Download CONCEPT OF COPPER MOBILITY AND

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. Heavy metals such as Cu and Zn are found the most detrimental to microbial
biomass. The order of toxicity of each metal to microbes was found as: Cu> Zn>> Ni or
Cd.
27
2.8 REFERENCES
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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. Effects of complexation by Cl, SO4, on
adsorption behavior on Cd on oxide surfaces. Envir. Sci. Technol. 16, 152-170.
Bohn, H. L. 1979. Soil Chemistry, pp.329. New York, John Wiley & Sons.
Bonaparte, R, and B. A. Gross. 1990. Field behaviour of double liner systems, waste
containment systems. In: Construction, Regulation, & Performance. pp. 52-53.
New York. Publication No. 26. ASCE.
Bartlett, R.J., James, B. R., 1983. Behaviour of chromium in soils. V. Fate of organically
Complexed Cr chromium (III) added to soil. J. Environ. Qual. 12, 169-172.
Camobreco, V.J., Richards , B.K., Steenhuis, T.S., Perverly, J.H. , McBride, M. B. 1996.
Movement of heavy metals through undisturbed and homogenized soil columns.
Soil Science. 161, 740-750.
Cherry, J.A., Robert W. Gillham, and James F. Baker., 1984. Contaminants in
groundwater. In: Chemical processes and groundwater contamination, pp 46-64.
Washington, D.C, National academy Press.
Conner, Jesse R. 1990. Chemical Fixation and Solidification of Hazardous Wastes. New
York, van Nostrand Reinhold Publishers.
Donor, H.E., 1978. Chloride as a factor in nobilities in Ni (II), Cu (II) and Cd (II) in soil.
J. Soil Sci, Soc. Am. 42, 882-885.
Edil, Tuncer. B. 1985. In: Appropriate Waste Containment Technology for Developing
Countries. (Kriton Curi, Ed.). New York, Plenum Press.
Elliott, H. A., Liberati, M.R., and Huang, C.P., 1986. Competitive adsorption of heavy
metals by Soils. J. Environ Qual. 15, 214-219.
49
EPA. 1990. Characterization of Municipal Solid Waste in the United States: Updated,
EPA-530-SW-90.
EPA. August, 1989. Requirements for Hazardous Waste Landfill Design, Construction,
and Closure. Cincinnati, USA.
Environment Canada. March 1991. The National Incinerator Testing and Evaluation
Program. Cat. No. En 21-97/1991. ISBN 0-662-58053-2. Hewson, Bridge and
Smith Ltd. Ottawa.
Environment Quebec. 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. Transport of cadmium in soils: experimental evidence
and Modelling Approaches. Soil Sci. Soc. Am. J. 58, 1316-1327.
Jones, L. H .P., and Jarvis, S. C. 1981. In: The fate of heavy metals. In: The chemistry of
soil processes. (Greenland, D.J. and Hayes, M.H., Ed.), New York John Wiley
and Sons.
Kabata-Pendias, A., and Pendias, H. 1984. Trace Elements in Soils and Plants. Boca
Raton , Florida, CRC Pres, Inc.
Kirov Nikolas, Y. 1975. Principle of Waste Management: Unit Operations and
Processes.Dept.of Fuel Technology, University of New South Wales. Kensington.
50
Korte, N.E., Skopp, J., Fuller, W.H.,Niebla, F.E. and B.A. Alesii. 1976. Trace element
movement in soils: Influence of soil physical and chemical properties. Soil Sci.
122, 350-359.
Lamy, I., Bourgeois, S., and Bermond, A. 1993. Soil cadmium mobility as a consequence
of sewage sludge disposal. J. Environ. Qual. 22, 731-737.
Laplante, B., Luckert, M. K. (Oct.7-9), 1992. Newsprint Recycling Policies: Will they
alleviate or exacerbate Canadian Landfill Problems? Proceedings, 14th Canadian
Waste Management Conference, Regina, Saskatchewan.
Leonard, Jean-François, Jacques L. September 1989. Rapport sur la production et le traitment des dèchets domestiques à Montreal. GRAIGE. Université du Quèbec à
Montréal. Quebec.
Marcus, Y. 1988. Ionic radii in aqueous solutions.Chemical Review, American Chemical
Society. pp 1475-1498.
McBride, M.B., 1989 Reactions controlling heavy metals solubility in soils. Advances in
Soil Science. 10, 1-57.
Martín-Sánchez, M.J., Camazano-Sánchez, M. 1993. Adsorption and mobility of
cadmium in natural, uncultivated Soils. J. Environ. Qual. 22, 737-742.
Montague, Peter. January 23, 1991. Why plastic landfill liners always fail. Rachel's
Hazardous Waste News #217, Environmental Research Foundation. Anna polis,
Md 21403, USA.
Murali, V. , and L.A.G. Aylmore. 1983. Competitive adsorption during solute transport in
soils. 2. 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.
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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
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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.
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Sivapullaiah, P.V., Sridharan, A., and Stalin, V.K. 2000. Hydraulic conductivity of
bentonite-sand mixtures. Canadian Geotechnical Journal, 37, 406-413.
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with calcium chloride solutions, J. of Geotech. And Geoenviron. Engrg. ASCE,
124(3), 231-241.
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hydraulic conductivity of bentonite-sand liners. Water Science Technology, 38 (2),
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NaCl solutions. Journal of Soil Science, 13 (1), 98-105.
Wu Guangxi, Loretta Y.Li. 1998. Modeling of heavy metal migration in sand/bentonite
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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
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6.
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in a copper contaminated sandy soil as affected by pH and solid and dissolved
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7.
Yoshinari, B.; Hiroshi, N.; Rie N.; Yohichi, M. Preparation of chitosan derivatives
containing methylthiocarbamoyl and phenylthiocarbamoyl groups and their selective
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8.
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 Sci. 2001, 166(1), 19-28.
9.
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Transport in Soils; Elsevier: Amsterdam, 1992.
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Academic & Professional: Glasgow, 1993.
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Retention in Clay Soils. PhD diss. McGill University, Montreal, QC, Canada, 1990.
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species and Co (II) complex ions on goethite. J. Colloid Interface Sci. 1974 ,49, 403409.
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139-224.
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883-891.
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20. Conner, J.R. Chemical Fixation and Solidification of Hazardous Wastes; Van Nostrand
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21. Baes, C.F., Jr.; Mesmer, R.E. The Hydrolysis of Cations; John Wiley & Sons: New
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23. Sposito G.; Lund, I. J.; Chang, A.C. Trace metal chemistry in arid-zone field soils
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Sarnia, Ontario, Canada.I: Thermodynamic assessment and chemical interpretations.
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metal ion levels. Water, Air, and Soil Pollution 1977, 8, 189-197.
26. Maguire, M.; Slavek, J.; Vimpany, I.; Higginson, F.R.; Pickering, W.F. Influence of
pH on copper and zinc uptake by soil clays. Australian J. Soil Res. 1981 , 19, 217-219.
27. Yong, R.N., Warkentin, B.P.; Phadungchewit, Y.; Galvez, R. Buffer capacity and
lead retention in some clay materials. Water, Air and Soil Pollution 1990, 53(1/2), 5367.
28. Kuo, S.; Baker, A.S. Sorption of copper, zinc, and cadmium by some acid soils. Soil
Sci. Soc. Am. J. 1980, 44(5), 969-974.
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Madison Waste Conference. Department of Engineering and Applied Science,
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30. Miller, W.P.; McFee, W.W.; Kelly, J.M. Mobility and retention of heavy metals in
sandy soils. J. Environ. Qual. 1983, 12, 579-584.
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retention forms in a Brazilian Oxisol. Rev. Bras. Ci. Solo, 1996, 20, 379-386.
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Mathematical models. Soil Sci. 1983a, 135(4), 143-150.
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review of experimental evidence of competitive adsorption and an evaluation of
simple competition models. Soil Sci. 1983b, 136(5), 279-290.
100
34. Christensen, T.H. Cadmium soil sorption at low concentrations: VI. A model for zinc
competition. Water, Air and Soil Pollution 1987, 34(3), 305-314.
35. Zasoski, R.J.; Burau, R.G. Sorption and sorptive interaction of cadmium and zinc on
hydrous manganese oxide. Soil Sci. Soc. Am. J. 1988, 52(1), 81-87.
36. Sarker, D.;Essington, M.E.; Misra, K.C. Adsorption of Mercury (II) by Kaolinite.
Soil Sci. Soc. Am. J. 2000, 64(6), 1968-1975.
37. 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.
38. 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.
39. SAS Institute. SAS system for Windows, ver. 8.00. SAS Institute: Cary, NC, 1999.
40. United States Environmental Protection Agency (USEPA). Understanding Variation in
Partition Coefficient, Kd , Values. Vol.1. The Kd model, Methods of Measurements, and
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1999.
41. Ma, Q.Y.; Lindsay, W.L. Measurement free zinc2+ activity in uncontaminated and
contaminated soils using chelation. Soil Sci. Soc. Am. J. 1995 , 57(4), 963-967.
42. Rai, D.; Zachara, J.; Schwab, A.; Schmidt, R.; Girin, D.; Rogers, J. Chemical
Attenuation Rates, Coefficients, and Constants in Leachate Migration. I. A Critical
review, EPRI Report EA-3356. Pacific Northwest Laboratories, Battelle Institute:
Richland, WA., 1984.
43. Cavallaro, N.; McBride, M.B. Activities of Cu+2 and Cd+2 in soil solutions as affected
by pH. Soil Sci. Soc. Am. J. 1980, 44(4), 729-732.
44. McBride, M.B. Copper (II) interaction with kaolinite factors controlling adsorption.
Clays Clay Miner. 1977, 26, 101-106.
45. Farrah, H.; Pickering, W.F. The adsorption of copper species by clays: II. Illite and
Montmorillonite, Aust. J. Chem. 1976, 29, 1649-1656.
101
46. McBride, M.B. 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
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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
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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. The demonstration of the Cu-Pb incompatibility versus Cu-Cd, in terms of Cu
adsorption by sand-bentonite liners.
8.3 RECOMMENDATIONS FOR F UTURE WORK
To give a final format to the concept of compartmental landfill, further research is
needed as follows:
i)
Compatibility tests need to be conducted using other toxic metals found in
landfill leachate based on their mobility (low, moderate and high).
ii)
Methods should be developed to characterize and segregate wastes based on
their individual, major heavy metal content and the compatibility of such
heavy metals.
161
iii)
Different methods should be developed to identify and label products at the
manufacturing level, according to heavy metal compatibility.
162
CHAPTER 9
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180
APPENDICES
181
Appendix for Chapter 5.
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%