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IJRRAS 15 (3) ● June 2013 www.arpapress.com/Volumes/Vol15Issue3/IJRRAS_15_3_19.pdf PREDICTION OF CONCENTRATION PROFILES OF CONTAMINANTS IN GROUNDWATER POLLUTED BY LEACHATES FROM A LANDFILL SITE Lukmon Salami 1, Olaosebikan A. Olafadehan2, Gutti Babagana3 & Alfred A. Susu4* Department of Chemical and Polymer Engineering, Lagos State University, Epe, Lagos, Nigeria 2,4 Department of Chemical Engineering, University of Lagos, Akoka, Yaba, Lagos, Nigeria 3 Department of Chemical Engineering, University of Maiduguri, Maiduguri, Borno State, Nigeria * Corresponding author: Professor Alfred A. Susu, [email protected] 1 ABSTRACT Point sources such as landfills can release high concentration of contaminants into the groundwater because of migration of leachate from its bottom. The leachate is generated primarily as a result of precipitation on an active landfill surface, leading to the transport of organic and inorganic contaminants from landfill waste which is subsequently discharged into groundwater in underlying aquifer. Landfill leachate has the potential to contaminate the surrounding environment and impair groundwater use. A one dimensional transport model was used to predict the concentration profiles of contaminants groundwater polluted by leachates from a landfill site using the finite difference approach implemented in Matlab 7.0. The concentration profiles for organic and inorganic pollutants indicated similar profiles, rising to a maximum with time and distance from the landfill. Three dimensional images were generated for the concentration profiles of all the contaminants. The data provided by Jhnamnani and Singh (2009) were used for the comprehensive prediction in this work. Keywords: Leachate; Landfill; Contaminants; Groundwater; Prediction of contaminant concentration profiles; Finite difference method. 1. INTRODUCTION A landfill site is a site for the disposal of waste materials by burial. It is also the oldest form of solid waste treatment. Historically, landfills have been the most common methods of organized waste disposal and remain so in many places around the world. Landfills may include internal waste disposal site (where a producer of waste carries out their own waste disposal at the place of production) as well as sites used by many producers. Many lands are also used for waste management purposes, such as the temporary storage consolidation and transfer or processing of waste materials (sorting, treatment or recycling). Areas near landfills have a greater possibility of groundwater contamination because of the potential pollution source of leachate originating from the landfill site. Such contamination of groundwater resource poses a substantial risk to public health and to the natural environment. The impact of landfill on the surface and groundwater has given rise to a number of studies in recent years (Saarela, 2001; Abu and Kofahi, 2001; Booser et al., 1999; Christensen et al., 1998; De Rosa et al., 1996 and Flyhammer, 1995). Many approaches have been used to assess the contamination of underground water. It can be assessed either by the experimental determination of the impurities or their estimation through mathematical modeling (Moo-Young et al., 2004, Hudak, 1998 and Stoline et al., 1993). Rain falling on the top of the landfill is the main contributor to the generation of leachate and is by far the largest contributor for modern sanitary landfills which do not accept liquid waste (Jhnamnani and Singh, 2009). In old unlined and un-engineered landfills, some leachates are produced during waste decomposition; additionally, surface water from surrounding water system, can sometimes run onto the waste (Renou et al., 2008). The decomposition of carbonaceous materials produces some additional water and a wide range of other materials including methane, carbon dioxide and a complex mixture of organic acids, aldehydes, alcohol and simple sugars, which dissolve in the leachate cocktail. The precipitation percolates through the waste and takes in dissolved and suspended components from the biodegrading waste, through physical and chemical reactions (Jhnamnani and Singh, 2009). The environmental risks of leachate generation arise from it escaping onto the environment around landfills, particularly to waste courses and groundwater. These risks can be mitigated by properly designed and engineered landfill sites. Such sites are those that are constructed on geologically impermeable materials or sites that use impermeable liners made of geotextile or engineered clay (Anne and Fred, 1993). The use of linings is now mandatory within both the United States and the European Union, except where the waste is closely controlled and genuinely inert (Anne and Fred, 1993). Most toxic and difficult materials are now specifically excluded from landfill 365 IJRRAS 15 (3) ● June 2013 Salami & al. ● Prediction of Concentration Profiles of Contaminants (EPA, 1997). However, despite much stricter statutory controls, the leachates from modern sites are currently stronger than ever as they contain a huge range of contaminants. In fact, anything soluble in the waste disposed will enter the leachate (EPA, 1995). Therefore, the aims and objectives of this work are: (i) the study of the natural attenuation of inorganic contaminants and (ii) the prediction of the contaminants concentration in subsurface region and groundwater. The predictive model uses the data generated by Jhamnani and Singh (2009) to produce three dimensional profiles of contaminant concentration in groundwater. If the concentration of contaminants in groundwater can be predicted, it helps to put in place remediation measures and proper waste management. The prediction of groundwater contaminants concentration therefore serves as a tool to sanitise our environment and improve the quality of human life. 2. THEORY AND NUMERICAL SIMULATION The governing one dimensional mass transport model used in this work was based on the dispersion model developed by Jhamnani and Singh (2009) and is presented in Equation (1). This equation was solved using finite difference method implemented on Matlab 7.0. The data for the characteristics of leachate for Bhalaswa landfill site and groundwater samples from nearby locations (Jhamnani and Singh, 2009) are shown in Table1. The model parameters are also taken from the same source and depicted in Table 2 and they were used for the simulation of contaminants migration from the landfill. Table1. Characteristics of Leachate from Bhalaswa landfills site and groundwater samples from nearby location Concentration in Landfill Leachate Parameters Concentration in groundwater samples at radial distance from landfill facility ≤75m 75-500m 500m1000m 1000m1500m Iron 20 7.04 6.53 5.11 3.61 (mg/L) Copper <10 0.1 0.08 0.06 0.05 (mg/L) Nickel <3 0.43 0.32 0.22 0.13 (mg/L) Zinc <10 3.37 3.27 2.14 1.11 (mg/L) Chloride 4000 1174.2 1032.24 845.45 543.12 (mg/L) Source: International journal of civil and environmental engineering (2009), 1(3):122 S/N 1 2 3 4 5 6 7 8 Table 2 Model parameters for simulation Model Parameter Unit Value Time S 50 Molecular diffusion coefficient m2/yr 0.027 Mechanical dispersion coefficient m2/yr 0-075 Effective molecular diffusion coefficient m2/yr 0-02 Dispersivity M 0.15 Advective velocity m/yr 0.5 Hydrodynamic dispersion m2/yr 0.095 Porosity 0.4 9 Retardation factor 10 Equivalent height of leachate M 10 11 t S 1 12 z M 1.5 1 366 1500m2000m 2000m5000m 1.73 0.64 0.02 0.01 0.43 0.43 0.06 0.02 324.23 135.36 IJRRAS 15 (3) ● June 2013 Salami & al. ● Prediction of Concentration Profiles of Contaminants Source: International journal of civil and environmental engineering,(2009),1(3):124 C Dh 2 C v C 2 t R f z R f z where Dh = hydrodynamic dispersion coefficient (1) = dispersivity v = advective velocity Rf = retardation factor Equation (1) in finite difference form can be written as: c m 1 i c m 1 i D C R m t Making C ci f m 1 i m i 1 h m m Cm Cm 2 C i C i 1 v i 1 i 2 2 z Rf z (2) the subject in equation (2); it yields: C D t Cm 2Cm Cm vt C i 1 i i 1 2 R f z R z D t R z m i h 2 m Ci 1 m i 1 (3) f h Let A = (4) 2 f vt 2 R f z B= (5) Substitute Equations (4) and (5) into Equation (3) and applying upwind correction, by replacing the forward difference in the 4th term with backward difference, we have: C m 1 i C m i A C m 2 Ci Ci 1 2 B m i 1 m C m i Ci 1 m (6) Expanding Equation (6); it becomes: C m 1 i C m i A C i 1 2 AC i A C i 1 2 B C i 2 B Ci 1 m m m m m (7) Grouping Equation (7); it gives: C m 1 i 1 2 A 2 B C m i A C i 1 A 2 B C i 1 m m (8) Equation (8) is the explicit finite difference approximation for the system. The value of t and z are chosen in such a way that the stability criteria of Equation (9) is satisfied. Dzt vzt 1 2 Rf z 2 Rfz 2 (9) The boundary condition (BC) is: CT t C 0 1 Hf f c, d t (10) t 0 Where CT = concentration of contaminant in source at any time t C 0 = initial concentration of contaminant in source Hf = equivalent height of leachate The initial condition (IC) is: (11) C z, o 0 According to Jhamnani and Singh (2009), advective dispersion transport in one dimension can be express as: fT c, nvzc nDh c z (12) The negative sign arises from the fact that the contaminant move from high to low concentrations. The total mass of contaminant transported out of the landfill up to some specific time t is obtained by integrating Equation (12). 367 IJRRAS 15 (3) ● June 2013 Salami & al. ● Prediction of Concentration Profiles of Contaminants Putting Equation (12) into Equation (10) yields: 1 CT t C 0 Hf t (nvzc nDh 0 c )d z (13) Applying numerical method to Equation (13), we have: t c t (14) v C ( 0 , t ) t D (0, t )t h z T t 0 z t 0 c Assuming the concentration gradient, 0 (Jhamnani and Singh, 2009); Equation (14) reduces to: z t n CT (0, t ) Co vz CT (0, t )t (15) H f t 0 CT (0, t ) Co n Hf Equation (15) can be written as: t 1 n n CT (0, t ) Co vz CT (0, t )t vzCT (0, t )t Hf H f t 0 (16) Rearranging Equation (16), it gives: CT (0, t ) t 1 n n vzCT (0, t )t Co vz CT (0, t )t Hf H f t 0 (17) Factorising the LHS of Equation (17), we have: t 1 n n 1 v t C ( 0 , t ) C v T z o z CT (0, t )t H f t 0 H f t 1 n Co v z CT (0, t )t H f t 0 CT (0, t ) n v z t 1 Hf (18) (19) 3. RESULTS The predictions of the variation of chloride concentration with time (at various depths) and distance (at various times) from the landfill are shown in Figures 1 and 2, respectively. The work of Jhamnani and Singh (2009) only showed the chloride concentration with time at only one depth of 5m below the bottom of the landfill. Our approach allowed us to indicate a 3-D chloride profile with distance and time (Figure 3). Figure 2 showed quite clearly that chloride concentration was negligible beyond a distance of 25m from the landfill for up to 30 years of operation of the landfill. We report in Figure 3 the 3-D profiles for chloride concentration with depth and time as variables. The advantage of depicting the profiles for various depths indicates that the shape of the profile was retained, except that the maximum decreased with time. We also showed the profiles for the heavy metals because of its impact on public health. Figures 4, 5 and 6 show the predicted zinc profiles from an initial value of <10 mg/L in the leachate concentration. Figures 7, 8 and 9 show corresponding profiles for nickel (initial leachate concentration of <3 mg/L), Figures 10, 11 and 12 for copper (initial leachate concentration of <10 mg/L) and Figures 13, 14 and 15 for iron (initial leachate concentration of 29 mg/L). All of the profiles for the heavy metals replicated all the features predicted for the chloride profiles. 368 IJRRAS 15 (3) ● June 2013 Salami & al. ● Prediction of Concentration Profiles of Contaminants Variation of chloride concentration with time at varying depths chloride concentration (mg/L) 1500 depth depth depth depth depth depth 1000 = 5m = 10m = 15m = 20m = 25m = 30m 500 0 0 10 20 30 time (years) 40 50 60 Figure 1. Variation of chloride concentration with time Variation of chloride concentration with distance 1800 time time time time time time chloride concentration (mg/L) 1600 1400 1200 = 5 years = 10 years = 15 years = 20 years = 25 years = 30 years 1000 800 600 400 200 0 0 5 10 15 distance (meters) 20 Figure 2. Variation of chloride concentration with distance 369 25 IJRRAS 15 (3) ● June 2013 Salami & al. ● Prediction of Concentration Profiles of Contaminants concentration-distance-time graph for chloride 2000 chloride concentration (mg/L) 1800 2000 1600 1400 1500 1200 1000 1000 500 800 600 0 200 400 150 60 100 50 distance (in meters) 200 40 20 0 0 0 time (in years) Figure 3. Concentration – distance – time graph with time for chloride Variation of zinc concentration with time at varying depths 4 depth depth depth depth depth depth zinc concentration (mg/L) 3.5 3 = 5m = 10m = 15m = 20m = 25m = 30m 2.5 2 1.5 1 0.5 0 0 10 20 30 time (years) 40 50 Figure 4. Variation of zinc concentration with time 370 60 IJRRAS 15 (3) ● June 2013 Salami & al. ● Prediction of Concentration Profiles of Contaminants Variation of zinc concentration with distance 4.5 time time time time time time 4 zinc concentration (mg/L) 3.5 3 = 5 years = 10 years = 15 years = 20 years = 25 years = 30 years 2.5 2 1.5 1 0.5 0 0 5 10 15 distance (meters) 20 25 Figure 5. Variation of zinc concentration with distance concentration-distance-time graph for zinc 5 zinc concentration (mg/L) 4.5 5 4 4 3.5 3 3 2 2.5 2 1 1.5 0 200 1 150 60 100 40 50 distance (in meters) 20 0 0 0.5 0 time (in years) Figure 6. Concentration – distance – time graph with distance 371 IJRRAS 15 (3) ● June 2013 Salami & al. ● Prediction of Concentration Profiles of Contaminants Variation of nickel concentration with time at varying depths 0.8 depth depth depth depth depth depth 0.6 0.5 0.4 0.3 0.2 0.1 0 0 10 20 30 time (years) 40 50 60 Figure 7. Variation of nickel concentration with time Variation of nickel concentration with distance 0.9 time time time time time time 0.8 0.7 nickel concentration (mg/L) nickel concentration (mg/L) 0.7 = 5m = 10m = 15m = 20m = 25m = 30m 0.6 = 5 years = 10 years = 15 years = 20 years = 25 years = 30 years 0.5 0.4 0.3 0.2 0.1 0 0 5 10 15 distance (meters) 20 Figure 8. Variation of nickel concentration with distance 372 25 IJRRAS 15 (3) ● June 2013 Salami & al. ● Prediction of Concentration Profiles of Contaminants concentration-distance-time graph for nickel 1 nickel concentration (mg/L) 0.9 1 0.8 0.8 0.7 0.6 0.6 0.4 0.5 0.4 0.2 0.3 0 200 0.2 150 60 100 50 distance (in meters) 0.1 40 20 0 0 0 time (in years) Figure 9. Concentration-distance-time graph for nickel Variation of copper concentration with time at varying depths 0.8 depth depth depth depth depth depth copper concentration (mg/L) 0.7 0.6 = 5m = 10m = 15m = 20m = 25m = 30m 0.5 0.4 0.3 0.2 0.1 0 0 10 20 30 time (years) 40 50 Figure 10. Variation of copper concentration with time 373 60 IJRRAS 15 (3) ● June 2013 Salami & al. ● Prediction of Concentration Profiles of Contaminants Variation of copper concentration with distance 0.9 time time time time time time copper concentration (mg/L) 0.8 0.7 0.6 = 5 years = 10 years = 15 years = 20 years = 25 years = 30 years 0.5 0.4 0.3 0.2 0.1 0 0 5 10 15 distance (meters) 20 25 Figure 11. Variation copper concentration with distance concentration-distance-time graph for copper 1 copper concentration (mg/L) 0.9 1 0.8 0.8 0.7 0.6 0.6 0.4 0.5 0.4 0.2 0.3 0 200 0.2 150 60 100 40 50 distance (in meters) 20 0 0 0.1 0 time (in years) Figure 12. Variation-distance-time graph for copper 374 IJRRAS 15 (3) ● June 2013 Salami & al. ● Prediction of Concentration Profiles of Contaminants Variation of iron concentration with time at varying depths 8 depth depth depth depth depth depth iron concentration (mg/L) 7 6 = 5m = 10m = 15m = 20m = 25m = 30m 5 4 3 2 1 0 0 10 20 30 time (years) 40 50 60 Figure 13. Variation of iron concentration with time Variation of iron concentration with distance 9 time time time time time time 8 iron concentration (mg/L) 7 6 = 5 years = 10 years = 15 years = 20 years = 25 years = 30 years 5 4 3 2 1 0 0 5 10 15 distance (meters) 20 Figure 14. Variation of iron concentration with distance 375 25 IJRRAS 15 (3) ● June 2013 Salami & al. ● Prediction of Concentration Profiles of Contaminants concentration-distance-time graph for iron 11 10 9 10 8 8 7 6 6 4 5 2 4 0 200 3 iron concentration (mg/L) 12 150 60 100 40 50 distance (in meters) 20 0 0 2 1 0 time (in years) Figure 15. Concentration-distance-time graph for iron 4. DISCUSSION The average concentrations of chlorides and heavy metals in leachate from Bhalaswa landfill site and in the groundwater samples at varying radial distances from the landfill are shown in Table 1. The average concentration of chlorides in the leachates from the Bhalaswa landfill site has been found to be 4000mg/L. For the purpose of simulation, the maximum concentration has been taken as 2000mg/L as the landfill has been started about 15 years back, with continuous addition of landfill mass at the top of it, resulting in progressive increase of its height to the present day condition of about 22m height (Jhamnani and Singh, 2009). Use of maximum concentration of chlorides as 2000mg/L thus seems quite reasonable (Thamnani and Singh, 2009). The maximum concentrations of copper, nickel and zinc were less than 10mg/L respectively while that of iron was 20mg/L. The use of maximum concentration of copper, nickel, zinc and iron as 0.1mg/L, 5.0mg/L, 5mg/L and 11mg/L respectively thus seems quite reasonable considering the concentration in ground water samples at radial distance. It can be seen that the variation of contaminants concentration show behaviour typical of a convectional landfill system. Simulated contaminants concentration in groundwater below the landfill facility increases, reaches a peak and then declines. Figures 1 and 2 show the variation of chlorides concentration with time and distance in landfill leachates while Figure 3 shows the concentration – distance – time graph for chloride. The graphs show the typical shape of a variation of contaminants with time and distance in landfill leachates. It was observed that as the depth increases, the time taken for the appearance of contaminants increases, that is, the depth is directly proportional to the time of appearance of contaminants. From Figure 1 it can be seen that at a depth of 6m, the concentration of chloride is about 1,150mg/L at the end of 15 years. The observed concentration of 1,174.2mg/L for chloride appears to be quite in agreement with the simulated concentration. At the age of 50years, the concentration of chloride at the depth of 6m will be about 500mg/L. From Figure 2, the concentration of chloride at the age of 5 years is the highest which is attributed to the fact that as time increases, the concentration of contaminants decreases with increase in depth. Figures 7 and 5 show the variation of zinc concentration with time and distance, respectively while Figure 8 shows the concentration - distance - time graph for zinc. The graphs also show the typical shape of variation of contaminants with time and distance, in landfill leachates. It was observed that as the depth increases, the time taken 376 IJRRAS 15 (3) ● June 2013 Salami & al. ● Prediction of Concentration Profiles of Contaminants for the appearance of contaminants increases which is in line with the chloride variation. From Figure 4, it can be seen that at a depth of 6m, the concentration of zinc is about 3.2mg/L at the age of 15years. The observed concentration of 3.37mg/L appears to be quite in agreement with the simulated concentration. At the age of 50 years, the concentration of zinc at the depth of 6m will be about 1.3mg/L. From Figure 5 the concentration of zinc at the age of 5 years is the highest which is in line with the fact that as time increases, the concentration of contaminants decreases with increase in depth. Figures 7 and 8 show the variation of nickel concentration with time and distance respectively while Figure 9 shows the concentration – distance – time graph for nickel. The graphs portray the typical shape expected for variation of contaminants in landfill leachates. It was also observed that as the depth increases, the time taken for the appearance of the contaminants increases. From Figure 9 the concentration of nickel at 6m depth is 0.6mg/L at the age of 15years while the observed concentration is 0.43mg/L. At the age of 50 years, the concentration of nickel at the depth of 6m would have reduced to about 0.35. From Figure 8 the concentration of nickel at the age of 5 years is the highest while the lowest concentration is at the age of 30 years. Figures 10 and 11 show the variation of copper concentration with time and distance respectively while Figure 12 shows the concentration – distance – time graph for copper. The graphs are in line with the typical shape expected of contaminants variation in landfill leachates. From Figure 10 the concentration of copper at the depth of 6m, at the age of 15years is about 0.58mg/L. The observed concentration of 0.1mg/L appears to deviates from the simulated concentration of approximately 0.58mg/L. At the age of 50years, the concentration of copper at the depth of 6m would have reduced to 0.25mg/L. From Figure 11 the concentration of copper at the age of 5years is the highest while the lowest concentration is at age of 30years which follows the same pattern with other contaminants. Figures 13 and 14 show the variation of iron concentration with time and distance respectively while Figure15 shows the concentration-distance-time graph for iron. The shapes of the graphs are in line with the shape of the graph of other contaminants in landfill leachates. From Figure 13, the concentration of iron at the depth of 6m at the age of 15years is about 6.8mg/L. The observed concentration of 7.04mg/L appears to be in agreement with the simulated concentration. At the age of 50years, at the depth of 6m, the concentration of iron would have reduced to about 2.8mg/L. From Figure 14, the concentration of iron at the age of 5years appears to be the highest while the lowest concentration is at 30years which is in line with the other contaminants from landfill leachates. 5. CONCLUSION The simulated concentrations of contaminants due to leachate are in consonance with the observed concentrations of contaminants due to leachate. The developed one dimensional transport model can be used as a tool to predict the concentrations of contaminants due to leachates in soil and groundwater. The natural attention of inorganic contaminants reduces the health and environmental risk posed by these contaminants by changing the amount of exposure, the exposure pathway or the toxicity of the chemicals. The leachate from Bhalaswa landfill is also of low quality because it exceeded the threshold limit set by the regulatory body. As the depth increases, the concentrations of contaminant in leachate decrease with increase in time. 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