INTRODUCTION

Liners play an important role in minimizing migration of contaminants. Liners are frequently constructed with natural materials serving as the primary barrier to contain chemicals and potentially harmful pollutants from municipal and toxic waste leachates. A major concern is the safe disposal of solid waste generated by growing Indian cities. With advancements new types of wastes are added and particularly urban areas are affected the most as the volume of waste starts increasing, municipalities face severe problems in managing the wastes (Mohammed et al., 2009; Safari, 2006; Erdogan, 2005).

Scientifically designed sanitary landfills are a solution but for Indian conditions locally available soils can be used as liner materials but they have their own advantages and disadvantages, a number of studies on liners have already been done but emphasis was on determining strength and stability and also on chemical compatibility. Adsorption phenomena have not been given much thought on the soil of Bangalore. Particularly hazardous waste might leach out from these liners and finally enter the ground water (Mohammed et al., 2008; Chalermyanont et al., 2009).

Heavy metals are toxic and non-biodegradable and probably have health effect. The accumulation of Cu ²⁺ in human body causes brain, skin, pancreas and heart diseases. Copper in soils may occur in several forms that are partitioned between the solution and the solid phases. Distribution of Cu between different soil constituents is mostly influenced by the presence of soil organic matter and Mn and Fe oxides. Cu shows a strong affinity for soil organic matter so that the organic-fraction Cu is high compared to that for other metals even though the absolute amounts are low. The most important sinks for Cu in soils are Fe and Mn oxides, soil organic matter, sulfides and carbonates while clay minerals and phosphates are of lesser importance. Adsorption maxima among soil constituents decrease in the order Mn oxide>organic matter>Fe oxide>clay mineral. Specific adsorption seems to play a more important role than nonspecific adsorption (i.e., cation exchange). Sorption isotherms indicate preferential adsorption of Cu onto soil organic matter associated with the clay fraction of the soil. Mn oxide and soil organic matter are the most likely to bind Cu in a non-exchangeable form. Sorption of Cu has been shown to follow either the Langmuir or the Freundlich isotherms. Cu in soil solution exists primarily in a form complexed with soluble organics. Complexation by organic matter in the form of humic and fulvic acids is an effective mechanism of Cu retention in soils. It has been shown that Cu is most extensively complexed by humic materials in comparison to other metals. The following preference series for divalent ions for humic acids and peat is indicated as follows: Cu>Pb>Fe>Ni = Co = Z>Mn = Ca (Nouri et al., 2001; El-Hattab et al., 2007).

The main aim of this study was to conduct sorption and leaching studies at constant initial concentration, sorption at constant pH, adsorption isotherms to predict the type of sorption taking place and to study the suitability of different combination of mixtures as liners based on sorption phenomenon. Kinetic studies to know the rate of sorption. The kinetic adsorption data can be processed to understand the dynamics of the adsorption reaction in terms of the order of the rate constant. Moreover, it is helpful for the prediction of adsorption rate, gives important information for designing and modeling the processes (Al-Mashreki et al., 2010; Mahjoor et al., 2009).

MATERIALS AND METHODS

The experimental works were started from October 2007 and have been carried till date; the following gives details of all materials used and the methods applied.

Chemicals: Copper nitrate was used as heavy metal for adsorption experiments. pH adjustments were carried out by using 0.1 N hydrochloric acid (HCl) and 0. 1N sodium hydroxide (NaOH).The chemicals used were supplied by Nice Chemicals of Analytical Grade (AR).

Adsorbent: Red soil of Bangalore collected from the Campus of Indian Institute of Science (IISc), Bangalore, India is taken as the main soil; IISc is selected as it is a big campus with minimal contamination. Approximately 10 kg soil samples were drawn below 15 cm from the ground surface. The soil was air dried and sieved to 2 mm sub samples of air dried soil were ground and sieved to obtain aggregates less than 0.1mm to ensure uniformity of the material (Morera et al., 2001; Germatas and Meng, 2003). Additives selected to enhance the properties were lime obtained as AR grade calcium hydroxide supplied by Qualigens Company, cement supplied by Associated Cement Company (ACC) grade 53 and fly ash obtained from Raichur Thermal Power Station (RTPS), Raichur, Karnataka, India. Fly ash, a finely divided powder, is a byproduct from coal combustion in power plants that requires ultimate disposal. The major constituents of fly ash are silica, alumina and iron oxide which are ideal metal adsorbents. Potential use of fly ash as an additive for liner material is obvious as it can be obtained cheaply in large quantities and can be used as an adsorbent. The physical properties of fly ash as obtained from Raichur Thermal Power Station revealed a specific gravity of 2.05, fineness of 356 m² kg^-1, lime reactivity of 83%, soundness (autoclave) of 0.06% and residue (in 45 microns) of 33.6 %. The energy dispersive spectroscopy results give the chemical composition of major elements in percent by weight as Ca (0.85%), Al (10%), Si (18%), Fe (12%), O (55%), Mg (2%) and C (8%). Also energy dispersive spectroscopy shows no detectable presence of any heavy metals. In order to calculate the presence of any background concentrations of the selected contaminants, batch leaching tests were performed according to American Standard Testing Methods (ASTM D3987-85, 2004). It was found that no traces of heavy metals were found in the soils.

The physico chemical properties of the selected soil are given in Table 1. The cation exchange capacity of the soil was determined by ammonium acetate extraction method (Jackson, 1958). Specific gravity test was conducted using a specific gravity bottle as per IS 2720. The liquid limit test of soil was determined by cone penetrometer method as per IS 2720. Liquid limit of a soil was taken as the water content corresponding to 20 mm penetration from a line drawn with respect to water content and penetration. The plastic limits of the soils were obtained as per standard method IS 2720. Shrinkage limit test was conducted as per IS 2720-1972. Grain-size distribution, the percentage of various sizes of particles in a given dry soil sample, was determined by particle size analysis or mechanical analysis. The mechanical analysis was performed in two stages: sieve analysis and sedimentation analysis. Bureau of Indian Standards (BIS) which is a regulatory body established by the government of India to standardize all testing methods and standard practices in the form of design codes, were considered in the study. These standards are consistent with ASTM and British standards. It takes into consideration Indian conditions which are unique due to geographical and climatic conditions. The surface area of the soil was determined by nitrogen adsorption method using BET equation, the instrument used was Micromeritics Rapid Surface Area Analyser, Model 2200 (Mohammed et al., 2009).

Batch adsorption tests: Batch adsorption tests were done as per ASTM D 4646-87 (reapproved 2001), a brief description is hereby provided for all the experiments carried out.

Table 1:	Physico- chemical properties of red soil

Samples with S/L ratios of 1:20, 1:30, 1:40, 1:100, 1:200, were taken and shaken for 24 h. Then, 100 mg L^-1 of contaminant was added proportionally to all samples and again shaken for 24 h. The samples were then removed, filtered and the filtered liquid was analyzed for its concentration. For pH dependent tests the procedure was the same as mentioned except for the pH of the contaminant which was first adjusted to the required pH and then the adsorbent was added maintaining a constant S/L ratio of 1:20 throughout. All pH adjustments were made using 0.1 N HCl and 0.1 N NaOH. This was done to prevent neutralization reaction from taking place because of the presence of lime in the adsorbent and also to mimic field conditions.

For different initial concentrations, about 5 g of adsorbent was taken in 100 mL of solution maintaining a S/L ratio of 1:20 and was subjected to shaking for 24 h, then 10 mg L^-1 of contaminant was added to all samples and again subjected to shaking for 24 h. The samples were then removed, filtered and the filtered liquid was analyzed for its concentration. The same procedure was applied with 20, 30 and 40 mg L^-1 as initial concentration of contaminant.

Kinetic studies were done by shaking 5 g of adsorbent in 100 mL of solution maintaining a S/L ratio of 1:20 and adding heavy metals in different concentrations of 10, 20 30 and 40 mg L^-1 at room temperature. Samples of 5 mL were collected at required intervals and centrifuged for 5 min. The clear solutions were analysed for residual heavy metal concentration in the solution.

Leaching test on soil and amended soils: A known percentage by weight of heavy metal as contaminant was mixed with soil or soil mixture and allowed to dry naturally for 7 days and also further extended to cure for 28 days. The standard procedures followed were as per ASTM (American Standard Testing Methods) D3987-85. A load ratio of 100 and 50 mg kg^-1 was maintained (1000 mL of 100 ppm contaminant solution was added to 1 kg of soil to give a load ratio of 100 mg kg^-1). Batch leaching test was done by taking a known weight of dried mixture and mixing it in a known volume of distilled water maintaining a solid to liquid (S/L) ratio of 1:20 and subjecting it to shaking for a period of 18 h in a sample shaker at a speed of 30 RPM. The leached solution was taken and after centrifuging and filtering the sample was sent for chemical analysis, the amount of contaminant leached was determined (ASTM D3987-85, 2004).

The concentration of copper was measured using Atomic Absorption spectrophotometer supplied by Perkin Elmer Corp AA200 Model, (flame type)

Adsorption models: Equilibrium isotherms for Copper were obtained by performing batch adsorption studies. Solutions of 100 mg L^-1 concentration were used. The adsorbed metal amount (q_e) per unit absorbent mass or sorption coefficient expressed as mg g^-1 was calculated as:

(1)

where, C_o is the initial heavy metal concentration, C_e is the concentration of heavy metal at equilibrium (mg L^-1), m is the soil mass (mg) and V is the solution volume (L).

Langmuir isotherm: Langmuir isotherm models the single coating layer on adsorption surface. This model supposes that the adsorption takes place at a specific adsorption surface. The attraction between molecules decreases as getting further from the surface (El-Said, 2010).

Freundlich isotherm: Freundlich isotherm was also used for modeling the adsorption on heterogeneous surfaces (Barakat, 2008).

Pseudo first-order kinetic model: The kinetic data were treated with the Lagergren first-order model which is the earliest known one describing the adsorption rate based on the adsorption capacity. It is generally expressed as follows:

(2)

where, qe and qt are the adsorption capacity at equilibrium and at time t, respectively (mg g^-1) and k₁ is the rate constant of pseudo first-order adsorption (min^-1). Equation 2 was integrated with the boundary conditions of t = 0 to t = t and qt = 0 to qt = qt and rearranged to the following linear equation:

(3)

If the pseudo first-order kinetics is applicable, a plot of log (qe-qt) versus t should provide a linear relationship from which k₁ and predicted qe can be determined from the slope and intercept of the plot, respectively (Oladoja et al., 2008).

Pseudo second-order kinetic model: Adsorption kinetic was explained by the pseudo second-order model given as follows:

(4)

where, k₂ (g/mg/min) is the second-order rate constant of adsorption. Integrating Eq. 4 for the boundary conditions q = 0 to q = qt at t = 0 to t = t is linearized to obtain the following equation:

(5)

The plot of t/qt versus t should show a linear relationship if the second-order kinetics is applicable. Values of k₂ and q_e were calculated from the intercept and slope of the plots of t/qt versus t.

There was a difference between experimental and theoretical values of sorption coefficient for both first order and second order kinetic models; hence a statistical method was used to find a comparison. The standard error of estimate is a measure of the accuracy of predictions. The Standard Error of Estimate (SEE) is defined by:

(6)

where, Q_exp and Q_the are the respective experimental and model estimated sorption coefficients and n is the number of observations. The values of SEE should be lower for higher accuracy (Silva et al., 2008).

Elovich kinetic model: Elovich kinetic equation is another rate equation based on the adsorption capacity which is generally expressed as:

(7)

where, α is the initial adsorption rate (mg/g/min) and β is the desorption constant (g mg^-1) during any one experiment. It is simplified by assuming αβot and by applying the boundary conditions qt = 0 at t = 0 and qt = qt at t = t Eq. 7 becomes form as followed:

(8)

If Cu²⁺ adsorption fits the Elovich model a plot of qt versus ln (t) should yield a linear relationship with a slope of (1/β) and an intercept of (1/β)xLn (αβ) Thus, the constants can be obtained from the slope and the intercept of the straight line.

The intraparticle diffusion model: The adsorption process required a multi-step involving the transport of solute molecules from the aqueous phase to the surface of the solid particles followed by diffusion of the solute molecules into the interior of the pores which is likely to be a slow process and is therefore, rate-determining step. The intraparticle diffusion model is explored by using the following equation:

(9)

where, C is the intercept and K_dif (mg/g¹/min^0.5) is the intraparticle diffusion rate constant. The plot of qt against t^0.5 may present a multi-linearity correlation which indicates that two or more steps occur during adsorption process. The rate constant K_dif directly evaluated from the slope of the regression line and the intercept is C (Kermani et al., 2006).

RESULTS

Adsorption at different dilution ratios: The dilution ratios were converted into concentration as this seems more practically feasible and results were presented in millimoles to normalize the effect of atomic numbers. From the given Fig. 1 it can be observed that at higher dilution ratio of 1:200 the concentration of contaminant was 0.7 mmol L^-1 and for the lowest dilution ratio of 1:20 the concentration of contaminant was 14 mmol L^-1. At very low concentration the sorption coefficient was highest and at a higher concentration the sorption coefficient was lowest for the different soil and soil mixtures taken. The dilution ratio and concentration are inversely related. At very low concentration the competition for adsorption sites between different cations was low hence maximum sorption takes place. It can also be observed that the adsorption behavior of all the mixtures was uniform with respect to concentration, although the degree of adsorption differs from material to material. Red soil with 3% cement undergoes maximum adsorption followed by 1% cement and 6 and 3% lime, similarly followed by red soil. Different mixtures of flyash show adsorption which seems insignificant compared to other mixtures.

Fig. 1:	Sorption behavior of copper for different mixtures of red soil at different dilution ratios converted to concentration

Cement being a calcareous material, specific adsorption of copper onto CaCO₃ may control Cu concentration in solution (Mohammed and Naik, 2010; Dang et al., 2009).

Adsorption characteristics with different initial concentrations: From Fig. 2, it can be seen that as the concentration goes on increasing, adsorption also increases, even here soil cement mixtures have given maximum adsorption compared to other mixtures. The experimental results show that the removal of Cu²⁺ was highly concentration dependent the increase in the uptake capacity of the sorbent with increasing Cu²⁺ concentration may be due to the increase of the sorbate quantity. At lower initial concentration sufficient adsorption sites are available for sorption of Cu²⁺ ions but there also exists number of competiting ions with higher energy which might de-sorb Cu²⁺ and occupy those sites. Similarly the number of Cu²⁺ ions at higher concentration is relatively more compared to the available adsorption sites. The difference in the removal capacity is a result of the difference in their chemical affinities and ion exchange capacities with respect to the chemical functional group on the surface of the adsorbent. In this case the availability of free adsorption sites dominates. Similar phenomenon have been reported by Atmani et al. (2009) and Mohammed and Naik (2011).

Adsorption at varying pH: From Fig. 3 it can be observed that as the pH goes on increasing adsorption also increases, at near neutral pH maximum adsorption takes place. Cu²⁺ adsorption increases with pH, Cu²⁺ hydrolyses at pH>7.7 and these hydrolyzed species are strongly adsorbed to soil surfaces. Cu forms complexes with inorganic and organic ligands that will affect its adsorption reactions with the soil surface and precipitation may be an important mechanism of retention. It has been reported that at a pH of 4, 50% adsorption of Cu²⁺ takes place, at this pH the adsorption proceeds as surface adsorption as the pH starts increasing hydroxides of copper are formed and precipitation becomes dominant. At neutral pH maximum adsorption takes place, stable complexes of copper are formed (Atmani et al., 2009; Shim et al., 2003).

Fig. 2:	Sorption behaviour of different mixtures of red soil with change in initial concentration

Fig. 3:	pH dependent adsorption of copper on red soil and its mixtures

Leaching behavior of copper with red soil: Leaching tests were performed on soil and soil mixtures, a load ratio of 50 and 100 mg kg^-1 was maintained. It can be seen from Fig. 4 that red soil leached to the maximum, both for 100 and 50 mg kg^-1 load ratio. It can also be observed that the load ratio does not have a remarkable effect and the variation was almost similar. Soil with lime and cement content has good retention capacity and resisted leaching. Investigations were also carried based on different curing periods of 7 and 28 days, the mode of curing was to cover the container with a polythene sheet and also for natural drying under shade at room temperature. Care was taken not to cover the containers airtight instead natural flow of air was maintained. Sub samples were drawn at 7 and 28 days intervals and subjected to leaching tests as discussed in the above section. It was found that initially after 7 days time the percentage leached was more as time lapsed over a period of 28 days the percentage of contaminant leaching reduced comparatively. Over increased curing time many reactions occur and specific adsorption of copper took place.

Cement due to hydrolysis and formation of a series of chemical complexes over a curing time of 28 days has retained copper. Lime also under goes hydration and forms calcareous compounds also with its high cation exchange capacity it retains cations. From the Fig. 4 it can also be seen that different percentages of flyash have also retained some amount of copper. This might be due to the presence of calcium, silica, alumina, iron etc. in flyash mixture (Nemr, 2009).

Monolayer adsorption capacity of red soil and its additives: Monolayer adsorption capacity was calculated from Langmuir’s equation the results are as tabulated. It can be observed from Table 2; red soil with 3% cement gave maximum adsorption capacity of 86.02 mg g^-1, red soil with 6% lime gave 85.63 mg g^-1 followed by 50% flyash and 3% lime. The R² values varied from 0.905 to 0.954 suggesting the isotherm model fits well. It has been suggested that a clay mineral exchange phase may serve as a sink for Cu in non-calcareous soils. In the same way the experimental data of batch adsorption studies was applied to Freundlich isotherm and concentration of metal adsorbed was obtained from this model as shown in Table 2. It can be seen that the concentration of adsorbed copper obtained is not conforming practically, even K_f and 1/n values are not practically consistent suggesting the model isotherm does not suit these materials (Oladoja et al., 2008).

Pseudo first order model for copper adsorption on red soil and its mixtures: As shown in Table 3, if the pseudo first-order kinetics is applicable, a plot of log (q_e-q_t) versus t should provide a linear relationship from which k₁ and predicted q_e can be determined from the slope and intercept of the plot, respectively. Linear regression analysis was done on the data and a straight line was fitted, also the equation of straight line was obtained. This linear equation was used to calculate the theoretical sorption coefficient value q_e and rate constant K₁. This procedure was applied to all the soil and soil mixtures and the results obtained are tabulated in Table 3.

From Table 3 the first order rate constant ranged from 0.095 to 2.7793 per min. The pseudo first orders theoretical values of q_e were obtained from the intercept of the linear plots and were compared with the experimental values, the R² values varied from 0.770 to 0.965.

Fig. 4:	Leaching behavior of copper with red soil and soil mixtures

Table 2:	Langmuir and Freundlich isotherm parameters for red soil and its additives

The pseudo first order suffered from inadequacies, the theoretical and experimental values did not match there were big differences. Discrepancies of this nature have been reported, it can be concluded that pseudo first order equation does not represent the adsorption process taking place for these sorbents. By comparing the rate constants it can be seen that no relationship can be established between different initial concentrations and also between different sorbents as the values are highly inconsistent and hence insignificant. The reason for such behavior of these sorbents is they are highly heterogeneous in nature and as such pseudo first order assumes adsorption taking place in one plane. Sorption with solid surfaces in particular soils which are rarely homogeneous and the effects of transport phenomena and chemical reactions are often experimentally inseparable. It has also been proved that pseudo first order is suitable at very low concentrations but in our case the concentration of contaminant taken was 100 mg L^-1 and the mass of the sorbent was 5 g.

Pseudo second order model for copper adsorption on red soil and its mixtures: From Table 3 the second order rate constant varied from 321.53 to 2174 g/mg/min. An inverse relationship exists between overall sorption rate and initial concentration, as the concentration increases the sorption rate decreases hinting at higher concentration the reaction goes for completion and all the reactants are converted into products. The values of K₂ are consistent, R² values varied from 0.9189 to 0.9423 which proves the model fits well. It can be inferred that second order was more suitable than first order which shows that along with adsorption many other processes are taking place like ion exchange, precipitation etc.

Pseudo second order gave better results than pseudo first order it has been reported that at high initial concentrations pseudo second order was more suitable and at low initial concentrations pseudo first order was suitable which has been proved true in this study as the initial concentration of copper taken was high and also mass of sorbate taken was 5 grams which is very high.

Estimation of model accuracy by Standard Error of Estimate (SEE): In order to compare the accuracy of these two models a statistical method was used to ascertain which model was more accurate. The model calculated values of sorption coefficient and experimental values for both the models showed variation, in order to access which model was better in terms of accuracy, this statistical method gives us a better idea. Also it has been found that lower the SEE value higher is the accuracy.

Table 3:	Comparison of the first order and second order adsorption rate constants for different initial copper concentrations with different sorbents

By comparing Table 4 it can be seen that the SEE values for first order varied from 1.076 to 10.315, where as for second order it varied from 0.861 to 1.862. Pseudo second order SEE values are lower than pseudo first order, hence it can be said that pseudo second order is more accurate than first order or pseudo second order is a better model when compared to first order for these mixtures, this confirms with the study of Silva et al. (2008).

Table 4:	Standard error of estimate (SEE) for red soil with copper

Elovich kinetic model for copper adsorption on red soil and its mixtures: From Table 5 correlation coefficients obtained by Elovich model varied from 0.72 to 0.995. It can also be observed that Elovich model gave us an account of the desorption process taking place, it can be seen from Table 5 that as the initial concentration was increasing desorption was decreasing in other words adsorption was increasing. This phenomenon was also seen during the experimental work, where in the sorption coefficient increased with increase in initial concentration. At lower concentration competing ions are dominant with higher energy whereas, at higher concentration more number of ions are present which play a role in adsorption. The correlation coefficients obtained are almost linear which shows the model fits well. Elovich model gave a good correlation for adsorption on highly heterogeneous surfaces like soil and also it shows that along with surface adsorption chemisorptions was also a dominant phenomenon taking place.

Intraparticle diffusion model for copper adsorption on red soil and its mixtures: The adsorption process required a multi-step involving the transport of solute molecules from the aqueous phase to the surface of the solid particles followed by diffusion of the solute molecules into the interior of the pores which is likely to be a slow process and is therefore, rate- determining step. The rate constant K_dif directly evaluated from the slope of the regression line and the intercept C and reported in Table 5.

The values of C ranged from 0.001 to 1.043 mg g^-1 which provide information about the thickness of the boundary layer, since the resistance to the external mass transfer increases as the intercept increases. R² values given in Table 5 ranged from 0.9 and 0.995. The linearity of the plots demonstrated that intra-particle diffusion played a significant role in the uptake of copper by sorbent. The intra-particle diffusion rate constants, K_dif, were in the range of 0.007 to 0.125 mg/g¹/min^0.5. However, low linearity can be seen for the adsorption of copper by different sorbents which indicates that both surface adsorption and intra-particle diffusion were involved in the rate-limiting step. However, still there is no sufficient indication about which of the two steps was the rate-limiting step. It has been reported that if the intraparticle diffusion is the sole rate-limiting step, it is essential for the qt versus t ^1/2 plots to pass through the origin which is not the case in this study.

Table 5:	Parameters obtained from elovich kinetics model and intraparticle diffusion model using different initial concentrations and different sorbents

DISCUSSION

Cement along with red soil has been very effective in retaining copper. The following reaction could be thought of; cement in the presence of moisture undergoes complex pozzolonic reaction that produces a variety of different compounds and gels. A portland cement particle is a heterogenous substance containing tricalcium silicate (C₃S), dicalcium silicate (C₂S), tricalcium aluminate (C₃A) and a solid solution described as tetracalcium alumino-ferrite (C₄AF). When the pore water soil encounters with the cement, hydration of the cement occurs rapidly and the major hydration products are formed. The products are hydrated calcium silicates, hydrated calcium aluminates and hydrated lime. The first two if the hydrated products are the main cementitious products formed and the hydrated lime deposited as a separate phase. These cement particles bind the adjacent cement grains together during hardening and form a hardened skeleton matrix which encloses unaltered soil particles (Nwachokor and Molindo, 2007).

The silicate and aluminate phases are internally mixed, so it is most likely that none is completely crystalline. Part of the Ca(OH)₂ may be mixed with other hydrated phases. Apart from primary process there is also an important secondary process in the cement clay interaction. The hydration of cement leads to a rise of pH value of the pore water which is caused by the dissolution of the hydrated lime. The strong bases dissolve the soil silica and alumina from both the clay minerals and amorphous materials on the clay particle surfaces. The hydrous silica and alumina will then gradually react with the calcium ions liberated from the hydrolysis of cement to form insoluble compounds which harden when cured to stabilize the soil. This is in accordance to the study done by Germatas and Meng (2003) and Ilemobayo and Kolad (2008).

Also presence of alumina, silica and calcium in soil and cement generates complexes and ligands which adsorb copper, the speciation of copper gives us an indication that the adsorption is highly pH dependent, at low pH adsorption is only nonspecific and occurs on surface of adsorbent, at pH 4 more than 50% of sorption takes place and the reaction changes from surface adsorption to complexation and precipitation, stable complexes start forming at a pH range of 5 to 7. Since for most of the landfills the average pH range of its leachate varies from 3.8 to 8.8, these materials have good potential as a landfill liner material. Similar observations were obtained by Bradl (2004), Cruz-Landero (2010) and Amu et al. (2005).

CONCLUSIONS

The following conclusion has been made:

ACKNOWLEDGMENTS

Sincere thanks for their encouragement to the HOD of the Department of Engineering Chemistry, Prof. Sanaulla PF of HKBK College of Engineering, Bangalore 5600 45. Appreciation is expressed to Mr. M. N. Zulfiqar Ahmed, Senior Lecturer, Dept. of Engineering Chemistry, HKBKCE for helping in setting up the experiments. We are highly indebted for the scholastic help rendered by Prof. P.V. Sivapullaiah, Ph.D., Prof of Civil Engineering, Indian Institute of Science (IISc.) Bangalore 560012 and Dr. Arif Ali Baig Moghal, Assistant Professor, Dept. of Civil Engineering, King Saud University, Riyadh, Kingdom of Saudi Arabia. Thanks are due to the critical review and constructive advises from anonymous reviewers which helped to improve the quality of this paper.