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CONCEPT OF COPPER MOBILITY AND COMPATIBILITY WITH LEAD AND CADMIUM IN LANDFILL LINERS by Saleh Kaoser A thesis submitted to the Faculty of Graduate Studies and Research in Partial fulfillment of the requirements for the degree of DOCTOR OF P HILOSOPHY Department of BioResource Engineering Macdonald Campus of Mc Gill University Montreal, Quebec, Canada December, 2003 ©Saleh Kaoser, 2003 i ABSTRACT Despite improved liner design, there are still reported incidences of landfill leachate, rich in heavy metals, percolating through to groundwater and threatening ecosystems. This thesis introduces the concept of segregating municipal solid wastes (MSW) according to their major heavy metals and their metal’s adsorption compatibility. Each segregated portion can be disposed in a different landfill compartment to minimize leaching of these heavy metals with the greatest bioactive impact. The validity of the concept was evaluated by batch and column retention mobility studies using copper (Cu) alone or with either lead (Pb) or cadmium (Cd) in solutions bearing various pHs. This was supported by selective sequential extraction (SSE) to determine the affinity to specific liner fractions. The following summarizes the procedure used. Beforehand, a soil column test using sand with 5 and 10% bentonite was conducted to develop an equation predicting liner permeability, k, under simulated field conditions. The column permeability test revealed that a liner with 5% bentonite resulted in a k value which respected the North American criteria of 10-5 m/s. In the batch experiments, solutions with Cu alone or with Cd or Pb, adjusted to pH of 3.7, 5.5 or 7.5, were applied to sand liners with 0%, 5% or 10% bentonite, having CEC’s of 2.0, 6.4, and 10.8 (cmol(+) kg-1 ), respectively. Bentonite, pH and Pb significantly affected Cu adsorption. Cu was adsorbed by the liners at pH < 6.5 whereas Cu precipitated at pH > 6.5. Cu retention was higher in the presence of Cd than in that of Pb, at all combinations of CEC and pH. Competition between metals was greater in liners with lower CEC and therefore fewer adsorption sites. Limiting Pb in a landfill compartment can improve Cu ads orption at pH’s below the precipitating threshold. In the SSE procedure, the liner samples were centrifuged, decanted from their solutions and each adsorption fraction analyzed for Cu content. Results indicated that the carbonate fraction adsorbed more Cu, and that Pb significantly increased the mobility of Cu due to competition for exchangeable sites. In the final soil column test using a sand liner with 5% bentonite, the leachate had an initial pH of 3.7. The leaching test confirmed the compatibility of Cu with Cd. The leaching of Cu was greater in the presence of Pb. Total metals in leachate was greater for ii the Cu-Cd solutions than for the Cu-Pb, because of Cd’s relatively high mobility. The sequential extraction results showed again that the carbonate fraction dominated metal adsorption. Total heavy metal leaching followed the order of Cu/Cd > Cu/Pb > Cu alone. Thus, disposing MSW in landfill compartments based on their heavy metal compatibility can minimize migration of heavy metals. iii RESUMÉ Malgré que les sites d’enfouissement sanitaires bénéficient de membranes d'étanchéité encore plus performantes, la percolation de lixiviat est un problème persistant qui met en dangers les nappes phréatiques environnantes à cause de leur contenu riche en métaux lourds. Une notion est donc introduite pour améliorer davantage l’adsorption des métaux lourds par les particules des membranes de sites d’enfouissement sanitaires construites d’un mélange de sable et de bentonite. Il s’agit de la ségrégation des ordures selon leur teneur en métaux. La ségrégation s’effectuerait de façon à regrouper les ordures avec des métaux lourds démontant une compatibilité d’adsorption par les particules de sables et bentonite. Chaque groupe d’ordure serait alors enfoui dans un compartiment différent du site d’enfouissement, pour que leur lixiviat contiennent des métaux lourds qui s’adsorbent mieux par la membrane d’étanchéité. Ce concept fut validé grâce à des études d’adsorption et de mobilité en série et en continue sur colonne, qui utilisaient du Cu seul ou en combinaison avec soit du Pb ou du Cd, dans des solutions de pH variés. Les échantillons de membrane naturelle (mélange de sable avec 0, 5 et 10% de bentonite) furent ensuite analysés en utilisant un procédé d’extraction séquentiel et sélectif (ESS) visant à établir l’affinité du Cu pour les diverses fractions adsorbante des particules de sables et bentonite. Le tout s’est réalisé comme suit. Avant de procédé à ces essais, la perméabilité de membranes de sables et de bentonite fut mesuré en laboratoire afin d'établir une équation permettant de prédire leur conductivité hydraulique, k, sous des conditions naturelles simulées. Cet essai démontra qu'une membrane de sable contenant 5% de bentonite donne une va leur de k respectant le critère nord américain de 10-5 m/s. Des études en série furent alors réalisées en exposant les échantillons de membranes de sable et de bentonite (0, 5 et 10%) à des solutions contenant du Cu seul ou en combinaison avec soit du Cd ou du Pb. Le pH de ces solutions fut ajusté à 3.7, 5.5 et 7.5. Les échantillons de membrane d'étanchéité de sable contenant 0%, 5% et 10% de bentonite offraient des pouvoirs d'échange cationiques (PEC) de 2.0, 6.4, et 10.8 (cmol (+) kg-1 ), respectivement. La teneur en bentonite, le pH, et la concentration de Pb eurent chacun une influence significative sur l'adsorption de Cu. A un pH sous 6.5, le Cu fut adsorbé par les échantillons de membrane, tandis qu'à un pH au-dessus de 6.5, le Cu fut iv surtout précipité. La rétention du Cu fut plus élevée en présence de Cd qu'en présence de Pb pour toutes les combinaisons de PEC et de pH. Les échantillons de membrane ayant un faible PEC ont engendré entre les métaux, une compétition d’adsorption plus serrée. Les essais ont démontré que lorsque la teneur des lixiviat est limité en Pb comparativement au Cd, l'adsorption du Cu est améliorée. L’extraction séquentielle du Cu des échantillons traités démontrait que les carbonates et hydroxydes adsorbaient la majorité du Cu, et que le Pb augmentait de façon significative la mobilité du Cu. Dans le dernier essai en continu, des colonnes d’échantillons de membrane de sable avec 5% de bentonite étaient exposés à un lixiviat simulé avec pH de 3.7. Cet essai de lixiviation confir ma la compatibilité du Cu avec le Cd. La perte par lixiviation de Cu fut plus élevée en présence de Pb qu'en son absence ou qu’en présence de Cd. La teneur totale en métaux du lixiviat fut plus élevée pour le lixiviat contenant du Cu et du Cd, comparativement au lixiviat contenant du Cu et du Pb, à cause de la mobilité élevée du Cd. De nouveau, l'extraction séquentielle démontra que l'adsorption des métaux fut dominée par les carbonates et les hydroxydes. Au total, les pertes en métaux lourds par lixiviation ont respecté l'ordre suivant : Cu/Cd > Cu/Pb > Cu seul. Par conséquent, la ségrégation et l’enfouissement des ordures, en fonction de leur teneur en métaux lourds, peut effectivement réduire la migration des métaux lourds des sites d’enfouissements sanitaire lorsque la membrane d’étanchéité est conçue d’un mélange de sable et de bentonite. v ACKNOWLEDGEMENT Above all, I thank the Almighty God for granting me the strength, patience, energy, and perseverance to bring my research to fruition. At each stage of my research, there was a new hand extended to me in generous support. However, I can only acknowledge a few of them in this short note. First of all, I am profoundly grateful to my thesis supervisor, Professor Suzelle Barrington, without whom this research work could not have been completed. She provided me with her valued insights and precious guidance. Her excellent judgment, remarkable patience, superlative vision, and amazing breadth of knowledge were continuously available to me. She had great understanding of the issues I had to face during my research period, both on academic and personal levels, and her unending faith and support were true incentives for me to carry out my research. In the department, I would like to acknowledge Dr. Vijaya Raghavan who, during his tenure as departmental chairman, encouraged me and repeatedly inquired about my progress. Also, I would like to offer a special thanks to Dr. Robert Kok, departmental chairman, whose seminars taught me to present my expertise in an effective and clear manner. I would like to extend my gratitude to Dr. Shiv O. Prasher, Dr. Edward McKyes, Dr. Chandra A Madramootoo, Dr. Robert Bonnell, Dr. Michael Ngadi and Dr. George Dodds as well, who always made sure of my wellbeing. Dr. John D. Sheppard was not an exception in this regard. He generously allowed me to use his lab facilities whenever I needed them. Dr. Robert S. Broughton, provided me with valuable suggestions in soil column setup. Also, I want to acknowledge the prompt and efficient work of Susan Gregus in our administrative department whenever problems or complications occurred with departmental students, including myself. Similar action was seen from Trish Singleton and Abida Subhan. I would like to thank all of them. I couldn’t have asked for better assistance. Besides our departmental faculty and staff, I would like to express my gratitude to Dr. William D. Marshall, Chairman, Department of Food Science and Agricultural Chemistry, at McGill University for allowing me to use his lab for AAS equipment and his wishes for my success. A special thanks goes to Dr. Maria Elektorowicz, department of Building, Civil & Environmental Engineering, Concordia vi University, who has given her valuable time to review my pa pers and provided suggestions and constructive criticism. Among my colleagues and friends I would like to mention Mari Shin, Gin-woo Kim, Leung Stanley Fahima Tani, and Sophie Morin with whom I shared our waste management laboratory. I will never forget them. Also, my friends, colleagues, and supporters, Mr. Hussein Sarwar and Mamun-ul Islam, provided their emotional support during my tenure at McGill. On a personal level, my special gratitude goes to Dr. Anna Ambrus, an English language specialist, my well wisher and personal friend whom I could turn to at any time. My heartfelt appreciation goes to my elderly mother to whom I could not provide sufficient care because of my engagements. She has given me unflinching support all these years and has never complained. Finally, I dedicate my dissertation to my father Abul Kalam Shamsul Hoque’s memory. He was the symbol of honesty, never faltering passion for knowledge, and he sacrificed his personal life for my education and that of my brothers and sisters. I would have loved him to see the results of my research work. My dedication also goes to my only beloved daughter Ridhwana Kaoser, my reason to carry on. vii TABLE OF CONTENTS Page ABSTRACT…………………………………………………………………….. i RESUME…………………………………………………………………… …. iii ACKNOWLEDGEMENT…………………………………………………….... v TABLES OF CONTENTS…………………………………………………....... vii LIST OF TABLES……………………………………………………………… xii LIST OF FIGURES…………………………………………………………....... xiv NOMENCLATURE……………………………………………………………. xvi CHAPTER 1: INTRODUCTION……………………………………….……. 1 1.1 Problem statement ……………………………………………………… 1 1.2 Quantities………………………………………………………………… 3 1.3 Health impact……………………………………………………………. 3 1.4 Research objectives…………………………………………………….. 5 1.5 Research scope ………………………………………………………….. 6 1.6 Notes on thesis organization……………………………………………. 6 1.7 References………………………………………………………………. 8 CHAPTER 2: LITERATURE REVIEW…………………………………… 12 2.1 Landfill liners……………………………………………………………. 12 2.2 •• Soil liners………………………………………………………………… 13 2.3 Liner leakage……………………………………………………………. 14 2.4 Causes of failure………………………………………………………… 15 2.5 Impact of liner leakage ………………………………………………….. 16 2.6 •• Properties of heavy metals………………………………………………. 17 2.7 Heavy metal adsorption mechanisms………………………………….. 20 2.7.1 Heavy metal interaction………………………………………………… 20 2.7.2 Organic-matter interaction…….. ……………………………………….. 22 2.7.3 Carbonate and oxide interactions ………………………………………. 23 2.7.4 Effects of redox processes……………………………………………… 24 viii 2.7.5 Effects of soil microbes…………………………………………………… 25 2.8 References………………………………………………………………. 27 Connecting statement…………………………………………………… 33 CHAPTER 3: COMPARTMENTS FOR THE MANAGEMENT OF MUNICIPAL SOLID WASTE………………………………………………… 34 3.1 ABSTRACT ………………………………………………………………… 34 3.2 INTRODUCTION …………………………………………………………… 35 3.3 MSW PRODUCTION AND LEACHATE CHARACTERISTICS ……...................... 3.4 PROCESS INFLUENCING HEAVY METAL MOBILITY………………………… 38 3.5 36 3.4.1 Heavy- metal interaction…………………………………. 38 3.4.2 Organic-matter interaction……………………………….. 40 3.4.3 Carbonate and oxide in teractions ………………………… 41 3.4.4 Effects of redox processes……………………………….. 42 MSW MANAGEMENT BY COMPARTMENTS………………………………… 43 3.5.1 Segregation of low-mobility metals ……………………… 43 3.5.2 Segregation of moderate mobility metals ………………… 44 3.5.3 Segregation of high-mobility metals……………………... 44 3.5.4 Waste sorting for segregation…………………………….. 44 3.5.5 Compartment liner design………………………………... 45 3.5.6 MSW Segregation………………………………………… 46 3.6 SUMMARY………………………………………………………………… 46 3.7 REFERENCES ……………………………………………………………… 48 CONNECTING STATEMENT………………………………………………… 60 CHAPTER 4: PRESSURE AND COMPACTION EFFECTS ON HYDRAULIC CONDUCTIVITY OF SAND-BENTONITE LINERS: A STUDY FOR VARIABLE AND UNPREDICTABLE FIELD CONDITION.. 61 4.1 ABSTRACT ………………………………………………………………… 61 ix 4.2 INTRODUCTION …………………………………………………………… 62 4.3 THEORETICAL APPROACH …………………………………………………. 63 4.4 MATERIALS AND METHODS……………………………………………….. 64 4.4.1 Experimental materials…………………………………………… 65 4.4.2 Methodology……………………………………………………… 65 4.5 RESULTS AND DISCUSSION…………………………………………………. 67 4.5.1 Swelling………………………………………………………….. 67 4.5.2 Impact of bentonite level………………………………………….. 68 4.5.3 Effect of compaction and hydraulic pressure on k………………… 68 4.5.4 Applying equation [5] to other research data……………………… 69 4.6 CONCLUSIONS …………………………………………………………… … 70 4.7 ACKNOWLEDGEMENT…………………………………………………….... 71 4.8 REFERENCES ………………………………………………………………. 72 CONNECTING STATEMENT………………………………………………… 83 CHAPTER 5: COPPER ADSORPTION WITH Pb AND Cd IN SAND-BENTONITE LINERS UNDER VARIOUS PHS. PART I. EFFECT ON TOTAL ADSORPTION……………………………………….. 84 5.1 ABSTRACT ………………………………………………………………… 84 5.2 INTRODUCTION …………………………………………………………… 85 5.3 MATERIALS AND METHODS………………………………………………... 87 5.4 5.3.1 Experimental materials…………………………………………..... 5.3.2 Experimental design……………………………………………… 89 RESULTS AND DISCUSSION ……………………………………………….. 87 90 5.4.1 Cu solubility……………………………………………………… 90 5.4.2 Copper adsorption for low heavy metal equivalence……………. 91 5.4.2.1 Initial solution pH of 3.7 and 5.5…………………………………. 91 5.4.2.2 Initial solution pH of 7.5………………………………………… 5.5 93 5.4.3 Absorption experiments with high cation equivalence…………... 94 5.4.4 Regression analysis………………………………………………. 94 CONCLUSION ……………………………………………………………… 96 x 5.6 ACKNOWLEDGEMENT …………………………………………………….. 5.7 REFERENCES ……………………………………………………………… 97 CONNECTING STATEMENT………………………………………………… 96 111 CHAPTER 6: COPPER ADSORPTION WITH Pb AND Cd IN SAND BENTONITE LINERS UNDER VARIOUS PHS. PART II. EFFECT ON ADSORPTION SITES…………………………………………. 112 6.1 ABSTRACT ………………………………………………………………… 112 6.2 INTRODUCTION …………………………………………………………… 113 6.3 METHODOLOGY…………………………………………………………… 115 6.4 6.3.1 Experimental materials…………………………………………… 115 6.3.2 Sample preparation………………………………………………. 116 6.3.3 SSE procedure…………………………………………………… 117 6.3.4 Statistical procedure and regression analysis……………………. 117 RESULTS AND DISCUSSION ……………………………………………….. 118 6.4.1 Adsorption under alkaline conditions……………………………. 118 6.4.2 Acid conditions and liner CEC higher than the solution equivalence………………………………………………………. 120 6.4.3 Acid conditions and liner CEC equal to or lower than the solution equivalence ……………………………………………… 122 6.5 CONCLUSION ………………………………………………………………. 123 6.6 ACKNOWLEDGEMENT …………………………………………………….. 6.7 REFERENCES ……………………………………………………………… 125 CONNECTING STATEMENT………………………………………………… 124 135 CHAPTER 7: EFFECT OF Pb AND C d ON Cu ADSORPTION BY SAND-BENTONITE LINERS……………………………………………….… 136 7.1 ABSTRACT ………………………………………………………………... 7.2 INTRODUCTION …………………………………………………………… 137 7.3 MATERIALS AND METHODS…………………………………………….… 139 7.3.1 Experimental setup……………………………………………..… 136 139 xi 7.4 7.3.2 Method………………………………………………………….… 140 7.3.3 SSE procedure…………………………………………………..… 141 7.3.4 Statistical procedure………………………………………………. 142 RESULTS AND DISCUSSION ……………………………………………..… 142 7.4.1 Leaching analysis……………………………………………….... 142 7.4.2 Sequential extraction analysis………………………………….… 143 7.5 SUMMARY………………………………………………………………… 144 7.6 ACKNOWLEDGEMENT …………………………………………………….. 7.7 REFERENCES ……………………………………………………………… 146 145 CHAPTER 8: GENERAL SUMMARY AND CONCLUSI ONS………….… 157 8.1 SUMMARY AND CONCLUSION ……………………………………………... 157 8.2 CONTRIBUTIONS TO THE KNOWLEDGE…………………………………….. 8.3 RECOMMENDATIONS FOR FUTURE WORK ………………………………….. 160 CHAPETR 9: GENERAL REFERENCES……………………………………. 159 162 APPENDICES…………………………………………………………………... 180 xii LIST OF TABLES Page CHAPTER 2 Table 2.1 Heavy-metal characteristics……………………………………….. 18 CHAPTER 3 Table 3.1 Countrywise waste production and waste components….………… 52 Table 3.2 MSW production in USA and Canada ……………………….……. 53 Table 3.3 Domestic and commercial waste components in Montreal….……. 54 Table 3.4 Segregated domestic and commercial waste components in Montreal………………………………………………………… 55 Table 3.5 Heavy metals and their corresponding waste components……….... 57 Table 3.6 Example of wastes containing compatible heavy metals ………….. 58 Table 3.7 Heavy metals in leachate in Quebec landfills……………………… 58 Table 3.8 Characteristics of heavy metals ……………………………………. 59 CHAPTER 4 Table 4.1 Shape factor for various soil particles……………………………... 75 Table 4.2 Physical and chemical characteristics of sand and bentonite used for the liner material………………………………………… 76 Table 4.3 Sand-bentonite liner properties ……………………………………. 77 Table 4.4a Applying equation [5] to other research using compacted laboratory sample………………………………………………….. 78 Table 4.4b Applying equation [5] to other research using consolidated laboratory samples………………………………………………… 79 CHAPTER 5 Table 5.1 Physical and chemical characteristics of sand and bentonite……… 102 Table 5.2 Cu adsorption with stabilization period…………………………… 103 Table 5.3 Equilibrium pH profile……………………………………………. 104 xiii LIST OF TABLES CONTD. Table 5. 4 Kd values of copper under different liner, pH and composite. solutions ………………………………………………….…….…. 105 CHAPTER 6 Table 6.1 Physical and chemical characteristics of sand and bentonite.…….. 128 Table 6.2a Equilibrium pH after 14 days of exposure to the experimental heavy metal solutions at an initial pH of 7.5…………………........ 129 Table 6.2b Equilibrium pH after 14 days of exposure to the experimental heavy metal solutions at an initial pH of 5.5……………………… 129 Table 6.2c Equilibrium pH after 14 days of exposure to the experimental heavy metal solutions at an initial pH of 3.7…………….….…….. 129 Table 6.3 Effect of Pb and Cd on Cu adsorption compared to Cu alone …...... 130 CHAPTER 7 Table 7.1 Physical and chemical characteristics of sand and bentonite…....... 149 Table 7.2 Total heavy metal retention in soil column and in different fractions………………………………………..…………………. 150 xiv LIST OF FIGURES Page CHAPTER 4 Figure 4.1 Particle size analysis ……………………………………………… 80 Figure 4.2 Rigid wall leaching cell…………………………………………… 81 Figure 4.3 Hydraulic conductivity (k) of sand and bentonite mixtures during swelling under an ?p of 7 kPa……………………………………. 82 CHAPTER 5 Figure. 5.1 Particle size analysis ………………………………………………. 106 Figure 5. 2 Solubility of Cu as a function of pH………………………………. 106 Figure 5.3 Cu absorption with time, for sand with 0% (a), 5% (b), and 10% bentonite (c), for an initial pH of 3.7, alone and with exposure to Pb and Cd. Cu, Pb, and Cd were all applied at a level of 1 meq/100g of liner material. The CEC of the liner a, b, and c are 2, 6.4, and 10.8 meq/100g of liner material respectively ……. 107 Figure 5.4 Cu absorption with time, for sand with 0% (a), 5% (b), and 10% bentonite (c), for an initial pH of 5.5, alone and with exposure to Pb and Cd. Cu, Pb, and Cd were all applied at a level of 1 meq/100g of liner material. The CEC of the liner a, b, and c are 2, 6.4, and 10.8 meq/100g of liner material respectively ….... 108 Figure 5.5 Cu absorption with time, for sand with 0% (a), 5% (b), and 10% bentonite (c), for an initial pH of 7.5, alone and with exposure to Pb and Cd. Cu, Pb, and Cd were all applied at a level of 1 meq/100g of liner material. The CEC of the liner a, b, and c are 2, 6.4, and 10.8 meq/100g of liner material respectively …..... 109 xv LIST OF FIGURES CONTD. Figure 5.6 Cu adsorption with time, for sand with 5% bentonite for an initial pH of 3.7, alone and with exposure to Pb and Cd. Cu alone was applied at a level of 4.8, with Pb and Cd, Cu applied at 2.4 meq/100g of liner material. Both Pb and Cd applied at 2.4 meq/100g of liner material. The CEC of the liner was 6.4 meq/100g of liner material……………………………….. 110 CHAPTER 6 Figure 6.1 Particle size analysis ………………………………………………. 131 Figure 6.2 Copper retention by liner particle fractions under initial pH of 7.5 in sand with 0%, 5%, and 10% bentonite mixtures………………. . 132 Figure 6.3 Copper retention by liner particle fractions under initial pH of 5.5 in sand with 0%, 5%, and 10% bentonite mixtures……………….. 133 Figure 6.4 Copper retention by liner particle fractions under initial pH of 3.7 in sand with 0%, 5%, and 10% bentonite mixtures ……………….. 134 CHAPTER 7 Figure 7.1 Particle size analysis………………………………………………. 151 Figure 7.2 Rigid wall leaching cell……………………………………………. 152 Figure 7.3 Experimental setup………………………………………………… 153 Figure 7.4a Mobility of Cd and Pb with Cu………………………………….… 154 Figure 7.4b Pore volume versus pH……………………………………….…… 154 Figure 7.5 Total Heavy Metal Leaching……………………………….……… 155 Figure 7.6 Metal retention in fractions through liner profile, for Cu alone (a), Cu with Cd (b), Cu with. Pb (c), Cd with Cu (d), and Pb with Cu (e). The total depth is 8 cm……………………………………. 156 NOMENCLATURE xvi ? (Angstrom)……………………….10-8 cm = 0.1nm A( m2 )………………………………..Cross sectional area of the test sample As……………………………………Arsenic Cd…………………………………...Cadmium Cu……………………………………Copper C0 (mg/L)……………………………Initial concentration of the supernatant Ce(mg/L)……………………………Equilibrium concentration of the supernatant Cua (cmol (+)/kg)……………………..Cu adsorbed as a fraction of that applied Cr……………………………………Chromium De …………………………………...Equivalent particle size diameter of the liner Db and Dp …………………………...Bulk and particle densities of the samples ? ……………………………………Dimensionless factor related to porosity Fe……………………………………Iron g (9.81 m/s2 )…………………………Gravitational constant Hg…………………………………… Mercury k (cm/s)………………………………Hydraulic conductivity Kd ……………………………………Distribution coefficient (Sorption affinity) ?L …………………………………...Depth of the liner L(m) …………………………………Depth of the test sample Ma (g)……………………………….. Mass of absorbent (liner material) Mn…………………………………...Manganese Ni…………………………………….Nickel n …………………………………….Volumetric porosity of the sample Pb……………………………………Lead ? P……………………………………Drop in Hydraulic pressure Pe……………………………………Equivalent pore size ?(kg/m3 )……………………………...Density of the fluid flowing through the liner Q(m3 )………………………………...Quantity of effluent collected Se…………………………………….Selenium NOMENCLATURE CONTD. xvii t(s)…………………………………...Time V(ml)………………………………...Volume of the supernatant ? …………………………………….Apparent average velocity xi …………………………………….Fraction of particles ai……………………………………..Shape factor of particles Zn……………………………………Zinc AAS…………………………………Atomic Adsorption Spectroscopy AAFRD……………………………...Alberta Agriculture, Food, and Rural Development ANOVA……………………………..Analysis of Variance ASTM……………… ………………American Society for Testing and Materials ATSDR……………………………...Agency for Toxic Substances and Disease Registry BaCl2 ………………………………. Barium Chloride CaCO3 ……………………………….Calcium Carbonate CCME………………………………. Canadian Council of Ministers of the Environment CEC [cmol(+)/kg]…………………...Cation Exchange Capacity CPE………………………………….Chlorinated polyethylene CQA…………………………………Construction Quality Assurance CSPE………………………………...Chlorosulfonated polyethylene CVD…………………………………Cardiovascular Diseases <d/l…………………………………..Below detection limit EPA………………………………….Environmental Protection Agency FeS2………………………………… Pyrite (Iron Disulfide) FMLs………………………………...Flexible Membrane Liners GERLED……………………………Group d'etude et de restauration des lieux d'elimination de dechets dangereux GNP…………………………………Gross National Product GPA…………………………………Global Programme of Action HDPE………………………………..High Density Polyethylene HNO3 ……………………………….Nitric Acid NOMENCLATURE CONTD. xviii IEC…………………………………..International Equipment Company, LDPE………………………………..Low Density Polyethylene LOI………………………………….Loss On Ignition MC………………………………….Moisture Content MENV………………………………Ministère de l'environnement du Québec MSW………………………………..Municipal Solid Waste mM………………………………….Millimol NaOH……………………………….Sodium Hydoxide NRCC……………………………….National Research Council of Canada OTA………………………………...Office of Technology Assessment PVC…………………………………Polyvinyl chloride RPAL………………………………. Risk and Policy Analysts Limited SSE………………………………….Selective Sequence Extraction TCE…………………………………Trichloroethylene UNEP ……………………………….United Nations Environmental Programme 1 CHAPTER 1 1. INTRODUCTION 1.1 PROBLEM STATEMENT Heavy-metal pollution has raised serious environmental concerns worldwide because bio-accumulation of these elements beyond the tolerance thresholds of living organisms poses long term risk to the earth’s ecosystem (Voegelin et al., 2003; Whittle and Dyson, 2002; Han et al., 2001; Oliver et al., 1999). Industrial, urban, and farming activities are the main sources of heavy metals in the environment (UNEP/GPA, 2003; RPAL, 2002; UNEP, 2000). Precise identification of the actual sources of heavy metal contamination of soil and water resources is urgent, due to the acute, severe, and persistent impacts of these pollutants on human health and on the sustainability of ecosystems. The main flows of heavy metals to the environment are from industrial and municipal wastes, both of which contain a variety of toxic heavy metals (Whittle and Dyson, 2002). For example, chemical and electronic industries mainly produce Hg; metallic ferrous mining and smelting industries produce Pb, Cd, As, Hg, Fe and Ni; metallurgical industries produce As, Cd, Cr, Cu, Mn, Ni, Pb, and Zn; the pulp and paper industry generates Zn, Cu, Cr, Ni, Al, Fe, and Mn (Brittle and Michaela, 2003; UNEP, 2000; Alloway and Ayres, 1993). Other industries, such as electroplating, leather, paint, and dye industries produce different sets of heavy metal containing wastes (Yong et al., 1992). In addition to various industries, many commercial establishments, such as retail stores, restaurants, laundr omats, garages, hospitals, schools, laboratories, and photo processors also contribute to the heavy metal load discharged into the environment. Storm water can be contaminated when runoff from areas receiving combined sewers contains zinc and cadmium from tire wear, lead from gasoline, and other metals from gutters. Of the 24.86, 16.93 and 0.1 Gg yr -1 discharges of Pb, Cd, and Cu, respectively, into UK waters, 166, 0.074, and 0.204 Mg yr -1 , originated from waste disposal facilities. (RPAL, 2002). Chen and Samira (2002) reported that deposition of Pb reached as high as 6 Gg yr-1 in Canada, USA, and some European countries. Lead levels up to 1000 mg kg-1 have been found in soils at shooting ranges contaminated by Pb ammunition (Murray 2 et al., 1997) and Pb concentrations as high as 62.15 g kg-1 were found near smelting operations (Temple et al., 1977). Elliott et al. (1989) measured soil Pb levels of 200 g kg-1 at a battery recycling plant and Sims (1986) reported levels up to 466 g kg-1 at hazardous waste sites. Households are also sources of contaminants. Home plumbing (copper and galvanized water pipes), household commodities (detergents, bath soaps) and solids from garbage grinders also contribute to wastewater heavy metal content. Heavy metals are generated by agricultural sources: fertilizers (As, Cd, Mn, and Zn), pesticides (As, Cu, Mn, Pb, and Zn) and farmyard manures (As, Cu, Mn, Ni, Pb, Se, and Zn) from enriched feed rations (Whittle and Dyson, 2002; Alloway, 1990). Runoff from livestock yards contains some amount of toxic substances due to the use of disinfectants in the cleaning of livestock shelters and metals used to handle wastes (AAFRD, 2002). For example, Fe, Zn, Cu, Pb, and Cd were found in beef feedlots at concentrations of 140, 30, 19, and 1.7 ppm, respectively (AAFRD, 2002; Overcash et al., 1983). The main source of heavy metal contamination has been landfill disposal sites. In 2000, there were 2,157 US municipal landfill sites in operation (City of Tempe, 2003). The Office of Technology Assessment (OTA, 1984) listed about 30 potential sources of groundwater contamination within six major categories. Among these categories, sources designed to store, treat, and/or dispose of substances include landfills, open dumps, surface impoundments, waste tailings, waste piles, materials stockpiles, above and underground storage tanks. Among all the sources, the leachate of municipal solid waste (MSW) always contained heavy metals since leachate is the product of all sorts of waste components that are disposed of in landfills all-together. The heavy metals commonly found in landfill leachate include Pb, Cd, Cu, Hg, Ni, Cr, Fe, and Se (Urase et al., 1997; Shuckrow and Touhill, 1981). Although the actual number and concentration of heavy metals in the leachate varies from one landfill to another, the concentrations of most heavy metals found in the landfill leachate are considerably above allowable concentrations. Moreover, it has been shown that all landfillliner materials eventually fail (Lee, 2002; Reinhart and McCreanor, 1999). Consequently, the potential risk of soil and groundwater contamination by heavy metals from landfills is also high. Furthermore, because household products are made from more and more 3 unusual chemical mixtures each year, landfill leachate becomes more toxic (Whittle and Dyson, 2002). Current landfill problems do not only include issues of land costs and scarcity, but also of chemicals leaching from landfill, groundwater contamination and its consequences. In designing a landfill site, the primary concern is to minimize the negative effects on the environment, referred to as "pollution". Pollution from landfills can take the following basic forms: (i) leachate production and off-site migration; (ii) gas emission; (iii) refuse dispersed by wind; and (iv) operational activities, of which the former two as well as migration are primary concerns. Moreover, some negative effects of pollution can persist even 50 to 100 years after the site is closed. 1.2 QUANTITIES In the United States, Canada, and the United Kingdom, respectively, about 230, 30, and 23.1 Tg yr -1 of MSW is produced (ACEID, 2002; USEPA, 2003b; Statistics Canada, 2003). In the United States 61% of MSW is disposed of in landfills (USEPA, 2003b). This MSW contains a significant quantity of heavy metals and other hazardous wastes (USEPA, 2003b; Statistics Canada, 2003). 1.3 HEALTH IMPACTS Health concerns are mainly triggered by the fact that most of the previously constructed landfills were not environmentally safe. The exposure of human or other living organisms to unsafe landfill residues is threefold: air emission, surface run-off, and leaching (Lee, 2002; Wentz, 1989). An analysis of landfills (Montague, 1990) shows that 86% of those studied have contaminated gr oundwater. A 2003 USEPA draft report (USEPA, 2003a) stated that environmental pollution causes a considerable number of human fatalities. The mortality rate due to lung and skin cancers has increased in recent years. More than 40% of the total deaths are caused by cardiovascular diseases (CVD) related to hypertension and arteriosclerosis. Lung cancer, asthma, and other respiratory diseases have also caused a considerable number of deaths, and their rates are increasing. Some of these symptoms are well established as being related to exposures to heavy metals. The effects of heavy metals on human health are very serious and vary from short-term (acute) to long term effects (Whittle and Dyson, 2002; Voegelin et al., 2003). Arsenic 4 compounds cause dermatitis, acute and chronic poisoning, and cancer (Karagas, 2002). For example, 0.7g of arsenic trioxide (As 2 O3 ) can be fatal if ingested (Abernathy and Morgan, 2001). Cadmium health hazards result from inhalation of fumes, dusts, and ingestion of contaminated food. The principle effects are carcinogenic and teratogenic, affecting liver, kidneys, lungs, blood (anæmia), which can further result in cardiovascular or hypertension problems (Waalkes, 2000). Copper toxicity presents gastrointestinal symptoms such as nausea and vomiting, and exposure to cupric sulphate can cause diarrhoea. Copper also can cause vascular injury and hæmolytic anæmia resulting in kidney and liver damage (USEPA, 1996). Lead has longer residence time in the environment, and consequently, the possibility of remaining in the food chain is higher. Lead can cause mental impairment in young children. Contamination may occur by ingestion or inhalation (Brittle and Michaela , 2003; Chen and Samira, 2002). Exposure to Pb before birth or in early child hood can reduce IQ levels by 5% to 10%. The effects of exposure can persist at least through age seven (Endres et al., 2002; Alloway, 1990). Lead can also cause a reduction in attention span and even insanity (Endres et al., 2002: Conner, 1990). Various mercury compounds possess varying toxicity levels. Mercury fumes can affect the central nervous system. As the bio-accumulation of mercury in fish is very high, the consumption of mercury-contaminated fish is the most common cause of mercury poisoning in people. When fish or other foods contaminated with Hg are consumed, mercury ingested in an organic form is transformed into methyl mercury chloride under the action of gastric acids. This organic mercury compound then passes into the blood stream and is carried to the central nervous system and the brain. The chemical symptoms of methyl mercury poisoning are: loss of sight and hearing; intellectual degeneration; progressive loss of movement co-ordination; numbness and tremors in the limbs and congenital disorders (Reeve, 2002; Alloway, 1990). Nickel and its compounds are inhalation hazards. Ni compounds are skin irritants, which can cause allergic dermatitis and are also carcinogenic (ATSDR, 1997). Since the most persistent toxic pollutants of landfill leachate are heavy metals, and since most of the landfill studied have leakage problems, alternative approaches for the retention of such heavy metals in liners have become increasingly important. One 5 option is to investigate the individual heavy metal characteristics and the interaction with their peers in the soil solution. A mixture of incompatible heavy metals leads to greater leaching due to the ion competition for the soil adsorption sites. Therefore, the proposed solution is to segregate the heavy metals on a compatibility basis with the aim of increasing the chances of their attenuation in the soil profile. 1.4 R ESEARCH OBJECTIVE The principle objective of this project was to develop and test a concept of heavy metal adsorption compatibility to reduce heavy metal mobility through landfill liners. Heavy metal leakage and migration in the soil profile have an adverse environmental effect. Despite technological development and improved liner-material applications , the migration of heavy metals continues to occur. This is attributed to the synergetic and antagonistic effect of different heavy metals which hinders their attenua tion within a single cell. Therefore, an alternative approach must be explored to reduce the risk of heavy-metal migration through landfill liners. By identifying the types of heavy metal in specific MSW and segregating and disposing these MSW on a basis of heavy metal compatibility, heavy metal mobility can be further attenuted by landfill liners. This will eventually lead to the concept of landfill compartmentalization of MSW components. This research was conducted in a view to minimizing heavy-metal migration to the groundwater, decreasing landfill liner cost. Since contaminants from landfills are carried by leachate,this is imperative to explore and measure the permeability (k values) of sand-bentonite liners under field conditions. To achieve this, the following research objectives were set: (i) To classify heavy metals under compatible and incompatible groups, in terms of synergetic and antagonistic interaction. (ii) To evaluate the hydraulic conductivity of the proposed sand-bentonite liner materials under simulated field conditions. 6 (iii) To establish empirical relationships between metal adsorption versus metal input concentrations using various compatible and incompatible combinations, solution pH and liner CEC. (iv) To measure the individual adsorption to/leaching from liner materials of heavy metals singly or in combination with compatible or incompatible heavy metals, in both batch and leaching tests. 1.5 R ESEARCH SCOPE This is a limited scope study, dealing with small samples of landfill liners exposed in the laboratory to simulated landfill leachate. Only three heavy metals are used - Cu, Cd and Pb -, in three combinations: Cu alone, Cu with Pb, and Cu with Cd. The experiments are limited to three types of sand-bentonite liner materials under three initial pH environments for the batch test and one liner material under one initial pH environment for the column test. 1.6 NOTES ON T HESIS ORGANIZATION Following the introduction and literature review on heavy metal interactions, properties and compatibilities, the subsequent 7 chapters are organized as follows: Chapter 3 deals with the concept of landfill compartmentalization based on heavy metal properties mentioned in the prece ding chapter. This concept has been published as a paper, entitled “Compartments for the Management of Municipal Solid Waste”in the Journal of Soil and Sediment Contamination, 2000, Vol. 9, Issue 5, pp. 503-522. It presents a method for the segregation of wastes according to their heavy metal contents, grouping them on a compatibility basis, and for their disposal into separate landfill compartments. In cha pter 4, the control of landfill leachate percolation rate through landfill liners, according to the EPA regulations (k<10-7 cm/sec), is experimented under the variable field conditions. The experimental results were submitted for publication to the Journal of Soil and Sediment Contamination under the title “Pressure and compaction effects on hydraulic conductivity of sand-bentonite liners: A study for variable and unpredictable field condition.” Chapter 5 pertains to the observation of metal 7 compatibility behaviour in sand-bentonite liners of the adsorption of a selected metal (Cu) tested alone and with Cd and Pb in a batch equilibrium test. This paper was submitted to the Journal of Environmental Science and Health under the title, ”Copper adsorption with Pb and Cd in sand-bentonite liners under various pHs. Part I. Effect on total absorption”. In this study, an empirical relationship predicting Cu adsorption as a function of pH, liner CEC, and levels of Cd or Pb has been established. Chapter 6 presents the results of a subsequent SSE procedure to reveal the distribution of Cu in different liner particle sites and established the empirical relationships predicting Cu adsorption in various liner fractions. The paper was submitted for publication to the Journal of Environmental Science and Health, under the title “Copper adsorption with Pb and Cd in sand-bentonite liners under various pHs. Part II. Effect on adsorption sites.” Chapter 7 describes the final experiment involving heavy metal leacing test using Cu alone and with Cd and Pb. The results of the leaching test demonstrate the mobility pattern of all three metals (Cu, Pb and Cd) and render the final results, confirming the compatibility of metals as suggested in the concept, which was consistent with the batch results. The paper was submitted to the Canadian Journal of Civil Engineering, under the title “Effect of Pb and Cd on Cu adsorption by sand-bentonite Liners”. Chapter 8 restates the central ideas of the thesis and concludes by suggesting future research work. Chapter 9 contains the general references for the whole thesis. Appendices are attached to present more complete sets of data. 8 1.7 R EFERENCES AAFRD. 2002. 8.0 Disposal of farm waste. In: Beneficial Management Practices, Environmental Manual for Feedlot Producers in Alberta , 95-100. Edmonton, Alta.: Alberta Agriculture, Food, and Rural Development. Available at www. agric .gov.ab.ca/livstock/beef/bmp/feedlot8.pdf. Accessed 1 October 2003) . Abernathy, C., and A. Morgan. 2001. Chapter 3. Exposure and health. In United Nations Synthesis Report on Arsenic in Drinking Water. n.p. Geneva: WHO. ACEID. 2002. Waste Fact Sheets Written for Key Stage 4 and A-level. Manchester, UK: Atmosphere, Climateand Environment Information Programme, Manchester Metro politan University. Available at:www.ace.mmu.ac.uk/Resources/Fact Sheets /Key_Stage_4/Waste/contents.html. Accessed 2 October 2003. Alloway, B.J. 1990. Heavy Metals in Soils. New York, N.Y.: John Wiley and Sons. Alloway, B.J, and D.C. Ayres. 1993. Chemical Principles of Environmental Pollution. Glasgow, UK: Blackie Academic & Professional. ATSDR. 1997. Toxicological Profile for Nickel (Update). Atlanta, GA: U.S. Department of Health and Human Services, Division of Toxicology, Agency for Toxic Substances and Disease Registry. Available at: www.atsdr.cdc.gov/toxprofiles/tp 15.html. Accessed 2 October 2003. Brittle, C., and Z. Michaela. 2003. Do newspapers lead with lead? A content analysis of how lead health risks to children are covered. J. Environ. Health 65(10): 17-22. Chen M., and H.D. Samira. 2002. Characterization of lead in soils of a rifle/pistol shooting range in central Florida, USA. Soil and Sediment Contamination 11(1): 1-17. City of Tempe. 2003. Sanitary Landfills. Tempe, AZ: Department of Public Works. Avail -able at:www.tempe.gov/publicworks/fspage/curbside/clandfill.htm. Accessed 2 October 2003. Conner, J.R. 1990. Chemical Fixation and Solidification of Hazardous Wastes. New York, N.Y.: Van Nostrand Reinhold. Elliott, H.A., J.H. Linn, and G.A. Shields. 1989. Role of Fe in extractive decontamination of Pb-polluted soils. Hazard. Waste Hazard. Mater. 6: 223-229. 9 Endres, J., J. Montgomery, and W. Patricia 2002. Lead poison prevention: A comparative review of brochures. J. Environ. Health , 64 (6): 20-24. Han, F.X., A. Banin, and G.B. Triplett. 2001. Redistribution of heavy metals in arid-zone soils under a wetting-drying cycle soil moisture regime. Soil Science 166 (1):1928. Lee, G.F. 2002. Solid Waste Management: USA Lined Landfilling Reliability. Submitted to Natural Resources Forum. Available at www.gfredlee.com/UNpaper -landfills .pdf. Accessed 2 October 2003. Karagas, M. 2002. Role of drinking water in skin and bladder cancer in New Hampshire. In: Arsenic in New England. A Multidisciplinary Scientific Conference. Manchester, New Hampshire. New Hampshire Consortium on Arsenic. Available at: www. dartmouth.edu/~Ecehs/ArsenicConference/karagas.html. Accessed 2 October 2003. Montague, P. 1990. The landfillers’ new plan: Megafills. Rachel’s Hazardous Waste News No. 164. Annapolis, MD: Environmental Research Foundation. Available at: www.rachel.org/bulletin/pdf/Rachels_Environmental_Health_News_973.pdf. Accessed 27 October 2003. Murray, K., A. Bazzi, and H. Sokol. 1997. Distribution and mobility of lead in soils at an outdoor shooting range. J. Soil Contam. 6 (1) : 79-93. OTA. 1984. Protecting the Nation's Groundwater from Contamination, OTA-O-233, Washington, D.C.: U.S. Congress, Office of Technology Assessment. Overcash, M.R., F.J. Humenik, and J.R. Miner. 1983. Livestock Waste Management, Vol. I, Boca Raton, Florida: CRC Press. Oliver, D.P., M.J. McLaughlin, R. Naidu, L.H. Smith, E.J. Maynard, and I.C. Calder. 1999. Measuring Pb bioavailability from household dusts using an in vitro model. Environ. Sci. Technol. 33 (24): 4434-4439. Reeve, D. 2002. Mercury-Health and environmental aspects of mercury. Materials Australia , 34 (1): 14-15. Reinhart, D., and P. McCreanor. 1999. Implications of Time/Space Variable Leachate Head on Liner Leakage. Orlando, Florida: College of Engineering, University of Central Florida. Ava ilable at: www. floridacenter .org/publications/ time_ space_ var_99-9.PDF. Accessed 27 October 2003. 10 RPAL. 2002. Scope for the use of economic instruments for selected persistent pollutants. Report to the Environmental Protection Economics Division, Department for Environment, Food and Rural Affairs. Risk and Policy Analysts Limited. London, Norfolk, UK: Risk and Policy Analysts Limited. Available at: www.defra. gov.uk/ environment/chemicals/econinst/pdf/economics_pollutants_pt1.pdf. Accessed 27 October 2003. Sims, R. 1986. Contaminated Surface Soils - In Place Treatment Techniques. Pollution Technology Reviews No. 132. Park Ridge, NJ: Noyes Publications. Shuckrow, A.J., and C.J. Touhill. 981. Management of Hazardous Waste Leachate, Draft -Report NTIS No: PB81-189359/HDM. Pittsburgh, Pennsylvania: Touhill, Shuckrow and Associates, Inc. Statistics Canada. 2003. Human Activity and the Environment: Annual Statistics 2002. Toronto, Ontario: Federal Publications Inc. Temple .P.J., S.N. Linzon, and B.L. Chai. 1977. Contamination of vegetation and soil by arsenic emissions from secondary lead smelters. Environ. Pollut. 12: 311-315. UNEP. 2000. Overview on Land-based Sources and Activities Affecting the Marine, Coastal and Associated Freshwater Environment in the Upper Southwest Atlantic Ocean. UNEP Regional Seas Reports and Studies No. 170. The Hague, The Nether lands: United Nations Environmental Programme. Available at:www.gpa.unep. org/documents/technical/rseas_reports/170-eng.pdf. Accessed 31 October 2003. UNEP/GPA. 2003. Heavy Metals. The Hague, The Netherlands: Global Programme of Action for the Protection of the Marine Environment from Land-based Activities Coordin ation Office, United Nations Environmental Programme. Available at: www.gpa.unep.org/pollute/metals.htm. Accessed 31 October 2003. Urase, T., M. Salequzzaman, S. Kobayashi, T. Matsuo, K. Yamamoto and N. Suzuki. 1997. Effect of high concentration of organic and inorganic matters in landfill leachate on the treatment of heavy metals in very low concentration level. Water Science and Technology, 36(12): 349–356. USEPA. 1996. Copper Metal; Toxic Chemical Release Reporting; Community Right-toKnow. Federal Register 16 (203) 40 CFR Part 372. Available at: www. epa.gov/ 11 docs/fedrgstr/EPA-TRI/1996/October/Day-18/pr-61DIR/pr-61.html. Accessed 31 October 2003. USEPA. 2003a. Human Health. United States Environmental Protection Agency’s Draft Report on the Environment, Chapter 4. Available at: www.epa.gov/ indicators/ roe/pdf/roeHealth.pdf. Accessed 31 October 2003. USEPA. 2003b. Summary of the EPA Municipal Solid Waste Program. United States Environmental Protection Agency, Waste and Chemicals Management Division. Available at: www.epa.gov/reg3wcmd/solidwastesummary.htm. Accessed 31 October 2003. Voegelin A., B. Kurt and K. Ruben. 2003. Heavy metal release from contaminated soils: Comparison of column leaching and batch extraction results. J. Environ. Qual. 32 (3): 865-875. Waalkes, M.P. 2000. Cadmium carcinogenesis in review. J. Inorganic Biochem. 82(1-4): 241-244. Wentz, C.A. 1989. Hazardous Waste Management, New York, N.Y.: McGraw-Hill, Inc. Whittle, A.J. and A.J. Dyson. 2002. The fate of heavy metals in green waste composting The Environmentalist 22: 13-21. Yong, R.N., A.M.O. Mohamed, and B.P. Warkentin. 1992. Principles of Contaminant Transport in Soils. New York, N.Y.: Elsevier. 12 CHAPTER 2 2. LITERATURE REVIEW 2.1 LANDFILL L INERS The discharge of pollutants from landfill operations can affect the quality of the surrounding surface and groundwater unless a satisfactory waste containment system is in place. The purpose of liners in a landfill base is to impede leachate migration to ground and surface water and to reduce contaminant concentration by adsorption of the suspended and dissolved pollutants (Lee, 2002; Reddy and Butul, 1999). Both natural and synthetic liners are used in a well-engineered and controlled landfill (Reddy and Butul, 1999; Davis and Cornwell, 1991). Geo-synthetic liners, known as flexible membrane liners (FMLs), and impermeable soil can both be used as liners. However, USEPA guidelines (2001) currently favour composite liners. A composite liner is a single liner made of two parts, a plastic liner, and compacted clay liner. Other liners, such as bentonite, bentonite mixtures, soil cement, and sprayed-on material (e.g., asphalt in cement, granite) although considered, have not been widely used as liners (CCME, 1991) . Since the 1970s, FMLs are increasingly used in landfills because of their low permeability and resistance to the attack of various chemicals (USEPA, 2001). This lining is accompanied by a drainage/collection system controlling and managing the leachate generated within the landfill for treatment. Geosynthetic materials consist of geo-textiles, geo-membranes, geo-grids and geo-composites (USEPA, 2001; Reddy and Butul, 1999; Koerner and Richardson, 1987). Flexible Membrane Liners can be: low density polyethylene (LDPE), high density polyethylene (HDPE), chlorinated polyethylene (CPE), chlorosulfonated polyethylene (CSPE), polyvinyl chloride (PVC), or butyl rubber and neoprene (CCME, 1991). However, it has been found that all liners fail (Lee, 2002; Reddy and Butul, 1999) and thereby, a growing attention is given to natural attenuation by engineered soil liners, since soils have a natural contaminant retaining capacity (USEPA, 2001). 13 2.2 SOIL LINERS Natural liners, consisting of sand-bentonite mixtures are widely used all over the world, including North America. Although sand-bentonite liners are suitable to impede the leakage of contaminant leachate from landfill in a cost effective manner, certain precautions need to be taken to achieve the required performance. Unless liners are carefully designed, cracking, due to swelling/ shrinkage, as well as piping due to erosion of liner may occur. These are caused by mixing an inappropriate ratio of particles in gap-graded liner materials. Bentonite is subject to significant volume change, therefore it is susceptible to lose its integrity because of swelling in aqueous environment or cracking during shrinkage. Using only bentonite or a high proportion of it for liner material is not recommended. To prevent piping or tunnelling, a minimum percentage of fine particles are essential. In sand-bentonite liners, a minimum of 5% bentonite is needed (Edil, 1985). Sand-bentonite liners are normally compacted to attain the desired permeability and stability for prolonged permeation of landfill leachate. Compacted soil mixtures have been used as landfill liners because of their specific physical and chemical properties: density, porosity, macro-structure features, soil fabric, mineralogical composition, buffering capacity, and liner permeability (USEPA, 2001; Cheremisinoff, 1990), which can be adjusted according to specific requirements. However, the compa tibility of soil liners has proved to be uncertain due to the interaction with a variety of hazardous chemicals. It is found that concentrated organic liquids could increase soil permeability by a factor of 100 to 1000 times. The following should be taken into consideration when soil liners are built: appropriate moisture content, type of equipment for optimum compaction, final hydraulic conductivity and quality control during liner placement (CCME, 1991). Soil liners can act as impermeable barriers and adsorbents or attenuating layers to reduce the concentration and movement of suspended and dissolved pollutants (USEPA, 2001; Cheremisinoff, 1990). The quality of soil for landfill liners depends on particle size and proportion of fine material (< 2 µm) and should contain at least 5% of fine material to form sufficiently impermeable liners. The predominant types of clay minerals are kaolinite, illite and montmorillonite because of their swelling capacity and low permeability. Among them, Na montmorillonite absorbs more interlayer water, but is more 14 susceptible to contaminant dispersion and internal erosion compared to Ca montmorillonite (Cheremisinoff, 1990). 2.3 LINER LEAKAGE Despite the usage of different types of engineered liners, the migration of contaminants still prevails due to landfill liner failure or inaccurate implementation (Lee, 2002; Reddy and Butul, 1999). Laine and Miklas (1989) investigated a total of 61 new or in-service geomembrane-lined waste storage facilities, using the electrical leak location method. Approximately 92% of the facilities tested were HDPE geomembranes, ranging in thickness from 1.52 mm to 2.54 mm. A total of 1,409 leaks were located at the 61 sites surveyed, with an average of 3.2 leaks per 929 m2 . It was found that 87% of the leaks were at the seams with the remaining 13% in the parent material. Laine and Miklas (1989) also added that if leaks occur in the primary liner, leaks can also be expected in the secondary liner. HDPE liners have been found to offer from 20 to 50 leaks per 400 m2 . Furthermore, leaks are difficult to detect in secondary liners once the landfilling operation has begun. Bonaparte and Gross (1990) reported on the leakage of double-liner systems at 23 landfills and 7 surface impoundment facilities, such facilities ranging from Group I to Group IV. Group I and II facilities are constructed with a geomembrane top liner and a geonet (Group I) or sand (Group II) leakage detection layer. Group III and IV facilities are constructed with a composite top lin er and geonet (for Group III) or sand (for Group IV) leakage detection layers. Leakage was found in the detection layer of all sixteen Group I and II type landfills studied. With one exception, all Group III landfill cells exhibited leakage while nine of the eleven Group IV facilities exhibited leakage. The leakage was attributed to the perforation of the top liners. The seven cells of Group I exhibited the top liner leakage, ranging from 4.67 to 252 L/ha/d, while the top liner of the nine Group II cells ranged from 4.67 to 187 L/ha/d. Bonaparte and Gross (1990) documented two cases of modern landfill liner leakage: the first 3 cell landfill with a double liner made up of a top sand leachate collection layer, then a geomembrane top liner, a geonet leakage detection layer, and a bottom composite bottom liner. This landfill exhibited high leakage rates through the top liner immediately 15 following construction due to high hydraulic heads above the liner. The average top liner leakage rate from cells 1 and 2 ranged from 16 to 43 L/ha/d during the second year of operation. In the second case, the landfill had a double-liner consisting of a sand leachate collection layer, a geomembrane top liner, a sand leakage detection layer, and a composite bottom liner. Leakage occurred in the top liner because construction water, compression water, and consolidation water were accumulated inside the cells. Flow occurred in Cell 1 after 46 months at a substantial rate of 18 L/ha/d. The average recorded flow rate increased to 122 L/ha/d at 52 months. The leakage was attributed to a hole in the geomembrane at the penetration point of the leachate collection pipe. Giroud and Bonaparte (1989) and Bonaparte and Gross (1990) discussed several examples of leak detection in double -lined landfills. In one case, the top liner of a landfill exhibited a total leakage rate of 2338 L/ha/d through the top geomembrane when the landfill was filled with 15 cm of water. A 4mm diameter circular hole could have sufficed in producing this leakage rate. In summary, properly constructed geomembrane top liners (Group I and II) that have undergone CQA (construction quality assurance) cannot consistently limit top liner leakage to a value under 46.76 L/ha/d. Furthermore, an action leakage rate of 50 L/ha /d is too restrictive and presents a performance standard that, if promulgated by US EPA, frequently will not be met by facilities that were constructed to present standards with rigorous third-party CQA programs. An action leakage rate of 200 L/ha/d appears to be reasonable for landfills built using rigorous third-party CQA programs. 2.4 CAUSES OF FAILURE California engineering-consulting firm (Lee, 2002) has confirmed that landfill liners will always fail, and for a variety of reasons. The leakage is caused by pinholes in the liner formed during manufacturing and holes created during the welding of the seams in the liner. Examination of actual landfill liners reveals that even the best seams contain some holes (Reinhart and McCreanor , 1999; Bonaparte and Gross , 1990). In addition, new scientific evidence indicates that HDPE (high density polyethylene) allows some chemicals to pass through quite readily (Montague, 1992). Dilute solutions of common solvents, such as xylenes, toluene, trichloroethylene (TC E), and methylene 16 chloride, penetrate HDPE in one to thirteen days. Even an HDPE sheet, 2.54 mm thick (the thickness used in the most expensive landfills) is penetrated by solvents in less than two weeks. A number of household chemicals such as naphthalene will degrade HDPE, leading to permeation and loss of strength. HDPE liners then become brittle and crack. Margarine, vinegar, ethyl alcohol, shoe polish, peppermint oil, and naphthalene were found to degrade HDPE. Besides household wastes, many others still unknown factors lead to HDPE liners "stress cracking" or "brittle fracture". The American Society for Testing Materials (Bonaparte and Gross, 1990) revealed that HDPE liners have failed from stress cracks after only two years of use. Natural clay often cracks, also by diffusion. Organic chemicals like benzene can permeate a meter clay landfill liner in approximately five years. Some chemicals can degrade clay. According to Bonaparte and Gross (1990), a naturally occurring clay soil is expected to rema in functional as a seepage barrier if compacted to high density and low hydraulic conductivity, and maintained at a stable environment (temperature, pressure, chemical and biological environment). In waste containment applications however, conditions change. The permeation of a compacted clay liner by chemicals is inevitable, and no compacted clay liner is either totally impervious or immune to chemical interactions of various types. Construction defects, poor installation procedures, and long-term aging are physical and chemical factors leading to landfill liner failure (Reinhart and McCreanor , 1999; Reddy and Butul, 1999). 2.5 IMPACT OF LINER LEAKAGE Landfill leachate contains a substantial amount of heavy metals and is a potential threat to groundwater resources (Whittle and Dyson, 2002). Unlike other organic contaminants, heavy metals do not decay (Voegelin et al., 2003; McBride et al., 2000; Morera et al., 2002). Furthermore, their antagonistic and synergetic characteristics lead to the competition for adsorption sites on soil particles (Han et al., 2001). Competing metal ions demonstrates higher mobility patterns in soil profiles. In Canada about 50% of groundwater contamination occurs due to landfill leakage (NRCC, 1987). For example, the city of Montreal faces serious incidences of heavy metals contaminating groundwater 17 in the vicinity of Riviere de Prairies, Saint-Michel, and Mercier, as a result of landfill leakage (GERLED, 1991). In USA, about 18% of 1250 sites were found contaminated by heavy metals (Acar, 1995). The problem of liner leakage can be significantly reduced by identifying and segregating heavy metal containing wastes, grouping them on a heavy metal compatibility basis and dispose of them in landfill compartments. To achieve this it is imperative to explore the individual heavy metal characteristics and their interaction. This segregation will enhance adsorption efficiency, and consequently, will reduce the potential risk of groundwater contamination. 2.6 PROPERTIES OF HEAVY METALS Heavy metals such as Pb, Cd, Cu, Hg, Fe, Se, Ni, As, Cr, and Zn that are commonly found in the landfill leachate greatly vary in their properties. The mobility and adsorbing capacity onto soil particles of such heavy metals also varies according to the chemical form in which it occurs. In a multiple ion system, the heavy metals react and compete with each other for adsorbing sites on the soil particles and prevent others from being retained. Table 2.1 presents the characteristics of such heavy metals. 18 Table 2.1 Heavy-metal characteristics Group in the Periodic table Arsenic VA Cadmium II B Copper IB Chromium VI B Lead IV A Ionic radius (Å) a* 0.88 0.91 0.72 0.58 1.55 Electronegtivity b* 2.18 1.69 2.0 1.66 2.33 Atomic Number/wt 33/74.92 48/112.41 29/63.54 24/51.99 82/207.2 c* Cd(S,A), Al(A), Mn(S,A), Zn(A), Se(A), Cr(A), Fe(A), Ni(S,A) Cu(A), Mn(A), Fe(A) Zn(A), Cd(S) Cu(OH) 2 = 2.2 x 10-20 CuCO3 = 1.4 x 10-10 CuS = 6.3 x 10-36 Cr(OH)2 = 2.0 x 10-16 Cr(OH)3 = 6.3 x 10-31 CrF3 = 6.6 x 10-11 Pb(OH)2 = 1.4x 10-20 PbCO3 = 1.4x 10-13 PbS = 3.0 x 10-28 Zn(S), Al(A), Cu(A,S), Pb(S), Se(A), Mn(S,A), Fe(S,A), Ni(S,A) Interacting ions A: antagonistic S: synergetic d* Solubility product e* As 2S3 +4H2O? 2HAsO2 +3H2S = 2.1 x 10-22 Precipitating pH f* 6-7 6-8 6-7 6= 6> Mobility in Clayey soil g* Moderate Moderate Low Low/High Low Compatibility h* Fe, Ni Cd, Hg Pb Zn Cu 70.2 0.4 9.0 5.6 14.2 0.025 0.005 = 1.0 0.05 0.01 Maximum concentration in landfills (mg/L) i* Maximum Allowable j* Concentration for Drinking water in Canada (mg/L) Zn (A), Mn (A) Cd(OH)2 = 5.2 x 10-15 CdCO 3 = 6.2 x 10-12 CdS = 3.6 x 10-29 Toxicity k* 40 200 1 20 100 Health impact l* Cause dermatitis, cancer .01g of As 2O 3 can be fatal if Ingested Carcinogenic, teratogenic affecting liver, kidneys, anaemia, blood pressure Gastrointesti-nal, hepatic, and renal effects like abdominal pain, vomiting, diarrhoea, and haemolysis. Carcinogenic, acute effects of chromates on skin and mucous membranes. Mental impairment in young children, reduce IQ levels 19 Table 2.1. Heavy-metal characteristics (contd) Group in the Periodic table Iron VIIIB Mercury IIB Selenium VIA Nickel VIII B Zinc II B 0.7 Ionic radius (Å) a* 0.72 1.03 1.4 0.67 Electronegtivity b* 1.6 2.0 2.55 1.91 Atomic Number/wt c* 26/55.845 80/200.59 34/78.96 28/58.69 Interacting ions A: antagonistic S: synergetic d* Cu(A), Zn(A), Cd(A,S) Cr(A), Mn(A) N/A Cu (A), Zn (A), Cd (A), Mn (A). Cu(A,S), Zn(S,A) Cd(S,A), Mn(A) Cu(A), Cd(A,S),Mn(A), Fe(A), Pb(A),As(A),Se(A), Mn(A), Ni(S,A) Fe(OH)2 = 7.9 x 10-15 FeCO3 = 3.5 x 10-11 FeS = 4.9 x10-18 Hg (OH)2 = 2.5 x 10-26 Hg2CO 3 = 8.9 x 10-17 HgS = 5.8 x 10-44 Ni(OH)2=5.4x10 -16 NiCO3 =1.4x10 -7 NiS =1.07x10-21 Zn(OH)2=6.8x10-17 ZnCO 3 =1.2 x 10-10 ZnS =2.0 x 10-25 >7 2-8 2-4 8> 6-8 Moderate to high High Moderate Moderate Low As, Ni Cd As, Fe Se, As, Fe Cr Maximum concentration in landfills (mg/L) i* 4,000 3.0 1.85 7.5 731 Maximum Allowable j* Concentration for Drinking water in Canada (mg/L) = 0.3 0.001 0.01 0.025 = 5.0 Relative Toxicity k* 3.33 1000 100 40 0.2 Fatigue, anorexia, dizziness, nausea, vomiting, headache, weight loss, shortness of breath, and greyish color to the skin Mercury fumes effects central nervous system. Loss of sight and hearing, intellectual degeneration, congenital disorders. Brittle hair, deformed nails, loss of feelings and control in arms and legs. Carcinogenic, skin irritants and cause allergic dermatitis Solubility product e* Precipitating pH f* Mobility in Clayey soil g* Compatibility h* Health impact l* 30/65.39 Vomiting, diarrhoea, chest pain and edema. Mutagenic effects. Ref: a*: Marcus (1988); b*: Kabata-Pendias (1984); c*, e*: Lide et al. (1994); d*, f, h*: Alloway (1990); g*, i*: Bagchi (1994); j*: Health Canada (1996); l*: McBride et al. (1994) 1 (mg/L) The relative toxicity was calculated as follows: Relative toxicity = Maximum allowable concentration for drinking water quality (mg/L) 20 2.7 H EAVY METAL ADSORPTION M ECHANISMS Since heavy metals migration to ground and groundwater is a serious concern, it needs to be dealt with to reduce the risk. It is therefore, imperative to understand the basic mechanisms of adsorption/desorption of heavy metals to and from soil profile. This will facilitate the prediction of the transport in groundwater from the standpoint of geochemistry, environmental chemistry, and other related disciplines. Soils have ability to adsorb metal ions from aqueous solution by various mechanisms which include physical and chemical adsorption, precipitation, and solid state diffusion. Metal ion transfer occurs at the solid–solution interface consisting of inorganic colloids (e.g., clay), metal oxides and hydroxides, metal carbonates and phosphates, organic matter, and living microorganisms (algae and bacteria). Another influencing parameter is the ligands in the solution responsible for the distribution of metal ions , inorganic and organic ligands such as humic and fulvic acids. Anthropogenic ligands like Nitrilotriacetic acid (NTA), Ethylenediamine Tetraacetic Acid (EDTA) and polyphosphates are also may be present in natural waters and soil. However, the most significant role in he avy metal retention, mobility and bioavailability is played by oxides of Fe, Al, and Mn as well as soil organic matter (Martinez and McBride 1998). The factors controlling exchange between heavy metal in the solution and the soil particles are: soil type, metal speciation, metal concentration, soil pH, solid:solution mass ratio, multiple ions in the solution (interacting ions)and contact time. Among them soil pH has the greatest effect of any single factor on the solubility or retention of metals, with a greater retention and lower solubility of metal cations which occurs at high soil pH (Kaoser et al. 2004b; Martinez and Motto 2000; Basta et al. 1993; Cavallaro and Mc Bride 1984). 2.7.1 H EAVY METAL INTERACTION Heavy metals in leachate can be present in the form of free and exchangeable ions and organic and inorganic complexes and can be precipitated by oxides of Fe, Al and Mn. They can also be bound within the crystalline lattice structure of primary minerals 21 (Gambrell 1994). Among the different forms, the water-soluble metals are the most mobile. These are free ions and soluble organic and inorganic complexes. The most stable heavy-metal forms are those incorporated into a crystal lattice structure by isomorphic substitution. The most unstable heavy metals are those exchangeable between the soil solution and the zone affected by the charged colloidal surfaces or the double diffuse layer (Tan 1993). Soil pH plays an important role as it establishes H+ concentration, H+ are strongly attracted by the soil surface negative charges and H+ are capable of replacing other cations (Gambrell 1994). Factors affecting the replacing power of any ion in the cation exchange complex are valence of the ion, its hydrated diameter and other ions in the solution. In most cases, higher valency provides greater degree of adsorption (Alloway and Ayres 1993). The strength with which cations of identical charge are held to the soil particle surfaces is inversely proportional to their hydrated radius (Bohn 1979). In the presence of multiple ions, competition between the metallic ions for adsorption sites occurs (Phadungchewit 1990), affects sorption in soil and consequently, gives rise to difficulties in assessing the process (Murali and Aymore 1983). The preferred soil adsorption of one heavy metal over another species is called selective adsorption and it is dominantly influenced by the ionic size of heavy metals (Elliott et al. 1986). On the basis of unhydrated radii, the expected order of selectivity is: Pb2+(0.12nm)>Cd2+ (0.097nm)> Zn2+(0.074nm)>Cu2+(0.072nm). The affinity of heavy-metal retention for illitecontaining carbonates and organics is: Pb>Cu>>Zn=Cd. For montmorillonite, two different patterns are obtained when applying a solution with a concentration of 1x10-3 M for each heavy metal: (1) for pH of 3, Pb> Cd> Zn> Cu, and (2) for pH >3, Pb > Cu> Zn > Cd. The change in selectivity is due to pH, the differences in soil particle characteristics, and the individual heavy-metal properties (Forbes et al. 1974). The influence of multiple ions on the sorption process is clear (Harter 1992; Wada and Abd-Elfattah 1981). Three metals (Cu2+ , Ni2+ and Co2+) can be ads orbed in the presence of Ca2+ in the order of Cu> Ni = Co. But, co-addition of up to 0.09 mmol/L Co2+ and 0.02 mmol/L Cu2+ in ternary systems, such as Co-Ni-Ca and Cu-Ni-Ca, slightly decrease Ni2+ sorption. Furthermore, higher Cu2+ addition always decreases the Ni2+ 22 sorption rate. Also, if more Ni2+ is added to the solution, Cu2+ and Ni2+ ions compete directly for exchange sites. Co2+ in the system slightly reduces Ni2+ sorption, indicating that Co2+ and Ni2+ are retained at different sites. However, Ni2+ addition has a depressive effect on the amount of Co2+ retained since Co2+ alters Mn-oxide surface characteristics. Their relative crystal field stabilization energies indicate that, once Co2+ has created an adsorption site by reducing an Mn ion, Ni2+ competes with Co2+ for site occupation, and Co2+ sorption is depressed by increasing Ni2+ activity. The presence of multiple heterogeneous ions not only interferes with heavy-metal mobility, but also triggers synergetic effect. In a solution culture study, modest levels of six different heavy metals used singly (Cd, Co, Cu, Mn, Ni, Zn) depressed growth of bush beans by an average of 12%. When six metals used together, the yield depression was 85% and synergetic in nature (Wallace 1994). 2.7.2 ORGANIC - MATTER INTERACTION A number of organic compounds, such as humic and fulvic acids, can form complexes with metal ions. Depending on the stability of the complexes, they can be soluble or insoluble in the soil solution (Tan 1993). When containing organic matter with a low C/N ratio, a high pH, and a high Ca2+ content in the exchange complex, soils are considered to have a high adsorptive capacity. Such organic matter with a low C/N ratio is highly humified and is slightly soluble (Martín-Sánchez 1993). The binding mechanism of heavy metals with organic matter includes complexation, adsorption, and chelation. Complexation occurs due to the reaction of a metallic cation with an inorganic or organic ligand. The common inorganic ligands forming complexes with the metallic ion include OH-, Cl- , SO4 2-, CO3 2-, PO33-, and CN-. The product of a metal cation and a ligand is called “a metal-co-ordination compound”. The complexes produced may be positive, negative, or neutral. Complexation by co-ordination with multidentate ligands is termed as chelation. To form complexes, metal ions need to be chelated by two or more functional groups of the organic fraction, such as carbonyl, carboxyl, alcohol, phenol, and methoxyl (Camobreco et al. 1996). 23 Different metals have different potentials for mobilization by organic and inorganic ligands (McBride 1989). Generally, metallic ion complexes with inorganic ligands are weaker than those with organic ligands and complexes formed with multidentate ligands are more stable than those formed with monodentate ligands. This complex formation in the soil solution results in competition between the ligands and the soil solids for the adsorption of heavy metals (Yong et al.1992). The complication in predicting heavy metal mobility arises when complexes, such as soluble organic ligands (fulvic acid) are unable to adsorb onto the soil-particle surfaces (Lamy et al.1993). Metallic-ion complexes with Cl- ions, sulphates, and organics interfere with their adsorption by the soil particles (Benjamin and Leckie 1982). Using leaching cells, Donor (1978) demonstrated that the transport of Ni, Cu, and Cd is 1.1 to 4 times faster in the presence of Cl- than in the presence of ClO 4 -, because Cl- does not form complexes with these metals. The water solubility of organic complexes depends on their stability (Tan 1993). In case of metal-fulvic acid complexes, the stability can be calculated as: log K = 2 log (H+)-log (M2+); where: M = metal ion; K = stability constant. The soil pH plays a vital role in the stability of heavy metal complexes since with increasing pH, the ionization of the functional groups (carboxyl, phenolic, alcoholic and carbonyl groups) is increased accordingly. For example, the stability constants of metalfulvic acid complexes such as Cu-FA, Zn-FA, and Mg-FA are 7.15, 5.40, and 3.42 at pH 3.5. However, at pH 5.5, the stability constants will be 8.26, 5.73, and 4.06 respectively (Tan 1993). Complexes with Cu are stable over a wide range of pH while for stabilized landfill leachate, the pH tends to vary between 6.29 to 6.99 (Shoiry 1993). The stability order of heavy-metal complexes with organic components of soil constituents is: Cu> Fe> Pb> Ni> Co> Mn> Zn (Jones and Jarvis 1981). 2.7.3 CARBONATE AND OXIDE INTERACTIONS Since pH controls acid-base reactions and influences equilibrium reactions, it determines the solubility of hydroxides, carbonates, sulphides, and other anions (Bagchi 1990). In acid conditions, heavy metal cations are most mobile, while at pH approaching neutrality and higher, immobilization occurs. For example, at pH>7, heavy metals, such as Pb and Cd, form insoluble carbonate minerals (Cherry et al. 1984). The higher the 24 carbonate content of a soil, the greater the amount of metals precipitated by the carbonate phase. However, if pH is decreased below the precipitation level of heavy metals (pH < 4, in case of Pb), dissociation of carbonates occurs, as carbonates tend to dissolve at low pH levels resulting in a minimum heavy-metal retention (Yong et al.1992). But as pH increases above 7, metals generally precipitate on the soil-solid surface or in the pore water. However, for even higher pH (pH>9) solubility increases for amphoteric metals like Ni and that of metal hydroxides. The solubility of metal hydroxides differs at the same pH (Conner 1990). Coprecipitation of metals with the hydrous oxides of Fe, Al, and Mn are important adsorptive mechanisms. Cu, Mn, Mo, Ni, and Zn are co-precipitated in Fe oxides while Co, Fe, Ni, Pb, and Zn are co-precipitated in Mn oxides (Sposito 1983). Metal ions are also co-precipitated with other secondary minerals. Thus, Cd, Co, Fe, and Mn coprecipitates with Ca carbonate, and Ni, Co, Cr, Zn, Cu, Pb, Ti, Mn, and Fe with clay minerals respectively. These metal compounds have their respective solubility products, and therefore, their mobility depends on the solubility of their hydroxides, carbonates, sulfates, chlorides, and sulfides. The solubility limits depend on the concentration of ionic species, temperature, pH, and redox potential (Yong et al. 1992). 2.7.4 EFFECTS OF REDOX PROCESSES Heavy metals such as Cr, Fe, Hg, As, Mn, and Se are referred to as redox elements or redox couples since they have more than one possible oxidation state (Cherry et al. 1984). However, Ag, Cu, Cd, and Zn, with only one valence state, can also be influenced by redox processes. Under very low redox conditions, Pb and Cd, with one oxidation state, form insoluble sulphide minerals. However, at a pH of 7 to 8, where redox conditions are not so low, they form ins oluble carbonate minerals. Arsenic, with two oxidation states, is insoluble with sulphide under very low redox conditions but does not form a carbonate under any Eh-pH conditions. The changes in redox potential affect the soil pH. Reducing conditions enhance pH value while oxidation brings pH down. Oxidation of pyrite (FeS2 ) in soil can significantly decrease pH (Alloway 1990). 25 2.7.5 EFFECTS OF SOIL M ICROBES Microbes in the soil affect the adsorption pattern of leachate heavy metals (Alloway and Ayres 1993). For example, Thiobacillus spp, catalyzes the oxidation of sulphides. Soil PbS, ZnS, and CuFeS2 , become oxidized, releasing metal cations such as Pb2+, Cu2+ , Zn2+, and Cd2+ into the soil solution where adsorption reactions occur. Sulphide oxidation causes an increase in soil acidity, enhancing metal mobility. Some microbes are able to methylate Se, As, and Hg into volatile forms (CH3HG+) and then diffuse them into the atmosphere as part of the gaseous exchanges of soil and atmospheric gases. The presence of microbes beneath a landfill is not permanent because of their heterotropic nature (survive on organic matter) and the decrease over time of the organics in landfill leachate (Bagchi 1990). Microbial heavy metal retention was studied by Büchel et al. 2003 using well characterized fungal and bacterial strains. Sorption of heavy metal to biomass was observed using the bacterium Escherichia coli for incubation of seepage water. It was further noted that heavy metal mobilization was due to alteration of the earth elements enhanced by high microbial activity and low pH. Ettajani et al. 2001 studied Cd transfer to microalgae such as Skeletonema costatum and Tetraselmis suecica, and oysters (Crassostrea gigas). It was concluded that Cd was absorbed by all the three microalgae but lesser extent to S. costatum than T. suecica. However, Cd was strongly bound to the cell wall for both the algal species. The retention of Cd in oysters was equally distributed between soluble and insoluble fractions in the oysters’ tissues. The retention of Cd in S. costatum, T. suecica and oysters was 9, 20, and 2% respectively of the applied concentration for 21 days. Another study by Pawlik (2000) found that Pb was accumulated by Stichococcus bacillaris, a ubiquitous green microa lga from aqueous solutions. Uptake by algal cells was low as 3-6% and dependent on Pb concentration, time of exposure and cell metabolism. Heavy metal uptake by algae was assessed under different conditions such as pH, time of algal residence in solution with the metal, and concentration of algal biomass by Hamdy (2000). The rate of uptake of metals such as Cr3+, Co2+, Ni2+ , Cu2+, and Cd2+.for the first 2h was significant. The weight of biomass was directly proportional to the amount of the metal uptake (5-15 mg range). 26 Generally, microbial biomass in soil can be 1-3% of the soil total organic matter. (Jenkinson and Ladd 1981). Heavy metal uptake by soil bio mass can be useful tool to retain metals, however, the concern arises due to the fact that increase of heavy metal concentration has toxic impact on the microbial communities in the soil as well. The change of pH of the metal carrying solution also a determining factor for the microbial population density. Chander and Brooks (1991) investigated the effects of heavy metals on microbial bio mass and found that the reduction of bio mass was about 50% of the control. 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Proc.Workshop on Soil Barriers to Control Groundwater Contamination at Landwaste Disposal Sites. Associate Committee on Geotechnical Research, National Research Council of Canada, Technical Memorandum No. 143, NRCC 28546. Montreal, QC: NRCC. Pawlik-Skowrońska B. 2000. Relationships between acid-soluble thiol peptides and accumulated Pb in the green alga Stichococcus bacillaris. Aquatic Toxicol 50(3): 221-230. 31 Phadungchewit, Y. 1990. The role of pH and soil buffer capacity in heavy metal retention in clay soils. PhD diss. Montreal, QC: McGill University, Dept. of Civil Engineering and Applied Mechanics. Reddy, D.V., and B. Butul. 1999. A Comprehensive Literature Review of Liner Failures and Longevity. Gainesville, Florida: Florida Center for Solid and Hazardous Waste Management. Available at: www.floridacenter.org/ publications /liner_failure_99.pdf. Accessed October 31 2003. Reinhart, D., and P. McCreanor. 1999. Implications of Time/Space Variable Leachate Head on Liner Leakage. Orlando, Florida: College of Engineering, University of Central Florida. Available at: www.floridacenter.org/ publications/ time_space _var _ 99-9.PDF. Accessed 27 October 2003. Shoiry, Jean. December, 1993. Caracterisation des eaux de lixiviation des lieux d'enfouisesement sanitaire et procedees de traitment applicables. Transfert Environnement Cours-Conference de Formation. Sposito, G. 1983. The chemical forms of trace metals in soils. In Applied Environmental Geochemistry. 123-170. I. Thornton, ed. London UK: Academic Press Inc. Tan, H.K. 1993. Principles of Soil Chemistry, 2nd ed. New York, N.Y.: M. Dekker, Inc. USEPA. 2001. Geosynthetic Clay Liners Used in Municipal Solid Waste Landfills GCL Technology Materials. EPA530-F-97-002. Solid Waste and Emergency Response (5306W). Washington, DC: United States Environmental Protection Agency. Available at: http://www.epa.gov/epaoswer/non-hw/muncpl/landfill/geosyn.pdf. Acc - essed 30 October 2003. Voegelin, A.B. Kurt, and K. Ruben. 2003. Heavy metal release from contaminated soils: Comparison of column leaching and batch extraction results. J. Environ. Qual. 32(3) : 865-875. Wada, K., and A. Abd-Elfattah. 1981. Adsorption of lead, copper, zinc, cobalt and cadmium by soils that differ in cation-exchange materials. J. Soil Sci. 32 (2): 271284. Wallace, A., and G.A. Wallace. 1994. A possible flaw in EPA's 1993 new sludge rule due to heavy metal interactions. Commun. Soil Sci. Plant Anal. 25(1&2): 129-135. 32 Whittle, A.J. and A.J. Dyson. 2002. The fate of heavy metals in green waste composting The Environmentalist 22: 13-21. Yong, R.N., A.M.O. Mohamed, and B.P. Warkentin. 1992. Principles of Contaminant Transport in Soils. New York, N.Y.: Elsevier. 33 CONNECTING STATEMENT Following the characterization of landfill leachate for heavy metal content, a theoretical concept of landfill compartmentalization is proposed in the following Chapter 3. It deals with the segregation of wastes according to their heavy metal content, and justifies this concept of segregation based on heavy metal compatibility in terms of adsorption in natural landfill liners. This paper was published in the journal of Soil and Sediment Contamination, 2000. Vol. 9, Issue 5, pp503 -522. Authors: 1) S. Kaoser, 2) S. Barrington, 3) M. Elektorowicz. The contributions of the authors are: Author (i) suggested the concept following a research on heavy metal characteristics and interactions, along with a laboratory study under the supervision of author (iii). Author (ii) further enhanced the concept, audited and contributed to the contents of the article. 34 CHAPTER 3 COMPARTMENTS FOR THE MANAGEMENT OF MUNICIPAL SOLID WASTE 3.1 ABSTRACT Despite technological developments and improved liner-material applications, heavy metals in landfill leachate still penetrate the soil profile, polluting the soil and groundwater. An alternative approach must therefore be explored to reduce heavy-metal migration in soil-bentonite landfill liners. By considering the interaction of different heavy metals and their synergetic and antagonistics behaviours, such an approach could be developed. Low mobility metals such as Cu2+ and Pb2+ inhibit the adsorption of Cd2+ which is a moderate-mobility metal and Cu2+ sorption is decreased by the presence of Zn2+ and Cd2+. Therefore, Zn2+, a low-mobility metal, cannot be grouped with Cu2+. This way, four compatible metal groups have been identified: (i) Low mobility: Pb2+, Cu2+, and Ag (ii) Low mobility: Zn2+ and Cr3+; (iii) Moderate mobility: As2+ , Fe2+ , and Ni2+; (iv) High mobility: Cd2+ and Hg2+. Cd2+ with a moderate mobility pattern is synergetic to Fe2+ and is more mobile with Ni2+. Therefore, Cd2+ is separated from the moderatemobility group and is consigned with Hg, a high-mobility metal. The liner materials suitable for Hg2+ are assumed to be suitable for Cd2+ as well. Based on this concept, and to reduce heavy metal mobility, wastes should be segregated on compatibility basis according to their heavy metal contents before being disposed in different individual compartments. For wastes containing several incompatible heavy metals, sorting should be based on the heavy-metal with the highest concentration. Another solution is the manufacturing of products using compatible heavy metal combinations and then labeling them accordingly. Such waste segregation and landfill compartmentalization will lower risks of ground water contamination and liner cost. KEY WORDS: liner, heavy metal, metal retention, synergism and antagonism, compartmentalization. 35 3.2 INTRODUCTION The current landfill liner technologies for municipal solid waste (MSW) remain unsatisfactory as heavy metal leakage is still observed and has an adverse environmental effect (Cherry et al. 1984; Hinz et Selim 1994). In flexible liners, leakage is caused by pinholes in the liner formed during manufacturing and holes created during seams welding. Examination of actual landfill liners reveals that even the best seams contain some holes. The average leachate leakage rate through the flexible membrane liner in the upper portion of double-lined sanitary landfills is estimated at 205 L/ha/day (Bonaparte and Gross 1990), and is much over the Action Leakage Rate of 47 L/ha/day (EPA 1989). For soil liners, physical and chemical factors account for the liner failure through cracking and changes in density and pore sizes. The physical failure mechanisms consist of volume change (swelling/shrinkage), desiccation, heaving and piping. The chemical mechanisms involve dissolution, flocculation, tunnelling, clay-mineral structure change, and syneresis, the spontaneous separation of liquid from the colloidal suspension due to contraction. (Edil1985). The migration rates of heavy metals through landfill liners vary according to their cation hydrated radius, mobility pattern and interactions, their ability to form inorganic and organic complexes, and their retention mechanisms (Yong et al. 1992; Bagchi 1990). Adding to the complexity, multiple ions in the solution significantly affect the individual heavy metal sorption kinetics because of competition for soil adsorption (Murali and Aylmore 1983). Heavy metals are toxic and their toxicity depends on their synergetic effects. Synergism occurs when the combined toxic effect of various metals is higher than the summation of their individual toxic effects. This refers to the ability of one metal to inhibit or stimulate the adsorption of other metals in soil or plants. (Kabata-Pendias, 1984). The synergetic effect depends on factors such as metal concentration ratio, pH level, and the number of metals involved in the solution (Wallace et al. 1977; Balsberg Påhlsson 1989). However, EPA's 1993 sludge rule for As, Cd, Cr, Cu, Pb, Hg, Mo, Ni, Se and Zn, ignores their synergetic interactions, and therefore, may be inadequate (Wallace 1994). 36 Consequently, it may be more appropriate to segregate heavy metals in landfills rather than to use single cells. This section will explore a MSW management strategy using a concept of compartments to segregate heavy metals into compatible groups, thus reducing their risk of migration. To establish compatible groups, the heavy-metal characteristics and the processes affecting their mobility will be reviewed along with their synergetic and antagonistic effects. 3.3 MSW PRODUCTION AND LEACHATE CHARACTERISTICS Landfill leachate migration is a serious environmental problem. MSW generation and therefore landfill leachate composition vary significantly among countries and in heavy metal content. Table 3.1 lists per capita MSW generation and MSW components in some developing and industrialized countries. Per capita MSW production not only varies between industrialized and developing countries, but also within these categories. Per capita waste production in industrial countries is higher than in developing countries. With 0.82 kg/p/day, Italy is an example of the least waste producing industrialized country. But this production rate is higher than that of the highest waste producing developing countries, such as Thailand (0.46 kg/p/day). Population growth and growing affluence are the two major factors responsible for the increase of MSW production in industrialized countries. Three additional parameters are: changes in demographic structure and work and eating patterns (Ting 1992). Among the industrial countries, Canada has the world's largest per capita waste production (1.7 kg/p/day) followed by the U.S.A. (1.6 kg/p/day) (Table 3.2). In 1988, the Canadian MSW generation was 21 million tonnes (0.8 tonnes/p/year) (Laplante 1992) and in 1989 the Montreal MSW generation was 1,149,100 tonnes. (Ville de Montreal 1991). Table 3 shows the different categories of solid waste generated in Montreal. The MSW composition of various countries has been evaluated by different researchers. However, the itemization procedure has not been consistent. This inconsistency is reflected by Tables 3.1, 3.2, and 3.3, comparing the MSW composition in different countries. The developing countries with a low GNP have a low production 37 of high-density wastes with the exception of high-income areas, such as cities, where the waste is similar to that of industrialized countries (Pickford 1981). High quantities of wastes like ash, dust, cinder, coal, and wood are found in countries such as India where they are used for domestic heating (Kirov 1975). Wastes, such as fruit and vegetable, coconut shell, rags, and bones are also common in developing countries where the natural recycling and recovering ratio is very high. Generally, in developing countries, putrescible organics form a larger portion of the MSW components than in industrialized countries (Kirov 1975). On the other hand, non-compostable components, such as metal, plastics, and glass constitute a higher percentage in industrialized countries (Table 3.1). In North America (Table 3.2) paper waste is a predominant percentage of the total waste. In 1989, paper represented 40% and 36% of the total waste in the U.S and Canada, respectively. MSW composition in the U.S. is very similar to that of several Canadian provinces. One tonne is estimated to occupy 2.6 cubic meters of landfill space (EPA 1990). For Montreal, MSW itemization has been further analyzed. Montreal MSW in general is the combination of domestic, institutional, secondary industrial, and commercial solid wastes (Table 3.3). MSW in Montreal is divided into 10 categories that are further subdivided into 47 sub-categories (Table 3.4) (Leonard 1989). The number of sub- categories within the respective waste categories are: (1) Paper/cardboard= 10; (2) Wood = 2; (3) Glass = 7; (4) Plastics = 4; (5) Ferrous metals = 2; (6) Non-ferrous metals = 4; (7) Putrescible materials = 1; (8) Garden residues = 2; (9) Hazardous materials = 9; and (10) Other wastes = 6. Table 4 also lists the type of heavy metals associated with each MSW category. A comprehensive list of heavy metals and their corresponding waste components are shown in Table 3.5. Leachate characteristics and seepage vary from one landfill to another, depending on the nature of the refuse, infiltrating precipitation, and the type of soil below the site. Generally, leachate contains a wide variety of inorganic and organic components, but soil and groundwater pollution results mainly from the inorganic components, such as heavy metals (Montague 1991). The heavy metals commonly found in leachate, their concentration, allowable concentrations according to the Quebec guideline, as well as their toxicity are shown in Table 3.7. Since a variety of wastes containing numerous 38 heavy metals are disposed of in the same landfill compartment, leachate behaviour is also complex because many heavy metals are antagonistic or synergetic to one another (Table 3.8). One heavy metal may hinder the sorption of another or exerts more toxicity in the environment in the presence of other metals. Such unpredictable leachate characteristics complicate the design and increase the construction and maintenance cost of landfill liners. The complex processes influencing the mobility pattern of different heavy metals is discussed below. 3.4 PROCESS INFLUECING H EAVY M ETAL MOBILITY The movement of heavy metals through landfill liners is related to their ability to interact and form various compounds in the soil solution. This mobility depends on their hydrogeochemistry controlled by pH, redox and ionic strength, the presence of surface sorption sites on clay colloid, sesquioxides, organic matter and competing or complexing ions, and finally soil microbes fostering chemical changes (Cherry et al. 1984; Erwin et al. 1997). 3.4.1 Heavy Metal Interaction Heavy metals in leachate can be present in the form of free and exchangeable ions and organic and inorganic complexes and can be precipitated by oxides of Fe, Al and Mn. They can also be bound within the crystalline lattice structure of primary minerals (Gambrell 1994). Among the different forms, the water-soluble metals are the most mobile. These are free ions and soluble organic and inorganic complexes. The most stable heavy-metal forms are those incorporated into a crystal lattice structure by isomorphic substitution. The most unstable heavy metals are those exchangeable between the soil solution and the zone affected by the charged colloidal surfaces or the double diffuse layer (Tan 1993). Soil pH plays an important role as it establishes H+ concentration, H+ are strongly attracted to the soil surface negative charges and H + are capable of replacing other cations (Gambrell 1994). Factors affecting the replacing power of any ion in the cation exchange complex are the valence of the ion, its hydrated diameter and other ions in the solution. 39 In most cases, higher valency provides greater degree of adsorption (Alloway and Ayres 1993). The strength with which cations of identical charge are held by the soil particle surfaces is inversely proportional to their hydrated radius (Bohn 1979). In the presence of multiple ions, competition between the metallic ions for adsorption sites occurs (Phadungchewit 1990), affects sorption in soil and consequently, gives rise to difficulties in assessing the process (Murali and Aymore 1983). The preferred soil adsorption of one heavy metal over another species is called selective adsorption and it is dominantly influenced by the ionic size of heavy metals (Elliott et al. 1986). On the basis of unhydrated radii, the expected order of selectivity is: Pb2+(0.12nm)>Cd2+ (0.097nm)> Zn2+(0.074nm)>Cu2+(0.072nm). The affinity of heavy-metal retention for illitecontaining carbonates and organics is: Pb>Cu>>Zn=Cd. For montmorillonite, two different pa tterns are obtained when applying a solution with a concentration of 1x10-3 M for each heavy metal: (1) for pH of 3, Pb> Cd> Zn> Cu, and (2) for pH >3, Pb > Cu> Zn > Cd. The change in selectivity is due to pH, the differences in soil particle characteristics, and the individual heavy-metal properties (Forbes et al. 1974). The influence of multiple ions on the sorption process is clear (Harter 1992; Wada and Abd-Elfattah 1981). Three metals (Cu2+, Ni2+ and Co2+) can be adsorbed in the presence of Ca2+ in the order of Cu> Ni = Co. But, co-addition of up to 0.09 mmol/L Co2+ and 0.02 mmol/L Cu2+ in ternary systems, such as Co-Ni-Ca and Cu-Ni-Ca, slightly decrease Ni2+ sorption. Furthermore, higher Cu2+ addition always decreases the Ni2+ sorption rate. Also, if more Ni2+ is added to the solution, Cu2+ and Ni2+ ions compete directly for exchange sites. Co2+ in the system slightly reduces Ni2+ sorption, indicating that Co2+ and Ni2+ are retained at different sites. However, Ni2+ addition has a depressive effect on the amount of Co2+ retained since Co2+ alters Mn-oxide surface characteristics. Their relative crystal field stabilization energies indicate that, once Co2+ has created an adsorption site by reducing an Mn ion, Ni2+ competes with Co2+ for site occupation, and Co2+ sorption is depressed by increasing Ni2+ activity. The presence of multiple heterogeneous ions not only interferes with heavy-metal mobility, but also triggers synergetic effect. In a solution culture study, modest levels of six different heavy metals used singly (Cd, Co, Cu, Mn, Ni, Zn) depressed growth of bush beans by an average of 12%. When six metals used together, the yield depression was 85% and synergetic in nature (Wallace 1994). 40 3.4.2 Organic Matter Interaction A number of organic compounds, such as humic and fulvic acids, can form complexes with metal ions. Depending on the stability of the complexes, they can be soluble or insoluble in the soil solution (Tan 1993). When containing organic matter with a low C/N ratio, a high pH, and a high Ca2+ content in the exchange complex, soils are considered to have a high adsorptive capacity. Such organic matter with a low C/N ratio is highly humified and is slightly soluble (Martín-Sánchez 1993). The binding mechanism of heavy metals with organic matter includes complexation, adsorption, and chelation. Complexation occurs due to the reaction of a metallic cation with an inorganic or organic ligand. The common inorganic ligands forming complexes with the metallic ion include OH-, Cl- , SO4 2-, CO3 2-, PO33-, and CN-. The product of a metal cation and a ligand is called “a metal-co-ordination compound”. The complexes produced may be positive, negative, or neutral. Complexation by co-ordination with multidentate ligands is termed as chelation. To form complexes, metal ions need to be chelated by two or more functional groups of the organic fraction, such as carbonyl, carboxyl, alcohol, phenol, and methoxyl (Camobreco et al. 1996). Different metals have different potentials for mobilization by organic and inorganic ligands (McBride 1989). Generally, metallic ion complexes with inorganic ligands are weaker than those with organic ligands and complexes formed with multidentate ligands are more stable than those formed with monodentate ligands. This complex formation in the soil solution results in competition between the ligands and the soil solids for the adsorption of heavy metals (Yong et al.1992). The complication in predicting heavy metal mobility arises when complexes, such as soluble organic ligands (fulvic acid) are unable to adsorb onto the soil-particle surfaces (Lamy et al.1993). Metallic-ion complexes with Cl- ions, sulphates, and organics interfere with their adsorption by the soil particles (Benjamin and Leckie 1982). Using leaching cells, Donor (1978) demonstrated that the transport of Ni, Cu, and Cd is 1.1 to 4 times faster in the presence of Cl- than in the presence of ClO 4 -, because Cl- does not form complexes with these 41 metals. The water solubility of organic complexes depends on their stability (Tan 1993). In case of metal-fulvic acid complexes, the stability can be calculated as: log K = 2 log (H+)-log (M2+); where: M = metal ion; K = stability constant. The soil pH plays a vital role in the stability of heavy metal complexes since with increasing pH, the ionization of the functional groups (carboxyl, phenolic, alcoholic and carbonyl groups) is increased accordingly. For example, the stability constants of metalfulvic acid complexes such as Cu-FA, Zn-FA, and Mg-FA are 7.15, 5.40, and 3.42 at pH 3.5. However, at pH 5.5, the stability constants will be 8.26, 5.73, and 4.06 respectively (Tan 1993). Complexes with Cu are stable over a wide range of pH while for stabilized landfill leachate, the pH tends to vary between 6.29 to 6.99 (Shoiry 1993). The stability order of heavy-metal complexes with organic components of soil constituents is: Cu> Fe> Pb> Ni> Co> Mn> Zn (Jones and Jarvis 1981). 3.4.3 Carbonate and Oxide Interactions Because pH controls acid-base reactions and influences equilibrium reactions, it determines the solubility of hydroxides, carbonates, sulphides, and other anions (Bagchi 1990). In acid conditions, heavy metal cations are most mobile, while at pH approaching neutrality and higher, immobilization occurs. For example, at pH>7, heavy metals, such as Pb and Cd, form insoluble carbonate minerals (Cherry et al. 1984). The higher the carbonates content of a soil, the greater the amount of metals precipitated by the carbonate phase. However, if pH is decreased below the precipitation level of heavy metals (pH < 4, in case of Pb), dissociation of carbonates occurs, as carbonates tend to dissolve at low pH levels resulting in minimum heavy-metal retention (Yong et al.1992). But as pH increases above 7, metals generally precipitate on the soil-solid surface or in the pore water. However, for even higher pH (pH>9) solubility increases for amphoteric metals like Ni and that of metal hydroxides. The solubility of metal hydroxides differs at the same pH (Conner 1990). Coprecipitation of metals with the hydrous oxides of Fe, Al, and Mn are important adsorptive mechanisms. Cu, Mn, Mo, Ni, and Zn are co-precipitated in Fe oxides while Co, Fe, Ni, Pb, and Zn are co-precipitated in Mn oxides (Sposito 1983). Metal ions are 42 also co-precipitated with other secondary minerals.Thus, Cd, Co, Fe, and Mn coprecipitates with Ca carbonate, and Ni, Co, Cr, Zn, Cu, Pb, Ti, Mn, and Fe with clay minerals respectively. These metal compounds have their respective solubility products, and therefore, their mobility depends on the solubility of their hydroxides, carbonates, sulfates, chlorides, and sulfides. The solubility limits depend on the concentration of ionic species, temperature, pH, and redox potential (Yong et al. 1992). 3.4.4 Effects of Redox Processes Heavy metals such as Cr, Fe, Hg, As, Mn, and Se are referred to as redox elements or redox couples since they have more than one possible oxidation state (Cherry et al. 1984). However, Ag, Cu, Cd, and Zn, with only one valence state, can also be influenced by redox processes. Under very low redox conditions, Pb and Cd, with one oxidation state, form insoluble sulphide minerals. However, at a pH of 7 to 8, where redox conditions are not so low, they form insoluble carbonate minerals. Arsenic , with two oxidation states, is insoluble with sulphide under very low redox conditions but does not form a carbonate under any Eh-pH conditions. The changes in redox potential affect the soil pH. Reducing conditions enhance pH value while oxidation brings pH down. Oxidation of pyrite (FeS2) in soil can significantly decrease pH (Alloway 1990). Microbes in the soil affect the adsorption pattern of leachate heavy metals (Alloway and Ayres 1993). For example, Thiobacillus spp, catalyzes the oxidation of sulphides. Soil PbS, ZnS, and CuFeS2 , become oxidized, releasing metal cations such as Pb2+ , Cu2+, Zn2+, and Cd2+ into the soil solution where adsorption reactions occur. Sulphide oxidation causes an increase in soil acidity, enhancing metal mobility. Some microbes are able to methylate Se, As, and Hg into volatile forms (CH3 HG+ ) and then diffuse them into the atmosphere as part of the gaseous exchanges of soil and atmospheric gases. The presence of microbes beneath a landfill is not permanent because of their heterotropic nature (survive on organic matter) and the decrease over time of the organics in landfill leachate (Bagchi 1990). 43 3.5 MSW MAMAGEMENT BY COMPARTMENTS MSW management by compartments containing compatible heavy metals is one concept for reducing heavy-metal interaction. Wastes are segregated according to their compatible heavy-metal content and disposed of in individual landfill compartments. These compartments could be introduced through the following five steps: 1. MSW segregation by the 3R-D basis (Reducing, Reusing, Recycling and Disposal) and MSW volume reduction by 50% 2. Identification of heavy-metal content of MSW 3. MSW segregation for compatible heavy-metal content 4. Liner design for individual compartments to hold compatible heavy metals 5. Waste disposal in landfill compartments. To apply compartments for MSW disposal, compatible heavy-metal groups must be established, based on mobility and heavy-metal interactions in clay soils. Heavy metals can be categorized in three groups according to their mobility in clay soils (Table 3.8). Cu, Cr3+, Pb, Zn 1. Low mobility: 2. Moderate mobility: As, Cd, Fe, Ni 3. High mobility: Hg, Cr6+ Precipitation and adsorption are immobilization processes for As, Cd, Hg, Ni, and Zn while cation exchange is an additional immobilization process for Cu, Cr, Fe, and Pb (Bagchi 1990). Each heavy-metal group classified under low, moderate, and high mobility needs further segregation according to the metal compatibility in terms of interaction and synergetic behaviour. 3.5.1 Segregation of Low-Mobility Metals Due to their synergetic and antagonistic behaviour, Pb-Zn, Zn-Cu, and Cu-Cr (Table 3.8) are not compatible and therefore, cannot be grouped together. Thus, Pb and Cu can be 44 grouped as low mobile metals with minimum antagonistic and synergetic interactions. In addition, Pb and Cu have similarities being very immobile, remaining in the upper layers of soils and being specifically adsorbed. The remaining metals, Zn and Cr (Cr 3+), belong to another group as no synergetic or antagonistic effects are detected. Cr 6+ with high mobility can be transformed to Cr3+ with a low mobility, under reduced condition, a pH of 5, and in the presence of organic matter. Since these conditions predominate in landfill leachate, Cr 3+ is the most common state. Thus, Cr can be classified as a low-mobility metal and can be grouped with Zn. 3.5.2 Segregation of Moderate Mobility Metals Metals like As, Cd, Fe, and Ni have moderate mobility in clay soils. However, synergetic effects exist between Cd-Fe and Cd-Ni (Table 3.1). Also Ni increases the solubility of Cd. Therefore, Cd must be removed from this group. Consequently, heavy metals, such as As, Fe, and Ni, form another group with no synergetic or antagonistic effect and Cd remains by itself. 3.5.3 Segregation of High-Mobility Metals Two metals, Cr+6 and Hg, fall in this category. Cr+6 is generally transformed to Cr+3 in landfill leachate, and then falls in the low mobility category. Therefore, Hg is alone but can be grouped with Cd, also left alone from the moderate mobility group as no synergetic effect is reported from their combination. The soil barrier required for a high mobility metal such as Hg will be suitable for Cd of moderate mobility. 3.5.4 Waste Sorting for Segregation The initial implementation stage of compartmentalization is sorting wastes according to their compatible heavy metal contents (Table 3.6). Although the discarded products do not necessarily contain single and compa tible heavy metals, sorting should be based on 45 heavy-metal concentration. The metal with the highest concentration should be first considered since minor concentrations have a lesser effect. Eventually, manufacturers could be required to label the heavy me tal content on products and to use compatible metal combinations during manufacturing. The implementation could begin in large industries where heavy metals are handled in large quantities and in small municipalities where communities can be easily trained to sort their wastes. 3.5.5 Compartment Liner Design Because the disposal of all wastes into a single landfill cell leads to complex interactions between heavy metals, waste segregation can greatly improve the prediction of heavy metal mobility. By taking into account the synergetic and antagonistic effects among them influencing mobility, heavy metals can be segregated as follows, based on their compatibility: ? Heavy metals of group I : Cu, Pb (low mobility) ? Heavy metals of group II : Zn, Cr3+ (low mobility) ? Heavy metals of group III: As, Fe, Ni (moderate mobility) ? Heavy metals of group IV : Cd, Hg (high & moderate mobility) The selection of liner material for each compartment proposed will be simplified as the leachate content is predictable. MSW compartments imply the production of leachate containing heavy metals of known mobility. For the MSW of groups I and II, the heavy metals in the leachate will show low mobility and a simple clay liner may suffice. MSW of group III with heavy metals of moderate mobility require a single geomembrane or a single composite liner such as 2 geomembranes separated by a layer of granular material and an overlying layer of clay. Finally, MSW of group IV, with highly mobile heavy metals, require a double composite liner made up of 2 geomembranes separated by a layer of clay and another overlying layer of clay. This concept requires testing. 46 3.5.6 MSW Segregation The segregation of MSW is a process which adds to the cost of handling and transportation. Nevertheless, it is an essential to the technique of compartments. Also, these additional costs can be compensated by the lower building costs for landfill liners. Sorting of MSW is most efficient when performed at the source (Peavy et al. 1985). The number of components segregated has a direct influence on the storage at the source and the transportation cost to the landfill. The use of compartments will be most practical and economical for large industries and businesses producing large quantities of a specific type of waste. For residential communities, the implementation of compartments will be more complicated because only small quantities of wastes are produced. One method of simplifying the approach for residential areas is asking residents to take their wastes to municipal collection posts on a monthly basis. 3.6 SUMMARY Heavy-metal mobility depends on the soil chemical processes, individual heavy-metal characteristics and their interactions. Multiple ions in solution compete for adsorption sites,even in reduced adsorption. The complexity of predicting heavy-metal movement in soils or liners can be avoided, environmental risks can be limited, and landfill liner costs can be reduced if heavy metals could be segregated into compatible groups. A revised MSW manageme nt procedure is proposed, based on waste segregation and landfill compartments. This method can simplify the design of landfill liners and reduces the risk of heavy metal migration. MSW compartments could offer the following major advantages: 1. Attenuate synergetic and antagonistic effects of heavy metals 2. Simplify the design of liners, especially for low mobility groups, promote their cost-effective construction and simplify their maintenance. 47 The successful implementation of this compartment technique depends upon the practical aspects of MSW segregation. Big industries producing large quantities of specific MSW will be able to use this technique with more ease and at a lower cost. This concept of compartmentalization merits further laboratory testing. 48 3.7 REFERENCES Alloway, B.J., and Ayres, D.C. 1993. Chemical Principles of Environmental Pollution. London, England. Blackie Academic and Professional Press. Alloway, B.J. 1990. Heavy Metals in Soils. New York, John Wiley & Sons. Bagchi, A.1990. Design, Construction, and Monitoring of Sanitary Landfill, NewYork, John Wiley & Sons. Balsberg Påhlsson, A. M. 1989. Toxicity of Heavy Metals (Zn, Cu, Cd, Pb) to Vascular Plants. Water, Air, and Soil Pollution 47: 287-319. Benjamin, M. M., and Leckie , J.O., 1982. 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September 1992.Chimiotox, Results of a Chemo-Toxic Evaluation of the Industrial Plants Targeted Under the St. Lawrence Action Plan. Vol. I. Erwin J. M. Temminghoff, Sjoerd E.A.T.M. Van der Zee. 1997. Copper mobility in a copper contaminated sandy soil as affected by pH and solid and dissolved organic matter. Environmental Sci.Technology. 31(4), 1109-1115. Forbes, E. A., P osner, A. M., and Quirk, J.P., 1974, The specific adsorption of inorganic Hg(II) species and Co(II) complex ions on geothite. J. Colloid Interface Sci. 49, 403-409. Fuller, W. H. 1977. Movement of selected metals, asbestos and cyanide in soil, application to waste disposal problem. EPA 600/2-77/020. Cincinnati, USA. Gambrell, R.P. 1994. Trace and toxic metals in wetlands. Journal of Envir. Qual. 23, 883-891. Harter, R.D. 1992. Competitive sorption of cobalt, copper, and nickel ions by a calciumsaturated soil. Soil Sci. Soc.Am. J. 56, 444-449. Hinz, C. and Selim, H.M. 1994. 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Simulations of competitive adsorption. Soil Science. 135, 203-213. Peavy, H.S., Rowe, D.R. and Tchobanoglous, G. 1985. Environmental Engineering, pp. 594-652. New York, McGraw Hill Publishing Company. Phadungchewit, Y. 1990. The Role of pH and Soil Buffer Capacity in Heavy Metal Retention in Clay Soils, p.176. Ph.D. thesis, McGill University, Montreal, Quebec, Canada. Pickford, J.1981. Solid waste management in developing countries. WEDC. Loughborough University, UK 51 Shoiry, Jean. December, 1993. Caracterisation des eaux de lixiviation des lieux d'enfouisesement sanitaire et procedees de traitment applicables. Transfert Environnement Cours-Conference de Formation. Sposito, G.1983.Applied Environmental Geochemistry , pp.123-170. Thornton, I.(Ed) Lo ndon, Acad Press. Tan, H. Kim. 1993. Principles of Soil Chemistry, 2nd edition, pp. 139-224. New York, M. Dekker, Inc. Ting-Leung Ng, G. (Oct. 7-9), 1992. The role of non-governmental organizations in waste minimization in urban areas. Proceedings, 14th Canadian waste management conference, Regina, Saskatchewan. Ville de Montreal. 1991. Le Défi Déchets, A Challenge for the Future: A Project for Montreal Towards the Integrated Management of Solid Waste and Recoverable Materials. Wada, K, and Abd-Elfattah, A. 1981. Adsorption of lead, copper, zinc, cobalt and cadmium by soils that differ in cation exchange materials. J. Soil Sci. 32, 271-284. Wallace, A., and Garn A. Wallace. 1994. A Possible Flaw in EPA’s 1993 new sludge rule due to heavy metal interactions. Commun. Soil Sci. Plant Anal. 25 (1&2), 129-135. Wallace, A., E.M. Romney. 1977. Synergistic Trace Metal Effects in Plants. In Commun. Soil Sci. Plant Anal. 8 (9), 699-707. Yong, R. N, Mohamed, A.M.O., and Warkentin, B.P. 1992. Principles of Contaminants T ransport in Soils. New York, Elsevier. 52 Table 3.1 Countrywise Waste Production and Waste Components COUNTRIES Bangladesh India (Calcutta) Bangkok Italy Netherlands U.K France 0.9 0.83 % Domestic Waste (kg) Per Person Per Day 0.35 0.42 0.46 0.82 WASTE COMPONENTS % % % % % % Organic garbage Fines Glass Vegetable Wood, coconut shell Paper and cardboard Rags Glass, crockery,bones Metal, tins Plastics Dust, ash, cinder Miscellaneous N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A 16.0 18.0 3.2 3.6 7.4 0.7 0.6 41.6 8.9 5.1 60.5 13.0 0.6 6.9 1.3 7.0 12.6 25 25 7 20 3 5 15 21 20 10 25 3 4 17 27 11 9 38 9 2.5 3.5 22 20 8 34 8 4 4 Total waste: N/A 100% 100% 100% 100% 100% Source: Environment Canada (1991); Pickford (1991); Ting (1992). 1.1 100% 53 Table 3.2 MSW Production in USA and Canada COUNTRIRS Domestic Waste (kg) Per Person Per Day UNITED STATES 1.6 CANADA 1.7 WASTE COMPONENTS % B.C % Paper 40 36 36 33 Glass 7 9 7.2 7.5 Yard + Food waste 25 22 31.5 32.2 Metals 8.5 11 6.5 6.8 Plastics 8 11 6.2 8.1 Others 11.5 11 12.6 12.4 100% 100% 100% Total waste: 100% Source: Environment Canada (1991); Laplante (1992). Ontario % Quebec % 54 Table 3.3 Domestic and Commercial Waste Components in Montreal Percentage weight WASTE COMPONENTS Domestic Commercial 31.20% 49.91% Glass 6.15% 3.23% Wood 2.33% 0.3 Plastic 6.31% 11.97% Non-ferrous metal 0.7% 0.73% Ferrous metal 3.72% 1.58% 24.47% 26.86 Papers/ cardboard Putrescible material Garden wastes 9.32% N/A Hazardous wastes 0.67% 0.11 14.93% 5.31 100% 100% Other wastes Total waste: Source: Ville de Montreal (1991). 55 Table 3. 4 Segregated Domestic and Commercial Waste Components in Montreal 1. PAPER/CARDBOARD i) ii) iii) iv) v) vi) vii) viii) ix) x) Newspaper Fine paper Alimentary paper Wrapping paper Glazed paper Other paper Plate carton Corrugated carton Alimentary carton Multi layered paper DOMESTIC (%) COMMERCIAL (%) 11.19 2.14 0.37 0.87 3.55 5.47 3.16 2.68 1.53 0.24 3.21 2.54 1.00 0.89 0.66 10.83 7.22 21.69 1.85 0.10 31.20 49.91 0.49 1.84 0.00 0.30 TOTAL WOOD (2) 3. GLASS i) Coloured wine glass ii) Clear alimentary glass iii) Color alimentary glass iv) Clear wine glass v) Reusable drink bottle vi) Plate/ flat glass vii) Other glass 2.33 0.30 1.55 2.55 0.68 0.40 0.22 0.20 0.55 0.21 2.06 0.46 0.07 0.03 0.07 0.32 TOTAL GLASS (7)......... 4. PLASTICS i) Reusable alimentary plastic ii) Plastic film iii) Rigid plastic container iv) Other plastics 6.15 3.23 0.08 3.44 1.46 1.52 0.00 5.57 0.78 5.63 TOTAL PLASTICS (4)...... 6.51 11.97 TOTAL PAPER (10) 2. WOOD i) Raw wood ii) Other wood HEAVY METAL Hg Al Cu As Se, As, Cr Se 56 Table 4 (suite) 5. FERROUS METALS DOMESTIC COMMERCIAL HEAVY METAL i) Alimentary ferr.metals ii) Other ferrous metals TOTAL FERROUS METALS (2) 2.15 1.57 3.72 0.60 0.98 1.58 Fe 6. NON-FERROUS METALS i) Reusable aluminium ii) Non-reusable aluminium iii) Copper iv) Other non-ferr. Metals TOTAL NON-FERR.METALS (4) 0.10 0.41 0.09 0.09 0.70 0.27 0.39 0.05 0.02 0.73 Al 24.47 26.86 Cu 2.04 7.29 9.32 N/A N/A N/A 7. PUTRESCIBLE MATERIALS GARDEN RESIDUES i) Grass ii) Leaves and branches TOTAL GARDEN RESIDUES (2) 8. HAZARDOUS MATERIAL i) Motor oil 0.02 ii) Radioactive waste 0.00 iii) Fluorescent ballast tube (PVC) 0.00 iv) Pesticides 0.00 v) Medicines 0.03 vi) Solid chem. materials 0.11 vii) Liquid chemical matl. 0.41 viii) Batteries 0.07 iv) Car batteries 0.03 TOTAL HAZARDOUS WASTES (9) 0.67 0.00 0.00 0.00 0.00 0.00 0.00 0.06 0.05 0.00 0.11 9. OTHER WASTES i) Rubber ii) Textile & leather iii) Household electric iv) Large household elect. v) Furniture vi) Miscellaneous * 0.26 2.80 0.29 0.21 0.20 0.26 0.08 0.01 0.00 0.00 3.01 3.76 100% 3.36 100% TOTAL OTHER WASTES (6) Total waste: Cu Pb, Ni, Zn, Ag, Cd Pb, Cu, Cd, Zn, Ni Cr Se, Cu Ag Zn, Cr, Ni, Cd Note: * Miscellaneous wastes include: Ceramic, rubble, porcelain, construction debris and non categorized articles. Source: Leonard (1989). 57 Table 3.5 Heavy Metals and their Corresponding Waste Components HEAVY MSW COMPONENTS METAL Ag Jewellery, flatware, coins, electroplated objects, photographic materials, electrical and electronic products, brazing alloys and solders, catalysts, dental and medical supplies, mirrors, bearings. As Pesticides, wood preservatives, glass, growth promoters for pigs and poultry, alloys and electronics Cd Phosphatic fertilizers (54-58%), sewage sludge (2-5%), Pb-Zn mining and smelting, cadmium plating wastes, negative electrode in rechargeable nickelcadmium and silver cadmium batteries, pigments, light and heat stabilizers in plastics, electronic applications, catalysts. Cr Electronics, high temperature batteries, magnetic tapes textile preservatives, wood preservatives (sodium dichromate), pigments and allied products, wash primers, phosphate coating, phosphate fertilizers, glass, ceramics, glues, fly -ash, fungicides. Cu Fly-ash from burning of coal, wood, oil etc, electricity cable and other wires, leafs previously received fungicidal sprays. Hg Thermometers, Hg-containing fungicides, commercial fertilizers, mirror, cosmetics, pigment, paper, paints, pesticides, pharmaceutical, catalysts for the production of vinyl chloride monomers and urethane foams. Ni Metal containing items, dry cell batteries, phosphate fertilizers, fly-ash, hightemperature and non-ferrous alloys, stainless steel, hydrogenation catalysts, ceramics, magnets, nickel-anodes used in plating operations. Pb Pd-acid batteries, fertilizers to grow vegetables, cement kiln dusts, lime, leadbearing pesticides, lead-bearing paints, lead plumbing materials, mining and smelting wastes. Se Electronic components, glass, plastics, ceramics, pigment, lubricants, dietary supplement with vitamin, coal. Zn Agrochemicals like fertilizers & pesticides 58 Table 3.6 Example of Wastes Containing Compatible Heavy Metals Compatible metal groups 1. Cu, Pb 2. Zn, Cr 3. As, Fe, Ni 4. Cd, Hg Wastes to be sorted and co-disposed as per compatible metal groups Electric cables, fly-ash from of coal, copper wires, Pb-acid batteries, Pb plumbing materials, cement kiln dusts. Agro-chemicals such as fertilizers, pesticides, magnetic tapes, glue, coloured glass. Growth promoters for poultry, ferrous metals, dry cell batteries, nickelanodes used in plating operations, stainless steel. Cadmium plating wastes, cadmium containing batteries, thermometers, Hgcontaining fungicides, mirrors, cosmetics, glazed paper. Table 3.7 Heavy Metals in Leachate in Quebec Landfills Heavy metals Concentration In Landfill in the leachate Leachate (mg/L) Allowable conc. in Allowable conc. in soil for category A * groundwater for (mg/kg) category A* (?g/L) Ftox ** Cu 0.060 - 0.11 50 25 40 Fe 107.900 - 279.8 N/A N/A 3.3 Pb 0.078 - 0.25 50 10 100 Mn 0.060 - 1,400 N/A N/A 10 Zn 0.000 - 1,000 100 50 20 Cr 0.079 - 1,79 75 15 66.66 Cd 0.011 - 0.165 1.5 1 1000 Hg (ug/L) 0.380 - 2.0 0.2 0.1 10000 Note: * A: Quebec guidelines 1 mg/L ** Ftox = Relative toxicity = Most stringent concentration (mg/l) Source: Shoiry (1993); Environment Quebec (September 1992). 59 TABLE 3.8 Characteristics of Heavy Metals Metal Valence Atomic number Atomic Ionic hydrated Electroradius(nm) radius (nm) negativity As +3,+5,-3 33 .080 ----- 2.18 Cu +2 29 .097 2.1 1.95 Cr +2 +3 24 .064 .052-.053 1.96 1.66 Fe +2, +3 26 .072 2.11 Pb +2, +4 82 .120 Hg 0, +2 80 .100 Ni +2 Zn +2 28 30 .067 .072 Synergetic elements ----- Antagonistic Solubility products elements Zn, Mn, Pb3 ((AsO4 )2 4.1x10-36 Cu 3 ((AsO4 )2 7.6x10-36 Ni3 ((AsO4 )2 1.9x10-26 Mobility in clay soils moderate Al, Mn, Zn, Cr, Fe, Se CuS CuCO3 Cu(OH)2 8.7x10-36 2.5x10-10 1.6x10-19 low ----- Mn, Fe, Cu Cr(OH)2 CrPO4 6.7x10-31 2.4x 10-23 Cr+3 - low Cr+6 - high 1.9 Cd Cu, Zn,Cr, Co, Mn FeS FeCO3 Fe(OH)2 4.9x10-18 3.5x10-11 7.9x10-15 Fe+3 -Low Fe+2 -high 1.87 2.1 ----- Cd, Zn PbS PbCO3 Pb(OH)2 8.4x10-28 1.5x10-13 2.5x10-16 2.42 2 ----- ----- Hg 2 S Hg 2 CO3 Hg(OH) 2 5.8x10--44 8.9x10-17 2.5x10-26 high 3.0x10--21 6.6x10--9 2.0x10-16 moderate 2.06 2.09 1.91 1.65 Cd, Mn, Ni Cu, Zn,Cd Ni Mn NiS NiCO3 Ni(OH)2 ZnS Cu,Cd, Pb,As, ZnCO3 Fe Se, Mn, Zn(OH)2 2.0x10--25 1.19x10--10 7.71x10-17 From: Bagchi (1990); Bartlett (1983); Conner (990); Fuller (1977); Kabata-Pendias (1984); Korte et al. (1976). Marcus (1988). low 60 CONNECTING STATEMENT To measure and predict the leachate permeability through landfill liners, a hydraulic conductivity test has been performed considering the variable field conditions related to moisture content, compaction and overhead pressures. Geosynthetic landfill liner materials (sand-bentonite mixture) were used. A common theoretical porous flow equation predicted the hydraulic conductivity (k). The liner containing 5% bentonite was found to respect EPA regulation (k< 10-7 cm/s). Chapter 4 describes this experiment. This paper is accepted for publication by the Journal of Soil and Sediment Contamination Authors: 1) S. Kaoser, 2) S Barrington, 3) M. Elektorowicz. The contributions of the authors are: Author (i) carried out the entire experiment and drafted the article. Author (ii) audited and contributed substantially to the contents. Author (iii) overviewed the paper and provided valuable suggestions. 61 CHAPTER 4 PRESSURE AND COMPACTION EFFECTS ON HYDRAULIC CONDUCTIVITY OF SAND-BENTONITE LINERS: A STUDY FOR VARIABLE AND UNPREDICTABLE FIELD CONDITION. 4.1 ABSTRACT Compacted sand and bentonite mixtures exhibit hydraulic conductivity values (k) mainly affected by bentonite level, hydraulic pressure (? P) and compaction conditions. This laboratory test is an effort to investigate the hydraulic conductivity behavior under increased overburden pressure, variable moisture content, and density to asses the probable scenarios in the field conditions. The hydraulic conductivity (k) of the two sand and bentonite mixtures were measured after compaction under three moisture levels (MC) of 14, 17 and 21%, and when consecutively exposed to three ?P of 42, 70, and 98 kPa. Bentonite swelling was found to take 9 days. Compacted at 17% MC, the 5 and 10% bentonite mixtures gave the lowest k of 10-7 to 10-9 m/s, respectively. The 5% bentonite mixture expressed more textural phase changes with increasing ?P due to the displacement of fine particles, compared to the 10% mixture. Compacted at moisture values higher than optimum, both bentonite mixtures had a lower k, compare to that obtained when compacted at lower than optimum moisture content, despite a higher porosity. Given the por osity of a sand-bentonite or silt/bentonite liner, and assuming no suffusion or particle washing within this liner, a standard theoretical porous flow equation was found to accurately predicted their k value. Key words: hydraulic conductivity, compacted sand and bentonite liners, modeling, compaction, porosity. 62 4.2 INTRODUCTION Sand-bentonite or silt/bentonite mixtures are commonly used in North America as low permeability liners for facilities such as landfills, ponds, and earthen storages (Kenney et al. 1992; CCME, 1991). In such mixtures, the bentonite reduces the hydraulic conductivity (k) while the sand or silt reduces problems of bentonite cracking under shrinkage. Bentonite levels of 5 to 15% are commonly used in landfill liners (Chapuis, 1990). However, the resulting saturated k may vary greatly depending upon the compaction at installation, the particle size distribution of the sand or silt, the level of bentonite used, the prevailing pH and the salt content of the seepage (Wareham et al., 1998). The variation in k can occur easily in a given soil as a result of changes in fabric, density, and water content as well as the method of compaction used (Sällfors and Högsta, 2002; Mitchell et al., 1965; Mitchell, 1976). Moreover, the laboratory procedures estimating hydraulic conductivity do not fully represent field conditions. Numerous researchers have conducted laboratory tests on hydraulic conductivity, but none of them represents the actual field conditions (Daniel et al., 1985). The overburden pressure on the landfill liners also varies as the average height of landfill rises to 24m (80 feet) exerting pressure of 155 kPa (22 psi) while the estimated density of the waste reaches 653 kg/m3 (Rowe, 1987). Furthermore, field overburden pressures vary as incoming wastes are compacted layer by layer, while leachate percolates continuously, making it difficult to predict the actual k value. A control on the final k of the liner after installation is necessary as legislation in both Canada and the United States require a k under 1 x 10-5 m/s for landfills and earthen storages used for hazardous waste and wastewaters (US-CFR 2002; Alston et al., 1997; Gleason, et al. , 1997). Empirical equations such as that presented by Abeele (1986) have been used to predict k of silt bentonite liners. Nevertheless, such empirical models cannot be easily adjusted for factors such as the suffusion or the washing of bentonite particles from the liner exposed to high hydraulic pressure (Marcotte et al., 1994). Suffusion occurs when some of the pore channels of the liner material are larger than the finest liner particles; then, these finest particles can be leached through the liner profile along with solution flow. Theoretical equations are generally preferred in establishing such relationships, as the impact of each factor can be better interpreted. 63 The k of sand or silt and bentonite liners has been examined for void ratios above 1.0 and for laboratory samples in a trial axial cell (Sivapullaiah et al., 2000; Stewart et al., 2000). Nevertheless, sand or silt and bentonite liners are generally compacted on site to void ratios under 1.0, then allowed to swell in place from exposure to the natural groundwater, and finally subjected to a gradually increasing hydraulic head as the landfill or earthen storage is filled. De Magistris et al. (1998) and Alston et al. (1997) have examined sand or silt and bentonite liners under compacted conditions, but have not arrived at a theoretical model to predict k. The objective of this paper was two fold: measure k of two sand-bentonite mixtures of 95/5% and 90/10%, under three hydraulic gradients (?P) and three densities obtained by compacting the samples at three moisture contents (MC), below, close and above OMC; and, compare the k values measured experimentally to that predicted by a standard theoretical porous flow equation. Suffusion will be expected to occur when the measured k value differs from that obtained theoretically, and is not stable as ?P changes. The important factors in this theoretical equation are the particle size distribution and porosity of the liner, determining its effective pore size and k. Conditions for full saturation and swelling were established initially to insure that all measurements were conducted under steady state. The k was measured in the laboratory, using compacted samples, initially swollen under low ?P and the n subjected to increasing ? P, as to simulate field conditions. According to Chapuis et al.( 1992) a 5 to 10% range of bentonite is quite common. 4.3 THEORETICAL APPROACH The k of a liner is mainly determined by its particle size distribution and porosity (Yong and Warkentin, 1975; Gnanapragasm et al. 1995). A perfect porous medium can be described in such terms, assuming no consolidation between particles and no particle washing or displacement (suffusion). A fluid’s drop in hydraulic head as it flows through a unit depth of porous medium such as that exhibited by a sand bentonite liner, can be defined as (Geankoplis, 1983; Kovacs, 1981): ?P / ?L = 5 µ ? ? / (De 2 ? g) [1] where ?P is the drop in hydraulic gadient across the depth of the liner, m; ?L is the depth of the liner, m; µ is the dynamic viscosity of the liquid flowing through the liner, Pa - s; ? is the 64 apparent average velocity of the liquid flowing through the liner (volumetric flow divided by the cross sectional area of the liner), m/s; ? is a dimensionless factor related to porosity, n, further described by equation [2]; De is the equivalent particle size diameter of the liner acting as a porous bed, further described by equation [3]; ? is the density of the fluid flowing through the liner, kg/m3 ; and g is the gravitational constant, 9.81 m/s2 . The porosity factor ? can be defined as: ? = n3 / (1-n)2 [2] where n is the volumetric porosity of the sample, dimensionless. The equivalent particle size is defined as: De = 1/( S (xi ai/di) ) [3] where xi is the fraction of particles wit h the average diameter size di and ai is the shape factor of these particles. Table 4.1 lists shape factors for various types of particles. Since the equivalent flow rate through the porous medium (?) can be defined as: ? = k (?P / ?L ) [4] then, the saturated hydraulic conductivity of the medium (k) can be solved by combining both equations [1] and [4]: k = De 2 ? g / (5 µ ?) [5] The equivalent pore size, Pe, of the por ous medium can also be defined in terms of particle size De : Pe = (De * 4N / (1-N)) [6] Thus, a given sand-bentonite mixture, compacted to a porosity of n, should exhibit k defined by equation [5] if no suffusion occurs. Similar remarks were made by Kenny et al. (1992) and Chapuis (1990). The following procedure was designed to test the validity of equation [5] for compacted sand-bentonite liners. 4.4 MATERIALS AND METHODS The experiment was conducted in two stages. The first stage investigated the time required to fully swell the sand-bentonite samples. This was determined by applying a low bottom water pressure (?P) of 7 kPa (Stern and Shackelford 1998) , to duplicate samples of sand with two bentonite levels (5 and 10%) and measuring the drop in k with time, from the flow 65 of water through the top opening. Full swelling was presumed to occur when a stable k value was obtained. The second stage investigated the effect of bentonite content (5 and 10%), compaction conditions (leading to specific soil porosity) and ? P on k, using fully swollen duplicated sand-bentonite samples. To obtain three different soil porosities, mixtures of each bentonite level were compacted using three different moisture contents (MC), then fully swollen and saturated, and finally exposed to three ?P while measuring k. 4.4.1 Experimental Materials The components of the experimental liners were commercial grade silica sand (40 mesh) and Na-bentonite (200 mesh). The results of the qualitative x-ray diffraction scan show that the predominant phase of the bentonite used was Montmorillonite (85% ± 5%), which is non-stoichiometric in composition. The chemical analysis conducted by X-Ray fluorescence for the sand-bentonite mixtures and their physicochemical properties are shown in Table 4.2. The sand particle size distribution was established (Fig. 4.1) by sieving mechanically, using a sieve stack of #20, #30, and #60, and by the hydrometer method. Particle density was determined by the pycnometer method (Blake et al., 1986). The pH was measured using a pH glass probe connected to a pH/ion meter (Corning, model 450), after soaking in deionized water for 24 h using a solid to liquid ratio of 1:1. Cation exchange capacity (CEC) was evaluated by determining the Ca, Mg, K and Na content, after soaking in a 0.1M BaCl2 solution (Hendershot et al., 1993). MC was measured gravimetrically after drying the moist samples for 24 hours in an oven at 105 0 C. 4.4.2 Methodology The saturated k of the sand-bentonite liners were measured using the constant head me thod (Wareham et al., 1998) and rigid wall permeameters built of steel and ABS (Acrylonitrile Butadiene Styrene) cylinders, for stages 1 and 2 of the project, respectively. The cylinders holding the sand-bentonite sample had an inside diameter of 100 mm and a height of 100 and 200 mm for stage 1 and stage 2, respectively (Fig. 4.2). Each cylinder was closed at the top and bottom by steel end plates held in place by threaded rods. To prevent leakage, 66 rubber O-rings were placed between the cylinder walls and the steel plates and were pressed in place by the screws. A porous filter net was used at the column base to hold the liner mixture in place. The top and bottom plates were taped to connect tubing to induce hydraulic pressure and collect the effluent. The first stage of the experiment was designed to determine the swelling period of the duplicate liners samples. The permeameters were manually filled with non-compacted sand with 5% and 10% bentonite, and at respective MC of 0.4% and 0.8%. Once placed in the permeameter, the weight and volume of each sample were measured to obtain its porosity and dry bulk density of 0.435 and 1.5 kg/L, respectively. All sand-bentonite samples were initially saturated by introducing water from bottom of the permeameter until the surface pounded. Then, the permeameters were subjected to a distilled water head of 7 kPa, and the top discharged volume was measured daily for nine days to determine k. After nine days, the effluent flow had stabilized and k had reached steady state. The low 7 kPa pressure applied at the bottom of the non-confined samples insured free swelling (Warkentin and Schofield, 1962). The second stage of the experiment was designed to simulate field conditions and establish the effect of compaction conditions, bentonite level and ?P on the k of sandbentonite liners. Duplicate 800 g samples of sand-bentonite liners were prepared, for each bentonite level (5 and 10%) and MC of 14, 17 or 21%. Dry sand and bentonite were first mixed thoroughly and then small amount of water was sprinkled in stages. The samples were well mixed following each addition. Once the desired moisture content was reached, the mixtures were compacted as specified in ASTM 698. To avoid annular space leakage, the inner wall of each permeameter was initially coated with grease. The initial bulk and dry densities of each sample were determined by measuring their volume once compacted in the permeameter. The experimental points of moisture contents and dry density for the liner materials of 5 and 10% bentonite contents were plotted and their respective optimum moisture contents were determined as 16.5 and 16.8% respectively. Their porosity was calculated as follows (Carter et al., 1993): Porosity (%) = [1 - (D b / Dp)] x 100 where Db and Dp are the bulk and particle densities of the samples, kg/L. [7] 67 Using 12 permeameters, all duplicate mixtures were allowed to swell under a head of 7 kPa for the swelling period found from in the first stage of the experiment. Thus, the sand-bentonite samples were exposed to conditions consistent with those occurring in the field condition prior to the actual operation of the landfill (EPA, 1989). Then, a higher pressure was applied through the top plate tap, by placing water in the top portion of the permeameter and increasing its pressure head using a regulated pressurized air cylinder linked to the permeameters through plastic tubing. Pressure levels of 42, 70, and 98 kPa were successively applied during a three day period, to represent field conditions, while constantly measuring k from the flow of water through the bottom plate tap, as: K? Q L ( ) At ?? [8] where Q is the quantity of effluent collected, m3; A is the cross sectional area of the test sample, m2 ; t is time, s; L is the depth of the test sample, m; and ?H is the total hydraulic head across the test sample, m. The ANOVA procedure was used to test the effect of bentonite, porosity and ? P on the k value of the duplicate samples (SAS Institute, 1990). The Least Square Method was used to identify the significantly different treatments. 4.5 RESULTS AND DISCUSSION Both the experimental bentonite and sand were alkaline in nature demonstrating a pH of 10 and 9, respectively (Table 4.2). The respective bentonite and sand CEC were 90 and 2 cmol+/kg, therefore giving a net CEC for the 5% and 10% sand-bentonite mixtures of 6.4 and 10.8 cmol+/kg, respectively. 4.5.1 Swelling The k value of all samples stabilized after nine (9) days, indicating that full swelling was reached for both the 5% and 10% bentonite samples (Fig. 4.3). On day one, the 5 and 10% sand-bentonite samples demonstrated k values ranging from 10-2 to 10-3 m/s while on day 9, k had dropped to 0.9x10-6 and 3.27 x10-8 m/s, respectively. From day 9 to 10, k was observed to vary by less than 0.5% and the swellin g was then considered complete. The 9- 68 day swelling period for bentonite is consistent with the findings of Baver et al., (1972). The results also confirmed that 5% bentonite content in sand suffices in lowering k below 10-6 m/s, under a liner porosity of 0.435, even under no compaction. Similar results were obtained by Sällfors and Öberg (2002). 4.5.2 Impact of Bentonite Level Bentonite level has a significant inverse effect on k (99% confidence level). At a dry bulk density of 1.73 g/cm3 and under a ? P of 42, 70, and 98 kPa, the 5% bentonite samples demonstrated k values of 170 x 10-8 , 618 x 10-8 and 1990 x10-8 m/s respectively, while the 10% bentonite yielded k values of 0.41 x 10-8 , 0.50 x 10-8 and 0.58 x10-8 m/s (Table 4.3). It can be presumed that higher bentonite levels lead to lower k values as observed by Sivapullaiah et al. (2000). 4.5.3 Effect of Compaction and Hydraulic Pressure on k Sand-bentonite k for the three different densities and three ?P are presented in Table 4.3, for the 5 and 10% bentonite mixtures. The upper most portion of the table presents the porosity values obtained after compacting the samples at three different MC. The optimum moisture content (OMC) for 5 and 10% bentonite content were found to be 16.5 and 16.8%, which are consistent with the other researchers (Wu et al., 1998; De Magistris et al., 1998). The porosity and particle size distribution of the liner materials (Fig. 4.1) were used to compute the factors ? and De [eq.2, 3]. The 5% and 10% bentonite mixtures offered theoretical De values of 20 and 10, respectively, indicating that bentonite level has an impact on the computed De as its shape factor has a value of 1000 when fully swollen. The central section of Table 3 presents the hydraulic conductivity values measured for both bentonite mixtures compacted under three moisture contents and the three ?P. For the 5% bentonite mixtures compacted at 20.4 and 17.0% MC, similar k values were obtained regardless of ?P. Compaction at 13.7% MC gave k much higher than those compacted under 20.4% MC and increasing significantly with ?P. For the 10% bentonite mixtures, similar tendencies were observed, except that the 98 kPa hydraulic pressure gave k 69 significantly but slightly higher k values. The lowest k was observed when the mixture was compacted under close to optimum MC. The two bottom sections of Table 4.3 give De calculated versus De measured. For some 10% bentonite samples, De calculated was slightly higher than De measured because the test was conducted with water, which had not been de-aired (de Magistris et al., 1998). The ratio of De measured to De calculated increased with ? P indicating its capacity to slightly change the texture of the liner material. This phenomenon was less predominant for the samples compacted at 20.4 and 17.0% MC, because of their lower porosity and therefore smaller and more stable pore sizes. As compared to the samples compacted under 13.4% MC, the 20.4% MC gave lower and more stable pore sizes despite a higher porosity. While being compacted under higher MC, clay particles experience less cohesion and can slip between sand particles more easily. Thus, compacting sand or silt bentonite liners at MC slightly above optimum values can result in lower k values, than when compacted at MC slightly under the optimum. As compared to the 5% mixtures, for the 10% bentonite mixture, k was similar for all three ?P and compaction levels, although significantly different. Also, De measured was close to De calculated. For the 10% bentonite mixtures, the bentonite particles formed such stable pores within the liner material, that ?P of 42 and 70 kPa had no effect on k. Only the 98 kPa ?P had a significant effect on k, although k remained under 10-8 m/s. Particle movement (suffusion) is a phenomena also observed by Marcotte et al. (1994) and Barrington et al. (1998). In the present research wor k, suffusion appeared to occur with bentonite content as high as 10%, pore diameters under 45 nm and ? P above 70 kPa. Compared to the 5% mixture, the 10% bentonite mixture demonstrated lower k values with compaction at 20.8% MC, as compared to 14.1% MC, even if the resulting porosity was higher. 4.5.4 Applying Equation [5] to Other Research Data Table 4.4a applies equation [5] to results obtained from similar research projects where sand or silt and bentonite mixtures had been compacted in the laboratory. De Magistris et al. (1998) soaked their samples and measured k under a ? P of 220 kPa, which was too high to allow for full swelling (Warkentin and Schofield, 1962) and to prevent suffusion. De 70 measured was much higher than the De calculated, using both shape factors of 1000 for fully swollen bentonite and 70 for non-swollen bentonite. The low bentonite content of 1 and 3% with large respective pore diameters of 144 and 129 nm calculated for non-swollen bentonite conditions, most likely contributed to suffusion along with the high ?P. Aston et al. (1997) swelled and measured k of silt and bentonite samples using a high ? P of 30 kPa most likely allowing for little swelling. Again De measured was higher than De calculated because the test was conducted under conditions leading to suffusion, using a low bentonite level of 5.5% and a large pore diameter of 274 nm calculated for non-swollen bentonite conditions. Table 4.4b applies equation [5] to k measured in the laboratory using silt and bentonite liners, which were saturated and then consolidated, in a tri-axial cell. Sivapullaia h et al. (1998) did not provide details pertaining to initial swelling conditions and the ? P used to measure k. Nevertheless, comparing De calculated to De measured, equation [5] gives the correct value for non-swollen conditions and bentonite levels exceeding 20%. Furthermore, the ratio of De measured to De calculated decreased with increasing bentonite content, indicating that no suffusion occurred for bentonite levels of 20% and more, even if Pe was relatively large at 460 nm. 4.6 CONCLUSION A laboratory test designed to measure the k value of 95/5% and 90/10% sand and bentonite mixtures under field conditions indicated that: (1) for 10cm deep laboratory samples, near full swelling can be reached after 9 days of soaking from the bottom, at a low pressure of 7 kPa; (2) a common theoretical porous flow equation can predict k for sand or silt bentonite mixtures, as long as the porosity is known and no suffusion occurs. Conditions for suffusion are related to total clay content, and need further investigation. (3) sand (particle size mostly between 250 and 500 µm) with 5% bentonite produce significantly lower k than mixtures with 10% bentonite, although the 5% mixtures compacted at a porosity of 0.375 or less, using a MC of 13.7%, will meet North American requirements of 10-5 m/s, after full swelling; 71 (4) compared to 5% bentonite mixtures, sand with 10% bentonite offers a more stable k under ?P as high as 98 kPa, because of less suffusion; (5) compacting sand or silt bentonite liners at MC slightly above optimum will result in a liner with lower k, than compacting at MC slightly below optimum, even though the same porosity is obtained. 4.7 ACKNOWLEDGEMENTS This work was financially supported by the Natural Sciences and Engineering Research Council of Canada. 72 4.8 REFERENCES ASTM D698-91. Laboratory Compaction Characteristics of Soil Using Standard Effort (12,400 ft-lbs). Abeele, W. V. 1986. The influence of bentonite on the permeability of sandy silts. Nuclear and Chemical Waste Management. 6, 81-88. Alston, C., Daniel, D.E., and Devroy, D.J. 1997. Design and construction of sand-bentonite liner for effluent treatment lagoon, Marathon, Ontario. Canadian Geotechnical Journal. 38, 841-852. Barrington, S. F., El Moueddeb, K., Jazestani, J., and Dussault, M. 1998. The clogging of non woven geotextiles with cattle manure slurries. Geosynthetics International. 5(3), 309-325. Baver, L.D., W.H. Gardner, and W.R. Gardner. 1972. Soil Physics. 4th Edition. John Wiley & and Sons. New York. Blake, G.R., and Hartage, K.H. 1986. Particle density. In: Method of Soil analysis, Part 1. Agronomy No.9. American Society of Agronomy, pp 377-382. (A. Klute, Eds). Madison, W1. Carter, M.R., and Ball, B.C., 1993. Soil Porosity. In: Soil Sampling and Methods of Analysis. Canadian Society of Soil Science. (Carter, M. R., Eds). Lewis Publishers. Canadian Council of Ministers of the Environment.1991. National Guidelines for the Land filling of Hazardous Waste. CCME-WM/TRE-28E. Chapuis, R.P., Lavoie, J., and Girard, D. 1992. Design, construction, performance, and repair of the soil-bentonite liners of two lagoons. Canadian Geotechnical Journal. 29(4), 638-649. Chapuis, R.P. 1990. Soil-bentonite liners: predicting permeability from laboratory tests. Canadian Geotechnical Journal. 27, 47-57. Daniel, D.E., Anderson, D.C., and Boynton, S.S. 1985. “Fixed-Wall versus Flexible Wall Permeameters,” Hydraulic Barriers in Soil and Rock, ASTM STP 874, pp. 107-126. De Magistris F.S., Silvestri, F., and Vianle, F. 1998. Physical and mechanical properties of Compacted silty sand with low bentonite fraction. Canadian Geotechnical Journal. 35, 909-925. 73 Environmental Protection Agency. 1989. Construction, Quality Assurance, and control: Construction of Clay Lines. In: Requirements for Hazardous Waste Landfill Design, Construction and Closure. EPA/625/4-B9/022. Seminar Publication. Foreman, D.E., and Daniel, D.E. 1986. Permeation of compacted clay with organic chemicals. J. Geotech. Engrg. ASCE. 112 (7), 669-681. Geankoplis, C.J. 1983. Transport processes and unit operations. Second edition. Allyn and Bacon Inc., Boston, USA. pp. 129-134. Gleason, M., Daniel, D., and Eykholt, G. 1997. Calcium and sodium bentonite for hydraulic containment applications. Journal of Geotechnical and Geoenvironmental Engineering, ASCE, 123 (5), 438-445. Gnanapragasm, N., Lewis, B., and Finno, R 1995. Microstructural Changes in Simulated Sand-Bentonite Soils When Exposed to Aniline, Journal of GeotechnicaEngineering, 121(2), 119-125. Hendershot, W.H., and Laland H. 1993. Ion exchange and exchangeable cations. In: Soil Sampling and Methods of Analysis. Canadian Society of Soil Science. (Carter, M.R.). Lewis Publishers. Kenney, T., Van Veen, W., Swallow, M., and Sungaila, M. 1992. Hydraulic conductivity of simulated SBMs. Canadian Geotechnical Journal, 29(3), 364-374. Kovacs, G. 1981. Seepage hydraulics. Elsevier Scientific Publishing Company, Amsterdam, the Netherlands. Pp.38-42; 240-243. Marcotte D., Marron J.C., and Fafard M. 1994. Washing of bentonite in laboratory hydraulic conductivity tests. Journal of Environmental Engineering. 120 (3), 691-698. Mitchell, J.K. 1976. Fundamentals of Soil Behavior, p 422. John Wiley and sons, NY. Mitchell, J.K., Hooper, D.R., and R.G. Campanella. 1965. Permeability of Compacted clay. Journal of the Soil Mechanics and Foundations Division. ASCE, 91 (SM4), 41-65. Rowe, R. Kerry. 1987. Pollutant Transport through barriers. Geotechnical Special Publication, (N13), ASCE, pp. 159-181. New York. Sällfors, G. and Öberg-Högsta, A. 2002. Determination of hydraulic conductivity of sandbentonite mixtures for engineering purposes. Geotechnical and Geological Engineering, 20, 65-80. 74 SAS Institute. 1990. SAS/STAT, User's Guide. Volume 2, 4th ed. Cary, NC, USA. SAS Institute Inc. Sivapullaiah, P.V., Sridharan, A., and Stalin, V.K. 2000. Hydraulic conductivity of bentonite-sand mixtures. Canadian Geotechnical Journal, 37, 406-413. Stern, R.T. and Shackelford, C.D. 1998. Permeation of sand-processed clay mixtures with calcium chloride solutions, J. of Geotech. And Geoenviron. Engrg. ASCE, 124(3), 231-241. Stewart, D.I., Cousens, T.W., and Tay, Y.Y. 2000. Design parameters for bentonite enhanced sand as a landfill liner. In: Proceedings of the Institution of Civil Engineers.Geotechnical Engineering, 137 (4), 189-195. US-CFR. 2002. Electronic Code of Federal Regulations, 40 CFR. Design and operating Requirements, Chapter I, part 264.301. Code of Federal Regulations. Washington DC.Wareham, D.G., Farajollahi, A., and Mike, M.W.1998. Influence of alkalinity on the hydraulic conductivity of bentonite-sand liners. Water Science Technology, 38 (2), 151-157. Warkentin, B.P., and Schofield, R.K. 1962. Swelling pressure of Na-montmorrillonite in NaCl solutions. Journal of Soil Science, 13 (1), 98-105. Wu Guangxi, Loretta Y.Li. 1998. Modeling of heavy metal migration in sand/bentonite and the leachate pH effect. Yong, R. N, and Warkentin, B.P. 1975. Soil properties and behavior. Elsevier Scientific Publishing Co., Amsterdam. 75 TABLE 4.1 Shape factor for various soil particles Type of soil particle Shape factor Recommended Use to compute data Na- montmorillonite 700 - 1000 1000 Kaolinite clay 30 - 70 70 Illite clay 20 - 60 N.A. Silt (2 to 20 µm) 10 - 50 25 Sand ( 20 to 2000 µm) 8 - 10 8 Gravel (larger than 2000 µm) 7 - 11 7 According to Kovacs (1981) 76 TABLE 4.2 Physical and chemical characteristics of sand and bentonite used for the liner material Properties of elements Liquid limit Plastic limit Particle density CEC Moisture content pH SiO2 CaO MgO Na2 O K2 O P2 O5 MnO TiO2 FeO Fe2 O3 Fe2 O3 (T) BaO Cr2 O3 Cu Zn LOI* Units Silica sand 40 Bentonite % % kg/L cmol/kg % N/A N/A 2.66 2 0.0 9 99.28 0.07 0.03 <d/l 0.10 0.012 <d/l 0.037 N/A N/A 0.07 21 <d/l 53 3 0.17 360 57 2.54 90 8.4 10 59.37 1.26 2.27 2.17 0.52 0.053 0.019 0.144 0.94 2.75 3.80 83 20 85 67 20 weight (%) weight (%) weight (%) weight (%) weight (%) weight (%) weight (%) weight (%) weight (%) weight (%) weight (%) mg/kg mg/kg mg/kg mg/kg weight (%) LOI*: Loss On Ignition <d/l: below detection limit Detection limit 0.006 0.0015 0.0095 0.0075 0.0025 0.0035 0.0030 0.0035 0.01 0.01 0.003 17 15 2 2 0.01 77 Table 4.3 Sand-bentonite liner properties Prope rty 5% bentonite mixture 10% bentonite mixture ____________________ ___________________ MC, % 13.7 20.4 17.0 14.1 20.8 17.4 Porosity 0.375 0.412 0.359 0.355 0.396 0.348 Dry bulk density, kg/L 1.66 1.56 1.70 1.71 1.60 1.73 ? 0.134 0.202 0.112 0.107 0.170 0.099 De calculated, nm* 20 20 20 10 10 10 Pe, nm 48 60 45 22 30 21 ________________________________________________________________________ Pressure, kPa k (10-8 m/s)** 42 170a 9.5d 3.6d 0.41e 0.24j 0.08h 70 618b 12.0d 8.45d 0.50e,f 0.24j 0.11h 98 1990c 12.6d 8.84d 0.58f 0.30g 0.18i ________________________________________________________________________ Pressure, kPa De measured (nm)*** 42 254 53.5 56.2 14.0 8.5 7.0 70 484 53.5 56.2 16.0 8.5 7.0 98 870 53.5 56.2 16.0 9.5 9.6 ________________________________________________________________________ Pressure, kPa De measured/ De calculated 42 127 25.75 26.6 1.4 0.85 0.7 70 242 25.75 26.6 1.6 0.85 0.7 98 435 25.75 26.6 1.6 0.95 0.96 ________________________________________________________________________ * De calculated using equation [3] a nd the particle size distribution and porosity of the sample. ** the k values with the same letter are not statistically different (95% confidence level). *** De obtained from equation [5] using the measured k value. 78 Table 4.4a Applying equation [5] to other research using compacted laboratory samples Property de Magistris et al. (1998) ____________________ Alston et al. (1997) ___________________ Bentonite, % Porosity ? De calculated, nm* 1 3 5.5 0.24 0.24 0.26 0.024 0.024 0.035 47 24 18 (swollen) 170 87 142 (swollen) Pe, nm 81 42 34 (swollen) 144 129 274 (non-swollen) ________________________________________________________________________ Pressure, kPa k (10-8 m/s) 220 2500 300 ---30 ------250 ________________________________________________________________________ Pressure, kPa De measured (nm)** 220 2310 800 ---30 ------613 ________________________________________________________________________ Pressure, kPa De measured/ De calculated 220 96.3 33.3 (swollen) ---26.6 9.2 (non-swollen) ---30 ------34.0 (swollen) ------4.3 (non-swollen) ________________________________________________________________________ * De calculated using equation [3] and the particle size distribution and porosity of the sample. ** De obtained from equation [5] using the measured k value. 79 Table 4.4b Applying equation [5] to other research using consolidated laboratory samples Property Sivapullaiah et al. 2000 Bentonite, % Porosity ? De calculated, nm* 10 15 20 50 0.50 0.50 0.50 0.45 0.50 0.50 0.50 0.30 9.6 6.5 4.9 2.0 (swollen) 98 75 60 27 (non-swollen) Pe, nm 39 24 39 27 (swollen) 401 273 460 360 (non-swollen) ________________________________________________________________________ k (10-10 cm/s) 300 100 65 7.8 ________________________________________________________________________ De measured, nm** 175 100 60 28 ________________________________________________________________________ De measured/ De calculated 18.2 15.4 12.2 14.0 (swollen) 1.8 1.3 1.0 1.0 (non-swollen) ________________________________________________________________________ * De calculated using equation [2] and the particle size distribution and porosity of the sample. ** De obtained from equation [5] using the measured k value. 80 100 90 % smaller than 80 70 60 50 40 5% bentonite 30 10% bentonite 20 10 0 1 10 100 Particle diameter (µm) Figure 4.1 Particle size analysis 1000 81 Perforation Top plate Threaded rod Deionized water 20 cm ~ 6 cm Compacted soil Rubber O ring Bottom plate ~2 cm Effluent collection 10 cm Figure 4. 2 Rigid wall leaching cell 82 10 K(10 -8 cm/s) 8 5% 10% 6 4 2 0 0 1 2 3 4 5 6 7 8 9 10 Time (days) Figure 4.3 Hydraulic conductivity (k) of sand and bentonite mixtures during swelling under an ?P of 7 kPa 83 CONNECTING STATEMENT To observe metal compatibility behaviour in sand-bentonite liners, the adsorption of a selected metal (Cu) was tested alone and with Cd and Pb in a batch equilibrium test. This batch experiment is described in Chapter 5. This paper is accepted by the Journal of Environmental Science and Health under the title” Copper adsorption with Pb and Cd in sand-bentonite liners under various pHs. Part I. Effect on total absorption”. Authors: 1) S. Kaoser, 2) S Barrington, 3) M. Elektorowicz, 4) and Li Wang. The contributions of the authors are: Author (i) carried out the entire experiment and drafted the article. Author (ii) audited and contributed substantially to its contents. Author s (iii) and (iv) overviewed the paper and pr ovided valuable suggestions. 84 CHAPTER 5 COPPER ADSORPTION WITH Pb AND Cd IN SAND-BENTONITE LINERS UNDER VARIOUS PH S . PART I. EFFECT ON TOTAL ADSORPTION 5.1 ABSTRACT Municipal solid wastes could be segregated based on their specific heavy metal content and disposing of them in separate landfill cells. Therefore, the objective of the project was to investigate the interaction between copper (Cu) and either lead (Pb) or cadmium (Cd) using equilibrium batch adsorption experiments. A first test consisted of soaking three types of sand-bentonite liner samples (0, 5 and 10% bentonite) with a respective cation exchange capacities (CEC) of 2, 6.4 and 10.8 cmol (+) /kg in one of nine solutions consisting of a combination of three pH levels (3.7, 5.5 and 7.5) and three heavy metal solutions (Cu alone, Cu with Cd, Cu with Pb) each offering a respective heavy metal equivalence of 1, 2 and 2 cmol(+)/kg of liner. A second test set consisted in soaking 5% bentonite liner samples in three solutions at a pH of 3.7, with either Cu alone or with Pb or Cd, at 4.8 cmol (+) /kg of liner. For up to 14 days, duplicate samples were sacrificed to determine the supernatant Cu level and pH. The results indicated that under acidic conditions (pH<6.5), the liner bentonite content, the solution pH and the presence of Pb or Cd significantly influenced Cu adsorption. Lead, and to a lesser extent Cd, competed with Cu for adsorption sites. Under alkaline conditions (pH>6.5), carbonate and hydroxyl precipitation governed and masked the Pb and Cd competition. Thus, at low pH, limiting the presence of Pb in landfill leachate can improve Cu adsorption. Key Words: Sand-bentonite liners, Cu, Cd, Pb, Heavy metal adsorption, Synergetic interactions. 85 5.2 INTRODUCTION The heavy metals in landfill leachate are an environmental threat, unless contained by an impermeable or adsorbed by a sand-bentonite liner. [1- 3] Heavy metals such as Cu, Cd, and Pb are found in landfill leachate at levels often exceeding their allowable limits and threatening the quality of groundwater. [2-5] The adsorption capacity of a sand-bentonite liner depends on the hydro-geochemistry of the system, as determined by the properties of the leachate and of the liner material: [6, 7] leachate pressure head, pH, redox potential, content in competing and complexing ions, liner hydraulic conductivity, surface sorption site density, sesquioxides and organic matter content. All factors are involved in the competition between heavy metals for the liner adsorption sites. [8-10] Competition for adsorption on the particles of liner material is governed by the individual properties of a cation: valence, hydrated diameter, electro-negativity, sorption affinity (Kd ), and the presence of other ions in solution. [8-10] In most cases, a higher valence provides a greater degree of adsorption. [9, 11] For cations of equal valence, the adsorption strength is inversely proportional to their hydrated radius. [12] In the presence of multiple ions, metallic ions compete for adsorption sites, and consequently, make the system more complex. [13, 14] The expected order of selectivity for non-hydrated ions is: Pb2+(0.12nm) > Cd2+ (0.097nm) > Zn2+(0.074nm) > Cu2+(0.072nm). The pH, carbonates and hydroxides, oxides, liner particles to solution mass ratio, individual metal characteristics, hydrolysis properties, and their concentrations can alter the preferential order. [7, 9] For example, on illite with carbonates and organic matter at a pH below 5, the selectivity order is Pb> Cu>> Zn= Cd. [15] For montmorillonite, two different patterns are obtained with each heavy metal at 1x10-3 M. At pH of 3, the preferential order is Pb > Cd > Zn > Cu, whereas at pH > 3, the order is Pb > Cu > Zn > Cd. [15] Multiple heavy metal sorption for the solid phases of different liner materials (Oxisols, Ultisols, and Alfisol) revealed the following selectivity order: Cr > Pb > Cu > Cd > Zn. Selectivity sequence was related to valence (trivalent Cr) and sorption affinity of the heavy metals. The order 86 of electro-negativity was not respected: Cu (1.9)> Pb (1.8)> Cd (1.7) > Cr (1.6) and Zn (1.6). [9] The system’s pH has a marked effect on heavy metal adsorption by the double diffuse layer of liner particles. [7, 16] The H+ ions are poorly hydrated and more strongly attracted by these negatively charged sites than are many hydrated metallic ions. [17] At low pH, H+ ions therefore displace hydrated metallic cations already adsorbed, or occupy sites that would otherwise be occupied by metallic cations.[18] Furthermore, acidic conditions dissolve carbonate and release its precipitated metals. [19] Solution pH affects heavy metal ion solubility, with precipitation occurring at pH above 6.5 as a result of complexing with OH- in solution and CO ?3 2 on the liner particle sur face. [18] However, at even higher pH (pH>8), solubility can increase for amphoteric metal ions such as Pb, Cd, Cu because higher OH- concentration results in the formation of soluble species such as Cu(OH) ?42 . [20, 21] Among heavy metals, synergy can influence adsorption by sand-bentonite liners. [22- 28] Synergy results from the competitive interactions among heavy metals and their associated ligands or molecules in the same phase. adsorbed over Zn. [9, 30] [29] It was found that Cu and Pb were preferentially In a soil column test using liner materials from different horizons, it was observed that Cu and Pb remained immobile, whereas Cd and Zn were translocated freely. [31] The degree of adsorption of a specific heavy metal may be decreased by the presence of a stronger one. [30, 32, 33, 34] The presence of other cations in the solution, irrespective of trace or major metals, may significantly affect the mobility of the metal of interest. [35] It was observed that the adsorption of Hg decreased in the presence of Ni and Pb at all pH levels in the range 3 to 9. [36] The sorption affinity (Kd) of any element is the ratio of the absorbate in unit mass of the solid to absorbate in the solution at equilibrium. [9] In a multi-component system, Kd can 87 be determined experimentally for each element present, at various conditions of pH and other relevant factors. The higher the Kd, the greater the adsorption. The preferential order of adsorption is then given by the order of the Kd of the individual elements, in descending order. Design concepts to improve the performance of landfill sand-bentonite liners require an understanding of the synergy occurring among heavy metals. Copper is relatively mobile, but Pb and Cd are expected to differently modify its mobility. Designed to explore the compatibility of Cu either with Cd or Pb, this study therefore simulated the exposure of sand-bentonite liners to landfill leachate containing Cu alone or with either Cd or Pb, at different pH levels, and at different solution equivalence versus the liner cation exchange capacity (CEC). 5.3 MATERIALS AND METHODS The experiment consisted of allowing three sets of sand-bentonite mixtures (0, 5 and 10% bentonite content) to swell in distilled water for 7 days and then, to be subjected to solutions of Cu alone or Cu in combination with either Pb or Cd. The samples and heavy metal solutions were agitated and the supernatant was collected at different times to determine its Cu content. The total amount of Cu retained by the individual sand-bentonite mixtures was calculated from the difference between the initial and the equilibrium solution Cu content at the end of the agitation period. 5.3.1 Experimental Materials The experimental sand-bentonite liner samples were made of commercial grade silica sand (40 mesh) and Na-bentonite (200 mesh). The qualitative X-ray diffraction scan showed that the predominant bentonite phase was montmorillonite (85% ± 5%), which is nonstoichiometric in composition. The results of the chemical analysis by X-ray fluorescence 88 are summarized in Table 5.1. The sand-bentonite mixtures were prepared at ratios of 100/0, 95/5 and 90/10. The particle size distribution of the sand (Fig.5.1) and bentonite was established by sieving mechanically and by using the hydrometer method on the particles finer than 1.0 mm. [37] The particle density was determined by the pycnometer method. [37] The pH was measured using (Corning pH/ion meter 450), after soaking in deionized water for 24 h using a solid to liquid ratio of 1:1. CEC was evaluated by determining the Ca, Mg, K and Na content, after soaking in a 0.1M BaCl2 solution. [38] Moisture content (MC) was measured gravimetrically after drying the moist samples for 24 hours in an oven at 1050C. The heavy me tal solutions were prepared by adding Cu, Pb and Cd nitrate salts to distilled water. Two sets of solutions were prepared. The first set contained 1.56 mM/L of Cu; or 1.56 mM/L each of Cu and Pb or 1.56 mM/L each of Cu and Cd. The second set contained 7.68 mM/L of Cu, or 3.84 mM/L each of Cu and Pb or 3.84mM/L each of Cu and Cd. Thus, the first set of solutions (Cu alone and Cu with either Pb or Cd) offered a cation level of 1, 2 and 2 cmol (+) /kg of liner sample, respectively, when the respective CEC of the 0, 5 and 10% bentonite samples was 2, 6.4 and 10.8 cmol (+) /kg. The second set of solutions (Cu alone and Cu with either Pb or Cd) offered a cation level of 4.8 cmol (+) /kg of sample respectively, when the CEC of the 5% bentonite samples was 6.4 cmol (+) /kg. The equivalence of the first set of solutions was chosen to investigate adsorption behaviour of metals of equal or lower equivalence to that of the liner material CEC, while the second set offered a higher metal equivalence.The initial pH of each solution was adjusted to the preset values (3.7, 5.5 or 7.5) by adding 0.05 M HNO3 or 0.05 M NaOH. These pH levels were selected after obtaining a solubility curve versus pH for a 1.57 x 10-3 M Cu solution and by observing the solution Cu drops for pH between 4.0 and 10 (Fig. 5.2). 89 5.3.2 Experimental Design Some 216 samples were used to test, in duplicate, the following treatment combinations: (3 sand-bentonite mixtures) x (3 pH levels) x (3 heavy metal solutions) x (2 replications) x (4 agitation periods). Samples of sand-bentonite mixtures representing various liner conditions were prepared, each weighing 2g and consisting of 0%, 5%, or 10% bentonite with silica sand. Each 2g sample was placed in a 50 ml polycarbonate centrifuge tube, wetted with 12.5 ml of deionized water and soaked for 7 days. Then, each tube received 12.5 ml of one of the following solutions: 1.56 mM/L of Cu, or 1.56 mM/L of Cu with 1.56 mM/L of Cd, or 1.56 mM/L of Cu with 1.56 mM/L of Pb at a pH of either 3.7, 5.5 or 7.5. Thus, each 2g sample of 0, 5 and 10% bentonite liner material offered a respective CEC of 2, 6.4, or 10.8 cmol (+) /kg when the 25ml of heavy metal solution offered a cation concentration of 1 cmol (+) /kg for Cu alone and 2 cmol (+) /kg for Cu with either Cd or Pb. After 1, 2, 7 and 14 days, two tubes of each solution and liner combination were centrifuged at 10000 rpm for 10 minutes using an IEC-2100R centrifuge (International Equipment Company, Needham Heights, MA, model 120). Subsequently, the equilibrium pH of the solutions was measured, and the supernatants were decanted for Cu quantification using AAS (model 903 single beam, GBC Scientific Equipment Pty Ltd. Dandenong, Australia). A second test was performed with 5% bentonite liner samples at an initial solution pH of 3.7, but a solution containing either 7.68 mM/L of Cu or 3.84 mM/L of Cu with 3.84 mM/L of Pb or 3.84 mM/L of Cu with 3.84 mM/L of Cd. These solutions offered cation levels of 4.8 cmol(+)/kg of sample when the liner samples had a CEC of 6.4 cmol (+) /kg. After 1, 2 and 7 days, two tubes of each solution and liner material combination were used to establish Cu sorption, as described above. 90 The SAS ANOVA procedure was used to analyze the effects of pH, CEC of the liner materials, Cd and Pb, on Cu ads orption. [39] A multiple linear regression procedure was also used to obtain an empirical relationship between Cu adsorption and solution pH, metal concentrations, and liner CEC. [39] The partition coefficients of Cu (Kd ) were calculated to analyze its affinity under different liner materials, solution concentrations and pH levels. [40] K d (ml/g) ? V(C 0 ? C e ) M aC 0 (1) Where, V = Volume of the supernatant, ml Ma = Mass of absorbent (liner material), g C0 = Initial concentration of the supernatant, mg/L Ce = Equilibrium concentration of the supernatant, mg/L 5.4 RESULTS AND DISCUSSION 5.4.1 Cu Solubility The solubility of Cu, as a function of solution pH, is illustrated in Fig 5.2. This test was conducted using a Cu level slightly higher than its solubility. At a pH of 4, the solubility of copper nitrate was just under 1.57 x 10-3 moles/L, but started to drop slightly at a pH of 5.5. At a pH of 6.5, the solubility dropped drastically to reach a low at a pH of 9. Thereafter, the solubility started to increase slowly. Solution pH determines which Cu species can be present and thus, its solubility. At a pH under 6, the predominant species for copper is Cu2+. As the pH increases from 6 to 8, it changes from Cu2+, to Cu (OH ) 02 , CuHCO 3? , CuCO 03 and finally to CuOH+.[21] As pH changes from 8 to 13, the predominant Cu species still changes from CuOH+ to Cu(OH) 02 91 to Cu (OH ) ?3 and even to Cu (OH ) ?4 2 . By forming neutral and anionic hydroxy-complexes, Cu species become progressively insoluble as the pH increases from 6 to 8. [41, 42] But as the pH further increases above 8, the Cu species become cationic, are subjected to hydration and regain some of their solubility. [21, 43, 44, 45] 5.4.2 Copper Adsorption at Low Solution Equivalence Figures 5.3, 5.4 and 5.5 illustrate the amount of Cu adsorbed by the liner samples, as compared to the respective initial solution Cu level, for pHs of 3.7, 5.5 and 7.5. Figs. 5.3a, b and c present the relative amount of Cu absorbed on the left, while on the right are the solution equilibrium pH’s for the three bentonite levels (0, 5 and 10%). In all three figures, the equivalence of the heavy metal solution was lower than the CEC of the 2g liner samples except for the cases of 0% liner bentonite content (100% sand) exposed to two metals, for which the solution equivalence was equal. 5.4.2.1 Initial solution pH of 3.7 and 5.5 With a solution pH of 3.7 and 0% bentonite and Cu alone, 68.2% of the total Cu applied was adsorbed by the liner material after 14 days, while with Cd or Pb, the adsorption reached 54% and 30%, respectively. Liner samples with 5% bentonite adsorbed 97.2% of the total Cu applied as Cu alone, whereas 93.2% and 76% were adsorbed with Cd or Pb respectively. Liner samples containing 10% bentonite adsorbed 98.6% of the total Cu applied alone, and 96.4% and 96% the presence of Cd or Pb, respectively. The Cu adsorption curves stabilized after 10, 2 and 2 days, for the 0%, 5% and 10% bentonite mixtures, indicating that for lower liner CEC close to the solution equivalence in cation, adsorption stability required more time (Table 5.2, Fig. 5.3). With a pH of 5.5, and 0% bentonite and Cu alone, the liner adsorbe d 70% of the total Cu applied, whereas with Cd or Pb, 50% and 27.4% was adsorbed, respectively. With 5% 92 bentonite and Cu alone, 97.4% adsorption occurred, while with Cd or Pb, 91% and 82.4% was adsorbed, respectively. With 10% bentonite and Cu alone, 99.2% of the total Cu applied was absorbed, while with Cd or Pb, 98 and 96% was absorbed, respectively (Fig. 5.4, Table 5.2). Table 5.4 shows the corresponding Kd values for Cu in each case. As with a pH of 3.7, the Cu adsorption curve stabilized after 10, 2 and 2 days, for the 0, 5 and 10% bentonite liners, respectively, with a solution pH of 5.5. The initial and 14 day equilibrium pH increased with bentonite level and with heavy metal equivalence in the solution (Table 5.3). Under both a pH of 3.7 and 5.5, the solutions with heavy metal equivalence (Cu with Cd or Pb) equal to the CEC of the liner produced lower equilibrium pH and therefore had a greater acidification effect (Table 5.3), because heavy metals tend to form complexes with OH- in solutions. For the same solution cation concentration, Pb expressed a stronger acidification effect than Cd, because of its smaller hydrated radius and greater surface charge density, which allows it to attract even more OH- in solutions. Because of the high magnesium and calcium oxide content (Table 5.1) higher bentonite content produced a higher equilibrium pH. Bentonite level significantly affected Cu adsorption after 14 days, and precipitation was limited as all equilibrium pHs were below 6.5. With 0% bentonite (sand alone) and in presence of Cu alone at half equivalence of the liner capacity, only 70% more or less of the Cu applied was adsorbed, indicating that other major cations (even H+) occupied the adsorption sites, and that Cu was unable to displace them. In addition, both Cd and Pb decreased the amount of Cu adsorbed, but Pb expressed stronger adsorption competition than Cd. Most of the applied Cu was adsorbed, where the liner samples, containing 5 and 10% bentonite, offered 220% and 440% more CEC than the solutio n equivalence. For liners with 0 and 5% bentonite content, Cu adsorption dropped in the presence of Cd, but more noticeably in the presence of Pb. The 10% bentonite samples offered such a high 93 density of adsorption sites, that Cu did not have to compete against Pb and Cd. The Kd values computed reflect these results (Table 5.4). 5.4.2.2 Initial solution pH of 7.5 With 0% liner bentonite content, the carbonates and hydroxide fractions precipitated 98% of total Cu applied. The presence of Cd had no significant effect, resulting in 98.6 Cu adsorption. The presence of Pb tended to diminish Cu adsorption to 95.2%, as might be expected from results indicating strong Pb competition for exchangeable sites. In liner with 5% bentonite content and Cu alone, 95.6% of the total Cu applied was adsorbed, while with Cd or Pb, 99.2 and 93.8% was adsorbed. With 10% liner bentonite content, 87.3% of the total Cu applied was adsorbed from solution with Cu alone, while with Cd or Pb, 87.6 and 85.2% were adsorbed, respectively. For all bentonite levels, Cu adsorption stabilized after 1 day (Fig.5.4, Table 5.2). Copper affinity for each individual case (K d) reflects these results (Table 5.4). Results indicated that higher bentonite levels induced higher initial and 14-day equilibrium pH. All equilibrium pH values approached or exceeded 8, except with 0% liner bentonite content and for the higher equivalence solutions where the equilibrium pH was in the range of 6.5 after 14 days (Table 5.3). As compared to the 0 and 5% liner bentonite content, that of 10% significantly reduced the amount of Cu adsorbed, because the equilibrium pH was near or above 8. At such pH, soluble Cu complexes with more available hydroxyl ions occur. Furthermore, Cd affected Cu adsorption almost to the same extent as did Pb, with 10% bentonite and at initial pH below 7.5, because Cd does not hydrolyze until a pH of 8, while Cu starts to hydrolyze at a pH of 6 and these hydrolyzed Cd species are preferentially adsorbed over free metal ion. [42 - 47] Therefore, at pHs as high as 8 induced 94 by 10% bentonite, Cd was adsorbed more rapidly than Cu, resulting in Cd having an effect similar to that of Pb. 5.4.3 Adsorption Experiments with High Cation Equivalence After 7 days, the second test investigating the effect of applying higher equivalence of heavy metal (Fig.5.6) at a solution pH of 3.7, demonstrated 53% adsorption for Cu applied in solution alone. When applied with Cd or Pb, 65% and 35% of Cu were adsorbed, respectively. Cadmium allowed almost 30% more Cu adsorption than did Pb (Table 5.2). For Cu alone, the liner samples adsorbed 1.62 mg Cu/g of sample, whereas when applied with either Cd or Pb, the sample adsorbed 1.0 and 0.53 mg Cu/g of sample, respectively. After 7 days, Cu adsorption had not stabilized. Thus, the ratio of liner CEC to the solution cation equivalence has a significant effect on Cu adsorption. As with previous tests conducted using a solution pH of 3.7 and 5.5, the initial and 7 day equilibrium pH increased for all the cases. Equilibriu m pH was higher for Cu/Cd and lower for Cu/Pb solution as compare to Cu alone (Table 5.3). 5.4.4 Regression analysis Three regression equations were obtained from the experimental data to express Cu adsorption (Cua) in terms of liner CEC and solution pH and metal content. Under alkaline conditions (pH>6.5) producing low Cu solubility and with a solution metal equivalence less than or equal to the liner CEC: Cua = 1.00 – 0.012* CEC+ 0.0151*Cd – 0.023*Pb r = 0.90 Where, Cua = Cu adsorbed as a fraction of that applied, unitless, CEC = liner cation exchange capacity, cmol (+)/kg of liner, and Cd and Pb = solution levels of Cd and Pb, respectively, cmol (+)/kg of liner. (2) 95 Liner CEC significantly decreased Cu adsorption because of its effect on further increasing the alkalinity of the equilibrium pH. Neither Cd nor Pb significantly influenced Cu adsorption (95% confidence level), although the regression coefficients for Cd and Pb are of the same magnitude as that for CEC, because they were applied at low levels of either 0 or 1 cmol (+) /kg, whereas the liner CEC value was much higher at either 2, 6.4 or 10.8 cmol (+)/kg. Under acidic conditions (pH<6.5) and liner CEC greater than the solution’s heavy metal equivalence, adsorption was rapid, can be expressed through the following regression equation: Cua = 1.00 - 0.034*(10.8-CEC) - 0.105*(Cu-1.0) - 0.04*Cd - 0.14*Pb r = 0.91 (3) Where, Cu = solution levels of Cu, cmol (+) /kg of liner. Under such conditions, most of Cu was adsorbed, and in direct proportion with liner CEC offering more adsorption sites than the solution equivalence. Adsorption was slightly decreased in the presence of Pb and, to a lesser extent Cd (95% confidence level). Thus, for liners being initially exposed to landfill leachate, adsorption is almost complete despite the presence of other heavy metals. The third equation is generated for acidic conditions (pH<6.5) and liner CEC equal to the solution’s heavy metal equivalence, where adsorption was much slower: Cua = 0.370 + 0.168*Cd – 0.087*Pb r = 0.99 (4) This third equation has no adsorption term as a function of Cu, since Cu levels were constant at 1 cmol (+)/kg of liner. Also, the values of Cd and Pb were either 0 or 1 cmol (+) /kg of liner for a liner CEC of 2 cmol (+) /kg of liner (0% liner bentonite level). Under such conditions, Pb decreased Cu adsorption while Cd increased its adsorption. 96 Therefore, within a sand-bentonite liner reaching heavy metal saturation, Cu adsorption would be improved in the presence of a compatible heavy metal such as Cd, as opposed to an incompatible heavy metal such as Pb. 5.5 CONCLUSION The effect of Cd and Pb on the adsorption of Cu by sand-bentonite liners may be summarized as follows: 1) Cu solubility and therefore mobility decreased as the solution equilibrium pH exceeded 6.5, whether or not compatible or incompatible heavy metals were present; 2) under acidic conditions (pH < 6.5) and a liner material offering an excess of adsorption sites, Cu was mostly adsorbed (97 to 99%) but Pb (by 3%), and to a lesser extent Cd (by 1%), increased its solubility by competing for adsorption sites. Thus, a freshly exposed sand-bentonite liner would adsorb most leachate metals, despite their incompatibility; 3) For a liner CEC equal to the solution heavy metal equivalence, only 30% of the Cu applied was adsorbed when Pb was also present, whereas in the presence of Cd, Cu adsorption was 54%. Thus, for a sand-bentonite liner reaching heavy metal saturation, the presence of compatible metals will decrease the mobility of Cu; 4) In general, Cd had a limited effect on Cu adsorption, whereas Pb had a much more pronounced effect. Thus, the presence of compatible metals such as Cd, have a positive effect on Cu adsorption while the presence of incompatible metals has an adverse effect. 5.6 ACKNOWLEDGEMENTS This project was funded by the Natural Science and Engineering Research Council of Canada. 97 5.7 R EFERENCES 1. Reddy, D.V.; Butul, B.A. Comprehensive Literature Review of Liner Failures and Longevity. 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Hydrolysis and dehydration reactions of exchangeable Cu2+ on hectorite. Clays Clay Miner. 1982, 30, 200-206. 47. Harter, R.D. Effects of pH on adsorption of lead, copper zinc and nickel. Soil Sci. Soc. Am. J. 1983, 47, 47-51. 102 Table 5.1. Physical and chemical characteristics of sand and bentonite Properties of elements Liquid limit Plastic limit Particle density CEC Moisture content pH SiO2 CaO MgO Na2 O K2 O P2 O5 MnO TiO2 FeO Fe2 O3 Fe2 O3 (T) BaO Cr2 O3 Cu Zn LOI* Units % % g/ml cmol/kg (%) weight (%) weight (%) weight (%) weight (%) weight (%) weight (%) weight (%) weight (%) weight (%) weight (%) weight (%) mg/kg mg/kg mg/kg mg/kg weight (%) LOI*: Loss On Ignition <d/l: below detection limit Silica sand 40 Bentonite N/A N/A 2.66 2 0.0 9 99.28 0.07 0.03 <d/l 0.10 0.012 <d/l 0.037 N/A N/A 0.07 21 <d/l 53 3 0.17 360 57 2.54 90 8.4 10 59.37 1.26 2.27 2.17 0.52 0.053 0.019 0.144 0.94 2.75 3.80 83 20 85 67 11.20 Detection limit 0.006 0.0015 0.0095 0.0075 0.0025 0.0035 0.0030 0.0035 0.01 0.01 0.003 17 15 2 2 0.01 103 Table 5.2. Cu adsorption with stabilization period. Bentonite Initial pH 3.7 Initial pH 5.5 Abs (%) Stab. (day) Abs (%) Stab (day) Abs (%) Stab. (day) Cu alone 68.2 10 70 10 98 1 Cu+Cd 54 10 50 10 98.6 1 Cu+Pb 30 10 27.4 10 95.2 1 Cu alone 97.2 2 97.4 2 95.6 1 Cu+Cd 93.2 2 91 2 99.2 1 Cu+Pb 76 2 82.4 2 93.8 1 Cu alone 98.6 2 99.2 2 87.3 1 Cu+Cd 96.4 2 98 2 87.6 1 Cu+Pb 96 2 96 2 85.2 1 HM (s) Initial pH 7.5 2.1 First experiment 0% 5% 10% 2.2 Second experiment 5% Cu alone 53 7 (not stabilized) Cu+Cd 65 7 (not stabilized) Cu+Pb 35 7 (not stabilized) Note: First exp. Second exp. Cu, Cd, and Pb each @ 1 meq/100g of liner material Cu alone @ 4.8 meq/100g of liner material Cu either with Cd or Pb @ 2.4 meq/100 g of liner material Cd and Pb each @ 2.4 meq/ 100g of liner material 104 Table 5. 3. Equilibrium pH profile. Initial pH HM (s) Initial Eq.pH Bentonite pH at Initial 14 days Eq.pH 0% pH at Initial 14 days Eq.pH 5% pH at 14 days 10% 3.1 First experiment Cu alone 3.7 6 5.2 6.6 6.2 6.9 6.4 Cu+Cd 3.7 5.9 5.1 6.3 5.7 6.7 6 Cu+Pb 3.7 5.5 4.9 6.0 5.3 6.2 5.9 Cu alone 5.5 6 5.5 6.6 6.2 7 6.4 Cu+Cd 5.5 5.9 5.3 6.5 5.8 6.8 6.2 Cu+Pb 5.5 5.5 5.2 6.3 5.4 6.4 5.8 Cu alone 7.5 8 7.9 9.2 8.5 9.4 8.7 Cu+Cd 7.5 7.6 6.7 8.1 7.5 8.8 7.9 Cu+Pb 7.5 7.8 6.3 9.4 8.4 9.4 8.5 pH at 7 days 3.2 Second experiment Cu alone 3.7 5.2 4.9 Cu+Cd 3.7 5.45 5.15 Cu+Pb 3.7 5.1 4.85 Note: First exp. Cu, Cd, and Pb each @ 1 meq/100g of liner material Second exp. Cu alone @ 4.8 meq/100g of liner material Cu either with Cd or Pb @ 2.4 meq/100 g of liner material Cd and Pb each @ 2.4 meq/ 100g of liner material 105 Table 5.4. Kd values of copper under different liner, pH and composite solutions. Kd (ml/g) Metals Initial pH 3.7 Bentonite 0% 5% 10% Initial pH 5.5 0% 5% Initial pH 7.5 10% 0% 5% 10% 4.1 First experiment Cu 8.52 12.15 12.32 8.75 12.17 12.32 12.25 11.95 10.91 Cu with Cd 6.75 11.65 12.05 6.25 11.37 12.25 12.32 12.4 Cu with Pb 3.75 9.5 3.42 10.3 11.9 12 4.2 Second experiment Cu 32.37 Cu with Cd 20.12 Cu with Pb 10.75 Note: Higher Kd values indicates that the metal is retained more strongly. 12 10.95 11.72 10.5 106 100 % smaller than 80 60 40 20 0 0 100 200 300 400 500 600 700 800 9 10 11 Particle diameter ( µm) Figure 5.1. Particle size analysis 100 Cu concentration (mg/L) 90 6.5 80 70 60 50 40 30 20 10 0 3 4 5 6 7 8 pH Figure 5.2. Solubility of Cu as a function of pH 107 1 7 0.8 6.5 a a' 0.6 6 0.4 5.5 0.2 5 0 4.5 1 7 b b' 6.5 Equilibrium pH Cua / Cut 0.8 0.6 0.4 6 5.5 0.2 5 0 4.5 1 7 c 0.8 c? 6.5 0.6 0.4 6 0.2 0 5.5 0 3.5 Cu+Pb 7 Time (day) 10.5 Cu+Cd 14 0 Cu Cu 3.5 7 10.5 Time (day) Cu+Pb 14 Cu+Cd Figure 5.3. Cu absorption with time, for sand with 0% (a), 5% (b), and 10% bentonite (c), for an initial pH of 3.7, alone and with exposure to Pb and Cd. Cu, Pb, and Cd were all applied at a level of 1 meq/100g of liner material. The CEC of the liner a,b, and c are 2, 6.4, and 10.8 meq/100g of liner material, respectively. 108 7 1 0.8 6.5 a a? 0.6 6 0.4 5.5 0.2 0 5 1 7 0.8 Equilibrium pH Cua/ Cut b 0.6 0.4 0.2 0 6.5 b? 6 5.5 5 7 1 c? c 0.8 6.5 0.6 0.4 6 0.2 5.5 0 0 3.5 Cu 7 Time (day) Cu+Cd 10.5 14 Cu+Pb 0 Cu 3.5 7 10.5 Time (day) Cu+Pb 14 Cu+Cd Figure 5.4. Cu absorption with time, for sand with 0% (a), 5% (b), and 10% bentonite (c), for an initial pH of 5.5, alone and with exposure to Pb and Cd. Cu, Pb, and Cd were all applied at a level of 1 meq/100g of liner material. The CEC of the liner a,b, and c are 2, 6.4, and 10.8 meq/100g of liner material respectively. 109 1 9.5 9 0.8 a a' 8.5 8 0.6 7.5 0.4 7 6.5 6 0 5.5 1 0.9 0.8 9.5 Equilibrium pH Cua/ Cut 0.2 b 0.7 0.6 0.5 0.4 0.3 9 b? 8.5 8 7.5 0.2 0.1 0 6.5 1 9.5 7 0.8 9 0.6 8.5 c 0.4 c? 8 0.2 7.5 0 0 Cu 3.5 7 10.5 Time (day) Cu+Cd 14 Cu+Pb 0 Cu 3.5 7 10.5 Time (day) Cu+Pb 14 Cu+Cd Figure 5.5. Cu absorption with time, for sand with 0% (a), 5% (b), and 10% bentonite (c), for an initial pH of 7.5, alone and with exposure to Pb and Cd. Cu, Pb, and Cd were all applied at a level of 1 meq/100g of liner material. The CEC of the liner a,b, and c are 2, 6.4, and 10.8 meq/100g of liner material respectively. 110 1 5.9 5.7 Equilibrium pH Cua/Cut 0.8 0.6 0.4 5.5 5.3 5.1 4.9 0.2 4.7 0 4.5 0 Cu 2 4 Time (day) Cu+Cd 6 Cu+Pb 8 0 2 Cu 4 Time (day) Cu+Cd 6 8 Cu+Pb Figure 5. 6. Cu adsorption with time, for sand with 5% bentonite for an initial pH of 3.7, alone and with exposure to Pb and Cd. Cu alone was applied at a level of 4.8, with Pb and Cd, Cu applied at 2.4 meq/100g of liner material. Both Pb and Cd applied at 2.4 meq/100g of liner material. The CEC of the liner was 6.4 meq/100g of liner material. 111 CONNECTING STATEM ENT After a batch experiment related to Cu total adsorption (chapter 5) , a subsequent SSE procedure was conducted to reveal the level of adsorption of Cu by different particles fractions of the liner. An empirical relationship was developed to predict Cu adsorption as a function of pH, liner adsorption site density, and level of Cd or Pb. Chapter 6 presents such results. The paper is accepted for publication by the Journal of Environmental Science and Health under the title”Copper adsorption with Pb and Cd in sand-bentonite liners under various pHs. Part II. Effect on absorption sites.” The authors were: 1) S. Kaoser, 2) S Barrington, 3) M. Elektorowicz, and 4) Li Wang. The contributions of the authors were: Author (i) carried out the entire experiment and drafted the article. Author (ii) audited and contributed substantially to the contents of the article. Author (iii) and (iv) overviewed the paper and provided valuable suggestions. 112 CHAPTER 6 COPPER ADSORPTION WITH Pb AND Cd IN SAND BENTONITE LINERS UNDER VARIOUS pHs. PART II. EFFECT ON ADSORPTION SITES. 6.1 ABSTRACT The project examined the effect of Pb and Cd on Cu adsorption using sand liners containing 0, 5 and 10% sodium bentonite and exposed to metal solutions at three pH levels (3.7, 5.5 and 7.5). Aliquots of 2g of liner material were exposed in duplicate, for 14 days, to solutions containing Cu alone or Cu with either Pb or Cd. Selective sequential extraction (SSE) was used to quantity the Cu adsorbed by each liner particle adsorption site (exchangeable, carbonate and hydroxide, oxides and residual). The results indicated that two main factors affected liner material behaviour in adsorbing Cu, besides Cd and Pb competition: pH either above or below 6.5; liner cation exchange capacity (CEC) greater or equal and greater than the solution cation equivalence. In general, the liner carbonate and hydroxide fractions precipitated the greatest amount of Cu, under all environmental conditions while the exchangeable, oxide and residual adsorbed more or less the same amount. Lead, and to a lesser extent Cd, significantly increase the mobility of Cu, by competing especially for the exchangeable sites. While Cd also competed against Cu for oxide adsorption, Pb competes for calcium and hydroxide precipitation. Lead, and to a lesser extent Cd, competed especially for the exchangeable site where the adsorption is more dynamic, and less for the more permanent adsorption sites, involving precipitation and electrostatic bonding. Key Words: Bentonite, Copper, Cadmium, Lead, Adsorption, Selective sequential extractions, Adsorption sites. 113 6.2 INTRODUCTION Sand-bentonite liners used for landfill containment must be designed to immobilize the heavy metals contained in the percolating leachate. [1-3] Immobilization can be improved by better controlling the synergy occurring between heavy metals. The segregation of municipal solid wastes (MSW) is therefore proposed to produce categories of MSW containing groups of compatible heavy metals so as to enhance immobilization. [4] It was previously demonstrated that Cd has little if any impact on Cu mobility while Pb, being incompatible with Cu, significantly decreases its adsorption or increases it mobility in sand-bentonite liners. [5] To further understand the synergistic effects of Pb and Cd on Cu mobility, selective sequential extraction (SSE) must be performed to measure the adsorption level of each liner particle fraction. Heavy metals are immobilized mainly by two mechanisms: adsorpt ion associated with four main liner particle fractions (exchangeable, oxides, organic matter and residual) and precipitation (carbonates and hydroxides). [6- 8] The exchangeable fraction is that which is retained by the diffuse layer created by the negatively charged sites at the surface of the colloidal particles of the liner material. [9] Metals in the outer sphere of this complex are hydrated and adsorbed to the liner’s negative surface sites by Coulomb and short range forces as a result of ion-dipole, dipoledipole and dipole-site interactions. Metals associated with these non-specific sites are exchangeable and potentially mobile, as they can be dynamically replaced by competing cations with a greater charge density. However, metals within the inner sphere of this complex are bound directly to the liner’s surface elements by interaction between the ions and the Coulomb forces, without hydration, and are therefore immobile. This is also called specific adsorption. Major cations, such as Na, Mg, and Ca have no adverse effect on the adsorbed metals due to the strong bond between the metallic ions and the liner elements. As the metal concentration increases in the solution, the exchange sites start to fill until the specific adsorption sites have been saturated. [10] The ionic strength of cations with identical charge is inversely proportional to their hydrated radius and this determines 114 the potential to compete for adsorption sites in the presence of multiple ions. Consequently, this adds complexity to the process. [11, 13, 14, 15] [11, 12] The preferred liner adsorption of one heavy metal over another is called selective adsorption. [11, 16, 17] Oxides of Fe, Al and Mn can adsorb a significant amount of heavy metals, especially under alkaline conditions. Under low pHs, such oxides acquire a positive charge [ ? Fe ? OH ?2 ] while under alkaline conditions, a negative charge [-Fe-O-] develops. The point of zero net proton charge (PZNPC) occurs when the oxide is neutral [-Fe-OH0 ]. The PZNPC differs among liners, depending on pH. [18, 19] Metal oxide and hydroxide behaviour also depends on pH, because as pH drops below 6, the solubility of Fe and Mn oxides increases, consequently, releasing into solution previously adsorbed metals. [20, 21] The metal fraction that is generally fixed within the lattice of silicate minerals is called the residual fraction. This fraction is relatively immobile as it becomes available only after digestion with strong acids at elevated temperatures. The carbonate fraction in the liner material and the free hydroxide anions in solution enhance the precipitation of heavy metals. With calcium carbonate, heavy metals replace Ca and form a three-dimensional crystal (e.g., CdCO3). In soils and natural liners with a low pH, carbonates tend to dissolve and release the precipitated heavy metals. The heavy metals adsorbed by each fraction can be extracted selectively using the appropriate reagents [22] : i) magnesium chloride solution to extract the soluble and exchangeable fractions; ii) acetic acid and an acetate buffer to extract the carbonate and hydroxide fractions; iii) hydroxylamine hydrochloride to extract the reducible Al, Fe and Mn oxides; iv) nitric acid, hydrogen peroxide and ammonium acetate to extract metals from organic matter; v) strong acids (HNO3 , HF, and HClO4 ) to dissolve the residual metal fraction. [23] 115 It was already demonstrated that the adsorption synergy occurring between heavy metals must be taken into account to improve the design of sand-bentonite liners. [5] This study therefore simulated the exposure of sand-bentonite liner material to landfill leachate containing Cu alone and in combination with either presumably a compatible (e.g. Cd) and an incompatible (e.g. Pb) metal. [4] The sequential selective extraction procedure (SSE) was used on the exposed liner samples to measure the role of each adsorption fraction in immobilizing Cu, when Cu reacts in synergy with other metals, specifically Cd and Pb. 6.3 METHODOLOGY The experiment consisted in allowing three types of liner material (sand alone and sand with 5 and 10% bentonite) to swell in distilled water for 7 days and then be subjected for 14 days to a solution containing Cu alone or Cu with either Pb or Cd. The samples were then centrifuged from their respective solutions and analyzed using SSE. 6.3.1 Experimental Materials The experimental sand and bentonite were commercial grade silica sand (40 mesh) and Na bentonite (200 mesh), respectively. Analysis by X-ray diffraction showed that the predominant phase of the bentonite was montmorillonite (85% ± 5%), which is nonstoichiometric in composition. Chemical characterization was achieved by X-ray fluorescence for the sand and bentonite (Table 6.1). The sand-bentonite mixtures were prepared at ratios of 100/0, 95/5 and 90/10. Fig. 6.1 illustrates the particle size distribution of the sand, established by mechanical sieving for the larger particles and the hydrometer method for the particles finer than 1.0mm. [24] The particle density was determined by the pycnometer method. [24] The sand and bentonite pH was measured using Corning pH/ion meter 450, after soaking in deionized water for 24h using a solid: liquid ratio of 1:1. Cation exchange capacity 116 (CEC) was evaluated by determining the Ca, Mg, K and Na displaced by soaking with a 0.1M BaCl2 solution. [25] By adding the required metal nitrate salts to distilled water, the experimental solutions (+) offered a cation level of 1 and 2 cmol /kg of liner sample for Cu alone and Cu in combination with either Pb or Cd, respectively. The respective CEC of the liner materials with 0, 5 and 10% bentonite was 2.0, 6.4 and 10.8 cmol (+)/kg. The initial pH of each solution was adjusted to the desired level (3.7, 5.5 or 7.5) by adding 0.05 M HNO3 or 0.05 M NaOH. These pH levels were selected based on the solubility behaviour of Cu indicating that the precipitation occurs above 6.5. [5] 6.3.2 Sample Preparation The experiment consisted in preparing 54 samples of sand-bentonite mixtures, each weighing 2g, and consisting of either 0, 5 or 10% bentonite with silica sand, exposed to one of three pHs (3.7, 5.5 and 7.5) and one of three heavy metal solutions (Cu alone or with either Pd or Cd). Each 2g of liner sample was placed in a 50ml polycarbonate centrifuge tube and soaked for 7 days in 12.5ml of deionized water. Then, each tube received 12.5ml of one of the following solutions: 1.56 mM/L of Cu, or 1.56 mM/L of Cu with 1.56 mM/L of Cd, or 1.56 mM/L of Cu with 1.56 mM/L of Pb at a pH of either 3.7, 5.5 or 7.5. Thus, each 2g sample of 0, 5 and 10% bentonite liner material offered a respective CEC of 2, 6.4, or 10.8 cmol cmol (+) (+) /kg when the 25ml of heavy metal solution offered a cation concentration of 1 /kg for Cu alone and 2 cmol (+) /kg for Cu with either Cd or Pb. After 14 days, duplicate liner samples were centrifuged at 10000 rpm for 10 minutes using an IEC-2100R centrifuge (model 120, International Equipment Company, Needham Heights, MA, USA) and the equilibrium pH of the supernatants was measured. The supernatants and liner samples were collected for Cu determination. While the supernatant Cu concentrations were reported earlier [5] , the liner samples were subjected to SSE to determine the Cu content of each adsorption fraction. Atomic Adsorption Spectroscopy (AAS, model 903 single beam, 117 GBC Scientific Equipment Pty Ltd. Dandenong, Australia) was used to quantify the Cu level of all extracted supernatants. 6.3.3 SSE Procedure Selective sequential extraction (SSE) was performed on each liner sample recovered from the heavy metal solution, using four different stages. [22] No analysis of organic fraction adsorption was performed as the artificial liner samples contained no organic matter. The first stage extracted the soluble fraction by washing each 2g liner sample with 20ml of deionized water for 30 minutes in a rotary shaker. The supernatant was removed for Cu determination and the remaining liner sample was then prepared for the second stage of extraction which pertained to the exchangeable fraction, which was extracted by agitating for 1.0h, each 2g liner sample with 10.7ml of 1 M MgCl2 (pH 7). The supernatant was removed for Cu determination and the remaining sample was saved for the third extraction process which pertained to the extraction of the carbonates and hydroxide fractions and was carried out by adding 10.7ml of 1 M NaOAc adjusted to pH 5 with acetic acid and by agitating continuously for 5h. After centrifugation, the supernatant was removed and analyzed for Cu. The remaining liner sample was used for the fourth stage pertaining to the extraction of Cu from the oxide fraction, achieved by adding 27ml of 0.04 M NH2 OH.HCl in 25% (v/v) acetic acid at pH 2.5 with occasional agitation for 6h at 960 C. Between stages, the remaining liner samples were transferred into individual Pyrex cylinders and allowed to dry for 72h in a ventilated hood. Liner samples were then agitated dry at 50 rpm for 24h, to allow them to regain their homogeneity lost during centrifugation. 6.3.2 Statistical Procedures and Regression Analysis The ANOVA procedure was used to test the effect of bentonite, pH, CEC, Pb and Cd on Cu adsorption. treatments. [26] The Least Square Method was used to identify the significantly different 118 A total of nine regression equations were obtained to represent Cu adsorption by each one of the three (3) fractions as a function of pH, liner CEC and Cu, Pb and Cd level under three (3) categories: i) alkaline conditions; ii) acid conditions and liner CEC greater than solution equivalence; and i i) acid conditions and liner CEC equal to or lower than the solution’s equivalence. 6.4 R ESULTS AND D ISCUSSION 6.4.1 Adsorption under Alkaline Conditions Figs. 6.2a, b and c illustrate the level of Cu remaining soluble and adsorbed by each fraction of the liner sample using an initial solution pH of 7.5. Except for Cu with Cd or Pb and 0% liner bentonite content, the 14 day equilibrium pH tended to increase above the initial pH of 7.5, in proportion with the bentonite content of liner sample (Table 6.2a). Cadmium with Cu produced the lowest equilibrium pH, followed by Cu alone and Cu with Pb. With 0% liner bentonite content, 2.0, 1.4 and 4.8% Cu remained soluble for Cu alone and with either Cd or Pb, respectively. The exchangeable fraction adsorbed 6.7, 0.8 and 0.8% Cu, while the carbonate and hydroxide fractions precipitated 51.2, 60.1 and 65.6% and the oxide fraction adsorbed 19.4, 21.6 and 15.2%, respectively. The residual fraction retained 20.7, 16.1 and 13.6% Cu. With 5% liner bentonite content, 4.4, 0.8 and 6.2% Cu remained soluble for Cu alone and with either Cd or Pb, respectively. The exchangeable fraction adsorbed 20.9, 13.6 and 6.6% Cu while the carbonate and hydroxide fractions precipitated 45.7, 64.2 and 61.6%, and the oxide fraction adsorbed 17.3, 13.0 and 11.4%, respectively. The residual fraction retained 11.7, 8.4 and 14.2% Cu, respectively. With 10% liner bentonite content, 12.7, 12.4 and 14.8% Cu remained soluble for Cu alone and with either Cd or Pb. The exchangeable fraction adsorbed 22.0, 8.6 and 4.3% 119 Cu, the carbonate and hydroxide fractions precipitated 34.2, 59.1 and 52.1% Cu and the oxide fraction adsorbed 13.0, 7.4 and 8.6% Cu, respectively. The residual fraction retained 18.1, 12.4 and 20.1% Cu, respectively. Under alkaline conditions, the following regression equations were obtained to represent Cu adsorption by each fraction. The residual fraction was not analyzed as its Cu level only varied with bentonite level, the bentonite containing more Cu within its crystalline formation than the sand. Exchangeable: Cua = 0.104 + 0.010*CEC - 0.092*Cd - 0.128*Pb r = 0.86 (1) r = 0.94 (2) r = 0.94 (3) Carbonate and hydroxide precipitate: Cua = 0.510 - 0.012*CEC + 0.176*Cd + 0.162* Pb Oxide: Cua = 0.2322 - 0.010*CEC - 0.026*Cd - 0.049*Pb Where, Cua = Cu adsorbed to the given soil fraction as a fraction of that applied, unitless, CEC = liner cation exchange capacity, cmol (+)/kg of liner, and Cd and Pb = solution levels of Cd and Pb, respectively, cmol (+)/kg of liner. Both Pb and Cd were exposed at a level of either 0 or 1 cmol whereas the liner CEC level was 2.0, 6.4 or 10.8 cmol (+) (+) /kg of liner samples, /kg. The effects of Pb and Cd on Cu adsorption are illustrated in Table 6.3 to better describe the regression equations. Liner bentonite content increased the 14 day equilibrium pH, and therefore increased the solubility of Cu at the expense of oxide adsorption. The governing factor under alkaline conditions is pH which affects mostly Cu solubility. Thus, for Cu alone and with either 120 Cd or Pb, the carbonate and hydroxide fractions precipitated the most Cu, followed by similar amounts being adsorbed by the exchangeable, the oxide and the residual fractions. Two pH ranges affect Cu solubility and therefore liner particle precipitation: from 6.5 to 8.0, where most Cu precipitates as Cu (OH ) 02 , CuHCO 3? , CuCO 03 , [27] and; above 8.0, where Cu regains its solubility by forming Cu (OH ) ?3 . [27] Therefore and at the expense of mostly the carbonate and hydroxide fractions, Cu solubility increased especially with 10% bentonite and even more so with Pb because of the 14 day equilibrium pH approached and even exceeded 8.0. Nevertheless, Pb and Cd introduced secondary effects on Cu solubility as for the same 14 day equilibrium pH, the solubility of Cu is not the same with Cd and Pb. For the same liner bentonite content in the presence of Pb, and to a lesser extent in the presence of Cd, Cu was desorbed from the excha ngeable sites to be mostly precipitated by the carbonate and hydroxide fractions, but also by the oxide fraction. Cadmium affected Cu precipitation by the carbonate and hydroxide fractions, mainly because of its effect on decreasing the equilibrium pH below the initial of 7.5. 6.4.2 Acid Conditions and Liner CEC Higher than the Solution Equivalence Since the initial solution pHs of 3.7 and 5.5 produced equilibrium pHs below 6.5, results obtained with both levels were not significantly different (Figs.6.3a, b, c and Figs. 6.4a, b, c). With 5% liner bentonite content, 2.8 to 2.6%, 6.8 to 9%, and 24 to 17.6% Cu remained soluble, for Cu alone and with either Cd or Pb, respectively. The exchangeable fraction adsorbed 12.7 to 18.0%, 9.0 to 12.4%, and 5.0 to 6.9% Cu for the same sample, respectively, while the carbonate and hydroxide fractions precipitated 55.0 to 48.1%, 64.8 to 62.7%, and 38.2 to 40.8% Cu and the oxide fraction adsorbed 17.3 to 21.6%, 8.6 to 6.5% and 25.9 to 25.4%, respectively. The residual fraction retained 12.3 to 9.7%, 10.8 to 9.4% and 6.9 to 9.4% Cu, respectively. 121 With 10% liner bentonite content, 1.4 to 0.8%, 3.6 to 2.0% and 4.0% Cu remained soluble, for Cu alone and with either Cd or Pb, respectively. The exchangeable fraction adsorbed 20.8 to 24.6%, 16.7 to 27.0% and 9.44 to 12.9% Cu, while the carbonate and hydroxide fractions precipitated 42.4 to 34.1, 47.6 to 38.2 and 47.0 to 43.0% Cu and the oxide fraction adsorbed 21 to 23.6, 21.6 to 20.7 and 28.1 to 28.0% Cu, respectively. The residual fraction retained 14.4 to 16.8%, 10.8 to 12.1% and 11.5 to 12.2% respectively (Figs. 6.3b, c and 6.4b, c). For a pH under 6.5 and a liner CEC higher than the solution equivalence, the following regression equations were obtained: Exchangeable: Cua = 0.029 + 0.019*CEC - 0.027*Cd - 0.104*Pb r = 0.91 (4) r = 0.58 (5) r = 0.90 (6) Carbonate and hydroxide precipitate: Cua = 0.474 - 0.068*CEC + 0.118*Cd - 0.006*Pb Oxide: Cua = 0.107 + 0.012*CEC - 0.070*Cd + 0.056*Pb The liner CEC had a major impact on Cu adsorption when alone and with either Cd or Pb. With respect to the heavy metal solution equivalence, its relative density of adsorption sites greatly increased with a liner bentonite content increasing from 5 to 10%. Again, the carbonate and hydroxide fractions precipitated the most Cu, followed by a similar level being adsorbed by the oxide and exchangeable fractions, and then the residual fraction. Lead and to a lesser extent, Cd, competed against Cu for the exchangeable adsorption sites for both the 5 and 10% liner bentonite content, with a direct increase in Cu solubility. As compared to Cu alone, Pb tended to compete against Cu for precipitation on the carbonate and hydroxide fraction shifting the adsorption towards the oxide 122 fraction, while Cd tended to compete against Cu for oxide adsorption shifting the precipitation towards the carbonate and hydroxides. Again, Pb and Cd had major effects with the 5% liner bentonite content but lesser effect with that of 10%, because of an increased ava ilability of adsorption sites. 6.4.3 Acid Conditions and Liner CEC Equal to or Lower than the Solution Equivalence This condition pertains to initial pHs of 3.7 and 5.5, and 0% liner bentonite content. Under such condition, 30.0 to 32.0, 46.0 to 50.0 and 70.0 to 72.4% Cu remained soluble, for Cu alone and either with Cd or Pb, respectively (Figs. 6.3a, b, c). The exchangeable sites adsorbed 6.1 to 6.3, 4.6 to 4.8 and 1.8 to 2.0% Cu, while the carbonate and hydroxide fractions precipitated 39.4 to 39.9, 27.2 to 31.4 and 13.8 to 15.2% and the oxide fraction adsorbed 10.8 to 17.3, 4.3 to 6.5 and 8.6 to 8.8%, respectively. The residual fraction retained 6.5 to 11.7, 13.7 to 13.8 and 1.4 to 5.8%, respectively (Figs. 6.3a and 6.4a). The following equations describe Cu adsorption either with Cd or Pb. The equations are generated for the 0% liner bentonite content (2 cmol (+) /kg of liner), with 1 cmol (+) Cu/kg of liner and either 1 cmol (+) Cd/kg of Cd or 1 cmol (+) Pb/kg of liner. Exchangeable: Cua = 0.0775 – 0.03*Cd – 0.06*Pb r = 0.98 (7) r = 0.98 (8) r = 0.99 (9) Carbonate and hydroxide precipitate: Cua = 0.125 + 0.17*Cd + 0.02*Pb Oxide: Cua = 0.0115 + 0.031*Cd + 0.076*Pb 123 Because the liner CEC was equal to the solution equivalence, heavy metals competed actively for adsorption sites. As for the 5 and 10% liner bentonite content, Pb and to a lesser extent Cd, competed against Cd for exchangeable sites, resulting in more Cu remaining soluble, compared to Cu alone. As compared to Cu alone, Pb, and to a great extent Cd, competed significantly against Cu for carbonate and hydroxide precipitation and for oxide adsorption, which resulted again in more Cu remaining soluble, as compared to Cu alone. 6.5 CONCLUSION Two main factors affected liner material behaviour in adsorbing Cu, besides Cd and Pb competition: pH either above or below 6.5; liner cation exchange capacity (CEC) greater or equal and lower than the solution cation level in terms of equivalence. Alone and in the presence of either Cd or Pb, Cu adsorption on sand-bentonite liner material demonstrated various affinities. Cu had more affinity for the exchangeable and oxide fractions when adsorption sites were available. For both alkaline and acidic conditions, Cu was mostly precipitated by the carbonate and hydroxide fractions, where relatively the same levels of Cu were adsorbed by the exchangeable sites, and the oxide and residual fractions. Also, Pb and to a lesser extent Cd, competed against Cu for the exchangeable sites and increase the soluble Cu level, as compared to Cu alone. Under alkaline condition, (pH above 6.5) and when alone or with either Cd or Pb, 85 to 88% of the Cu was precipitated by the carbonate and hydroxide fractions. As compared to Cu alone, Pb and to a lesser extent Cd, competed for oxide adsorption and shifted Cu towards carbonate and hydroxide precipitation. While Pb resulted in a higher 14 day equilibrium solution pH exceeding 8.0, which increased Cu solubility, Cd had a lowering effect on such pH and resulted in even less Cu being soluble. 124 Under acidic condition, (pH below 6.5), and 0% liner bentonite content, adsorption sites were limited and both Cd and Pb competed with Cu for oxide adsorption and carbonate and hydroxide precipitation. With 5 and 10% liner bentonite levels, adsorption sites were more abundant and Cu could shift towards carbonate and hydroxide precipitation while Cd competed against it for oxide adsorption. With Pb and as bentonite liner level increased from 0, to 5 and then 10%, Pb competition for carbonate and hydroxide precipitation against Cu dropped, reducing its shifting effect on Cu for oxide adsorption. 6.6 ACKNOWLEDGEMENT The authors gratefully acknowledge the financial contribution of the Natural Sciences and Engineering Research Council of Canada. 125 6.7 R EFER ENCES 1. Lee, G. F. Solid Waste Management: USA Lined Landfilling Reliability. Submitted to Natural Resources Forum, http://www.gfredlee.com/UNpaper-landfills.pdf. (accessed Oct, 1 2003). 2. Yong, R.N.; Bentley, S.P.; Harris, C.; Yaacob, W.Z.W. Selective sequential extraction analysis (SSE) on estuarine alluvium soils. In Geoenvironmental Engineering; Yong, R.N.; Thomas, H.R., Eds; Thomas Telf ord: London, 1999; 118126. 3. Reddy, D.V.; and Butul, B.A. Comprehensive Literature Review of Liner Failures and Longevity. Report to the Florida Center for Solid and Hazardous Waste Management University of Florida, 1999. http://www.floridacenter.org/ publications /liner_failu re_99.pdf (accessed Oct 2, 2003). 4. Kaoser, S.; Barrington, S.; Elektorowicz, M. Compartments for the management of municipal solid waste. Soil and Sediment Contamination. 2000, 9(5), 503-522. 5. Kaoser, S.; Barrington, S.; Elektorowicz, M.;Wang, L. Copper adsorption with Pb and Cd in sand-bentonite liners under various pHs. Part I. Effect on total adsorption. J. Environ. Sci. Health, Part A, accepted. 2004. 6. Solís, G.J.; Alonso, E.; Riesco, P. Distribution of metal extractable fractions during anaerobic sludge treatment in southern Spain WWTPS. Water, Air, and Soil Pollution 2002, 140 (1-4), 139-156. 7. Morera, M.T.; Echeverria, J.C.; Garrido, J.J. Mobility of heavy metals in soils amended with sewage sludge. Can. J. Soil Sci. 2001 , 81 (1), 405-414. 8. Yanful, E.K.; Quigley, R.M.; Nesbitt, H.W. Heavy metal migration at a landfill site, Sarnia, Ontario, Canada- I: Thermodynamic assessment and chemical interpretations. Applied Geochemistry. 1988, 3, 523-533. 9. Jiang, X.; Zhou, J.; Zhu, M.; He, W.; Yu, G. Charge characteristics on the clay surface with interacting electric double layers. Soil Science 2001, 166 (4), 249-254. 10. Garcia-Miragaya, J.; Cardenas, R.; Page, A.L. Surface loading effect on Cd and Zn sorption by kaolinite and montmorillonite from low concentration solutions. Water, Air, and Soil Pollution 1986, 27(1/2), 181-190. 126 11. Gomes, P.C.; Fontes, M.P.F.; da Silva, A.G.; de S. Mendonça, E.; Netto, D.A.R. Selectivity sequence and competitive adsorption of heavy metals by Brazilian soils. Soil Sci. Soc. Am. J. 2001, 65, 1115-1121. 12. Bohn, H. L. Soil Chemistry; John Wiley & Sons: New York, 1979. 13. Kim, K.-H.; Kim, S.-H. Heavy metal pollution of agricultural soils in central regions of Korea. Water, Air, and Soil Pollution. 1999 , 111(1-4), 109-122. 14. McBride, M.B.; Brian, K.R.; Tammo, S.; John, J.R.; Sebastien, S. Mobility and solubility of toxic metals and nutrients in soil fifteen years after sludge application. Soil Science 1997, 162(7), 487-500. 15. Phadungchewit, Y. The Role of pH and Soil Buffer Capacity in Heavy Metal Retention in Clay Soils, Ph.D. diss., McGill University, Dept. of Civil Engineering and Applied Mechanics, Montreal, QC, Canada. 1990; pp. 176. 16. Han, F.X.; Banin, A.; Triplett, G.B. Redistribution of heavy metals in arid-zone soils under a wetting-drying cycle soil moisture regime. Soil Science 2001, 166(1), 19-28. 17. Elliott, H.A.; Liberati, M.R.; Huang, C.P. Competitive adsorption of heavy metals by soils. J. Environ. Qual. 1986, 15 (3), 214-219. 18. Spadini, L.; Schindler, P.W.; Charlet, L.; Manceau, A.; Ragnarsdottir, K.V. Hydrous ferric oxide: evaluation of Cd-HFO surface complexation models combining Vdk ECAFS data, potentiometric titration results, and surface site structures identified from mineralogical knowledge. J. Colloid and Interface Sci. 2003, 266, 1-18. 19. McLean, J.E.; Bledsoe, B.E. Behaviour of metals in soils. In EPA Ground Water Issue EPA/540/S-92/018. 1-20. Washington, D.C.: Office of Solid Waste and Emergency Response, United States Environmental Protection Agency, 1992. http:// www.epa.gov/ada/download/issue/issue14.pdf. Accessed 2 November 2003. 20. Nordstrom, D.K.; Alpers, C.N.; Jennifer, A.C.; Taylor, H.E.; Blaine, R.; McCleskey, J.W.; Ogle, B.S.O.; Cotsifas, J.S.; Davis, J.A. Geochemistry, toxicity, and sorption properties of contaminated sediments and pore waters from two reservoirs receiving acid mine drainage. U.S. Geological Survey Toxic Substances. Hydrology Program Proceedings of the Technical Meeting Charleston South Carolina, Water-Resources Investigation Report 99-4018A, March 8-12, 1999, 1 (3). 127 21. Essen, J.; El Bassam, N. On the mobility of Cadmium under aerobic soil conditions. Environ. Pollut. Ser. A, 1981; 15-31. 22. Tessier, A.; Cambell, P.G.C.; Bisson, M. Sequential extraction procedures for the speciations of particulate trace metals. Anal. Chem. 1979, 51(7), 844-851. 23. Balasoiu, C.; Zagury, G.; Deschenes, L. Partitioning and speciation of chromium, copper, and arsenic in CCA-contaminated soils: influence of soil composition. Sci. Tot. Environ. 2001, 280(1-3), 239-255. 24. Blake G.R.; Hartage, K.H. Bulk density. In Methods of Soil Analysis, Part 1; Klute, A., Ed.; Am. Soc. Agron. and Soil Sci. Soc. Am.: Madison, WI. 1986; 363-375. 25. Hendershot, W.H.; Lalande H. Ion exchange and exchangeable cations. In Soil Sampling and Methods of Analysis. Carter, M.R., Ed.; Lewis Publishers: Boca Raton, FL., 1993; 167-170. 26. SAS Institute. SAS system for Windows, ver. 8.00. SAS Institute: Cary, NC., 1999. 27. Baes, C.F., Jr.; Mesmer, R.E. The Hydrolysis of Cations; John Wiley & Sons: New York, 1976. 128 Table 6.1. Physical and chemical characteristics of sand and bentonite Properties of elements Liquid limit Plastic limit Particle density CEC Moisture content pH SiO2 CaO MgO Na2 O K2 O P2 O5 MnO TiO2 FeO Fe2 O3 Fe2 O3 (T) BaO Cr2 O3 Cu Zn LOI* Units % % g/ml cmol/kg (%) weight (%) weight (%) weight (%) weight (%) weight (%) weight (%) weight (%) weight (%) weight (%) weight (%) weight (%) mg/kg mg/kg mg/kg mg/kg weight (%) LOI*: Loss On Ignition <d/l: below detection limit Silica sand 40 Bentonite N/A N/A 2.66 2 0.0 9 99.28 0.07 0.03 <d/l 0.10 0.012 <d/l 0.037 N/A N/A 0.07 21 <d/l 53 3 0.17 360 57 2.54 90 8.4 10 59.37 1.26 2.27 2.17 0.52 0.053 0.019 0.144 0.94 2.75 3.80 83 20 85 67 11.20 Detection limit 0.006 0.0015 0.0095 0.0075 0.0025 0.0035 0.0030 0.0035 0.01 0.01 0.003 17 15 2 2 0.01 129 Table 6.2a. Equilibrium pH after 14 days of exposure to the experimental heavy metal solutions at an initial pH of 7.5 Bentonite level Cu alone Cu with Cd Cu with Pb % 0 7.9 6.7 6.3 5 8.5 7.5 8.4 10 8.7 7.9 8.5 Table 6.2b. Equilibrium pH after 14 days of exposure to the experimental heavy metal solutions at an initial pH of 5.5. Bentonite level Cu alone Cu with Cd Cu with Pb % 0 5.5 5.3 5.2 5 6.2 5.8 5.4 10 6.4 6.2 5.8 Table 6.2c. Equilibrium pH after 14 days of exposure to the experimental heavy metal solutions at an initial pH of 3.7. Bentonite level Cu alone Cu with Cd Cu with Pb % 0 5.2 5.1 4.9 5 6.2 5.7 5.3 10 6.4 6 5.9 130 Table 6.3. Effect of Pb and Cd on Cu adsorption compared to Cu alone Liner Fractions Soluble Metal combinations pH Cu-Cd Exchangeable Cu-Pb Cu-Cd Cu-Pb Carbonate and Hydroxides Cu -Cd Cu-Pb Oxides Cu-Cd Cu-Pb Bentonite (%) 0 * * * * 3.7 5 * * 10 * * 0 5.5 5 * 10 * * * * * * * 0 7.5 5 * 10 * Note: The * sign indicates that the adsorption value is highly significant (95% confidence level) compared to Cu alone. % smaller than 131 100 90 80 70 60 50 40 30 20 10 0 0 100 200 300 400 Particle diameter ( µm) Fig. 6.1. Particle size analysis 500 600 700 800 132 (a) 75 60 0% 5% 10% 45 30 15 0 (b) Cu adsorption (%) 75 0% 5% 10% 60 45 30 15 0 (c) 75 0% 5% 10% bentonite 60 45 30 15 0 Soluble Exchange Carbonate Oxides Residue Figure 6.2. Copper adsorption by sand bentonite liner fractions under initial pH of 7.5 with 0%, 5%, and 10% liner bentonite content with Cu alone (a) and with exposure to Cd (b) and Pb (c). 133 (a) 75 60 0% 5% 10% 45 30 15 0 (b) 75 Cu adsorption (%) 0% 5% 10% 60 45 30 15 0 (c) 75 0% 5% 10% bentonite 60 45 30 15 0 Soluble Exchange Carbonate Oxides Residue Figure 6.3.Copper adsorption by sand bentonite liner fractions under initial pH of 5.5 with 0%, 5%, and 10% liner bentonite content with Cu alone (a) and with exposure to Cd (b) and Pb (c). 134 (a) 75 60 0% 5% 10% 45 30 15 0 (b) 75 Cu adsorption (%) 0% 5% 10% 60 45 30 15 0 (c) 75 0% 5% 10% bentonite 60 45 30 15 0 Soluble Exchange Carbonate Oxides Residue Figure 6.4. Copper adsorption by sand bentonite liner fractions under initial pH of 3.7 with 0%, 5%, and 10% liner bentonite content with Cu alone (a) and with exposure to Cd (b) and Pb (c). 135 CONNECTING STATEMENT To test Cu mobility and compatibility, either with Cd or Pb, in sand-bentonite liners, a column leaching test was performed. The results were compared with these of the preceding batch experiment. Chapter 7 deals with that experiment. This paper is accepted by the Canadian Journal of Civil Engineering under the tilte “Effect of Pb and Cu on Cu Adsorption by Sand-Bentonite Liners”. . The authors were : 1) S. Kaoser, 2) S Barrington, 3) M. Elektorowicz, and 4) Li Wang. The contributions of the authors are: Author (i) carried out the entire experiment and drafted the article. Author (ii) audited and contributed substantially to the contents of the article. Author (iii) and (iv) overviewed the paper and provided valuable suggestions. 136 CHAPTER 7 EFFECT OF Pb AND Cd ON Cu ADSORPTION BY SAND-BENTONITE LINERS 7.1 Abstract: The mobility of Cu alone and in the presence of Cd (compatible) and Pb (incompatible) was studied using laboratory columns packed with a 95w% sand and 5w% bentonite mixture. The liner material was subjected to one of four heavy metal solutions using a pressure head of 7kPa: 2.0cmol(+) Cu /L solution; 1.0cmol(+) Cu and 1.0cmol(+) Cd /L solution for a total of 2.0cmol(+) /L solution; 1.0cmol(+) Cu and 1.0cmol(+) Pb /L solution for a total of 2.0cmol(+) /L solution. The effluents and the liner samples were analyzed for Cu, Pb and Cd. The breakthrough curves indicated that Cd was the most mobile of the metal cations, while Pb was the least. Total metal adsorption was greatest for Cu with Pb, followed by that of Cu with Cd and then that of Cu alone showing that Cu is compatible with Cd but not Pb. The selective sequential extraction (SSE) analysis indicated that most of the Cu was precipitated by the carbonate and hydroxide fractions. Key words: Cd, Pb, Cu, sand-bentonite liners, adsorption, leaching. 137 7.2 Introduction Despite the existing MSW landfill disposal practices, leaching still occurs leading to heavy metal migration towards the groundwater, whether the liners are built of synthetic materials or of sand and bentonite mixtures (Lee, 2002; Reinhart and McCreanor 1999). In terms of groundwater contamination, the elements of greatest concern are Cadmium (Cd), Chromium (Cr), Copper (Cu), lead (Pb), Nickel (Ni), and Zinc (Zn) because they are often found in large amounts in landfill leachate and are potentially toxic to plants and animals (Thornton et al. 2003; Whittle and Dyson 2002; Urase et al. 1997; Cherry et al. (1984). The leaching of heavy metal from landfill can be attenuated by improving the liner adsorption which depends on physical and chemical factors. Physical factors include the liner particle sizes and permeability. A percentage exceeding 5% for clay particle (less than 2 micron) is preferred (e.g. bentonite) for metal adsorption because clay particles offer larger surface charge densities and swelling capacity. The liner thickness, hydraulic conductivity and leachate pressure head affect the leachate retention time within the liner layer. Chemical factors include individual metal concentration and liner CEC. Kaoser et al. (2004b) found that Cu adsorption curves stabilized after 10, 2 and 2 days, for the 0%, 5% and 10% bentonite mixtures (liner CEC of 2, 6.4, and 10.8 cmol(+) /kg respectively) with heavy metal equivalence of 1 and 2 cmol(+) /kg of liner, indicating that for lower liner CEC close to the solution cation equivalence, adsorption stability required more time. This was obvious especially for initial leachate pH of 3.7 and 5.5. However, with higher solution concentration at 4.8 cmol(+) /kg, adsorption required more than 7 days to stabilize. The level of adsorption of heavy metals by sand-bentonite landfill liners varies according to their mobility pattern, their interaction and their ability to form inorganic and organic complexes. Consequently, leachate containing multiple ions of different physical and chemical properties affects the individual heavy-metal sorption kinetics because of competition for adsorption sites on particles of the sand-bentonite liner (Gomes et al. 2001; Yong et al. 1992). The preferred adsorption of one heavy metal species over another is called selective adsorption (Harter 1992). The selectivity order is influenced by the valency and the ionic size of the heavy metals once hydrated (Gomes et al. 2001; Elliott et al. 1986). On the basis 138 of unhydrated radii, the expected order of selectivity is: Pb2+ (0.12nm radius) > Cd2+ (0.097nm radius) > Cu2+ (0.072nm radius). Once hydrated the selectivity order becomes inversely proportional to the hydrated radius (Morera et al. 2001; Gomes et al. 2001). Thus, the selectivity order is: Cd (0.23nm radius) < Cu (0.21nm radius) <Pb (0.187nm radius). Smaller ions with the same valency, such as Cd compared to Pb, have higher charge densities and attract more water molecules resulting in a larger hydrated radius. Metals with higher hydrated radius exert weaker Coulombic forces of attraction (Young 1992). Therefore, Cd is expected to be mobile as compared to Pb, because of its larger hydrated radius. Apart from the selectivity order, multiple metal ions in the solut ion will yield unanticipated chemical combinations because of the synergetic and antagonistic effects which they exert on each other (Kim 2003). The complexity of such process increases with the number of individual heavy metals present. Kaoser et al. (2000) suggested the segregation of MSW based on the adsorption compatibility of their heavy metal content. For example, Cu, Pb, Cr 3+ and Zn can be grouped as low mobility metals. However, due to the synergetic and antagonistic behaviour between Pb-Zn and Zn-Cu, Zn needs to be segregated into another low mobility but compatible category. Using batch tests, Kaoser et al. (2004b, c) demonstrated that more Cu is adsorbed in the presence of Cd, as compared to Pb and therefore, Cu is incompatible with Pb, despite both metals falling into the same low mobility category. Under both alkaline and acidic conditions, Pb and to a lesser extent Cd, competed with Cu for exchangeable sites. This competition lead to more Cu remaining soluble with Pb and more Cu being precipitated on the carbonate fraction in the presence of Cd or being adsorbed by the oxide fraction in the presence of Pb. To further test this compatibility concept using dynamic conditions, the present study explored the mobility patterns of Cu alone and with either Cd or Pb, using laboratory sand-bentonite columns. A sand-bentonite mixture composed of 5wt% Na bentonite and 95wt% commercial grade sand was used as they represent the most common type of landfill liner used in North America (Whittle and Dyson 2002; Han et al. 2001; KabataPendias 1984). Three different permeate solutions (Cu alone, Cu with Cd, Cu with Pb) 139 were leached through in triplicate soil columns to observe adsorption levels of heavy metals. 7.3. Materials and Methods The liner material consisted of 95% by weight commercial grade silica sand (40 mesh) and 5% by weight Na bentonite, also named national bentonite (200 mesh). The physical and chemical properties of the sand and bentonite were determined separately in the laboratory using X-ray diffraction. The sand particle size distribution was established by sieving mechanically (ASTM-E11), in a sieve stack of #20, #30, and #60 (0.850, 0.300, and 0.250mm respectively), and by the hydrometer method (Fig. 7.1). This analysis indicated that the sand particle size ranged from 50 to 600 µm. Particle density was determined by the pycnometer method (Blake and Hartage 1986). The pH was measured using a pH probe, after soaking in deionised water for 24 hours. Cation exchange capacity (CEC in cmol (+) /kg of liner material) was determined by quantifying Ca, Mg, K and Na released with soaking in a 0.1M BaCl2 solution (Hendershot and Lalande 1993). Moisture content, measured gravimetrically after drying in an oven for 24 h at 1050C, is presented as dry over wet mass (Table 7.1). By adding the required metal (Cu, Cd or Pb) nitrate salts to distilled water, the experimental solutions offered a cation level of 10cmol(+) /L of permeant solution. The CEC of the liner material with 5% bentonite was 6.4cmol(+)/kg. The initial pH of each solution was adjusted to the desired level of 3.7 by adding 0.05 M HNO3 or 0.05 M NaOH. This pH level represents normal leachate pH leading to the high solubility behaviour of Cu which tends to precipitated only above a pH of 6.5. 7.3.1. Experimental set-up Rigid wall permeameters were used to subject the sand-bentonite liner material to various heavy metals solutions. Each permeameter consisted of an ABS (Acrylonitrile Butadiene Styrene) cylinder with an inside diameter of 100 mm and a height of 200 mm, closed at the top and bottom by steel end plates held in place by threaded rods (Fig. 7.2). To prevent leakage, rubbers O-rings were pressed between the cylinder walls and the steel plates by means of screws. The top and bottom plates were fitted with sewage locks and 140 each had a perforation to create a pressure head and allow for the drainage and collection of effluents. A total of 12 rigid ABS wall permeameters were used to test the effect of four triplicate solutions (Fig.7.3). The permeameters were then subjected to a constant head pressure of 7 kPa (1 psi), and the leachate was collected after each pore volume eluted (200 ml). Pore volume, namely the net internal void or pore space within the solid core, was calculated by subtracting from the total volume, the volume occupied by the soil particles. This pressure head produce a flow rate of 100ml/day or 0.5 pore volume per day, which is that recommended by Fuller (1982) as being the most favourable for laboratory simulation of landfill seepage. 7.3.2. Method Each of the 12 permeameters was filled with 800 g of dry clean sand-bentonite mixture, placed without compaction according to the Fuller (1982) procedure. The air-dried liner material was spooned into the column in 2cm layers and uniformly packed with a round end glass rod. The procedure was repeated until the permeameters contained 800 g of dry liner material. The experiment was conducted in two stages. In the first stage, all samples were subjected to swelling by applying a low bottom water pressure head (?P) of 7kPa for 9 days, a period considered sufficient to reach full swell (Kaoser et al. 2004a). In the second stage, the permeameters received one of four heavy metal solutions: 2.0 cmol(+) Cu /L solution; 1.0 cmol(+) Cu and 1.0 cmol(+) Cd /L solution for a total of 2.0 cmol(+) /L solution; 1.0 cmol(+) Cu and 1.0 cmol(+) Pb /L solution for a total of 2.0 cmol(+) /L solution, and; a blank or control solution. The strength of the heavy metal permeant solution was selected so that 15 pore volumes (3.0L) of solution would offer equivalence in heavy metals equal to that of the 800 g of liner material. The liner material offered a CEC of 6.4cmol(+) /kg and its pore volume represented 200 ml. The heavy metal solutions were con tinuously fed into the permeameters to maintain a hydraulic head of 700 mm over a liner depth of 100 mm corresponding to a hydraulic pressure of 7 kPa. This constant hydraulic head produced an effluent flow rate of about 0.5 pore volume (100 ml)/day. As suggested by Fuller (1982), this is the most favourable rate 141 for gravity control in landfill liner and soil column leaching tests. Each effluent pore volume was sampled to measure its pH and Cu, Pb and Cd. All heavy metal quantification was conducted using atomic absorption spectrometry (AAS). Each of the permeant solutions was allowed to leach continuously until the breakthrough curve was obtained. A total of 20 pore volumes were applied to each column. The liner specimens were then extruded and cut into four equal segments, each representing a slice of 2 cm in depth, and each segment was thoroughly mixed and analyzed using selective sequential extraction (SSE). 7.3.3 SSE procedure Selective sequential extraction (SSE) was performed on each liner sample recovered from the heavy metal solution, using four different stages (Tessier et al. 1979). SSE is the extraction of heavy metals, in sequence, from different soil fractions (soluble, exchangeable, carbonate, oxides-hydroxides, and residue) using selective acids and reagents. No analysis of organic fraction adsorption was performed as the artificial liner samples contained no organic matter. The first stage extracted the soluble fraction by washing each 2g liner sample with 20ml of deionized water for 30 minutes in a rotary shaker. The supernatant was removed for heavy metal quantification and the remaining liner sample was then prepared for the second stage of extraction which pertained to the exchangeable fraction, which was extracted by agitating for 1.0h, each 2g liner sample with 10.7ml of 1 M MgCl2 (pH 7). The supernatant was removed for heavy metal quantification and the remaining sample was saved for the third extraction process which pertained to that of the carbonates and hydroxide fractions and was carried out by adding 10.7ml of 1 M NaOAc adjusted to pH 5 with acetic acid and by agitating continuously for 5h. After centrifugation, the supernatant was removed and analyzed for heavy metals. The remaining liner sample was used for the fourth stage pertaining to the extraction of heavy metals from the oxide fraction, achieved by adding 27ml of 0.04 M NH2OH·HCl in 25% (v/v) acetic acid at pH 2.5 with occasional agitation for 6h at 960C. The residual fraction was quantified by digesting with a strong acid at 5000 C. Between stages, the remaining liner samples were transferred into individual Pyrex cylinders and allowed to dry for 72h in a ventilated hood. Liner samples were then 142 agitated dry at 50 rpm for 24h, to allow them to regain their homogeneity lost during centrifugation. 7.3.4 Statistical procedure The ANOVA procedure was used to test the effect of Pb and Cd on Cu adsorption. The Least Square Method was used to identify the significantly different treatments (SAS 1999). The coefficient of variance ranged between 1 and 6%. 7.4 Results and Discussion 7.4.1 Leaching analysis The heavy metal content of the leachate with pore volume leached is illustrated in Fig. 7.4a. The Cu content of the leachate was greatest when Cu was in the presence of Pb, moderate when Cu was in the presence of Cd, and least when Cu was alone. Thus, Pb, and to a lesser extent, Cd competed for adsorption against Cd as the permeate seeped through the liner material. The leachate Cd level offered the highest C/Co value, indicating the relative high mobility of Cd, while the leachate Pb level offered the lowest C/Co values indicating the relatively low mobility of Pb. Interestingly enough, the leachate Cu level offered a C/Co value slightly higher than that of Pb, indicating that Cu is slightly more soluble than Pb. The average breakthrough point, or the number of pore volumes at which the leachate metal concentration is equal to that of the permeate, occurred after 17 pore volumes for Cu alone, as compared to 16 and 17 when Cu was applied in combination with Pb or Cd, respectively (Fig. 7.4a). For Cu, alone or with either Cd or Pb, no significance in breakthrough points was observed among triplicate heavy metal combinations despite different levels of adsorption. Such phenomenon results from Cd’s relatively large hydrated radius once hydrated and therefore its blockage of adsorption sites for itself as well as for Cu. In comparison, Pb’s relatively small hydrated radius leads to its more intensive adsorption and no adsorption blocka ge for Cu. Both Cu and Cd have higher hydrated radii of 0.21 and 0.23 nm respectively, as compared to Pb with a hydrated radius of 0.187 nm. A larger hydrated radius leads to weaker Coulombic or 143 ionic forces of attraction since these forces are inversely proportional to the square of the distance between the ionic centres of positive and negative charges (1/r2 ). The leachate pH change with pore volume is illustrated by Fig. 4b. Initially, the leachate pH was alkaline because of the high pH of the liner mixture, while the permeate was acidic, because of its heavy metal content. As the heavy metals contained in the permeate solution are adsorbed by the soil, major cations such as Ca2+ and Na+ are released, the leachate acquires alkalinity. With leaching, major soil cations become depleted and the permeating solution pH remains acidic. The pH dropped quickly below 6.0, after 2 pore volumes for Cu alone, and 4 and 5 pore volumes for Cu with Pb and for Cu with Cd, respectively. Such acidic conditions created early at the on-set of the infiltration process prevented all heavy metals from being mostly precipitated. The leachate pH remained unchanged for all solutions after 16 to 17 pore volumes of leachate. The total amount of heavy metal lost as leachate is illustrated in Fig. 7.5. The most heavy metal leached throughout the test liner material was associated with the Cu with Cd solution, because of the high mobility of Cd. The lowest amount of heavy metal leached was associated with the solution containing only Cu. Thus, the solution containing Cu and Pb produced an intermediate level of heavy metal leaching. The amount of total metal leaching followed the order of Cu/Cd > Cu/Pb > Cu alone. The leaching amounts of metals for all the cases were significant at 95% confidence level. 7.4.2 Sequential extraction analysis Table 7.2 illustrates the percentage of Cu, Pb and Cd adsorbed by the total mass of liner material which followed the following order: Pb with Cu (56.0%) > Cu alone (51.3%) > Cu with Cd (45.0%) > Cu with Pb (28.9%) > Cd with Cu (19.2%). A distinct adsorption pattern was found for Pb with Cu as compared to Cd with Cu and Cu alone. This is consistent with the result observed by Camobreco et al. (1996) who obtained a 30, 15 and 12% leaching of Cd, Cu and Pb, based on their initial concentration. Along with Table 7.2, Fig. 7.6 shows the adsorption of Cu with column depth in cm, and by the different liner fractions, for Cu alone, and with either Pb or Cd. For all metals, the adsorption was greatest within the top layer and dropped with depth. The carbonate 144 and hydroxide fraction precipitated the most heavy metals generally followed by adsorption by the oxide fraction and the exchangeable sites. For Cu alone, the percentage adsorption with layers L1, L2, L3 and L4 (L1 pertaining to the column top) were 14.4, 13.4, 12.4, and 11.1%, respectively for a total of 51.3%. For Cu applied with Cd, the adsorption percentages for layers L1, L2, L3 and L4 dropped to 12.5, 11.6, 11.0, and 9.9%, respectively. For Cu with Pb, the adsorption of layers L1, L2, L3 and L4 dropped even more to 9.5, 8.6, 6.4, and 4.4%, respectively. The adsorption of Cd with Cu for layers L1, L2, L3 and L4 was 5.5, 4.8, 4.5, and 4.4%, respectively, while that of Pb with Cu was 18.0, 14.8, 12.6, and 10.6%. Thus, Cd is an extremely mobile cation, demonstrating limited adsorption as opposed to Pb, which is highly adsorbed. These results are consistent with those of Voegelin et al. (2003), and Hooda and Alloway (1998). In terms of Cu adsorption, Pb dropped the level of precipitation by the carbonates and hydroxides and the level of adsorption by the oxides and the exchangeable sites. The drop in Cu adsorption by each one of these fractions was accompanied by a paralleled high level of Pb. Although Cd also dropped the level of Cu adsorbed by each fraction to a lesser extent than Pb, its own adsorption was relatively limited for all fractions except for its precipitation on carbonate and hydroxide. Thus, Cd blocked adsorption without being itself adsorbed, due to its larger hydrated radius (0.23nm) and weaker Coulombic forces of attraction. These results are in good agreement with Kaoser et al. (2004b and c). 7.5. Summary The segregation of Pb rather than Cd from Cu, can therefore lead to less Cu being leached from a natural landfill liner such as sand-bentonite. The adsorption order under this dynamic set up was: Cu alone > Cu with Cd > Cu with Pb. This was demonstrated by less 50% more Cu being leached out of the liner material in the presence of Pb as compare to that in the presence of Cd. The SSE analysis in individual column layers reveals that the mass of heavy metal adsorption dropped with depth within the column. This suggests the preferential movement of leachate within the macro-pores of the liner, with percolation depth. Nevertheless, some 15 pore volumes of solution were required to fully saturate the CEC 145 of the liner material, when the breakthrough points occurred after 16 to 18 pore volumes. Thus, CEC is a reliable value to estimate the adsorption capability of a liner material. The SSE analysis of individual column layers also indicated that the carbonate and hydroxide fractions played a key role in precipitating all heavy metals, followed by adsorption by the oxides and the exchangeable sites. During percolation, Pb competed against and displaced Cu from its precipitation on the carbonate and its adsorption on the oxides and exchangeable sites. On the other hand, Cd also competed to a lesser extent, but only blocked Cu adsorption without occupying the adsorption sites. 7.6 Acknowledgement The authors wish to acknowledge the financial contribution of the Natural Science and Engineering Research Council of Canada. 146 7.7 References Blake G.R., and Hartage, K.H. 1986. Bulk density, In: Methods of Soil Analysis, Part 1, A. Kute (ed). Agronomy series. Am. Soc. Agronomy and Soil Sci. Soc. Am., Inc., Publ. Madison, W1. 9: 363-375. Camobreco, V.J., Richards, B.K., Steenhuis, T.S., Peverly, J.H., and McBride, M.B. 1996. Movement of heavy metals through undisturbed and homogenized soil columns. Soil Sci. 161 (11): 740-750. Cherry, J.A., Gillham, R.W., and Baker, J.F. 1984. Contaminants in groundwater. In: Groundwater Contamination. 46-64. Washington, D.C.: National Acade my Press. Elliott, H.A., Liberati, M.R., and Huang, C.P. 1986. Competitive adsorption of heavy metals by soils. J. Environ Qual. 15 (3):214-219. Fuller, W.H. 1982. Methods for conducting soil column tests to predict pollutant movement. In Land Disposal: Ha zardous Wastes, 87-105. D. Schultz, ed. EPA 600/9-82-002. Washington, D.C.: United States Environmental Protection Agency. Gomes, P.C., Fontes, M.P.F., da Silva, A.G., de Mendonça, S. E., and Netto, A.R. 2001. Selectivity sequence and competitive adsorption of heavy metals by Brazilian soils. Soil Sci. Soc. Am. J. 65 (4): 1115-1121. Han, F.X., Banin, A. and Triplett, G.B. 2001. Redistribution of heavy metals in arid-zone soils under a wetting-drying cycle soil moisture regime. Soil Science 166 (1): 19-28. Harter, R.D. 1992. Competitive sorption of cobalt, copper, and nickel ions by a calciumsaturated soil. Soil Sci. Soc.Am. J. 56 (3): 444-449. Hendershot, W.H., and Lalande, H. 1993. Ion exchange and exchangeable cations. In: Soil Sampling and Methods of Analysis. 167-170. M.R. Carter, ed. Boca Raton, FL: Lewis Publish- ers. Hooda, P.S., and Alloway, B.J. 1998. Cadmium and lead sorption behaviour of selected English and Indian soils. Geoderma, 84 (1-3): 121-134. Kabata-Pendias, A., and Pendias, H. 1984. Trace elements in soils and plants. Boca Raton, Fl.: CRC Press Inc. Kaoser, S., Barrington, S., and Elektorowicz, M. 2000. Compartments for the management of municipal solid waste. Soil and Sediment Contamination, 9 (5): 503-522. 147 Kaoser, S., Barrington, S., and Elektorowicz, M. 2004a. Pressure and compaction on hydraulic conductivity of sand-bentonite liners: A study for variable and unpredictable field condition (accepted to the Journal of soil and sedimentation). Kaoser, S., Barrington, S., Elektorowicz, M., and Wang, Li. 2004b. Copper adsorption with Pb and Cd in sand-bentonite liners under various pHs. Part I. Effect on total absorption (accepted to the Journal of Environmental Science and Health). Kaoser, S., Barrington, S., Elektorowicz, M., and Wang. Li. 2004c. Copper adsorption with Pb and Cd in sand-bentonite liners under various pHs. Part II. Effect on adsorption Sites. (accepted to the Journal of Environmental Science and Health). Kim, K-H. and Kim, S-H. 1999. Heavy metal pollution of agricultural soils in central regions of Korea. Water, Air and Soil Pollution, 111(1-4): 109-122. Lee, G.F. 2002. Solid waste management: USA lined land filling reliability. Natural Resources Forum 25 (4). Available at www.gfredlee.com/UNpaper-landfills.pdf. Accessed 31 October 2003. Morera, M.T., Echeverria, J.C., and Garrido, J.J. 2001. Mobility of heavy metals in soils amended with sewage sludge. Can. J. Soil Sci. 81(1): 405-414. Reddy, D.V., and Butul, B. 1999. A comprehensive literature review of liner failures and longevity. Gainesville, Florida: Florida Center for Solid and Hazardous Waste Manage- ment. Available at: www.floridacenter.org/publications/ liner_failure_ 99. pdf. Accessed Oct. 31 2003. Reinhart, D., and McCreanor, P. 1999. Implications of time/space variable leachate head on liner leakage. Orlando, Florida: College of Engineering, University of Central Florida. Available at:www.floridacenter.org/publications/time_space_var_99-9.PDF. Accessed 27 October 2003. SAS Institute. 1999. SAS system for windows, version 8.00. Cary, NC, USA: SAS Institute. Tessier, A., P. G. C. Cambell, and M. Bisson. 1979. Sequential extraction procedures for the speciations of particulate trace metals. Analytical Chemistry, 51(7) : 844-851. Thornton, S.F., Tellam, J.T. and Lerner, D.N. 2003. Attenuation of landfill leachate by clay liner ma terials in laboratory columns, 2. Behaviour of inorganic contaminants. Waste Manage- ment and Research, 19: 70-88. 148 Urase, T., Salequzzaman, M., Kobayashi, S., Matsuo, T., Yamamoto, K., and Suzuki, N. 1997. Effect of high concentration of organic and inorganic matters in landfill leachate on the treatment of heavy metals in very low concentration level. Water Science and Technology, 36 (12): 349–356. Voegelin, A., Kurt, B., and Ruben, K. 2003. Heavy metal release from contaminated soils: Comparison of column leaching and batch extraction results. J. Environ. Qual. 32 (3): 865-875. Whittle, A.J. and Dyson, A.J. 2002. The fate of heavy metals in green waste composting The Environmentalist, 22:13-21. Yong, R.N., Mohamed, A.M.O., and Warkentin, B.P. 1992. Princ iples of Contaminant Transport in Soils, Elsevier, New York. 149 Table 7.1. Physical and chemical characteristics of sand and bentonite Properties of elements Liquid limit Plastic limit Particle density CEC Moisture content pH SiO2 CaO MgO Na2 O K2 O P2 O5 MnO TiO2 FeO Fe2 O3 Fe2 O3 (T) BaO Cr2 O3 Cu Zn LOI* Units % % g/ml cmol/kg (%) weight (%) weight (%) weight (%) weight (%) weight (%) weight (%) weight (%) weight (%) weight (%) weight (%) weight (%) mg/kg mg/kg mg/kg mg/kg weight (%) LOI*: Loss On Ignition <d/l: below detection limit Silica sand 40 Bentonite N/A N/A 2.66 2 0.0 9 99.28 0.07 0.03 <d/l 0.10 0.012 <d/l 0.037 N/A N/A 0.07 21 <d/l 53 3 0.17 360 57 2.54 90 8.4 10 59.37 1.26 2.27 2.17 0.52 0.053 0.019 0.144 0.94 2.75 3.80 83 20 85 67 11.20 Detection limit 0.006 0.0015 0.0095 0.0075 0.0025 0.0035 0.0030 0.0035 0.01 0.01 0.003 17 15 2 2 0.01 150 Table 7.2. Total Heavy Metal Adsorption in Soil Column and in Different Fractions Liner fractions Metal applied (cmol (+) ) Cu Concentration of metal retained (cmol (+) ) Percent metal retained (%) Cd Pb Cu Cd Pb Cu Cd Pb 1. Cu alone 4 Soluble Exchangeable Carbonate/ hydroxide Oxide Residue -- -- 2.05 0.07 0.27 1.16 0.38 0.17 -- -- 51.32 3.33 13.15 56.32 18.82 8.39 -- -- 2. Cu with Cd 2 Soluble Exchangeable Carbonate/ hydroxide Oxide Residue 2 -- 0.89 0.06 0.09 0.62 0.08 0.05 0.38 0.04 0.03 0.24 0.05 0.02 -- 44.97 6.96 9.71 68.96 8.92 5.47 19.17 12.10 7.40 62.56 11.94 6.00 -- 3. Cu with Pb 2 Soluble Exchange Carbonate/ hydroxide Oxide Residue -- 2 0.58 0.10 0.04 0.25 0.14 0.04 -- 1.12 0.02 0.16 0.67 0.19 0.09 28.85 17.33 6.86 44.31 24.56 6.93 -- 56 1.61 14.10 59.64 16.78 7.86 Note: In column 4, the bold numbers represent the percent metal retained of the total metal applied, while the liner fractions show the percentage of retained metal in each fraction. 151 % smaller than Fig. 7. 1. Particle size analysis 100 90 80 70 60 50 40 30 20 10 0 0 100 200 300 400 500 Particle diameter ( µm) 600 700 800 152 Fig 7. 2. Rigid wall leaching cell. Perforation Top plate Threaded rod Deionized water 20 cm ~ 6 cm Compacted soil Rubber O ring Bottom plate ~2 cm Effluent collection 10 cm 153 Figure 7. 3. Experimental setup Permeant solutions Cu Cu+Cd Cu+Pb Leachate collection by each pore volume until “breakthrough curve” Measure of Cu, Pb, and Cd content in the leachate of each pore volume Determination of Cu, Pb, and Cd content in four different layers of the solid fraction. (SSE) Blank 154 Fig.7. 4a. Mobility of Cd and Pb with Cu 1.1 Cd with Cu 1 Cu with Pb 0.9 Cu with Cd Leachate C/Co 0.8 Cu alone 0.7 Pb with Cu 0.6 0.5 0.4 0.3 0.2 0.1 0 0 2 4 6 8 10 12 14 16 18 20 Pore volume (200 mL) Fig. 7. 4b. Pore volume versus pH. 9 8 7 Cu with Cd Cu with Pb pH Cu alone 6 5 4 1 2 3 4 5 6 7 8 9 10 11 12 Pore volume (200 mL) 13 14 15 16 17 18 19 20 155 Fig. 7. 5. Total Heavy Metal Leaching 1.1 1 0.9 Total leachate (C/Co) 0.8 0.7 0.6 0.5 0.4 0.3 0.2 Cu alone Cu+Cd 10 14 Cu+Pb 0.1 0 0 2 4 6 8 12 Pore volume (200ml) 16 18 20 156 Fig. 7. 6.Metal retention in fractions through liner profile, for Cu alone (a), Cu with Cd (b), Cu with.Pb (c), Cd with Cu (d), and Pb with Cu (e). The total depth is 8 cm. 2 a) 3 4 5 6 7 8 2 b) 3 4 5 6 7 8 c) Depth of liner (cm) 2 3 4 5 6 7 8 d) 2 3 4 5 6 7 8 e) 2 3 4 5 6 Sol Ex Car Ox Res 7 8 0 0.2 0.4 0.6 0.8 1 Retained metals concentartion (Cmol(+) / kg) 1.2 1.4 1.6 157 CHAPTER 8 8. GENERAL SUMMARY A ND CONCLUSIONS 8.1 SUMMARY AND CONCLUSION Heavy metal pollution from landfill and surface impoundments through natural sandbentonite liners is a threat to the environment. Despite improved liner-material applications, heavy metals in landfill leachate still penetrate the soil profile, polluting the soil and groundwater. Research concerning the attenuation of heavy metal migration has focused on liner design parameters – composition, thickness and drainage. However, the literature indicated that there are synergetic and antagonistic interactions between heavy metal cations relative to the availability of adsorption sites in liners and thus to the degree to which a liner layer could retain a particular cation. This led to the idea of segregating MSW into categories based on their heavy metals content and according to the degree of adsorption compatibility between these heavy metals. Each segregated MSW category needs to be disposed of in a different compartment of a landfill, so as to insure maximum adsorption of the heavy metal content. To achieve this ultimate goal, a concept has been developed and tested to minimize the seepage and impact of heavy me tal mobility. The study included five consecutive steps: Step (i) involved a research work on heavy metal characteristics, mobility through liner profile and their interactions. This approach to enhancing the attenuation of heavy metal migration through the liner profile lead to the specific studies. Step (ii) involved a laboratory soil column experiment to test liner hydraulic conductivity behaviours. Since landfill leachate is the main carrier of heavy metal to the ground water, the permeability (k) of the liners needed to be tested for consistency with the regulation set by EPA. The liners tested were 95/5 and 90/10 sand–bentonite mixtures. Variable pressures, moisture contents were considered to represent field conditions. The test has successfully established the fact that a common theoretical porous flow equation can predict k for sand or silt bentonite mixtures as long as the porosity is known and no suffusion occurs. It was further established that sand with a particle size distribution between 250 and 500 µm, with a minimum of 5% bentonite mixtures compacted 158 at a porosity of 0.375 or less, under an MC of 13.7%, produce significantly lower k and will meet North American requirements of 10-7 cm/s, after full swelling. Step (iii) involved a series of batch experiments on Cu alone and either with Cd or Pb to investigate their kinetics, interaction and adsorption pattern in liner materials. The selected liner contained 5 and 10% bentonite, consistently with the preceding soil column test. Both acidic (pH < 6.5) and alkaline (pH > 6.5) environments were considered since leachate pH varies from 4 to 9 depending on the aging period of the landfill. The results revealed the effect of Cd and Pb on the adsorption of Cu by sand-bentonite liners. It was found tha t for neutral to alkaline pH (pH > 6.5) Cu mobility decreased significantly due to precipitation, and the metal compatibility factor did not play a major role. However, in acidic condition (pH < 6.5) while liner material offered an excess of adsorption sites, Pb competed strongly with Cu, while Cd competed to a much lesser extent for adsorption sites. Under this pH environment but under conditions of liner CEC equal or lower than heavy metal equivalents of the leachate, only 30% of the Cu applied was adsorbed in composite with Pb, and 54% was adsorbed in composite with Cd. It was concluded that Cd competed less for adsorption sites with Cu, than did Pb. This suggests that Cu can be adsorbed in liners with greater efficiency in the presence of Cd, than in the presence of Pb. To study Cu affinity to the different liner adsorption fractions of the liners, a consecutive selective extractions (SSE) procedure was conducted in step (iv). The results revealed that the carbonate fraction dominated the Cu adsorption, followed by the oxide and exchangeable fraction. It was found that Cu had more affinity for the exchangeable and oxide fractions when adsorption sites were available. In presence of Pb, more Cu was found in the soluble fraction suggesting an adverse impac t of Pb on Cu adsorption in general. The results of SSE indicated that the liner carbonate fraction absorbed the greatest amount of Cu, and that Pb significantly increased the mobility of Cu due to greater competition for exchangeable sites. In step (v), a leaching test of Cu, Cd and Pb was performed to determine the mobility pattern and compatibility among them in landfill liner with 5% bentonite content. The selection of 5% bentonite was based on the result of the permeability (k) test in step (ii) that satisfied the EPA regulation of allowable k < 10-7 cm/s. 159 According to the above, Pb has a greater influence on copper adsorption than does Cd. The retention of Cu alone was higher than when either of the other metals was in the solution since there was no interference or competition among metals for adsorption sites. The retention order for Cu was: Cu alone > Cu with Cd > Cu with Pb. The retention order of Cd and Pb with Cu, and Cu alone, was Pb > Cu > Cd. When the total metal concentration was considered, the total leaching was found to be in the order: Cu/Cd > Cu/Pb > Cu alone. This order of total leaching was due to higher leaching of Cd and that of Cu with Pb, as found in their percentages of retention in such combinations. It can therefore be stated that for total metal adsorption, Cu and Pb are compatible, but if Cu is the principal metal to be adsorbed, Cu is more compatible with Cd than with Pb. The SSE analysis in layers revealed that the top layer of the column retained more metals than in the following layers and the least metal was retained at the bottom layer. Comparing the metals, Pb retention was greater in the upper layers than was Cd. In the soil fractions, carbonate fractions dominated the metal retention in all cases. The precipitation as hydroxides and carbonates played an important role in Cu retention. When Cu was alone in the solution, metal retention in different particle fractions was quite distinct since there was no competition among metals for the adsorption sites. When Cu was applied in composite with Cd or Pb, the retention of Cu in various fractions was similar although the retention order shifted on occasion. This suggests the interaction, interference and competition among metals for adsorption sites. As a whole, Pb was found to be the least mobile metal and Cd the most mobile. The compatibility of these metal combinations can be a useful tool for designing landfill liners and in other waste disposal systems. 8.2 CONTRIBUTIONS TO THE KNOWLEDGE. The conventional methods of MSW management involve disposing of all sorts of wastes in one landfill compartment. This results in the interaction of multiple incompatible heavy metals. Their synergistic and antagonistic effects hinder their efficient absorption on the liner particles. An alternative approach proposes the identification of the different compatible and incompatible heavy metals and the segregating wastes according to such heavy metal content. This method can facilitate 160 attenuation without the interference of incompatible metals. Another advantage over the present system includes minimum liner material requirements, reliable cost-effective liner design, and lower leachate heavy metals content, and a cheaper leachate treatment system. For example, this study indicated that if Pb/Cu and Cd/Cu were placed in sperate landfill compartments, the liner thickness for Cu retention could be reduced by 16% in the case of Cd/Cu versus Pb/Cu. The research work has the following contributions: 1. A concept of compartmental landfill disposal system based on segregating wastes according to their content of compatible groups of heavy metals. 2. A theoretical equation predicting k values for sand-bentonite liners under variable field conditions. 3. An empirical relationship predicting Cu adsorpt ion as a function of pH, liner CEC, and level of Cd or Pb. 4. 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Batch experiment results under pH at 3.7 The adsorption units are in mg/L Cu adsorption with Pb and Cd in pH 3.7 at 0% bentonite Day 0 1 2 7 14 3.7 Concen. 50 44.4 41.8 37.3 35 Equi.pH 5.4 5.45 5.1 4.9 With Pb 3.7 Adsorbed 0 5.6 8.2 12.7 15 3.7 Concen. 50 39.6 36.4 25.7 23 Equi.pH 5.8 5.9 5.3 5.1 With Cd 3.7 Adsorpt 0 10.4 13.6 24.3 27 Cu adsorption with Pb and Cd in pH 3.7 at 5% bentonite Day 0 1 2 7 14 3.7 Concen. 50 15 15 13.1 12 Equi.pH 5.95 5.9 5.4 5.3 With Pb 3.7 Adsorbed 0 35 35 36.9 38 3.7 Concen. 50 8 9 3.7 3.4 Equi.pH 6.3 6 5.86 5.73 With Cd 3.7 Adsorbed 0 42 41 46.3 46.6 Cu adsorption with Pb and Cd in pH 3.7 at 10% bentonite Day 0 1 2 7 14 3.7 Concen. 50 3.5 3 2.2 2 Equi.pH 6.2 6.1 5.9 5.9 With Pb 3.7 Adsorbed 0 46.5 47 47.8 48 3.7 Concen. 50 2.5 2.1 1.6 1.8 Equi.pH 6.65 6.5 6.2 6 With Cd 3.7 Adsorbed 0 47.5 47.9 48.4 48.2 182 Appendix for Chapter 5. Batch experiment results under pH at 5.5 The adsorption units are in mg/L Cu adsorption with Pb and Cd in pH 5.5 at 0% bentonite Day 0 1 2 7 14 5.5 Concen. 50 40.2 44.2 37.6 36.3 Equi.pH 5.5 5.3 5.2 With Pb 5.5 Adsorbed 0 9.8 5.8 12.4 13.7 5.5 Concen. 50 26.4 34.4 24.4 25 Equi.pH 5.8 5.55 5.3 With Cd 5.5 Adsorbed 0 23.6 15.6 25.6 25 Cu adsorption with Pb and Cd in pH 5.5 at 5% bentonite Day 0 1 2 7 14 5.5 Concen. 50 18 11.8 8.8 8.8 Equi.pH 6 6 5.58 5.4 With Pb 5.5 Adsorbed 0 32 38.2 41.2 41.2 5.5 Concen. 50 9 5 4.8 4.5 Equi.pH 6.45 6.35 5.9 5.8 With Cd 5.5 Adsorbed 0 41 45 45.2 45.5 Cu adsorption with Pb and Cd in pH 5.5 at 10% bentonite Day 0 1 2 7 14 5.5 Concen. 50 4.2 2.8 2.1 2 Equi.pH 6.3 6.3 5.95 5.8 With Pb 5.5 Adsorbed 0 45.8 47.2 47.9 48 5.5 Concen. 50 3.25 1.3 1.4 1 Equi.pH 6.8 6.6 6.25 6.2 With Cd 5.5 Adsorbed 0 46.75 48.7 48.6 49 183 Appendix for Chapter 5. Batch experiment results under pH at 7.5 The adsorption units are in mg/L Cu adsorption with Pb and Cd in pH 7.5 at 0% bentonite Day 0 1 2 7 14 7.5 Concen. 50 1.1 1.7 2.6 2.4 Equi.pH 7.8 6.6 6.3 6.3 With Pb 7.5 Adsorbed 0 48.9 48.3 47.4 47.6 7.5 Concen. 50 0.7 1.2 0.7 0.7 Equi.pH 7.57 6.75 6.78 6.7 With Cd 7.5 Adsorbed 0 49.3 48.8 49.3 49.3 Cu adsorption with Pb and Cd in pH 7.5 at 5% bentonite Day 0 1 2 7 14 7.5 Concen. 50 0.8 1.4 4.2 3.1 Equi.pH 9.4 8.55 8.56 8.4 With Pb 7.5 Adsorbed 0 49.2 48.6 45.8 46.9 7.5 Concen. 50 0.2 0.6 0.3 0.4 Equi.pH 8.15 7.54 7.4 7.8 With Cd 7.5 Adsorbed 0 49.8 49.4 49.7 49.6 Cu adsorption with Pb and Cd in pH 7.5 at 10% bentonite Day 0 1 2 7 14 7.5 Concen. 50 8 11.3 12.4 12.4 Equi.pH 9.4 8.6 8.72 8.5 With Pb 7.5 Adsorbed 0 42 38.7 37.6 37.6 7.5 Concen. 50 0.4 0.4 6 6.2 Equi.pH 8.8 8.1 8 7.9 With Cd 7.5 Adsorbed 0 49.6 49.6 44 43.8 184 Appendix for Chapter 6. Selective Sequential Extraction (SSE) eesults for liner with 0% bentonite cotent pH 3.7 (0% B) Cu Fractions Retained mg Soluble 0.4 Exchange 0.076 Carbonate 0.4922 Oxides 0.135 Residue 0.1468 pH 3.7 (0% B) Cu+Cd Fractions Retained mg Soluble 0.575 Exchange 0.057 Carbonate 0.39196 Oxides 0.054 Residue 0.17204 pH 3.7 (0% B) Cu+Pb Fractions Retained mg Soluble 0.875 Exchange 0.022 Carbonate 0.172 Oxides 0.108 Residue 0.073 Cu Retained (%) 32 6.08 39.376 10.8 11.744 pH 5.5 (0% B) Cu Cu Retained Retained mg (%) 0.375 30 0.0785 6.28 0.49862 39.8896 0.216 17.28 0.08188 6.5504 pH 7.5 (0% B) Cu Cu Retained Retained mg (%) 0.025 2 0.08382 6.7056 0.64 51.2 0.243 19.44 0.25818 20.6544 Cu+Cd Retained (%) 46 4.56 31.3568 4.32 13.7632 pH 5.5( 0% B) Cu+Cd Cu+Cd Retained Retained mg (%) 0.625 50 0.06 4.8 0.34 27.2 0.054 4.32 0.171 13.68 pH 7.5( 0% B) Cu+Cd Cu+Cd Retained Retained mg (%) 0.0175 1.4 0.01042 0.8336 0.7515 60.12 0.27 21.6 0.20058 16.0464 Cu+Pb Retained (%) 70 1.76 13.76 8.64 5.84 pH 5.5( 0% B) Cu+Pb Cu+Pb Retained Retained mg (%) 0.9075 72.6 0.025 2 0.19 15.2 0.1105 8.84 0.017 1.36 pH 7.5( 0% B) Cu+Pb Cu+Pb Retained Retained mg (%) 0.06 4.8 0.01 0.8 0.82 65.6 0.19 15.2 0.17 13.6 185 Selective Sequential Extraction (SSE) eesults for liner with 5% bentonite cotent pH 3.7 (5% B) Cu Fractions Retained mg Soluble 0.035 Exchange 0.1589 Carbonate 0.68694 Oxides 0.216 Residue 0.15316 pH 3.7 (5% B) Cu+Cd Fractions Retained mg Soluble 0.085 Exchange 0.1122 Carbonate 0.81 Oxides 0.108 Residue 0.1348 pH 3.7 (5% B) Cu+Pb Fractions Retained mg Soluble 0.3 Exchange 0.063 Carbonate 0.47722 Oxides 0.324 Residue 0.08578 Cu Retained (%) 2.8 12.712 54.9552 17.28 12.2528 pH 5.5 (5% B) Cu Cu Retained Retained mg (%) 0.0325 2.6 0.22444 17.95 52 0.60134 48.1072 0.27 21.6 0.12172 9.7376 *** pH 7.5 (5% B) Cu Cu Retained Retained mg (%) 0.055 4.4 0.261 20.88 0.57138 45.7104 0.216 17.28 0.14662 11.7296 1.25 Cu+Cd Retained (%) 6.8 8.976 64.8 8.64 10.784 pH 5.5(5% B) Cu+Cd Cu+Cd Retained Retained mg (%) 0.1125 9 0.155 12.4 0.78422 62.7376 0.081 6.48 0.11728 9.3824 pH 7.5( 5% B) Cu+Cd Cu+Cd Retained Retained mg (%) 0.01 0.8 0.17 13.6 0.8025 64.2 0.162 12.96 0.1055 8.44 Cu+Pb Retained (%) 24 5.04 38.1776 25.92 6.8624 *** pH 5.5(5% B) Cu+Pb Cu+Pb Retained Retained mg (%) 0.22 17.6 0.0864 6.912 0.5096 40.768 0.317 25.36 0.117 9.36 *** pH 7.5( 5% B) Cu+Pb Cu+Pb Retained Retained mg (%) 0.0775 6.2 0.08214 6.5712 0.77 61.6 0.143 11.44 0.17736 14.1888 186 Appendix for Chapter 6. Selective Sequential Extraction (SSE) eesults for liner with 10% bentonite cotent pH 3.7 (10% B) Cu Fractions Retained mg Soluble 0.0175 Exchange 0.26 Carbonate 0.53 Oxides 0.263 Residue 0.1795 pH 3.7 (10% B) Cu+Cd Fractions Retained mg Soluble 0.045 Exchange 0.2084 Carbonate 0.59492 Oxides 0.27 Residue 0.13168 pH 3.7 (10% B) Cu+Pb Fractions Retained mg Soluble 0.05 Exchange 0.118 Carbonate 0.587 Oxides 0.351 Residue 0.144 Cu Retained (%) 1.4 20.8 42.4 21.04 14.36 *** pH 5.5 (10% B) Cu Cu Retained Retained mg (%) 0.01 0.8 0.30762 24.6096 0.4268 34.144 0.295 23.6 0.21058 16.8464 1.25 Cu+Cd Retained (%) 3.6 16.672 47.5936 21.6 10.5344 pH 5.5(10% B) Cu+Cd Cu+Cd Retained Retained mg (%) 0.025 2 0.33768 27.0144 0.478 38.24 0.25836 20.6688 0.15096 12.0768 Cu+Pb Retained (%) 4 9.44 46.96 28.08 11.52 pH 5.5(10% B) Cu+Pb Cu+Pb Retained Retained mg (%) 0.05 4 0.161 12.88 0.537 42.96 0.35 28 0.152 12.16 *** pH 7.5 (10% B) Cu Cu Retained Retained mg (%) 0.15875 12.7 0.275 22 0.428 34.24 0.162 12.96 0.22625 18.1 1.25 *** pH 7.5( 10% B) Cu+Cd Cu+Cd Retained Retained mg (%) 0.155 12.4 0.10847 8.6776 0.73883 59.1064 0.0927 7.416 0.155 12.4 1.25 *** pH 7.5( 10% B) Cu+Pb Cu+Pb Retained Retained mg (%) 0.185 14.8 0.054 4.32 0.6513 52.104 0.1081 8.648 0.2516 20.128 187 Appendix for Chapter 7. pH profile of pore volumes PoreV Cu CuCd CuPb 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 7.5 5.8 5.5 5.4 5.2 5.2 5.1 5 5 5 4.9 4.9 4.9 4.9 4.9 4.9 4.8 4.7 4.7 4.7 8.1 7 6.5 6 5.8 5.6 5.5 5.4 5.2 5.1 5 4.8 4.8 4.7 4.6 4.5 4.5 4.5 4.5 4.5 7.8 6.5 6 5.8 5.5 5.5 5.4 5.4 5.3 5.1 5.1 5.1 5 5 5.1 5 4.9 4.9 4.9 4.9 188 Appendix fo Chapter 7. Leaching test results, measured by AAS. Cu leaching only Col 1 mg/L Col 2 mg/L Col 3 mg/L 20.24 25.5 30 45.385 62.7 86.28 123.42 156.28 190 236.28 290.5 380 410.45 480 530.5 580.33 620 625 635.25 635 20 28.6 35.25 40.45 63.17 88.2 127.1 153.42 197.71 230.57 290 382.2 415 486.25 533.25 585.66 625.66 630.66 632.25 635.25 18.17 28.218 34.35 41.243 68.7 84.85 120.57 150.57 193.42 239.14 290.25 386.25 415.21 480.75 535.25 580.66 626.66 630 635.166 635.25 Cu C/Co Cu C meq Cu Co meq Sol.Conc per PV (mg) Cum. adsorbed (mg) 0.03064 0.04318 0.05225 0.06666 0.10206 0.13603 0.19466 0.24144 0.30483 0.37033 0.45676 0.60242 0.65079 0.75903 0.83876 0.91621 0.98213 0.98913 0.99805 0.99954 0.6127844 0.8636054 1.0449123 1.3331865 2.041251 2.7206538 3.8931378 4.828733 6.0966859 7.4066031 9.1351147 12.04849 13.015873 15.180604 16.775249 18.324259 19.642674 19.782625 19.961036 19.990768 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 127.092 127.092 127.092 127.092 127.092 127.092 127.092 127.092 127.092 127.092 127.092 127.092 127.092 127.092 127.092 127.092 127.092 127.092 127.092 127.092 2541.84 123.198 121.6041 120.452 118.6201 114.1207 109.8033 102.3527 96.40733 88.35 80.026 69.042 50.52867 44.38133 30.62533 20.492 10.64867 2.270667 1.381333 0.2476 0.058667 1304.611 Pore V. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 % adsorbed Cu alone: 51.30% 189 Appendix for Chapter 7. Leaching test results, measured by AAS. Cu leaching with Cd Col 4 mg/L Col 5 mg/L Col 6 mg/L mg/L 16.19 18.19 24.413 30.6 45.92 50.71 76.78 94.85 120.42 160.57 200.25 245.5 271.5 280.76 290.5 308 310.66 315 315.3 315 mg/L 16.194 18.21 28.58 30.87 42.43 57.71 76.28 90 120.57 160.57 200.5 246.25 270 278.5 280 310.4 316.66 315.7 315 315 mg/L 17.578 19.239 27.28 30.04 44.04 57.24 70.57 90.57 125.92 169.14 205 243.75 272.5 277.5 289 308 315 315.9 315 315 Cu C/Co Cu C meq 0.052416 0.058371 0.084215 0.096004 0.138892 0.173795 0.234612 0.288946 0.384929 0.514357 0.635498 0.77162 0.853975 0.877852 0.901709 0.971894 0.988596 0.993086 0.991723 0.991408 0.524156 0.583714 0.842151 0.960039 1.388915 1.737954 2.346122 2.889455 3.849285 5.143571 6.354976 7.716195 8.539745 8.778523 9.01709 9.718944 9.885962 9.930 864 9.917225 9.914078 Cu Co meq Sol.Conc per PV (mg) Cum. adsorbed (mg) 63.546 63.546 63.546 63.546 63.546 63.546 63.546 63.546 63.546 63.546 63.546 63.546 63.546 63.546 63.546 63.546 63.546 63.546 63.546 63.546 1270.92 60.2152 59.836733 58.194467 57.445333 54.72 52.502 48.637333 45.184667 39.085333 30.860667 23.162667 14.512667 9.2793333 7.762 6.246 1.786 0.7246667 0.4393333 0.526 0.546 571.6664 Pore V. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 % adsorbed Cu with Cd: 44.90% 190 Appendix for Chapter 7. Leaching test results, measured by AAS. Cu leaching with Pb Col 7 mg/L Col 8 mg/L Col 9 mg/L Cu C/Co Cu C meq Cu Co meq 0.050968 0.083515 0.159017 0.271323 0.365268 0.499365 0.626759 0.733317 0.799106 0.836276 0.90066 0.916659 0.936099 0.987998 0.992709 0.997692 1.020772 1.021821 1.023395 1.026018 0.509678 0.835154 1.590166 2.713226 3.652682 4.993653 6.267586 7.333165 7.991062 8.362761 9.006599 9.166588 9.360988 9.879982 9.927087 9.97692 10.20772 10.21821 10.23395 10.26018 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 Sol.Conc per PV (mg) Cum. adsorbed (mg) 63.546 63.546 63.546 63.546 63.546 63.546 63.546 63.546 63.546 63.546 63.546 63.546 63.546 63.546 63.546 63.546 63.546 63.546 63.546 63.546 1270.92 60.3072 58.23893 53.44113 46.30453 40.33467 31.81333 23.718 16.94667 12.766 10.404 6.312667 5.296 4.060667 0.762667 0.463333 0.146667 -1.32 Pore V. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 16.194 25.194 50.48 86.8 117.41 157.71 200.57 230.71 253.42 266.14 286.25 290 300.25 310.5 315.75 317.5 325.5 326.5 328 325.5 16.194 26.194 50.486 86.802 115.38 157.71 196.28 236.28 252 264.71 288.75 287.5 290.78 315.75 315.25 316.25 327.25 325.25 327.25 330.25 16.194 28.218 50.607 85.02 115.38 160.57 200.57 232 256.28 266.28 283.5 296.25 301.25 315.5 315.24 317.24 320.24 322.24 320.24 322.24 % adsorbed Cu with Pb: -1.48667 -1.65333 366.8565 28.86% 191 Appendix for Chapter 7. Leaching test results, measured by AAS. Cd leaching with Cu Col 4 mg/L Col 5 mg/L Col 6 mg/L Cd C/Co Cd C meq Cd Co meq 0.168667 0.34279 0.433528 0.504577 0.579421 0.725315 0.80182 0.848375 0.915391 0.965801 0.986736 0.969775 0.964318 0.967195 0.994268 0.999902 0.999546 0.998123 0.999013 1.000021 1.686668 3.427897 4.335282 5.04577 5.794213 7.253145 8.018195 8.483749 9.153908 9.658011 9.867362 9.697746 9.643184 9.671948 9.942681 9.999021 9.995463 9.98123 9.990126 10.00021 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 Sol.Conc per PV (mg) Cum. adsorbed (mg) 112.411 112.411 112.411 112.411 112.411 112.411 112.411 112.411 112.411 112.411 112.411 112.411 112.411 112.411 112.411 112.411 112.411 112.411 112.411 112.411 2248.22 93.451 73.87767 63.67767 55.691 47.27767 30.87767 22.27767 17.04433 9.511 3.844333 1.491 3.397667 4.011 3.687667 0.644333 0.011 0.051 0.211 0.111 -0.00233 431.1433 Pore V. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 114 190 240.5 280.5 329 404 451.5 480 510.5 540.5 550.8 545.8 540 540.35 559 559 561.9 562.5 562 561.9 94 194 245 283.8 324 405 454 474 514 546 555.2 548.9 544 549 556.5 560.5 561.5 561.5 560.5 562 76.4 194 245.5 286.5 324 414 446.5 476.5 519 542 557.8 540.5 542 541.5 561 566.5 562 559 562 562.3 % Cd adsorbed with Cu: 19.17% 192 Appendix for Chapter 7. Leaching test results, measured by AAS. Pb leaching with Cu Col 7 mg/L Col 8 mg/L Col 9 mg/L 10 16.66667 36.66667 43.33333 56.6667 83.33333 163.3333 196.6667 270 343.3333 396.6667 496.6667 630 663.3333 763.3333 830 930 990 1010 1030 10 16.66667 36.6667 43.33333 63.33333 96.66667 163.3333 236.6667 296.6667 383.3333 430 530 563.3333 670 736.6667 863.3333 983.3333 1003.333 1030 1030 10 16.66667 43.33333 50 83.3333 103.3333 170 196.6667 276.6667 363.3333 396.6667 496.6667 596.6667 663.3333 716.6667 930 996.6667 1030 1030 1030 Pb C/Co Pb C meq Pb Co meq Sol.Conc per PV (mg) Cum. adsorbed (mg) 0.009653 0.016088 0.037538 0.043973 0.065423 0.091163 0.159803 0.202703 0.271343 0.350708 0.393608 0.490133 0.575933 0.642428 0.713213 0.844058 0.936293 0.972758 0.987773 0.994208 0.09652 5 0.160875 0.375375 0.439725 0.654226 0.911626 1.598026 2.027027 2.713428 3.507078 3.936079 4.90133 5.759331 6.424281 7.132132 8.440583 9.362934 9.727585 9.877735 9.942085 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 207.2 207.2 207.2 207.2 207.2 207.2 207.2 207.2 207.2 207.2 207.2 207.2 207.2 207.2 207.2 207.2 207.2 207.2 207.2 207.2 4144 205.2 203.8667 199.4222 198.0889 193.6444 188.3111 174.0889 165.2 150.9778 134.5333 125.6444 105.6444 87.86667 74.08889 59.42222 32.31111 13.2 5.644447 2.533333 1.2 2320.889 Pore V. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 % Pb adsorbed with Cu: 56%