PHYSICOCHEMICAL CHARACTERISTICS AND AMENABILITY TO BENCH SCALE TREATMENTS OF SOME INDUSTRIAL EFFLUENTS AND SEDIMENTS IN LAGOS METROPOLIS BY CHIONYEDUA THERESA, ONWORDI MATRIC. NUMBER: 66909 B.Sc (Industrial Chemistry), M.Sc (Analytical Chemistry), (Ibadan) A Thesis in the Department of Chemistry Submitted to the Faculty of Science in Partial Fulfilment of the Requirements for the Degree of DOCTOR OF PHILOSOPY of the UNIVERSITY OF IBADAN June, 2014. UNIVERSITY OF IBADAN LIBRARY PHYSICOCHEMICAL CHARACTERISTICS AND AMENABILITY TO BENCH SCALE TREATMENTS OF SOME INDUSTRIAL EFFLUENTS AND SEDIMENTS IN LAGOS METROPOLIS BY CHIONYEDUA THERESA, ONWORDI MATRIC. NUMBER: 66909 B.Sc (Industrial Chemistry), M.Sc (Analytical Chemistry), (Ibadan) A Thesis in the Department of Chemistry Submitted to the Faculty of Science in Partial Fulfilment of the Requirements for the Degree of DOCTOR OF PHILOSOPY of the UNIVERSITY OF IBADAN June, 2014. i UNIVERSITY OF IBADAN LIBRARY DEDICATION This research work is dedicated to God the All Sufficient, and to unborn generations. ii UNIVERSITY OF IBADAN LIBRARY ABSTRACT Untreated industrial effluents discharged into land and surface water are of great environmental concern. Previous studies on physicochemical characteristics of effluents and sediments from discharged points of various industrial sectors in Lagos metropolis are inadequate in scope, duration, chemical treatment options and estimated pollutant discharge load which are of importance in waste water management. This study was designed to investigate the physicochemical characteristics of effluents and sediments from industrial estates in Lagos metropolis, to estimate pollutant loads and determine appropriate chemical treatment options. Effluents (75) and sediments (72) were sampled once in two months from the discharge points of textile, pharmaceutical, food and beverages, basic metals and paint industries from November 2006 to May 2008. Total Dissolved Solids (TDS), pH, alkalinity, total hardness, sulphate, phosphate, nitrate, chloride, Dissolved Oxygen (DO), Biochemical Oxygen Demand (BOD), Chemical Oxygen Demand (COD)] and heavy metals were determined in the effluents according to APHA methods and compared with National Environmental Standards and Regulations Enforcement Agency (NESREA) limits. Organic carbon, pH and heavy metals were also analysed in the sediments. Multiple Linear Regression (MLR) was used to estimate organic load and eutrophication potential. Industrial effluent with the highest level of pollutant load was subjected to bench scale treatments using ferrous chloride, ferrous sulphate, Fenton’s reagent, alum, and activated carbon separately. Efficiency of treatment was ascertained by determination of TDS and COD. The Pearson correlation at p = 0.05 was used to establish the significance between the levels of metals in the effluents and sediments.. Physicochemical characteristics (mg/L) of effluents from textile and pharmaceutical industries were 1160±890 and 630±260 for TDS, 316±280 and 230±97 for alkalinity, 60±32.and 53±44.for total hardness, 156±73 and 34±36 for sulphate, 1.0±0.3 and 0.7±0.6 for phosphate, 60.0±40.0 and 75±45 for nitrate, 65±19 and 61±40 for chloride, 1.8±1.7 and 1.3±1.2 for DO, 209±72 and 150±96 for BOD, 343±130 and 245±140 for COD, 0.7±0.3 and 0.5±0.2 for Zn, 1.5±1.0 and 0.3±1.1 for Cu, 2.5±0.3, 0.03±0.03 for Cr and corresponding pH was 10.6±1.1 and 5.5±1.2 respectively. Effluents from foods and beverages, basic metal and paint industries had lower levels of TDS, alkalinity, phosphate, and chloride. Nitrate, DO, BOD, and COD levels exceeded NESREA limits for all effluents. The pH and organic carbon iii UNIVERSITY OF IBADAN LIBRARY ranged from 4.2-7.5 and 0.2-4.5% respectively for the sediments with the levels (µg/g) of Zn, Cr and Cu ranged from 196-3270, 7-49 and 21-244 respectively. Metal levels in sediments were higher than in effluent. Significant correlations were observed for Zn (0.64) and Cr (0.75) between effluents and sediments. The linear regression coefficients for organic load and eutrophication modelling ranged from 0.638-0.999 and 0.301-0.999 respectively. Efficiency of treatment options for COD reduction was in the order: Fenton’s reagent (90%) > ferrous chloride (87%) > ferrous sulphate (66%) > alum (61%) > activated carbon (47%) while significant TDS reduction was not achieved. Industrial effluents and related sediments in Lagos metropolis were highly polluted. Applications of Fenton’s reagent and ferrous chloride were the appropriate chemical treatment options for organic pollutants in textile effluent. Keywords: Industrial effluent and sediment, Heavy metal pollution, Lagos metropolis, Pollutant discharge load. . Word count: 496 iv UNIVERSITY OF IBADAN LIBRARY ACKNOWLEDGEMENTS My sincere gratitude and deepest appreciation goes to my supervisor, Prof. P.C. Onianwa for his invaluable guidance, constructive criticism and encouragement during the course of this project. I am grateful to Professor A. A. Adesomoju, the Head, Department of Chemistry, University of Ibadan, past Heads of the Department (Profs Oderinde. I. A. and Osibanjo, O.), and all members of staff of the Department. I want to specially appreciate my parents Mr. & Mrs. A. P. C. Onwordi for their prayers and love, and my sisters and their husbands; Mr & Mrs. M. A. Ajufo, and Mr. & Mrs. M. Okibuzor for your prayers, concerns and love. I appreciate my brother, Mr. C. A. Onwordi for his cares. To my cousins, the Nwaegbe’s and the Chalokwu’s, I say, thank you. Thanks go to Dr. A. R. Ipeaiyeda, Dr. I. A. Adejoro, Dr I. A. Oladosu, Dr N. O. Obi-Egbedi, Dr E. U. Etim, Dr O. A. F. Olayiwola, Dr J. E. Emurotu, Mr. I. U. Umoren and Mr.O. A. Ayodele for their moral and technical support. My appreciation goes to numerous people who assisted me with sampling and much needed support and guidance. Appreciation goes to members of staff and students of Chemistry Department, Lagos State University, for all their encouragements at the various stages of the research. My gratitude goes to academic and non-academic staff of the Faculty of Science, Lagos State University for all their cares and advice. I acknowledge the management and staff of Lagos State University for various forms of support granted during the course of the programme. My unreserved thanks go to the management and staff of Evans Medical Plc, especially to the staff of the Quality Assurance Department for granting me a laboratory bench space for the research. To my friends and colleagues in no particular order, Mr and Mrs B. A. Oguntade, Dr and Dr (Mrs) O. Wusu, Mr and Dr (Mrs) S. A. Oyeyiola, Engr and Dr (Mrs) S.O. Ezekiel, Mr A. O Adeagbo, Dr O. T Ojuromi, Mr V. O. Adedeji, Mr R. O. Olorunnimbe, Mr J. O. Adebayo, Miss T. C. Falaye, Mr O. Ogundele, among too numerous to list, I say a big thank you! To all my spiritual leaders, I say a special thank you for your prayers and moral support. v UNIVERSITY OF IBADAN LIBRARY To the Student Economics Development Initiative (SEDI) family: Mr. T, Igbekele, Mr.V. Oyibo, and Mr. J. Nwafo, I also say thank you for your technical supports. Above all, I thank the All Sufficient God, for giving me life to fulfil my dream in the academic sector and for endowing me with divine intellectual ability. Chionyedua Theresa, ONWORDI June, 2014. . vi UNIVERSITY OF IBADAN LIBRARY CERTIFICATION I certify that this work was carried out by Miss C. T. ONWORDI in the Department of Chemistry, University of Ibadan. Supervisor P.C. Onianwa B.Sc., M.Sc., Ph.D. (Ibadan) Professor of Analytical/Environment Chemistry Department of Chemistry University of Ibadan, Ibadan. vii UNIVERSITY OF IBADAN LIBRARY TABLE OF CONTENTS Page Dedication i Abstract ii Acknowledgment iv Certification vi Table of Contents vii List of Tables xiii List of Figures xvii Acronyms and Abbreviations xx 1.0 CHAPTER ONE: INTRODUCTION 1 1.1 Industrial pollution and the environment 1 1.1.1 Impact of industrial emission on land 2 1.1.2 Impact of industrial emission on water bodies 2 1.1.3 Impact of industrial emission on the atmosphere 4 1.2 Pollution From Industrial effluents In Lagos State 5 1.3 Aims and Objectives of study 11 2.0 CHAPTER TWO: LITERATURE REVIEW 2.1 Quality and chemistry of industrial effluents 13 2.1.1 Impact of industrial effluent on water bodies 15 2.1.2 Impact of industrial effluent on land 17 2.2 Effluent treatment 18 2.2.1 Biological treatment methods 19 2.2.1.1 Conventional activated sludge 20 2.2.1.2 Sequence batch reactors 21 2.2.1.3 Membrane bioreactors (MBRs) 21 2.2.1.4 Anaerobic sludge reactors 22 2.2.1.5 Anaerobic filters 22 viii UNIVERSITY OF IBADAN LIBRARY Page 2.2.1.6 Anaerobic film reactors / fluidized bed 23 2.2.2 Chemical/ Physical treatment methods 23 2.2.2.1 Ozonization 24 2.2.2.2 Ion exchange 24 2.2.2.3 Adsorption 26 2.2.2.4 Membrane filtration 26 2.2.2.5 Chemical oxidation 27 2.2.2.6 Chlorination 27 2.2.2.7 Neutralization 27 2.2.2.8 Coagulation 27 2.3 The Lagos environments 28 2.4 Use of multivariate statistical techniques to generate linear regression model for environmental studies 31 2.4.1 Effluent quality model 32 2.4.2 Model classification 33 2.5 Application of multiple linear regression model to water quality prediction 35 3.0 CHAPTER THREE: MATERIALS AND METHODS 3.1 Design of study 36 3.2 Description of study locations 39 3.3 Sampling 39 3.3.1 Preparation of sample containers 39 3.3.2 Collection of effluent 39 3.3.3 Collection of drainage channel sediment 47 3.3.4 Collection of soil 47 3.4 Analysis of effluent samples for physicochemical parameters 47 3.4.1 Determination of pH 47 3.4.2. Determination of conductivity 47 3.4.3 Determination of total dissolved solids 48 3.4.4 Determination of alkalinity 48 3.4.5 Determination of total hardness 48 ix UNIVERSITY OF IBADAN LIBRARY Page 3.4.6 Determination of calcium and magnesium 49 3.4.7 Determination of chloride 50 3.4.8 Determination of sulphate 51 3.4.9 Determination of phosphate 52 3.4.10 Determination of nitrate 53 3.5 Analysis of effluent samples for heavy metals 55 3.6 Analysis of effluent samples for gross organic pollutants 55 3.6.1 Determination of dissolved oxygen 57 3.6.2 Determination of biochemical oxygen demand 58 3.6.3 Determination of chemical oxygen demand 60 3.7 Analysis of sediment for physicochemical parameters 61 3.7.1 Determination of sediment pH 61 3.7.2 Determination of sediment particle size distribution 62 3.7.3 Determination of organic carbon content of sediment 63 3.8 Determination of metals in sediment 64 3.9 Analysis of soil 64 3.10 Chemical treatment of textile effluent. 65 3.10.1 Effluent treatment procedures 66 3.10.2 Analysis of the parameter determined in the treated effluent. 66 3.11 Quality control/Quality assurance (QC/QA) 67 3.11.1 General quality control. 67 3.11.2 Recovery study 67 3.11.3 Analysis of standard reference material 68 3.11.4 Quality control of BOD determination 72 3.11.5 Quality control of COD determination 72 3.12 Application of modeling to studying effluent quality 72 3.12..1 Modeling to estimate gross organic pollution and eutrophication potential 72 3.13 Statistical analyses and calculation of special parameters 74 3.13.1 Contamination factor 74 3.13.2 Pollution load index 75 3.13.3 Geo accumulation index 75 x UNIVERSITY OF IBADAN LIBRARY Page 4.0 CHAPTER FOUR: RESULTS AND DISCUSSION 4.1 General characteristics of the effluents 76 4.1.1 Characteristics of effluents in Ikeja industrial estate 76 4.1.2 Characteristics of effluents in Isolo industrial estate 85 4.1.3 Characteristics of effluents in IIupeju industrial estate 91 4.2 Characteristics of effluents according to types of industries 98 4.2.1 Multivariate analysis for heavy metals in types of industry. 107 4.3 Effect of seasonal variation on the effluents characteristics 108 4.3.1 pH and alkalinity 108 4.3.2 Total dissolved solids and conductivity 122 4.3.3 Total hardness, calcium and magnesium 122 4.3.4 Sulphate and chloride 122 4.3.5 Phosphate and nitrate 127 4.3.6 Dissolved oxygen, biochemical oxygen demand and Chemical oxygen 127 demand 4.3.7 Lead 130 4.3.8 Cadmium 130 4.3.9 Zinc 130 4.3.10 Copper 133 4.3.11 Chromium 133 4.3.12 Nickel 133 4.4 Characteristics of effluents channels sediments 136 4.4.1 Lead 139 4.4.2 Cadmium 141 4.4.3 Zinc 141 4.4.4 Copper 141 4.4.5 Chromium 142 4.4.6 Nickel 142 4.5 Multivariate analysis of heavy metals distribution in sediments 143 4.6. Comparison of metals In sediments with sediment quality guidelines 146 4.7 Correlation of metals In effluents and sediment 150 xi UNIVERSITY OF IBADAN LIBRARY Page 4.8 Soil qualities around the effluent channels 151 4.8.1 Characteristics of Soil around Ikeja industrial estate 151 4.8.2 Characteristics of soil around Isolo industrial estate 159 4.9 Comparison of heavy metals in soils studied with soil quality guidelines 167 4.10 Effect of seasonal variation on the soil characteristics 167 4.10.1 pH and organic matter content 167 4.10.2 Soil particle size characteristics 174 4.10.3 Lead 174 4.10.4 Cadmium 175 4.10.5 Zinc 177 4.10.6 Copper 179 4.10.7 Chromium 179 4.10.8 Nickel 179 4.11 Assessment of level of contamination of soils around the industrial estate 182 4.11.1 Assessment of contamination factor 182 4.11.2 Assessment of pollution index 184 4.11.3 Assessment of geo-accumulation index 184 4.12 Treatment of effluent 184 4.12.1 Treatment with inorganic coagulants 184 4.12.2 Treatment with Fenton’s reagent 192 4.12.3 Treatment with charcoal. 195 4.12.4 pH variation during treatment of textile effluent 195 4.12.5 TDS variation during treatment of textile effluent 198 4.12.6 TSS variation during treatment of textile effluent 198 4.13 Estimates of gross organic pollution and eutrophication potential 198 4.13.1 Model result of multiple linear regressions of physicochemical data for predictive estimation of gross organic pollutant (as COD). 198 4.13.2 Model results of multiple linear regression of physicochemical data for predictive estimation of eutrophication potential (via phosphate levels) 203 xii UNIVERSITY OF IBADAN LIBRARY Page 5.0 CHAPTER FIVE: CONCLUSION AND RECOMMENDATION 5.1 Summary and conclusion 205 5.1.1 Effluent characteristics 205 5.1.2 Pollution of sediments of effluent channels 205 5.1.3 Soil pollution 205 5.1.4 Effluent treatment 205 5.1.5 Model estimation of COD and phosphate 205 5.2 Recommendations 206 References 207 Appendices 237 xiii UNIVERSITY OF IBADAN LIBRARY LIST OF TABLES Table No. Title Page Table 1.1 List of Industrial estates in Lagos State 8 Table 2.1 Pollutants characteristics of wastes from some Lagos industries. 27 Table 3.1 Industries sampled from various industrial estate 43 Table 3.2 Description of sample locations and code 44 Table 3.3 Operating conditions for the atomic absorption spectrophotometer 54 Table 3.4 Results of recovery study for lead 69 Table 3.5 Results of recovery study for cadmium 70 Table 3.6 Results of analysis of standard reference material 71 Table 3.7 Comparison of BOD and COD of standards with experimental Values 73 Table 4.1a Physicochemical characteristics of effluents from Ikeja Industrial 77 Estate. Table 4.1b Physicochemical characteristics of effluents from Ikeja Industrial 78 Estate. Table 4.2 Comparison of physicochemical characteristics of the effluents of Ikeja Industrial Estate with effluent quality standards 80 Table 4.3 Pearson correlation coefficients amongst parameters of effluents at the Central Treatment Plant. 81 Table 4.4 Pearson correlation coefficients amongst parameters of Hoesch Pipe Mills effluents. 82 Table 4.5 Pearson correlation coefficients amongst parameters of Berger Paint effluents. 83 Table 4.6 Pearson correlation coefficients amongst parameters of Galvanizing Industries Limited effluents. 84 Table 4.7 Pearson correlation coefficients amongst parameters of Woolen & Synthetic Textile Manufacturing limited effluents. 86 Table 4.8 Pearson correlation coefficients amongst parameters of May & Baker effluents. 87 Table 4.9a Physicochemical characteristics of effluents from Isolo Industrial 88 Estate Table 4.9b Physicochemical characteristics of effluents from Isolo Industrial Estate. 89 xiv UNIVERSITY OF IBADAN LIBRARY Table No. Title Page Table 4.10 Comparison of physicochemical characteristics of the effluents of Isolo and Ilupeju Industrial Estates with effluent quality standards. 90 Table 4.11 Pearson correlation coefficients amongst parameters of John Wax Nigeria Ltd. effluents. 92 Table 4.12 Pearson correlation coefficients amongst parameters of Afrab chem 93 Ltd effluents. Table 4.13 Pearson correlation coefficients amongst parameters of Viju industries effluents. 94 Table4.14a Physicochemical characteristics of effluents from Ilupeju Industrial Estate 95 Table 4.14b Physicochemical characteristics of effluent from Ilupeju industrial 96 estate contd. Table 4.15 Pearson correlation coefficients amongst parameters of Paterson 99 Zonchouis effluents . Table 4.16 Pearson correlation coefficients amongst parameters of Canal 100 effluents. Table 4.17 Physicochemical characteristics of effluents from different classes of industries 101 Table 4.18 Principal component analysis of data of industrial effluent metal 109 concentrations Table 4.19a Average seasonal effluent quality at Ikeja industrial estate 112 Table 4.19b Seasonal concentrations (mg/L) of heavy metals in effluent at Ikeja industrial estate. 114 Table 4.20a Average seasonal effluent quality at Isolo industrial estate 115 Table 4.20b Seasonal concentrations (mg/L) of heavy metals in effluent at Isolo industrial estate 117 Table 4.21a Average seasonal effluent quality at Ilupeju industrial estate 118 Table 4.21b Seasonal concentrations (mg/L) of heavy metals in effluent at Ilupeju industrial estate 120 Table 4.22 pH, organic matter and particle size characteristics of sediments from effluent channel around Ikeja, Isolo and Ilupeju industrial estates 137 Table 4.23 Concentrations of metals (μg/g) in sediments from effluent channel around Ikeja, Isolo and Ilupeju industrial estates 138 Table 4.24 Pearson correlation coefficient results between parameters determined in effluent channels sediments 140 xv UNIVERSITY OF IBADAN LIBRARY Table No. Title Page Table 4.25 Principal component analysis of sediment for heavy metal data 144 Table 4.26 Comparison of heavy metals in sediment channel with sediment quality guidelines 149 Table 4.27 Correlation coefficient (p = 0.05) for heavy metal levels in sediment/effluent 151 Table 4.28 pH, organic matter and particle size characteristics of soil from Ikeja industrial estate 152 Table 4.29 Concentrations of heavy metals (μg/g) in soil from Ikeja industrial estate 153 Table 4.30 Pearson correlation coefficients for metal contents of soil around Ikeja industrial estate 158 Table 4.31 pH, organic matter and particle size characteristics of soil from Isolo industrial estate 160 Table 4.32 Concentrations of heavy metals (μg/g) in soil from Isolo industrial estate 161 Table 4.33 Pearson correlation coefficients for metal contents of soil around Isolo industrial estate 166 Table 4.34 Soil quality criteria for some countries compared with levels (μg/g) obtained from this study 168 Table 4.35a Average seasonal physicochemical quality of soil from Ikeja industrial estate 169 Table 4.35b Seasonal concentrations (μg/g) of metals in soil from Ikeja industrial estate 170 Table 4.36a Average seasonal physicochemical quality of soil from Isolo 171 industrial estate Table 4.36b Seasonal concentrations (μg/g) of metals in soil from Isolo industrial estate 172 Table 4.37 Soil contamination factors for the industrial estates studied 183 Table 4.38 Geo-accumulation index values for metals in soil from the industrial estates studied 185 Table 4.39 Changes in TDS (mg/L) values during treatment of textile effluent 199 Table 4.40 Changes in TSS (mg/L) values during treatment of textile effluent 201 xvi UNIVERSITY OF IBADAN LIBRARY Table No. Title Page Table 4.41 Result of multiple linear regression of physicochemical data for predictive estimation of COD as indicator of gross organic pollution 202 Table 4.42 Result of multiple linear regression of physicochemical data for predictive estimation of phosphate as indicator of eutrophication potentials 204 xvii UNIVERSITY OF IBADAN LIBRARY LIST OF FIGURES Figure Title Page No. Fig 1.1 Map of Lagos State, showing Ikeja, Isolo, Mushin and other Local Government Areas 6 Fig 1.2 The spatial distribution of industrial estates within Lagos Metropolis. 9 Fig 3.1 Map of Ikeja Industrial Estate showing sample locations 40 Fig 3.2 Map of Isolo Industrial Estate showing sample locations 41 Fig 3.3 Map of Ilupeju Industrial Estate showing sample locations 42 Fig 4.1 Principal component bi-plot of data on effluents metals concentrations 110 Fig 4.2 Cluster analysis dendrogram of effluents types for six metal concentrations. 111 Fig. 4.3 Variations of pH of effluents with sampling period 121 Fig. 4.4 Variations of alkalinity of effluents with sampling period 121 Fig.4.5 Variations of TDS of effluents with sampling period 123 Fig.4.6 Variations of conductivity of effluents with sampling period 123 Fig.4.7 Variations of total hardness of effluents with sampling period 124 Fig.4.8 Variations of calcium contents of effluents with sampling period 124 Fig.4.9 Variations of magnesium contents of effluents with sampling period 125 Fig.4.10 Variations of sulphate contents of effluents with sampling period 125 Fig.4.11 Variations of chloride contents of effluents with sampling period 126 Fig.4.12 Variations of phosphate contents of effluents with sampling period 128 Fig.4.13 Variations of nitrate contents of effluents with sampling period 128 Fig.4.14 Variations of DO contents of effluents with sampling period 129 Fig.4.15 Variations of BOD of effluents with sampling period 129 Fig.4.16 Variations of COD of effluents with sampling period 131 Fig.4.17 Variations of lead contents of effluents with sampling period 131 Fig.4.18 Variations of cadmium contents of effluents with sampling period 132 Fig.4.19 Variations of zinc of effluents with sampling period 134 xviii UNIVERSITY OF IBADAN LIBRARY Figure Title Page No. Fig.4.20 Variations of copper contents of effluents with sampling period 134 Fig. 4.21 Variations of chromium contents of effluents with sampling period 135 Fig.4.22 Variations of nickel contents of effluents with sampling period 135 Fig.4.23 Principal component bi-plot of sediments metals data 145 Fig.4.24 Correspondence analysis relay plot of sediment for six metals 147 Fig.4.25 Hierrarchical cluster analysis dendrogram for sediment sample 148 Fig.4.26 Principal component bi-plot of data in soil metals concentrations in Ikeja industrial estate 155 Fig.4.27 Correspondence analysis relay plot of soil for six metals in Ikeja industrial estate 156 Fig.4.28 Cluster analysis dendrogram for soil using the metals concentrations in Ikeja 157 Fig.4.29 Principal component bi-plot of data in soil metals concentrations in Isolo industrial estate 163 Fig.4.30 Cluster analysis dendrogram of data on soil metals in Isolo industrial estate 164 Fig.4.31 Correspondence analysis relay plot of soil for six metals in Isolo industrial estate 165 Fig.4.32 Variations of pH of soil with sampling period 173 Fig.4.33 Variations of organic matter content of soil with sampling period 173 Fig.4.34 Variations of lead contents of soil with sampling period 176 Fig.4.35 Variations of cadmium contents of soil with sampling period 176 Fig.4.36 Variations of zinc contents of soil with sampling period 178 Fig.4.37 Variations of copper contents of soil with sampling period 180 Fig.4.38 Variations of chromium contents of soil with sampling period 180 Fig.4.39 Variations of nickel contents of soil with sampling period 181 Fig.4.40 Trend in COD reduction during treatment with FeCl2.4H2O 186 Fig.4.41 Trend in COD reduction during treatment with FeSO4.7H2O 188 xix UNIVERSITY OF IBADAN LIBRARY Figure Title Page No. Fig.4.42 Trend in COD reduction during treatment with FeCl2.4H2O + 188 FeSO4.7H2O Fig.4.43 Trend in COD reduction during treatment with FeCl3 190 Fig.4.44 Trend in COD reduction during treatment with alum 190 Fig.4.45 Trend in COD reduction during treatment with various inorganic 191 coagulants Fig.4.46 Trend in COD reduction during treatment with FeCl2.4H2O + H2O2 193 Fig.4.47 Trend in COD reduction during treatment with FeSO4.7H2O + H2O2 193 Fig.4.48 Trend in COD reduction during treatment with FeCl2.4H2O + FeSO4.7H2O + H2O2 194 Fig.4.49 Trend in COD reduction during treatment with fenton’s reagents 194 Fig.4.50 Trend in COD reduction during treatment with charcoal 196 Fig.4.51 Variations in pH values during various treatment options 197 xx UNIVERSITY OF IBADAN LIBRARY LIST OF ACRONYMS AND ABBREVIATIONS AF Anaerobic Filter AFB Anaerobic Fluidized Bed API Active Pharmaceutical Ingredient BOD Biochemical Oxygen Demand CA Correspondence Analysis CF Contamination Factor COD Chemical Oxygen Demand DA Discriminant Analysis DO Dissolved Oxygen FA Factor Analysis FEPA Federal Environmental Protection Agency GPS Global Positioning System HCA Hierarchical cluster analysis HRT Hydraulic Retention Time Igeo Geo-accumulation index LASEPA Lagos State Environmental Protection Agency MBRs Membrane Bioreactors NA Not Available ND Not Determined NESREA National Environmental Standards and Regulations Enforcement Agency PAC Poly-Aluminium Chloride PAFS Poly-Alumino-Iron Sulphate Coagulant PAHs Poly-Aromatic Hydrocarbons PAS Poly-Aluminium Sulphate PC Principal Component PCA Principal Component Analysis PCBs Polychlorinated Biphenyls PFS Poly-Ferric Sulphate PLI Pollution Load Index xxi UNIVERSITY OF IBADAN LIBRARY POME Palm Oil Mill Effluent PZ Paterson Zochonics SBRs Sequence Batch Reactors SS Suspended Solids TDS Total Dissolved Solid TSS Total Suspended Solid UASB Up-Flow Anaerobic Sludge Blanket VOCs Volatile Organic Compounds VSS Volatile Suspended Solid WHO World Health Organisation xxii UNIVERSITY OF IBADAN LIBRARY xxiii UNIVERSITY OF IBADAN LIBRARY CHAPTER ONE INTRODUCTION 1.1 INDUSTRIAL POLLUTION AND THE ENVIRONMENT Industrial pollution has been and continues to be a major factor causing the degradation of the environment around us, affecting the water we use, the air we breathe and the soil we live on. Amongst these, the pollution of water is arguably the most serious threat to current human welfare. Water is polluted not only by industries but also by households. Both industries and household wastewater contain chemicals and biological matter that impose high demands on the oxygen present in water. Polluted water thus contains low levels of dissolved oxygen as a result of the heavy Biochemical Oxygen Demand (BOD) and Chemical Oxygen Demand (COD) placed by industrial and household waste materials discharged into water bodies and water systems. In addition to low levels of dissolved oxygen in water, industrial wastes (effluents) also contain chemicals and metals that are directly harmful to human health and the ecosystem (Samarghandi et al., 2007; Adekunle and Eniola, 2008; Mahdieh and Amirhossein, 2009). Environmental pollution due to increase of industrial activities are one of the most significant problems of the century. Pollution of soil and water may be derived from human activities such as industrial production, agriculture, burning of fossil fuels, mining and metallurgical processes and waste disposal (Guiliano et al., 2007). Industries which use large amounts of water in their processes include chemicals manufacturing, steel plants, metal processors, etc. Effluents and most products from industries create serious pollution to water bodies and soils (Baskaran et al., 2009). Partially or untreated industrial effluents may contain high levels of pollutants which in water body systems cause increase in BOD, COD, Total Dissolved Solids (TDS), Total Suspended Solids (TSS), toxic metals such as Cd, Cr, Ni and Pb, and feacal coliform, and hence make such water unsuitable for drinking, irrigation, and aquatic life support. Industrial wastewaters impacts include high BOD from biodegradable wastes such as those from human sewage, pulp and paper industries, slaughter houses, tanneries and chemical industry (Emongor et al., 2005; Otokunefor and Obiukwu, 2005; Phiri et al., 2005). 1 UNIVERSITY OF IBADAN LIBRARY 1.1.1 Impact of Industrial Emission on Land Soil physiochemical properties are adversely affected by high concentrations of heavy metals, rendering contaminated soils unsuitable for crop production (Udom et al., 2004; Hernandez- Allica et al., 2007). Metals can also be transported through soil water resulting into soil contamination and inhibiting growth of plants (Sharma et al., 2008). Soils contaminated with toxic metals from point sources are potential exposure routes for surrounding population (Hindwood et al., 2004; Carrizales et al., 2006; Pruvot et al., 2006). The heavy metals accumulate in the plant material grown in these soils, which ultimately enters the human body through the food chain directly or indirectly. For example, the leather processing industry discharges huge volumes of waste water to irrigate agricultural lands in some communities. This waste water contains some proteins released during enzymatic processing of leather, and contaminants such as salts and chromium, that negatively affect soil processes and crop production (Alvarez-Bernal et al., 2006). Continuous disposal of such partially treated effluents results in soil and groundwater pollution not only in the effluent irrigated lands, but also the surrounding farmlands, through leaching/percolation and run-off from the effluent irrigated land. Contamination of both soil and groundwater (shallow and deep aquifers) quality may usually be quite evident, since drinking water in such vicinities may turn brown due to the presence of lignin in affected areas (Sundari and Kanakarani, 2001). 1.1.2 Impacts of Industrial Effluents on Water Bodies Worldwide, water bodies are the primary receptacles for disposal of waste, especially the effluents from industries that are near them. Industries turn out wastes which are peculiar in terms of type, volume and frequency. The effluents often have the capacity to alter the physical, chemical and biological characteristics of the receiving water body. The extent of discharge of domestic and industrial waste is such that rivers receiving untreated effluent cannot give dilution necessary for their survival as good quality water sources (Sangodoyin, 1991). Industrial effluents are a main source of direct and often continuous input of pollutants into aquatic ecosystems with long-term implications on ecosystem functioning including changes in food availability and an extreme threat to the self-regulating capacity of the biosphere. These industrial discharge or wastes include heavy metals, pesticides, polychlorinated biphenyls (PCBs), dioxins, poly-aromatic hydrocarbons (PAHs), petrochemicals, phenolic compounds and microorganisms (Botkin and Kelly, 1998; Vesiland et al., 1990; Fakayode, 2005; Nubi et al., 2008). The initial effect of waste is to degrade the 2 UNIVERSITY OF IBADAN LIBRARY physical quality of the water. Later biological degradation becomes evident in terms of number, variety and organization of the living organisms in the water (Gray, 1989). Industrial wastes containing high concentration of microbial nutrients obviously promote an after growth of significantly high coliform types and other microbial forms. Some heavy metals contained in these effluents have been found to be carcinogenic while other chemicals equally present are poisonous depending on the dose and duration of exposure. Undoubtedly, wastewaters from industries and residential areas discharged into another environment without suitable treatment could disturb the ecological balance of such an environment (Botkin and Kelly, 1998). According to Perry et al. (2007), nitrogen, phosphorus, or both, may cause aquatic biological productivity to increase, resulting in low dissolved oxygen and eutrophication of lakes, rivers, estuaries, and marine waters. Eutrophication affects the aesthetics of water bodies (lakes, rivers), and results in odour and appearance problems. Besides adding to nutrient-content of the water, addition of some forms of nitrogen and phosphorus will increase BOD and COD (Mahdieh and Amirhossein, 2009). Increased nitrogen levels adversely affect cold-water fish more than they do warm water fish. Accumulation of trace metals occur in upper sediment in aquatic environment by biological and geochemical mechanisms and become toxic to reproduction and lower species diversity (Praveena et al., 2007). Textile industries wastes typically include high concentrations of caustic chemicals of high pH values, between 10.0 and 11.0. The net effect is a variation of the acidic or basic nature of the water bodies (Comprehensive Industry Document Series, 1998). The release of coloured effluents into water bodies by textile industries is undesirable, not only due to their colour, but also because many synthetic dyes and their breakdown products are toxic and/or mutagenic (Saratale et al., 2009; Ben-Younes et al., 2011). The pollution effects of untreated wastewaters of the steel industry, if discharged into the receiving water bodies are: toxicity to aquatic life, reduction of Dissolved Oxygen (DO), silting due to suspended solids, taste and odour problems, temperature rise affecting the DO and aquatic life, and formation of oil slicks due to floating oil. Considerable quantities of suspended and colloidal matter in the discharged effluents reduce the penetration of sunlight. Suspended and colloidal matter can also smother bottom dwelling aquatic organisms, 3 UNIVERSITY OF IBADAN LIBRARY affecting the life of water bodies, and leading to heavy siltation which affects the flow. Untrapped oil and grease from the effluent lead to formation of ugly oil slicks and iridescent colour which cause poor aesthetics. Oil slicks also reduce the diffusion of oxygen from the atmosphere to water, thereby affecting self purification. Where steel plants are located on the coasts, wave action invariably brings back oil and grease to the shore and spoils the beaches (Comprehensive Industry Document Series, 1998). There is thus a challenge of providing water in adequate quantity and of required quality to minimize hazards to human health and conserve the water bodies and the environment. 1.1.3 Impacts of Industrial Emission on the Atmosphere Air pollution is one of the most serious issues in urban areas, due to its adverse effects on human health. In developing countries, an estimated 0.5-1.0 million people die prematurely each year as a result of exposure to urban air pollution (Kojima, 2001).Air quality standards, established for the protection of human health and environment, are being exceeded by mega- cities (Lin et al., 2001). Limits and the requirements for improved critical levels for air pollutants in the European Union highlight the need for both extensive and intensive monitoring programs to determine long-term critical exposure levels for damages (Mills et al., 2000/2001). Energy related activities (production, transformation and use) are the major air polluters for almost all air pollutants. Gaseous pollutants include sulphur dioxide, nitrogen oxides (NOx), ozone, carbon monoxide, Volatile Organic Compounds (VOCs), hydrogen sulphide, hydrogen fluoride and various gaseous forms of metals. These pollutants are emitted from both mobile and stationary sources which include fossil fuel, fired power plants, smelters, industrial boilers, petroleum refineries, incinerators and manufacturing facilities (Yusuf et al., 2013).With that, industry emits a large portion of greenhouse gases and is the major emitter of SO2. In1996, industrial sources contributed about 20%, 17% and 22% of SO2, NOx and CO2 respectively, to total EU emissions of air pollutants (European Environment Agency, 2000), while in 2004 the manufacturing industry contributed 17% in particulate matter, 15% in ground-level ozone formation precursors and 14% in acidifying substances (European Environment Agency, 2007).The impacts of air pollution include chronic respiratory illness, cardiovascular defects, cancer and even loss of life. Other consequences of air pollution include corrosion to various materials which cause damage to cultural resources, acid rain, damage of crops and other vegetation, including damage to aesthetic values, and reduced 4 UNIVERSITY OF IBADAN LIBRARY visibility (Yusuf et al., 2013). Industrial emissions contain VOCs which emanate from industrial activities of various industrial units, and the products associated with them. These industrial units include ceramic and tile, lime and cement, metal plating, refineries, slaughter houses, metal foundries, food industries, detergents, dry cleaning, dyeing industries, tanneries, dairies, oil mills, fisheries, hydrometallurgical processes, aluminium recycling, piggeries, poultry raising, breweries, cosmetics, canning industries, pharmaceuticals, wood processing units, paper mills, production of construction materials etc. VOCs are carbon-based compounds that have vapour pressures that make them prone to significantly vapourise and enter the atmosphere (USEPA, 2005; EU, 2005). Studies have shown that VOCs enter the human bloodstream through inhalation, ingestion and dermal penetration (ATSDR, 2001). In the Niger Delta region of Nigeria, the operations of petroleum industries especially the upstream and downstream sectors as well as a variety of other anthropogenically related activities including biomass combustion, refuse burning and traffic emissions release a barrage of substances like volatile organics, oxides of carbon, nitrogen, sulphur, particulate matter, heavy metals and other toxics at levels that most times exceed both the national and international guidelines ( Tawari and Abowei, 2012). Apart from compromising the quality of the atmosphere, air pollution in the region is observed to have local and regional effects. Such effects include acid rain, water pollution, soil pollution, and injuries to plants and wildlife. Air pollution also damages materials and artifacts (Tawari and Abowei, 2012). 1.2 POLLUTION FROM INDUSTRIAL EFFLUENTS IN LAGOS STATE Lagos is the most populated city in Nigeria and the second largest in Africa (Fig 1.1). Lagos lies between longitude 2° 42’ E and 3° 42’ E and Latitude 6° 23’N and 6° 41’N, which is approximately 1,000 sq. km in size with a coastline of about 180km (Akinmoladun and Adejumo, 2011). About 22% of the total landmass is made up of a network of creeks, rivers and lagoons (Adewuyi and Adeleye, 2013). The soils are mostly hydromorphic, clayey and sandy. It contains over 70% of Nigeria’s manufacturing industries (Oketola and Osibanjo, 2007) with the highest level of estimated emission of 8000 tons of hazardous wastes per year, most of which is discharged directly into the Lagos lagoon and cuts across the southern 5 UNIVERSITY OF IBADAN LIBRARY 520000 530000 540000 550000 560000 570000 580000 N I fako Ijaiye Epe Agege Kosofe Alimosho Oshodi lu in o Isolo h ho m us S M Ajero. re o s d ule La g lan I felodun n Ojo u r ai Amuwo S M Badagry Ibeju-LekkiOdofin Apapa Etiosa KEY Sampled LGAs OTHER LGA RIVER OCEAN 9000000 0 9000000 Km 520000 530000 540000 550000 560000 570000 580000 Fig 1.1: Map of Lagos State, showing Ikeja, Isolo, Mushin and other Local Government Areas. 6 710000 720000 730000 740000 UNIVERSITY OF IBADAN LIBRARY 710000 730000720000 740000 part of the metropolis, linking the Atlantic Ocean (in the west and south), and Lekki Lagoon (in the east). It is about 6354.788sq km in area and 285 km in perimeter. The lagoon is generally shallow with a depth of between 0.3 and 3.2 m in most parts with the exception of some dredged parts, notably in the Lagos Harbour, where depth is greater than 10 m. The tidal range is 0.3-1.3 m. The lagoon provides places of abode and recreation, means of livelihood and transport, a dumpsite for residential and industrial discharge and a natural shock absorber to balance forces within the natural ecological system. The Lagos lagoon consists of three main segments namely the Lagos Harbour Segment, the Metropolitan and the Epe Division Segment (Ajao, 1996; IPEP, 2006; Adesalu et al., 2010). Concentrations of some heavy metals at the site of sewage disposal in Lagos lagoon are: 0.07ppm Hg; 0.008ppm Pb; 0.66ppm Fe; 0.10ppm Cu and 0.03ppm Ni (Fodeke, 1979). It was reported that fish caught at the sites of industrial sewage discharge in the Lagos lagoon had high concentrations of heavy metals in them. These heavy metals are potentially dangerous to fish and man (FAO/WHO, 1972; Okoye, 1991). Adebayo et al. (2007) reported that there was decreasing concentration of metals (Fe, Cu, Cd, Mn, Cr, Zn, Mn, Pb, Cr, Cu, and Hg) from their source towards the Lagoon. Waters in the lagoon and ocean adjoining Lagos were found not to meet required standards and were thus are not fit for human consumption (Sangodoyin, 1995, Adebayo et al., 2007). Lagos is home to some twenty- three (23) industrial estates (Table 1.1, Fig 1.2). It is the most developed area of the country with about a total of 5,000 ha of land occupied by the industrial estates (Soneye, 1997). The oldest industrial estate in Lagos is the Apapa Industrial Estate which occupies 100 hectares of land while the newest industrial estates are the Amuwo and Ikorodu/Odoguyan estates. The three largest industrial estates in terms of area occupied are Ikorodu/Ibese, Oregun and Ogba estates. The lack of inadequate treatment of drinking water is one of the most critical crises in developing countries. Rivers, streams, wells, and borehole water are usually used as supplements for the scarce pipe borne water for drinking with little or no treatment. Drinking such water has led to the outbreak of epidemics such as cholera and other water related diseases on several occasions (United States Food and Drug Administration (USFDA), 1993; Frontiers, 1996; Umeh et al., 2004). The supply of water to several cities is limited, and in many cases, water supply is chronically insufficient for the inhabitants. Despite the 7 UNIVERSITY OF IBADAN LIBRARY Table 1.1: List of industrial estates in Lagos State Area Estate LGA Year of Development (hectares) Apapa Apapa 1951 100 Mushin Mushin 1957 30 Ijora Apapa 1958 160 Badiya Apapa 1958 15 Matori Mushin 1958 120 Ikeja Ikeja 1959 180 Agidingbin Ikeja 1962 97 Ilupeju Mushin 1966 110 Oshodi Oshodi/Isolo 1968 120 Iganmu Surulere 1968 80 Ogba Ikeja 1969 414 Yaba/Oyadiran Lagos Mainland 1970 20 Ilasamaja Mushin 1971 60 Oregun Ikeja 1971 500 Isolo Oshodi/Isolo 1972 45 Gbagada Somolu 1974 50 Ikorodu/Ibeshe Ikorodu 1976 1580 Akowonjo Alimosho 1976 50 Kirikiri Apapa 1981 30 Surulere Surulere 1981 20 Abesan/Ipaja Alimisho 1981 100 Amuwo Amuwo-Odofin NA NA Ikorodu/odogiyan Ikorodu NA NA Source: Soneye (1997) 8 UNIVERSITY OF IBADAN LIBRARY 5 2 0 0 0 0 5 3 0 0 0 0 5 4 0 0 0 0 5 5 0 0 0 0 5 6 0 0 0 0 5 7 0 0 0 0 5 8 0 0 0 0 LEGEND State Boundary Main Road Minor Road Ijaiye Railway Line Abule- Odogunyan Meiran Egba Industrial Estates Studied Ogba Iyana Other Industrial Estates Ipaja Abesan Agidi Town -Ipaja -ngbi River/Creek/Lagoon Atlantic Ocean Egan Ejigbo Ibese 1,115 0 1,115 2,230 3,345 Metres Igando Ikotun Iba Town Isolo Shomolu Ilasama ja Agbara Iba Estate Badagry Abule Ado Amuwo Iganmu Okokomaiko LASU Volks Odofin wagen Ijora O j o Tinubu Lekki SQ. Obalende Iyana Iba Amuwo CMSSurulere Trade TBS Kirikiri Apapa ress W ay. xp Lekki E p e E Fair Adeola ode. st. Eko Ajah Hotel 5 2 0 0 0 0 5 3 0 0 0 0 5 4 0 0 0 0 5 5 0 0 0 0 5 6 0 0 0 0 5 7 0 0 0 0 5 8 0 0 0 0 Figure 1.2: The spatial distribution of industrial estates within Lagos Metropolis. 9 710000 720000 730000 740000 UNIVERSITY OF IBADAN LIBRARY 710000 720000 730000 740000 inadequacy of water supply, the management and conservation of the available water bodies is generally poor. Industrial growth is fast increasing globally and so also 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 the untreated waste water or poorly treated effluents accrued from industrial activities, which have rendered many water bodies unsuitable for both primary and/or secondary usage (Ogbeibu and Edutie, 2002). These industrial estates were established and operated without proper consideration of their environmental impacts. As a result, rivers and streams received large volumes of industrial effluents daily, which frequently were beyond the auto in-stream recovery capacities. Liquid wastes are commonly discharged into sewers or rivers, which in many countries is subject to legislation governing treatment before discharge. In most states in Nigeria this legislation is not effectively enforced, and liquid wastes are discharged into water bodies or allowed to infiltrate into the soil. Where available indiscriminate disposal of liquid wastes pose a major pollution threat to both surface and groundwater.There are about 2000 industries in Lagos metropolis with industrial units such as chemical industries, metal industries, oil refineries, petrochemicals, tanneries, pharmaceuticals, textiles, etc of which many discharge their effluents into Shasa and Odo Iya Alaro streams in the densely populated and heavily industrialized Ikeja suburb (Oyeyiola et al., 2013). There are documented incidents of pollution caused by discharge of untreated industrial effluents to water bodies in Lagos metropolis (Novick, 1999; WHO, 2003). The water quality characteristics of the Shasa and Odo Iya Alaro streams, such as pH, BOD, turbidity, color, total suspended solid, oil and grease, pesticides and heavy metals as well as total coliform exceed the limits of WHO and Federal Environmental Protection Agency. All fresh water bodies in Lagos state and the Ogun river drain into the Lagos Lagoon. According to the industrial database research sponsored by the World Bank in 1996-1997, only seven (7) industries installed wastewater treatment facilities out of the 534 industries which answered to the questionnaires from the 2,000 industries in Lagos State (WES, 1997). A central treatment plant (the Ikeja Industrial Effluent treatment plant) was constructed by the Western Marketing Board, WEMABOD Estates Limited in 1965. It had the capacity to process 19,000 cubic metres of industrial waste water daily. It received wastewater from various industries, treated these and discharged into Shasa stream. A similar treatment plant for about twenty industries was constructed in Agbara Industrial Estate at Agbara between Lagos and Ogun States, which 10 UNIVERSITY OF IBADAN LIBRARY discharges treated water into the Owo river (Sowunmi, 2001). Wastewater from Iganmu and Apapa industrial estates are discharged directly into the Lagos Lagoon, Port Novo and Ebute Metta Creeks Apart from wastewater from Iganmu industrial estate, Nigerian Breweries which carries out physical and biological treatment (WES, 1997). It is noteworthy that the central wastewater treatment plant constructed by WEMABOD has remained dysfunctional and disused since the early 1980’s; hence the wastewater that should have been treated at this facility is discharged untreated into a nearby stream through an underground sewer to Shasa stream. There are no central treatment plants in the other industrial estates, although a few industries partially treat their effluents whenever regulatory bodies propose visits to their premises. Meanwhile, monitoring by the regulatory body, Lagos State Environmental Protection Agency (LASEPA), is not effectively carried out. LASEPA’s functions include the monitoring and control of the disposal of wastes in Lagos State, and advising the State government on all environmental management policies. The policies on environmental management in the various instruments of intervention are rarely enforced. Previous studies on the characteristics of industrial effluents in Lagos metropolis have been very limited with respect to the class of industries studied, duration of studies and types of parameters investigated. In view of this, there has been the need to further comprehensively study the physicochemical characteristics of effluents from many more industries located in various industrial estates in Lagos. 1.3 AIMS AND OBJECTIVES Environmental pollution associated with industrial is essential the problem of disposal of solid wastes and effluents ( liquid or gaseous). All three types of wastes have the potential of ultimately polluting water. Polluted water, in addition to other effects, directly affects, soil not only in industrial areas but also in agricultural fields as well as the beds of rivers, thereby creating secondary effects. Farmers irrigate their gardens with water obtained from rivers and streams, which receive effluents from these industries. This practice has been on for a very long time, unabated. This research is therefore aimed at studying the degree of contamination of industrial effluents generated from three major industrial estates in Lagos metropolis and also to investigate the extent to which the effluents can be treated. In view of the problem stated in section 1.2, the specific objectives of this study were as follows: 11 UNIVERSITY OF IBADAN LIBRARY  To determine the levels of physicochemical parameters and heavy metals in industrial effluents from selected industries in Ikeja, Isolo and Ilupeju industrial estates.  To determine physicochemical characteristics and heavy metals contents of sediments of some effluent channels.  To determine physicochemical characteristics and heavy metals contents of soil samples besides the effluent channels, within the vicinities of the industries.  To investigate the treatability of some of the effluents, using chemical methods.  To apply the Multiple Linear Regression to generate models for estimating organic pollutant load (as COD) and eutrophication potentials (as phosphate) of the effluents. 12 UNIVERSITY OF IBADAN LIBRARY CHAPTER TWO LITERATURE REVIEW 2.1 OUALITY AND CHEMISTRY OF INDUSTRIAL EFFLUENTS Production of wastes is a normal consequence of industrial activities. Unfortunately, our inability to anticipate or predict the types and magnitude of undesired consequences of unbridled release of effluents in our environment, coupled with the growth of industrialisation, have resulted in massive and destructive operations in our ecosystems. In most cases, effluents are discharged into rivers, estuaries, lagoons, or the sea without any form of treatment. Also, despite the treatment being employed by some industries, it is still impossible to remove all undesirable properties from effluents (Uaboi-Egbenni et al., 2009). The effluents of pharmaceutical, dyeing, printing, photography, textile and cosmetics industries contain dyes (McMullan et al., 2001). Pulp and paper industry produces a large quantity of wastewater of high organic strength (Moiseev et al., 2004; Satyawali et al., 2009). 3 For most modern operations, approximately 60 m of wastewater is generated for every tonne of paper produced (Thompson et al., 2001). Paper manufacturing processes include debarking, pulping, separation of pulp from cooking liquor, bleaching, stock preparation, and making the final paper products (Tezel et al., 2001). The pulping and bleaching processes produce the largest volume of wastewater whose composition is highly variable, depending on the implemented technologies (Mounteer et al., 2007; Ugurlu and Karaoglu, 2009). Paper mill effluents are loaded with organic matter with relatively high COD of about 800–4400 mg/L, BOD5 of about 300–2800 mg-/L, and color of 1200–6500 color units (Buzzini et al., 2007; Singhal and Thakur, 2009; Sheldon et al., 2012). Paper mill waste also contain processed by-products such as extractives, carbohydrates, lignin and its derivatives, resin acids, sterols, and various chlorinated organic substances (Aaltonen et al., 2000; Kostamo et al., 2004; Leiviska et al., 2009). Effluents from textile industries are a complex mixture of many polluting substances such as organo-chlorine based pesticides, heavy metals, pigments and dyes. Heavy metals have been associated with the textile effluents (Ekhaise and Anyansi 2005). Among the various types of 13 UNIVERSITY OF IBADAN LIBRARY organics present, colour-producing substances are the most difficult to remove. Colour contributes to increased BOD and COD. Azo dyes are used extensively in the textile and dyestuff industries; these dyes have a complex structure some of which are carcinogenic and mutagenic (Tripathi et al., 2007). Azo dyes are xenobiotics compounds characterized by the presence of one or more azo groups (–N=N–). They are considered a very important group of synthetic colourants. Of the annual world-wide production of dyes more than 50% are azo dyes, and around 2000 of them are used in the textile, leather, plastics, paper, cosmetics and foods industries (Stolz, 2001). Dispersed blue 79 (DB79) is one of the colorants with more application in the textile industry. It is used in the dyeing of polyester, nylon, diacetate and triacetate of cellulose as well as in acrylic fibers. The reductive cleavage of the azo linkage of the DB79 resulting in the formation of the amine 2-bromine-4,6-dinitroaniline (BDNA), which is toxic and mutagenic, and of the amine NN-disubstituted 1,4-diaminobencene. Pharmaceuticals released into the environment are an important group of environmental pollutants (Jorgensen and Halling-Sorensen, 2000). Some studies have found pharmaceuticals in drinking water (Webb et al., 2003) and hospital wastewater (Suarez et al., 2009). Pharmaceutical industry wastewaters may contain organic solvents, catalysts, additants, reactants, intermediates, raw materials and Active Pharmaceutical Ingredients (APIs) (Sreekanth et al., 2009) which make them difficult to treat. The presence of toxic or recalcitrant substances in such wastewater results in lower COD removal efficiencies (Chelliapan et al., 2006). It has been estimated that up to half of the pharmaceutical wastewater generated worldwide is released without any treatment (Enick and Moore, 2007). Paint making involves the mixture of pigment, binder, thinner and additives which when spread in a thin film forms a solid, adherent surface coating. The pigment gives the paint colour and hiding power; the binder acts as a film-former, which holds the pigment on the painted surface; the thinner brings the pigment-binder mixture to a suitable consistency for application, and the additives impart special properties such as rapid drying. The binder with the thinner is called the “vehicle” because together these two components carry the pigment to a surface. The pigment, the binder, and some additives (calcium carbonate, and aluminum silicate) make up the non-volatile matter, which becomes the dried paint film (Dey et al., 2004). There are two types of paints, water based paint and alkyd paint. Water based paint are also referred as “latex” paint. Water based paints can easily be apply to a surface, wash away with soap, dry faster, and have fewer odors than solvent paints. Alkyd paint is referred to as 14 UNIVERSITY OF IBADAN LIBRARY “oil-based” paint. Alkyd paints create more pollution than water based paint. The mixing of these different chemical compounds results in excessive discharge of some organic and inorganic substances to the environment (Morgan, 1982). Paint industry wastewaters are characterized by substantial organic matter, high salinity, sulphate and suspended solids contents (Aboulhassan et al., 2006). According to the World Health Organization (WHO 1984), the metals of importance in paint industrial wastewater are mercury, copper, chromium, zinc, iron and lead. Paint effluent is highly polluted in terms of COD, suspended solid (SS), lead, zinc, iron and copper Industrial effluents from soap manufacturing industries are known to contain complex chemicals; e.g, most of which are very toxic and capable of destroying the microbial habitats in an adverse way. For example, characterisation of the composite wastewater from both soap and food processing plants indicated that the waste was highly contaminated with organic compounds as indicated by COD and BOD values (El-Gohary el al., 1987). Effluents from food and beverages industries contributed significant pollution load to the Alaro river in Ibadan (Ipeaiyeda and Onianwa, 2011). The identified pollutants in the effluents were organic load, suspended solids, phosphate, nitrate and chloride which led to its significant pollution. The receipt of the effluents has rendered the river unwholesome for some beneficial purposes such as cooking, drinking, irrigation and aquatic life support (Ipeaiyeda and Onianwa, 2011). Brewery, the alcohol producing industry, is one of the major polluting industries. Brewing involves the making of fermented alcoholic beverages, such as beer and ale from cereal grains. Brewery wastes are composed mainly of liquor pressed from the wet grain and wash water from the various departments. After the distillation of the alcohol, the residue remaining is referred to as "distillery slops", or "still bottoms". The brewing industry consumes much water about 10 gallons of processed water / gallon of product. The BOD (260 mg/L); COD (854 mg/L); TSS (1050 mg/L) and Electrical Conductivity (3585 μmhos/cm) of the waste water are higher than the regulatory limits (Noorjahan and Jamuna, 2012). 2.1.1 Impact of Industrial Effluents on Water bodies Water is essential to all forms of life and makes up 50-97% of the weight of all plants and animals and about 70% of human body (Buchholz, 1998). Water is also a vital resource for 15 UNIVERSITY OF IBADAN LIBRARY agriculture, manufacturing, transportation and many other human activities. Despite its importance, water is the most poorly managed resource in the world (Fakayode, 2005). Ground and surface waters can be contaminated by several sources. In farming areas, the routine application of agricultural fertilizers is the major source of pollutant (Altman and Parizek, 1995, Emongor et al., 2005). In urban areas, the careless disposal of industrial effluents and other wastes may contribute greatly to the poor quality of the water (Furtado et al., 1998, Chindah et al., 2004; Ugochukwu, 2004; Emongor et al., 2005). In recent years anthropogenic activity has introduced significant amounts of trace metals into aquatic environments (Puig et al., 1999). Anthropogenic water pollution is connected with sewage (industrial and communal), with lixiviation of chemical matter from dumping grounds of different waste materials, and with falls of atmospheric dust (Kabata-Pendias and Pendias, 1999). Industrial effluents from various industries like textile, dyestuffs, paper and pulp, distillery, olive oil mill and metal industries, etc. are the major contributors to water pollution as they create more subtle effects on behavior, reproduction or even survival of biotic communities (Tripathi et al., 2007). A study on the impact of industrial effluents on the water quality of a river in Nigeria (Fakayode, 2005) showed that the chemical parameters studied were above the allowable limits and also tended to accumulate downstream. The increasing demand on water arising from the fast growth of industries has put pressure on the limited water resources. While most people in urban cities of the developing countries have access to piped water, several others still rely on borehole and river water for domestic use. Most of the rivers in the urban areas of the developing world are the end points of effluents discharged from the industries. Industrial effluents, if not treated and properly controlled can also pollute ground water (Olayinka and Alo, 2004). Therefore, bore holes and rivers generally have poor quality water in affected areas. Since people use untreated waters from these sources, the result is continuous outbreaks of diseases such as cholera, bilharzia, diarrhoea and others (Umeh et al., 2004). NEST (1991) studied the effect of thick black effluent from two breweries on Ikpoba River in Benin City, Nigeria and concluded that there was a decrease in dissolved oxygen, an increase in BOD, a massive increase in COD and an equally massive increase in total suspended solids, all of which have effect on the flora and fauna of aquatic life. Today in Nigeria, industrial effluents especially from textile mills have turned the water of Iya Alaro river and Shasha river permanently bluish-green as well as increased the pH, sodium compounds and lead contents (NEST, 1991). Industrial effluent characteristics provide basic information 16 UNIVERSITY OF IBADAN LIBRARY about the integrity of the aquatic habitat within such rivers and streams into which they are discharged. Most of these effluents pose inestimable harm to which the microbial entity is the most adversely affected (Barnes, 2004; Dallas, 2004). Most of the industrial effluents are known to contain significant concentrations of inorganic chemicals which affect not only the normal pH values but also lead to the hardness of the water (Ogunfowokan and Fakankun, 1998). These aquatic environments may put some components of the habitat to disadvantage. Industrial effluents from soap manufacturing industries are known to contain complex chemicals e.g most of which are very toxic and are capable of destroying the microbial habitats in an adverse way. For example, El-Gohary et al. (1987) presented the wastewater management of an industrial complex which produced different products, such as soap, perfume extract, macaroni, jam and juices. A continuous monitoring programme of effluents and characterisation of the composite wastewater from both the soap and brewery manufacturing processes indicated that the waste was highly contaminated with organic compounds as indicated by the high COD and BOD values. Moreover, effluent from the soap manufacturing plant contained significant concentrations of oil and grease amounting to 563 mg/L. Soap manufacturing effluent and the combined wastes discharged from the whole industrial complex were subjected to different treatment processes namely dissolved air flotation, chemical coagulation, sedimentation and biological treatment via a completely mixed activated sludge process (Agedengbe et al., 2003). 2.1.2 Impact of Industrial Effluent on Land. Crowe et al. (2002) observed that exposure of oil sand effluents had an inhibitory effect on the germination of several plant species (tomato, clover, wheat, rye, pea, reed canary grass, loblolly pine). In addition, they observed that clover and tomato seeds’ germination were most affected. Powell et al. (1996) also observed that fresh weights of seedlings were significantly reduced by treatment waters, while dry weights were generally unaffected when these plants were exposed to pollution stress. Caywela et al. (1996) and Zheng et al. (1998) found that seeds in iron-rich media germinated later than the control and showed physical deformities at the seedling stage. Soil kept moist with high sulphate waters (>1200 ppm) had a negative impact on the rate or percent of germination of barley and reed canary grass seeds, as compared to the controls (Golder Associates Ltd, 1997). The problem of environmental pollution on account of essential industrial growth is in practical terms, the problem of disposal of industrial effluents. All three types of wastes 17 UNIVERSITY OF IBADAN LIBRARY (solid, liquid, and gaseous) have the potential of ultimately polluting water. Polluted water in addition to other effects, directly affects soil in industrial and agricultural areas, as well as the beds of rivers, thereby creating secondary sources of pollution (Barman et al., 2000; Kisku et al., 2000). Use of industrial effluent and sewage sludge on agricultural land has become a common practice in Nigeria as a result of which toxic metals (Pb, Hg, Zn, Cu, etc) can be transferred and concentrated into plant tissues from the soil. These metals have damaging effects on plants themselves and may become a health hazard to man and animals. Above certain concentrations and over a narrow range, the heavy metals become toxic (Babich et al., 1982). In addition to providing large quantities of water, some effluents contain considerable amount of essential nutrients which may prove beneficial for plants. The bio-accumulation of heavy metals in different terrestrial biota was investigated in Kenting National Park, Taiwan and high concentrations of Cd, Hg, and Sn were found in snail, earthworm, crab, lizard, snake, and bat. Similarly, high level of Hg was found in invertebrates, amphibians and reptiles which revealed a strong influence from industrial effluents (Hsua et al., 2006). The effluents released from the sugar industry contain high degree of organic pollutants which has effect on aquatic and terrestrial ecosystems. The use of this industrial effluent and sewage sludge on agricultural land is a common practice in many places, especially Nigeria (Odjegba and Bamgbose 2012), as a result of which these organic pollutants can be transferred and get accumulated into plant tissues from the soil. These organic pollutants have damaging effects on the plants themselves and may become a health problem to man and animals (Ayyasamy et al., 2008). The effluents also alter the physicochemical characteristics, flora and fauna of receiving aquatic bodies. In addition, the sugar factory effluent discharged to the environment poses a serious health hazard to the rural and semi-urban populations that use stream and river water for agriculture and domestic purposes. 2.2 EFFLUENT TREATMENT Control of pollution is one of the prime concerns of society today. With economic constraints on pollution control processes, affordable and effective methods have become a necessity. Industries have four possibilities for disposal of their wastewaters, namely: 18 UNIVERSITY OF IBADAN LIBRARY  They may treat their wastewaters separately in an industrial wastewater treatment plant prior to discharge into a water course.  They may discharge their wastewaters into municipal treatment plants for complete treatment.  Their wastewaters can be pre-treated at the industrial wastewater treatment plant prior to discharge into the municipal sewerage system.  Industrial wastewaters can be discharged directly into water course without any treatment (industries will prefer this method because of the economic advantage). Treatment of industrial effluents could be achieved by biological methods, chemical methods and physical methods. Biological methods are those that involve living organisms using organic, or in some instances, inorganic, substances for food, completely changing their chemical and physical characteristics. The biological treatment of industrial effluents is achieved with the aid of microorganisms (Frank, 2001). Chemical methods include chemical precipitation, chemical oxidation or reduction, formation of an insoluble gas followed by stripping, and other chemical reactions that involve exchanging or sharing electrons between atoms. Physical treatment methods include sedimentation, flotation, filtering, stripping, ion exchange, adsorption, and other processes that accomplish removal of dissolved and undissolved substances without necessarily changing their chemical structures (Frank, 2001 ). The chemical and physical methods of industrial effluent treatment processes remove organic pollutants at low level; they are highly selective to the range of pollutants removed and prohibitively expensive (Tripathi et al., 2007). 2.2.1 Biological Treatment Methods Biological treatment methods use microorganisms, mostly bacteria, in the biochemical decomposition of wastewaters to stable end products. The biological or biochemical treatment, unlike other forms, is in a large scale and the widest spread method of treating sewage. The method is based on the biochemical oxidation of organic and inorganic 19 UNIVERSITY OF IBADAN LIBRARY substances due to the activity of microorganisms using impurities such as a nitrite substrate, - 2- and forming harmless oxidation products: water, CO2, NO3 and SO4 ions and also biochemical matter. The degree of decomposition of organic compounds in biological treatment is characterised by the ratio BOD/COD. The greater the biological oxidation of a given waste, the higher is this ratio (Nsi, 2007). In biochemical treatment of effluents, the numerous organisms used constitute bacteria. A wide range of bacteria has been recorded (Ugoji and Aboaba, 2004; Iyagba et al., 2008), but the dominant aerobic genera appear to be gram-ve rods; pseudomonas, achromobacter, alcaligens and flavobacterium. Fungi are normally outnumbered 8:1 by bacteria. Autotrophic bacteria tend to be more predominant in the lower layers of the biofilms with nitrosomonas oxidizing nitrite to nitrate (Christopher, 2002). Protozoa, according to Ajao et al. (2011) and algae, according to Laliberte et al. (1994), have also been implicated in the biological treatment of effluent from pharmaceutical companies. Generally, biological treatment methods can be divided into aerobic and anaerobic methods, based on availability of dissolved oxygen. Aerobic applications include activated sludge, sequence batch reactors and membrane batch reactors (LaPara et al., 2002; Suman Raj and Anjaneyulu, 2005; Noble, 2006; Chang et al., 2008 and Chen et al., 2008). Anaerobic methods include anaerobic sludge reactors, anaerobic film reactors and anaerobic filters (Gangagni et al., 2005; Enright et al.,2005; Chelliapan et al., 2006; Oktem et al., 2007; Sreekanth et al., 2009). The advantages of anaerobic treatment over aerobic processes is its ability to deal with high strength wastewater, with lower energy inputs, sludge yield, nutrient requirements, operating cost, space requirement and improved biogas recovery. 2.2.1.1 Conventional activated sludge Conventional activated sludge with a long hydraulic retention time (HRT) has historically been the method of choice for the treatment of pharmaceutical industry wastewater (El- Gohary and Abou-Elea, 1995; Oz et al., 2004). It has a lower capital cost than more advanced treatment methods and a limited operational requirement; it is generally more environmentally friendly than chlorination. However, high energy consumption, the production of large amounts of sludge (Sreekanth et al., 2009) and operational problems including colour, foaming and bulking in secondary clarifiers are associated with activated sludge plants (Oz et al., 2004). Factors which affect the efficiency of activated sludge facilities for the treatment of pharmaceutical wastewater include HRT, temperature, pH, dissolved oxygen (DO), organic load, microbial community, presence of toxic or recalcitrant substances and the batch operation of pharmaceutical production facilities (LaPara et al., 20 UNIVERSITY OF IBADAN LIBRARY 2001; LaPara et al., 2002; Suman Raj and Anjaneyulu, 2005). These variables require modification for adaptation to pharmaceutical industry wastewater. 2.2.1.2 Sequence batch reactors A number of pilot scale studies were conducted using Sequence batch reactors (SBRs) and Membrane bioreactors (MBRs) in an attempt to improve the effectiveness of activated sludge treatment (Clara et al. 2005; Radjenovic et al. 2007). SBR is an activated sludge method of treatment in which separate tanks for aeration and sedimentation are not required and there is no sludge return. This type of process is ideal for use in small systems or when land is limited (Ileri et al., 2003). In one study, removal rates of 82 % BOD, 88 % COD, 96 % NH3 and 98 % Suspended solids (SS) from domestic and pharmaceutical wastewater were achieved with a SBR operated for a 4 h aeration and a 60 min sedimentation period (Ileri et al., 2003). In another study, slightly lower removal efficiencies of between 63-69 % of COD levels were achieved using SBR technology (Aguado et al., 2008). 2.2.1.3 Membrane bioreactors (MBRs) Membrane bioreactors (MBRs) are known to be effective for the removal of bulk organics and can replace traditional methods or operate in combination with conventional AS systems or as hybrid systems (Noble, 2006). The main advantages of MBRs over AS is that they require less space for operation (Yang et al., 2006), and can also treat variable wastewater compositions (Chang et al., 2008). High COD and BOD removal have been demonstrated in 3 pharmaceutical production facilities (De Wever et al., 2007). For example, a 10 m per day capacity MBR operated at a pharmaceutical facility in Taiwan, removed 95 % of COD and 99 % of BOD (Chang et al., 2008). However, complete removal of all APIs is rare (Helmig et al., 2007). While the MBR removed 17- α-estradiol, 17-β-estradiol, 17-α-dihydroequilin, trimegestone, estriol, medrogestone, norgestrel, and estradiol valerate to near and below the detection limits, estrone, ethinyl estradiol, and venlafaxine, a selective serotonin reuptake inhibitor (SSRI), were shown to be more resistant to the MBR treatment (Helmig et al., 2007). One explanation for this is that pharmaceutical compounds are generally smaller than the membrane pores and so only substances sorbed on particles are retained (Radjenovic et al., 2007). In order to remove the smaller compounds, membranes such as those used in reverse osmosis or nanofiltration are used. However, these are expensive, and limited in widespread use (Clara et al., 2005). 21 UNIVERSITY OF IBADAN LIBRARY 2.2.1.4 Anaerobic sludge reactors Up-flow anaerobic sludge blanket (UASB) technology, normally referred to as UASB reactor, is a form of anaerobic digester that is used in the treatment of wastewater (Borja et al., 1994a). UASB was developed by (Lettinga et al., 1980) whereby this system has been successful in treating a wide range of industrial effluents including those with inhibitory compounds. The UASB reactor is a methanogenic (methane-producing) digester that evolved from the anaerobic clarigester. The underlying principle of the UASB operation is to have an aerobic sludge which exhibits good settling properties (Lettinga, 1995). So far, UASB has been applied for the treatment of potato wastewater (Kalyuzhnyi et al., 1998, Lettinga et al., 1980, Parawira et al., 2006); domestic wastewater (Barbosa and Sant’Anna, 1989; Behling et al., 1997); slaughterhouse wastewater (Sayed et al., 1984); Palm oil mill effluent (POME) (Borja and Banks, 1994c). UASB has a relatively simple design where sludge from organic matter degradation and biomass settles in the reactor. Organic matter from wastewater that comes in contact with sludge will be digested by the biomass granules. 2.2.1.5 Anaerobic filters The anaerobic filter (AF) has been widely applied in the beverage, food-processing, pharmaceutical and chemical industries due to its high capability of biosolids retention. Anaerobic filter were favoured for wastewater treatment as a results of the following:  High substrate removal efficiency  It requires a smaller reactor volume which operates on a shorter hydraulic retention times (HRT), (Borja and Bank, 1994b),  The ability to maintain high concentrations of biomass in contact with the wastewater without affecting treatment efficiency(Reyes et al., 1999; Wang and Bank, 2007),  Tolerance to shock loadings (Reyes et al., 1999; Van Der Merwe and Britz, 1993). Also, construction and operation of anaerobic filter is less expensive and small amounts of suspended solids in the effluent eliminate the need for solid separation or recycle (Russo et al. 1985). Anaerobic filters have been applied to treat various types of wastewater including soybean processing wastewater (Yu et al., 2002a); wine vinases (Nebot et al., 1995; Perez et al., 1998); land fill leachate (Wang and Bank., 2007), municipal wastewater (Bodkhe, 2008), brewery wastewater (Leal et al., 1998), slaughterhouse wastewater (Ruiz et al., 1997), drug wastewater (Gangagni et al., 2005), and beet sugar water (Farhadian et al., 2007). 22 UNIVERSITY OF IBADAN LIBRARY 2.2.1.6 Anaerobic film reactors / fluidized bed The anaerobic fluidized bed (AFB) reactor is a type of reactor device that can be used to carry out a variety of multiphase chemical reactions. It comprises small media, such as sand or granular activated carbon, to which bacteria attach. Good mass transfer resulting from the high flow rate around the particles, less clogging and short-circuiting due to the large pore spaces, formed through bed expansion and high specific surface area of the carriers (Borja et al., 2001) due to their small size make fluidized bed reactors highly efficient. However, the lapses include difficulty in developing strongly attached biofilm containing the correct blend of methanogens, detachment risks of microorganisms, negative effects of the dilution near the inlet as a result of high recycle rate and high energy costs due to the high recycle rate are the main drawbacks of this system (Ersahin et al., 2011). Investigations have been carried out on the application of fluidized bed to treat cutting-oil wastewater (Perez et al., 2007); real textile wastewater (Sen and Demirer, 2003); slaughterhouse wastewater (Toldra et al., 1987); wine and distillery wastewater (Garcia et al. 1998),(Sowmeyan and Swaminathan, 2008); ice-cream wastewater (Borja and Bank, 1995a), (Hawkes et al., 1995); pharmaceutical effluent (Saravanane et al., 2001), and POME (Borja and Bank., 1995b) A lab-scale AFB reactor using cultivated polyvinyl alcohol gel beads with a diameter of 2–⁻ mm, to treat corn steep liquor was investigated by Zhang et al. (2009). The effective volume of the reactor was 3.9 L. Influent COD concentration varied within 2100-12900 mg/L. COD removal efficiencies of 96% and 91% were achieved at Organic Loading Rate (OLRs) of 27.5 3 and 25 kg COD/m .day with HRTs of 10 h and 6 h, respectively. 610 g/L of biomass concentration was achieved by the biomass attachment of 1.02 g volatile suspended solid/g PVA-gel beads. 2.2.2 Chemical and Physical Methods Chemical and Physical methods include ozonisation, adsorption, ion exchange, membrane filtration, chemical oxidation etc (Arutchelvan et al., 2006; Aksu and Gonen, 2006; Meghraj et al., 2003; Das and Mishra, 2010). These process are high energy consuming, non economic and release effluents and waste waters which require further treatment. Also, complete removal of the pollutants is not possible by the use of physical and chemical processes 23 UNIVERSITY OF IBADAN LIBRARY (Camargo et al., 2003; Shen and Wang, 1994; Wang and Chirwa, 1998). 2.2.2.1 Ozonisation Chemical oxidation with ozone can be used to treat organic pollutants or act as disinfectant agents. Ozone has been applied to the treatment of waters primarily due to its strong disinfection and sterilisation properties (Araña et al., 2002). Ozone is a powerful oxidant that can oxidize a great number of organic and inorganic materials. Ozone based technologies research is also focused on the catalytic ozonation where the presence of catalyst significantly improved the oxidation rate of organic compounds compared to non catalytic ozonisation. The ozonisation processes are possibly one of the most effective methods for treatment of wastewater containing organic products such as effluents from chemical and agrochemical industries, textile industry, paints, etc. (Guendy, 2007). The use of high oxidation potential like ozonisation has recently received much attention in wastewater treatment studies. A Ozoniser in which the oxygen molecules in the gas were dissociated to form ozone was used in many cases (Matheswaran et al., 2007).The disadvantages associated with the process are high operating cost and cost of equipment. The cost of the equipment is very high and also it requires high voltage and electricity for its operation (Larsen et al., 2004; Gharbani et al., 2010). The main mode of action in the ozonation process is the formation of OH- radicals due to ozone decay in the water, but there are also ozone molecules present for chemical attack. This increases the oxidation capacity (Ternes et al. 2003). However, the characteristics of the wastewater such as pH, temperature and concentration of organic pollutant play an important role in organic degradation (Yogeswary et al., 2005). Similarly, Wang et al. (2008), have studied the treatment of industrial effluents by with varied ozone concentrations. Initial pH and dosages of catalyst were also varied. The remaining organic matter was removed by BAF (Biological aerated filter), and the effect of hydraulic retention time (HRT) of BAF was also considered. Their result showed that under optimum pH and dose of H2O2 about 30% COD could be removed by ozonisation alone. 2.2.2.2 Ion exchange Ion exchange involves the removal of an ion from an aqueous solution by replacing with another ionic species. Ion exchange can be used for the removal of undesirable anions and cations from a wastewater. Cations are usually exchanged for hydrogen or sodium ions, and anions for hydroxyl ions. Ion exchange resins consist of an organic or inorganic network 24 UNIVERSITY OF IBADAN LIBRARY structure with attached functional groups. There are natural and synthetic materials available which are specially designed to enable ion exchange operations at high levels. Most ion exchange resins used in wastewater treatment are synthetic resins made by the polymerisation of organic compounds into a porous three-dimensional structure. Synthetic and industrially produced ion exchange resins are mainly made up of polystyrene and polyacrylate which are in the form of small and porous beads. Also there are some naturally occurring minerals which have ion exchange properties. The most common one is aluminium silicate minerals which are also called zeolites (Raghu et al., 2007). There are different zeolites made up of various ionic materials which have affinity towards some particular metals. The main characteristics of the ionic resins are material properties such as adsorption capacity, porosity, density etc (Zorpas et al., 2010).Ion exchange resins are called cationic if they exchange positive ions and anionic if they exchange negative ions. Cation exchange resins are comprised of acidic functional groups, such as sulphonic groups, whereas anion exchange resins have basic functional groups, such as amine. The strength of the acidic or basic character depends upon the degree of ionisation of the functional groups, similar to the situation with soluble acids or bases. Thus, a resin having sulphonic acid groups would act as a strong acid cation exchange resin. The main disadvantages associated with ion exchange methods are the high cost of the ion exchange resins and the selective nature of the resins in the removal of selective contaminant only. Furthermore, complete removal of the contaminant is not possible. Besides, ion exchange can be used for limited cycles only because passing concentrated metal solution the matrix gets easily owned out by organics and other solids in the wastewater after several use. Moreover ion exchange is sensitive to pH of the solution (Liotta et al., 2009; Sapari et al., 1996). A comparative study of alginate beads and an ion-exchange resin for the removal of heavy metals from a metal plating effluent was studied (Silva et al., 2008). It was observed that alginate beads removed lower amounts of heavy metals than the resin, but exhibited faster uptake kinetics. The comparison of dry protonated alginate beads with the resin supports the potential of the biosorbent for the treatment of industrial effluents. Howard et al. (2009) used ion exchange technology in the purification of gold mine water for discharge or re-use as industrial or potable water. The technology produced water quality that met the limits for discharge into water bodies. Gayathri and Senthi- Kumar (2010) also conducted the recovery and reuse of hexavalent chromium from aqueous solutions by a hybrid technique of electrodialysis and ion exchange. The different modes of operation, such as batch 25 UNIVERSITY OF IBADAN LIBRARY recirculation process, batch recirculation process with continuous dipping and continuous process, were carried out to remove and recover the chromium from the effluent. The reductions of chromium were found to be 98.69%, 99.18% and 100%, respectively. 2.2.2.3 Adsorption Adsorption is a widely used method for the treatment of industrial wastewater containing colour, odour, heavy metals and other inorganic and organic impurities, (Lai and Lin, 2003; Ganji et al., 2005; Al Rekabi et al., 2007; Georg Steinhauser 2008; Patel and Vashi, 2010). Adsorbent materials are basically derived from low-cost agricultural wastes,activated carbon prepared from various raw materials such as sawdust, groundnut husk, nut shells, coconut shells etc (Olayinka and Osinowo, 1999; Brown et al. 2000; Fischer et al., 2002; Zawani et al., 2009).These adsorbents are used for the effective removal and recovery of organic and metal pollutants from wastewater streams (Basso et al., 2002 and Park et al., 2006). It is a complex process affected by several factors. Mechanisms involved in the adsorption process involves the particle size, surface area and porosity of adsorbent etc (Gardea-Torresdey et al., 2004).This method suffers from low adsorption capacity and in some cases complete removal is not possible and the cost of adsorbent is high. After use the disposal of adsorbents create problems. 2.2.2.4 Membrane filtration Membrane filtration technique has received a significant attention for the wastewater treatment. It considers the application of hydraulic pressure to bring about the desired separation through the semi permeable membrane (Chen et al., 2005). Membranes are of different pore size and it is necessary to select membranes of appropriate pore size for specific purpose so that effluent and wastewater could be purified and permeable membrane could be recycled a number of times. There are three types of membrane filtration. These are ultra-filtration, nanofiltration and reverse osmosis (Chauhan and Rekha, 2004; Al-Rekabi et al., 2007). Various other types of membranes such as inorganic, polymeric, and liquid membranes can be used for this process. The main problem associated with this process is incomplete removal of contaminants, high energy requirement, high cost of the membrane and longevity of the membrane. After long term use the membranes get clogged with the contaminants present in the waste water and are damaged due to extra pressure on it. 26 UNIVERSITY OF IBADAN LIBRARY 2.2.2.5 Chemical oxidation In this process the waste materials from the industrial waste water are removed by the help of chemical oxidation using various chemicals mainly hydrogen peroxide (Dias-Machado et al., 2006; Ksibi, 2006). There are many disadvantages associated with this process like the high cost of the chemicals, emission of various harmful by products, hazardous constituents like secondary effluent problem along with the production of harmful gases 2.2.2.6 Chlorination Chlorine, a strong oxidizing chemical, is used to kill bacteria and to slow down the rate of decomposition of the wastewater. Bacterial kill is achieved when vital biological processes are affected by the chlorine. Another strong oxidizing agent that has also been used as an oxidizing disinfectant is ozone. Chlorination has been shown to be effective for the removal of pharmaceuticals including 17α- ethinylestradiol and 17 β-estradiol (Alum et al., 2004) and sulfonamides (Qiang et al., 2006). Chlorine dioxide is also effective for the removal of sulfamethoxazole, roxithromycin, 17α-ethinylestradiol and diclofenac (Khetan and Collins, 2007). 2.2.2.7 Neutralization A chemical process commonly used in many industrial wastewater treatment operations is neutralization. Neutralization consists of the addition of acid or base to adjust pH levels back to neutrality. Since lime is a base it is sometimes used in the neutralization of acid wastes. 2.2.2.8 Coagulation Coagulants used for treatment of both water and wastewater treatment are predominantly inorganic salts of ferric iron or aluminium. Aluminium salts, including aluminium sulphate and aluminium chloride, in addition to pre-hydrolysed metal-ion reagents such as poly- aluminium sulphate and poly-aluminium chloride (PAC), are the coagulants most widely used for water treatment (Bratby, 1980; ACWA, 2000). A possible alternative is to use iron- based coagulants, such as ferric chloride, ferric sulphate, or poly-ferric sulphate instead of the aluminium salts (Tenny and Derka, 1992; Jiang et al., 1996; Jiang and Graham, 1998). However, the coagulants composed by a mixture of both aluminium and iron salts can also be applied in water treatment. A study conducted by Jiang and Graham (2003) demonstrated that a poly-alumino-iron sulphate coagulant (PAFS) achieved, in water treatment tests, either a greater or similar removal in terms of percentage of colour and dissolved organic carbon in 27 UNIVERSITY OF IBADAN LIBRARY comparison with other conventional coagulants. The authors demonstrated that the PAFS allowed lower residual metal-ion concentrations in the drinking water when compared to poly-ferric sulphate, ferric sulphate, and aluminium sulphate 2.3 THE LAGOS ENVIRONMENT Lagos is highly populated and there are quite a number of industries distributed across the city, a development that has put the city under serious pressure in terms of environmental safety. Consequently, the city and its environs have witnessed environmental degradation leading to decreased biodiversity and extinction of many sensitive organisms (Odjegba and Bamgbose, 2012). Most of the industries in Lagos State do not control their wastewater effluents by processing, waste recycling or end-of-pipe treatment. Thus, effluents with some levels of toxic substances are being discharged into the environment untreated The discharge of partially treated or untreated industrial effluents into water bodies is a common phenomenon in Lagos metropolis. Table 2.1 lists the sources/types of industrial wastes in the Lagos area which are discharged either directly or indirectly into the lagoon systems, and their potential threats to aquaculture. These wastes and the resultant pollutants could bring about reduction in dissolved oxygen levels, eutrophication and increase in temperature. They could also cause increased organic load, increased turbidity as well as damage to nurseries and spawning grounds. Due to inability to sort waste at source, household and industrial waste, including toxic ones, are often handled together, leading to soil and ground water pollution (UNESCO, 2000). The indiscriminate discharge of chemical toxins especially Pb, Cd, Cr, Co, etc. into the environment ensure their transfer into plants, animals and man. The high concentration of heavy metals in irrigation waters could results in death of crops, interfere with uptake of other essential nutrients or form objectionable deposits on fruits. This may render the edible portions of plants toxic to humans and grazing animals (UNESCO, 2000). A study on fundamentals and trends of water services in Nigeria urban settlement Sangodoyin (1990) showed concern in an increase in the level of pollutants in surface and ground water which services as a source of domestic water and sometimes drinking water for rural and urban dwellers in Nigeria. Sangodoyin (1995) studied characteristics and control of industrial effluents generated pollution and showed that increase industrial activities have led to 28 UNIVERSITY OF IBADAN LIBRARY Table 2.1: Pollutant characteristics of wastes from some Lagos industries. Source Pollutants Threat to Aquaculture References Brewing: Organic pollutant, Low dissolved oxygen, Ajao e.g.Dumex Industry alcoholic brewer waste, eutro-phication, (1990) (Nig) Ltd., Vitamalt biodegradables, Zn, Pb, pollutants get clogged in Plc Ca, Mg, sulphate the gills of fish Pulp & Paper: Sulphate, free chloride, Moderately toxic and can Ajao e.g. WTN Plc caustic soda, chlorine easily be bioaccumula- (1996) dioxide and hypochlorite ted. Elevated levels of some metals in sediments and biota. Soap Detergent, caustic soda Prevent penetration of FEPA Manufacturing: e.g. from washing and light, low dissolved (1992) Lever Brothers Plc, cooling water, sulphur- oxygen in water, prolific Lorry ated detergent, oil, growth of blue-green Parks/Mechanic grease, engine oil, petrol. algae and bacteria, eutrophication. Plastics Industry: Hot polymer by- Increases the tempera- FEPA Lotus Plastic products, calcium, tin ture; bioaccumulation of (1992) and lead. metals often result. Foods Industry: Organic pollutants and Eutrophication, pollut- FEPA e.g. International biodegradables. Elevated ants get clogged in the (1992) Quality Food (Nig) water temperature gills of the fish; low Ltd., Unilever dissolved oxygen. Brothers Plc Chemical Industry: Acid, alkali, caustic Low dissolved oxygen, FEPA Henly Industries soda, insecticides, reduced temperature, (1992) (Nig) Ltd, Drury detergent, free chloride, highly toxic to fish, (Nig) Ltd,, Reckitts rodenticides, polymer obstruct balance of food and Beckiser, and nylon. chain and bioaccu- Colodense (Nig) mulation. Ltd. Pharmaceuticals: Free chloride, detergent, Bioaccumulation, toxic FEPA Pharma Deko, pesticides, organic to fish, fish kill and low (1992) Evans (Nig) Ltd., pollutants and trace dissolved oxygen. Glaxo Smithkline metals, alkali. Plc Metal Finishing: Acids, alkali, fluoride, Highly toxic, fish kill, FEPA e.g. John Edge inorganic salt, cyanide, low dissolved oxygen, (1992) Company (Nig), phosphate, toxic metals - high BOD. Beta Glass Zn, Pb; S, sulphur 29 UNIVERSITY OF IBADAN LIBRARY Source Pollutants Threat to Aquaculture References compounds Industries Oil and grease. Undesirable thick black Ajao Generating flames of oil in sheltered (1990) Hydrocarbon waters, reduction in light Wastes: penetration, loss of plankton, reduced dissolved oxygen, anoxic condition in bottom sediments, risk to aquatic organisms and fish- eating birds. 30 UNIVERSITY OF IBADAN LIBRARY pollution of our major rivers and streams in Nigeria. Effluents from industries have been known to contaminate water, soil and air with associated heavy disease burden and eventual shorter life expectancy in developing countries (WHO, 2003). The presence of poisonous chemicals in industrial effluents may pose health threats to humans and animals. Studies relating to the physicochemical characteristics of different types of industries in Lagos are few. Textile industry wastewaters are one of the principal sources of pollution in the world (Olayinka and Alo (2004). In particular, the release of coloured effluents into the environment is undesirable, not only due to their colour, but also because many synthetic dyes and their breakdown products are toxic and/or mutagenic (Saratale et al., 2009; Ben Younes et al., 2011). The physicochemical pollutant indicators from textiles industries (Adebayo et al., 2007; Awomeso et al., 2010) were studied in Lagos, and it was noted that the levels of pH, alkalinity, biological oxygen demand, chemical oxygen demand and dissolved oxygen were higher than the guideline limits of NESREA. The effect of textile effluent on groundwater was studied by Olayinka and Alo (2004). The study showed that the effluents had high values of BOD5 (100–390 mg/l) and COD (204–2000 mg/l) and were highly coloured. These values were higher than the effluent limits stipulated by the Federal Ministry of Environment for textile industries. 2.4 USE OF MULTIVARIATE STATISTICAL TECHNIQUES TO GENERATE LINEAR REGRESSION MODEL FOR ENVIRONMENTAL STUDIES The application of different multivariate statistical techniques, such as principal component analysis (PCA), factor analysis (FA) and discriminant analysis (DA), helps in the interpretation of complex data matrices to better understand the water quality and ecological status of the studied systems. It allows the identification of possible factors/sources that influence water systems and offers a valuable tool for reliable management of water resources as well as rapid solution to pollution problems (Helena et al. 2000; Lee et al. 2001; Adams et al. 2001; Wunderlin et al. 2001; Reghunath et al. 2002; Simeonova et al. 2003; Simeonov and Tisitouridou, 2004; Papatheodorou et al. 2007).Multivariate statistical techniques has been applied to characterise and evaluate surface and freshwater quality, and it is useful in verifying temporal and spatial variations caused by natural and anthropogenic 31 UNIVERSITY OF IBADAN LIBRARY factors linked to seasonality. For example, Zou and Yu (1996) have used a general dynamic factor model to reduce the high dimensionality of the original matrix variables in order to detect trends in time series. Meng and Maynard (2001) used cluster and factor analysis to identify geochemical regions within a watershed in Brazil. Silva and Williams, (2001) combined multivariate statistical analyses with GIS (Geographical Information System) analysis to determine if a correlation existed between water quality and landscape characteristics in a watershed in southern Ontario (Canada). Singh et al. (2004) and Singh et al. (2005) used multivariate statistical techniques to evaluate spatial and temporal variations in water quality of the Gomti River (India). Similarly, Shrestha and Kazama (2007) also used multivariate statistical techniques to assess the surface water quality of the Fuji River Basin in Japan. These techniques, especially PCA, have also been used to evaluate the water quality monitoring stations (Ouyang, 2005). Furthermore, long term hydrochemical data of shallow water bodies have been evaluated using factor analysis and discriminant analysis (Medina- Gomez and Herrera-Silverira, 2003; Solidoro et al., 2004). 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 models (containing all the independent variables). Best subset procedure based 2 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). SPSS software can be used to generate Linear Regression Model using the data obtained from the effluent quality analysis. 2.4.1 Effluent Quality Model 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. waste water). 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. Regression models, linear time series models, non linear time series analysis and artificial neural 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 streams (Ongley and Booty, 1999). 32 UNIVERSITY OF IBADAN LIBRARY 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 the 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. 2.4.2 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 parameters estimates are usually simpler than stochastic predictions of the probability distributions of these 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. 33 UNIVERSITY OF IBADAN LIBRARY 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 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. 34 UNIVERSITY OF IBADAN LIBRARY 2.5 APPLICATION OF MULTIPLE LINEAR REGRESSION MODEL TO WATER QUALITY PREDICTION Multiple regression analysis is a statistical tool for understanding the relationship between two or more variables. Multiple regression examines the relation between a single dependent variable and a set of independent variables to best represent the relation in the population. The technique is used for both predictive and explanatory purposes within experimental or non experimental designs (Tinsley and Brown 2000). When there are an arbitrary number of explanatory variables, the linear regression model takes the following form: Y = β0 + β1.X1 + β2.X2 + . . . . . . . . . . + βm.Xm + eij where Y represents the dependent variable and X1. . . Xu represent the different independent variables, β0,. . . .. . . ..,βm represent the regression coefficient and e represents the random error (Freund and Wilson 1998). The error term e represents the collective unobservable influence of any omitted variables. In a linear regression each of the terms being added involves unknown parameters, which are estimated by “fitting” the equation to the data using least-squares. It has been used to investigate relationships between water quality parameters (physicochemical and biological) and landscape characteristics (Sliva and Williams 2001; Wang 2001; Singh et al. 2005; Amiri and Nakane 2009) and to investigate between water quality and pathogen indicators (Mallin et al. 2000; Crowther et al. 2001). MLR models typi- cally make use of easily measured environmental and water-quality variables to estimate pollutant concentration. 35 UNIVERSITY OF IBADAN LIBRARY CHAPTER THREE MATERIALS AND METHODS 3.1 DESIGN OF STUDY The experimental procedures were designed in line with the broad aims and objectives of the study as indicated in section 1.3., i.e. to determine the characteristics of the effluents of some industries located in three industrial estates in Lagos, and determine suitable chemical treatment options. The design components included the following: (a) Selection of Industrial Estates for the Study: Three estates were selected for this study. They were sufficiently representative of the industrial estates in the Lagos area with respect to age, types of industries within the estates, and accessibility to the effluent channels. In each estate, only some of the industries were investigated. The list of estates and the industries investigated are given below: (i) Ikeja Industrial Estate:  a pharmaceutical industry - May & Baker (Nigeria) Plc;  a paints industry - Berger Paints (Nigeria) Plc;  a textile industry - Woolen and Synthetic Textile Manufacturing Limited;  two basic metal industries – Galvanizing Industries Limited, and Hoesch Pipe Mills Ltd.  a central effluent treatment plant for the estate, owned and operated by WEMABOD. (ii) Isolo Industrial Estate:  two pharmaceutical industries – Johnson Wax Nig. Ltd, and Afrab Chem Ltd.  a food and beverages industry - Viju Industries Ltd. (N.B: this industry is now located in Ikeja, but was located in Isolo at the time of the study). 36 UNIVERSITY OF IBADAN LIBRARY (iii) Ilupeju Industrial Estate:  a conglomerate (varied products) industry – PZ Industries Nig. Plc.  a nearby canal receiving industrial wastewater discharge. (b) Study of Effluent Characteristics: Location of Sampling Points: For a particular factory/industry, effluents samples were collected along the discharge channel, at an accessible point closest to the factory. Two points at about 100 m apart were sampled along the canal, while the underground tunnel at the treatment plant was also sampled. Frequency/Duration of Sampling: Sampling was carried out bimonthly at each sampling location, and lasted a period of 20 months (November 2006 to May 2008), covering (two wet and two dry seasons). Parameters Determined:  Physicochemistry - pH, conductivity, total dissolved solid, alkalinity, total hardness, calcium, magnesium, chloride, sulphate, phosphate and nitrate.  Heavy metals - Pb, Cd, Zn, Cu, Cr and Ni  Gross Organic Pollutants - dissolved oxygen, biochemical oxygen demand, and chemical oxygen demand. (c) Study of Sediments Obtained from Effluent Channels: Sediments are good indicators of depositions from fluid/aquatic systems, as sediment quality reflect long-term characteristics of the fluid medium above the sediments. Thus, even for an effluent channel, the drainage sediment is a good sink for indirectly monitoring pollutant in the liquid effluents. Location of Sampling Points: Sediments were obtained at the locations where effluents were sampled. However, it was not possible to obtain sediments at all effluent sampling points, as no sedimentation occurred at such points. Frequency/Duration of Sampling: Sediment sampling was of the same frequency and duration as the effluent sampling. Parameters Determined:  Physicochemistry - pH, % organic carbon and mechanical property (particle size)  Heavy metals- Pb, Cd, Zn, Cu, Cr and Ni 37 UNIVERSITY OF IBADAN LIBRARY (d) Study of Topsoil Quality Around Some Effluent Channels: Quality of topsoil beside the effluent channels was studied at two of the estates because it was observed that the sediments were often scooped out of the channels and deposited on adjoining grounds. The topsoil quality was thus determined to examine the relationship with the qualities of the sediments and effluents. Location of Sampling Points: Samples of topsoil collected at various points directly beside (not more than one metre) the drainage channel. At Isolo estate, six sampling points were utilized around the channel, while at Ikeja estate, four locations were utilised. Frequency/Duration of Sampling: Frequency and duration were same as for effluents and sediments. Parameters Determined: Same as for sediments. (e) Treatment of Textile Effluent: From the results obtained from the effluent characterization study, it was noted that the most highly organically polluted effluents were those obtained from the textile and pharmaceutical industries with extreme values of BOD and COD. The textile industry effluents were then selected for testing the efficiencies of a variety of treatment methods for the reduction of the organic load. The following treatment options were investigated:  Treatment with Fenton’s reagents (FeCl2.4H₂O and H2O2 ; FeSO4.7H2O and H2O2; FeCl2 .4H₂O + FeSO4.7H2O and H2O2.  Treatment with a variety of inorganic coagulant chemicals – FeCl2.4H₂O, FeSO4.7H2O, FeCl3, alum  Treatment with activated charcoal  Simple exposure of effluent to the dark and under sunlight, with no addition of chemicals. Parameters for Monitoring the Effectiveness of Treatment: pH, total dissolved solid, total suspended solids and chemical oxygen demand. (f) Model Estimate of Organic Pollutant Loading and Potential for Eutrophication: The results obtained from the physicochemical characterisation were utilised for developing a model for predicting the phosphate and COD levels in effluents based on the observed levels of other parameters. Such COD levels reflect the potential for organic loading, while the phosphate levels reflect the potential for eutrophication. Both modeled parameters are 38 UNIVERSITY OF IBADAN LIBRARY significant for predicting the impact of the effluents on the receiving streams and Lagoon. The modeling was based on application of stepwise multiple linear regressions to obtain a model equation for estimating:  organic pollutant load (as chemical oxygen demand), and  potential for eutrophication (as phosphate concentrations) 3.2 DESCRIPTION OF STUDY LOCATIONS The various industrial estates studied and the effluents sampling points from the industries are shown in Figure 3.1, Figure 3.2 and Figure 3.3. Table 3.1 gives the site codes, while Table 3.2 describes the features of the sampling locations. Appendix 1 shows the GPS derived geographical locations of the sampling points. 3.3 SAMPLING 3.3.1 Preparation of Sample/Sampling Containers polyethene containers for effluents samples were washed with detergent and rinsed with water. The containers were soaked in 2 M nitric acid overnight and then washed with distilled water prior to sampling. Winkler bottles for dissolved oxygen were also cleaned as described for the polythene containers. The hand trowel used for collecting sediments/soil samples was washed with detergent and rinsed with distilled water prior to sampling. Clean hand trowel was used to collect the sediments/soil samples 3.3.2 Collection of Effluent At each sampling point, a composite effluent sample was collected by dipping a clean polythene plastic cup into effluent at the discharge point and pouring into the storage clean polythene plastic bottle for physicochemical parameters. Samples for heavy metal were collected into other polythene plastic bottles and preserved by addition of 2 mL of conc. nitric acid to 1L of effluent at the site. By this method a total of 60 effluent samples were obtained from various industries in Ikeja Industrial Estate during the study period. The corresponding numbers for Isolo and Ilupeju estates were 55 and 47. 39 UNIVERSITY OF IBADAN LIBRARY Figure 3.1: Map of Ikeja Industrial Estate showing sample locations. 40 UNIVERSITY OF IBADAN LIBRARY Figure ⁻.2 :Map of Isolo Industrial Estate showing sample locations. 41 UNIVERSITY OF IBADAN LIBRARY Figure 3.3: Map of Ilupeju Industrial Estate showing sample locations. 42 UNIVERSITY OF IBADAN LIBRARY Table 3.1: Industries sampled from various industrial estates Name of Industrial Estate Industry Sampled Classification Site Code Ikeja Industrial Estate Hoesch Pipe Mills Ltd. Basic metal KHO Ikeja Industrial Estate Berger Paints (Nigeria) Plc Paint industry KBP Ikeja Industrial Estate Galvanizing Industries Limited Basic metal KGI Ikeja Industrial Estate Woolen and Synthetic Textile Textile industry KWS Manufacturing Limited Ikeja Industrial Estate May & Baker (Nigeria) Plc Pharmaceutical KMB Ikeja Industrial Estate Central Treatment Plant - KJE Isolo Industrial Estate Johnson Wax Nig. Ltd Pharmaceutical SJW Isolo Industrial Estate Afrab Chem Pharmaceutical SAF Isolo Industrial Estate Viju industries Food &beverages SVJ Ilupeju Industrial Estate PZ Industries Nig. Plc Conglomerate* LPZ Ilupeju Industrial Estate Canal ** - LCA * varied product (pharmaceuticals, cosmetics and house hold appliances) types industry. ** Canal receives effluents discharged from several industries 43 UNIVERSITY OF IBADAN LIBRARY Table 3.2: Description of sample locations and code. Sample Type of Location Location Features Code Sample KHO-EF Effluent Ikeja Industrial Estate Open drainage from metal industry, close to abandoned treatment plant; shallow - about 1.5m depth; varied colours and offensive odour (Appendix 2) KBP- EF Effluent Ikeja Industrial Estate Open drainage from paint company; shallow with about 1.5m depth; varied colours and offensive odour KGI- EF Effluent Ikeja Industrial Estate Open drainage from galvanizing industry; shallow with about 1.5m depth; varied colours and offensive odour KWS- EF Effluent Ikeja Industrial Estate Open drainage from textile company; shallow with about 1.5m depth; varied colours and offensive odour KMB- EF Effluent Ikeja Industrial Estate Open drainage from pharmaceutical company; shallow with about 1.5m depth; varied colours and offensive odour (Appendix 3) KJE- EF Effluent Ikeja Industrial Estate Open sewer (abandoned treatment plant) with vegetation; receives all the effluents on the estate; have a depth of about 3.0m, coloured with offensive odour (Appendix 4) SJW- EF Effluent Isolo Industrial Estate Open drainage from pharmaceutical company; has a depth of about 1.0m; varied colours and offensive odour (Appendix 5) SAF- EF Effluent Isolo Industrial Estate Open drainage from pharmaceutical company; has a depth of about 2.0m; varied colours and offensive odour (Appendix 6) SVJ- EF Effluent Isolo Industrial Estate Open drainage from food and beverage industry; has a depth of about 1.0m; varied colours and offensive odour 44 UNIVERSITY OF IBADAN LIBRARY Table 3.2: contds. Sample Type of Location Location Features Code Sample LPZ- EF1 Effluent Ilupeju Industrial Estate Open drainage from a conglomerate company; has a depth of about 1.0m; varied colours and offensive odour LPZ- EF2 Effluent Ilupeju Industrial Estate Open drainage from a conglomerate company ; has a depth of about 1.0m ; varied colours and offensive odour LPZ- EF3 Effluent Ilupeju Industrial Estate Collective effluent joint from a conglomerate company ; has a depth of about 2.5m ; varied colours and offensive odour (Appendix 7) LCA-EF1 Effluent Ilupeju Industrial Estate Canal with vegetation ; weeds at the edge, it is about 3.0m deep, coloured with offensive odour (Appendix 8) LCA-EF2 Effluent Ilupeju Industrial Estate Canal with vegetation; weeds at the edge, it is about 3.0m deep, coloured with offensive odour KJE-SD Sediment Ikeja Industrial Estate collective effluent point ;Open sewer , marshy with vegetation KHO-SD Sediment Ikeja Industrial Estate metal company; open drainage SVJ-SD Sediment Isolo Industrial Estate food and beverage industry; open drainage LPZ-SD1 Sediment Ilupeju Industrial Estate a conglomerate; open drainage LPZ-SD2 Sediment Ilupeju Industrial Estate a conglomerate; open drainage LPZ-SD3 Sediment Ilupeju Industrial Estate a conglomerate; open drainage LCA-SD1 Sediment Ilupeju Industrial Estate canal with vegetation LCA-SD2 Sediment Ilupeju Industrial Estate canal with vegetation 45 UNIVERSITY OF IBADAN LIBRARY Table 3.2: contd. Sample Type of Location Location Features Code Sample KBP-SL1 Topsoil Ikeja Industrial Estate roadside soil around paint company KGI-SL2 Topsoil Ikeja Industrial Estate roadside soil around galvanizing company KWS-SL3 Topsoil Ikeja Industrial Estate roadside soil around textile company KMB-SL4 Topsoil Ikeja Industrial Estate roadside soil around pharmaceutical company SJW-SL1 Topsoil Isolo Industrial Estate roadside soil around pharmaceutical company SJW-SL2 Topsoil Isolo Industrial Estate roadside soil around pharmaceutical company SJW-SL3 Topsoil Isolo Industrial Estate roadside soil around pharmaceutical company SAF-SL4 Topsoil Isolo Industrial Estate roadside soil around pharmaceutical company SAF-SL5 Topsoil Isolo Industrial Estate roadside soil around pharmaceutical company SVJ-SL6 Topsoil Isolo Industrial Estate roadside soil food and beverage industry K-CTR-SL1 Topsoil Control soil for Ikeja Industrial Estate soil residential area outside the estate ; open area with few houses K-CTR-SL2 Topsoil Control soil for Ikeja Industrial Estate soil residential area outside the estate ;open area with few houses S-CTR-SL1 Topsoil Control soil for Isolo Industrial Estate soil residential area outside the estate; open area with few houses S-CTR-SL2 Topsoil Control soil for Isolo Industrial Estate soil residential area outside the estate; open area with few houses 46 UNIVERSITY OF IBADAN LIBRARY 3.3.3 Collection of Drainage Channel Sediment Sediment samples were collected at a depth of 0-15cm using a clean hand trowel at the drainage channels. Samples were collected the sampling points as composites and were stored in polythene bags. The sediment samples were transported to the laboratory, and air- dried for 10-15days. Non-soil materials (such as wood, stones, and dirt) were removed by hand picking, and the samples then crushed manually, using porcelain mortar and pestle. The sediments were then sieved using a 2 mm mesh sieve, and stored in polythene bags for analysis. Total number of sediments collected was: Ikeja Industrial estate - 20, Isolo Industrial Estate - 5, Ilupeju Industrial Estate - 47. 3.3.4 Collection of Soil Soil samples were collected beside the effluent channels. These were obtained as composite samples and stored in polythene bags. They were treated in same manner as the sediment samples. 3.4 ANALYSIS OF EFFLUENT SAMPLES FOR PHYSICOCHEMICAL PARAMETERS All analysis were carried out using standard procedures of the American Public Health Association (APHA-AWWA-WEF, 1998) 3.4.1 Determination of pH pH was determined by electrometric method using a pH meter (Mettler Toledo, model MP 220). Buffer tablets were used to prepare buffer 4, 7 & 9. Each of the tablets was dissolved in 100mL of distilled water respectively. The pH meter was calibrated by alternatively dipping the electrode into buffer solutions of pH 4.00, 7.02 & 9.02 3.4.2 Determination of Conductivity Conductivity was determined by electrometric method using a conductivity meter (Jenway, model 4076). The meter was calibrated with saturated solution of KCl to give a reading of 1420 μS/cm prior to use. 47 UNIVERSITY OF IBADAN LIBRARY 3.4.3 Determination of Total Dissolved Solids Total Dissolved Solids was determined by electrometric method using a TDS meter (Jenway, model 4076). 3.4.4 Determination of Alkalinity Alkalinity was determined by titrimetry method using methyl orange indicator. Reagents: Sodium carbonate, 0.1M Standard volumetric solution: anhydrous sodium carbonate (5.300 g) which have been dried,cooled in a dessicator, was weighed out dissolved in distilled water, and diluted to 1 litre in a volumetric flask. Hydrochloric acid, 0.1M Standard volumetric solution: concentrated hydrochloric acid (10 mL) was added to distilled water and diluted to 1 Litre. The solution was standardized against 0.1M sodium carbonate, using methyl orange indicator. Methyl orange indicator solution: methyl orange (1 g) was dissolved in distilled water and diluted to 1 Litre. Phenolphthalein indicator solution: phenolphthalein dye (1.0 g) was dissolved in 100 mL ethanol (95%), and 100 mL of distilled water was added, and the mixture then stirred. The solution was stored in a reagent bottle and labeled. Procedure: Total Alkalinity: An aliquot of the sample (50-100 mL) was added 2 to 3 drops of methyl orange indicator solution was added. It was titrated with the standard acid from yellow to an orange colour end point. Calculation: Vol of 0.1M HCl x 5000 Total Alkalinity (mg/L) = Vol of Sample 3.4.5 Determination of Total Hardness, Total Hardness was determined by EDTA titrimetry procedure using solochrome black T indicator. 48 UNIVERSITY OF IBADAN LIBRARY Reagents: Buffer (pH 10): concentrated ammonium hydroxide (NH4OH) (143 mL) was added to 16.9 g of ammonium chloride (NH4Cl) and diluted to 250 mL with water. Erichrome Black T Indicator: erichrome black T solid indicator (0.2 g) was dissolved in 15 mL of triethanolamine and 5mL of absolute ethanol. Standard 0.01M EDTA Titrant: Sodium ethylenediaminetetraacetate dihydrate o Na2H2EDTA.2H2O was dried at 80 C for 1 hour and cooled in a dessicator. 3.723 g was accurately weighed and dissolved in water and diluted to 1000 mL. The solution was standardised against standard zinc sulphate solution. The solution was stored in polyethylene bottle. Procedure: Total hardness: 2 mL of pH 10 buffer was added to 50 mL of sample in a conical flask, followed by 2 drops of Eriochrome Black T, this was titrated with 0.01M EDTA from wine- red to blue colour end point. Blank was run in a similar way as sam ples, and values were subtracted from that of sample. Calculation: Titre mL 1000 Total hardness (mg CaCO3 /L) = Vol. of sample (mL) 3.4.6 Calcium and Magnesium Calcium was determined by EDTA titrimetry procedure using calcon indicator after making the pH of sample solution sufficiently high that the magnesium is precipitated as hydroxide while Magnesium was determined by the difference in titre values between total hardness and calcium. Reagents: Sodium Hydroxide 1.0 M: sodium hydroxide (40.0 g) was dissolved in 1000 mL of water. Solid Calcon Indicator. 49 UNIVERSITY OF IBADAN LIBRARY Procedure Calcium Hardness: 2 mL of NaOH solution or a volume sufficient to produce a pH of 12 to 13 was added to 50 mL sample in conical flask, followed by the addition of 0.1g of Calcon indicator. This was stirred, and titrated with 0.01M EDTA with continuous stirring to a blue end- point. Blank was run in a similar way as samples, and values were subtracted from that of sample. Calculations: 3.4.7 Determination of Chloride Chloride was determined by titrimetrically with mercuric nitrate reagent and diphenylcarbazone indicator. Reagents: Hydroquinone solution: hydroquinone (1.0 g) was dissolved in 100 mL of water. The solution was freshly prepared when needed. Nitric acid, approximately 0.05 N: concentrated nitric acid (3.2 mL) was diluted to 1 Litre. Sodium hydroxide, approximately 0.05 M: sodium hydroxide (2 g) was dissolved in water and diluted to 1 Liter. Mercury (II) nitrate standard solution: mercury (II) nitrate, Hg(NO3)2.H2O, (5.04 g) was dissolved in 50 mL of water containing 0.5 mL of concentrated nitric acid, diluted to 1L and filtered. This solution was standardized against 25 mL of sodium chloride standard solution, and adjusted so that - 1.0 mL ≡ 1.0 mg Cl The solution was stored in a brown bottle. Sodium chloride standard solution: sodium chloride, NaCl (1.648 g) dried, cooled in a dessicator was dissolved in water and diluted to 1 Litre, so that - 1.0 mL ≡ 1.0 mg Cl 50 UNIVERSITY OF IBADAN LIBRARY Indicator solution: diphenylcarbazone (0.5 g) and bromophenol blue (0.05 g) were dissolved in 100 mL of ethanol (95% v/v) and stored in a brown bottle. Procedure: A measured volume (V) of sample was placed in a conical flask and diluted or evaporated down as necessary to about 50 mL. 5 mL of hydroquinone solution was added to mask chromate and iron (III) ions. Five to ten drops of indicator solution was added. If a blue, blue violet or red colour developed, 0.05 M nitric acid was added drop wise until the colour changes to yellow and 1mL in excess was added. If a yellow or orange colour developed 0.05M sodium hydroxide was added until the colour changed to blue violet and 0.05 M nitric acid was added until the colour changed to yellow, and then 1mL in excess was added. Mercury (II) nitrate standard solution was then titrated against the solution. Blank correction was carried out by titrating against 50 mL of distilled water. Calculation: V V₂ x 1000 Concentration of chloride ( mg/L) = V V1 = Volume (mL) of mercury (II) nitrate solution required for sample. V2 = Volume (mL) of a mercury (II) nitrate solution required for blank. V = Volume (mL) of sample taken. 3.4.8 Determination of Sulphate Sulphate was determined by the turbidimetric method involving the precipitation of sulphate ions as barium suphate and stabilisation with conditioning reagent. Reagents: Barium chloride crystals: Conditioning reagent: 75 g of sodium chloride, 30 mL of concentrated hydrochloric acid, 100 mL of 95% propan-2-ol and 50 mL of glycerol were dissolved in 300 mL of water, and mixed well. Sulphate standard solution: anhydrous sodium sulphate (0.1479 g) was dissolved in I L of distilled water. 2- 1.0 mL = 100 µg SO4 51 UNIVERSITY OF IBADAN LIBRARY Procedure: Preparation of calibration graph : Appropriate amount of sulphate standard solution was 2- measured into 100 mL volumetric flasks to cover the range up to 10..0 mg SO4 (0.5, 1, 2, 2- 3, 4, 6, 8 and 10 mg SO4 ) and the solutions were made up to 50 mL with water. Sulphate determination was continued according to the procedure described below, and a blank simultaneously carried out. Treatment of test and blank solutions: A quantity (50 mL) of sample was measured into 250 mL beaker. 5 mL of the conditioning reagent was added and the mixture stirred with a magnetic stirrer at a constant speed. While the solution was being stirred, about 0.5 g of barium chloride crystals was added, and the stirring continued for exactly one minute at the same speed. Immediately after one minute the absorbance was measured at 420 nm wavelength using double beam uv/visible cecil 3025 spectrophotometer The readings were taken at intervals of 30 seconds over a period of four minutes, and the maximum reading was recorded. Typical calibration graph obtained is given in Appendix 9. The number of µg of sulphate in the sample was estimated from calibration curve, and the concentration calculated as: No of mg SO₄ from calibration extrapolation SO₄ (mg/L) = Volume of Sample (mL) 3.4.9 Determination of Phosphate Phosphate was determined by the colorimetric method involving the reaction of phosphate ions with ammonium molybdate and potassium antimony tartarate in acid medium to form phosphomolybdic acid. This is then reduced to intensely coloured molybdenum blue complex with ascorbic acid. Reagents: Sulphuric acid, approximately 10M: concentrated sulphuric acid (139 mL) was carefully added to water. The mixture was cooled and diluted to 1000 mL. Ammonium molybdate solution: ammonium molybdate tetrahydrate (NH4)6Mo7O24.4H2O (40 g) was dissolved in 1000 mL distilled water and stored in a glass-stoppered bottle. Ascorbic acid solution 0.01M: ascorbic acid (3.4 g) was dissolved in 200 mL water. This o solution was stable for just one week at 4 C. Potassium antimonyl tartarate solution: potassium antimonyl tartarate, K(SbO)C4H4O.½H2O, 52 UNIVERSITY OF IBADAN LIBRARY (2.743 g) was dissolved in 1000 mL water and stored in a glass-stoppered bottle. Stock phosphate solution: anhydrous potassium dihydrogen phosphate, KH2PO4, (219.5 mg) was dissolved in water and diluted to 1000 mL. 3- 1.0 mL ≡ 50 µg PO4 Phosphate working solution: 5 mL of the stock phosphate solution was diluted to 500 mL with water. 3- 1.0 mL 2.5 µg PO4 Combined reagent: The above reagents were mixed to obtain 500 mL combined reagent: 250 mL of 10M sulphuric acid, 75 mL of ammonium molybdate solution, 25 mL of potassium antimonyl tartarate, 150 mL of ascorbic acid solution. At each addition, the combined reagent was properly mixed and allowed to reach room temperature. The reagent was stable for 4 hours. Procedure Preparation of calibration graph: Accurately measured amounts of phosphate working 3- solution covering the range of 1 to 15 µg PO4 were placed in stoppered 50 mL volumetric flask. These were diluted as necessary to 40 mL of distilled water. 40 mL of distilled water was placed in another flask for use as blank. The process was continued according to the procedure described below. Treatment of test and blank solution: A sample solution of 40 mL was placed into a 50mL stoppered volumetric flask while 40 mL of distilled water as blank was placed in another flask. 8 mL of the combined reagent was added and the content mixed and diluted to 50 mL with distilled water. These were allowed to stand for 10 minutes, and the absorbance measured at 880nm with a double beam uv/visible spectrophotometer (Cecil 3025). Typical calibration graph obtained is given in Appendix 10. The number of µg of phosphate in the sample was estimated from calibration curve, and the concentration calculated as: No of mg PO₄ from calibration extrapolation PO₄ (mg/L) = Volume of Sample (mL) 3.4.10 Determination of Nitrate Nitrate was determined by phenoldisulphonic acid method (Marczenko, 1986). The nitration of 2, 4-phenoldisulphonic acid forms 6-nitrophenol-2, 4-disulphonic acid which, on subsequent addition of ammonium hydroxide solution produces a yellow coloured compound. 53 UNIVERSITY OF IBADAN LIBRARY Reagents: Phenoldisulphonic acid: solution in concentrated H2SO4: phenol (12.5 g) was dissolved in 75 mL of concentrated H2SO4, 37.5 mL of 13 % oleum added and the mixture was stirred well. The solution was heated in 250 mL conical flask for 2 hours on a boiling water-bath with occasional stirring. Stock nitrate solution: potassium nitrate, KNO3, 0.7128 g which has been dried, cooled in a desiccators, was weighed out, dissolved in distilled water and diluted to 1000 mL. The solution was preserved with 2 mL chloroform per litre. - 1.0 mL≡100 μg NO3 -N Nitrate working solution: 25.0 mL of the stock nitrate solution was diluted to 500 mL with distilled water. - 1.0 mL ≡ 5.0 μg NO3 -N Silver sulphate solution: silver sulphate (1.10 mg) was dissolved in 250mL of distilled water. - 1.0 mL ≡1.0 mg of Cl Calcium carbonate suspension: CaCO3 (1.0 g) was mixed with 100 mL of distilled water. Procedure: Preparation of calibration graph: Standards were prepared to cover 0, 5, 10, 20, 30, 40 and – 50 g NO3 N / 50 mL by diluting 0.0, 1.0, 2.0, 4.0, 6.0, 8.0 and 10.0mL of the standard nitrate solution to 50mL with distilled water in a volumetric flask. Blank was also prepared - using only 50mL of distilled water and the determination of NO3 -N was carried out as described below. The standards were treated in the same manner as samples. A calibration - graph of absorbance of the standards against number of microgram of NO3 -N / 50 mL was constructed. Treatment of test and blank solutions: High chloride causes low results owing to the reaction between HCl and HNO3 when the phenoldisulphonic acid in concentrated sulphuric acid is added to some samples; - - + 3Cl + NO3 + 4H → Cl2 + NOCl + 2H2O Large amount of chloride was precipitated as AgCl and filtered beforehand by the addition of slightly less than stoichiometric quantity of silver sulphate to the sample. 50 mL of sample was measured into a 100 mL beaker, neutralised by the addition of 1 mL of CaCO3 o suspension and evaporated to dryness in a water bath at 105 C.The cooled residue was treated with 2mL of phenoldisulphonic acid reagent and stirred well. After 5 minutes, it is 54 UNIVERSITY OF IBADAN LIBRARY diluted with 10mL of water and transferred quantitatively to a 50 mL standard flask. Concentrated ammonia solution was added until the solution became intensely golden yellow, then 2 mL was added in excess and diluted to mark with distilled water. The absorbance of the golden yellow colour was measured at 410nm with a double beam uv/visible cecil 3025 spectrophotometer. Typical calibration graph obtained is given in Appendix 11. Number of mg of nitrate in the sample was estimated from calibration curve, and the concentration calculated as: Calculation: 3.5 ANALYSIS OF EFFLUENT SAMPLES FOR HEAVY METALS Sample of 200 mL was measured into a 500 mL beaker, and 5 mL of concentrated nitric acid was carefully added. This solution was concentrated to 20 mL by heating on a water bath for few hours. The concentrated extract was cooled and transferred into a 50 mL standard flask, then made up to mark with distilled water. Heavy metal (Pb, Cd, Zn, Cu, Cr and Ni) contents of the samples were determined using atomic absorption spectrophotometer (Perkin Elmer, Model AAnalyst 200, Waltham, USA) involving direct aspiration of the aqueous solution into air- acetylene flame. Table 3.3 gives the detailed information on the atomic absorption spectrophotometer operating conditions and the range of standards for each metal. . A reagent blank was prepared and analysed. Heavy metal concentrations of a series of standards (BDH )were determined and a calibration graph constructed. From the graph the concentrations of heavy metals in the sample were calculated. Calculation: 3.6 ANALYSIS OF EFFLUENT SAMPLES FOR GROSS ORGANIC POLLUTANTS All analysis were carried out using standard procedures of American Public Health Association (APHA-AWWA-WPCF, 1998). 55 UNIVERSITY OF IBADAN LIBRARY Table 3.3: Operating conditions for the atomic absorption spectrophotometer. Wavelength Slit Width Lamps Current Range of Working Element Flame Type (nm) (nm) (mA) Standards (mg/L) Pb 283.3 2.7 10 Air-C2H2 0-10 Cd 228.8 2.7 4 Air-C2H2 0-1 Zn 213.9 2.7 15 Air-C2H2 0-1.6 Cu 324.8 2.7 15 Air-C2H2 0-6.4 Cr 357.9 2.7 25 Air-C2H2 0-6.4 Ni 232.0 1.8 25 Air-C2H2 0-4 Source: Perkin Elmer (2007). 56 UNIVERSITY OF IBADAN LIBRARY 3.6.1 Determination of Dissolved Oxygen. Dissolved oxygen was determined by Winkler’s Method. This method is based on the addition of manganous sulphate solution and alkali-iodide-azide solution to the water sample. This results in the formation of manganous hydroxide precipitate. Any dissolved oxygen present in sample solution then rapidly oxidizes an equivalent amount of the dispersed manganous hydroxide precipitate to form higher hydroxide. Subsequent acidification of this solution liberates iodine in an amount chemically equivalent to the original dissolved oxygen content of the sample. The iodine is then determined by titration with a standard solution of sodium thiosulphate Reagents: Alkaline iodide solution: sodium hydroxide (400 g) was dissolved in 560 mL of water, after which 90 g of sodium iodide was added and the solution kept hot until the iodide had completely dissolved. The solution was cooled and diluted to 1 Litre. No iodine was liberated when 1 mL is diluted to 50 mL and acidified. Alkaline iodide-azide solution: alkaline iodide solution (1 Litre ) was mixed with 300mL of 2.5% sodium azide solution. Concentrated sulphuric acid Manganese (II) sulphate solution: hydrated manganese (II) sulphate, MnSO4.5H2O, (500 g) was dissolved in water, filtered and made up to 1 Litre. o Potassium iodate, 0.025M : Potassium iodate was dried at 120 C for 2 hours, cooled in a dessicator, 0.892 g was weighed out, dissolved in water and diluted to 1 Litre. The solution was stored in a glass-stoppered bottle. Sodium thiosulphate, approximately 0.025M: sodium thiosulphate pentahydtate, Na 2S2O8.5H2O, (6.3g) was dissolved in 1 Litre of copper- free water, 1mL of chloroform was added to stabilize the solution. This was stored in a brown bottle. Sodium thiosulphate working solution: The 0.025M sodium thiosulphate solution was diluted to strength of 0.0125M. For standardization, 10 mL of 0.025M potassium iodate was measured by pipette into a conical flask containing 100 mL water and 1mL of potassium iodide solution was added, followed by 2 mL of conc. sulphuric acid. The content was mixed thoroughly, and then titrated with the 0.0125M thiosulphate, adding 2mL of starch solution just before the end point. 57 UNIVERSITY OF IBADAN LIBRARY Sampling: Glass bottles (250 mL capacity) with narrow neck and well-fitted ground-glass stoppers were used for sampling. These sampling bottles were cleaned with 5M sulphuric acid and then rinsed thoroughly with water. Synthetic detergent was used for cleaning. The bottles were subsequently kept clean by acidic iodine solution of the Winkler procedure and required no further treatment apart from thoroughly rinsing with tap water before use. For the purpose of sampling industrial effluent and canal water, the sampling bottles were held horizontally at the surface so that the water enters gently, without bubbling. As the bottle fills, it was brought gradually to the vertical position. Finally the Winkler reagent was added immediately the sample was taken, as changes in the concentration of dissolved oxygen may occur rapidly. Procedure: To the sample, 1 mL of manganese (II) sulphate solution was added well below the surface of the liquid and 1 mL of alkaline-iodide-azide solution was added at the surface. The stopper was carefully replaced so as to avoid inclusion of air bubbles, and the content thoroughly mixed by inverting and rotating the bottle several times. When the precipitate had settled to the lower third of the bottle, the mixing was repeated and the precipitate allowed to settle completely leaving a clear supernatant liquid. 2 mL of concentrated sulphuric acid was added with measuring cylinder. The stopper was replaced and the content thoroughly mixed by rotation. A 100 mL of the solution was measured into a conical flask and the iodine immediately titrated with standard thiosulphate solution, while 2 mL of starch indicator solution was added towards the end of the titration. Calculation: 3.6.2 Determination of Biochemical Oxygen Demand (BOD5) Biochemical Oxygen Demand (BOD5) was determined by Dilution Method. 250 mL glass bottles with narrow neck and well-fitted ground-glass stoppers were used. These bottles were cleaned with 5 M sulphuric acid and then rinsed thoroughly with water. Synthetic detergents were used for cleaning. 58 UNIVERSITY OF IBADAN LIBRARY Reagents: Dilution water: Stock solutions of the following pure chemicals are required. Calcium Chloride solution: calcium chloride, CaCI2, (27.5 g), was dissolved in 1Litre of water. Iron (III) chloride solution: iron (III) chloride hexahydrate, FeCl3.6H2O, (0.125 g), was dissolved in 1 Litre of water. Magnesium sulphate solution: magnesium sulphate heptahydrate, MgSO4.7H2O, (25 g), was dissolved in 1 Litre of water. Phosphate buffer stock solution: potassium dihydrogen phosphate, KH2PO4, (42.5 g) was dissolved in 700 mL of water, and 8.8 g of sodium hydroxide was added. 2 g of ammonium sulphate was added, and diluted to 1 Litre. To every Litre of freshly distilled water, 1 mL of each reagent was added. The water was taken to incubation temperature and saturated with oxygen by bubbling air through it for forty eight hours, and the water used soon after saturation. Procedure: Preliminary treatment of sample: for pH of the sample that was not between 6.5 and 8.5, sufficient alkali or acid was added to bring it within the range. The amount of acid or alkali added is determined on a separate sample. Dilution of sample: With some samples more than one dilution was carried out. For example: industrial effluents vary so much in character that no limits of dilution can be suggested. A range of dilution, for example 1 : 5, 1:15, 1:50, 1:150, was carried out and the best dilution that gave at least 30% of the oxygen remaining after 5 days was used. Samples which were stored in a refrigerator were allowed to reach room temperature before dilutions were made. The following principles were strictly adhered to in preparing the dilutions: (i) The sample and dilution water was mixed thoroughly but violent agitation leading to the formation of minute air bubbles was avoided. Mixing was accomplished by careful repeated inversion of a measuring cylinder containing the sample and dilution water. (ii) The diluted mixture were transferred to two incubator bottles (or more when replicate results were required) by careful pouring. Exactly the same mixing and transfer mixing technique was used for all samples. (iii) Bottles of the dilution water used in the test were prepared at the same time as the sample dilution to permit determination of the blank. Carrying out the test: The initial concentration of dissolved oxygen in one bottle of the mixture of sample and diluted water, and in one of the bottles containing only dilution water was 59 UNIVERSITY OF IBADAN LIBRARY determined immediately. The other bottles (those containing the mixture of sample and dilution water, and that containing the plain dilution water to act as a blank) were placed in the dark at room temperature. After 5 days, the dissolved oxygen in the diluted sample and the blank was determined. In both case, the azide method described for dissolved oxygen determination was used. Calculation: Where: x = volume of 0.0125 M thiosulphate required for 100mL of the original dilution (mL) y= volume of 0.0125 M thiosulphate required for 100mL of incubated dilution (mL) a = volume of diluted water to 1 volume of sample (mL) z = difference between volumes of 0.0125 M thiosulphate requires for 100mL of dilution water before and after incubation (mL). (The blank correction). 3.6.3 Determination of Chemical Oxygen Demand Chemical Oxygen Demand was determined by the method of oxidation with potassium dichromate, followed by titrimetric determination of the unreacted dichromate with iron (II) sulphate heptahydrate. Reagents: Iron (II) sulphate, 0.125 M: iron (II) sulphate heptahydrate, FeSO4.7H2O, (34.75 g) was dissolved in 100mL of 1:3 diluted sulphuric acid , and diluted to 1 Litre. Potassium dichromate, 0.125 M: potassium dichromate, K2Cr2O7, (6.129 g) was dissolved in water and diluted to 1 Litre. Iron (II)-1, 10-phenanthroline indicator solution: iron (II) sulphate heptahydrate, FeSO4.7H2O, (3.475g) was dissolved in 500 mL of water. To this was added 7.427 g of 1,10- phenanthroline monohydrate and the mixture was shaken until dissolved. Silver sulphate solution: silver sulphate (1.25 g) was dissolved in 100mL 1:1 sulphuric acid solution. 60 UNIVERSITY OF IBADAN LIBRARY Sulphuric acid: concentrated. Procedure: 10 mL of 0.125 M potassium dichromate was accurately measured and transferred with 30mL of concentrated sulphuric acid into a 250 mL round bottom flask and mixed well. The flask was cooled in a tray containing cold water, 1 mL of silver sulphate solution was added followed by 20mL of sample and few anti-bumping granules are added. The flask was fitted to the reflux condenser and boil under reflux for two hours. The content was allowed to cool, and 60 mL of distilled water was added and the content was allowed to cool. A drop of indicator solution was added and the residual dichromate was titrated with 0.125M Iron (II) sulphate added from a burette. A blank determination was carried out in a similar way using 20mL of distilled water instead of sample. The sample titration is subtracted from the blank titration to give the volume of potassium dichromate equivalent to the oxygen demand. Since 1.0 mL of 0.125 M potassium dichromate = 1.0mg of oxygen Calculation: 3.7 ANALYSIS OF SEDIMENT FOR PHYSICOCHEMICAL PARAMETERS The following parameters were determined in sediment: pH, organic carbon, particle size distribution (i.e., mechanical properties) and heavy metals (Pb, Cd, Zn, Cu, Cr and Ni). The analyses were carried out using standard procedures. 3.7.1 Determination of Sediment pH pH was determined by electrometric method (Page,1982), using pH meter (Mettler Toledo, model MP 220). The pH meter was calibrated as stated in 3.4.1 Procedure: Sediment samples were sieved (2 mm mesh), dried in an oven at 105 C for 4 hrs, and cooled in a desiccator. 20 g of the dry sample was weighed into a 50 mL plastic container. 20 mL of deionised water was added and the mixture was allowed to stand for 30 minutes with occasional stirring, using a glass rod. After calibration of the pH meter, the pH of the sediment 61 UNIVERSITY OF IBADAN LIBRARY suspensions, were taken using the pH meter. The electrode was rinsed with deionised water and wipe with a dry tissue paper after each reading. 3.7.2 Determination of Sediment Particle Size Distribution Particle size analysis was done by Bouyoucos hydrometer method (Gee and Bauder, 1979) Reagent: Sodium hexametaphosphate dispersing agent (5%): sodium hexametaphosphate (50 g) was weighed and dissolved in 1000 mL water. Procedure: 50.0g of 2mm sieved air-dried sediment was weighed into a 400mL beaker. 50 mL of 5.0% sodium hexametaphosphate was added along with 100 mL of water. The suspension was stirred and allowed to stand for 20 minutes, then stirred again for 15 minutes with a mechanical shaker. After the shaking, the suspension was transferred to a glass cylinder. With the hydrometer in the suspension, water was added making the volume of water to be 1000 mL. The cylinder was covered with a tight fitting rubber bung and the suspension was mixed by inverting the cylinder carefully. The cylinder was place on a flat surface and the time noted. The hydrometer was gently placed immediately into the cylinder and after 40 seconds the first hydrometer and temperature readings of the suspension were taken (H1 and T1). After this the cylinder was allowed to stand for three hours and the second hydrometer and temperature readings were taken (H2 and T2). The first reading measures the percentage of silt and clay in suspension. The second reading indicates the percentage of 2- micron (total) clay in o o the suspension. Results were corrected to a temperature of 68 F. For every degree over 68 F, o 0.2 are added to hydrometer reading before computation and for under 68 F, 0.2 was subtracted from hydrometer reading. 2.0 are subtracted from every hydrometer reading to compensate for the added dispersing agent. Calculation: % Sand = 100.0 – [H1 + 0.2 (T1 -68) – 2.0]2 % Clay = [H2 + 0.2 (T2 – 68)2] % Silt = 100.0 – (% sand + % clay) 62 UNIVERSITY OF IBADAN LIBRARY 3.7.3 Determination of Organic Carbon Content of Sediment Organic Carbon was determined by the method of Nelson and Sommers (1996). The method measures the amount of degradable organic matter in sediment and soil. Sediment and soil organic carbon is almost completely oxidized with potassium dichromate (K2Cr2O7) in the presence of H2SO4, leaving the fraction present in inorganic form. The excess dichromate is determined by titration with ferrous sulphate Reagents: Potassium dichromate (K2Cr2O7) 6M: potassium dichromate (49.04 g) was dissolved in water and diluted to 1000 mL. Ferrous sulphate 0.5 M: ferrous sulphate heptahydrate, FeSO4.7H2O, (140 g) was dissolved in 300 mL of water containing 15 mL of concentrated sulphuric acid, and diluted to 1000 mL. This solution was standardized before use by titrating against 10 mL 6 M K2Cr2O7. Concentrated sulphuric acid. O-phenanthroline- ferrous complex 0.025M (ferroin): Ferroin indicator was prepared by dissolving 14.85 g of O-phenanthroline monohydrate and 6.95 g of FeSO4.7H2O in water, and diluting to 1000 mL. Procedure: 1.00 g of sieved sediment samples was weighed in duplicate and transferred to 250 mL Erlenmeyer flasks. 10 mL of 6M K2Cr2O7 solution was pipetted into the flask, and the flask was swirled gently to disperse the soil/sediment. 20 mL of concentrated H2SO4 was rapidly added and the flask immediately swirled gently until soli/sediment and reagents were mixed. The flask was allowed to stand on a sheet of asbestos for about 30 minutes. 100 mL of distilled water was added after standing for 30 minutes. 3-4 drops of ferroin indicator was added and titrated with 0.5 M ferrous sulphate solution. As the end point was approached, the solution takes on a greenish cast and then changes to dark green. At this point, the ferrous sulphate was added drop by drop until the colour changed sharply from blue to red (maroon colour) in reflected light against a white background. The blank titration was made in a similar way but without sediment, to standardize the dichromate. Calculation: 63 UNIVERSITY OF IBADAN LIBRARY Correction factor, f = 1.33 me = molarity of solution x volume (mL) of solution used % Organic matter in soil/sediment = % Organic carbon x 1.729 3.8 DETERMINATION OF METALS IN SEDIMENT Heavy metals were determined by atomic absorption spectrophotomettry of wet-digested samples. Reagents: Nitric acid, 2 M: Concentration nitric acid (127 mL) was accurately measured into 1000 mL volumetric flask which contained 200 mL of water. The mixture was made up to mark with distilled water. Procedure: Sieved air-dried sediment (5.0 g) was weighed into 250 mL beaker; 50 mL of 2 M nitric acid was measured and added to the sediment sample. The mixture was swirled gently and the content boiled on a water bath with a watch glass lid for 50 minutes. On cooling, the extract was filtered through Whatman No. 1 filter paper into a clean 50ml standard flask. A reagent blank was also prepared. Heavy metals (Pb, Cd, Zn, Cu, Cr and Ni) were determined using an atomic absorption spectrophotometer as previously described in section 3.5. Calculation: A = concentration (µg /mL) of metal in the sample from AAS reading B = concentration (µg /mL) of metal in the blank from the AAS reading W = Weight (g) of soil sample used for extraction V = Volume (mL) of the extract used 3.9 ANALYSIS OF SOIL The following parameters were determined in soil: pH, organic carbon, particle size distribution or Mechanical properties and heavy metals (Pb, Cd, Zn, Cu, Cr and Ni). The analyses were carried out as described for sediment (Sections 3.7 and 3.8). 64 UNIVERSITY OF IBADAN LIBRARY 3.10 CHEMICAL TREATMENT OF TEXTILE EFFLUENT. The study is about the use of flocculation/ coagulation method in the treatment of industrial effluents. From the results obtained from the effluent characterisation study, it was noted that the most highly organically polluted effluents were those obtained from the textile and pharmaceutical industries with extreme values of BOD and COD. The textile industry effluents were then selected for testing the efficiencies of a variety of treatment methods for the reduction of the organic load. Effluent generated from Woolen and Synthetic Textile Manufacturing Limited located in Ikeja Industrial Estate was collected from the discharged channel with aid of pre washed polyethene container into pre washed 25 Litre plastic containers. The sample was transported to the laboratory. The pH, TDS, TSS, COD, and Conductivity were determined on the sample prior to treatment. TDS will give an idea of the level of inorganic components in the effluent. COD indicates the level of organic load in the effluent; TSS will give the level of suspended solutes, while conductivity indicates the levels of dissolved solutes. The monitoring was done for 30 days in order to establish the optimum results for the parameters monitored. The following categories of treatment options were employed: (a) Application of Fenton’s reagent  FeCl₂ .4H₂O + H2O2,  FeSO4.7H2O + H2O2  FeCl₂ .4H₂O + FeSO4.7H2O + H2O2 (b) Application of Inorganic Coagulants.  FeCl₂.4H₂O  FeSO4.7H2O,  FeCl₃  Alum (c) Application of other materials for treatment  Activated charcoal.  Sunlight  Darkness 65 UNIVERSITY OF IBADAN LIBRARY 3.10.1 Effluent Treatment Procedures Coagulation studies were performed by the jar test. Twelve jars test were used and were labeled appropriately with the concentration and type of coagulant used. Into each of the jar was added 1L volume of effluent from the textile industry with known initial pH, TDS, TSS and COD concentration. The jars were labeled 1- 12, and into each of the jar were added the following amount concentrations of coagulant: Jar 1 →Only the effluent sample, kept in the sunlight (No coagulant) Jar 2 →Only the effluent sample, kept in the Dark (No coagulant) Jar 3 → 1000 mg FeCl₂ .4H₂O 3 Jar 4 → 1000 mg FeCl₂ .4H₂O + 10 cm of H2O2 Jar 5 → 1000 mg FeSO4.7H2O 3 Jar 6 → 1000 mg FeSO4.7H2O +10 cm H2O2 Jar 7 → 500 mg each of FeCl₂ .4H₂O + FeSO4.7H2O 3 Jar 8 → 500 mg each of FeCl₂ .4H₂O and FeSO4.7H2O + 10 cm of H2O2 Jar 9 → 1000 mg FeCl3 Jar 10 → 1000 mg Alum Jar 11 → 1000 mg Charcoal Jar 12 → Control. Each of the jar was shaken for 1hour at 225 rev/ mm with Gallenkamp orbital shaker after which the samples were analysed for pH, TDS, TSS and COD. The samples were monitored for 30 days at various times intervals. pH , TDS, and COD were monitored daily for the first 14 days while subsequently, monitoring was done in once in two days for the rest of the treatment period. TSS was monitored for three days intervals for the period of treatment. 3.10.2: Analysis of the Parameters Determined in the Treated Effluent.  Determination of pH: as described in section 3.4.1  Determination of conductivity: as described in section 3.4.2  Determination of COD: as described in section 3.6.3  Determination of TSS: Total suspended solids in the treated samples were determined gravimetrically by evaporating 20 ml of treated samples in a pre-weighed evaporating o dish at 110 C and expressing it in mg/L. 66 UNIVERSITY OF IBADAN LIBRARY The removal efficiency (% Removal) was calculated from the following formula: Where, Co is initial COD value before treatment, C is the COD value after treatment. 3.11 QUALITY CONTROL/QUALITY ASSURANCE (QC/QA) 3.11.1 General Quality Control. The following general quality control measures were carried out throughout the course of this research:  Sampling containers were properly cleaned with 2M HNO3 acid and washed with plenty of distilled water.  Analytical grade reagents were used.  Certified Reference material GLAURM, an urban soil secondary material prepared by participants in the EU URBSOIL project (Davidson et al., 2006) was analysed.  oEffluent samples were kept at 4 C and were allowed to assume room temperature before analyses were done. 3.11.2 Recovery Study Recovery study was carried out on selected soil (six samples)/sediment (seven samples) based on the average levels of metals in the samples. This involved spiking of a previously analysed soil /sediment sample with a calculated amount of analyte standard and reanalyzing the spiked sample following the same method and procedure used for the unspiked soil/sediment sample to determine the percentage of the added standard that can be recovered. A total of five soils and two sediments samples were used and spiked with standard solutions of Pb while one soil and five sediments samples were used and spiked with standard solutions of Cd. Procedure: 5.0 g of the previously analysed soil/sediment was weighed into a crucible.1 mL of the working standard solution was spiked on the cone soil/sediment inside the crucible and this was dried in o the oven at 105 C for one hour. The soil/sediment was then cooled in a dessicator and 67 UNIVERSITY OF IBADAN LIBRARY homogenized using a glass rod. 1.0 g of the homogenized soil/sediment was weighed out and digested as described in Section 3.8. The sample was digested, was then analysed for metals as previously described. Calculation: Comment of the Obtained Recovery Values: The mean percentage recoveries were 92±13 for Pb and 86.1±8.5 for Cd as presented in Tables 3.4 and 3.5 respectively, are indicative of a high degree of accuracy in the analyses. 3.11.3 Analysis of Standard Reference Material Reagents: Aqua-regia acid mixture: concentrated hydrochloride acid, (300 mL) about 36% m/m (11 N) was carefully mixed with 100mL of concentrated nitric acid at a ratio of 3:1 in a 500mL reagent bottle. Procedure: 1.0 g of GLAURM reference standard material was weighed into 250 mL beakers. 10mL of aqua regia was measured and added to the standard sample. The mixture was swirled gently, and the content boiled on a water bath with a watch glass lid for 50 minutes. On cooling, the extract was filtered through Whatman No. 1 filter paper into a clean 50 mL standard flask. The content was made up to mark with water, and stored for determination of heavy metal concentration using atomic absorption spectrophotometer. A reagent blank was carried out in the similar way but without sample. The values obtained are compared with the reference values in Table 3.6 68 UNIVERSITY OF IBADAN LIBRARY Table 3.4: Results of recovery study for lead. Original Mass of Concentration Volume of Increased Total Conc. Result of Mean Sample Sample Sample of Standard Standard Concentration Expected by Re- % % Code Concentration Taken Spiked Spiked Achieved Analysis analysis Recovery Recovery (µg/g) (g) (µg/mL) (mL) (µg/g) (µg/g) (µg/g) LPZ-SL2 (7) 129 5.0 750 1.0 150 279 253 82.7 SJW.SL⁻(3) 44.4 5.0 250 1.0 50 94.4 97.7 107 SAF-SL4(4) 27.0 5.0 150 1.0 30 57.0 57.3 101 92±13 KHO-SD(9) 328 2.5 750 1.0 300 628 592 88 KHO-SD(7) 262 2.5 750 1.0 300 562 491 76.3 SJW-SL1(7) 13.2 5.0 75 1.0 15 28.2 25.5 82 SJW-SL2(8) 3.21 5.0 25 1.0 5 8.21 8.5 106 69 UNIVERSITY OF IBADAN LIBRARY Table 3.5: Results of recovery study for cadmium Original Mass Concentration Result of Volume of Increased Total Conc. % Mean Sample Sample of of Standard Re- Standard Concentration Expected Recovery % Code Concentration Sample Spiked analysis Spiked Achieved by Analysis Recovery (µg/g) Taken (µg/mL) (µg/g) (mL) (µg/g) (µg/g) (g) KJE-SD(6) 10.6 5.0 50 1.0 10 20.6 18.4 72 LPZ-SD2(7) 1.00 5.0 5 1.0 1 2.00 1.81 81 LCA-SD1(10) 1.02 5.0 5 1.0 1 2.02 1.92 90 86.1±8.5 KJE-SD(9) 14.6 5.0 75 1.0 15 29.6 27.5 86 KJE-SD(10) 23.2 5.0 125 1.0 25 48.2 47.1 95.6 KWS-SL5(10) 2.30 5.0 25 1.0 5 7.3 6.9 92 70 UNIVERSITY OF IBADAN LIBRARY Table 3.6: Results of analysis of standard reference material (GLAURM) Metals Measured Concentrations Certified Reference Values (μg/g) (μg/g) Pb 440 389±25 Cd 1.0 <0.74 Zn 165 117±11 Cu 117 111±5 Cr 54 43.2±3.0 71 UNIVERSITY OF IBADAN LIBRARY Comment of the Obtained Reference Standard Values: Student t-test (tcal.= 2.045; ttab = 2.132) showed no significant difference in the certified reference values for the metals and the measured values (at 95% confidence limit) indicating good quality control and data validation. The recovery range of 100.9-135.1% with mean of 118±15 was obtained for the metals, relative to the certified reference values. 3.11.4 Quality Control for BOD Determination The accuracy of the BOD determination was checked using standard mixture of glucose and o glutamic acid. 150 mg each of glucose and glutamic acid (both dried at 103 C for 1hour) was dissolved in 1 litre of distilled water. 1 to 50 dilution water was prepared and the BOD was then determined as described in section 3.4.3b. Theoretically, the BOD should be approximately 220 mg/L. Table 3.7 gives result of comparison of the theoretical BOD value with those obtained from the quality evaluation. The average value of 235±39 mg/L BOD obtained from the study was comparable (106.8% recovery) to the theoretical value of 220 mg/L BOD, indicating good accuracy. 3.11.5 Quality Control of COD Determination Accuracy of the COD determinations was validated by determination of the COD of potassium hydrogen phthalate which has a theoretical COD value of 500 mg/L. Potassium hydrogen o phthalate, (HOOCC6H4COOK), was lightly crushed and dried to constant weight at 120 C. 425 mg was dissolved in water and diluted to 1000 mL. COD of this solution was then determined as previously described (section 3.6.3). Table 3.7 gives the results of this quality check. The average value of 494±34 mg/L COD obtained from the control check represented a 98.8% recovery, and a good accuracy. 3.12 APPLICATION OF MODELING TO STUDY OF EFFLUENT QUALITY 3.12.1 Modeling to Estimate Gross Organic Pollution and Eutrophication Potential The data obtained for physicochemical parameters were subjected to Stepwise Multiple Linear Regression using the SPSS 19.0 software package (Lynwood et al., 1990; Sharma and Jain, 2005). The regression was designed to enable the estimation of gross organic pollution level 72 UNIVERSITY OF IBADAN LIBRARY Table 3.7: Comparison of BOD and COD of standards with experimental values. Date of Analysis Reference Parameters Nov.’ Jan. March May July Sept.’ Nov’ Jan’ March May Mean ±S.D Value 06 ’07 ’07 ’07 ’07 07 07 ’08 ’08 ’08 BOD mg/L 245 305 255 235 267 190 257 183 195 215 235±39 220 COD mg/L 455 505 560 495 510 485 475 440 502 515 494±34 500 73 UNIVERSITY OF IBADAN LIBRARY and the eutrophication potential. The following data were used to run the regression for the estimation of gross organic pollution (as indicated by COD levels): conductivity, Ca, Mg, alkalinity, total hardness, sulphate, phosphate, nitrate, chloride. Eutrophication potential was estimated by determination of phosphate level, using the model. The data used for this purpose were those of conductivity, Ca, Mg, alkalinity, total hardness, sulphate, phosphate, nitrate, chloride, BOD and COD. The quality of the regression was based on the regression coefficients values. 3.13 STATISTICAL ANALYSES AND CALCULATION OF SPECIAL PARAMETERS. Varieties of statistical tools were used to process the data obtained in the analysis namely:  t-test  analysis of variance  correlation coefficient  principal component analysis (PCA)  graphical biplot  hierarchical cluster analysis (HCA)  correspondence analysis (CA). All statistical treatments mentioned above were performed using Microsoft Excel, SPSS (version19) and Paleontological Statistics Software package for education and data analysis (PAST, version 1.38). 3.13.1: Contamination Factor (CF) The level of contamination of soil by metals is expressed in terms of a contamination factor (CF) calculated as: 74 UNIVERSITY OF IBADAN LIBRARY Where the contamination factor CF<1 refers to low contamination 1≤CF<3 means moderate contamination 3≤CF≤6 indicates considerable contamination CF>6 indicates very high contamination 3.13.2: Pollution Load Index (PLI). Each industrial estate was evaluated for the extent of metal pollution by employing the method based on the pollution load index (PLI) developed by Thomilson et al. (1980) as follows 1/n PLI= (CFI×CF2×CF3× CFN) Where n is the number of metals studied (six in this study) and CF is the contamination factor calculated in 3.13.1 The PLI provides simple but comparative means for assessing a site quality, where a value of PLI <1 denote perfection PL1=1 present that only baseline level of pollutants are present PLI>1 would indicate deterioration of site quality. 3.13.3: Geo-accumulation index (Igeo). Enrichment of metal concentration above baseline concentration was calculated using the method proposed by (Muller, 1969).This assesses the metal pollution in terms of seven (0 to 6) enrichment classes (Appendix 12) ranging from background concentration to very heavily pollute as follows: 75 UNIVERSITY OF IBADAN LIBRARY CHAPTER FOUR RESULTS AND DISCUSSION 4.1 GENERAL CHARCTERISTICS OF THE INDUSTRIAL EFFLUENTS 4.1.1. Characteristics of Effluents in Ikeja Industrial Estate Effluents obtained from Berger Paint, Galvanizing Industries Limited, and Woolen & Synthetic Textile Manufacturing Limited had basic pH while the effluents from the Central Treatment Plant and Hoesch Pipe Mills Ltd. were acidic with low pH values (Tables 4.1a and - - - 4.1b). Mean level of TDS, alkalinity, Ca, Mg, total hardness, SO4² and PO4³ , NO3 , BOD, COD were significantly different among the six effluents sampled in Ikeja industrial estate. The effluents at Ikeja Industrial Estate were coloured and had foul odours which is an indication of high level of dissolved solutes in the effluent. Effluent from Woolen & Synthetic Textile Manufacturing Limited had the highest conductivity (9100±3800 µs/cm) while Hoesch Pipe Mills Ltd. had the lowest conductivity (4700±1500 µs/cm). Woolen & Synthetic Textile Manufacturing Limited is a textile industry that makes use of inorganic compounds as their raw material. This could be the reason why the conductivity was high. - - 3- The SO4² , Cl and PO4 concentrations were all below the FEPA (1991) standard for all - effluent except Berger Paint with SO4² value of 577±51 mg/L. The DO levels of all the effluents were below 3.4 mg/L. This is an indication of high load of organic and inorganic compounds in the effluents. The low DO concentration of the effluents may facilitate the release of nutrients in the sediment which results in eutrophication of the water bodies (Sondergaard et al., 2003; Jin et al., 2006). The effluents pH range of the central treatment plant (3.3-8.0) and May & Baker (4.1-7.0) were acidic, while Berger Paint, Galvanizing Industries Limited and Woolen & Synthetic Textile Manufacturing limited were basic with values of 6.5-11.0, 7.2-12.8 and 9.2-12.6 respectively. The acidic nature of the central treatment plant (5.6±1.4) and May & Baker 76 UNIVERSITY OF IBADAN LIBRARY *Table 4.1a: Physicochemical characteristics of effluents from Ikeja Industrial Estate. Total 2- 3- - Site Code pH Conductivity TDS Alkalinity Ca Mg SO4 PO4 NO3 Hardness Mean 5.6±1.4 7300±1300 663±190 170±91 23±11 4.3±2.4 49±12 220±40 0.57±0.31 57±56 KJE-EF Range 3.3-8.0 5000-9000 435-999 36-294 9-47 1.1-9.6 35-78 162-185 0.12-1.04 10-196 Mean 6.9±0.6 4700±1500 374±110 105±27 19±11 7.1±9.7 82±39 34±24 0.43±0.24 59±64 KHO-EF Range 5.9-8.0 3000-8000 245-599 38-135 4-47 1.8-25.4 22-146 12-92 0.11-0.83 7-221 Mean 8.5±1.8 7200±2500 1610±170 216±120 15±10 3.9±3.4 60±33 577±51 0.90±0.31 67±37 KBP-EF Range 6.5-11.0 4000-12000 1350-1860 65-392 5-37 0.4-9.7 23-104 488-658 0.64-1.06 12-152 Mean 9.9±1.7 8600±3000 690±310 334±310 11±9 3.1±1.4 57±35 110±48 1.75±0.58 63±35 KGI-EF Range 7.2-12.8 6000-16000 316-1160 65-1120 3-35 0.6-6.0 15-112 44-187 1.03-2.58 8-143 Mean 10.6±.1.1 9100±3800 1160±890 316±280 17±13 3.1±2.1 60±32 156±73 0.99±0.29 60±40 KWS-EF Range 9.2-12.6 4000-16000 604-3600 75-795 4-43 1.3-8.4 21-98 123-363 0.62-1.46 14-152 Mean 5.5±1.2 6600±2100 838±310 208±55 NA NA NA 73±36 0.34±0.22 79±35 KMB-EF Range 4.1-7.0 5000-12000 455-1470 86-271 NA NA NA 33-164 0.15-0.75 29-155 *All units in mg/L except pH with no unit, and conductivity in μS/cm NA- Not Available 77 UNIVERSITY OF IBADAN LIBRARY * Table 4.1b: Physicochemical characteristics of effluents from Ikeja Industrial Estate. - Site Code Cl DO BOD COD Pb Cd Zn Cu Cr Ni Mean 108±51 1.1±1.5 288±130 473±190 0.04±0.05 0.01±0.01 0.97±0.51 0.049±0.023 0.059±0.025 0.075±0.032 KJE-EF Range 32-201 0.0-3.6 159-535 298-963 0.006-0.15 0.002-0.029 0.39-1.94 0.019-0.083 0.038-0.128 0.042-0.154 Mean 60±37 3.4±0.5 157±66 262±96 0.17±0.07 0.011±0.002 0.72±0.24 0.03±0.01 0.15±0.11 0.04±0.02 KHO-EF Range 24-131 3.0-4.7 65-291 111-382 0.06-0.25 0.007-0.014 0.31-1.05 0.01-0.05 0.03-0.4 0.01-0.08 Mean 45±14 1.3±1.7 221±130 367±150 0.08±0.12 0.007±0.003 0.69±0.14 0.48±0.08 0.020±0.003 0.04±0.01 KBP-EF Range 25-69 0.0-4.2 89-559 151-671 0.04-0.44 0.003-0.014 0.46-0.89 0.4-0.67 0.016-0.027 0.001-0.05 Mean 101±95 1.9±1.8 231±79 399±110 0.05±0.06 0.007±0.003 0.51±0.20 1.06±0.55 0.036±0.027 0.069±0.059 KGI-EF Range 20-255 0.2-4.0 181-424 281-606 0.006-0.15 0.002-0.014 0.19-0.79 0.15-1.96 0.011-0.08 0.008-0.22 Mean 65±19 1.8±1.7 209±72 343±130 0.28±0.09 0.009±0.004 0.67±0.29 1.45±0.93 2.54±0.26 0.056±0.041 KWS-EF Range 38-101 0.2-4.3 130-385 150-617 0.18-0.46 0.003-0.015 0.17-1.24 0.32-3.29 2.0-2.9 0.01-0.15 Mean 93±57 0.2±0.2 239±93 382±110 0.07±0.04 0.013±0.013 0.67±0.24 0.05±0.02 0.05±0.01 0.05±0.03 KMB-EF Range 29-187 0.0-0.6 147-451 253-631 0.03-0.15 0.001-0.045 0.18-0.98 0.02-0.08 0.04-0.06 0.01-0.10 *All units in mg/L except pH with no unit, and conductivity in μS/cm 78 UNIVERSITY OF IBADAN LIBRARY (5.5±1.2) effluents could bring about redox changes in the sediments of the receiving water body, and causes algae bloom in the water body (Ahmad et al., 2010). The effluent characteristics were compared with the regulatory limits of the Federal Ministry of Environment (Federal Environmental Protection Agency, 1991) and the World Bank (1995) (Table 4.2) that is based on the assimilative capacity of the receiving water for all categories of industries. The mean BOD and COD of all the effluents as shown in Table 4.1b exceeded the guideline values. This is an indication of high load of gross organic pollutants in the effluents. The nitrate level of all the effluents exceeded the guideline values of 20 mg/L. The high nitrate level in the effluents will increase growth of algae in the water bodies as a result of eutrophication. The mean concentrations of the heavy metals, Pb, Cd, Zn, Cu, Cr, and Ni, are all within the FEPA guideline values except Cu in Galvanizing Industries Limited effluent (1.06±0.55 mg/L) and Woolen & Synthetic Textile Manufacturing Ltd (1.45±0.93 mg/L); Cr value in Woolen & Synthetic Textile Manufacturing Ltd effluent was (2.54±0.26 mg/L). The high level of Cu in Galvanizing Industries Limited and Woolen & Synthetic Textile Manufacturing Ltd could be as a result of the nature of raw materials used. Galvanizing Industries Limited is a basic metal type of industry. The Woolen & Synthetic Textile Manufacturing Ltd is a textile industry that makes use of dyes. The mean concentration of Zn in the effluents exceeded the guideline limit for World Bank (1995) while Cr in Hoesch Pipe Mills Ltd effluent exceeded the World Bank limit. Tables 4.3, 4.4, 4.5, 4.6, 4.7 and 4.8 show the Pearson correlation coefficients amongs pairs of the physicochemical parameters of effluents of Ikeja Industrial Estate. There were 2- - significantly positive correlation between DO and SO4 (r = 0.664); and DO and Cl (r=0.655) for Central Treatment Effluent Plant (Table 4.3), while effluent form Hoesch Pipe - Mills Ltd effluent had positive correlation between alkalinity and SO4² (r = 0.690); Mg and COD (r = 0.645). Total hardness and Ca (r = 0.714); DO and Mg (r = 0.642) and Total hardness and nitrate (r = 0.725) (Table 4.4). This showed that any condition that affects one will affect the other. Berger Paint effluent showed negative correlation between DO and Mg (r = -0.642) (Table 4.5). Galvanising Industries Limited effluent had strong correlation between Ca and Total hardness (r = 0.753) and nitrate and COD (r = 0.753) (Table 4.6). 79 UNIVERSITY OF IBADAN LIBRARY * Table 4.2: Comparison of physicochemical characteristics of the effluents of Ikeja Industrial Estate with effluent quality standards. This Study FEPA World Parameters Bank KJE-EF KHO-EF KBP-EF KGI-EF KWS-EF KMB-EF (1991) (1995) pH §5.6 6.9 8.5 § § § 9.9 10.6 5.5 6.0 -9.0 6.0 -9.0 Colour Cloudy Colourless Coloured Colourless Coloured Cloudy NA NA Odour Foul Foul foul Foul Foul Foul NA NA Conductivity 7300 4700 7200 8600 9100 6600 NA NA TDS 663 374 1610 690 1160 838 2000 NA Alkalinity 170 105 216 334 316 208 NA NA Ca 23 19 15 11 17 ND 200 NA Mg 4.3 7.1 3.9 3.1 3.1 ND 200 NA Total hardness 49 82 60 57 60 ND NA NA 2- SO 4 220 34 § 577 110 156 73 500 NA 3- PO 4 0.57 0.43 0.90 1.75 0.99 0.34 5 NA - NO § § § § § § 3 57 59 67 63 60 79 20 NA - Cl 108 60 45 101 65 93 600 NA DO 1.1 3.4 1.3 1.9 1.8 0.2 NA NA BOD § § § § § §288 157 221 231 209 239 30 50 COD § § § § § §473 262 367 399 343 382 80 250 Pb 0.04 0.17 0.08 0.05 0.28 0.07 <1.0 0.10 Cd 0.01 0.011 0.007 0.007 0.009 0.013 <1.0 0.10 Zn 0.97 0.72 0.69 0.51 0.67 0.67 <1.0 0.50 Cu 0.049 0.03 0.48 1.06§ 1.45§ 0.05 <1.0 0.50 Cr 0.059 0.15 0.02 0.036 2.54§ 0.05 <1.0 0.10 Ni 0.075 0.04 0.04 0.069 0.056 0.05 <1.0 0.50 *All units in mg/L except pH with no unit, and conductivity in μS/cm NA- Not Available Figures with § are out of the limits . 80 UNIVERSITY OF IBADAN LIBRARY Table 4.3: Pearson correlation coefficients amongst parameters of effluents at the Central Treatment Plant. T. 2- 3- - - Conductivity TDS Alkalinity Ca Mg Hardness SO4 PO4 NO3 Cl DO BOD COD Conductivity 1 TDS 0.195 1 Alkalinity -0.285 0.389 1 Ca 0.073 0.134 0.262 1 b Mg 0.185 0.197 0.187 .880 1 T. Hardness 0.352 0.216 -0.487 0.605 0.624 1 2- b SO4 0.663 -0.538 0.051 -0.027 -0.187 -0.276 1 3- PO4 -0.068 0.477 0.621 -0.29 -0.391 -0.573 -0.314 1 - NO3 0.341 0.139 -0.141 -0.088 0.005 0.083 -0.417 0.165 1 - b Cl 0.541 0.294 0.035 -0.256 -0.186 -0.153 -.851 0.539 0.242 1 b a a DO -.811 0.008 0.357 -0.178 -0.272 -0.382 .664 0.126 -0.133 -.655 1 a BOD .662 0.102 -0.033 -0.186 -0.023 -0.121 -0.269 -0.022 0.54 0.227 -0.326 1 b COD 0.579 0.29 0.289 -0.177 -0.115 -0.322 -0.265 0.311 0.322 0.35 -0.278 .892 1 a b Significant at the 0.05 level. Significant at the 0.01 level. 81 UNIVERSITY OF IBADAN LIBRARY Table 4.4: Pearson correlation coefficients amongst parameters of Hoesch Pipe Mills effluents. T. 2- 3- - -Conductivity TDS Alkalinity Ca Mg Hardness SO4 PO4 NO3 Cl DO BOD COD Conductivity 1 TDS 0.26 1 a Alkalinity 0.112 -0.645 1 Ca -0.18 -0.398 0.164 1 Mg 0.29 -0.292 0.274 0.024 1 a a T. Hardness 0.18 -0.433 0.321 0.724 0.681 1 2- a a SO4 0.077 0.592 -0.690 -0.716 -0.07 -0.585 1 3- PO4 -0.295 -0.247 0.000 0.357 -0.451 -0.086 -0.096 1 - a NO3 0.702 0.238 0.131 0.104 -0.253 -0.027 -0.209 -0.034 1 - a a Cl 0.736 0.03 0.271 -0.054 0.693 0.525 -0.193 -0.549 0.342 1 a b DO 0.188 0.487 -0.657 -0.566 -0.067 -0.457 0.894 0.127 -0.186 -0.177 1 b BOD -0.421 0.179 -0.221 -0.525 -0.626 -0.821 0.434 0.307 -0.17 -0.546 0.243 1 a b b COD -0.453 0.146 -0.298 -0.489 -0.645 -0.798 0.334 0.222 -0.207 -0.492 0.213 0.920 1 a b Significant at the 0.05 level. Significant at the 0.01 level. 82 UNIVERSITY OF IBADAN LIBRARY Table 4.5: Pearson correlation coefficients amongst parameters of Berger Paint effluents. T. 2- 3- - -Conductivity TDS Alkalinity Ca Mg Hardness SO4 PO4 NO3 Cl DO BOD COD Conductivity 1 TDS 0.497 1 Alkalinity -0.193 0.085 1 Ca -0.044 0.63 0.25 1 b Mg 0.920 0.181 -0.274 -0.222 1 T.Hardness 0.198 0.337 0.202 0.511 -0.005 1 2- a SO4 -0.275 -0.061 -0.04 0.177 -0.352 0.641 1 3- PO4 0.47 0.324 0.553 0.329 0.433 0.372 0.02 1 - a NO3 0.393 0.43 -0.165 0.405 0.234 0.725 0.389 0.226 1 - Cl -0.063 0.611 0.435 0.570 -0.291 0.012 -0.365 0.048 0.129 1 a DO -0.477 0.291 0.068 0.166 -0.642 -0.170 0.182 -0.475 -0.049 0.511 1 BOD 0.420 -0.010 -0.211 -0.200 0.529 -0.243 -0.620 -0.120 -0.200 -0.131 -0.375 1 b COD 0.155 -0.260 -0.066 -0.139 0.312 -0.087 -0.462 -0.115 -0.192 -0.214 -0.478 0.907 1 a b Significant at the 0.05 level. Significant at the 0.01 level. 83 UNIVERSITY OF IBADAN LIBRARY Table 4.6: Pearson correlation coefficients amongst parameters of Galvanizing Industries Limited effluents. 2- 3- - - Conductivity TDS Alkalinity Ca Mg T.Hardness SO4 PO4 NO3 Cl DO BOD COD Conductivity 1 TDS 0.578 1 Alkalinity a .681 0.358 1 Ca -0.385 -0.017 -0.091 1 Mg a -0.233 -0.221 0.244 .688 1 T.Hardness a -0.576 -0.368 -0.212 .753 0.567 1 2- SO4 -0.009 0.024 -0.238 0.019 0.034 -0.021 1 3- PO4 -0.134 0.234 -0.416 0.329 -0.299 0.034 0.182 1 - NO3 0.169 0.279 -0.268 0.438 0.037 0.181 0.199 0.318 1 - Cl 0.591 0.015 0.224 -0.052 0.155 -0.079 0.006 -0.323 0.473 1 DO a -0.403 0.277 -0.400 0.272 -0.364 0.087 -0.258 .642 0.077 -0.600 1 BOD a b 0.682 0.246 0.773 -0.415 0.007 -0.264 -0.128 -0.346 -0.437 0.261 -0.552 1 COD b 0.317 -0.141 0.467 -0.552 -0.014 -0.291 -0.020 -0.334 -0.753* 0.019 -0.546 0.840 1 a b Significant at the 0.05 level. Significant at the 0.01 level. 84 UNIVERSITY OF IBADAN LIBRARY Woolen & Synthetic Textile Manufacturing limited effluent had strong positive correlation between conductivity and BOD (r = 0.703); phosphate and nitrate (r = 0.673) (Table 4.7) while May & Baker effluent had correlation between COD and DO (r = 0.653) (Table 4.8). Analysis of variance (ANOVA) was carried out to check for significant difference among the physicochemical parameters for each of the industries. ANOVA at (p=0.05) showed statistically significant difference in total hardness in Berger Paint effluent, Galvanising - Industries Limited and Woolen &Synthetic Textile manufacturing Ltd effluents. SO4² and BOD showed statistically significant difference for Hoesch Pipe Mill Ltd effluents and May & Baker effluents respectively while Central Treatment Plant effluent showed no statistically significant difference between the physicochemical parameters. Analysis of variance (p=0.05) showed statistically significant difference between the effluents studied at Ikeja industrial estate for pH, conductivity, TDS, Ca, Mg total hardness, sulphate, phosphate, DO, Pb, Cu, Cr, and Zn indicating constituents concentrations varied spatially and temporally. Levels of alkalinity, nitrate, chloride, BOD, COD, Cd and Ni showed no significant difference among various sites (effluents). 4.1.2 Characteristics of Effluents in Isolo Industrial Estate Table 4.9 shows the results of the industrial effluents characteristic of Isolo Industrial Estate. Effluents of Viju Industries (Food &Beverages) were mostly acidic with low pH values of 4.0±0.9. The pH values of Johnson Wax (7.5±1.1 and Afra Chem industries (7.2±1.1) (pharmaceuticals) were within the FEPA (1991) guidelines (6.0-9.0).The conductivity was relatively high as shown on Table 4.9; this may be due to the nature of the raw materials 2- (inorganic based) used in the industries. The TDS alkalinity, calcium, magnesium, TH, SO4 3- and PO4 were all within FEPA (1991) guideline limits. The nitrate concentrations was within the FEPA guideline limit for Viju industries with value being 16±3 mg/L, while Johnson Wax and Afra Chem industries had values that exceeded the limit of 20 mg/L. The low DO concentrations of the effluents from Johnson Wax (2.2±1.2 mg/L); Afra Chem (1.5±0.8 mg/L) and Viju industries (1.9±1.3 mg/L). The low DO values facilitate the release of nutrients in the sediment which results in eutrophication of the water bodies. The mean BOD and COD of all the effluents exceeded the guideline values (Table 4.10). This is an indication of gross organic pollutants loading of the effluents. The mean concentrations of the heavy metals studied were all within the FEPA (1991) guideline limit for the effluents. 85 UNIVERSITY OF IBADAN LIBRARY Table 4.7: Pearson correlation coefficients amongst parameters of Woolen & Synthetic Textile Manufacturing Limited effluents. 2- 3- - - Conductivity TDS Alkalinity Ca Mg T.Hardness SO4 PO4 NO3 Cl DO BOD COD Conductivity 1 TDS 0.577 1 Alkalinity -0.404 -0.236 1 Ca -0.175 -0.245 0.075 1 Mg -0.146 0.059 -0.088 0.492 1 T.Hardness -0.037 -0.228 -0.241 0.505 0.121 1 2- SO4 0.022 -0.056 -0.290 -0.234 -0.343 -0.338 1 3- PO4 0.303 -0.377 -0.147 0.098 -0.194 0.087 0.102 1 - a NO3 0.441 -0.202 -0.152 0.334 0.003 0.485 -0.143 0.673 1 - b Cl 0.519 0.766 -0.291 -0.045 -0.203 0.126 -0.022 0.002 -0.028 1 DO -0.176 -0.155 -0.267 0.612 0.010 0.138 0.418 0.204 0.169 0.093 1 a BOD -0.703 -0.484 0.599 -0.289 -0.232 -0.365 0.150 -0.152 -0.246 -0.630 -0.081 1 a b COD -0.759 -0.283 0.493 -0.171 -0.233 -0.194 0.065 -0.335 -0.314 -0.367 0.104 0.899 1 a b Significant at the 0.05 level. Significant at the 0.01 level. 86 UNIVERSITY OF IBADAN LIBRARY Table 4.8: Pearson correlation coefficients amongst parameters of May & Baker effluents. 2- 3- - - Conductivity TDS Alkalinity SO4 PO4 NO3 Cl DO BOD COD Conductivity 1 TDS -0.095 1 Alkalinity -0.100 -0.378 1 2- SO4 0.006 -0.192 0.205 1 3- PO4 0.104 0.123 0.069 -0.026 1 - NO3 -0.353 0.333 0.209 -0.538 -0.144 1 - Cl 0.262 -0.138 -0.172 0.490 0.471 -0.398 1 DO 0.578 0.563 -0.336 -0.309 -0.222 0.068 -0.332 1 BOD -0.392 -0.586 0.327 0.119 -0.422 0.352 -0.011 -0.631 1 a b COD -0.341 -0.630 0.400 -0.055 -0.181 0.425 0.068 -0.653 0.925 1 a b Significant at the 0.05 level. Significant at the 0.01 level. 87 UNIVERSITY OF IBADAN LIBRARY * Table 4.9a: Physicochemical characteristics of effluents from Isolo Industrial Estate. Total 2- 3- - Site Code pH Conductivity TDS Alkalinity Ca Mg SO4 PO4 NO Hardness 3 Mean 7.5±1.1 4900±1400 535±130 204±71 13±15 7.2±7.9 47±41 13±9 0.59±0.49 75±64 Range 5.7-8.9 3330-7300 323-723 99-313 4-54 1.3-28.6 21-163 5-27 0.16-1.41 18-224 Mean 7.2±1.1 6500±2600 526±170 288±130 15±15 13.2±14.0 59±47 17±18 1.15±0.74 80±33 Range 5.7-9.2 2500-10500 341-950 142-500 3-52 1.7-39.4 22-184 1-57 0.10-1.89 5-116 Mean 4.0±0.9 4600±1800 477±150 94±35 -** - - 172±19 0.57±0.21 16±3 Range 3.3-5.4 3000-7000 360-731 55-150 NA NA NA 149-199 0.22-0.72 13-19 *All units in mg/L except pH with no unit, and conductivity in μS/cm. ** Not Determined NA- Not Available 88 SVJ-EF SAF-EF SJW-EF UNIVERSITY OF IBADAN LIBRARY *Table 4.9b: Physicochemical characteristics of effluents from Isolo Industrial Estate. Site - Code Cl DO BOD COD Pb Cd Zn Cu Cr Ni Mean 48±9 2.2±1.2 92±66 153±97 0.09±0.13 0.026±0.062 0.43±0.14 0.37±1.02 0.02±0.03 0.17±0.47 Range 33-61 0.4-3.7 22-265 35-391 0.006-0.402 0.00-0.20 0.14-0.57 0.01-3.28 0.00-0.08 0.00-1.50 Mean 44±12 1.5±0.8 120±53 201±100 0.22±0.44 0.06±0.17 0.39±0.17 0.60±1.2 0.004±0.004 0.03±0.02 Range 20-60 0.5-2.6 39-189 51-349 0.01-1.43 0.00-0.56 0.17-0.69 0.02-5.41 0.00-0.01 0.01-0.09 Mean 50±20 1.9±1.3 147±75 269±140 0.18±0.35 0.002±0.002 0.92±0.16 0.10±0.19 0.003±0.000 0.03±0.04 Range 26-82 0.0-2.9 62-216 105-385 0.01-0.80 0.000-0.006 0.80-1.18 0.01-0.44 0.003-0.003 0.01-0.11 *All units in mg/L except pH with no unit, and conductivity in μS/cm. 89 SVJ-EF SAF-EF SJW-EF UNIVERSITY OF IBADAN LIBRARY * Table 4.10: Comparison of physicochemical characteristics of the effluents of Isolo and Ilupeju Industrial Estates with effluent quality standards. World This Study FEPA Bank Parameters (1991) SJW-EF SAF-EF SVJ-EF LPZ-EF LCA-EF (1995) § pH 7.5 7.2 4.0 6 6.5 6.0 -9.0 6.0 -9.0 Colour Clear Clear Cloudy Cloudy Coloured NA NA Odour Odourless Foul Foul Foul Foul NA NA Conductivity 4900 6500 4600 3117 4065 NA NA TDS 535 526 477 385 467 2000 NA Alkalinity 204 288 94 128 164 NA NA Ca 13 15 ** 18 21 200 NA Mg 7.2 13.2 ** 5.3 4.5 200 NA Total hardness 47 59 ** 71 69 NA NA 2- SO 4 13 17 172 10 10 500 NA 3- PO 4 0.59 1.01 0.57 1 0.9 5 NA - NO § § § § 3 75 80 16 106 137 20 NA ₋ Cl 48 44 50 28 41 600 NA DO 2.2 1.5 1.9 0.9 0.2 NA NA § § § § § BOD 92 120 147 136 126 30 50 § § § § § COD 153 201 269 246 225 80 250 Pb 0.09 0.22 0.18 0.02 0.01 <1.0 0.10 Cd 0.026 0.06 0.002 0.04 0.012 <1.0 0.10 Zn 0.43 0.39 0.92 0.25 0.47 <1.0 0.50 Cu 0.37 0.6 0.1 0.22 0.36 <1.0 0.50 Cr 0.02 0.004 0.003 0.006 0.02 <1.0 0.10 Ni 0.17 0.03 0.03 0.04 0.06 <1.0 0.50 *All units in mg/L except pH with no unit, and conductivity in μS/cm , ** Not Determined, NA- Not Available. § Figures with are out of the limits 90 UNIVERSITY OF IBADAN LIBRARY Cr concentrations were below the detection limit. Tables 4.11-4.13 show correlation coefficients between pairs of the physicochemical parameters of effluents at Isolo Industrial Estate. There were high correlations between conductivity vs TDS (r = 0.721); TDS vs. nitrate (r = 0.679); and alkalinity vs. sulphate (r = 0.709) in Johnson Wax effluent (Table 4.11). Afra Chem effluent also showed good correlations between the pairs of TDS and alkalinity (r = 0.710), alkalinity and sulphate (r = 0.632), calcium and magnesium (r=0.700), and sulphate and chloride (r = 0.679) (Table 4.12). Viju Industries effluent had strong correlations for - TDS vs. Cl (r = 0.906) (Table 4.13). Analysis of variance (p=0.05) showed significant differences among the sites in Isolo Industrial Estate for the parameters pH, alkalinity, sulphate, nitrate and Zn. Levels of conductivity, TDS, Ca, - Mg, TH, phosphate, Cl , DO, BOD, COD, Pb, Cd, Cu, Cd, and Ni did not show significant differences among various effluents. Significant differences occurred for pH in John Wax effluent, pH and DO for Afra Chem effluent, while Viju industries effluent showed no significant difference for all the physicochemical parameters. 4.1.3 Characteristics of Effluents in Ilupeju Industrial Estate Table 4.14a and 4.14b give the characteristics of effluents from Ilupeju Industrial Estate. Effluents of Paterson Zochonis (LPZ-EF) had a pH range of 5.0-7.01 with mean value of 6.0±0.7; the effluents were slightly acidic. The pH range of the canal (LCA-EF) that receives various effluents from the industrial estate was 5.6-7.4 with mean of 6.5±0.6. This slightly acidic pH will support the release of metals from the sediments. The effluents from Paterson Zochonis and the Canal were coloured with foul odours. The Canal was also void of aquatic life. The conductivities of effluents of Paterson Zochonis and the Canal were (3120±960 μS/cm) and (4070±1400 μS/cm) respectively. Alkalinity, calcium, magnesium, total hardness, sulphate, phosphate and chloride levels differed significantly among the various sampling points of the Paterson Zochonis and Canal effluents. The DO of effluents at Paterson Zochonis was 0.85±0.70 mg/L and at the Canal 0.20±0.50 mg/L. The low DO value could be responsible for the non-existence of aquatic life in the Canal. Effluents BOD and COD at Paterson Zochonis and the Canal exceeded the guideline values (Table 4.10). 91 UNIVERSITY OF IBADAN LIBRARY Table 4.11: Pearson correlation coefficients amongst parameters of John Wax Nigeria Ltd. effluents. 2- 3- - - Conductivity TDS Alkalinity Ca Mg T.Hardness SO4 PO4 NO3 Cl DO BOD COD Conductivity 1 a TDS 0.721 1 a Alkalinity 0.638 0.349 1 Ca -0.210 0.033 -0.268 1 b Mg -0.188 0 -0.236 0.992 1 b b T.Hardness -0.375 -0.076 -0.356 0.971 0.962 1 2- a a SO4 0.643 0.276 0.709 -0.343 -0.293 -0.365 1 3- PO4 -0.419 -0.285 -0.278 0.181 0.132 0.169 -0.288 1 - a a NO3 0.647 0.679 0.163 -0.195 -0.174 -0.326 0.224 -0.156 1 - - Cl 0.166 0.083 0.628 0.199 0.195 0.109 0.183 0.245 0.028 1 DO -0.032 0.102 -0.023 -0.575 -0.581 -0.523 -0.239 -0.528 0.22o -0.208 1 BOD -0.005 -0.099 0.537 -0.198 -0.134 -0.118 0.621 -0.179 -0.083 0.266 -0.042 1 b COD -0.021 -0.057 0.504 -0.219 -0.167 -0.131 0.619 -0.138 -0.059 0.274 -0.024 0.989 1 a b Significant at the 0.05 level. Significant at the 0.01 level. 92 UNIVERSITY OF IBADAN LIBRARY Table 4.12: Pearson correlation coefficients amongst parameters of Afrab chem Ltd effluents. 2- 3- - - Conductivity TDS Alkalinity Ca Mg T.Hardness SO4 PO4 NO3 Cl DO BOD COD Conductivity 1 TDS -0.009 1 a Alkalinity 0.119 0.710 1 Ca -0.281 -0.178 -0.203 1 a Mg -0.066 -0.288 0.064 0.700 1 b b T.Hardness -0.188 -0.243 -0.205 0.948 0.845 1 2- a SO4 -0.017 0.015 0.632 0.053 0.510 0.095 1 3- PO4 -0.296 -0.232 -0.260 0.277 -0.014 0.205 -0.246 1 - NO3 0.217 0.243 0.233 0.338 0.12 0.205 0.06 0.36 1 - a Cl -0.337 -0.407 0.166 0.123 0.386 0.093 0.679 -0.090 -0.141 1 b DO 0.863 0.245 0.368 -0.344 -0.168 -0.318 0.113 -0.155 0.530 -0.363 1 BOD 0.208 0.346 0.434 0.305 0.480 0.424 0.067 0.110 0.367 -0.112 0.205 1 b COD 0.140 0.371 0.399 0.258 0.446 0.399 -0.003 0.096 0.294 -0.18 0.151 0.984 1 a b Significant at the 0.05 level. Significant at the 0.01 level. 93 UNIVERSITY OF IBADAN LIBRARY Table 4.13: Pearson correlation coefficients amongst parameters of Viju industrieseffluents. 2- 3- - - Conductivity TDS Alkalinity SO4 PO4 NO3 Cl DO BOD COD Conductivity 1 TDS 0.817 1 Alkalinity -0.245 -0.176 1 2- SO4 0.388 -0.114 0.214 1 3- PO4 0.299 0.469 0.702 -0.057 1 - NO3 0.438 0.535 0.245 0.38 0.232 1 - a Cl 0.857 0.916 0.122 0.225 0.604 0.711 1 a DO -0.57 -0.906 0.245 0.509 -0.459 -0.228 -0.69 1 BOD 0.583 0.255 -0.675 0.522 -0.575 0.319 0.249 0.013 1 b COD 0.519 0.212 -0.699 0.485 -0.635 0.31 0.192 0.041 0.996 1 a b Significant at the 0.05 level. Significant at the 0.01 level. 94 UNIVERSITY OF IBADAN LIBRARY * Table 4.14a: Physicochemical characteristics of effluents from Ilupeju Industrial Estate. T. 2- 3- Site Code pH Conductivity TDS Alkalinity Ca Mg Hardness SO PO -4 4 NO3 LPZ-EF1 Mean 6.1±0.8 3100±1200 453±92 134±81 17±10 5.5±3.4 70±33 9±8 1.1±0.8 144±36 Range 5.1-7.3 2000-5000 342-630 41-295 5-33 2.3-13,2 37-143 2-29 0.1-2.7 100-301 LPZ-EF2 Mean 5.7±1.3 3860±1200 414±120 136±80 13±8 4.9±3.7 59±35 12±6 1.2±0.9 130±60 Range 3.4-7.0 2000-5000 274-581 11-236 6-30 1.6-12.2 29-133 4-23 0.1-2.4 10-179 LPZ-EF3 Mean 6.3±0.8 2900±876 295±66 115±49 19±9 5.1±2.9 74±32 13±16 0.6±0.4 54±25 Range 5.2-7.5 2000-4000 179-401 53-190 8-35 1.4-10.8 43-138 2-54 0.4-1.7 25-91 LPZ-EF Mean 6.0±0.7 3120±960 385±60 128±56 18±9 5.3±2.9 71±32 10±8 1.0±0.5 106±23 Range 5.0-7.1 2000-4330 288-472 37-214 6-33 5.3-2.9 38-138 2-26 0.3-1.9 61-137 LCA-EF1 Mean 6.4±0.7 4000±1300 454±140 156±72 21±15 4.5±2.5 71±47 7±3 0.9±0.5 113±52 Range 5.3-7.5 2000±6000 209-631 70-280 5-56 1.8-9.6 12-189 4-13 0.2-1.5 15-163 LCA-EF2 Mean 6.6±0.8 4130±1400 480±130 173±91 22±10 4.5±2.4 66±29 13±10 1.0±0.7 161±88 Range 5.9-8.2 2000-6000 270-673 73-357 11-43 1.8--8.7 22-122 4-37 0.2-2.5 10-305 LCA-EF Mean 6.5±0.6 4070±1400 467±130 164±81 21±13 4.5±1.8 69±38 10±6 0.9±0.5 137±64 Range 5.6-7.4 2000-6000 275-652 73-319 9-50 2.0--6.5 17-156 4-22 0.2-2.0 13-213 *All units in mg/L except pH with no unit, and conductivity in μS/cm. 95 UNIVERSITY OF IBADAN LIBRARY * Table 4.14b: Physicochemical characteristics of effluent from Ilupeju Industrial Estate contd. - Site Code Cl DO BOD COD Pb Cd Zn Cu Cr Ni Mean 33±27 0.9±1.2 146±130 264±170 0.014±0.018 0.01±0.01 0.16±0.08 0.12±0.18 0.007±0.008 0.05±0.06 LPZ-EF1 Range 6-87 0.0-3.0 33-462 63-601 0.006-0.064 BDL-0.04 0.03-0.25 0.04-0.63 BDL-0.03 BDL-0.17 Mean 38±23 0.4±0.9 123±160 227±260 0.03±0.01 0.05±0.12 0.32±0.20 0.14±0.08 0.003±0.000 0.07±0.06 LPZ-EF2 Range 10-76 0.0-2.4 14-470 20-752 0.02-0.05 0.003-0.325 0.03-0.61 0.05-0.26 BDL 0.008-0.139 Mean 18±11 0.8±0.9 138±140 238±230 0.03±0.07 0.08±0.24 0.30±0.14 0.35±0.19 0.01±0.01 0.03±0.03 LPZ-EF3 Range 8-47 0.0-2.3 35-427 61-726 0.006-0.24 BDL-0.77 0.02-0.59 0.047-0.66 0.003-0.040 BDL Mean 28±18 0.85±0.7 136±119 246±180 0.02±0.03 0.04±0.12 0.25±0.10 0.22±0.10 0.006±0.006 0.04±0.05 LPZ-EF Range 11-65 0.0-1.9 30-453 57-693 0.01-0.12 BDL -0.38 0.03-0.40 0.05-0.35 0.003-0.02 0.01-0.13 Mean 38±17 0.3±0.6 141±130 241±210 0.01±0.01 0.01±0.02 0.38±0.44 0.42±0.31 0.01±0.02 0.06±0.12 LCA-EF1 Range 10-65 0.0-1.9 36-463 65-741 0.01-0.04 BDL-0.07 0.03-1.28 0.05-0.92 BDL-0.06 0.01-0.39 LCA-EF2 Mean 45±26 0.14±0.3 111±65 210±130 0.02±0.02 0.01±0.02 0.57±0.75 0.31±0.20 0.02±0.06 0.05±0.11 96 UNIVERSITY OF IBADAN LIBRARY - Site Code Cl DO BOD COD Pb Cd Zn Cu Cr Ni Range 14-81 0.0--1.0 11-197 20-404 0.01-0.08 BDL-0.05 0.03-2.08 0.05-0.59 BDL-0.19 0.01-0.37 Mean 41±21 0.20±0.5 126±74 225±130 0.01±0.01 0.012±0.017 0.47±0.59 0.36±0.22 0.02±0.04 0.06±0.12 LCA-EF Range 12-73 0.00-1.4 27-258 51-426 BDL-0.04 BDL-0.05 0.03-1.59 0.05-0.64 BDL-0.13 BDL-0.38 *All units in mg/L except pH with no unit, and conductivity in μS/cm, BDL(Below detection limit = <0.0064 mg/L for Pb, <0.0004 mg/L for Cd, < 0.0031 mg/L for Cr, <0.0082 mg/L for Ni 97 UNIVERSITY OF IBADAN LIBRARY The nitrate level of the Paterson Zochonis effluents 106±23 mg/L and the Canal 137±64 mg/L all exceeded the FEPA guideline values of 20 mg/L. High nitrate level in the effluents will increase growth of algae in the water bodies. Concentrations of Pb ,Cd ,Zn, Cu, Cr, Ni were within the FEPA (1991) guideline values (Table 4.10). Cd and Cr were below their detection limits. Table 4.15 and 4.16 give the correlation coefficients between pairs of the physicochemical parameters of effluents studied at the two locations at Ilupeju Industrial Estate. There was positive correlation between conductivity and calcium (r = 0.643), conductivity and nitrate (r = 0.6880); TDS and alkalinity (r = 0.688); while negative correlation exist between conductivity and TH (r = -0.6460) for Paterson Zochonis effluents (Table 4.15). The canal effluents showed good correlation between the following parameters as shown in (Table 4.16), TDS and alkalinity (r = 0.696); TDS and BOD (r = 0.724), TDS and COD (r = 0.7290); calcium and TH (r = 0.738); phosphate and chloride (r = 0.640). Analysis of variance (p=0.05) showed significant difference among the sites in Ilupeju Industrial Estate for the parameters TDS, nitrate and Cu, indicating constituents concentration variation. ⁻- - - Levels of pH, conductivity, alkalinity, Ca, Mg, TH, PO4 , SO4² , Cl ,DO, BOD, COD, Pb, Cd, Zn, Cr, and Ni did not show any significant difference. There was no significant difference in Paterson Zochonis and Canal effluents for all the physicochemical parameters analysed. 4.2: CHARACTERISTICS OF EFFLUENTS ACCORDING TO TYPES OF INDUSTRIES The results of effluent characteristics obtained were examined for patterns related to industry types, i.e pharmaceuticals, textiles, paints, foods and beverages, basic metals, and conglomerates and mixed/collective. For each industry type, results were pooled for all estates. Pharmaceutical Industry: The parameters of concern in pharmaceutical Industry are TDS, BOD and COD values which may be in the tens of hundreds of milligrams per litre. Effluents of pharmaceutical industries had pH range of 4.1-9.2 with mean of 6.8±1.4 (Table 4.17). The conductivity levels (6000±2200 μS/cm) were much higher than (290 μS/cm) work reported by Adeyeye et al., 2007. The effluents were coloured with foul odour. The TDS, alkalinity, TH, Ca, 2- ⁻ - Mg, SO4 , PO4 and Cl levels were within the FEPA (1991) and World Bank (1995) limits. The - NO3 levels (78±45 mg/L) are not within the FEPA (1991) of 20 mg/L. 98 UNIVERSITY OF IBADAN LIBRARY Table 4.15: Pearson correlation coefficients amongst parameters of Paterson Zonchouis effluents . 2- 3- - - Conductivity TDS Alkalinity Ca Mg T.Hardness SO4 PO4 NO3 Cl DO BOD COD Conductivity 1 TDS 0.457 1 a Alkalinity 0.475 0.688 1 a Ca -0.643 -0.274 -0.091 1 b Mg -0.514 -0.63 -0.328 0.785 1 a b b T. Hardness -0.646 -0.476 -0.213 0.956 0.903 1 2- b SO4 0.784 0.191 0.170 -0.589 -0.273 -0.469 1 3- b PO4 0.598 0.326 0.774 0.044 -0.036 0.011 0.286 1 - a NO3 0.688 0.585 0.504 -0.152 -0.228 -0.253 0.508 0.551 1 - Cl 0.076 -0.150 0.240 0.092 0.191 0.045 -0.194 0.326 0.327 1 a DO -0.709 -0.222 -0.458 0.371 0.032 0.322 -0.512 -0.524 -0.528 -0.423 1 BOD 0.278 0.516 0.134 0.012 -0.206 -0.157 -0.023 -0.016 0.521 0.005 -0.269 1 b COD 0.147 0.491 0.128 0.136 -0.105 -0.041 -0.142 -0.050 0.480 0.049 -0.221 0.986 1 a b Significant at the 0.05 level. Significant at the 0.01 level. 99 UNIVERSITY OF IBADAN LIBRARY Table 4.16: Pearson correlation coefficients amongst parameters of Canal effluents. 2- 3- - - Conductivity TDS Alkalinity Ca Mg T.Hardness SO4 PO4 NO3 Cl DO BOD COD Conductivity 1 b TDS 0.949 1 Alkalinity 0.627 0.696* 1 Ca -0.537 -0.434 0.048 1 Mg -0.22 -0.207 -0.584 0.005 1 a T. Hardness -0.32 -0.173 -0.049 0.738 0.334 1 2- SO4 0.21 0.433 0.474 -0.101 -0.447 -0.09 1 3- b PO4 0.478 0.505 0.775 0.101 -0.439 -0.053 0.308 1 - NO3 -0.303 -0.175 -0.140 0.457 0.057 0.070 0.245 -0.094 1 - b b b a Cl 0.773 0.780 0.833 -0.064 -0.593 -0.198 0.372 0.646 0.042 1 DO 0.075 -0.112 0.069 0.002 -0.442 -0.548 -0.35 0.116 0.239 0.358 1 a BOD -0.600 -0.724 -0.357 0.233 -0.193 -0.081 -0.449 -0.427 -0.015 -0.468 0.393 1 a b COD -0.612 -0.729 -0.384 0.276 -0.111 -0.043 -0.500 -0.467 0.057 -0.477 0.408 0.991 1 a b Significant at the 0.05 level. Significant at the 0.01 level ** 100 UNIVERSITY OF IBADAN LIBRARY * Table 4.17: Physicochemical characteristics of effluents from different classes of industries. Effluent types/ Total 2- 3- Parameters pH Conductivity TDS Alkalinity Calcium Magnesium Hardness SO4 PO4 Mixed /collective effluent 6.2±1.1 5140±2000 532±180 166±82 22±12 4.4±2.4 63±33 80±100 0.79±0.52 Basic metals 8.4±2.0 6650±3000 532±280 220±240 15±11 5.1±5.2 70±38 71±53 1.09±0.80 Pharmaceuticals 6.8±1.4 6000±2200 633±260 233±97 14±15 10±11 53±44 34±36 0.65±0.57 Paint 8.5±1.8 7200±2500 1610±180 216±120 15±10 3.0±3.4 60±33 577±51 0.90±0.13 Textile 10.6±1.1 9100±3800 1160±890 316±270 17±13 3.1±2.1 40±32 156±73 0.99±0.29 Conglomerate 6.0±0.7 3120±960 385±60 128±56 17.7±9.2 5.3±2.9 71±32 10.3±8.2 0.97±0.49 Food & beverage 4.0±0.9 4600±1800 477±150 94±35 ** ** ** 172±19 0.57±0.21 FEPA(1991) 6.0 - 9.0 NA 2000 NA 200 200 NA 500 5 World Bank (1995) 6.0 - 9.0 NA NA NA NA NA NA NA NA *All units in mg/L except pH with no unit, and conductivity in μS/cm. NA – not available; ** not determined, 101 UNIVERSITY OF IBADAN LIBRARY * Table 4.17: Physicochemical characteristics of effluents from different classes of industries contd. Effluent types/ - - Parameters NO3 Cl DO BOD COD Pb Cd Zn Cu Cr Ni Mixed /collective effluent 110±78 64±46 0.5±1.0 180±130 308±210 0.034±0.029 0.013±0.016 0.64±0.62 0.26±0.26 0.034±0.040 0.071±0.089 Basic metals 61±50 80±74 2.7±1.5 194±81 330±120 0.11±0.08 0.009±0.004 0.62±0.24 0.55±0.65 0.09±0.10 0.06±0.04 Pharmaceuticals 78±45 61±40 1.3±1.2 150±96 245±140 0.053±0.042 0.012±0.013 0.48±0.20 0.048±0.033 0.030±0.024 0.043±0.021 Paint 67±37 45±14 1.3±1.7 221±130 367±150 0.08±0.12 0.007±0.003 0.69±0.14 0.48±0.08 0.02±0.00 0.04±0.00 Textile 60±32 65±19 1.8±1.7 209±72 343±130 0.28±0.09 0.01±0.00 0.67±0.29 1.45±0.93 2.54±0.26 0.06±0.04 Conglomerate 106±23 28±18 0.85±0.74 136±120 246±180 0.03±0.03 0.04±0.12 0.25±0.10 0.22±0.10 0.01±0.00 0.05±0.04 Food & beverage 15.9±2.5 50±20 1.9±1.3 147±75 269±140 0.04±0.02 0.003±0.002 0.92±0.16 0.02±0.02 0.01±0.00 0.04±0.04 FEPA(1991) 20 600 NA 30 80 < 1.0 < 1.0 < 1.0 < 1.0 < 1.0 < 1.0 World Bank (1995) NA NA NA 50 250 0.1 0.1 0.5 0.5 0.1 0.5 *All units in mg/L except pH with no unit, and conductivity in μS/cm, NA – not available 102 UNIVERSITY OF IBADAN LIBRARY The DO values were low (1.3±1.2 mg/L). The BOD (150±96 mg/L) and COD (245±140 mg/L) values exceeded the limits of 30 mg/L and 80 mg/L; and 50 mg/L and 250 mg/L for FEPA (1991) and World Bank (1995) respectively (Table 4.17). The release of effluents with high levels of BOD and COD into rivers or stream water will have adverse effects on aquatic life due to depleted oxygen level in water. Similar conclusions were also drawn by Shah (1999) and Khan and Noor (2002). The high levels of BOD and COD could be attributed to the organic nature of the raw materials used in the industry. The TDS and BOD levels were higher than the work reported by (Tariq et al., 2006; Bakare et al., 2009). Levels of heavy metals (Pb, Cd, Zn, Cu, Cr and Ni) were within the limits of FEPA (1991) and World Bank (1995). However, levels of Pb, Cd, and Cr were lower than the levels reported by (Adeyeye et al., 2007 and Tariq et al., 2006) but the Cu levels ( 0.26 mg/L) in this study are higher than (0.15 mg/L and 0.22 mg/L) for the work carried out by (Adeyeye et al., 2007 and Tariq et al., 2006) respectively. The levels of the metals were lower than similar work carried out on heavy metals concentrations in some pharmaceutical industrial effluents (Anyakora et al., 2011; Lokhande et al., 2011; Ramola and Singh, 2013) Textile Industry: The parameters of concern in textile Industry are pH, colour, TDS, BOD, COD and Cr. pH values ranged within 9.2-12.6 with mean being 10.6±1. That is, these effluents were mostly alkaline in nature, and did not meet required guidelines of 6.0- 9.0.Similarly, alkaline pH values for textile effluents have been noted by others Rao et al., 1993; Aslam et al., 2001; Olayinka and Alo, 2004; Mahmood et al., 2005; Ahmed, 2007; Rahman et al., 2008; Madukasi et al., 2013; Ben-Younes et al., 2013). The alkaline nature of the effluents was as a result of the use of sodium hydroxide during one of the process of manufacturing textile materials. High pH inhibits the growth of aquatic macrophytes by impairing iron (Fe) and phosphorus uptake (Argo, 2003). The effluents had varied colours which could be attributed to the types of dye used for the production of particular textile. Azo dyes constitute the largest and most diverse group of dyes used. Azo dyes are a group of compounds characterized by the presence of one or more azo bonds (- N = N -) along with one or more aromatic systems. Despite their low toxic effect on receiving bodies, these dyes create an aesthetic problem, colouration of water bodies (Rajamohan and Karthikeyan, 2004) and were found to be the most recalcitrant compounds (Maeir et al., 2004). The conductivity values (9100±3800 μS/cm; 4000-16000 μS/cm) were much higher than levels reported for a textile effluent (3.4-14.5 μS/cm) by Adeyeye and Ayejuyo, 2002 but lower than work done by (Ben-Younes et al., 2013). The values of TDS are high (1160±890 mg/L) but still within 103 UNIVERSITY OF IBADAN LIBRARY the standard limits of 2000 mg/L. Similar results were obtained by (Olayinka and Alo, 2004). 2- 3- - Alkalinity, TH, Ca, Mg, SO4 , PO4 and Cl were below the FEPA (1991) and World Bank (1995) limits. The DO values were low (1.8±1.7 mg/L), while BOD and COD values exceeded the limits. The DO levels were higher than similar work of (Olayinka and Alo, 2004; Adebayo et al., 2007) while the BOD and COD levels are within the values stated by Olayinka and Alo, 2004 but lower than values reported by (Varma and Sharma, 2011; - Madukasi et al., 2013; Ben-Younes et al., 2013). The NO3 levels (60±32 mg/L) were not - within the FEPA (1991) of 20 mg/L. The results of NO3 levels obtained were higher than work done by (Olayinka and Alo, 2004). Levels of heavy metals (Pb, Cd, Zn and Ni) were within the limits while Cu (1.45±0.93) mg/L and Cr (2.54±0.26 mg/L) values are higher than the FEPA (1991) and World Bank (1995) limits respectively. The high levels of Cu and Cr in textile effluents could be related to the various pigments and additives used in the manufacturing of textile materials.The Cr levels were higher than results stated by (Olayinka and Alo, 2004; Ugoji and Aboaba, 2004; Adebayo et al., 2007; Deepali, 2010). The levels of the metals were higher compared to the works done by (Adebayo et al., 2007; Younes et al., 2013). 2- 3- Paint Industry: The parameters of concern in paint Industry are pH, TDS, SO4 , PO4 - ,NO3 , BOD ,COD and Cr. pH values were 8.5±1.8 which ranged between 6.5-11.0. The pH was mostly basic; this could be due to the use of ammonia solution as buffer in water based paints. The values of TDS are high (1610±180 mg/L) but still within the standard limits of 2000 mg/L, similar result was obtained in the work done by (Jolly et al., 2012). TDS includes inorganic salts such as carbonates, sulphates, chlorides, calcium and magnesium. Alkalinity, ⁻ - TH, Ca, Mg, PO4 and Cl were within the FEPA (1991) and World Bank (1995) limits. The - Cl levels are similar to the work carried out by Onuegbu et al., 2013 with value 49 mg/L. 2- - The SO4 (577±51) mg/L and NO3 (67±37 mg/L ) values are not within the FEPA (1991a) of 2- - 500mg/L and 20 mg/L respectively. The SO4 and NO3 levels were higher compared with Onuegbu et al., 2013 work. The DO values were low (1.3±1.7 mg/L), while BOD and COD values exceeded the limits. The BOD and COD values were similar to the results by Onuegbu et al., 2013 but the COD values obtained are lower compared with Madukasi et al. (2013) and Onuegbu et al. (2013) works. The presence of organic compounds such as nitro cellulose used as thickener, alkyd resins and acrylic /styrene co-polymer used as dispersants 104 UNIVERSITY OF IBADAN LIBRARY and binders could be the cause of high content of biodegradable organic matter which impacts the BOD. The availability of oxidizable inorganic compounds such as pigments and additives are responsible for the impact on COD. The metal values are lower than the standards (Table 4.17) though Zn values were higher than World Bank (1995) standard. The levels of metals obtained were higher compared with the work reported by Jolly et al. (2012). Similar work reported by Malakootian et al., (2009) ; Lokhande et al., (2011); Madukasi, et al., (2013) and Onuegbu et al., (2013) gave a higher levels of the metals analysed. The discharge of effluents with high levels of Zn to nearby water bodies, though unusual in that it has low toxicity to man, but relatively high toxicity to fish (Oke et al., 2006). Basic Metal Industry: The parameters of concern in Basic Metal Industry are pH, TDS, 2- - SO4 , NO3 , and metals (Zn, Cu, Cr and Ni). pH values ranged within 5.9-12.8 with mean 2- 3- being 8.4±2.0, the effluents were mostly basic. The TDS, alkalinity, TH, Ca, Mg, SO4 , PO4 - and Cl levels were within the FEPA (1991) and World Bank (1995) limits, however DO - values 2.7±1.5 mg/L were low. The BOD and COD values exceeded the limits. The NO3 levels (60±32 mg/L) are not within the FEPA (1991) of 20 mg/L. The values of metals analysed are below the FEPA (1991) limit though Zn and Cu values were higher than World Bank (1995) limits. The levels of Pb, Cd. Cr and Ni were lower than work reported by Sharma and Forster (1994) and Ayenimo et al. (2005). The levels of Cu (0.55±0.65 mg/L) are higher compared with values obtained by Sharma and Forster (1994) and Ayenimo et al. (2005). Foods & Beverages Industry: The parameters of concern in Foods & Beverages Industry 2- are pH, TDS, SO4 , BOD, COD and Zn. pH values ranged between 3.3-5.4 with mean being 4.0±0.9. The effluents were generally acidic. The observed mean pH of the effluent (4.0±0.9) was much lower than pH 6.0, 8.0 and 8.3 reported for effluents from Nasco, Cadbury Plc in Nigeria (Chukwu, 2008) and Dairy industry (Cristian, 2010). The effluents were acidic and have the potential to acidify the receiving water bodies thereby rendering it unwholesome for aquatic life. Aquatic biota is sensitive to extremes of pH. The effluent may possibly contain + weak acids such as CO2 and hydrated metal ions for instance Al (H2O)6 , which contribute to the acidity. The conductivity levels (4600±1800 μS/cm) were much higher than levels 105 UNIVERSITY OF IBADAN LIBRARY reported for a cocoa industry liquid effluent (10 μS/cm) investigated by (Adeyeye et al., 2- 2005). TH, Ca, and Mg were not determined due to the matrix nature of the effluents. SO4 , 3- - - PO4 , NO3 and Cl were below the FEPA (1991) and World Bank (1995) limits. The levels of 3- - - 2- PO4 , NO3 and Cl were lower while SO4 was higher than reported work by (Ipeaiyeda and Onianwa, 2011). The release of detergents used for washing in the factories is a probable source of phosphate in the effluents. Though the phosphate levels are within the limits of 5.0 mg/L, it is nevertheless not too low as to encourage the proliferation of algae growth in the water bodies.The DO values were low (1.9±1.3 mg/L). BOD and COD values were about five times and four times respectively higher than FEPA 1991 limit. The DO range is far below the recommended minimum of 5.0 mg/L for aquatic life and could encourage septic condition in the water bodies. This condition is detrimental to aquatic life. The major pollutant in the effluents from food and beverage industries is organic material which is decomposable by micro-organisms in the water bodies. In breaking down the organic pollutant, the micro-organism consumes the available oxygen in the water. The susceptibility of aquatic life to toxic substance in the effluent is expected to increase when the level of dissolved oxygen is depleted. The effluents BOD levels are far below the BOD level of 1318 mg/L reported for a similar food manufacturing and beverage industries in Ghana (Acquah, 1998). Pb, Cd, Cu, Cr and Ni values were below the limits. However, Zn levels were about two times higher than the World Bank limit. The release of effluents with high level of Zn into water bodies will have effect on the aquatic life. The levels of the metals were lower than the reported works of (Amuda et al., 2006; Ipeaiyeda and Onianwa, 2011; Vanerkar et al., 2013). Conglomerate Industry: pH values ranged within 5.0-7.1 with mean being 6.0±0.7. The pH values are slightly acidic. The acidic nature of the effluent could be attributed to the nature of the raw material used for the manufacturing of the various products which includes range of soaps, cosmetic products, pharmaceutical, etc. The pH values are similar to work done by Ajibola and Ladipo (2009). The conductivity levels (3120±960 μS/cm) were almost the same for a similar work reported with value 3320 μS/cm by Sanyaolu et al. (2013). The high values obtained in this work are similar to the result obtained by Nwaedozie (2006). The parameter of concern can make receiving water bodies unsuitable for direct domestic use. This can be said that high conductivity gives an indication of dissolved ions in the effluent samples, hence gives high total dissolved solid which is potentially unhealthy.The total hardness, Ca, 106 UNIVERSITY OF IBADAN LIBRARY 2- 3- - - Mg, SO4 , PO4 , NO3 and Cl were below the FEPA (1991) and World Bank (1995) limits. - NO3 values were five times higher than FEPA limit of 20 mg/L, also greater than nitrate 2- levels reported by Ajibola and Ladipo (2009) and Sanyaolu et al. (2013). The levels of SO4 , 3- - PO4 and Cl were lower than work reported Ajibola and Ladipo (2009) and Sanyaolu et al. (2013). The DO values 0.85±0.74 mg/L are low, which suggested that the industry was releasing some organic substances that were high oxygen-demanding wastes (Emongor et al., 2005), thus cannot support aquatic life. The BOD values are five times and three times higher than FEPA (1991) and World Bank (1995) limits respectively while COD values are three times higher than FEPA limit. The DO, BOD and COD levels were lower than work reported by Ajibola and Ladipo (2009). Pb, Cd, Zn, Cu, Cr and Ni values were below the FEPA (1999) and World Bank (1991) limits. The levels of metals were lower compared to the works done by Ajibola and Ladipo (2009) and Sanyaolu et al. (2013). However, continues discharge of effluent with low level of metal will pose a great danger to the receiving water bodies, its inhabitants (fishes and other aquatic organisms) as well as those who make use of the water for direct domestic purposes. Mixed/ Collective Effluent Type: pH values ranged from 3.3-8.0 with a mean of 6.2±1.1. 2- ⁻ - The pH was mostly acidic. TH, Ca, Mg, SO4 , PO4 and Cl were below the FEPA (1991) - and World Bank (1995) limits. NO3 values are six times higher than FEPA limit of 20 mg/L. The mixed effluents had the least value of DO (0.5±1.0) mg/L compared to other types of industries. The BOD values were six times and four times higher than FEPA (1991) and World Bank (1995) respectively. COD values were four times higher than FEPA (1991) limit. Pb, Cd, Cu, Cr and Ni values are low than the FEPA (1991) limit, however Zn values are higher than the World Bank (1995) limit. Analysis of variance (p=0.05) shows significant difference among the types of industries for pH, conductivity, TDS, alkalinity, Ca, total hardness, sulphate, nitrate, dissolved oxygen, Pb, Cd, Zn, Cu, Cr and Ni indicating constituent concentration varied spatially and temporally. Levels of Mg, phosphate, chloride, biochemical oxygen demand and chemical oxygen demand did not show significant difference among the types of industries. 4.2.1: Multivariate Analysis for Heavy Metals in Types of Industry. Principal Component Analysis (PCA) was used to categorize the various types of industries 107 UNIVERSITY OF IBADAN LIBRARY according to the similarities in the heavy metal levels. PCA of heavy metal for seven effluents types indicated that the first two components (PC1 and PC2) accounted for 97.3% of the total variation (Table 4.18). A plot of the first two components showed four different groups characterized by their metal concentrations. Cluster 1 was characterized by food & beverages types of effluent with the highest Zn concentrations, cluster 2 by pharmaceuticals, collective and conglomerate types of effluents with least metal (Pb, Cd, Cu, Cr and Ni) concentrations, cluster 3 by Paint and Basic metal types of effluents with highest Ni and Cd concentrations and finally cluster 4 with Textile type of effluents with the highest metal (Pb, Cu and Cr) concentrations. From the PCA biplot (Fig 4.1), Textile types of effluents stand to contribute Pb, Cu and Cr to the environmental media. Cluster formed by the PCA were confirmed by the cluster analysis dendrogram in Figure 4.2, where four different clusters were noticed. Cluster 1 dominated by food & beverages types of effluent, cluster 2 by pharmaceuticals, collective and conglomerate types of effluents having similar metal concentrations, cluster 3 by paint and basic metal effluents having very similar metal concentrations and finally cluster 4 with textile effluents. Generally, textile effluents have the highest load of the metal studied while the foods & beverage effluent had the least concentrations of the metal studied except Zn concentrations. The order of the metal concentrations in the types of effluents studied is textile > paint and basic metal > pharmaceuticals, collective and conglomerate > foods & beverages. 4.3 EFFECT OF SEASONAL VARIATION ON THE EFFLUENTS CHARACTERISTICS Table 4.19, 4.20 and 4.21 show the seasonal average concentrations of effluent quality parameters from Ikeja, Isolo and Ilupeju industrial estate respectively. 4.3.1 pH and Alkalinity pH values indicated no seasonal trend (Figure 4.3). The alkalinity was generally higher in the dry seasons compared to the wet seasons for most of the sites (Table 4.19, 4.20 and 4.21). The high alkalinity could be as a result of detergents and soap based products or raw materials like calcium carbonate that are used in these industries. High alkalinity effluents discharged in water bodies will neutralize acidic pollution from rainfall or basic inputs thereby maintaining the buffering capacity of a water body. Figure 4.4 shows the monthly trends in alkalinity. 108 UNIVERSITY OF IBADAN LIBRARY Table 4.18: Principal component analysis of data of industrial effluent metal concentrations. Percentage Cumulative Eigen Values variance percentage PC 1 2.68724 77.963 77.963 PC 2 0.66571 19.314 97.28 PC 3 0.070936 2.058 99.3 4 PC 4 0.021465 0.62276 99.96 PC 5 0.001084 0.031457 99.99 PC 6 0.000378 0.010972 100 109 UNIVERSITY OF IBADAN LIBRARY 2 Zn food&bev 1 paint co llective 0 Nbiasic Pb Cd CCur textile pharm -1 congl 0 1 2 3 4 Component 1 Fig 4.1: Principal component biplot of data on effluents metals concentrations. 110 UN Component 2IVERSITY OF IBADAN LIBRARY 0 -0.1 -0.2 -0.3 -0.4 -0.5 -0.6 -0.7 -0.8 -0.9 -1 -1.1 -1.2 -1.3 -1.4 -1.5 -1.6 -1.7 -1.8 -1.9 1 2 3 4 5 6 7 8 Obsarvations Fig. 4.2: Cluster analysis dendrogram of effluents types for six metal concentrations. 111 UN SimilarityIVERS food&bevITY pharm O collectiveF IB conglAD basicAN paintLIB textileRARY * Table 4.19a: Average seasonal effluent quality at Ikeja Industrial Estate. Wet Season Dry Season Site T. T. Code pH Cond TDS Alkalinity Ca Mg pH Cond TDS Alkalinity Ca Mg Hardness Hardness KJE-EF 5.7±1.8 7830±1200 749±200 157±110 22±14 4.6±2.7 52±15 5.4±0.8 6500±1300 534±100 190±55 25±5 3.8±2.1 45±5 KHO-EF 6.9±0.6 4670±1750 355±95 110±18 23±12 5.9±1.8 91±28 6.9±0.8 4750±1300 402±140 98±40 12±7 8.9±11.0 68±52 KBP-EF 8.5±1.7 7500±2300 1670±200 254±100 17±11 3.7±3.2 79±30 8.6±2.1 6750±3100 1529±120 158±120 11±7 4.1±4.1 32±9 KGA-EF 9.5±1.6 7500±1600 536±200 250±130 15±10 3.5±1.4 80±25 10.5±2.0 10300±4000 921±330 461±480 6±4 2.6±1.3 24±9 KWS-EF 10.6±1.0 9330±4700 1330±1100 269±250 21±15 3.7±2.4 78±25 10.7±1.4 8750±2600 903±350 385±340 11±8 2.0±0.8 33±21 KMB-EF 5.8±1.3 6830±2600 938±350 195±61 ** ** ** 5.1±1.1 6250±1300 689±190 227±45 ** ** ** *All units in mg/L except pH with no unit, and conductivity in μS/cm ** not determined 112 UNIVERSITY OF IBADAN LIBRARY Table 4.19a contd.: Wet Season Dry Season Site Code SO 2- PO 3-4 4 NO 3- Cl- DO BOD COD SO 2-4 PO 3- 4 NO 3- Cl- DO BOD COD KJE-EF 197±27 0.6±0.4 75±67 126±47 0.7±1.4 315±160 495±250 255±27 0.5±0.2 29±15 81±51 1.7±1.7 248±26 439±66 KHO-EF 22±10 0.4±0.2 66±78 59±32 3.2±0.3 137±40 246±85 51±29 0.5±0.3 49±41 61±50 3.7±0.7 187±93 285±120 KBP-EF 596±45 1.0±0.1 85±36 46±18 1.3±2.0 186±64 336±140 546±49 0.8±0.1 40±22 44±5 1.2±1.3 273±101 414±170 KGA-EF 111±39 1.7±0.6 68±45 118±105 1.9±1.7 214±54 383±130 108±66 1.8±0.7 56±15 76±87 2.1±2.2 256±110 424±92 KWS-EF 135±11 1.0±0.4 69±50 72±20 1.8±1.8 182±42 315±100 188±120 1.0±0.2 46±16 55±11 1.8±1.7 251±95 385±69 KMB-EF 63±19 0.4±0.2 79±20 83±53 0.3±2.0 190±51 340±86 89±52 0.3±0.3 78±54 107±68 0.0±0.0 312±100 444±120 *All units in mg/L except pH with no unit, and conductivity in μS/cm 113 UNIVERSITY OF IBADAN LIBRARY Table 4.19b: Seasonal concentrations (mg/L) of heavy metals in effluent at Ikeja Industrial Estate. Wet Season Dry Season Site Code Pb Cd Zn Cu Cr Ni Pb Cd Zn Cu Cr Ni KJE-EF 0.04±0.04 0.01±0.01 1.15±0.56 0.04±0.03 0.06±0.03 0.08±0.04 0.05±0.07 0.01±0.01 0.70±0.32 0.07±0.01 0.05±0.01 0.07±0.01 KHO-EF 0.17±0.08 0.01±0.00 0.76±0.28 0.03±0.01 0.12±0.08 0.05±0.02 0.17±0.07 0.01±0.00 0.67±0.20 0.03±0.01 0.21±0.13 0.03±0.02 KBP-EF 0.11±0.16 0.008±0.004 0.75±0.15 0.51±0.08 0.022±0.004 0.04±0.01 0.045±0.005 0.006±0.002 0.61±0.09 0.44±0.04 0.019±0.002 0.041±0.007 KGI-EF 0.03±0.04 0.006±0.004 0.50±0.21 1.10±0.46 0.03±0.03 0.05±0.02 0.07±0.08 0.008±0.001 0.53±0.23 1.00±0.74 0.04±0.03 0.10±0.09 KWS-EF 0.32±0.09 0.009±0.004 0.58±0.27 1.20±0.87 2.46±0.27 0.05±0.05 0.23±0.05 0.010±0.003 0.79±0.30 1.83±1.01 2.66±0.21 0.06±0.02 KMB-EF 0.05±0.03 0.018±0.014 0.58±0.24 0.05±0.02 0.05±0.01 0.04±0.02 0.08±0.05 0.005±0.004 0.68±0.25 0.04±0.02 0.05±0.01 0.07±0.04 114 UNIVERSITY OF IBADAN LIBRARY * Table 4.20a: Average seasonal effluent quality at Isolo Industrial Estate. Wet Season Dry Season Site Code T. T. pH Conductivity TDS Alkalinity Ca Mg pH Conductivity TDS Alkalinity Ca Mg Hardness Hardness 6.9±1.0 4720±1200 507±137 194±78 18±18 10.0±9.3 59±51 8.4±0.6 5165±1731 575±130 219±69 6±2 3.0±1.4 29±7 6.5±0.7 5170±2200 491±77 268±120 18±18 17.9±16.0 71±58 8.3±0.6 8500±1600 577±260 317±160 11±10 6.1±5.1 41±18 4.4±1.0 5670±1500 548±160 89±6 ** ** ** 3.4±0.1 3000±0 370±14 103±67 ** ** ** *All units in mg/L except pH with no unit, and conductivity in μS/cm ** not determined 115 SVJ-EF SAF-EF SJW-EF UNIVERSITY OF IBADAN LIBRARY Table 4.20a contd.: Wet Season Dry Season Site Code SO 2-4 PO 3- - - 4 NO3 Cl DO BOD COD SO 2- 4 PO 3- 4 NO - 3 Cl - DO BOD COD 11±8 0.5±0.5 60±81 47±8 2.0±1.3 89±88 144±130 17±11 0.7±0.5 96±21 49±11 2.4±1.1 96±16 167±41 16±20 1.1±0.8 64±34 49±9 1.0±0.5 116±62 200±120 18±18 1.2±0.8 103±11 38±14 2.3±0.5 126±44 203±97 173±25 0.6±0.1 16±3 59±21 1.3±1.5 158±84 281±150 170±10 0.5±0.4 16±2 37±14 2.8±0.2 131±86 250±170 *All units in mg/L except pH with no unit, and conductivity in μS/cm 116 SVJ-EF SAF-EF SJW-EF UNIVERSITY OF IBADAN LIBRARY Table 4.20b: Seasonal concentrations (mg/L) of heavy metals in effluent at Isolo Industrial Estate. Wet Season Dry Season Site Code Pb Cd Zn Cu Cr Ni Pb Cd Zn Cu Cr Ni 0.06±0.11 0.006±0.007 0.44±0.11 0.06±0.03 0.03±0.03 0.02±0.01 0.14±0.18 0.06±0.10 0.40±0.19 0.8±1.6 0.006±0.006 0.40±0.73 0.055±0.07 0.006±0.006 0.42±0.15 0.07±0.06 0.005±0.005 0.02±0.02 0.48±0.67 0.14±0.28 0.35±0.21 1.39±2.68 0.003±0.000 0.032±0.036 0.018±0.019 0.002±0.003 0.94±0.21 0.16±0.24 0.003±0.000 0.04±0.06 0.42±0.53 0.0004±0.000 0.89±0.13 0.01±0.00 0.003±0.000 0.013±0.001 117 SVJ-EF SAF-EF SJW-EF UNIVERSITY OF IBADAN LIBRARY * Table 4.21a: Average seasonal of effluent quality at Ilupeju Industrial Estate. Wet Season Dry Season Site T. T. Code pH Conductivity TDS Alkalinity Ca Mg Hardess pH Conductivity TDS Alkalinity Ca Mg Hardess LPZ-EF1 6.0±0.9 2830±1200 440±110 116±77 16±11 5.4±4.0 71±39 6.1±0.9 3500±1300 473±73 162±91 18±11 5.7±2.9 69±29 LPZ-EF2 5.4±1.4 3600±1300 381±120 108±76 16±9 6.1±3.8 68±38 6.4±0.8 4500±710 495±122 206±42 7±1 1.9±0.4 36±10 LPZ-EF3 6.3±0.8 2830±750 281±67 97±48 18±11 6.1±3.1 77±37 6.4±1.0 3000±1200 317±68 142±43 20±6 3.5±1.8 69±26 L PZ-EF 5.9±0.8 3000±840 370±64 109±58 17±10 5.8±3.3 73±36 6.2±0.6 3290±1200 409±54 409±54 18±10 4.5±2.2 67±29 LCA-EF1 6.4±0.8 4330±1400 477±150 170±85 23±19 4.3±2.9 86±58 6.2±0.8 3500±1300 419±130 134±50 18±6 4.9±2.1 52±13 LCA-EF2 6.8±0.9 4500±1500 519±120 197±110 25±13 4.9±2.7 74±35 6.4±0.6 3580±1200 421±150 136±57 17±4 3.8±2.0 53±11 LCA-EF 6.6±0.6 4420±1400 498±130 183±95 24±16 4.6±2.0 80±46 6.3±0.7 3540±1200 420±140 135±54 15±6 4.4±1.7 53±12 *All units in mg/L except pH with no unit, and conductivity in μS/cm 118 UNIVERSITY OF IBADAN LIBRARY Table 4.21a contd.: Wet Season Dry Season Site Code SO 2-4 PO 3- 4 NO - 3 Cl - DO BOD COD SO 2- PO 3- - -4 4 NO3 Cl DO BOD COD LPZ-EF1 8±5 1.1±1.0 125±21 30±24 0.7±1.0 101±57 203±120 11±12 1.2±0.6 171±39 38±34 1.3±1.4 213±180 357±210 LPZ-EF2 9±4 0.9±1.0 111±62 39±27 0.6±1.0 75±52 163±120 20±4 2.0±0.1 177±3 34±9 0.0±0.0 242±320 386±520 LPZ-EF3 14±20 0.7±0.5 71±14 19±14 1.0±1.1 135±120 234±180 10±11 0.5±0.1 28±2 15±3 0.6±0.4 141±190 244±320 L PZ-EF 10±7 0.9±0.6 102±23 30±21 0.8±0.8 108±44 208±90 11±11 1.0±0.4 111±27 29±16 0.9±0.8 177±188 303±270 LCA-EF1 7±3 1.1±0.5 127±56 43±15 0.3±0.8 100±65 174±110 8±3 0.5±0.2 92±46 30±20 0.2±0.2 201±190 340±290 LCA-EF2 11±7 1.2±0.7 152±81 51±27 0.2±0.4 121±58 231±130 16±14 0.6±0.4 175±110 36±23 0.1±0.2 96±81 178±150 LCA-EF 9±4 1.2±0.5 139±65 47±21 0.2±0.6 111±54 202±110 12±7 0.5±0.2 133±73 33±22 0.1±0.2 148±100 260±170 *All units in mg/L except pH with no unit, and conductivity in μS/cm 119 UNIVERSITY OF IBADAN LIBRARY Table 4.21b: Seasonal concentrations (mg/L) of heavy metals in effluent at Ilupeju Industrial Estate. Wet Season Dry Season Site Code Pb Cd Zn Cu Cr Ni Pb Cd Zn Cu Cr Ni LPZ-EF1 0.02±0.02 0.01±0.01 0.13±0.19 0.16±0.23 0.003±0.000 0.06±0.06 0.01±0.00 0.003±0.005 0.19±0.03 0.07±0.02 0.01±0.01 0.05±0.08 LPZ-EF2 0.03±0.01 0.07±0.14 0.28±0.17 0.16±0.09 0.003±0.000 0.06±0.06 0.02±0.00 0.008±0.001 0.40±0.30 0.10±0.01 0.003±0.000 0.09±0.06 LPZ-EF3 0.05±0.10 0.13±0.31 0.30±0.18 0.32±0.22 0.003±0.000 0.03±0.03 0.01±0.00 0.002±0.002 0.3±0.1 0.4±0.2 0.01±0.02 0.02±0.02 L PZ-EF 0.03±0.04 0.06±0.15 0.23±0.11 0.21±0.11 0.003±0.000 0.05±0.050 0.01±0.00 0.004±0.003 0.27±0.09 0.22±0.08 0.01±0.01 0.04±0.05 LCA-EF1 0.01±0.01 0.02±0.03 0.32±0.40 0.39±0.35 0.01±0.02 0.09±0.15 0.01±0.00 0.007±0.006 0.46±0.55 0.46±0.29 0.003±0.000 0.02±0.02 LCA-EF2 0.02±0.03 0.01±0.02 0.56±0.79 0.34±0.20 0.03±0.08 0.07±0.15 0.01±0.00 0.013±0.02 0.57±0.81 0.26±0.24 0.005±0.004 0.014±0.011 LCA-EF 0.02±0.02 0.014±0.020 0.44±0.59 0.36±0.23 0.02±0.05 0.08±0.15 0.01±0.00 0.01±0.01 0.51±0.68 0.36±0.24 0.004±0.002 0.018±0.011 120 UNIVERSITY OF IBADAN LIBRARY 14.0 12.0 KJE-EF 10.0 KHO-EF KBP-EF 8.0 KGI-EF 6.0 KWS-EF KMB-EF 4.0 SJW-EF SAF-EF 2.0 SVJ-EF 0.0 LPZ-EF LCA-EF Sampling period Fig. 4.3: Variations of pH of effluents with sampling period. 1200 1000 KJE-EF KHO-EF 800 KBP-EF KGI-EF 600 KWS-EF 400 KMB-EF SJW-EF 200 SAF-EF SVJ-EF 0 LPZ-EF LCA-EF Sampling period Fig. 4.4: Variations of alkalinity of effluents with sampling period. 121 UN Alkalinity (mg/L) pH I Nov-06 V Dec-06 E Jan-07 R Feb-07 S Mar-07 I Apr-07 T May-07 Y Jun-07 O Jul-07 Aug-07 F Sep-07 IB Oct-07 Nov-07 A Dec-07 D Jan-08 A Feb-08 N Mar-08 Apr-08 LI May-08 BRARY The acidic nature of the industrial effluents of the central effluent treatment plant, May & Baker and Viju industries Ltd reflected on the low average alkalinity levels compared to effluents of Berger Paint, Galvanizing Industries Limited and Woolen and Synthetic Textile Manufacturing Limited that are basic. The alkalinity of the basic effluents is quite higher compared to the acidic effluents. The highest alkalinity levels were obtained during the months of Nov. 2007 and Jan. 2008 (the dry seasons) for Galvanizing Industries Limited and Woolen and Synthetic Textile Manufacturing Limited effluents 4.3.2 Total Dissolved Solids (TDS) and Conductivity Figure 4.5 and 4.6 give the monthly trends in TDS and conductivity respectively. The TDS level and conductivity are higher in the wet season than the dry season for most of the + + + 2+ 2+ - effluents. High values of EC shows that inorganic ions such as H , Na , K , Mg , Ca , Cl , 2- SO4 , HCO⁻ - e.t.c. are present in reasonable concentrations in the wastewater; Such ions have major influence on the conductivity of water. For TDS, high values obtained indicate that reasonable amount of solid matter were present in the wastewater as dissolved substances. (Uwidia and Ukulu, 2013).Basic effluents had higher TDS and conductivity compared to acidic effluents, this could be associated to the formation of hydroxide ions of various inorganic components of the effluents. The highest conductivity was observed in the month of September, 2007 which corresponded to end of the wet season. 4.3.3 Total hardness, Calcium and Magnesium Figures 4.7 to 4.9 show the monthly trends in total hardness, calcium and magnesium contents of the effluents. The levels were higher in the wet season than the dry season for most of the effluents. The highest values of TH, calcium and magnesium were observed in the month of July, 2007 for most of the effluents. There was no seasonal trend for the parameters for all the effluents. Analysis of variance (p = 0.05) showed significant difference in levels of total hardness, calcium and magnesium among effluents. 4.3.4 Sulphate and Chloride Figures 4.10 and 4.11 show the monthly trends in sulphate and chloride of the effluents. The sulphate levels are higher in the dry season compared to the wet season for most of the 122 UNIVERSITY OF IBADAN LIBRARY Fig. 4.5: Variations of TDS of effluents with sampling period. Fig. 4.6: Variations of conductivity of effluents with sampling period. 123 UNIVERSITY OF IBADAN LIBRARY Fig. 4.7: Variations of total hardness of effluents with sampling period. Fig. 4.8: Variations of calcium contents of effluents with sampling period. 124 UNIVERSITY OF IBADAN LIBRARY Fig. 4.9: Variations of magnesium contents of effluents with sampling period. Fig. 4.10: Variations of sulphate contents of effluent with sampling period. 125 UNIVERSITY OF IBADAN LIBRARY Fig. 4.11: Variations of chloride contents of effluent with sampling period. 126 UNIVERSITY OF IBADAN LIBRARY effluents. This may be due to low dilution of the effluent and higher production at the various industries. The chloride levels were higher for wet season for most of the effluents. From November, 2006 to May, 2008, the trend in chloride levels was notably higher for central treatment plant effluents, Galvanizing Industries Limited and May & Baker. 4.3.5 Phosphate and Nitrate Figure 4.12 and 4.13 show monthly variations of phosphate and nitrate in the effluents respectively. Average phosphate levels for the effluent show no significant difference between the wet and dry season. Average nitrate levels for the effluent showed significant difference between the wet and dry seasons. The nitrate levels were higher in the wet season than the dry season for most of the effluents. Over 500% highest level of nitrate was observed in September, 2007 in most of the effluents. 4.3.6 Dissolved Oxygen, Biochemical Oxygen Demand and Chemical Oxygen Demand Dissolved oxygen, biochemical oxygen demand and chemical oxygen demand are very important indicator parameters for the quality of effluents. From Tables 4.19a, 4.20a and 4.21a, seasonal DO levels show no significant difference between the seasons. The levels of DO in the effluents were generally low. This could be as a result of high level of anions and organic pollution in the effluents (D’ Amello, 2007). The environmental implication of low DO is the death of aquatic organism in the water body that receives these effluents (Chapelle and Petts, 2004). Figure 4.14 showed monthly variations of DO in the effluents. The DO level is quite low between September, 2007 and May, 2008 for most of the effluents. Variations in Biochemical Oxygen Demand are shown in Figure 4.15. Significant difference was observed between the wet season and dry season. The level of BOD is lower in the wet season compared to dry season. The low level could be attributed to dilution of the effluents as a result of rain or higher production. 127 UNIVERSITY OF IBADAN LIBRARY Fig. 4.12: Variations of phosphate contents of effluent with sampling period. Fig. 4.13: Variations of nitrate contents of effluents with sampling period. 128 UNIVERSITY OF IBADAN LIBRARY Fig. 4.14: Variations of DO contents of effluents with sampling period. Fig. 4.15: Variations of BOD of effluents with sampling period. 129 UNIVERSITY OF IBADAN LIBRARY The monthly variations of figure 4.16 show similar trends in COD levels of the effluents as reported for the BOD. Effluents rich in organic and inorganic compounds were responsible for the high levels of COD. An increase in COD levels for most of the effluents was observed in September 2007, November 2007, March 2008, and May 2008 with a decreased level in November 2006, January 2007 and May 2007. Analysis of variance (p = 0.05) showed no significant difference in COD levels during the period of sampling. 4.3.7 Lead Lead levels were generally higher in the dry season but there were no significant variation observed in the seasons however Woolen & Synthetic Textile Manufacturing Ltd and Berger Paint had higher levels in wet season (Table 4.19b, 4.20b and 4.21b). The dry season levels of lead were higher for other effluents. The number of effluent samples that were below detection limit was 30 out of 105. Figure 4.17 shows the trend of Pb concentrations with time. November 2006, March 2007, May 2007, July 2007, September 2007 and November 2007 showed high Pb concentrations, while January 2007, January 2008, March 2008 and May 2008 showed low Pb concentrations. The seasonal variation is arbitrary in pattern. T- test analysis (p = 0.05) showed no significant difference in Pb levels between the dry season and wet season. Exposure to ambient environmental lead has been associated with a wide range of metabolic disorders and neuropsychological problems especially in children (Fatoki and Ogunfowokan, 2002; Basha et al., 2005; Greben et al., 2005; Wu et al., 2008). 4.3.8 Cadmium Cd levels in most of the effluents analysed were higher during the dry season but were no significant variation observed in the seasons (Table 4.19b, 4.20b and 4.21b). About 14 samples out of 105 samples analysed had Cd level below the detection limit. Figure 4.18 shows the variations of Cd concentrations with time. July 2007 and November 2007 showed high Cd concentrations where November 2006, January 2007, March 2007 and May 2007, September 2007, January 2008, March 2008 and May 2008 showed low Cd concentrations. The seasonal variation is arbitrary in pattern. T-test analysis (p = 0.05) showed no significant difference in Cd levels between the dry season and wet season. 4.3.9 Zinc Zn levels in most of the effluents analysed were higher during the dry season but were no significant variation observed in the seasons. (Table 4.19b, 4.20b and 4.21b). None of the 130 UNIVERSITY OF IBADAN LIBRARY Fig. 4.16: Variations of COD of effluents with sampling period. Fig. 4.17: Variations of lead contents of effluents with sampling period. 131 UNIVERSITY OF IBADAN LIBRARY Fig.4.18: Variations of cadmium contents of effluents with sampling period. 132 UNIVERSITY OF IBADAN LIBRARY effluent samples had Zn level below the detection limit. Figure 4.19 shows the variations in Zn concentrations with time. January 2007, March 2007 and May 2007 showed high Zn concentrations while November 2006, July 2007, September 2007, November 2007, January 2008, March 2008 and May 2008, showed low Zn concentration. T-test analysis (p = 0.05) showed no significant difference in Zn levels between the dry season and wet season. 4.2.10 Copper Copper level in most of the effluents analysed were higher during the wet season compared to dry season. Cu levels (Table 4.19b) for effluents from Woolen & Synthetic Textile Manufacturing limited were not within the regulatory limits for both the wet season (1.5 ± 0.9 mg/L) and dry season (1.4 ±1.0 mg/L). The high levels of Cu in textile effluents could be related to the various dyes used in the manufacturing of textile materials. Figure 4.20 shows the variations in concentrations with time. July 2007 and November, 2007 showed high Cu concentrations where November 2006, January 2007, March 2007, May 2007, September 2007, January 2008, March 2008 and May 2008 showed low Cu concentration. T-Test analysis (p = 0.05) showed no significant difference in Cu levels between dry season and wet season. 4.3.11 Chromium Chromium levels in most of the effluents analysed were higher during the dry season compared to the wet season. Woolen & Synthetic Textile Manufacturing limited had Cr levels (Table 4.19b) high for both the wet season (2.52 ± 0.17 mg/L) and dry season (2.55±0.32 mg/L). About 36 samples out of 105 samples of the effluents analysed had Cr levels below the detection limit. Chromium was not detected in effluents sample of Viju industries Ltd (food and beverages industry). Figure 4.21 shows the trend in Cr concentrations with time. The levels of Cr in both the wet season and dry season seem to be the same in most of the effluents sampled. T-test analysis (p = 0.05) showed no significant difference in Cr levels between the dry season and wet season. 4.3.12 Nickel Nickel levels in most of the effluents analysed were higher during the dry season compared to the wet season. 27 samples of effluents analysed had their levels below the detection limit for Ni. Figure 4.22 shows the trend in Ni concentrations with time. March 2007 and November 133 UNIVERSITY OF IBADAN LIBRARY Fig.4.19: Variations of zinc contents of effluents with sampling period. Fig. 4.20: Variations of copper contents of effluents with sampling period. 134 UNIVERSITY OF IBADAN LIBRARY Fig. 4.21: Variations of chromium contents of effluents with sampling period. Fig. 4.22: Variations of nickel contents of effluents with sampling period. 135 UNIVERSITY OF IBADAN LIBRARY 2007 showed high Ni concentration while November 2006, January 2007, May 2007, July 2007, September 2007, January 2008, March 2008 and May 2008 showed low Ni concentrations. T-test analysis (p = 0.05) showed no significant difference in Ni levels between dry season and wet season. From the study, it is noticeable that heavy metal levels were higher in the dry season than the wet season. This could be attributed to high volume of production in most of the industries during the dry season and low dilution of the effluents. The low levels of heavy metal in wet season could be associated to dilution of the effluent by rain. It is noteworthy that effluents from textile industry are burden with high level of chromium and copper. 4.4: CHARACTERISTICS OF EFFLUENT CHANNELS SEDIMENTS Table 4.22 gives the pH, organic matter and particle size characteristics of sediments from effluents channel around Ikeja, Isolo and Ilupeju Industrial Estates while Table 4.23 gives the levels of metals in sediment from effluents channel around Ikeja, Isolo and Ilupeju Industrial Estates. The sediments were predominantly sandy with low % organic matter. The sandy nature is comparable to the particle size in the soil around the industrial areas. The range of sediments pH were 4.2-6.7 for KJE-SD and 6.7-7.5 for KHO-SD in Ikeja industrial estate, 6.1-6.5 for SVJ-SD in Isolo industrial estate, while 6.7-7.0 for LPZ-SD and 6.3 - 7.3 for LCA-SD in Ilupeju industrial estate. The low pH values of KJE-SD sediment (5.8±0.8) may allow the release of some toxic metals that would otherwise be adsorbed to sediment (Mouvet and Borg, 1983). The high levels of heavy metals in the effluents channel in KJE could have contributed in the elevated level of metals in the sediment (Table 4.1). pH variation in sediments influences the release or adsorption of each metal into sediment fraction. High pH lowers desorption of metals and possesses high buffering capacity against acidic conditions that may be created as a result of wastes accumulation. Association of metals with the carbonate, Fe-Mn-Oxides and organic fraction are affected by pH variations (Ikem et al., 2003). Cd, Cu and Zn are mostly bound to carbonate and Fe-Mn oxides fraction. Low pH is known to influence the sorption of Pb by organic fraction in sediments (Bodar et al., 2006; Vamerali et al., 2010). 136 UNIVERSITY OF IBADAN LIBRARY Table 4.22: pH, organic matter and particle size characteristics of sediments from effluent channel around Ikeja, Isolo, Ilupeju industrial estates. Organic Organic Sand Clay Silt Site Code pH Carbon Matter (%) (%) (%) (%) (%) Mean 5.8±0.8 2.4±1.3 4.2±2.2 92.8 ±1.8 4.0 ±1.2 3.2±1.1 KJE-SD Range 4.2-6.7 1.0-4.0 1.8-7.0 90.2-95.7 2.6-5.4 1.7-4.9 Mean 7.0±0.2 3.5±0.7 6.2±1.6 92.0±1.7 2.9±0.8 5.2±1.2 KHO-SD Range 6.7-7.50 2.0-4.5 3.5-9.8 90.2-95.5 1.9-4.4 3.0-6.9 M ean 6.3± 0.1 2.7± 0.2 4.6± 0.3 92.2± 0.9 4.5± 0.8 3.3± 0.8 SVJ-SD Range 6.1-6.5 2.6-3.0 4.4-5.1 91.2-93.5 3.3-5.2 2.6-4.3 Mean 6.4± 0.2 2.2± 1.0 3.8± 1.7 94.1± 1.5 2.3± 0.9 3.7± 1.0 LPZ-SD1 Range 6.2-6.9 0.7-3.1 1.1-5.3 91.7-95.9 1.0-4.0 2.4-5.3 Mean 6.1±0.2 2.5±0.7 4.5±2.0 93.6±0.6 2.1±0.6 4.3±0.5 LPZ-SD2 Range 5.9-6.4 1.8-4.0 3.2-8.8 92.9-94.7 1.0-2.9 3.4-5.2 Mean 6.1±0.2 1.8±1.4 3.2±2.5 95.0±1.0 2.3±0.5 3.7±3.7 LPZ-SD3 Range 5.9-6.6 0.4-3.9 0.6-6.7 93.1-96.1 1.9-2.9 1.9-14.0 Mean 6.3±0,3 2.0±1.0 3.5±1.9 94.3±0.8 2.3±0.5 3.5±0.6 LPZ-SD Range 6.1-7.0 0.6-3.5 1.1-6.7 92.9-95.5 1.6-2.9 2.4-4.5 Mean 6.8±0.5 1.4±1.1 2.5±1.9 86.0±1.6 8.3±1.2 5.7±1.4 LCA-SD1 Range 6.1-7.4 0.3-4.0 0.6-6.8 83.9-88.7 6.9-10.0 4.4-8.1 Mean 6.8±0.3 1.1±1.1 1.9±1.9 85.7±1.7 8.6±1.1 5.5±1.1 LCA-SD2 Range 6.4-7.2 0.2-4.0 0.3-6.8 83.7-88.7 7.0-10.9 4.3-7.4 Mean 6.8±0.4 1.3±1.1 2.2±1.8 85.9±1.5 8.4±1.1 5.6±1.1 LCA-SD Range 6.3-7.3 0.3-4.0 0.5-6.8 84.3-88.7 7.0-10.5 4.4-7.3 137 UNIVERSITY OF IBADAN LIBRARY Table 4.23: Concentrations of metals (µg/g) in sediments from effluent channel around Ikeja, Isolo, and Ilupeju industrial estates. Site Code Lead Cadmium Zinc Copper Chromium Nickel Mean 80± 39 13±4.3 2210 ±500 128±62 31±11 34±10 KJE-SD Range 31-150 7.8-23.2 1440-3270 87-244 12-49 21-55 Mean 453±320 19.9±1.4 2200±320 49 ±5 28±7 22±6 KHO-SD Range 229-1127 16.4-21.7 1460-2440 44-56 17-39 17-33 Mean 46± 33 0.43± 0.26 326± 82 70± 15 15.8± 2.3 6.5± 3.5 SVJ-SD Range 10-93 0.14-0.66 196-423 57-92 13.1-19.1 2.6-10.2 Mean 165± 34 1.0± 0.4 257± 46 74± 28 20± 3 10± 4 LPZ-SD1 Range 121-218 0.3-1.6 208-327 33-115 14-26 4.5-16.6 Mean 71±30 0.7±0.5 226±9 92±57 13±4 8±4 LPZ-SD2 Range 35-129 0.2-1.6 211-237 25-201 6.7-18.7 4.4-16.3 Mean 62±34 1.2±1.2 231±47 65±20 18±5 9±4 LPZ-SD3 Range 32-134 0.2-4.5 179-353 38-101 11.7-27.3 4.2-13.3 Mean 105±27 1.0±0.6 242±18 75±27 18±4 9±3 LPZ-SD Range 67-165 0.4-2.4 223-280 33-115 12.2-24.6 4.7-14.8 Mean 155±20 0.9±0.4 271±61 71±30 18±6 10±3 LCA-SD1 Range 127-182 0.4-1.5 221-404 27-115 10.2-27.2 6.1-15.7 Mean 149±20 0.8±0.5 328±80 69±43 20±9 12±3 LCA-SD2 Range 127-195 0.2-1.7 238-476 21-162 11-33 6.51-15.8 LCA-SD Mean 152±16 0.9±0.4 300±59 70±31 19±7 11±2 Range 132-188 0.4-1.3 237-414 27-131 11.1-28.1 6.4-14.7 138 UNIVERSITY OF IBADAN LIBRARY It was observed that the levels of the heavy metals studied were highest in KHO-SD; this could be due to the high organic matter content and the pH of the sediment since organic matter has been found to influence heavy metal adsorption in sediments. The levels of heavy metals in sediments studied were in the order of Zn> Pb > Cu> Cr>Ni> Cd (Table 4.23). Table 4.24 shows the Pearson correlation coefficient between the pair of parameters determined in the effluent channels. There were weak correlations between pairs of heavy metals. ANOVA at 95% confidence limit showed no significant difference among the parameters for pH, Cu, % sand, %clay and %silt, while Pb, Cd, Zn, Cr and Ni. 4.4.1: Lead Levels of Pb were highest in KHO-SD (453±320 µg/g) followed by CA-SD (152 ±16 µg/g) and LPZ –SD (105±27 µg/g) (Table 4.23). The levels are about 13, 15 and nine folds; four,five and three folds; three, three and two folds greater than the average level in sediment quality guidelines (CCME,1995; MOEE,1993 and ANZECC/ARMCANZ,2000) respectively. The results obtained for KHO-SD and LPZ –SD are greater than report by (Ajibola, and Ladipo, 2011) on the levels of Pb in sediment effluent channels of PZ (56±46 µg/g) and steel industry (69±27 µg/g). The high levels of this metal in sediment could be related to the type of raw materials utilize in the manufacturing process. The high degree of Pb in the sediment will have a negative effect on aquatic life. This effect will arise when metals are released into the water column; hence bioconcentrates in the skin, bones, kidneys, and liver of fish (Falconer, 1999). Also when lead concentrations in algae exceed 500 ppb, enzymes needed for photosynthesis are inhibited. When less photosynthesis takes place, the algae will produce less food and therefore will not grow as much. Reduction in algal growth leads to less food for animals; this has negative effect on the entire ecosystem. Fish are more sensitive to high lead concentrations than algae. When lead levels exceed 100 ppb, gill function is affected (Falconer, 1999). Embryos and fry are more sensitive to the toxic effects of lead than the adult fish (Falconer, 1999). Lead is more toxic at lower pH and in soft water (Falconer, 1999; Wright and Welbourn, 2002). The increase in the levels of Pb was in the order of KHO-SD > LCA-SD > LPZ-SD > KJE- SD > SVJ-SD. The levels of Pb were higher in the sediments compared with the levels in the soil around the industrial estates. 139 UNIVERSITY OF IBADAN LIBRARY Table 4.24: Pearson correlation coefficient results between parameters determined in effluent channel sediments. pH Pb Cd Zn Cu Cr Ni OC OM Sand Clay Silt pH 1 b Pb .465 1 b Cd 0.183 0.543 1 b b Zn 0.040 0.400 0.926 1 a a Cu -0.261 -0.192 0.090 0.248 1 b b Cr -0.009 0.209 0.552 0.616 0.212 1 b b b b Ni -0.123 0.125 0.717 0.790 0.331 0.709 1 b b b OC -0.058 0.338 0.406 0.371 0.172 0.026 0.171 1 b b b b OM -0.038 0.367 0.405 0.367 0.155 0.009 0.171 0.990 1 b a a Sand -0.457 -0.097 0.169 0.140 0.034 0.102 0.107 0.258 0.255 1 b a b b b Clay 0.389 -0.033 -0.238 -0.178 0.007 -0.131 -0.091 -0.343 -0.334 -0.933 1 b b Silt 0.292a 0.198 -0.008 -0.054 -0.032 -0.127 -0.106 0.132 0.115 -0.552 0.326 1 a Significant at the 0.05 level. b Significant at the 0.01 level. 140 UNIVERSITY OF IBADAN LIBRARY 4.4.2: Cadmium The Cd ranged between 16.4 μg/g and 21.7 μg/g with mean values of 19.9±1.4 μg/g for KHO-SD (Basic metal industry type) while the SVJ-SD (Food & Beverage) ranged within 0.14-0.66 with mean values of 0.43±0.26 μg/g. The concentrations of Cd in sediments from industrial channels in Ikeja industrial Estate were higher than the corresponding soil (Table 4.29). This reflects the effect of sediments as a sink for metals. The levels of Cd in LPZ-SD and LCA-SD from effluent channels in Ilupeju industrial estate were 1.0±0.6 μg/g and 0.9±0.4 μg/g respectively. The levels in KJE-SD, KHO-SD, SVJ-SD, LPZ-SD and LCA-SD were about 22 and 8; 33 and 13; one respectively; 2 and 1; 2 and 1 folds greater than the average level in sediment quality guidelines (CCME, 1995 and ANZECC/ARMCANZ, 2000) respectively. The increase in the levels of Cd are in the order of KHO-SD > KJE-SD> LPZ-SD > LCA-SD > SVJ-SD. 4.4.3: Zinc The level of Zn in KJE-SD (effluent channels from Ikeja Industrial Estate) was highest with value 2213±504 μg/g while the lowest value was obtained from LPZ-SD (effluent channels from Ilupeju Industrial Estate) 242±18 μg/g. The high level of Zn concentration in the effluent channels from Ikeja industrial estate could be associated to anthropogenic sources. The concentration of Zn was higher in the soil quality studied from Ikeja industrial area compared to the soil quality from other industrial estates. The results obtained for KHO-SD (2200±320 µg/g) and LPZ –SD (242±18 µg/g) are greater than report gave by (Ajibola and Ladipo, 2011) on the levels of Zn in sediment effluent channels of PZ (141±54 µg/g) and steel industry (614±380 µg/g). The levels in KJE-SD, KHO-SD, SVJ-SD, LPZ-SD and LCA- SD were 18 and 11; 18 and 11; 3 and 2; 2 and 2; 2 and 2 folds greater than the average level in sediment quality guidelines (CCME, 1995 and ANZECC/ARMCANZ, 2000) respectively. The increase in the levels of Zn was in the order of KJE-SD >KHO-SD > SVJ-SD > LCA-SD > LPZ-SD. The high concentration of Zn though not usually a cause for human health have greater impacts in the health of aquatic ecosystem. 4.4.4: Copper Copper concentrations in the sediments are in the decreasing order of KJE-SD (128±62µg/g); LPZ-SD (75±27); LCA-SD (70±31µg/g); SVJ-SD (70±15µg/g); and KHO-SD (49±5). The 141 UNIVERSITY OF IBADAN LIBRARY average Cu content in soils/sediments is considered to be about 30 mg/kg (Adriano, 2001). The levels in sediment studied are greater than the average kevel. The levels of Cu in sediments channel of Ikeja Industrial Estate (KJE-SD and KHO-SD) are higher than the levels in soils around the estate (Table 4.29). Variations in the concentrations of the metals examined reflect anthropogenic input from the industries. The results obtained for LPZ –SD (75±27 µg/g) is greater than report by (Ajibola, and Ladipo, 2011) on the levels of Cu in sediment effluent channels of PZ (22.4±8.8 µg/g) while KHO-SD (49±5 µg/g) is less than the steel industry (73±18 µg/g). The levels in KJE-SD, KHO-SD, SVJ-SD, LPZ-SD and LCA- SD were about 4, 8 and 2; 1, 3 and 1; 2, 4 and 1; 2, 5 and 1; 2, 4 and 1folds greater than the average level in sediment quality guidelines (CCME, 1995; MOEE, 1993 and ANZECC/ARMCANZ, 2000) respectively. Although copper toxicity in humans is rare, aquatic organisms are potentially at risk from Cu exposures (Adriano, 2001). 4.4.5: Chromium The Cr ranged between 12 μg/g and 49 μg/g with mean values of 31±11 μg/g for KJE-SD (Central treatment plant) while the LCA-SD (effluent channels from Ilupeju Industrial Estate) ranged between 6.4-14.7 μg/g with mean values of 11±2 μg/g. The average Cr concentration in soils/sediments was reported to be 40-50 mg/kg (Adriano, 2001). All the sediment samples had lower Cr concentrations than the average reported by Adriano (2001). The results obtained for LPZ -SD (18±4 µg/g) and KHO-SD (basic metal) (28±7 µg/g) are lower than report gave by (Ajibola, and Ladipo, 2011) on the levels of Cr in sediment effluent channels of PZ (144±24 µg/g) and steel industry (146±23 µg/g). The levels in KJE-SD, KHO-SD, SVJ-SD, LPZ-SD and LCA-SD were about one fold greater than the average level in sediment quality guidelines (CCME, 1995 and MOEE, 1993 sediment qualities) for all the sediments studied respectively. The levels of Cr in the sediments were higher than the levels in the soil around the estates. Variations in the concentrations of the metals examined reflect anthropogenic input from the industries. The levels of Cr in the sediments are in the order of KJE-SD >KHO-SD > LPZ-SD > SVJ-SD > LCA-SD. 4.4.6: Nickel The level of Ni in KJE-SD (effluent channels from Ikeja Industrial Estate) was highest with mean value 34±10 μg/g while the lowest mean value was obtained from SVJ-SD (Food & beverages) 6.5±3.5 μg/g. The average Ni concentration in soils/sediments is reported as 20 142 UNIVERSITY OF IBADAN LIBRARY mg/kg. All the sediment samples except KJE-SD and KHO- SD had lower Ni concentrations than the average reported by Adriano (2001). However, Ni is relatively less toxic to invertebrates; it can be toxic to aquatic organisms such as reduction in skeletal calcification and diffusion capacity of gills (Moore, 1991). The sediments from KJE-SD and KHO- SD may affect fish when eventually discharged into the water bodies. The results obtained for LPZ -SD (9±3 µg/g) and KHO-SD (22±6 µg/g) are lower than report gave by (Ajibola, and Ladipo, 2011) on the levels of Ni in sediment effluent channels of PZ (41±28 µg/g) and steel industry (107±41 µg/g). The levels in KJE-SD (two folds), while others (KHO-SD, SVJ-SD, LPZ-SD and LCA-SD) are one fold greater than the average level in (MOEE, 1993) quality guideline. The levels of Ni in the sediments were higher than the levels in the corresponding soils around the various estates. The levels of Ni in the sediments are in the order of KJE-SD >KHO-SD > LCA-SD > LPZ-SD > SVJ-SD. Generally, the concentrations of metals in the sediment were higher in effluents channels from Ikeja industrial estates compared with other studied estates. 4.5 MULTIVARIATE ANALYSIS OF HEAVY METALS DISTRIBUTION IN SEDIMENTS. In order to reduce the relatively large number of variables to a smaller number of orthogonal factors, the original data obtained was processed by multivariate statistical methods. The distribution pattern of individual associations of elements in sediments were determined by Principal Component Analysis and Correspondence Analysis (CA). Table 4.25 shows the eigen values from the PCA of the sediments analysed. Based on the eigen values, the first two factors with the highest values were used. The two factors explained 96.3% of the total variance. The first factors explained 69.4% of the total variance which was strongly and positively related to Pb and Cd while the second explaining 26.9% of the total variance showed negative loadings in Zn, Cr, Ni and Cu as shown in Figure 4.23. The first component which accounts for 69.4% variance indicates that points KJE-SD and KHO-SD had elevated levels of total metals levels. As shown from figure 4.23, the points were not clustered meaning that there were variations in metal levels in sediments for the various points. This could be as a result of various industrial processes. Points to the left SVJ- 143 UNIVERSITY OF IBADAN LIBRARY Table 4.25: Principal component analysis of sediments for heavy metal data. Initial Eigen Percentage Cumulative Factor Values Variance Percentage 1 3.788 69.39 69.39 2 1.470 26.92 96.31 3 0.116 2.12 98.43 4 0.078 1.43 99.86 5 0.005 0.09 99.95 6 0.002 0.04 100 144 UNIVERSITY OF IBADAN LIBRARY 3 2 Pb KHO-SD 1 0 LCA-SD Cd LLPCZA-S-S1DD12 SVJL-PSZD-SD3 CZrn LPZ-SD2 -1 Ni -2 Cu KJE-SD -3 -1 0 1 2 3 4 Component 1 Fig. 4.23: Principal component bi-plot of sediment metal data. 145 U Component 2NIVERSITY OF IBADAN LIBRARY SD, LPZ-SD1, LPZ-SD2, LPZ-SD3, LCA-SD1 and LCA-SD2 from the origin indicate low levels of Pb, Cd, Zn, Cu, Cr and Ni while points on the right KJE-SD and KHO-SD of the origin indicate high levels of Pb, Cd, Zn, Cu, Cr and Ni. Figure 4.24 shows the CA relay plot of the categorisation result of the heavy metals which confirm that Zn and Cd are anthropogenic by source and Ni, Cr, Pb and Cu from geological bedrock formation. The Hierarchical cluster analysis (HCA) were classified into three clusters (Figure 4.25).There were three sites in cluster 1 (SVJ-SD, LPZ-SD2, LPZ-SD3), three sites in cluster 2 (LPZ-SD1, LCA-SD1, LCA-SD2) and two sites in cluster 3 (KJE-SD, KHO-SD). The decreasing pollution order of heavy metal concentrations in the effluent channel sediments are Cluster 3 > cluster 2 > cluster1. From the statistical analysis, it was established that sediments samples from effluent channels from Ikeja industrial area had highest concentration of heavy metal. 4.6. COMPARISON OF METALS IN SEDIMENTS WITH SEDIMENT QUALITY GUIDELINES. Table 4.26 shows the levels of the metals studied compared to Canadian sediment quality guidelines, Ontario sediment quality guidelines and Australian sediment Quality Guidelines. Pb levels in SVJ-SD are less than Australian sediment Quality Guidelines but greater than the other two quality guidelines. The other sediments studied had the levels of Pb greater than Canadian sediment quality guidelines and Ontario sediment quality guidelines. Cd in SVJ-SD sediment is lower than the three quality standard guidelines, while LPZ-SD and LCA-SD are lower than Australian sediment Quality Guidelines. Cr levels in all the sediments are lower than the sediment quality standard guidelines while Ni in SVJ-SD; LPZ-SD and LCA-SD are lower than the Ontario sediment quality guidelines. Generally, the sediments studied had Pb, Cd, Zn, and Cu higher than the sediment quality guidelines. 146 UNIVERSITY OF IBADAN LIBRARY 100 90 80 70 60 50 40 30 20 10 Fig. 4.24: Correspondence analysis relay plot of sediments for six metals. 147 U Relay indexNIVER ZnSITY Cd OF Ni IBA CrDAN Pb L Cu IBRARY 0 -100 -200 -300 -400 -500 -600 -700 1 2 3 4 5 6 7 8 9 Observations Fig 4.25: Hierarchical cluster analysis dendrogram for sediment samples. 148 U SimilarityNIVER SVJ-SDSIT LPZ-SD2Y LPZ-SD3OF LPZ-SD1 IBA LCA-SD1DA LCA-SD2N KHO-SDLIB KJE-SDRARY Table 4.26: Comparison of heavy metals in sediment channel with sediment quality guidelines. Australia This Study Canada Canada- & New (CCME, Ontario Zealand 1995) (MOEE, (ANZECC/ 1993) ARMCANZ, 2000) Pb 80 453 46 105 152 35 31 50 Cd 13 19.9 0.43 1 0.9 0.6 0.6 1.5 Zn 2210 2200 326 242 300 123 120 200 Cu 128 49 70 75 70 35.7 16 65 Cr 31 28 15.8 18 19 37.3 26 NA Ni 34 22 6.5 9 11 NA 16 NA NA- not available 149 U Site Code NIV KJE-SD ER KHO-SD SITY SVJ-SD OF LPZ-SD IBA LCA-SD DAN LIBRARY 4.7 CORRELATION OF METALS IN EFFLUENTS AND SEDIMENT Heavy metals are natural constituent in sediments but due to industrialization, the levels have increased greatly. The contamination of sediments of water bodies is one of the worst problems in the environment due to the sediment acting as both a sink and a source for heavy metal to aquatic system. The sediments along the effluent channels reflected significant level of heavy metal accumulation when compared to levels in the effluent. The heavy metals analysed for effluents and sediments showed positive significant correlations for Zn (r = 0.644) and Cr (r = 0.748) while Pb (r = 0.404) and Ni (r =0.464) showed low positive significant correlations as shown in (Table 4.27). This shows that Zn and Cr in the effluent and sediments originated from a similar source. Therefore means that, these metals levels in sediments are anthropogenic in nature. 4.8: SOIL QUALITIES AROUND THE EFFLUENT CHANNELS 4.8.1. Characteristics of Soil around Ikeja industrial estate The pH of soil in Ikeja industrial estate ranged between 6.4 and 8.5, while the control sample pH for Ikeja industrial estate ranged within 6.2-7.8. The soil pH in Ikeja industrial estate was near neutral. The % organic matter in Ikeja soil ranged between 0.7 and 7.1 while points KBP-SL1 and KMB-S4 had very high organic matter content for all the months sampled. % sand ranged between 75.2 and 97.2, while point KBP-SL1 had the highest means value 95.0±2.0 for all the period sampled. % clay and % silt ranged between 1.7 and 10.0 and 0.2 and16.9 respectively while KMB-SL4 had the highest value of % clay and % slit for all the period of sampled (Table 4.28). Pb concentrations were found to be quite high, ranging from 5 µg/g - 217 µg/g. Point KGI- SL2 had the highest concentration of 100±70 µg/g (Table 2.29) At this point it was observed that the sediments from the effluent channel are often excavated to the surface top soil. Cd concentration was found to be high. The concentrations ranged from 0.3µg/g-5.1µg/g, with KMB-S4 having the highest concentration of 3.0±1.3 µg/g. The high level of Cd in the soil could also be attributed to overflow of effluent from KMB- EF channel to the surface of the road. The highest concentration of Cd was observed in KMB-EF. 150 UNIVERSITY OF IBADAN LIBRARY Table 4.27: Correlation coefficient (p=0.05) for heavy metal levels in sediment/effluent. Heavy metals Pb Cd Zn Cu Cr Ni Coefficient 0.404 -0.251 0.644* -0.279 0.748* 0.461 * strong correlation. 151 UNIVERSITY OF IBADAN LIBRARY Table 4.28: pH, organic matter and particle size characteristics of soil from Ikeja Iindustrial Estate. Organic Organic Sand Clay Silt Site Code pH Carbon Matter (%) (%) (%) (%) (%) Mean 6.4±0.4 2.0±1.4 3.0±2.5 95.0±2.0 3.1±0.8 2.5±1.4 KBP-SL1 Range 5.8-6.9 0.6-4.10 0.7-70 91.5-97.2 2.2-4.8 0.2-47 Mean 6.2±0.6 3.1±1.1 5.4±2.0 91±2.5 2.6±1.0 6.3±2.2 KGI-SL2 Range 5.0-6.9 0.7-41 1.2-7.1 85.9-93.2 2.0-5.0 3.7-10.1 Mean 6.5±0.7 2.4±0.8 4.1±1.3 89.7±4.3 5.1±2.3 5.3±2.2 KWS-SL3 Range 5.6-7-8 1.0-3.7 1.7-6.4 84.7-95.8 2.0-8.2 2.1-8.4 Mean 7.5±0.3 2.5±1.1 4.3±1.9 86.8±7.7 5.9±3.3 7.5±`4.9 KMB-SL4 Range 7.0-7.9 0.6-4.1 1.1-7.1 75.2-96.5 1.7-10.0 1.4-16.9 Mean 7.3±0.7 2.5±0.9 4.4±1.6 93.4±9.6 4.3±1.6 5.6±2.5 K-SL Range 6.4-8.5 0.8-4.2 1.3-7.4 86-118.7 2.4-6.3 2.4-8.6 Mean 7.3±0.5 1.0±0.4 1.6±0.6 8 8.8±4.3 5.5±2.5 5 .7±2.6 K-CTR-SL1 Range 6.2-7.8 0.6-1.6 1.0-2.8 83.2-94.2 2.3-9.9 2.0-10.1 Mean 7.3±0.5 1.0±0.2 1.7±0.4 90±3.4 4.8±1.8 4.9±2.5 K-CTR-SL2 Range 6.2-7.8 0.7-1.3 1.2-2.2 86.2-95.5 2.9-8.0 1.3-8.7 Mean 7.3±0.5 1.0±0.3 17±0.5 89.6±3.7 5.2±2.1 5.3±2.5 K-CTR-SL Range 6.2-7.8 0.7-1.5 1.1-2.5 85.0-94.4 2.6-8.9 1.9-9.4 152 UNIVERSITY OF IBADAN LIBRARY Table 4.29: Concentrations (µg/g) of heavy metals in soil from Ikeja Industrial Estate. Sample Lead Cadmium Zinc Copper Chromium Nickel Code Mean 36±39 2.0±0.5 193±130 37±17 15±12 8.1±4 KBP-SL1 Range 5-125 1.2-2.8 99-5110 23-72 6-48 2-17 Mean 100±70 2.0±1.4 335±44 70±44 24±21 12±8 KGI-SL2 Range 30-217 0.3-47 249-422 22-176 4-65 5-27 Mean 40±21 1.3±0.6 181±78 26±2 17±4 9±3 KWS-SL3 Range 11-75 0.5-2.3 99-321 22-30 8-23 6-14 Mean 29±8 3.0±1.3 414±180 28±11 16±7 8.2±4 KMB-SL4 Range 21-42 0.7-5.1 221-767 17-43 9-29 3-15 Mean 52±21 2.0±0.6 280±98 41±14 18±8 10±3 K-SL Range 20-90 1.1-2.90 201-505 23-73 7-32 7-16 Mean 60±12 2.2±1.0 120±22 71±14 98±7 19±5 K-CTR-SL1 Range 38-77 1.0-4.4 96-162 42-97 87-111 11-25 Mean 58±10 2.0±0.3 114±20 70±14 99±7 18±3 K-CTR-SL 2 Range 39-70 1.1-2.2 97-166 44-99 90-109 14-22 Mean 59±11 2.0±0.6 117±21 71±14 99±7 18±4 K-CTR-SL Range 38-72 1.0-3.0 97-164 43-98 88-109 13-24 153 UNIVERSITY OF IBADAN LIBRARY Zn ranging from 99-767 µg/g, with point KMB-EF having the highest mean level of 414±175 µg/g. The levels of clay were observed to be high for this soil and clay binds up metal because clay minerals are hydrous aluminium, magnesium or iron silicates. It therefore means that the metals are associated to the clay particles in the soil. Cu ranged from 17-176 µg/g with point KGI-SL2 had the highest concentration of 70±44 µg/g. Cr and Ni ranged from 4-65 µg/g and 2-27 µg/g respectively with point KGI-SL2 had the highest concentration of 24±2 µg/g and 12±8 µg/g respectively. The high concentration of Cr and Ni in point KGI-SL2 could be associated to the high level of % organic matter. The control soil samples (K-CTR-SL) are higher in Pb, Cu, Cr and Ni when compared to the soil from Ikeja industrial estate (K-SL). Generally, soil collected around KGI-SL2 (basic metal industry) had the highest concentrations of Pb, Cu, Cr and Ni while concentrations of Cd and Zn were highest in KMB-SL4 (pharmaceutical industry) sampling point. The PCA diagram in Figure 4.26 shows KGI-SL2 with the highest concentrations of Pb, Zn, Cu, Ni and Cr. The corresponding analysis in Figure 4.27 shows the relative abundance of the metals which were in the order: Zn > Cd > Pb > Ni > Cu > Cr. The cluster analysis dendrogram in Figure 4.28 shows three clusters of KBP-SL1 and KWS-SL3, K-CTR-SL1 and K-CTR-SL2; and KGI-SL2 and KMB-SL4. Table 4.30 shows the Pearson correlation coefficient results between parameters determined in soil around Ikeja industrial estate. There were low correlation between Pb and Cd (r=0.268); Pb and %organic matter (r = 0.311); Pb and Cu (r = 0.285); Zn and % Silt (r = 0.327) while negative low correlation existed between Ni and %organic matter (r = -0.289); at 95% confidence level. At 99% confidence level there were correlation between % organic matter and Zn; Cd and Ni. From the analysis of variance at 95% confidence limit, there were significant difference in pH, %organic matter, %sand and clay, %silt, Pb, Zn, Cu, Cr and Ni between the sampling 154 UNIVERSITY OF IBADAN LIBRARY 3 KGI-SL2 2 Pb 1 Zn Cu 0 KM B-SL4 Cd Ni K-CTR-SL1 KKWBP Cr K-CTR-SL2 S--SSLL31 -2 -1 0 1 2 3 Component 1 Fig 4.26: Principal component bi-plot of data on soil metals concentrations in Ikeja Industrial Estate. 155 U Component 2NIVERSITY OF IBADAN LIBRARY 100 90 80 70 60 50 40 30 20 10 Fig 4.27: Corresponding analysis relay plot for six metals in Ikeja Industrial Estate. 156 UN Relay indexIVERSI ZnTY O CdF IB PbA NDi A Cu N LI Cr BRARY 0 -10 -20 -30 -40 -50 -60 -70 -80 -90 1 2 3 4 5 6 7 Observations Fig 4.28: Cluster analysis dendrogram for soil using the metals concentrations in Ikeja Industrial Estate. 157 U SimilarityNIVERS KWS-SL3ITY KBP-SL1 OF K-CTR-SL1IBAD K-CTR-SL2AN KGI-SL2LIBR KMB-SL4ARY Table 4.30: Pearson correlation coefficients for metal contents of soil around Ikeja Industrial Estate. pH Pb Cd Zn Cu Cr Ni % OC % OM % Sand % Clay % Silt pH 1 a Pb -0.325 1 a Cd 0.213 0.268 1 Zn -0.084 0.191 0.033 1 a Cu 0.074 0.285 0.082 -0.229 1 b b b Cr 0.409 0.160 0.128 -0.534 0.487 1 b b b b b Ni 0.134 0.468 0.420 -0.355 0.531 0.692 1 b a b b a % OC -0.369 0.312 0.005 0.574 -0.216 -0.580 -0.289 1 b a b b a b % OM -0.369 0.311 0.002 0.575 -0.209 -0.576 -0.289 1.000 1 % Sand -0.120 -0.050 -0.199 -0.206 -0.021 -0.074 -0.083 -0.162 -0.161 1 b % Clay 0.240 -0.155 0.019 -0.004 -0.077 0.183 0.013 -0.112 -0.114 -0.846 1 a a b b b b % Silt 0.006 0.193 0.289 0.327 0.085 -0.032 0.108 0.346 0.346 -0.918 0.566 1 a Significant at the 0.05 level. b Significant at the 0.01 level. 158 UNIVERSITY OF IBADAN LIBRARY points during the period of sample. Cd shows no significant difference between the sampling points. Paired student t-test (95% confidence limit) showed no significant difference in Pb and Cd concentrations between soil from the ikeja industrial estate and the control soil while Zn, Cu, Cr and Ni concentrations showed a significant difference between the soil sample and the control soil. 4.8.2 Characteristics of Soil around Isolo Industrial Estate The pH of soil in Isolo industrial estate ranged within 6.7-8.8 while the control pH ranged within 7.2-8.6. The soil pH was near neutral (Table 4.31). This was similar to the pH range obtained from Ikeja industrial estate soil. The % organic matter ranged from 0.5-5.7 while SJW-SL1 had the highest level of % organic carbon of 2.2±2.1 for all the months sampled. % sand ranged within 75.9-97.0 with point SAF-SL4 had the highest level of 94.1±1.9 for all the period of sampling. % clay and % silt ranged within 2.6-14.0 and 0.4-18.4 respectively with SJW-SL1 had the highest value of % clay 87.9±6.6 and % silt 4.6±5.9 for all the period of sampling. Pb concentration in soil was found to be high compared to the control soil; this ranged within 3-65 µg/g with points SJW-SL3 and SAF-SL5 had high concentrations for all the period of sampling (Table 4.32). Cd concentration ranged within 0.12-1.75 µg/g with point SJW-SL3 and SVJ-SL6 had high concentration of 0.82±0.48 µg/g and 0.76±0.54 µg/g respectively for all the period of sampling. The Cd concentration in the soil at SJW-SL3 are higher compared to the control soil, but the average Cd in the soil samples(S-SL) were lower than the average of the control(S-CTR-SL). There was no significant difference in Cd between the soil sample from the estate and the control sample. Zn ranged within 13-135 µg/g with SJW-SL3 and SVJ-SL6 having the highest concentration of 57±22 µg/g and 56±40 µg/g respectively. The soil sample (S-SL) Zn average was lower than the average control (S-CTR-SL). There was no significant difference in Zn between the soil sample and the control sample. 159 UNIVERSITY OF IBADAN LIBRARY Table 4.31: pH, organic matter and particle size characteristics of soil from from Isolo Industrial Estate. Organic Organic Site Code pH Sand Clay Silt Carbon Matter (%) (%) (%) (%) (%) Mean 7.9±0.8 1.3±1.2 2.2±2.1 87.9±6.6 7.5±4.0 4.6±5.9 SJW-SL1 Range 7.0-9.1 0.3-3.3 0.5-5.7 75.9-96.0 3.6-14.0 0.4-18.4 Mean 7.8±0.8 0.7±0.4 1.2±0.7 92.7±4.1 5.1±1.8 2.2±2.7 SJW-SL2 Range 7.1-9.1 0.4-1.5 0.6-2.7 81.9-95.0 3.6-8.4 0.2-9.7 Mean 7.5±0.8 1.2±0.5 2.2±0.9 92.4±1.5 5.1±0.9 2.4±1.5 SJW-SL3 Range 6.4-8.5 0.4-2.0 0.9-3.4 90.9-96.0 3.6-6.2 0.4-4.5 Mean 7.7±08 0.7±0.3 1.2±0.4 94.1±1.9 4.7±1.5 1.3±0.7 SAF-SL4 Range 6.6-8.8 0.4-1.2 0.8-2.1 92.2-97.0 2.6-7.2 0.4-2.4 Mean 7.9±0.8 1.3±1.1 2.2±1.8 93.4±1.4 5.1±0.8 1.3±0.6 SAF-SL5 Range 6.6-8.9 0.4-3.2 0.6-5.5 91.9-96.0 3.6-5.8 0.4-2.5 Mean 7.4±0.9 1.3±0.7 2.2±1.2 92.3±3.2 4.4±1.2 3.3±3.5 SVJ-SL6 Range 6.5-8.7 0.6-2.8 1.0-4.9 86.6-95.7 2.6-5.9 0.6-9.8 Mean 7.7±0.7 1.1±0.4 1.9±0.6 92.1±1.6 5.3±1.2 2.5±1.0 S-SL Range 6.7-8.8 0.6-1.6 1.1-2.8 89.6-94.2 3.8-7.8 1.4-4.7 Mean 7.6±0.5 0.5±0.1 0.9±0.2 85.2±4.6 7.1±2.4 7.7±3.1 S-CTR-SL1 Range 7.2-8.6 0.3-0.7 0.5-1.2 76.0-90.1 4.6-12.3 3.4-11.7 Mean 7.8±0.4 0.5±0.1 0.9±0.2 87.6±1.8 5.2±0.8 7.2±1.3 S-CTR-SL2 Range 7.3-8.3 0.3-0.8 0.6-1.4 85.5-90.7 3.8-6.4 5.4-9.4 Mean 7.7±0.2 0.5±0.1 0.9±0.2 86.4±3.0 6.2±1.3 7.5±1.9 S-CTR-SL Range 7.4-8.0 0.4-0.7 0.6-1.2 80.7-90.3 4.2-8.7 5.0-10.6 160 UNIVERSITY OF IBADAN LIBRARY Table 4.32: Concentration of metals (µg/g) of soil from Isolo Industrial Estate. Site Code Lead Cadmium Zinc Copper Chromium Nickel Mean 18±7 0.49±0.43 42±24 15±7 5.9±2.2 4.9±2.4 SJW-SL1 Range 12-32 0.12-1.37 13-76 7-33 3.0-9.0 2.1-9.3 Mean 14±6 0.72±0.42 49±21 17±13 5.1±3.1 6.0±3.1 SJW-SL2 Range 3-21 0.32-1.70 30-96 1-43 2.2-10.1 1.3-11.9 Mean 26±11 0.82±0.48 57±22 41±56 8.4±3.7 7.5±3.3 SJW-SL3 Range 15-44 0.16-1.52 35-96 11-195 3.9-16.4 2.8-11.3 Mean 20±6 0.66±0.33 45±20 11.4±3 5.9±1.7 5.6±2.8 SAF-SL4 Range 13-29 0.30-1.10 21-79 7-15 2.6-6.6 1.9-11.3 Mean 20±11 0.61±0.31 46±25 21±20 6.0±2.0 4.6±2.3 SAF-SL5 Range 10-40 0.20-1.13 23-85 6-76 1.7-8.2 1.7-8.8 Mean 28±20 0.76±0.54 59±40 40±14 7.8±1.9 5.3±2.3 SVJ-SL6 Range 13-65 0.17-1.75 33-135 13-64 4.9-9.5 2.4-8.6 Mean 21±8 0.68±0.33 50±22 24±15 6.5±1.3 5.6±1.7 S-SL Range 13-33 0.36-1.29 30-86 14-66 4.2-8.0 2.8-8.2 Mean 2±1 0.67±0.40 58±16 23±5 9.5±2.7 7.7±2.4 S-CTR-SL1 Range 1-3 0.32-1.53 30-82 15-31 6.8-14.0 5.2-12.5 Mean 3±1 0.95±0.33 58±11 21±2 8.4±0.5 8.7±2.4 S-CTR-SL2 Range 1-4 0.54-1.65 43-78 17-25 7.7-9.4 6.3-13.2 Mean 3±1 0.81±0.23 58±11 22±3 8.9±1.4 8.2±1.5 S-CTR-SL Range 1-3 0.54-1.15 44-80 16-26 7.2-11.1 6.2-11.1 161 UNIVERSITY OF IBADAN LIBRARY Cu concentration was highest in points SJW-SL3 and SVJ-SL6 with average concentration of 41±56 µg/g and 40±14 µg/g respectively. The concentration of Cu in the soil sample was higher than the concentration in the control sample. There was no significant difference in Cu between the soil sample and the control sample. Cr concentration ranged from 1.7-16.4µg/g with points SJW-SL3 and SVJ-SL6 had the highest concentration. The average Cr concentration in SJW-SL3 and SVJ-SL6 are 8.4±3.7 µg/g and 7.8±1.9µg/g respectively. Ni concentration ranged within 1.3-11.9 µg/g with point SJW-SL3 having the highest concentration of 7.5±3.3 µg/g. Paired student t-test showed significant difference in Cr and Ni concentrations between sample soils and control soil at 95% confidence level. Generally, sampling points SJW-SL3 and SVJ-SL6 had the highest concentrations of the metals analysed as shown in the PCA diagram in Figure 4.29. The cluster analysis dendrogram in Figure 4.30 shows three clusters for the sampling points which are in line with the PCA deduction The corresponding analysis in Figure 4.31 shows the relative abundance of the metals. This shows the relative abundances are in order of Ni > Cr >Zn > Cd > Cu > Pb. Table 4.33 shows the Pearson correlation coefficient results between the parameters determined in soil around Isolo industrial estate. There are low correlation between Ni and % sand (r = -0.258); % organic matter and % clay(r = 0.286) at 95% confidence level. At 99% confidence level there are low correlation between % organic matter and Pb (r = 0.335); Cd and Ni (r = 0.293). Analysis of variance at 95% confidence limit showed significant difference for Pb, Cu, Ni, %organic matter, %sand, %clay and %silt between the sampling points. pH, Cd, Zn and Cr showed no significant difference between sampling points during the period of sampling. Paired student t-test (95% confidence limit) showed significant difference in Pb, Cr and Ni concentration between soil from Isolo Industrial Estate and the control. Cd, Zn, and Cu concentration did not show any significant difference between sample soil and the control soil. 162 UNIVERSITY OF IBADAN LIBRARY 3 2 SVJ-SL6 Pb Cu SJW-SL3 1 SAF-SL5 Zn 0 Cd Cr SJW-SL1 SAF-SL4 SJW-SL2 Ni -1 S-CTR-SL1 S-CTR-SL2 -1 0 1 2 3 Component 1 Fig 4.29: Principal Component bi-plot of data on soil metals concentrations in Isolo Industrial Estate. 163 UN Component 2IVERSITY OF IBADAN LIBRARY 0 -1 -2 -3 -4 -5 -6 -7 -8 -9 -10 -11 -12 -13 -14 -15 -16 -17 -18 -19 1 2 3 4 5 6 7 8 9 Observations Fig 4.30: Cluster analysis dendrogram of data on soil metals in Isolo Industrial Estate. 164 U SimilarityNIVER SAF-SL4S SJW-SL1ITY SAF-SL5 O SJW-SL2F I S-CTR-SL1BA S-CTR-SL2DA SJW-SL3N L SVJ-SL6IBRARY 100 90 80 70 60 50 40 30 20 10 Fig 4.31: Correspondence analysis relay plot for heavy metals in Isolo Industrial Estate. 165 U Relay indexNIVERS NiITY O CrF IBZnA Cd DAN Cu LIB Pb RARY Table 4.33: Pearson correlation coefficient for metal contents of soil around Isolo Industrial |Estate. pH Pb Cd Zn Cu Cr Ni % OC % OM % Sand % Clay % Silt pH 1 Pb -0.204 1 b Cd 0.396 -0.186 1 b b Zn -0.320 0.492 -0.114 1 Cu -0.052 0.190 0.173 -0.045 1 Cr -0.192 -0.028 0.182 0.047 -0.050 1 b b Ni -0.117 0.055 0.293 0.477 0.138 0.120 1 b % OC -0.122 0.335 0.102 0.013 0.216 -0.008 0.015 1 b b % OM -0.122 0.335 0.102 0.013 0.216 -0.008 0.015 1.000 1 b a % Sand 0.003 0.337 -0.095 -0.038 -0.038 -0.026 -0.258 -0.123 -0.123 1 a a b % Clay 0.033 -0.175 -0.004 -0.035 -0.057 -0.020 -0.022 0.286 0.286 -0.635 1 b b b % Silt -0.020 -0.327 0.125 0.064 0.082 0.046 0.335 -0.013 -0.013 -0.892 0.218 1 a Significant at the 0.05 level. b Significant at the 0.01 level. 166 UNIVERSITY OF IBADAN LIBRARY 4.9: COMPARISON OF HEAVY METALS IN SOILS STUDIED WITH SOIL QUALITY GUIDELINES Metal levels in the study were compared with the soils from industrial and commercial areas criteria for Canada, Hong kong, Netherland, Norway and Switzerland (Table 4.34).The average Pb, Cd and Zinc in soil from Ikeja industrial estate (this study) exceeded the Norwegian and Swiss guides while the metal levels in Isolo industrial estate soil (this study) were lower than the above guides. The averages Cu, Cr, and Ni in the soils from both studies were below the guidlelines of Canada, Hong Kong, Netherland, Norway and Switzerland. Cd and Zn were established as metals of significant environmental concern at Ikeja industrial estate soil compared with Isolo industrial estate soil. Levels of Cu, Cr, and Ni did not reveal appreciable contamination from the activities around the industrial estate. This could have a link to the geological formation of soil in this area. (Zupan et al., 2000). 4.10: EFFECT OF SEASONAL VARIATION ON THE SOIL QUALITY 4.10.1: pH and % Organic matter The pH of the Ikeja and Isolo industrial estate soil were generally near basic for both estate while the control for both estate were near basic too as shown in Table 4.35a and Table 4.36a respectively. There were no clear seasonal variations for soil pH as shown from the monthly variation in pH values in Figure 4.32. Soils generally had pH values within the range of 4.0 – 8.5, 3+ due to buffering by Al at the lower end and CaC03 at the upper end of the range (Wild, 1998). Generally, soils have several mechanisms which serve to buffer pH to varying exchange reactions. Since soils are affected by change on redox potential which occurs in most tropical soils, the pH range recorded in these soils indicate oxidizing conditions. Heavy metals are most mobile and bioavailable under acidic conditions. The bioavailability of heavy metal ions in soil is influential by many factors. The most important factor affecting metal availability is soil pH (Tordoff et al., 2000; Jordan et al., 2002). Brady (1984) stated that the normal pH is 5 – 7 in soils of humid regions and 7-9 in soils of arid regions. The optimal pH for most arable crops is 6.5 on mineral soils and 5.5 on peaty soil which means that the upper most layer of the regolith does support crop production and vegetation favourably. The percentage organic matter ranged within 1.3 – 7.4 and 1.1 – 2.8 for Ikeja and Isolo Industrial Estate respectively as shown in Table 4.35a and Table 4.36a while the controls respectively ranged within 1.1 – 2.5 and 0.6 – 1.2. The seasonal variations in % organic matter are shown in Figure 4.33 167 UNIVERSITY OF IBADAN LIBRARY Table 4.34: Soil quality criteria for some countries compared with level (μg/g) obtained from this study. Country Definition Pb Cd Zn Cu Cr Ni Reference Mean 52± 21 2.0 ± 0.6 208 ± 98 41 ± 14 18 ± 8 10 ± 3 This study Ikeja, Nigeria (This study) Range 20 – 90 1.1 – 2.90 201 – 505 23 – 73 7 – 32 7 – 16 This study Mean 21 ± 8 0.68 ± 0.33 50 ± 22 24 ± 15 6.5 ± 1.3 5.6 ± 1.7 This study Isolo, Nigeria (This study) Range 13 – 33 0.36 – 1.29 30 – 86 14 – 66 4.2 – 80 2.8 – 8.2 This study Reimann et al. Norway 50 1.0 150 100 100 30 (1997) Reimann et al. Netherland action level 530 12 720 190 380 210 (1997) Guide FOEFL (1987) Switzerland 50 0.8 200 50 - - Value Canada Industrial 600 22 360 91 87 50 CCME (1999) Canada Commercial 260 22 360 91 87 50 CCME (1999) Hong Kong 90 0.9 59 16 - - Chen et al. ( 1997) 168 UNIVERSITY OF IBADAN LIBRARY Table 4.35a: Average seasonal physicochemical quality of soil from Ikeja Industrial Estate. Wet Season Dry Season Site Code pH % OC % OM % Sand % Clay % Silt pH % OC % OM % Sand % Clay % Silt KBP-SL1 6.5±0.2 1.8±1.6 3.1±2.8 95.3±1.7 2.9±0.6 1.9±1.4 6.4±0.5 1.7±1.4 2.9±2.5 94.0±2.2 3.3±0.9 2.9±1.5 KGI-SL2 6.3±0.5 3.0±0.9 5.2±1.5 91.6±1.5 2.4±0.5 6.0±1.9 6.2±0.7 3.2±1.3 5.6±2.2 90.7±3.0 2.8±1.1 6.6±2.7 KWS-SL⁻ 6.5±0.6 2.1±0.5 3.6±0.8 89.9±5.3 4.9±2.9 5.2±2.5 6.5±0.8 2.6±0.9 4.4±1.6 89.5±4.1 5.2±2.1 5.3±2.1 KMB-SL4 7.6±0.2 3.0±1.2 5.1±2.1 87.7±9.6 4.9±3.5 7.7±6.7 7.5±0.4 2.2±1.0 3.7±1.8 86.1±7.1 6.5±3.3 7.4±4.0 K-SL 7.7±0.7 2.7±1.0 4.7±1.8 98.5±14.1 4.2±2.0 5.7±3.3 6.9±0.6 2.4±0.9 4.2±1.5 90.1±3.6 4.4±1.4 5.5±2.2 K-CTR-SL1 7.3±0.4 0.7±0.1 1.1±0.2 88.1±5.6 6.9±3.4 5.0±2.4 7.4±0.6 1.2±0.3 2.0±0.5 89.3±3.8 4.5±1.3 6.2±2.9 K-CTR-SL2 7.3±0.3 0.8±0.2 1.5±0.3 90.5±3.2 5.8±2.0 3.7±1.7 7.3±0.6 1.1±0.2 1.9±0.3 90.2±3.9 4.2±1.4 5.6±2.8 K-CTR-SL 7.3±0.4 0.8±0.1 1.3±0.2 89.3±4.2 6.4±2.7 4.4±1.8 7.3±0.6 1.1±0.2 2.0±0.4 89.7±3.8 4.4±1.3 5.9±2.8 169 UNIVERSITY OF IBADAN LIBRARY Table 4.35b: Seasonal concentration (µg/g) of metals in soil from Ikeja Industrial Estate. Wet Season Dry Season Site Code Lead Cadmium Zinc Copper Chromium Nickel Lead Cadmium Zinc Copper Chromium Nickel KBP-SL1 18±15 1.7±0.5 119±15 42±17 21±19 8±7 47±46 1.9±0.6 242±155 34±19 11±4 8±2 KGI-SL2 73±49 1.0±0.5 334±13 91±59 21±20 9±4 117±80 2.1±1.7 335±58 55±27 26±24 14±9 KWS-SL⁻ 37±14 1.5±0.5 131±29 27±2 17±2 10±3 42±26 1.1±0.7 214±84 24±1 17±5 9±3 KMB-SL4 35±8 3.0±1.9 329±113 35±11 15±9 8±5 26±6 2.2±0.7 470±194 23±8 16±6 8±4 K-SL 43±14 1.9±0.5 239±26 52±14 19±9 9±2 58±23 1.8±0.8 307±121 34±9 18±9 10±4 K-CTR-SL1 54±15 1.8±0.8 117±5 67±17 101±3 16±5 64±9 2.5±1.0 122±29 74±13 96±9 20±5 K-CTR-SL2 54±12 1.7±0.4 110±2 65±14 102±7 17±3 61±9 1.7±0.3 117±27 74±14 97±8 19±3 K-CTR-SL 54±14 1.8±0.6 113±3 66±15 101±5 17±4 62±9 2.1±0.6 119±27 74±13 97±8 20±4 170 UNIVERSITY OF IBADAN LIBRARY Table 4.36a: Average seasonal physicochemical quality of soil from Isolo Industrial Estate. Wet Season Dry Season Site Code pH % OC % OM % Sand % Clay % Silt pH % OC % OM % Sand % Clay % Silt SJW-SL1 7.8±0.6 1.7±1.4 2.9±2.4 84.0±7.5 8.1±4.2 7.9±8.0 8.0±0.9 1.0±1.2 1.7±2.4 90.5±5.0 7.0±4.1 2.4±3.0 SJW-SL2 7.8±0.8 0.5±0.2 0.9±0.3 94.2±0.5 4.4±0.5 1.4±0.0 7.9±0.9 0.8±0.5 1.4±0.8 91.8±5.2 5.6±22 2.7±3.5 SJW-SL⁻ 7.0±0.7 1.7±0.3 2.9±0.4 92.8±2.2 4.4±1.0 2.8±2.0 7.8±0.7 1.0±0.4 1.7±0.7 92.2±0.8 5.6±0.5 2.2±1.2 SAF-SL4 7.7±0.9 0.7±0.2 1.2±0.4 93.7±1.6 4.7±1.2 1.6±0.8 8.1±0.7 0.7±0.3 1.2±0.5 94.3±2.1 4.7±1.8 1.0±0.6 SAF-SL5 7.0±1.1 1.3±1.3 2.3±2.2 93.2±0.2 5.5±0.1 1.4±0.0 8.0±0.9 1.3±1.0 2.2±1.8 93.6±1.9 4.9±1.0 1.3±0.8 SVJ-SL6 7.1±0.9 0.8±0.1 1.4±0.2 94.5±1.6 3.9±1.5 3.9±1.5 7.7±0.7 1.6±0.8 2.7±1.3 90.9±3.3 4.7±0.9 4.4±4.2 S-SL 7.4±0.7 1.1±0.4 1.9±0.7 92.1±1.7 5.2±0.5 2.8±1.4 7.9±0.8 1.0±0.4 1.8±0.6 92.2±1.7 5.4±1.6 2.3±0.6 S-CTR-SL1 7.6±0.6 0.5±0.1 0.9±0.1 87.0±4.6 7.3±1.8 5.7±3.4 7.6±0.5 0.5±0.1 0.9±0.2 83.9±4.5 7.0±2.9 9.1±2.1 S-CTR-SL2 7.9±0.4 0.6±0.1 1.0±0.3 87.5±1.9 5.2±0.8 7.4±1.3 7.8±0.4 0.5±0.1 0.9±0.2 87.6±1.9 5.2±0.8 7.2±1.4 S-CTR-SL 7.7±0.1 0.5±0.1 0.9±0.2 87.3±3.1 6.2±1.2 6.5±2.0 7.7±0.2 0.5±0.1 0.9±0.2 85.8±0.5 6.1±1.5 8.1±1.7 171 UNIVERSITY OF IBADAN LIBRARY Table 4.36b: Seasonal concentration (µg/g) of metals in soil from Isolo Industrial Estate. Wet Season Dry Season Site Code Lead Cadmium Zinc Copper Chromium Nickel Lead Cadmium Zinc Copper Chromium Nickel SJW-SL1 18±9 0.39±0.47 33±4 17±1 5±3 4±2 18±6 0.56±0.44 48±30 14±10 6±2 5±3 SJW-SL2 11±4 0.86±0.13 35±4 20±17 3±1 4±3 16±7 0.63±0.53 58±23 15±12 7±3 7±3 SJW-SL⁻ 24±7 0.72±0.54 39±4 74±83 9±5 9±3 28±13 0.89±0.48 68±21 19±7 6±3 6±3 SAF-SL4 21±8 0.63±0.30 37±2 13±4 5±2 5±4 19±6 0.69±0.37 50±26 11±3 6±2 6±2 SAF-SL5 14±5 0.47±0.18 32±3 34±28 4±2 3±1 25±12 0.71±0.35 56±30 12±5 7±1 6±2 SVJ-SL6 22±9 0.57±0.46 38±6 46±8 6±2 3±1 33±25 0.88±0.59 74±48 36±17 9±1 7±2 S-SL 18±6 0.61±0.26 36±1 34±21 5±1 5±2 23±10 0.73±0.39 59±25 18±4 7±1 6±2 S-CTR-SL1 2±1 0.67±0.41 64±20 21±2 9±3 7±3 2±0.5 0.66±0.43 54±13 24±6 10±3 8±3 S-CTR-SL2 3±1 0.80±0.21 67±12 21±2 8±0.4 9±3 3±0.3 1.04±0.37 52±5 20±3 9±1 9±2 S-CTR-SL 2±1 0.74±0.21 66±14 21±2 9±2 8±2 3±0.3 0.85±0.25 53±7 22±4 9±1 8±2 172 UNIVERSITY OF IBADAN LIBRARY Fig. 4.32: Variations of pH of soils with sampling period. Fig. 4.33: Variations of soil organic matter content with sampling period. 173 UNIVERSITY OF IBADAN LIBRARY The values of % OM as shown in Tables 4.35a and 4.36a for Ikeja and Isolo wet season value are 4.7±1.5 and 1.9±0.7 respectively while the dry season values are 4.2 ± 1.5 and 1.8 ± 0.6. Alloway, (1990) stated that the higher the organic matter content, the more it absorbs heavy metals. This may indicates that organic matter may play a significant role in the absorption of these metals in soils from the different locations of the study area. Organic matter content plays an important role in the soil structure, water retention and in the formation of complexes (Alloway and Ayres, 1997). pH and organic matter content of the soil were not significantly different from one season to another. 4.10.2 Soil Particle Size Characteristics Tables 4.35a and 4.36a gave the percentage sand, clay and silt content of the soil within the study area for Ikeja and Isolo industrial area respectively. The wet season % sand and silt values were: 98.5 ± 14.1 and 5.7 ± 3.3; 92.1 ± 1.7 and 2.8 ± 1.4 respectively. The dry season values were 90.1 ± 3.6 and 5.5 ± 2.2; 92.2 ±1.7 and 2.3 ± 0.6 respectively. T-test (95% confidence level) showed no significant different between the wet seasons and dry season for the soil samples and the control sites. 4.10.3 Lead Pb concentrations obtained are shown in Tables 4.29 and 4.32 for Ikeja and Isolo Industrial area respectively. The Pb concentrations in soil samples from Ikeja industrial area (52 ± 21 μ g/g) which are lower than the control site (59 ± 11μg/g), there was no significant difference between the values. The Industrial activities within Ikeja industrial estate had no significant effect on the soil within the estate. Isolo industrial area Pb concentrations were higher in the soil samples (21±8 μg/g) than the control site (3±1 μg/g). There was significant difference between the values. This shows that the activity of the industries within the Isolo industrial estate had a significant impact on the soil within the estate. Accumulation factors were calculated by dividing each metal level in different sites by levels in control. Accumulation factor for Pb in the soil from Ikeja industrial estate and Isolo Industrial Estate are 0.9 and 9.0 respectively. Thus, Isolo Industrial Estate soil had significant contamination of Pb in the top soil. 174 UNIVERSITY OF IBADAN LIBRARY Lead concentration showed seasonal variation with the dry season having the highest levels for all sample sites (Table 4.35b and 4.36b). The monthly variations in Figure 4.34 showed increased concentration of Pb during the months of January 2007 and November 2007. This invariably was the dry months of the year which explains the elevated levels of Pb. During these months, the Pb only accumulates in the soil either through atmospheric deposition, effluent discharge or evacuation of sediments unto Land, in contrast to the wet months of March 2007, May 2007, July 2007 and September 2007 in which precipitation leaches particulate Pb from the top soil, thereby reducing it concentration. Level of Pb (52 ± 21 μg/g) and (21 ± 8 μg/g) in Ikeja and Isolo industrial areas respectively are lower than that obtained in top soils (81 μg/g) in Ibadan Nigeria (Onianwa et al., 2001), around Auto workshops (805 μg/g), gas stations (392 μg/g) and motor parks (266 μg/g) in Ibadan city (Onianwa et al., 2001). 4.10.4 Cadmium Cd concentrations for Ikeja and Isolo industrial estates are 2.0±0.6 μg/g and 0.68±0.33 μg/g respectively as shown in Tables 4.29 and 4.32. The Cd concentrations of Ikeja and Isolo soil compared to the control showed no significance difference. There were no seasonal variations (Table 4.35b and 4.36b) for Ikeja and Isolo industrial estate soils respectively in Cd levels. The accumulation factors of cadmium in the soil were Ikeja soil – 0.8 and Isolo – 1.0 respectively. These reflect relatively low Cd levels in the estates soils. The average concentration of Cd in the earth’s crust is above 0.1 μg/g (Alloway,1997). Cd content of soil was determined by the chemical composition of the parent rock. The content varies between 0.1 –0.3 μg/g in igneous rocks and 3–11 μg/g in sedimentary rocks (Pager et al., 1987). The average content of Cd in unpolluted soil lies between 0.07 and 1.1μg/g. Generally, most soils can be expected to contain <1μg/g Cd except those contaminated from discrete sources or development in parent materials with anomalously high Cd contents, such as black shales. The monthly variation in Figure 4.35 shows no spatial variation in concentration of Cd in the soils sample. Cd levels in soils around Isolo industrial area are 0.68±0.33 μg/g and the control + 0.81 0.23 μg/g were relatively close to global background concentrations hence may not be considered contaminated with Cd. 175 UNIVERSITY OF IBADAN LIBRARY Fig. 4.34: Variations of lead contents of soils with sampling period. 6.0 SJW-SL1 5.0 SJW-SL2 SJW-SL3 4.0 SAF-SL4 SAF-SL5 SVJ-SL6 3.0 KBP-SL1 KGI-SL2 2.0 KWS-SL3 KMB-SL4 1.0 K-CTR-SL1 K-CTR-SL2 0.0 S-CTR-SL1 S-CTR-SL2 Sampling period Fig. 4.35: Variations of cadmium contents of soils with sampling period. 176 UNI Cd (μg/g) VERSITY OF IBADAN LIBRARY Ikeja industrial area soil Cd concentrations are 2.0±0.6 μg/g and the control 2.0±0.6 μg/g was higher than the global background concentration. In the USA, a survey of soils from uncontaminated sites revealed a range of Cd contents from 0.005 to 2.4 μg/g, while in England and Wales Cd content of 1.2μg/g was reported. However, the background Cd level in soils apparently should not exceed 0.5 μg/g (0.07–1.1 μg/g). Values higher than the background Cd levels reflect anthropogenic impact. It was observed that the Cd in soil increases with Zn content and these elements are much higher in surface soil (0 – 15cm) than sub surface soil (15 – 60cm) (Jarup et al., 1998), though Cd tend to move down the profile more readily than Pb or Cu. Biddappa et al. (1982) found that Cd, Ni and Zn were readily leached down columns of two Japanese soils than Cu and Pb are thus constitute a greater ground water pollution risk. Cadmium is most mobile in acidic soils within the range of pH 4.5–5.5, which means that Cd was not expected to be of environmental significance with the studied area. Levels of Cd (2.0 μg/g and 0.68 μg/g) respectively for Ikeja and Isolo industrial estate were lower than that obtained in contaminated soils (3865 μg/g) in Albania (Shallar et al., 1998) and 2.2 μg/g obtained in top soils in Ibadan, Nigeria (Onianwa et al., 2001). The Cd level of (2.0 μg/g) in this study was comparable with levels reported for in Auto-workshops (1.48 μg/g), gas station (1.13 μg/g) and motor packs (1.37μg/g) in Ibadan city (Onianwa et al., 2001) 4.10.5 Zinc Zn concentrations obtained in Tables 4.29 were higher than average control soils levels for Ikeja industrial estate soil while Isolo industrial estate soil (Table 4.32) had its Zn level lower than the control soil but there is no significant difference. The levels of Zn in the study areas were affected by seasonal factors with the dry season having elevated concentrations of the metal (Table 4.35b and 4.36b). The monthly variations of Zn in Figure 4.36 shows month of January 2007 having elevated concentrations of the metal for the study areas. Accumulation factors of 2.4 and 0.9 for Ikeja and Isolo industrial estate soil respectively indicated high level of Zn contamination at the Ikeja industrial estate soil. Zn levels of 280 μg/g and 50 μg/g around Ikeja and Isolo industrial estate are higher than 177 UNIVERSITY OF IBADAN LIBRARY 900.0 800.0 SJW-SL1 SJW-SL2 700.0 SJW-SL3 600.0 SAF-SL4 SAF-SL5 500.0 SVJ-SL6 400.0 KBP-SL1 KGI-SL2 300.0 KWS-SL3 200.0 KMB-SL4 K-CTR-SL1 100.0 K-CTR-SL2 0.0 S-CTR-SL1 S-CTR-SL2 Sampling period Fig. 4.36: Variations of zinc contents of soils with sampling period. 178 UNI Zn(μg/g) VERSITY OF IBADAN LIBRARY 48μg/g obtained in topsoil in Ibadan Nigeria (Onianwa et al., 2001). The average Zn levels in Ikeja industrial estate was higher than that level reported for topsoil in Abakaliki, Nigeria (78μg/g) (Chukwuma, 1996), Hong Kong (59μg/g) (Chen et al., 1997). 4.10.6 Copper The Cu levels in Ikeja and Isolo industrial estate showed seasonal variation as shown in (Table 4.35b and 4.36b) with higher level of Cu in wet season (Figure 4.37). The levels of Cu in soil around Isolo industrial estate are higher than the control (Table 4.32) while the soil around Ikeja industrial estate had lower levels of Cu than the control soil (Table 4.29). Accumulation factors of 0.6 for Ikeja industrial estate and 1.1 for Isolo industrial estate, this indicates low level of Cu contamination in the study area. Average soil Cu level in Ikeja (41μg/g) and Isolo (24μg/g) industrial estate are higher than 17μg/g obtained in top soil of Ibadan Nigeria (Onianwa, 2001), but lower than the average Cu level in Auto workshops (410μg/g), gas stations (117μg/g) and motor parks (59μg/g) in Ibadan city (Onianwa et al., 2001). 4.10.7 Chromium There were no seasonal variation in Cr levels in soil at the Ikeja and Isolo industrial estate (Tables 4.35b and 4.36b). The monthly variation as shown in Figure 4.38, indicated that level of Cr in the control soil sample for Ikeja industrial estate are visibly higher in the metal than the soil around the estate. The level of Cr in the Isolo industrial estate soil shows significantly difference with that of the control soil. Accumulation factor of 0.2 and 0.7 for Ikeja and Isolo industrial soil respectively did not indicate any significant contamination of soil with Cr. Chromium levels were more abundant in soil around Ikeja industrial area and its control compared to Isolo industrial area and its control. The average Cr level of 18μg/g and 6.5μg/g for Ikeja and Isolo industrial respectively in this study was lower than levels reported in Auto workshops (33.0μg/g), gas station (32.2μg/g) and motor parks (36.3μg/g) in Ibadan city (Onianwa et al., 2001) 4.7.8 Nickel There were no seasonal variations in levels of Ni in the Ikeja and Isolo industrial estate soil. The monthly variations in Ni (Figure 4.39) were similar to that of Cd in which the dry months recorded high level of accumulation of the metal on the top soil. 179 UNIVERSITY OF IBADAN LIBRARY 250.0 SJW-SL1 SJW-SL2 200.0 SJW-SL3 SAF-SL4 150.0 SAF-SL5 SVJ-SL6 100.0 KBP-SL1 KGI-SL2 KWS-SL3 50.0 KMB-SL4 K-CTR-SL1 0.0 K-CTR-SL2 S-CTR-SL1 S-CTR-SL2 Sampling period Fig. 4.37: Variations of copper contents of soils with sampling period. Fig. 4.38: Variations of chromium contents of soils with sampling period. 180 U Cu (μg/g) NIVERSITY OF IBADAN LIBRARY 30.0 SJW-SL1 25.0 SJW-SL2 SJW-SL3 20.0 SAF-SL4 SAF-SL5 15.0 SVJ-SL6 KBP-SL1 10.0 KGI-SL2 KWS-SL3 5.0 KMB-SL4 K-CTR-SL1 0.0 K-CTR-SL2 S-CTR-SL1 S-CTR-SL2 Sampling period Fig. 4.39: Variations of nickel contents of soils with sampling period. 181 UN Ni (μg/g) IVE Nov-06R Dec-06S Jan-07I Feb-07T Mar-07Y Apr-07 O May-07Jun-07F Jul-07 IB Aug-07A Sep-07Oct-07D Nov-07ADec-07Jan-08 FeNb-08 Mar-08 L Apr-08 I May-08 BRARY Nickel levels at the industrial estates had no significant difference with the level in the control soils. The accumulation factor of Ikeja industrial estate soil – 0.6 and that of Isolo industrial estate soil 0.7 further justify the low level of Nickel in the soil around the industrial estate soils. Top soils throughout the world contain background Ni concentration within the broad range of 1 to 100ppm (Kabata and Pendicis, 1984). The study areas were considered not contaminated since their Ni levels generally fall below 100ppm. The Ni levels of Ikeja industrial estate – 10μg/g and Isolo industrial estate – 5.6μg/g were lower than levels reported for in Auto workshops (15.1μg/g), gas stations (11.1μg/g) and motor parks (11.5μg/g) in Ibadan city (Onianwa et al., 2001).Average Ni levels of Ikeja industrial estate compared closely with topsoil of Ibadan. Nigeria (Onianwa, 2001) while Isolo industrial estate soil is lower than soils reported by (Onianwa 2001; Chukwuma, 1996). 4.11: ASSESSMENT OF LEVEL OF CONTAMINATION OF SOILS AROUND THE INDUSTRIAL ESTATE For a better estimation of anthropogenic input, sampling sites were assessed for the degrees of contamination. Contamination Factors (CF), Pollution load Index (PLI) and Geo accumulation index (Igeo) were used for the estimations. 4.11.1: Assessment of Contamination Factor Table 4.37 shows the soil contamination factors for the industrial estates studied, (see section 3.14.1) Where the contamination factor: CF<1 refers to low contamination; 1≤CF<3 means moderate contamination; 3≤CF≤6 indicates considerable contamination CF>6 indicates very high contamination. From the contamination factors categories described above, average K-SL had no contamination in all the metals except Cd that is moderately contaminated. Average S-SL suffered very high contamination in Pb level; moderately contamination in Cd and no contamination for Zn, Cu, Cr and Ni. 182 UNIVERSITY OF IBADAN LIBRARY Table 4.37: Soil contamination factors for the industrial estates studied. Site Code Pb Cd Zn Cu Cr Ni K-SL 0.88 1.00 2.39 0.58 0.19 0.56 S-SL 7.00 0.84 0.86 1.10 0.73 0.68 183 UNIVERSITY OF IBADAN LIBRARY 4.11.2: Assessment of Pollution Load Index Each industrial estate was also evaluated for the magnitude of metal pollution, using the pollution load index (PLI) developed by Thomilson et al., (1980) (see section 3.14.2). The PLI provides simple but comparative means for assessing a site quality, where a value of PLI <1 denote perfection: PL1=1 present that only baseline level of pollutants are present and PLI>1 would indicate deterioration of site quality. Average K-SL and Average S-SL pollution load index is 0.712 and 1.18 respectively. The soil metal levels in Ikeja industrial area in terms of pollution load shows perfection while Isolo industrial area indicates deterioration of site with the metals studied. 4.11.3: Assessment of Geo-accumulation Index. Enrichment of metal concentration above baseline concentration was calculated using the method proposed by (Muller, 1969). Assesses the metal pollution in terms of seven (0 to 6) enrichment classes (Appendix 12) ranging from background concentration to very heavily polluted (see section 3.14.3). Table 4.38 shows the calculated Geo-accumulation index values. It was evident from the table that the uncontaminated to moderately contaminated Igeo value of 0 to 1 of class 1 was observed in K-SL for Zn with value 0.64. S-SL soils show moderately/strongly contaminated of Igeo class 3 for Pb in soil (2.20). The other metals analysed had values of Igeo <1 which showed non contamination. Generally, as revealed from the three pollution assessment methods, soil of average S-SL was pollution impacted more than average K-SL. 4.12: TREATMENT OF EFFLUENT 4.12.1 Treatment with Inorganic Coagulants Treatment with FeCl2.4H2O: Fig 4.40 shows the trend in COD reduction during treatment with ferrous chloride. An immediate and effective reduction (85%) in COD was obtained on the first day. Thereafter the degree of reduction declined slightly but this varied generally within 60% - 90% during the remaining study period. The reduction in COD was remarkably 184 UNIVERSITY OF IBADAN LIBRARY Table 4.38: Geoaccumulation index values for metals in soil from the industrial estates studied. Site Code Pb Cd Zn Cu Cr Ni K-SL -0.77 -0.58 0.64 -1.36 -2.90 -1.40 S-SL 2.20 -0.84 -0.84 -0.45 -1.03 -1.14 185 UNIVERSITY OF IBADAN LIBRARY Fig 4.40: Trend in COD reduction during treatment with FeCl₂.4H₂O. 186 UNIVERSITY OF IBADAN LIBRARY 2+ different from the control. The coagulant (Fe ) neutralizes the suspended solids portion from the waste water (usually color-producing organic substances) which carries negative charges, making them less stable in suspension. The flocs formed settle under gravity to the bottom of the jar, hence clearer solution was obtained. The pH of this medium was acidic; this could also have contributed to the coagulation process. 2FeCl2+ 2Ca(HCO3)2------------> 2Fe(OH)2 + 2CaCl2 + 4CO2 Treatment with FeSO4.7H2O: Fig 4.41 shows the trend in COD reduction during treatment with ferrous sulphate heptahydrate. Day 1 gave 62% reduction in COD while an increase in COD reduction (90%) was observed on Day 2. Subsequently, a decrease in COD reduction was observed till day 16, after which this increased gradually to 90% on Day 22. Optimal reduction level was observed on Day 22. Basic reactions occurring during the coagulation process for ferrous sulphate is given in the following equation: - 2- FeSO4 + 2HCO3  Fe(OH)2 + SO4 + 2CO2 Hydrolysis of FeSO4 during coagulation results in the formation of corresponding gel-like hydroxides and some positively charged mononuclear and poly-nuclear species. These positively charged compounds combine with negatively charged colloidal particles present in the wastewater by charge neutralization mechanism; and at the time of settling under gravity these hydroxides and complexed hydroxides sweep away remaining uncharged/charged colloidal particles of the wastewater with them and precipitate out. Treatment with combination of FeCl2.4H2O and FeSO4.7H2O: Fig 4.42 shows the trend in COD reduction during treatment with a combination of ferrous chloride pentahydrate and ferrous sulphate heptahydrate. 55% COD reduction was achieved on the first day of application of the coagulants. Subsequently, the effectiveness of treatment decreases and then rose to a maximum of about 89% on Day 22. Thus, an optimal COD reduction was achieved on Day 22. The combination of these coagulants did not give a better COD reduction as compared to when they were singly used. 187 UNIVERSITY OF IBADAN LIBRARY 120 100 80 FeSO₄ .7H₂O 60 Sample in sunlight Sample in dark 40 Control 20 0 1 2 3 4 5 6 7 8 9 10111213141618202224262830 Days Fig 4.41: Trend in COD reduction during treatment with FeSO₄.7H₂O. 100 90 80 70 60 FeCl₂. 4H₂O +FeSO₄ .7H₂O 50 Sample in sunlight 40 Sample in dark 30 Control 20 10 0 1 3 5 7 9 11 13 16 20 24 28 Days Fig 4.42: Trend in COD reduction during treatment with FeCl₂.4H₂O +FeSO₄.7H₂O. 188 UN %COD reduction %COD reduction IVERSITY OF IBADAN LIBRARY Treatment with FeCl3: The trend in COD reduction during treatment with ferric chloride is illustrated in Fig 4.43. 20% reduction occurred on the first day. The best reduction achievable was an 87% reduction which was achieved on the seventh day. Ferric chloride when added in water yields ferric and chloride ions. Ferric ions neutralise negatively charged colloidal particles and agglomerisation of the colloidal particles can occur or they may combine with hydroxide ions to form ferric hydroxide which adsorbs colloidal particles, providing clarification of effluent. Treatment with Alum: 62% COD reduction occurred on the first day, and this improved to yield the optimum reduction of 90% on the second day (Fig 4.44). 3+ The trivalent metallic ions (Al ) combine with hydroxide ion to rapidly form hydroxide precipitate. The colloidal particles formed provide condensation site where the precipitate can form and therefore, the colloid becomes entrapped in the precipitate and settles with it. The pH of the medium is low; therefore an increase in hydrogen ion concentration would neutralize the negative charged carbon surface. This enhances the adsorption of the negatively charged organic species because of reduction in the forces of repulsion between adsorbent and adsorbate. A12(SO4)3 + 3Ca(HCO3)2 ------------> 2Al(OH)3 + 3CaSO4 + 6CO2 Comparative COD reduction is represented in Figure 4.45 for the inorganic coagulants. It was observed that FeCl2.4H2O gave a 86.9% reduction in COD on the first day of the treatment followed by FeSO4.7H2O (65.5%), 61.4% Alum, FeCl2.4H2O + FeSO4.7H2O (53.3%) while FeCl3 gave the least reduction of 15.5%. During the first 14 days of the treatment process, FeSO4.7H2O gave a reduction of 66.2% followed by 60.9% for FeCl2.4H2O; then FeCl2.4H2O + FeSO4.7H2O achieved59.8%, while FeCl3 gave the least reduction of 20.9%. Subsequently after the first 14 days of monitoring, the average reduction result is in the order of FeSO4.7H2O> Alum > FeCl2. 4H2O > (FeCl2. 4H2O + FeSO4.7H2O4) > FeCl3. The efficiency of COD reduction was better with the iron salts (ferrous ions) than alum, possibly because the former is a better reducing agent. The results obtained in this study is similar to that obtained by Birdie and Birdie, 1994; Arceivala, 1998; Choudhary and Ojha, 2011. It was reported that a faster sedimentation of sludge by iron salts with lime than alum salt was observed. The sludge formation is as a result of removal of suspended solid which. 189 UNIVERSITY OF IBADAN LIBRARY Fig 4.43: Trend in COD reduction during treatment with FeCl₃. Fig 4.44: Trend in COD reduction during treatment with Alum. 190 UNIVERSITY OF IBADAN LIBRARY Fig 4.45: Trend in COD reduction during treatment with various inorganic coagulants. 191 UNIVERSITY OF IBADAN LIBRARY will indirectly affect the reduction of COD. Generally, the efficiencies of the inorganic coagulants were in the order: FeCl2.4H2O> FeSO4.7H2O > Alum > (FeCl2. 4H2O + FeSO4.7H2O) > FeCl3. 4.12.2 Treatment with Fenton’s Reagent Treatment with FeCl2.4H2O + H2O2: Day 1 gave 78% COD reduction followed by an increase in reduction on Day 3 to 90% (Fig 4.46). The effectivenss of COD reduction nd stabilized at about this value up to the 22 day. Treatment with FeSO4.7H2O and H2O2: Fig 4.47 shows the trend in COD reduction during treatment with FeSO4.7H2O and H2O2. A 92% reduction was attained even on the first day. There were subsequently minor variations, but the reduction generally stabilised at about this value.even up to the last day Treatment with FeCl2.4H2O + FeSO4.7H2O and H2O2: Fig 4.48 shows the trend in COD reduction during treatment with FeCl2.4H2O + FeSO4.7H2O and H2O2. Day 1 gave 80% COD reduction while an increase in reduction was observed on Day 2 (87%). The degree of reduction varied mildly as time progressed, but eventually stabilized at about 95% Figure 4.49 compares the COD reduction with the use the various Fenton’s reagents. It was observed that FeSO4.7H2O + H2O2 gave 92.5% reduction on the first day of the treatment, followed by FeCl2.4H2O + FeSO4.7H2O + H2O2 (81.3%) and FeCl2.4H2O + H2O2 (75.7%). During the first 14 days of the treatment, the trend in the reductions was in the order: FeSO4.7H2O + H2O2 (64.7%) > FeCl2.4H2O + FeSO4.7H2O + H2O (56.5%) > FeCl2.4H2O + H2O2 (41.8%). The principle/reactions behind the application of Fenton’s reagent for this purpose derive from the following (Wardman and Candeias, 1996): 2+ 3+ • − Fe + H2O2 → Fe + OH + OH (chain initiation) (1) 2+ The ferrous iron (Fe ) starts the reaction and catalyses the decomposition of H2O2 192 UNIVERSITY OF IBADAN LIBRARY • 2+ − 3+ OH + Fe → OH + Fe (chain termination) (2) 100 90 80 70 60 FeCl₂. 4H₂O +H₂O₂ 50 Sample in sunlight 40 30 Sample in dark 20 Control 10 0 1 2 3 4 5 6 7 8 9 10111213141618202224262830 Days Fig 4.46: Trend in COD reduction during treatment with FeCl₂.4H₂O +H₂O₂. Fig 4.47: Trend in COD reduction during treatment with FeSO₄.7H₂O + H₂O₂ . 193 UN %COD reduction IVERSITY OF IBADAN LIBRARY Fig 4.48: Trend in COD reduction during treatment with FeCl₂.4H₂O +FeSO₄.7H₂O + H₂O₂. Fig 4.49: Trend in COD reduction during treatment with Fenton’s reagents. 194 UNIVERSITY OF IBADAN LIBRARY Hydroxyl radicals react with ferrous ion to form hydroxide and ferric ions. 3+ The formed ferric ions (Fe ) decompose hydrogen peroxide in water and oxygen (forming ferrous ions and radicals): 3+ 2+ + Fe + H2O2 ↔ Fe − OOH + H (3) 2+ • 2+ Fe − OOH ← HO2 + Fe (4) The radical formed reacts with organics (RH) by abstraction of proton from the organic to • form organic radicals (R ). • • RH + OH → H2O + R + further oxidation 4.12.3 Treatment with Charcoal Reduction during treatment with charcoal was 18% on Day 1. A gradual increase occurred, and up to 90% reduction was achieved by Day 22 (Fig 4.50). Charcoal treatment option performed better than simple exposure to darkness or sunlight. Generally, the sample kept in the dark gave a better COD reduction on Day 1 (19.6%) compared to the sample kept in sunlight, this could be as result of activities of microorganism. Summarily, the order of efficiencies of the various chemicals used was: Fenton’s reagent > FeCl2.4H2O > FeSO4.7H2O > FeCl2.4H2O + FeSO4.7H2O > alum > activated charcoal > FeCl3. 4.12.4: pH Variation during Treatment of Textile Effluent Figure 4.51 shows the variation of pH values during treatment of the effluent with various coagulants. The initial pH of the textile effluent before treatment was 10.4. On Day 1 of the treatment, the samples that were treated with inorganic and Fenton’s reagent were acidic while the samples kept in the dark, sunlight and treated with activated charcoal gave basic values. As treatment progressed, the treatments with sunlight, exposure to the dark, and addition of charcoal gave pH range within the FEPA regulatory guideline for wastewater (6- 9), while the other treatment options yielded acidic effluents. Therefore, the use of activated 195 UNIVERSITY OF IBADAN LIBRARY charcoal for the regulation of pH for textile effluent may be encouraged. 100 90 80 70 60 Charcoal 50 Sample in sunlight 40 Sample in dark 30 Control 20 10 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 16 18 20 22 24 26 28 30 Days Fig 4.50: Trend in COD reduction during treatment with charcoal. 196 UN %COD reduction IVERSITY OF IBADAN LIBRARY 12 Sample in sunlight 11 Sample in dark 10 FeCl₂ .4H₂O 9 FeCl₂ .4H₂O+H₂O₂ 8 FeSO₄ .7H₂O 7 FeSO₄ .7H₂O +H₂O₂ 6 FeCl₂ .4H₂O +FeSO₄ .7H₂O 5 FeCl₂ .4H₂O+FeSO₄ .7H₂O+H₂O₂ 4 FeCl₃ Alum 3 Charcoal 2 Control 1 0 1 2 3 4 5 6 7 8 9 10111213141618202224262830 Days Fig 4.51: Variations in pH values during various treatment options. 197 U pH NIVERSITY OF IBADAN LIBRARY 4.12.5: TDS Variation during Treatment of Textile Effluent Table 4.39 shows the variation of TDS levels during treatment of textile effluent. The initial TDS of the effluent before treatment was 780 mg/L. On the first day of treatment, treatment options of FeCl2.4H2O + H2O2, FeCl2.4H2O + FeSO4.7H2O + H2O2 and FeCl3 gave increase in value of TDS as 1254, 874 and 1425 mg/L respectively while the other treatment gave a lower value compared with the control. Similarly, the treatment options of light, dark, FeCl2. 4H2O, FeSO4.7H2O , FeSO4.7H2O + H2O2, FeCl2. 4H2O + FeSO4.7H2O, alum and activated charcoal gave reduction in TDS values as the treatment days progressed while the other treatment options had increase in value as the treatment days progressed. The order of reduction in TDS was: FeCl3 > (FeCl2.4H2O + FeSO4.7H2O + H2O2) >(FeCl2.4H2O + H2O2) > (FeSO4.7H2O + H2O2 ) > (FeCl2. 4H2O + FeSO4.7H2O) > FeCl2. 4H2O > alum > activated charcoal > FeSO4.7H2O. The effectiveness is in order of FeSO4.7H2O > alum = (FeCl2. 4H2O + FeSO4.7H2O) > charcoal = FeCl2. 4H2O = (FeSO4.7H2O + H2O2) > (FeCl2.4H2O + FeSO4.7H2O + H2O2 ) > (FeCl2.4H2O + H2O2) > FeCl3. 4.12.6: TSS Variation during Treatment of Textile Effluent Table 4.40 shows the variation of TSS during the treatment of textile effluent. The initial TSS level of the raw effluent before treatment was 135 mg/L. The various treatment options all had increase in the value of TSS throughout the treatment days. This could be associated to suspension of some of the coagulated particles. The TSS could be taken care of by passing the treated effluent through sand filter before discharging into an open sewer. 4.13 ESTIMATES OF GROSS ORGANIC POLLUTION AND EUTROPHICATION POTENTIAL. 4.13.1 Model Result of Multiple Linear Regressions of Physicochemical Data for Predictive Estimation of Concentration of a Gross Organic Pollutant (as COD). The Multiple Linear Regression Analysis between “gross organic pollution” in the form of COD, and the other explicative variables extracted from the Factor Analysis presented the final regression equations as expressed in Table 4.41. 198 UNIVERSITY OF IBADAN LIBRARY TABLE 4.39: Changes in TDS (mg/L) values during treatment of textile effluent Day Day Day Day Day Day Day Day Day Day Day Treatment Options/Date 1 2 3 4 5 6 7 8 9 10 11 Sample in Light 760 684 798 760 570 608 741 646 836 874 722 Sample in Dark 779 684 684 646 494 532 608 627 627 627 646 FeCl₂ .4H2O 760 893 1045 950 779 741 665 684 627 684 646 FeCl₂.4H2O +H₂O₂ 1254 1311 1425 1368 1045 1121 1083 1140 1007 1045 874 FeSO₄ .7H2O 684 589 855 779 475 532 608 494 323 494 437 FeSO₄.7H2O +H₂O₂ 760 722 950 912 646 703 665 703 570 589 475 FeCl₂.4H2O +FeSO₄ .7H2O 703 646 855 855 589 589 608 532 570 532 285 FeCl₂.4H2O +FeSO₄.7H2O +H₂O₂ 874 836 1064 1045 684 817 798 855 684 760 627 FeCl₃ 1425 1444 1900 1805 1235 1406 1425 1539 1292 1121 912 Alum 703 703 988 988 646 741 722 760 684 722 646 Charcoal 760 684 988 665 418 570 589 608 627 589 532 Control 703 646 665 665 513 589 570 570 570 627 551 199 UNIVERSITY OF IBADAN LIBRARY TABLE 4.39 contd.: Day Day Day Day Day Day Day Day Day Day Day Treatment options/Date 12 13 14 16 18 20 22 24 26 28 30 Sample in Light 703 798 950 893 893 912 855 836 855 817 912 Sample in Dark 551 627 665 608 684 665 665 608 627 684 589 FeCl₂ .4H2O 570 627 570 608 646 665 627 627 646 513 703 FeCl₂.4H2O +H₂O₂ 874 950 912 874 950 969 931 912 798 722 741 FeSO₄ .7H2O 475 456 551 494 532 513 532 475 475 532 570 FeSO₄.7H2O +H₂O₂ 494 532 627 551 627 646 608 912 532 532 608 FeCl₂.4H2O +FeSO₄ .7H2O 475 551 551 570 627 589 570 551 475 570 627 FeCl₂.4H2O +FeSO₄.7H2O +H₂O₂ 627 703 684 665 741 931 722 608 532 532 722 FeCl₃ 1273 1368 1368 1368 1425 1539 1501 1444 1045 570 1140 Alum 608 684 665 627 703 722 665 608 551 950 342 Charcoal 684 608 608 589 646 646 627 570 893 266 551 Control 551 608 646 570 646 646 646 570 589 589 551 200 UNIVERSITY OF IBADAN LIBRARY TABLE 4.40: Changes in TSS (mg/L) value during treatment of textile effluent. Treatment Options/Date Day 0 Day 2 Day 4 Day 6 Day 8 Day 12 Day 16 Day 20 Day 24 Day 28 Sample in Light 135 1040 1035 1009 880 850 705 680 500 500 Sample in Dark 135 400 550 864 790 785 610 640 500 500 FeCl₂ .4H2O 135 440 570 429 402 425 650 720 300 302 FeCl₂ .4H2O +H₂O₂ 135 1200 1020 1344 1100 1050 805 320 250 255 FeSO₄ .7H2O 135 400 580 984 890 780 605 320 400 402 FeSO₄ .7H2O +H₂O₂ 135 400 575 740 690 580 530 380 700 705 FeCl₂ .4H2O + FeSO₄ .7H2O 135 280 300 380 350 345 310 280 300 306 FeCl₂ .4H2O + FeSO₄ .7H2O +H₂O₂ 135 280 340 620 615 600 390 360 400 410 FeCl₃ 135 1280 1080 884 870 850 390 400 200 220 Alum 135 400 420 280 300 290 395 400 1700 1500 Charcoal 135 1320 1420 1590 1285 1109 210 200 250 240 Control 135 1360 1420 1672 1492 1290 390 320 500 510 201 UNIVERSITY OF IBADAN LIBRARY Table 4.41: Result of multiple linear regression of physicochemical data for predictive estimation of COD as indicator of gross organic pollution. Site code Effluent Types Equation R² KJE-EFF Mixed industries 0.954 - - KHO-EFF Basic metal 4.897Mg + 2.580Cl + 117 PO₄³ 0.905 KBP -EF Paint industry 2- -3468 4.75 SO4 9.12 Cl + 4.82 TH – 0.033 Cond 0.740 -- KGI-EF Basic metal 418 + 0.017 Cond – 2.585 NO3 0.770 KWS-EF Textile industry 655 - 0.027 Cond – 21.7 Mg 0.698 2- -- SJW-EF Pharmaceutical industry 0.390 TDS – 0.450 Alk - 10.79 SO4 + 31.07 NO3 0.933 SAF-EF Pharmaceutical industry 0.007 Cond 0.932 2- -- SVJ-EF Food & beverages industry 0.415 - 0.008 TDS + 0.024 SO4 + 0.076 NO3 0.999 LPZ-EF Conglomerate 0.855 TDS 0.638 LCA-EF Mixed industries 6.943 Ca + 16.14 Mg 0.846 202 UNIVERSITY OF IBADAN LIBRARY 2 These determination coefficients R for gross organic pollutant (COD) ranged within 0.638- 0.999. This means that 63.8% - 99.9% dependent variable (COD) variance was explained by the independent variables (parameters reflected in the final equation). With the regression equations generated from the data obtained in the analysis of the various effluents, the gross organic pollutant (COD) can be estimated from the equations. 4.13.2 Model Results of Multiple Linear Regression of Physicochemical Data for Predictive Estimation of Eutrophication Potential (via Phosphate Levels) The equations obtained for the various effluent types are represented in Table 4.43. The independent variables that are retained in the final equations are different for various effluents. The regression values as shown in Table 4.42 ranged within 0.301-0.999 with KHO-EF (Basic metal Industry) having the least value while KBP-EF (Paint Industry) had 2 the highest R value. 203 UNIVERSITY OF IBADAN LIBRARY Table 4.42: Result of multiple linear regression of physicochemical data for predictive estimation of phosphate as indicator of eutrophication potential. Site Code Effluent Types Equation R² KJE-EFF Mixed industries 3- -PO4 0.03 Alk – 0.078 Mg + 0.004TH + 0.002 Cl 0.970 KHO-EFF Basic metal 3- -PO4 0.642 - 0.004 Cl 0.301 KBP -EF Paint industry 3- - 2- -PO4 1.370 + 0.001 Alk + 0.015 Mg - 0.001 BOD - 0.011 Cl -0.002 SO4 + 0.001 NO3 0.999 +0.007 Ca KGI-EF Basic metal 3- PO4 2.368 + 0.060 Ca - 0.395 Mg 0.640 KWS-EF Textile industry 3- - -PO4 0.704 + 0.05 NO3 0.493 KMB-EF Pharmaceutical industry 3- PO4 0.00001 Cond – 1.479 COD 0.714 SJW-EF Pharmaceutical industry 3- 2 - - PO4 11.848 - 0.025 TDS + 0.045 Alk + 1.245 SO4 - - 2.232 NO3 + 0.038 Cl + 0.044 0.981 COD - 0.221 BOD. SAF-EF Pharmaceutical industry 3- 2 -PO4 0.012 Alk - 1.375 SO4 - 1.856 NO3 0.954 SVJ-EF Food & beverages 3- PO4 0.273 + 0.005Alk -0.109 COD 0.917 industry LPZ-EF Conglomerate 3- - 2PO4 0.046 Cl + 0.006 TH + 0.008 SO4 - – 0.001 Alk – 0.032 Mg – 0.000006COD - 0.998 0.173 LCA-EF Mixed industries 3- PO4 0.006 Alk 0.939 204 UNIVERSITY OF IBADAN LIBRARY CHAPTER FIVE SUMMARY, CONCLUSIONS AND RECOMMENDATIONS 5.1 SUMMARY AND CONCLUSIONS 5.1.1 Effluent Characteristics Most of the effluents had high levels of nitrate COD and BOD, which is indicative of a high level of gross organic pollution, and likelihood to cause eutrophication of receiving water bodies. Chromium and copper were also problematic in textile effluents. In most cases, the characteristics of the effluents did not meet the required limiting guidelines of the regulatory agency in Nigeria. However, the textile and pharmaceutical industrial sector tended to discharge the most polluted effluents. Seasonal variation was not an important factor in determining effluent qualities. 5.1.2 Pollution of Sediments of Effluent Channels The sediment quality of the effluent channels showed some level of contamination with heavy metals, which correlated with the levels in the effluents. Chromium and zinc were generally more significant in this respect. 5.1.3 Soil Pollution Soil pollution with heavy metals was not significant, and did not appear to be correlated with levels in effluents and sediments. 5.1.4 Effluent Treatment Fenton’s reagent was very effective for the treatment of the textile effluents, achieving a COD reduction of about 90% within the first day of treatment. Treatment with ferrous chloride was the second best of the chemical treatment options investigated. 5.1.5 Model Estimation of COD and Phosphate Various equations have been generated for various effluent types, that can serve as a future 205 UNIVERSITY OF IBADAN LIBRARY means of estimating eutrophication potential (phosphate level) and gross organic pollution load (COD levels) in which data for effluent quality are available. 5.2 RECOMMENDATIONS (i) Regulatory bodies should enforce the standard regulatory limits for effluents discharge to land and water bodies. Results from this study show that the industries are currently mostly not compliant. (ii) The model derived from this study for estimating gross organic pollution and eutrophication potential should be further developed to make it more accurate and applicable to estimating other important parameters. (iii) Studies should be extended to other industrial estates and industry types in the metropolis. 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Chemometric characterisation of soil and plant pollution. Part 1: multivariate data analysis and geostatistical contaminations in soil. Environmental Science and Pollution Research, 7: 89-96. 236 UNIVERSITY OF IBADAN LIBRARY Appendix 1: Coordinates of the sampling locations. Coordinates of the Sampling Elevation above Sea Name of Industrial Estate Industry Sampled Location level Ikeja industrial estate Hoesch Pipe Mills Ltd. N 06⁰ 36 02.8 E 003⁰ 19 50.1 40 m Ikeja industrial estate Berger Paints (Nigeria) Plc N 06⁰ 06 50.9 E 003⁰ 20 09.0 45 m Ikeja industrial estate Galvanizing Industries Limited N 06⁰ 36 46.0 E 003⁰ 20 09.5 45 m Woolen and Synthetic Textile N 06⁰ 36 37.6 E 003⁰ 20 10.3 42 m Ikeja industrial estate Manufacturing Limited Ikeja industrial estate May&Baker (Nigeria) Plc N 06⁰ 36 32.7 E 003⁰ 20 18.5 47 m Ikeja industrial estate Central Treatment Plant N 06⁰ 36 33.0 E 003⁰ 19 54.9 49 m N 06⁰ 31 32.7 E 003⁰ 19 32.7 23 m Isolo industrial estate Johnson Wax Nig. Ltd N 06⁰ 31 31.2 E 003⁰ 19 48.9 20 m N 06⁰ 31 28.5 E 003⁰ 19 48.6 18 m Isolo industrial estate Afrab Chem N 06⁰ 31 25.4 E 003⁰ 19 48.3 16 m N 06⁰ 31 24.4 E 003⁰ 19 48.1 20 m Isolo industrial estate Viju industries N 06⁰ 31 35.5 E 003⁰ 20 04.1 39 m PZ Industries Nig. Plc N 06⁰ 33 11.7 E 003⁰ 21 24.7 32 m Ilupeju industrial estate N 06⁰ 33 09.9 E 003⁰ 21 25.6 29 m N 06⁰ 33 09.2 E 003⁰ 21 27.8 31 m Ilupeju industrial estate Canal * N 06⁰ 32 57.9 E 003⁰ 21 26.9 29 m N 06⁰ 34 56.9 E 003⁰ 21 26.0 33m * Canal receives effluents from industries 237 UNIVERSITY OF IBADAN LIBRARY Appendix 2: Effluent channel of Hoesch Pipe Mill Ltd in Ikeja Industrial Estate Appendix 3: Effluent channel of May & Baker Nigeria Ltd in Ikeja Industrial Estate 238 UNIVERSITY OF IBADAN LIBRARY Appendix 4: Effluent channel at the central treatment plant receiving all effluents in Ikeja Industrial Estate. Appendix 5: Effluent channel of Johnson Wax in Isolo Industrial Estate. 239 UNIVERSITY OF IBADAN LIBRARY Appendix 6: Effluent channel of Afrab Chem in Isolo Industrial Estate. Appendix 7: Effluent channel of PZ in llupeju Industrial Estate 240 UNIVERSITY OF IBADAN LIBRARY Appendix 8: A canal receiving wastewater from several industries in llupeju Industrial Estate. 241 UNIVERSITY OF IBADAN LIBRARY Appendix 9: Calibration graph for determination of sulphate. Appendix 10: Calibration graph for determination of phosphate. 242 UNIVERSITY OF IBADAN LIBRARY Appendix 11: Calibration graph for nitrate. 243 UNIVERSITY OF IBADAN LIBRARY Appendix 12: Geo-accumulation index of heavy metals in soil. I-geo I-geo. Grade Soil Quality 5-10 6 Extremely contaminated 4-5 5 Strong/ extremely contaminated 3-4 4 Strongly contaminated 2-3 3 Moderately/ Strongly contaminated 1-2 2 Moderately contaminated 0-1 1 Uncontaminated/ moderately contaminated <0 0 Uncontaminated 244 UNIVERSITY OF IBADAN LIBRARY