Abstract
In this study, the ability of low-cost composite adsorbents to treat organic compounds in terms of chemical oxygen demand (COD), total nitrogen (TN), and total phosphorus (TP) was investigated. The composite adsorbents were composed of washed sea sand (WSS), dewatered alum sludge (DAS), zero-valent iron (ZVI), and granular activated carbon (GAC). The removal efficiency of COD in landfill leachate by a composite adsorbent (composed of WSS (40%), DAS (40%), ZVI (10%), and GAC (10%) in weight) was 79.93 ± 1.95%. The corresponding adsorption capacity was 8.5 mg/g. During batch sorption experiments, the maximum COD removal efficiencies given by DAS, WSS, ZVI, and GAC were 16, 51.3, 42, and 100.0%, respectively. The maximum removal efficiencies of the above composite adsorbent for TN and TP were 84.9 and 97.4%, respectively, and the adsorption capacities were 1.85 and 0.55 mg/g, respectively. The Elovich isotherm model gave the best fit for COD, TN, and TP adsorption. This composite adsorbent can treat more than one contaminant simultaneously. The application of DAS and ZVI to make an efficient adsorbent for wastewater treatment would be a good re-use application for them, which would otherwise be landfilled directly after their generation.
HIGHLIGHTS
This investigates the applicability of composite adsorbents made of low-cost/waste materials, in landfill leachate treatment.
The composite adsorbent has considerable treatment potential.
This can be an initial study leading to further studies.
The use of waste materials makes treatment economically and practically feasible.
This is also a sustainable re-use application for the waste to be dumped.
INTRODUCTION
Landfill leachate is one of the unavoidable products of landfilling. Rainwater percolates through waste layers of landfills to produce leachate, which drains into groundwater and leads to water quality deterioration. Landfill leachate usually contains a variety of potentially hazardous inorganic and organic compounds. The characteristics of landfill leachate depend on the constituents contained in the solid waste having been dumped. There are many factors affecting the quality of leachate: age, precipitation, seasonal weather variation, waste type, and composition (Renou et al. 2008). Even within a single landfill site, variability is frequently evident. The characteristics of the landfill leachate can usually be represented by the basic parameters, such as chemical oxygen demand (COD), biological oxygen demand (BOD), the ratio BOD/COD, pH, suspended solids (SS), ammonia nitrogen (NH3-N), Total Kjeldahl Nitrogen (TKN), inorganic salts, and heavy metals (Renou et al. 2008). In the deposited wastes, usually, organics are still present even after thorough waste separation, mainly due to dirty packages and other remains that could not be completely separated; thus, microbial processes dominate the stabilization of the waste and lead to the generation of landfill gas and dictate the amount and composition of the leachate (Gotvajn & Gotvajn 2015). Organic pollutants include pesticides, fertilizers, hydrocarbons, phenols, plasticizers, biphenyls, detergents, oils, greases, pharmaceuticals, proteins, and carbohydrates (Ali et al. 2012). The organic matter present in landfill leachate is biodegradable, but also refractory to biodegradation. Untreated landfill leachates can permeate into groundwater or mix with surface waters and contribute to the pollution of soil, groundwater, and surface waters. During the decomposition process of organic pollutants, the dissolved oxygen in the receiving water may be consumed at a greater rate than it can be replenished, causing oxygen depletion and severe consequences for the stream biota. Wastewater with organic pollutants contains large quantities of SS, which reduce the light available to photosynthetic organisms and, on settling out, alter the characteristics of the river bed, rendering it an unsuitable habitat for many invertebrates (Rashed 2013). Among the substances contained in landfill leachate are several compounds classified as potentially hazardous: bio-accumulative, toxic, genotoxic (chemical compounds that damage the genetic information within a cell causing mutation that may lead to cancer), and they could have an endocrine disruptive effect (Renou et al. 2008). If the landfill leachate is managed poorly, and engineering methodologies such as liner systems and non-permeable outer layers are avoided, water quality and other environmental issues would arise along with health issues, thus treatment of landfill leachate is important (Michael-Kordatou et al. 2015). Hazardous substances from the landfill leachate should be caught and removed properly, to avoid spreading in the receiving environment.
Landfill leachate can be treated by different physico-chemical and biochemical methods and their combinations. Often, biochemical processes are employed if biotreatability in terms of low toxicity and at least moderate biodegradability of the leachate is indicated (Kurniawan et al. 2006; Renou et al. 2008). Landfill leachate treatment methods have been explained by Raghab et al. (2013) as follows: aerobic biological treatment such as activated sludge, aerated lagoons, and stabilization ponds; anaerobic biological treatment such as anaerobic lagoons, reactors; physiochemical treatment such as air stripping, pH adjustment, chemical precipitation, oxidation, and reduction; coagulation using lime, alum, ferric chloride, and land treatment and advanced techniques such as carbon desorption and ion exchange. Efficient treatment methods must be matched with the actual characteristics of a particular leachate and they could vary with time. Efficient techniques for the removal of highly toxic organic compounds from water are coagulation, filtration with coagulation, precipitation, ozonation, adsorption, ion exchange, reverse osmosis, and advanced oxidation process (Rashed 2013). Out of the main types of treatment for landfill leachates such as coagulation/flocculation, chemical oxidation, and reverse osmosis (Renou et al. 2008; Sadegh et al. 2017), adsorption, a physiochemical process, is one of the most popular methods to treat landfill leachate (Güneş 2014; Gotvajn & Gotvajn 2015) due to its simplicity, efficiency, and economic benefits (Sadegh et al. 2017; Crini et al. 2019).
Low-cost and locally available adsorbents would play a vital role in landfill leachate treatment using adsorption. Materials such as agricultural wastes, industrial solid wastes, biomass, clay minerals, and zeolites are among such materials (Halim et al. 2010; Dada et al. 2012; Farouq & Yousef 2015; Gisi et al. 2016; Hossain et al. 2016; Sadegh et al. 2017). Previous studies have investigated different low-cost adsorbents for wastewater treatment. Limestone could effectively remove ammonia and metals from landfill leachate (Aziz et al. 2001). Rice husk, an agricultural waste, has been used as an adsorbent for many organic and inorganic pollutants (Chuah et al. 2005). Granular activated carbon (GAC) is widely applied (Dada et al. 2012; Fu et al. 2014; Kulikowska 2016; Erabee et al. 2018; Crini et al. 2019; Kuang et al. 2020), but either expensive or not readily available. Zero-valent iron (ZVI) represents the most common metallic reducing agent for the treatment of toxic contaminants, since it is available at low-cost, particularly as a waste (scrap) material (Bigg & Judd 2000). Indeed, it has been reported that many types of scrap steel can be substituted for iron with little change in reaction efficiency (Gillhan & O'Hannesin 1994). Alum sludge is a by-product of using aluminium sulphate as a coagulant in the purification of drinking water in water treatment plants (Yang et al. 2006a, 2006b). According to the same authors, in many countries, dewatered alum sludge (DAS), which possesses only minor soil fertility benefits, is disposed of as waste in a landfill. Obri-Nyarko et al. (2014) stated that there is also evidence that lots of efforts have been made to elucidate the mechanisms of contaminants removal by diverse reactive media and the factors controlling these. However, the same authors stressed the need for more research as an understanding of these mechanisms is still lacking, particularly for newly discovered materials.
A composite adsorbent made of several low-cost adsorbents would possess more advantages as a treatment method for landfill leachate. The availability of multiple properties caused by more than one material would lead to the simultaneous occurrence of several processes. According to Zhou et al. (2014) and Hadjar et al. (2004), the combination of multiple materials can reduce costs, increase the number of mechanisms available for single- or multi-contaminant removal, and enhance and accelerate removal rates. The highlighted advantage of using a composite adsorbent made of low-cost and locally available materials for landfill leachate treatment would be a possibility to treat several contaminants simultaneously, the ability to reduce the cost for treatment, and the easiness to find the materials as the larger proportion is from naturally available or waste materials (Kulikowska 2016; Sadegh et al. 2017). Further, there is an ability to composite a high percentage of low-cost materials and a low percentage of high-cost materials, which would enhance treatment potential compared to these materials as single adsorbents. Therefore, considering all these requirements, it is high time to investigate techniques, which are low-cost, highly effective, and easy to install, for treating landfill leachate-contaminated groundwater. Hence, this study aimed to investigate the adsorbability of composite adsorbents made of low-cost materials, washed sea sand (WSS), DAS and ZVI, and high-cost GAC, for COD, TN, and TP removal. The objectives were to obtain the removal efficiencies of the individual and composite adsorbents for COD, TN, and TP; to optimize the contact time and adsorbent dosage for each type of adsorption process, and to obtain the best-fit isotherm model characterizing each adsorption process.
METHODOLOGY
Overall procedure
First, all the selected raw materials were collected and processed under the desired conditions. The selected materials were WSS, GAC, and ZVI. Raw landfill leachate was diluted 100 times and then characterized. A 100-time diluted landfill leachate was the adsorbate for all the batch sorption experiments. Three composite adsorbents (C1, C2, and C3) were made by mixing the above four materials into three different proportions. COD, TN, and TP were the target parameters. The following steps were done for COD, TN, and TP separately: first, kinetic experiments were conducted for individual materials and three composite adsorbents. The aim of the kinetic experiments was to optimize the contact time for each adsorbent. Based on the highest removal efficiency of the desired parameter, the best composite adsorbent was selected, and the selected composite adsorbent was continued to be subjected to the next experimental step, whereas the other two composite adsorbents were not subjected to further testing for the parameter of concern. Next, adsorption isotherm experiments were conducted for individual materials along with the selected composite adsorbent. The aim of conducting the adsorption isotherm experiments was to optimize the adsorbent mass. In carrying out the adsorption isotherm experiments, the optimized contact time for each material was used. Finally, a series of batch sorption experiments were conducted for the selected composite adsorbent by varying the initial adsorbate concentration, pH, and stirring temperature, at the optimum contact time and adsorbent mass. Finally, a verification batch test was conducted for the selected composite adsorbent at the optimum contact time and optimum adsorbent mass and at the best pH, temperature, and initial adsorbate concentration. Adsorption isotherms were fitted for each adsorption experiment conducted above using the following isotherm models: Linear, Langmuir, Freundlich, Elovich, and Temkin. The adsorption capacities for each case were also computed.
Raw material collection and characterization
The first step of the experiment was to collect the raw materials used as adsorbents. DAS was obtained from the sludge treatment unit of a local water treatment plant. ZVI was obtained from an iron workshop. Sea sand was obtained from a beach. GAC (particle size < 600 μm) was purchased.
Landfill leachate rich in organic matter and nutrients was collected from the composting yard of a sanitary landfill. This leachate had been generated from organic wastes. Leachate samples were transported to the laboratory, and stored at 4 °C. Leachate was considered as stable as the pH was greater than 5. The characteristics of raw landfill leachate are given in Table 1. Table 1 and supplementary material show the methods used to analyze the wastewater parameters in accordance with Clesceri et al. (1998). COD was measured using the closed reflux method using a COD analyzer and 0–1,500 mg/L of COD vials. pH, conductivity, and oxidation–reduction potential (ORP) were measured using a pH meter, a conductivity meter, and an ORP meter, respectively. TN and TP were measured by the persulfate digestion method using an autoclave and a spectrophotometer at the wavelengths of 225 and 275 nm for TN and 830 nm for TP.
Parameter . | Value . | Method . |
---|---|---|
COD | 93,300 mg/L | Closed reflux method |
pH | 8.07 | pH meter |
Conductivity | 3,301 μS/cm | Conductivity meter |
ORP | −257 mV | ORP meter |
TN | 950.8 mg/L | Persulfate method |
TP | 35.9 mg/L | Persulfate method |
Parameter . | Value . | Method . |
---|---|---|
COD | 93,300 mg/L | Closed reflux method |
pH | 8.07 | pH meter |
Conductivity | 3,301 μS/cm | Conductivity meter |
ORP | −257 mV | ORP meter |
TN | 950.8 mg/L | Persulfate method |
TP | 35.9 mg/L | Persulfate method |
Preparation of the composite adsorbents
Three different composites (C1, C2, and C3) were prepared for batch sorption experiments, with different proportions of selected raw materials as indicated in Table 2. All the particle sizes other than GAC were below 150 μm, and the GAC particle size was 600 μm. All raw materials were mixed by adding about 60% (by weight) of water until the mixture became mouldable. The mixture was allowed to dry in an oven at 104 °C for 24 h (Halim et al. 2010). This hardened composite media was crushed and sieved into a powder below 600 μm particle diameter. All three composites were prepared using the same procedure.
Material . | Proportions (%) on weight basis . | ||
---|---|---|---|
C1 . | C2 . | C3 . | |
DAS | 35 | 40 | 45 |
WSS | 35 | 40 | 45 |
ZVI | 15 | 10 | 5 |
GAC | 15 | 10 | 5 |
Material . | Proportions (%) on weight basis . | ||
---|---|---|---|
C1 . | C2 . | C3 . | |
DAS | 35 | 40 | 45 |
WSS | 35 | 40 | 45 |
ZVI | 15 | 10 | 5 |
GAC | 15 | 10 | 5 |
Batch sorption experiments
The batch sorption experiments were of two types: kinetic experiments and adsorption isotherm experiments. A series of six 250-mL conical flasks, each with 100 mL of 100 times diluted landfill leachate, was placed on a magnetic stirrer at 400 rpm, assuming that the shaking speed allows the whole surface area of adsorbents to come in contact with contaminants over the whole period of each experiment. The study was performed at 25 °C so as to be representative of environmentally relevant conditions.
Kinetic experiments
The flasks were allowed for adsorption to take place during six different contact time values as 15, 30, 60, 90, 120, and 180 min with a constant adsorbent mass of 10 g. Experiments were conducted for all the single and composite adsorbents. After allowing for sedimentation, the supernatants were filtered using 1.2-μm filter papers on a vacuum filtration apparatus. Then, the supernatants were analyzed in terms of COD, TN, TP, pH, ORP, and conductivity. The removal efficiencies of COD, TN, and TP were computed using the contaminant concentration values in the adsorbate (raw influent) and each supernatant.
The models named pseudo-first-order and pseudo-second-order equations were used to analyze the adsorption kinetics of an adsorbate and correlations between adsorbed amounts and time.
Pseudo-first-order equation
Pseudo-second-order equation
Adsorption isotherm experiments
This experiment series was done to obtain the optimum adsorbent mass at the optimized contact time (which had been determined from the kinetic experiments) for every single adsorbent and the selected composite from the kinetic experiments. The mass of adsorbents used for each flask was 3, 6, 9, 10, 12, 15, and 18 g. The wastewater parameters were measured, and the removal efficiencies of COD, TP, and TN were also computed. Then, the results of each adsorption isotherm experiment were fitted to several adsorption isotherm models, to find out the best fit model. Linear, Langmuir, Freundlich, Elovich, and Temkin were the selected adsorption isotherm models (Langmuir 1918; Dada et al. 2012; Aljeboree et al. 2017).
Sensitivity analysis
Using the optimized contact time and adsorbent dosage, batch sorption experiments were conducted by varying the initial adsorbate concentration (160–796 mg/l), pH (2.78–12.56), and the stirring temperature (25–55 °C) to find out their effect on the COD removal efficiency.
Verification test
Finally, a verification test was conducted for the best composite adsorbent for COD adsorption. A batch sorption experiment was conducted for four replicates of the best composite adsorbent at its optimum contact time (from the kinetic experiments), optimum adsorbent mass (from the adsorption isotherm experiments), and optimum initial pH, temperature, and adsorbate concentration (from the sensitivity analysis).
RESULTS AND DISCUSSION
Kinetic experiments
Optimum contact time for COD removal
Reaching the optimum value means that the functional groups of the adsorbent would be efficiently filled by the contaminants (Kuang et al. 2020). Then a decrease in the removal efficiency at the latter periods may occur due to the fact that the collisions between the adsorbate particles and adsorbent, which may result in the desorption of the adsorbate (Said et al. 2014). The COD removal efficiency of the C2 reached its maximum at about 30 min, after which it almost entered the equilibrium state. As the adsorption progresses, an equilibrium of adsorption of the solute between the adsorbate and adsorbent is attained (Rashed 2013). The results showed that the rates of COD removal of all the adsorbents were relatively high before the maximum removal efficiency due to the availability of more vacant sites for adsorption (Thinojah & Ketheesan 2022). This could happen due to the diffusion process where the adsorbate species are most probably transported from the bulk of the solution into the solid phase through intra-particle diffusion, which is often the rate-limiting step in many adsorption processes (Doke & Khan 2012).
Since the C1 and C2 provided less treatment potential for COD, those two adsorbents were not subjected to further experiments in terms of COD removal.
Optimum contact time for TN removal
Material . | Equation . | Determination coefficient (R2) . |
---|---|---|
WSS | t/qt = 0.637t – 4.7115 | 0.8352 |
ZVI | t/qt = 0.4127t – 1.4904 | 0.9256 |
GAC | t/qt = 0.4939t – 11.626 | 0.8465 |
DAS | t/qt = 0.1776t + 0.6607 | 0.8404 |
C1 | t/qt = 1.4176t – 60.207 | 0.5781 |
C2 | t/qt = 0.5593t + 16.096 | 0.2445 |
C3 | t/qt = 0.2737t + 2.2352 | 0.9262 |
Material . | Equation . | Determination coefficient (R2) . |
---|---|---|
WSS | t/qt = 0.637t – 4.7115 | 0.8352 |
ZVI | t/qt = 0.4127t – 1.4904 | 0.9256 |
GAC | t/qt = 0.4939t – 11.626 | 0.8465 |
DAS | t/qt = 0.1776t + 0.6607 | 0.8404 |
C1 | t/qt = 1.4176t – 60.207 | 0.5781 |
C2 | t/qt = 0.5593t + 16.096 | 0.2445 |
C3 | t/qt = 0.2737t + 2.2352 | 0.9262 |
Optimum contact time for TP removal
Adsorption isotherm experiments
Optimum dosage for COD removal
The optimum dosage for TN removal
The optimum dosage for TP removal
Adsorption isotherm models
COD adsorption
Adsorbent . | Adsorption isotherm equation . | ||||
---|---|---|---|---|---|
Linear isotherm . | Langmuir isotherm . | Freundlich isotherm . | Temkin isotherm . | Elovich isotherm . | |
WSS | R² = 0.2035 | R² = 0.656 | R² = 0.4054 | R² = 0.1663 | R² = 0.7835 |
ZVI | R² = 0.68 | R² = 0.52 | R² = 0.59 | R² = 0.69 | R² = 0.87 |
GAC | R² = 0.7051 | R² = 0.6189 | R² = 0.3964 | R² = 0.4931 | R² = 0.0001 |
DAS | R² = 0.24 | R² = 0.73 | R² = 0.5773 | R² = 0.2413 | R² = 0.8262 |
C2 | R² = 0.0286 | R² = 0.2709 | R² = 0.1035 | R² = 0.0269 | R² = 0.8391 |
Adsorbent . | Adsorption isotherm equation . | ||||
---|---|---|---|---|---|
Linear isotherm . | Langmuir isotherm . | Freundlich isotherm . | Temkin isotherm . | Elovich isotherm . | |
WSS | R² = 0.2035 | R² = 0.656 | R² = 0.4054 | R² = 0.1663 | R² = 0.7835 |
ZVI | R² = 0.68 | R² = 0.52 | R² = 0.59 | R² = 0.69 | R² = 0.87 |
GAC | R² = 0.7051 | R² = 0.6189 | R² = 0.3964 | R² = 0.4931 | R² = 0.0001 |
DAS | R² = 0.24 | R² = 0.73 | R² = 0.5773 | R² = 0.2413 | R² = 0.8262 |
C2 | R² = 0.0286 | R² = 0.2709 | R² = 0.1035 | R² = 0.0269 | R² = 0.8391 |
refers to the amount of metal ion adsorbed at equilibrium (mg/g). refers to the equilibrium concentration (mg/L).
Adsorbent . | Best-fitted isotherm . | R2 . | Adsorption capacity (mg/g) . |
---|---|---|---|
WSS | Elovich | 0.78 | 7.0 |
ZVI | Elovich | 0.83 | 2.3 |
GAC | Linear | 0.62 | 24.4 |
DAS | Elovich | 0.87 | 1.7 |
C2 | Elovich | 0.84 | 8.5 |
Adsorbent . | Best-fitted isotherm . | R2 . | Adsorption capacity (mg/g) . |
---|---|---|---|
WSS | Elovich | 0.78 | 7.0 |
ZVI | Elovich | 0.83 | 2.3 |
GAC | Linear | 0.62 | 24.4 |
DAS | Elovich | 0.87 | 1.7 |
C2 | Elovich | 0.84 | 8.5 |
TN adsorption
Table 6 illustrates the different isotherm models for each adsorbent to represent the TN adsorption during the adsorption isotherm experiments (Section 3.2.2). Table 7 and Figure 13 give the adsorption capacities and the best-fitted isotherm models for each adsorbent for adsorption of TN. The TN adsorption on C2 was represented the best by the Elovich isotherm model at an R2 value of 0.94, which was the best fit among all the tested adsorbents except DAS and ZVI, R2 of whose best-fitted models were slightly higher. This implies that the TN removal of C2 could mainly be due to adsorption. The adsorption capacity of C2 for TN was 1.85 mg/g. It is to be noted that ZVI has given the best adsorption capacity for TN, which was 12.53 mg/g, and C2 is the lowest performer among all the tested adsorbents. It is also to be noted that WSS and DAS have also performed better than GAC. This observation shows that WSS, DAS and ZVI could be efficient adsorbents individually for TN removal though they were lower performers than GAC and C2 in COD removal. Hence, in order to produce a better composite that can be applied for multiple contaminant removal simultaneously, further experiments are needed with composite adsorbents composed of all these materials at different compositions. Halim et al. (2012) obtained an adsorption capacity of 37.59 mg/g for ammonia (in terms of ammonia nitrogen, this adsorption capacity is 30.96 mg/g) adsorption, using a composite adsorbent consisting of GAC, zeolite, limestone, and rice husk out of which GAC and zeolite are well-known adsorbents at different composition to the C2. However, it is interesting to observe that ZVI used for the present research (a waste material left out from a metal working shop) has provided a considerably high adsorption capacity, which could be on a par with that of Halim et al. (2012), given the used ZVI being a waste material.
Adsorbent . | Adsorption isotherm equation . | ||||
---|---|---|---|---|---|
Linear isotherm . | Langmuir isotherm . | Freundlich isotherm . | Temkin isotherm . | Elovich isotherm . | |
WSS | R² = 0.0278 | R² = 0.4946 | R² = 2E-07 | R² = 0.0213 | R² = 0.1451 |
ZVI | R² = 0.6699 | R² = 0.8713 | R² = 0.9065 | R² = 0.9388 | R² = 0.9241 |
GAC | R² = 0.5892 | R² = 0.8805 | R² = 0.6063 | R² = 0.5449 | R² = 0.8893 |
DAS | R² = 0.6942 | R² = 0.6402 | R² = 0.5888 | R² = 0.6988 | R² = 0.9479 |
C2 | R² = 0.1743 | R² = 0.7568 | R² = 0.1297 | R² = 0.053 | 242 R² = 0.9386 |
Adsorbent . | Adsorption isotherm equation . | ||||
---|---|---|---|---|---|
Linear isotherm . | Langmuir isotherm . | Freundlich isotherm . | Temkin isotherm . | Elovich isotherm . | |
WSS | R² = 0.0278 | R² = 0.4946 | R² = 2E-07 | R² = 0.0213 | R² = 0.1451 |
ZVI | R² = 0.6699 | R² = 0.8713 | R² = 0.9065 | R² = 0.9388 | R² = 0.9241 |
GAC | R² = 0.5892 | R² = 0.8805 | R² = 0.6063 | R² = 0.5449 | R² = 0.8893 |
DAS | R² = 0.6942 | R² = 0.6402 | R² = 0.5888 | R² = 0.6988 | R² = 0.9479 |
C2 | R² = 0.1743 | R² = 0.7568 | R² = 0.1297 | R² = 0.053 | 242 R² = 0.9386 |
refers to the amount of metal ion adsorbed at equilibrium (mg/g). refers to the equilibrium concentration (mg/L).
Adsorbent . | Fitted isotherm . | R2 . | Adsorption capacity (mg/g) . |
---|---|---|---|
WSS | Langmuir | 0.4946 | 7.01 |
ZVI | Temkin | 0.9388 | 12.53 |
GAC | Elovich | 0.8893 | 3.70 |
DAS | Elovich | 0.9479 | 4.92 |
C2 | Elovich | 0.9386 | 1.85 |
Adsorbent . | Fitted isotherm . | R2 . | Adsorption capacity (mg/g) . |
---|---|---|---|
WSS | Langmuir | 0.4946 | 7.01 |
ZVI | Temkin | 0.9388 | 12.53 |
GAC | Elovich | 0.8893 | 3.70 |
DAS | Elovich | 0.9479 | 4.92 |
C2 | Elovich | 0.9386 | 1.85 |
TP adsorption
Adsorbent . | Adsorption isotherm equation . | ||||
---|---|---|---|---|---|
Linear isotherm . | Langmuir isotherm . | Freundlich isotherm . | Temkin isotherm . | Elovich isotherm . | |
WSS | R² = 0.4681 | R² = 0.0584 | R² = 0.303 | R² = 0.4476 | R² = 0.2166 |
ZVI | R² = 0.8312 | R² = 0.8273 | R² = 0.9143 | R² = 0.9175 | R² = 0.9457 |
GAC | R² = 0.1887 | R² = 0.7196 | R² = 0.1446 | R² = 0.2712 | R² = 0.4604 |
DAS | R² = 0.227 | R² = 0.8417 | R² = 0.1176 | R² = 0.2436 | R² = 0.4518 |
C2 | R² = 0.5372 | R² = 0.477 | R² = 0.6285 | R² = 0.8347 | R² = 0.9372 |
Adsorbent . | Adsorption isotherm equation . | ||||
---|---|---|---|---|---|
Linear isotherm . | Langmuir isotherm . | Freundlich isotherm . | Temkin isotherm . | Elovich isotherm . | |
WSS | R² = 0.4681 | R² = 0.0584 | R² = 0.303 | R² = 0.4476 | R² = 0.2166 |
ZVI | R² = 0.8312 | R² = 0.8273 | R² = 0.9143 | R² = 0.9175 | R² = 0.9457 |
GAC | R² = 0.1887 | R² = 0.7196 | R² = 0.1446 | R² = 0.2712 | R² = 0.4604 |
DAS | R² = 0.227 | R² = 0.8417 | R² = 0.1176 | R² = 0.2436 | R² = 0.4518 |
C2 | R² = 0.5372 | R² = 0.477 | R² = 0.6285 | R² = 0.8347 | R² = 0.9372 |
refers to the amount of metal ion adsorbed at equilibrium (mg/g). refers to the equilibrium concentration (mg/L).
Adsorbent . | Fitted isotherm . | R2 . | Adsorption capacity (mg/g) . |
---|---|---|---|
WSS | Linear | 0.4681 | 1.69 |
ZVI | Elovich | 0.9457 | 0.99 |
GAC | Langmuir | 0.7196 | 0.49 |
DAS | Langmuir | 0.8417 | 0.39 |
C2 | Elovich | 0.9372 | 0.55 |
Adsorbent . | Fitted isotherm . | R2 . | Adsorption capacity (mg/g) . |
---|---|---|---|
WSS | Linear | 0.4681 | 1.69 |
ZVI | Elovich | 0.9457 | 0.99 |
GAC | Langmuir | 0.7196 | 0.49 |
DAS | Langmuir | 0.8417 | 0.39 |
C2 | Elovich | 0.9372 | 0.55 |
Sensitivity analysis for COD adsorption
Effect of pH
Effect of initial adsorbate concentration
Effect of temperature
Verification test for COD
Table 10 shows the results of the verification test conducted on COD adsorption at the optimum contact time, adsorbent dosage, initial pH (5.55), initial adsorbate concentration (398 mg/l), and temperature (35 °C). The average removal efficiency of COD by the four replicates was 79.93 ± 1.95%. The results show that the COD removal efficiency by C2 at the optimum conditions has increased by more than 50% compared to both the kinetic and adsorption isotherm experiments conducted earlier. This implies that with further research, the adsorption capacity of the composite adsorbent could be further enhanced.
Replicates . | Contact time (min) . | Adsorbent mass (g) . | Equilibrium COD (mg/l) Ce . | Efficiency (%) . |
---|---|---|---|---|
1 | 30 | 10 | 86.93 | 78.16 |
2 | 30 | 10 | 75.41 | 81.05 |
3 | 30 | 10 | 71.22 | 82.11 |
4 | 30 | 10 | 85.88 | 78.42 |
Mean value | 79.93 |
Replicates . | Contact time (min) . | Adsorbent mass (g) . | Equilibrium COD (mg/l) Ce . | Efficiency (%) . |
---|---|---|---|---|
1 | 30 | 10 | 86.93 | 78.16 |
2 | 30 | 10 | 75.41 | 81.05 |
3 | 30 | 10 | 71.22 | 82.11 |
4 | 30 | 10 | 85.88 | 78.42 |
Mean value | 79.93 |
Removal of COD, TN, and TP due to adsorption
Adsorption is a complicated process, in which there could be different physical and chemical interactions between the solutes in the adsorbate and adsorbent. Physical adsorption and chemical adsorption are classified according to the bonding forces between the adsorbent and adsorbate; physical adsorption happens due to the attraction force of weak Van der Waal while chemical sorption happens due to the chemical bonding between adsorbate and adsorbent; and when there is concentration difference between adsorbate and adsorbent, the adsorbate molecules in solution move and bind onto the surface of adsorbate (Sazali et al. 2020). Being a weak process in terms of the binding reactions, only a small amount of COD could be removed by physical adsorption. The adsorption process has become complex due to the large number of variables involved such as electrostatic, dispersive, and chemical interactions, intrinsic properties of the solute (solubility and ionization constants), and the intrinsic properties of the adsorbent (such as pore size distribution), solution properties (in particular pH), and the temperature of the system (Moreno-Castilla 2004). Each adsorbent has its own characteristics such as porosity, pore structure, and nature of its adsorbing surfaces (Rashed 2013). Hence, a composite adsorbent composed of multiple materials possessing different functional groups could execute adsorption more efficiently than that of a single adsorbent. As chemical adsorption provides a stronger binding force compared to physical adsorption, a larger amount of adsorbate could be adsorbed via chemical adsorption (Gisi et al. 2016) by the adsorbents tested in this research. Gotvajn & Gotvajn (2015) on the treatment of sanitary landfill leachate using different methods, using a composite medium of gravel, sand and zeolite, obtained 61% COD removal in a subsurface constructed wetland system. The same system showed ammonium nitrogen and orthophosphate-phosphorus removal efficiencies of 37–68% and around 30–83%, respectively (Gotvajn & Gotvajn (2015). Further, Güneş (2014) identified that when the adsorption process of GAC is combined with the coagulation/flocculation process, the COD removal efficiency was 56–63% with the optimum GAC dosage of 18 g/L in a 12-h contact period. The above authors identified that when the adsorption process is combined with coagulation/flocculation process (which removes particles and could be considered as a pre-treatment for adsorption), the efficiency increases.
The mechanism of adsorption described by the Elovich model is based on chemical reactions which are responsible for adsorption. The Elovich isotherm model was the best-fitted isotherm model for C2 for COD, TN, and TP adsorption. According to Elovich (1962), this isotherm is based on a chemisorption kinematic principle in heterogeneous surfaces, and it has the capability to present the rates of adsorption and desorption. It is based on a kinetic principle assuming that the adsorption sites increase exponentially with adsorption, which implies multilayer adsorption (Farouq & Yousef 2015). The equation may be used as a basis for a quantitative interpretation of rates of adsorption and desorption.
In DAS, there are abundant amorphous aluminium ions, which are very effective for phosphorus removal in wastewater because the ions enhance the process of adsorption and chemical precipitation (Yang et al. 2006a, 2006b). Using DAS is environmentally and economically beneficial. Sea sand is formed naturally as a result of the weathering process, and it is an abundant material. Silicon dioxide is the most common chemical composition in sea sand. According to the same author, some research has been done to remove Cu(II) from aqueous solutions using treated sea sand.
According to the results of the present and past research, the removal efficiency greatly depends on the contact time, adsorbent dosage, initial pH, initial adsorbate concentration, temperature, rotational speed, etc. The COD removal efficiency by the composite adsorbent 2 increased significantly when the batch sorption experiment was carried out at the optimum conditions. Hence, there is a great possibility to improve the treatment efficiencies of the adsorbents subjected to the batch sorption experiments in this research. The results also revealed that the output could be changed by altering the composition of the composite adsorbent as different compositions of the composite adsorbent gave different performances for different parameters. In addition, the treatment efficiency of these single and composite adsorbents could be increased by subjecting them to different processes to enhance the adsorption properties. The fact that a composite made of several unprocessed low-cost materials gave a good performance would imply that they would give far better performances after being subjected to different property enhancement processes.
CONCLUSION AND RECOMMENDATIONS
The aim of this research was to investigate the treatment potential of composite adsorbents made of WSS, DAS, ZVI, and GAC in treating organic matter (COD), total nitrogen (TN), and total phosphorus (TP) in landfill leachate. Hence, three composite adsorbents were prepared by varying the composition of the above materials. Two types of batch sorption experiments were conducted, namely, kinetic experiments and adsorption isotherm experiments. Seven types of adsorbents, namely WSS, DAS, ZVI, GAC, composite 1 (C1), composite 2 (C2), and composite 3 (C3) were subjected to batch sorption experiments.
In both the kinetic and adsorption isotherm experiments, GAC had the highest COD removal efficiency. C2 had the second highest COD removal efficiency in the kinetic experiments, whereas its COD removal efficiency in the adsorption isotherm experiments was the third highest and was slightly lower than that of WSS. A sensitivity analysis was conducted for C2 to find the effects of pH, temperature, and initial COD concentration on COD adsorption. Accordingly, three optimum values were obtained for pH, temperature, and the initial COD concentration. After that, a verification test was conducted for C2 by carrying out a batch sorption experiment for four replicates of C2 at its optimum time, adsorbent mass, pH, temperature, and initial COD concentration. In the verification test, the average removal efficiency of C2 was 79.93 ± 1.95%. The COD removal efficiency of C2 had improved by more than 50%. The COD adsorption capacities of C2, GAC, WSS, ZVI, and DAS were 8.5, 24.4, 7.0, 2.3, and 1.7 mg/g, respectively. Hence, it can be concluded that C2 is a potential low-cost adsorbent for COD removal in landfill leachate.
The maximum TN removal efficiencies of WSS, ZVI, DAS, C2, GAC, C1, and C3 were 93.6, 91.9, 86.8, 84.9, 62.3, 52.2, and 52.2%, respectively. The maximum TP removal efficiencies of C2, WSS, C1, ZVI, DAS, GAC, and C3 were 97.4, 97.4, 96.1, 94.4, 92.7, 85.7, and 89.9%, respectively. Unprocessed WSS, ZVI, DAS, and C2 are better at TN and TP removal efficiencies than those of GAC, which is a well-known and expensive adsorbent, whereas all the other materials are low-cost or waste materials. WSS, DAS, and ZVI were obtained free of charge, whereas GAC was purchased. Hence, it can be concluded that WSS, ZVI, DAS, and the composite adsorbents subjected to batch sorption experiments under this research have the potential to treat COD, TN, and TP in landfill leachate simultaneously. Further, it can be concluded that the adsorption characteristics of individual materials can enhance the adsorption characteristics of a composite adsorbent.
When compared with individual materials, the importance of the composite adsorbent lies in the fact that it can treat more than one contaminant simultaneously. In addition, the application of DAS and ZVI as efficient adsorbents is a sustainable approach as proper disposal sites are highly limited. The results of the present research show the ability of the best-performing composite adsorbent to be used as a packing medium in any landfill leachate treatment system or any treatment system targeting COD, TN, and TP of any given wastewater of the same characteristics as landfill leachate. A packing medium can be either a filtration medium in ‘pump-and-treat’ treatment systems or a reactive medium in permeable reactive barriers.
This research was done over 180 min and for a mass range of 3–18 g. Testing composites with the same materials at different proportions is recommended as further research in order to check any improvement to the treatment efficiency. Furthermore, bench-scale, pilot-scale, and field-scale experiments are recommended to be conducted to check the practical applicability of this type of composite adsorbent. In order for the effluent of this type of composite adsorbent to comply with the discharge standards, the treatment system having the composite medium should be either pre-treated or post-treated.
ACKNOWLEDGEMENT
The technical officer, Ms D.A.M. Nimal Shanthi, of the Department of Civil and Environmental Engineering, Faculty of Engineering, University of Ruhuna is greatly acknowledged.
DATA AVAILABILITY STATEMENT
All relevant data are included in the paper or its Supplementary Information.
CONFLICT OF INTEREST
The authors declare there is no conflict.