14 1.86 0.49 0.98 0.35 0.87 0.28 0.48 0.40 Ni 1.23 1.69 1.24 1.72 0.45 0.01 0.48 0.44 0.10 0.10 0.35 0.10 Zn 8.30 6.54 54.5 106 6.14 2.66 7.21 11.8 51.1 52.5 42.8 6.60 302 UNIVERSITY OF IBADAN LIBRARY Appendix 50: Description of sampling locations at Adeti, Oyika and Anne OYIKA ADETI ANNE 303 UNIVERSITY OF IBADAN LIBRARY Appendix 51: Description of sampling locations at Ojutu, Gbodofon and Olumirin OJUTU GBODOFON OLUMIRIN 304 UNIVERSITY OF IBADAN LIBRARY Appendix 52: Description of sampling locations at Osun, Yeyekare and Olumirin OSUN YEYEKARE OLUMIRIN 305 UNIVERSITY OF IBADAN LIBRARY Appendix 53: Description of sampling location at Asejire ASEJIRE 306 UNIVERSITY OF IBADAN LIBRARY LIST OF ACRONYMS/ABBREVIATIONS AAS Atomic Absorption Spectrophotometer APHA American Public Health Association AWWA American Water Works Association BOD Biochemical Oxygen Demand CBSQG Concensus Based Sediment Quality Guidelines of Wisconsin CECe Effective Cation Exchange Capacity COD Chemical Oxygen Demand Conc. Concentration DO Dissolved Oxygen EEC European Economic Community ERL Effect Range Low ERM Effect Range Medium FMHE Federal Ministry of Health and Environment FTU Formazin Turbidity Unit NCSS Number Cruncher Statistical System PCA Principal Component Analysis SON Standard Organization of Nigeria SPSS Statistical Package for Social Sciences TDS Total Dissolved Solids TS Total Solid TSS Total Suspended Solid USEPA United State Environmental Protection Agency UV/Visible Ultraviolet/Visible WHO World Health Organization WPCF Water Pollution Control Federation xx UNIVERSITY OF IBADAN LIBRARY