The ability of a composite adsorbent composed primarily of various waste materials to adsorb heavy metals, NH3-N, and chemical oxygen demand (COD) from landfill leachate was investigated through batch sorption experiments. The study determined the optimal contact time and adsorbent dosage for the removal of Pb, Zn, Cu, Fe, NH3-N, and COD to be 15, 90, 30, 180, 30, and 30 min, respectively. The corresponding optimum adsorbent dosages were determined to be 5, 30, 5, 15, 5, and 30 g, respectively. The composite adsorbent exhibited high removal efficiencies, achieving the following maximum values: 96.4% for Pb, 92.7% for Zn, 60.3% for Cu, 87.1% for Fe, 75.0% for NH3-N, and 67.5% for COD. Pb and Fe showed the best fit with a Langmuir isotherm model, with corresponding adsorption capacities of 0.0165 and 1.14 mg/g, respectively. For Zn, Cu, NH3-N, and COD, the equilibrium data demonstrated the best fit with an Elovich isotherm model, with adsorption capacities of 0.004, 0.005, 0.016, and 4.29 mg/g, respectively. The kinetic data followed the pseudo-second-order kinetic model. It presented a potential solution for the disposal of the waste from which it was derived.

  • This investigates the applicability of a composite adsorbent 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 reuse application for the waste to be dumped.

Water is undeniably the most crucial resource on Earth. However, with the world's development, water pollution is escalating day by day, leading to a scarcity of clean water and causing suffering for many people. There are numerous factors contributing to water pollution, and one significant cause is the generation of wastewater from solid waste production. Both municipal and industrial solid waste production have rapidly increased due to the rise in affluence and continuous growth in industrial and commercial sectors worldwide over the past few decades (Renou et al. 2008). Consequently, the proper disposal of municipal and industrial solid waste has become a pressing global issue. If the waste generated by municipal and industrial operations is not adequately managed, it can have a detrimental impact on the quality of natural surface and groundwater systems.

There are several methods available for solid waste disposal. According to Kumar (2021), sanitary landfilling is currently the most commonly employed approach for eliminating municipal solid waste (MSW) in many countries. However, landfilling presents a major challenge in the form of leachate generation. Landfill leachate refers to an aqueous solution that contains various toxic pollutants, including heavy metals, organic pollutants, and nitrogenous compounds, which are formed as precipitation percolating through waste accumulated in landfill sites (El-Saadony et al. 2023). The characteristics of landfill leachate are influenced by several factors such as the composition of the deposited waste materials, moisture movement within the landfill, specific climate conditions, refuse quality, and particularly, the age of the landfill (Alkalay et al. 1998). In addition to these factors, Mohammadi et al. (2023) investigated how leachate characteristics change due to seasonal variations. According to their study, chemical oxygen demand (COD) and total kjeldahl nitrogen (TKN) levels in landfill leachate were higher during the spring season. However, when considering cations, no significant differences in cation concentrations were identified between different seasons. Furthermore, they explained that there was no significant difference in most of the metal ions, except for Na+, across different seasons. Furthermore, Mohammadi et al. (2022) found that landfill leachate contains microplastics. The same authors explained that the high salinity, hot climate conditions, and low rainfall in the research area resulted in an increased presence of microplastics.

Heavy metals, organic contaminants, and nitrogenous compounds are among the pollutants commonly found in landfill leachate. Heavy metals, classified as nonbiodegradable components (Bailey et al. 1999), pose a threat to living organisms, causing health issues in both fauna and flora. Lead (Pb) is a prevalent heavy metal found in drinking water and wastewater, and its consumption can lead to health hazards such as mental retardation, decreased blood hemoglobin levels, and damage to the nervous system (Sazali et al. 2020). Organic contaminants, including herbicides, pesticides, and plant and animal tissues, have been identified as potential sources of adverse impacts on the environment and human health (Mandal et al. 2016). Nitrates and nitrites, on the other hand, can cause poisoning, particularly in infants and children, resulting in symptoms such as methemoglobinemia, which is a fatal disease (Knobeloch et al. 2000). Moreover, these compounds contribute to the formation of potential nitrogen-based carcinogens, which have been linked to the development of stomach cancer. When leachate seeps into the ground or mixes with runoff, it pollutes nearby groundwater and surface water sources, adversely affecting aquatic organisms such as fish and amphibians (Gola et al. 2016). Therefore, it is crucial to treat landfill leachate before it mixes with surface and groundwater.

Various studies have been conducted on the treatment of landfill leachate. Several methods, including coagulation, filtration with coagulation, precipitation, ozonation, adsorption, ion exchange, reverse osmosis, and advanced oxidation processes, have been employed for the removal of organic pollutants from polluted water and wastewater (Rashed 2013). However, these methods often involve high capital and operational costs, limiting their widespread application (Rashed 2013). The removal of heavy metals typically requires the use of chemical reagents to precipitate these metals from the solution (Aziz et al. 2001). Among the available techniques for wastewater treatment, the adsorption process using solid adsorbents shows promise as one of the most effective methods for treating and removing organic contaminants (Aziz et al. 2001). Adsorption can also be effective in treating heavy metals and ammonia. Two commonly used adsorbents are activated carbon and zeolite (ZE). Activated carbon is particularly effective in removing organic compounds and heavy metals. Zeolites, on the other hand, have shown good adsorption capacity for lead ions within a pH range of 6–8 (Ahmed et al. 2006). However, many of the currently used adsorbents are expensive and not readily available. Therefore, it is crucial to conduct research on low-cost adsorbents. In addition, combining multiple adsorbents into a composite material has shown promising results. Consequently, investigating the removal of multiple pollutants from landfill leachate using a composite adsorbent (CA) made from waste materials would be timely and of great importance.

The aim of the present research was to investigate the applicability of a CA made from multiple waste materials, namely, roof waste (RW), brick and mortar waste (BMW), concrete waste (CW), biochar (BC), sawdust (SD), sugar cane bagasse powder (SCBP), coconut coir pith (CCP), dewatered alum sludge (DAS), washed sea sand (WSS), zero valent iron (ZVI), and 10% granular activated carbon (GAC), in the treatment of heavy metals, ammonia nitrogen (NH3-N), and COD in landfill leachate using batch sorption experiments. The objectives were to optimize the contact time and adsorbent dosage, estimate adsorption capacities, and determine the best-fit adsorption model for each adsorbate. Batch sorption experiments are used to identify suitable adsorbents for the treatment of pollutants in wastewater. The aforementioned waste materials were combined to form a single adsorbent due to the proven chemical and physical properties exhibited by each of these waste materials. Whether considered as a whole or as individual components, these waste materials have shown their efficacy in treating particular pollutants in wastewater. Building waste materials, such as clay, Portland cement, fine and coarse aggregate, and admixtures like fly ash and plasticizers (Mohamed & Antia 1998), possess a high capacity for removing heavy metals. Clay minerals possess excellent sorption, ion exchange, and expansion properties, which makes them widely employed for adsorbing various pollutants from large volumes of aqueous solutions (Zhao et al. 2011). Section 2.2 provides a more detailed description of research that has been conducted using the aforementioned adsorbents for wastewater treatment. According to Metcalf & Eddy (2003), the application of adsorption to wastewater treatment often involves mixtures of organic compounds. While the adsorptive capacity of an individual compound in a solution with multiple compounds may vary, the total adsorptive capacity of the adsorbent can be greater than that of a single compound (Metcalf & Eddy 2003). Semi-synthetic adsorbents, which combine natural materials with chemicals, exhibit high adsorption capacities and are cost-effective compared to synthetic adsorbents (Sazali et al. 2020). Since the CA is composed of a mixture of natural materials combined with GAC, its adsorption capacity is expected to be higher than that of a single material. The novelty of this research lies in the effectiveness of the CA for the simultaneous removal of multiple pollutants, including heavy metals, COD, and nitrogen. Furthermore, the CA's ability to effectively remove these pollutants suggests its broad applicability and potential as a promising solution for treating various types of wastewater. This research represents a significant advancement in the development of sustainable and efficient wastewater treatment technologies, which are crucial for environmental protection and human health.

Adsorption

Adsorption is a technique used to separate substances from gas or liquid phases by binding them onto the surface of an adsorbent (Yusuff et al. 2013). This method employs selective separation mechanisms, where one or more components from a mixture are adsorbed onto a surface. Adsorbents can be classified as synthetic, natural, or semi-synthetic (Sazali et al. 2020). According to Naftz et al. (2002), adsorption is the process in which an aqueous chemical species attaches to a solid surface. Most adsorption reactions are reversible and occur at relatively rapid rates. Some reactions are specific, preferentially attaching to particular sites, while others are less specific and compete with other ions for attachment sites. Adsorption can be categorized as physical or chemical depending on the bonding forces between the adsorbent and adsorbate. Physical adsorption occurs due to the weak Van der Waals attraction force, whereas chemical sorption happens through chemical bonding between the adsorbate and adsorbent. When there is a concentration difference between the adsorbate and the adsorbent, the adsorbate molecules in solution move and bind onto the surface of the adsorbent (Sazali et al. 2020). In simpler terms, adsorption involves the bonding of molecules between the adsorbent and adsorbate. While most solids possess adsorbent characteristics, the performance of adsorption varies depending on their functional groups. Currently, adsorption technology is being adopted for treating textile effluents that contain mixtures of dyes and heavy metals (Sazali et al. 2020). The extent of inhibition caused by competing compounds is related to the size of the molecules being adsorbed, their adsorptive affinities, and their relative concentrations. It is important to note that adsorption isotherms can be determined for heterogeneous mixtures of compounds (Metcalf & Eddy 2003).

Potential low-cost adsorbents

RW and BMW, which are composed of clay, have been found to be effective for wastewater treatment. Wasted ‘clay tiles’, commonly known as ‘roof tiles’, were chosen as the RW for this research. Roof tiles are a popular choice for covering roof areas. Furthermore, it has been proven that quarry dust, which is often included in construction and demolition waste, exhibits high removal efficiencies for heavy metals in landfill leachate. Another beneficial material for wastewater treatment is BC, which efficiently removes various organic, inorganic, and microbial contaminants. BC is a carbon-rich solid derived from biomass, such as wood or manure, through a process of heating with limited or no oxygen (Sohi 2012). ZVI is a commonly studied metallic reducing agent for treating a range of toxic contaminants. It is particularly attractive due to its low cost and availability as waste scrap material. Numerous studies have shown that ZVI can effectively reduce chlorinated hydrocarbons (Gillham & O'Hannesin 1994), nitrates (Siantar et al. 1996), and Cr6+ (Gould 1982). Fenglian et al. (2014) reported that ZVI is widely utilized for the treatment of toxic contaminants in groundwater and wastewater. Various waste materials have been successfully used to produce activated carbon, including waste wood, bagasse, coir pith, orange peel, coffee husk, pinecone, coconut tree, sunflower seed hull, pine-fruit shell, hazelnut husks, rice hulls, oil palm shell, and coconut husk (Aljeboree et al. 2017). SD has demonstrated effective adsorption properties for heavy metals and has been widely used as an adsorbent (Sazali et al. 2020). Familusi et al. (2018) found that SD effectively improved the physicochemical parameters of water samples but did not provide sufficient removal of bacteriological parameters. Renu & Singh (2017) explained that sugarcane bagasse, which is rich in hydroxyl and phenolic groups, can be chemically modified to enhance its adsorption capacity for pollutants. The same authors discovered that CCP exhibited a high removal efficiency of 70% for chromium. Treated sea sand has also proven effective for Cu (II) removal from aqueous solutions (Nagasinghe et al. 2021). GAC is a conventional reactive material widely used in ex situ pump-and-treat techniques for groundwater treatment, as well as in water and wastewater treatment plants. Batch sorption experiments on total iron removal conducted by Dayanthi et al. (2023), using a CA composed of 40% BMW and BC, 10% ZE, and 10% GAC, showed a removal efficiency of 95.0%. In comparison, high-cost ZE and GAC achieved removal efficiencies of 89.7% and 93.0%, respectively. The CA demonstrated a total iron removal efficiency of 99.2% under optimum conditions, with an average adsorption capacity of 1.3 mg/g. Regarding the removal of COD in landfill leachate, a CA consisting of 40% WSS, 40% DAS, 10% ZVI, and 10% GAC achieved a removal efficiency of 79.93 ± 1.95% and an adsorption capacity of 8.5 mg/g (Perera & Dayanthi 2023). The maximum removal efficiencies for TN and TP using the same CA were 84.9 and 97.4%, respectively, with adsorption capacities of 1.85 and 0.55 mg/g, respectively. The Elovich isotherm model gave the best fit for COD, TN, and TP adsorption (Perera & Dayanthi 2023).

Furthermore, biosorbents have also been identified as low-cost wastewater treatment materials in past studies. Keshtkar et al. (2019) asserted that industrial remnants and agricultural byproducts can serve as cost-effective biosorbents. Among these adsorbents, materials like agricultural and gardening residues stand out as economically viable choices due to their low energy requirements for production. For instance, Dobaradaran et al. (2016a) investigated the ability of Padina sanctae-crucis algae to remove fluoride by 97% from the aqueous phase. Dobaradaran et al. (2014) noted that shrimp shell waste can serve as an environmentally friendly, effective, and affordable adsorbent for removing fluoride from aqueous solutions, particularly from industrial wastewaters. Moreover, Dobaradaran et al. (2015) demonstrated that seed ash from Moringa oleifera can effectively remove fluoride (F) from aqueous solutions. The extent of F removal varied from 7.23 to 81.14% depending on variables such as the initial F concentration, pH level, ashing temperature, contact time, and the amount of biomass used. Dobaradaran et al. (2016b) investigated the efficiency of treating Pb2+ and Cu2+ from aqueous solutions using cuttlebone. Furthermore, Dobaradaran et al. (2017) investigated the adsorption capacity of Sargassum hystrix algae for fluoride in aqueous solutions. These authors conducted a series of batch experiments, and the results revealed that 100% fluoride removal efficiency was achieved with a 40 g/L adsorption capacity within 60 min of contact time for an initial fluoride concentration of 5 mg/L. In a study conducted by Mahvi et al. (2018), the efficiency of Ziziphus leaves in the removal of F from aqueous solutions was measured. This study found that at a biosorbent dose of 10 g/L, a contact time of 90 min, and an initial F concentration of 12 mg/L, the greatest removal efficiency reached 100%. In addition, Abtahi et al. (2017) conducted sorption experiments to remove fluoride from both synthetic and natural waters using a composite coagulant made from polyaluminum chloride-chitosan (PACl-Ch). The results showed that the PACl-Ch coagulants exhibited excellent performance in reducing fluoride levels while maintaining low residual aluminum levels across various operational conditions. Furthermore, Ghasemi et al. (2016) conducted research using Sargassum hystrix algae from the Persian Gulf coastline in Bushehr, Iran, as a biosorbent for extracting Fe (II) from aqueous solutions. The ability of cuttlebone, specifically the dead biomass of cuttlefish bone, to adsorb lead(II) and copper(II) from aqueous solutions was also identified. Cuttlebone demonstrated substantial potential for self-purification in marine environments and as an efficient medium for metal ion removal from water and wastewater. It had maximal adsorption capacities (qm) of 45.9 mg/g for Pb2+ and 39.9 mg/g for Cu2+, highlighting its effectiveness in metal ion adsorption (Dobaradaran et al. 2017b).

In addition, the efficiency of Rhizopus oryzae fungal biomass in removing fluoride from aqueous solutions was examined by Tangestani et al. (2021). Their batch system experiments determined that a 2 g/L biosorbent dose, a 30-min contact period, and a 2 mg/L initial fluoride concentration were the optimal conditions for achieving a maximum fluoride removal rate of 90.5%. These studies collectively show the feasibility of using fungal biomass as a biosorbent to remove fluoride from aqueous solutions. Shams et al. (2013a) discovered that MSW compost ash exhibited remarkable adsorption properties for removing phosphorus (P) from aqueous solutions. This finding has significant implications for P removal in wastewater treatment and agricultural runoff, offering economically and environmentally desirable potential. Enriching adsorbents with P has also been shown to improve soil quality and accelerate plant growth, making P-enriched compost ash a promising soil amendment. Furthermore, a series of batch experiments were conducted to investigate the applicability of waste aluminum filings as a cost-effective adsorbent for removing fluoride (F) from aqueous solutions (Shams et al. 2013b). Baziar et al. (2017) effectively utilized multiwall carbon nanotubes, which are graphite sheets twisted into tube-like structures, for the treatment of microcystin-LR, a toxin produced by cyanobacteria.

The effect of the particle size of adsorbents on adsorption

The studies conducted by Naeem et al. (2019) and Amarasinghe (2011) investigated the effectiveness of different particle sizes of adsorbent materials for the removal of heavy metals, specifically cadmium (Cd) and lead (Pb), from water. Naeem et al. (2019) found that a particle size of 1 mm for BC was highly effective, removing 99% of Cd from water. This suggests that BC can be a promising adsorbent material for Cd removal from water. On the other hand, Amarasinghe (2011) found that a particle size of 0.85 mm for CCP was effective for removing both Cd and Pb from an aqueous medium. This indicates that CCP may be an effective adsorbent material for the removal of multiple heavy metals from water. In simpler terms, Yang et al. (2006) investigated that smaller particles of sludge improve the adsorption process. This, in turn, leads to a faster reduction of phosphorus in the liquid and a lower concentration of phosphorus at equilibrium, indicating greater removal of phosphorus from the liquid. Their study explained that the particle size ranging from 0.063 to 0.125 mm showed effective removal efficiency for phosphorus. Vinayakamoorththy & Dayanthi (2019) conducted a study where they utilized recycled building waste materials, including CW, BMW, flooring waste, and RW, to remove total iron from landfill leachate. Their findings showed that a particle size range of 0.425 mm resulted in up to 90% removal of total iron. Furthermore, in the study conducted by Ikenyiri & Ukpaka (2016), the adsorption capacity of three different particle sizes (0.8, 1.4, and 3.5 mm) of wood-based adsorbents for the removal of crude oil was investigated. The results revealed that the adsorbent with the smallest particle size (0.8 mm) exhibited the highest adsorption capacity, while those with larger particle sizes (1.4 and 3.5 mm) exhibited lower adsorption capacities. On the basis of an adsorption test on dye, Rehman et al. (2013) stated that the increase in adsorption capacity with the decreasing particle size suggests that a dye preferentially adsorbs on the outer surface and does not fully penetrate the particle due to steric hindrance of large dye molecules. The particle size of an adsorbent plays a very important role in the adsorption capacity of a dye, and the relationship between adsorption capacity and particle size depends on two criteria: (i) the chemical structure of the dye molecule (its ionic charge) and its chemistry (its ability to form hydrolyzed species) and (ii) the intrinsic characteristics of the adsorbent (its crystallinity, porosity, and rigidity of the polymeric chains) (Iqbal et al. 2011).

These findings suggest that there is a direct correlation between surface area and adsorption capacity, with larger surface areas resulting in higher adsorption capacities. Conversely, smaller surface areas are associated with lower adsorption capacities. These results have important implications for the development of efficient and effective methods for the removal of contaminants from wastewater using adsorption. By considering the particle size of the adsorbent material, it may be possible to optimize the adsorption process and improve its efficiency. The different optimal particle sizes observed in these studies may be attributed to differences in the adsorbent materials used, the type of heavy metal being removed, and the water conditions tested. Overall, these studies demonstrate the potential of waste materials as adsorbents for removing heavy metals and other types of adsorbates from water. They also highlight the importance of considering the optimal particle size for effective adsorption.

Composite adsorbent preparation

Each of the chosen materials for preparing the CA (RW, DAS, CCP, SCBP, BMW, SD, BC, WSS, ZVI, GAC, and CW) was collected and subjected to a series of preparation steps. These steps included thorough cleaning through multiple washings, oven-drying at 104 °C for 24 h, and sieving to achieve the required particle size. The selected particle size ranges for each material are shown in Table 1. When selecting the particle size range of each material, the results of the relevant past research were used as much as possible. However, the particle size of the ZVI could not be changed manually, and hence, no attempt was taken to change the particle size range.

Table 1

Particle size ranges for selected materials

MaterialParticle size range (mm)Reference
Saw dust 0–0.85 Ikenyiri & Ukpaka (2016)  
Coconut coir pith 0–0.85 Amarasinghe (2011)  
Biochar 0–1.18 Naeem et al. (2019)  
Dewatered alum sludge 0–0.075 Yang et al. (2006)  
Sugar cane bagasse 0–1.7 – 
BMW, concrete waste and roofing waste 0–0.425 Vinayakamoorththy & Dayanthi (2019)  
ZVI 0 – 0.075 – 
MaterialParticle size range (mm)Reference
Saw dust 0–0.85 Ikenyiri & Ukpaka (2016)  
Coconut coir pith 0–0.85 Amarasinghe (2011)  
Biochar 0–1.18 Naeem et al. (2019)  
Dewatered alum sludge 0–0.075 Yang et al. (2006)  
Sugar cane bagasse 0–1.7 – 
BMW, concrete waste and roofing waste 0–0.425 Vinayakamoorththy & Dayanthi (2019)  
ZVI 0 – 0.075 – 

The CA was prepared with equal proportions of already prepared materials by adding water of 60% by weight and allowed to harden about 1 day. Hardened mixture was crushed to have a powder and sieved to get particles that passed through the sieve of 1.7 mm and stored in an air tight container. Figures 1 and 2 show individual and composite adsorbents, respectively. Figure 3 depicts the variation of particle size of the CA.
Figure 1

Individual adsorbents.

Figure 1

Individual adsorbents.

Close modal
Figure 2

Composite adsorbent.

Figure 2

Composite adsorbent.

Close modal
Figure 3

Particle size distribution for each adsorbent.

Figure 3

Particle size distribution for each adsorbent.

Close modal

Cost estimation for composite adsorbent preparation

Table 2 shows the cost estimate for producing 1 kg of the CA. The cost of preparing 1 kg of the CA is USD 0.41, whereas 1 kg of GAC costs USD 3.84. The cost of using GAC is 9.4 times higher than using the CA.

Table 2

Cost estimate for the preparation of the 1 kg of composite adsorbent

AdsorbentAmount for 1 kg of CA (g)Unit price (for 1 kg) (USD)Price for making 1 kg of the composite adsorbent (USD)
RW 90.9 Free 
BMW 90.9 Free 
CW 90.9 Free 
BC 90.9 0.65 0.06 
SD 90.9 Free 
SCBP 90.9 Free 
CCP 90.9 Free 
DAS 90.9 Free 
WSS 90.9 0.02 0.0016 
ZVI 90.9 Free 
GAC 90.9 3.84 0.35 
Total price for making 1 kg of composite adsorbent   0.41 
AdsorbentAmount for 1 kg of CA (g)Unit price (for 1 kg) (USD)Price for making 1 kg of the composite adsorbent (USD)
RW 90.9 Free 
BMW 90.9 Free 
CW 90.9 Free 
BC 90.9 0.65 0.06 
SD 90.9 Free 
SCBP 90.9 Free 
CCP 90.9 Free 
DAS 90.9 Free 
WSS 90.9 0.02 0.0016 
ZVI 90.9 Free 
GAC 90.9 3.84 0.35 
Total price for making 1 kg of composite adsorbent   0.41 

Landfill leachate

Landfill leachate was collected from a sanitary landfill, which receives MSW from seven wards. Upon collecting and transporting the landfill leachate to the laboratory, the original leachate sample was initially characterized and subsequently stored at 4 °C inside a refrigerator until all the batch sorption experiments were completed. During the batch sorption experiments, the refrigerated leachate was retrieved, allowed to reach ambient temperature, and then diluted by a factor of 50. This 50-fold diluted sample was employed as the influent for all the batch sorption experiments. For each analysis, the target parameter in the influent (i.e., the 50-fold diluted landfill leachate) was measured in replicates, mirroring the approach used for the supernatant samples obtained after the sorption experiments. This ensures that any changes in water quality during storage could not have had a significant impact on the results. The wastewater parameters were analyzed according to the Standard Method of the Examination of Water and Wastewater (1998). Raw leachate parameters (pH, conductivity, oxidation-reduction potential (ORP), temperature, turbidity, heavy metals, COD, and NH3-N) (Supplementary material) were analyzed. Table 3 shows the raw leachate characteristics.

Table 3

Raw leachate characteristics and the method/apparatus used for the analysis

ParameterValueStandard deviationMethod/main apparatus
COD 63,600 mg/L 25.50 Closed reflex method/incubator 
Pb 28.5 mg/L 1.03 Atomic absorption spectrophotometer (AAS) 
Cu 14.6 mg/L 0.94 AAS 
Zn 40.6 mg/L 1.70 AAS 
Fe 654.2mg/L 2.47 UV/Vis spectrophotometer 
NH3-N 36.1 mg/L 0.31 Kjeldahl distillation method 
pH 6.6 0.12 pH meter 
Turbidity 110 turbidimetric turbidity units (NTU) 2.45 Turbidity meter 
Temperature 30 °C 0.14 Thermometer 
Conductivity 31.4 mS/cm 0.25 Conductivity meter 
ORP −209 mV 3.08 ORP meter 
ParameterValueStandard deviationMethod/main apparatus
COD 63,600 mg/L 25.50 Closed reflex method/incubator 
Pb 28.5 mg/L 1.03 Atomic absorption spectrophotometer (AAS) 
Cu 14.6 mg/L 0.94 AAS 
Zn 40.6 mg/L 1.70 AAS 
Fe 654.2mg/L 2.47 UV/Vis spectrophotometer 
NH3-N 36.1 mg/L 0.31 Kjeldahl distillation method 
pH 6.6 0.12 pH meter 
Turbidity 110 turbidimetric turbidity units (NTU) 2.45 Turbidity meter 
Temperature 30 °C 0.14 Thermometer 
Conductivity 31.4 mS/cm 0.25 Conductivity meter 
ORP −209 mV 3.08 ORP meter 

Batch sorption experiments

Batch sorption experiments were conducted in two categories: kinetic experiments and adsorption isotherm experiments. Initially, kinetic experiments were conducted to optimize the contact time for the CA. Subsequently, adsorption isotherm experiments were performed using the optimized contact time to determine the optimum mass that yields maximum removal efficiency for the considered parameter.

Kinetic experiments

Kinetic experiments were performed by contacting 10 g of the CA with 150 ml of 50 times diluted landfill leachate at different contact times (15, 30, 60, 90, 120, and 180 min) at 25 °C with a stirring speed of 400 rpm. Each sample was covered with an aluminum foil and left to settle for 24 h. After settling, each sample was filtered using Whatman-branded GF-3 graded 1.2 μm filter paper. Thereafter, the filtrate was stored in sampling bottles for analysis. The filter papers had a diameter of 5.5 cm and an exceptional temperature resistance up to 550 °C.

Pseudo-first- and second-order equations were applied to the results obtained from the kinetic experiments, and the best-fitted kinetic model for each adsorbate was identified to illustrate adsorption kinetics. In order to analyze the adsorption kinetics of the adsorbate, correlations between adsorbed amounts and time will be observed by testing different mathematical expressions corresponding to pseudo-first-order and second-order equations.

Pseudo-first-order equation (Lagergren 1898):
formula
(1)
where is the adsorption capacity at equilibrium (mg/g), is the adsorption capacity at time t (mg/g), and kad is the pseudo-first-order rate constant of the adsorption (min−1).

If this equation is applicable, a plot of log (qeqt) versus t should give a straight line (Fierro et al. 2008).

Pseudo-second-order equation (Blanchard et al. 1984):
formula
(2)
where h is (mg g−1min−1) and k is the pseudo-second-order rate constant of adsorption (g mg−1min−1).

If pseudo-second-order kinetics is applicable, the graph of t/qt versus t should give a straight line. qe, k, and h can be determined from the slope and the intercept of the graph (Kumar et al. 2010).

Adsorption isotherm experiments

The adsorption isotherm experiments were conducted to determine the optimum adsorbent dosage for the removal of Pb, Zn, Cu, Fe, NH3-N, and COD by using different adsorbent masses (5, 10, 15, 20, 25, and 30 g) with 150 mL of 50 times diluted landfill leachate at the optimized contact time for each contaminant. The temperature and stirring speed were set at 25 °C and 400 rpm, respectively. After the experiment, each sample was covered with aluminum foil and left to settle for 24 h. After settling, each sample was filtered using 1.2 μm filter paper and stored in sampling bottles for analysis.

Sensitivity analysis

The effect of temperature, stirring speed, initial pH, and initial adsorbate concentration was determined using the already optimized contact time and adsorbate dosage for Pb, Fe, and COD adsorption onto the CA.

Effect of temperature

The effect of temperature on the removal efficiencies of Pb, Fe, and COD was examined by varying the temperature from 25 to 60 °C (25, 30, 35, 40, 50, and 60 °C), using the obtained optimum contact time and adsorbent dosage for each adsorbate at 400 rpm.

Effect of stirring speed

The effect of stirring speed on the removal efficiencies of Pb, Fe, and COD was investigated by varying the stirring speed from 50 to 1,100 rpm (50, 200, 400, 600, 800, and 1,100 rpm), using the obtained optimum contact time and adsorbent dosage for each adsorbate at 25 °C.

Effect of initial adsorbate concentration

The effect of initial adsorbate concentration on Pb, Fe, and COD was examined by varying the initial concentrations of Pb, Fe, and COD in the solution, using the obtained optimum contact time and adsorbent dosage at 25 °C with 400 rpm. Here, the initial ion concentration was changed by diluting the raw leachate by factors of 10, 20, 30, 40, 50, and 60 times.

Effect of initial pH

The effect of initial pH on Pb, Fe, and COD was investigated by varying the pH of the solution from 1 to 12, using the obtained optimum contact time and adsorbent dosage with 400 rpm at 25 °C.

Adsorption isotherms

Finally, adsorption isotherms were plotted using the data obtained from the adsorption isotherm experiments for each adsorbate. The Langmuir, Freundlich, Temkin, Elovich, and Dubinin–Raduskevich (D-R) isotherm models were used.

Langmuir isotherm
The linear form of Langmuir adsorption isotherm is shown in Equation (3) (Langmuir 1918), where qe is the contaminant adsorbed at equilibrium (mg/mg), qm is the maximum adsorption capacity (mg/mg), Ce is the equilibrium concentration (mg/L), KL is the adsorption coefficient that can be obtained from the graph of Ce/qe against Ce, and Co is the initial concentration of the adsorbate in solution (Farouq & Yousef 2015).
formula
(3)
Freundlich isotherm
Freundlich isotherm can be linearized as shown in Equation (4) (Freundlich 1906a, 1906b):
formula
(4)
where 1/n is the adsorption intensity (L/mg) and KF is the adsorption capacity (mg/g), which can be determined from the slope and intercept the graph of log (qe) versus logCe correspondingly.
Temkin isotherm
It is considered that adsorption energy linearly decreases with the coverage as per the Temkin isotherm, which is shown in Equation (5) (Temkin & Pyzhev 1940).
formula
(5)

Temkin constant related to the heat of adsorption and the equilibrium binding constant corresponding to the maximum binding energy (L/mg) are denoted by B and ET, respectively. ET and B can be determined from the intercept and the slope of the linear plot of experimental data of qe versus ln(Ce).

Elovich isotherms

Equation (6) (Elovich 1962) displays the Elovich isotherm model.

KE denotes Elovich constant. qm and KE can be obtained from the slope and intercept, respectively, using the graph of ln(qe/Ce) versus qe (Farouq & Yousef 2015).
formula
(6)
Dubinin–Raduskevich isotherm
Equation (7) denotes the Dubinin–Raduskevich (D-R) isotherm (Dubinin et al. 1947):
formula
(7)
where β is the free energy of sorption and qm is D-R isotherm constant related with the degree of sorbate sorption by the reactive material surface.
Equation (8) expresses ε, which is known as Polanyi potential.
formula
(8)
where R is the gas constant (Jmol−1 K−1) and T is the absolute temperature.

Kinetic experiments

One of the crucial parameters that influence adsorption performance is the contact time. The results of the kinetic experiment are shown in Figures 4 and 5. The removal efficiency of Pb decreased linearly with increase of the contact time, whereas that of Fe increased linearly with the increase of the contact time. The removal efficiencies of Zn, Cu, NH3-N, and COD did not follow a unique correlation with the contact time. The optimum contact time values of Pb, Zn, Cu, Fe, NH3-N, and COD were 15, 90, 30, 180, 30, and 30 min, respectively, with removal efficiencies of 92.7, 97.0, 64.8, 96.2, 75.0, and 33.2%, respectively. After reaching the optimum contact time, the removal efficiency of Zn, Cu, NH3-N, and COD decreased. The maximum removal efficiency of Pb occurred at the first selected contact time with a high removal efficiency, and hence, the respective contact time was considered the optimum value. The removal efficiency of Fe gradually increased, and the maximum efficiency occurred at the last selected contact time. Since the maximum removal efficiency was very high, the kinetic batch sorption experiment for Fe was not extended, and the corresponding contact time for the maximum efficiency was considered the optimum value. For activated carbon, the maximum adsorption was noted at pH above the neutral.
Figure 4

Variation of removal efficiencies of heavy metals versus contact time.

Figure 4

Variation of removal efficiencies of heavy metals versus contact time.

Close modal
Figure 5

Variation of removal efficiencies of COD and NH3-N versus contact time.

Figure 5

Variation of removal efficiencies of COD and NH3-N versus contact time.

Close modal

Table 4 depicts the summary of kinetic experimental results.

Table 4

Summary of kinetic experimental results

Adsorbate Time (min)Zn
Pb
Cu
Fe
COD
NH3-N
Removal efficiency (%)SDRemoval efficiency (%)SDRemoval efficiency (%)SDRemoval efficiency (%)SDRemoval efficiency (%)SDRemoval efficiency (%)SD
15 6.91 1.01 92.69 0.14 54.09 1.40 85.84 0.62 19.81 0.51 62.5 1.22 
30 75.09 0.20 88.78 0.14 64.81 0.28 86.58 1.25 33.18 0.13 75 0.82 
60 68.68 3.02 81.80 0.72 63.81 2.80 92.810.04 0.06 19.03 0.19 37.5 1.63 
90 97.04 0.10 71.72 0.14 16.88 0.56 85.88 1.25 5.66 0.32 37.5 2.45 
120 44.510.29 0.50 74.68 1.43 25.47 8.39 84.82 0.62 3.77 0.06 62.5 0.82 
180 45.09 0.10 72.91 0.29 26.91 0.28 96.23 0.62 13.21 0.13 62.5 0.08 
Adsorbate Time (min)Zn
Pb
Cu
Fe
COD
NH3-N
Removal efficiency (%)SDRemoval efficiency (%)SDRemoval efficiency (%)SDRemoval efficiency (%)SDRemoval efficiency (%)SDRemoval efficiency (%)SD
15 6.91 1.01 92.69 0.14 54.09 1.40 85.84 0.62 19.81 0.51 62.5 1.22 
30 75.09 0.20 88.78 0.14 64.81 0.28 86.58 1.25 33.18 0.13 75 0.82 
60 68.68 3.02 81.80 0.72 63.81 2.80 92.810.04 0.06 19.03 0.19 37.5 1.63 
90 97.04 0.10 71.72 0.14 16.88 0.56 85.88 1.25 5.66 0.32 37.5 2.45 
120 44.510.29 0.50 74.68 1.43 25.47 8.39 84.82 0.62 3.77 0.06 62.5 0.82 
180 45.09 0.10 72.91 0.29 26.91 0.28 96.23 0.62 13.21 0.13 62.5 0.08 

According to Robati (2013), kinetic modeling is a useful tool that enables the determination of sorption rates and provides valuable insights into the possible reaction mechanisms involved. By using kinetic modeling, it is possible to establish rate expressions that accurately describe the sorption process and shed light on the underlying chemical and physical mechanisms. In essence, kinetic modeling not only helps quantify the rate of sorption but also provides a deeper understanding of the underlying chemical and physical processes. Therefore, it is a valuable tool for optimizing sorption processes and developing efficient wastewater treatment strategies. According to Raj Somera et al. (2019), several mathematical models are used to describe the kinetics of adsorption. These models are employed to describe the kinetic process of adsorption and the adsorption mechanism. Based on the kinetic experimental results for Pb, Zn, Cu, Fe, NH3-N, and COD in the present research, the results agreed with the pseudo-second-order equation with high determination coefficients. Table 5 summarizes the pseudo-second-order equation for each adsorbate. Figure 6 depicts the pseudo-second-order kinetic models for Pb, Zn, Cu, Fe, COD, and NH3-N. It is noteworthy that the COD adsorption has not been fitted as well as the others. This discrepancy could be due to the fact that other treatment mechanisms have also contributed to the reduction of COD, most likely through the biodegradation of biodegradable organic matter.
Table 5

Pseudo-second-order equation for each adsorbate

AdsorbateEquationR2
Pb y = 165.08x − 826.01 0.9985 
Zn y = 194.35x − 3603.8 0.9293 
Cu y = 964.04x − 10619 0.8564 
Fe y = 0.5391x + 2.2447 0.9901 
COD y = 0.6893x + 0.9081 0.4440 
NH3-N y = 146.36x + 2462.5 0.8320 
AdsorbateEquationR2
Pb y = 165.08x − 826.01 0.9985 
Zn y = 194.35x − 3603.8 0.9293 
Cu y = 964.04x − 10619 0.8564 
Fe y = 0.5391x + 2.2447 0.9901 
COD y = 0.6893x + 0.9081 0.4440 
NH3-N y = 146.36x + 2462.5 0.8320 
Figure 6

The best-fitted kinetic model for Pb, Zn, Cu, Fe, COD, and NH3-N: pseudo-second-order.

Figure 6

The best-fitted kinetic model for Pb, Zn, Cu, Fe, COD, and NH3-N: pseudo-second-order.

Close modal

The pseudo-second-order kinetic model is based on the assumption that the rate-limiting step may be chemical sorption or chemisorption involving valence forces through the sharing or exchange of electrons between the adsorbent and adsorbate (Ho & McKay 1999). Previous research has shown that the kinetics of metal ion adsorption have obeyed the pseudo-second-order model in various studies: Pb2+ adsorption on sludge-derived BC at initial pH 2.0–5.0 (Lu et al. 2012), Cu(II) and Pb(II) ions for the studies of kinetic and adsorptive characterization of BC in metal ion removal (Kołodyńska et al. 2012), lead adsorption mechanism onto pinewood and rice husk-derived biochars (Liu & Zhang 2009), and removal of different metals (Cd, Cr, Cu, and Pb) from acidic solutions using plant waste-derived BC as an adsorbent (Khare et al. 2017). It is worth noting that the pseudo-second-order model fits the kinetic adsorption of metal ions better than the pseudo-first-order model (Tan et al. 2015). Kinetic adsorption experiments conducted by Bulgariu & Balgariu (2018) on Pb and Cu removal by a functionalized soybean adsorbent were best fitted with the pseudo-second-order model. Onwordi et al. (2019) conducted batch sorption experiments with bean husk and fish scale as adsorbents to remove Pb, the kinetics of which were best fitted to a pseudo-second-order model. Furthermore, most studies related to the adsorption of organic contaminants onto biochars have also followed the pseudo-second-order model, indicating a chemisorption process (Tan et al. 2015). Jia et al. (2013) investigated the sorption of oxytetracycline from an aqueous solution to maize-straw-derived BC, and the resulting data fit well with the pseudo-second-order kinetics model.

Figure 7 and Table A-1 (Supplementary material) illustrate the changes in pH, conductivity, and ORP observed during the kinetic experiments. The pH and conductivity increased as a result of adsorption. Conductivity serves as a measure of water's ability to conduct electrical current, which is directly related to the concentration of ions in the water. These ions originate from dissolved salts, as well as inorganic substances such as sulfides, chlorides, alkalis, and carbonate compounds. In terms of ORP, it is influenced by the presence of oxygen in the landfill leachate. The ORP value quantifies the capacity of a substance to either oxidize or reduce another substance. A positive ORP value indicates an oxidizing agent, while a negative reading suggests a reducing agent. Consequently, the variation in ORP after each experiment revealed a decrease in the final ORP value compared to the initial value. Wuana & Okieimen (2011) noted that ORP is one of the key variables governing the potential release of stored pollutants into the aqueous phase during sorption, particularly for heavy metals. The decrease in ORP observed in this study can be attributed to the process of adsorption, indicating a predominant occurrence of reductive reactions. It is worth noting that pH, conductivity, and ORP did not vary significantly with respect to the contact time. It is worth noting that pH, conductivity, and ORP did not vary significantly with respect to the contact time. The final pH, conductivity, and ORP results are influenced by the reactions occurring within the sample. For all the samples used in the kinetic experiments, except for the contact time, all other conditions remained constant. As depicted in Figure 4, the removal efficiency of Pb and Fe did not show significant variation with respect to contact time. Similarly, the reactions responsible for altering pH, conductivity, and ORP may not have a strong time-dependent component within the experimental time frame. Consequently, there might have been little to no change in these water quality parameters over time.
Figure 7

Variation of indicator parameters during the kinetic experiments.

Figure 7

Variation of indicator parameters during the kinetic experiments.

Close modal

Adsorption isotherm experiments

Adsorption equilibrium information is the most crucial aspect required for a comprehensive understanding of an adsorption process. Proper comprehension and interpretation of adsorption isotherms are vital for enhancing the overall understanding of adsorption mechanisms and designing effective adsorption systems (El-Khaiary 2008).

Figures 8 and 9 display the results of the relationship between removal efficiency and adsorbent mass for each adsorbate. Notably, there exists a distinct correlation between the removal efficiency of each heavy metal and the adsorbent mass. Specifically, the removal efficiencies of Cu, Fe, and Pb demonstrate a linear relationship, while that of Zn exhibits a curvilinear correlation with the adsorbent mass (Figure 8). The optimized adsorbent dosages for Pb, Zn, Cu, Fe, NH3-N, and COD were determined to be 5, 30, 5, 15, 5, and 30 g, respectively, at the corresponding optimized contact times (15, 90, 30, 180, 30, and 30 min, respectively). At these optimized dosages, the removal efficiencies of Pb, Zn, Cu, Fe, NH3-N, and COD were found to be 96.4, 92.7, 60.3, 87.1, 75.0, and 67.5%, respectively.
Figure 8

Variation of removal efficiencies of heavy metals with adsorbent mass.

Figure 8

Variation of removal efficiencies of heavy metals with adsorbent mass.

Close modal
Figure 9

Variation of removal efficiencies of COD and NH3-N with adsorbent mass.

Figure 9

Variation of removal efficiencies of COD and NH3-N with adsorbent mass.

Close modal

However, it is important to note that the observed results sometimes differed from the expected outcome, which anticipated an increase in adsorption capacity with the adsorbent dosage due to the availability of a greater number of vacant sites for ion absorption. In certain cases, an increase in adsorbent dosage led to a decrease in adsorption capacity. Padmavathy et al. (2016) explained that this could occur due to particle aggregation caused by higher adsorbent dosages, limiting the accessibility of adsorbate molecules to active sites on the adsorbent surface. In addition, Hameed et al. (2017) observed that while an increase in adsorbent dosage results in an increased amount of adsorbed dye, the adsorption density (amount adsorbed per unit mass) decreases. The authors suggested that the increase in available adsorption sites by increasing the adsorbent dose is accompanied by a decrease in adsorption density, primarily because some adsorption sites remain unsaturated during the adsorption reaction, while the number of available sites for adsorption increases with the adsorbent dose.

Aziz et al. (2001) stated that more than 90% of copper (Cu) with concentrations up to 50 mg/L could be removed from a solution using a limestone quantity of more than 20 ml (equivalent to 56 g). In batch experiments conducted by Aziz et al. (2001) using limestone and activated carbon, both materials exhibited similar metal removal efficiencies of approximately 95%. Analysis of the limestone media after filtration indicated that adsorption and absorption processes were involved in the removal mechanisms (Aziz et al. 2001). Furthermore, Aziz et al. (2001) described a study on the use of limestone for removing manganese (Mn) from water. In their study, Mn solution with a concentration of 1 mg/L was shaken with limestone (containing 53.9% CaCO3 and 5.2% MgCO3), gravel, crushed brick, or with no solid media at various pH values. At a final pH value of 8.5, limestone achieved a 95% removal of Mn, crushed brick achieved 82% removal, gravel achieved 60% removal, and the removal efficiency for aeration and settlement with no solid media was less than 15%. These results indicated that rough solid media and the presence of carbonate are beneficial for the precipitation of Mn in water. The filtration results indicated that at an input pH of 7 with an initial Mn concentration of 1 mg/L, a retention time of 1.35 h, a media depth of 500 mm, and a flow rate of 20 ml/min, limestone (SiO2) exhibited good removal efficiency (above 90%) compared to gravel media (Aziz et al. 2001). Limestone primarily consists of CaCO3 and also contains silica. In the present research, the CA consisted of 30% building waste (CW, roofing waste, and BMW) on a weight basis, making the limestone content significant. In addition, the waste sludge-based sorbent, which accounts for 10% of the CA on a weight basis, contains two chemicals available in limestone, namely, CaCO3 and silica. Therefore, the CA is expected to exhibit similar heavy metal removal capabilities as those observed with limestone in the aforementioned study. As explained in Section 2.2, the low-cost materials composing the CA possess various functional groups favorable for treating wastewater parameters. The CA demonstrated high removal efficiencies for four heavy metals, COD, and NH3-N. This ability to treat multiple parameters simultaneously is likely due to the diverse properties of the 10 different materials used. Table 6 depicts the summary of the adsorption isotherm experimental results.

Table 6

Summary of adsorption isotherm experiment results

Zn
Pb
Cu
Fe
COD
NH3-N
Adsorbate Mass (g)Removal efficiency (%)SDRemoval efficiency (%)SDRemoval efficiency (%)SDRemoval efficiency (%)SDRemoval efficiency (%)SDRemoval efficiency (%)SD
49.32 1.01 96.39 0.14 60.29 0.28 85.84 0.06 23.35 0.20 75.00 1.13 
10 62.89 0.20 90.91 0.43 58.47 1.40 85.28 0.06 37.89 0.30 50.00 2.26 
15 67.080.58 1.01 95.30 0.57 58.13 0.56 87.110.29 0.50 26.89 0.10 37.50 0.34 
20 90.49 0.20 93.33 0.29 58.99 0.28 86.02 1.25 23.74 0.41 62.50 0.91 
25 69.67 0.50 89.86 0.14 59.06 1.68 84.92 0.02 31.60 0.20 62.50 1.13 
30 92.700.058 0.10 77.50 5.73 60.15 0.56 85.95 0.02 67.49 0.10 37.50 0.26 
Zn
Pb
Cu
Fe
COD
NH3-N
Adsorbate Mass (g)Removal efficiency (%)SDRemoval efficiency (%)SDRemoval efficiency (%)SDRemoval efficiency (%)SDRemoval efficiency (%)SDRemoval efficiency (%)SD
49.32 1.01 96.39 0.14 60.29 0.28 85.84 0.06 23.35 0.20 75.00 1.13 
10 62.89 0.20 90.91 0.43 58.47 1.40 85.28 0.06 37.89 0.30 50.00 2.26 
15 67.080.58 1.01 95.30 0.57 58.13 0.56 87.110.29 0.50 26.89 0.10 37.50 0.34 
20 90.49 0.20 93.33 0.29 58.99 0.28 86.02 1.25 23.74 0.41 62.50 0.91 
25 69.67 0.50 89.86 0.14 59.06 1.68 84.92 0.02 31.60 0.20 62.50 1.13 
30 92.700.058 0.10 77.50 5.73 60.15 0.56 85.95 0.02 67.49 0.10 37.50 0.26 

Figures 1012 and Table A-2, A-3 and A-4 (Supplementary material) depict the variations in pH, conductivity, and ORP during the adsorption isotherm experiments. The pH increased in all solutions targeting Zn, Pb, Cu, Fe, and COD. The final pH value appeared to be relatively independent of the adsorbent mass. In addition, the final conductivity of all experiments was higher than the initial conductivity, and it increased with the adsorbent mass for all parameters except Fe. Conversely, the final pH remained relatively constant during the kinetic experiments (refer to Figure 7). The final ORP values were generally lower than the initial values, except for a few data points related to Zn. This suggests a net occurrence of reductive reactions in all experiments. There seems to be a correlation between the final ORP and the optimal adsorbent mass. Specifically, the optimum masses for Zn and Cu were determined to be 30 and 5 g, respectively. These optimal masses corresponded to the lowest final ORP values for both Zn and Cu. Therefore, the occurrence of net reductive reactions correlates with the removal efficiencies. In the Fe experiment, the ORP rapidly decreased until reaching the optimal mass of 15 g, after which it remained relatively constant, confirming the aforementioned observation. The final ORP in the experiment targeting Pb remained relatively constant. However, no such correlation was observed between the final ORP and the optimal adsorbent mass for the COD experiment. This indicates that the net occurrence of reductive reactions was not the sole major factor contributing to COD removal.
Figure 10

pH variation during the adsorption isotherm experiments.

Figure 10

pH variation during the adsorption isotherm experiments.

Close modal
Figure 11

Conductivity variation during the adsorption isotherm experiments.

Figure 11

Conductivity variation during the adsorption isotherm experiments.

Close modal
Figure 12

ORP variation during the adsorption isotherm experiments.

Figure 12

ORP variation during the adsorption isotherm experiments.

Close modal

Adsorption isotherms

The experimental maximum adsorption capacities of Pb, Zn, Cu, Fe, NH3-N, and COD were 0.016, 0.004, 0.005, 1.14, 0.016, and 4.29 mg/g, respectively. The best-fitted isotherm model for Pb was identified as the Langmuir isotherm with an R2 value of 0.9571. Fe, Zn, Cu, NH3-N, and COD were best fitted with the Elovich isotherm, and the determination coefficients were 0.9391, 0.9627, 0.9411, 0.8950, and 0.9712, respectively. Table 7 represents a summary of the adsorption isotherm models considered for each adsorbate. Table 8 depicts a summary of the adsorption capacities of each adsorbate. Figure 13 shows the best-fitted models for each parameter. Bulgariu & Balgariu (2018) obtained adsorption capacities of approximately 84.7 and 43.3 mg/g, respectively, for an adsorbent made of soy waste biomass. However, the soy waste biomass used in their study had been functionalized with an industrial sulfur-based chelating agent, which is a precipitation agent used in industrial wastewater treatment. This agent was used for the removal of some heavy metals from aqueous solutions. Onwordi et al. (2019) obtained adsorption capacities of 0.9895 and 0.858 mg/g for two different adsorbents, namely, soybeans and fish scale, respectively. However, the fish scale had undergone pulverization as a pretreatment. In contrast, the CA used in the present research has not undergone any processing. Therefore, if individual materials were given chemical treatment, the adsorption capacity could be enhanced. Halim et al. (2012) reported adsorption capacities (according to the Langmuir model) of 30.96, 22.99, and 0.12 mg/g for NH3-N, COD, and iron (Fe), respectively. In their study, composite adsorption media were produced using activated carbon (AC), ZE, and low-cost media such as limestone and rice husk carbon (RHC) for the treatment of semiaerobic stabilized landfill leachate. The final composition of the proposed composite media for optimum leachate treatment consisted of 45% ZE, 15.31% limestone, 4.38% AC, and 4.38% RHC as adsorbents, and 30% ordinary Portland cement (OPC) used as a binder. It should be noted that GAC and ZE are well-known adsorbents.
Table 7

Isotherm models considered for each adsorbate

AdsorbateAdsorption isotherm equations
Freundlich isothermLangmuir isothermTemkin isothermElovich isothermDubinin–Radushkevich isotherm
Pb 
R² = 0.6381 

R² = 0.9571 

R² = 0.4965 

R² = 0.9188 

R² = 0.6622 
Zn 
R² = 0.4134 

R² = 0.2783 

R² = 0.3670 

R² = 0.9627 

R² = 0.2739 
Cu 
R² = 0.0241 

R² = 0.0038 

R² = 0.1432 

R² = 0.9411 

R² = 0.0246 
Fe – 
R² = 0.0502 

R² = 0.1390 

R² = 0.9391 

R² = 0.2114 
NH3-N 
R² = 0.6257 

R² = 0.6439 

R² = 0.6218 

R² = 0.8950 

R² = 0.6654 
COD 
R² = 0.0056 

R² = 0.2692 

R² = 0.0024 

R² = 0.9712 

R² = 0.0028 
AdsorbateAdsorption isotherm equations
Freundlich isothermLangmuir isothermTemkin isothermElovich isothermDubinin–Radushkevich isotherm
Pb 
R² = 0.6381 

R² = 0.9571 

R² = 0.4965 

R² = 0.9188 

R² = 0.6622 
Zn 
R² = 0.4134 

R² = 0.2783 

R² = 0.3670 

R² = 0.9627 

R² = 0.2739 
Cu 
R² = 0.0241 

R² = 0.0038 

R² = 0.1432 

R² = 0.9411 

R² = 0.0246 
Fe – 
R² = 0.0502 

R² = 0.1390 

R² = 0.9391 

R² = 0.2114 
NH3-N 
R² = 0.6257 

R² = 0.6439 

R² = 0.6218 

R² = 0.8950 

R² = 0.6654 
COD 
R² = 0.0056 

R² = 0.2692 

R² = 0.0024 

R² = 0.9712 

R² = 0.0028 
Table 8

Summary of the adsorption capacity for each adsorbate

AsorbateAdsorption capacity (mg/g)Amount of adsorbate that can be removed using 1 kg of composite adsorbent (mg)
Pb 0.016 16 
Zn 0.004 
Cu 0.005 
Fe 1.14 1,140 
COD 4.29 4,290 
NH3-N 0.016 16 
AsorbateAdsorption capacity (mg/g)Amount of adsorbate that can be removed using 1 kg of composite adsorbent (mg)
Pb 0.016 16 
Zn 0.004 
Cu 0.005 
Fe 1.14 1,140 
COD 4.29 4,290 
NH3-N 0.016 16 
Table 9

Summary of sensitivity analysis results

AdsorbateEffect of temperature variation
Effect of speed variation
Effect of initial ion conc. variation
Effect of pH variation
Optimum temperature (°C)Removal efficiency (%)SDOptimum speed (rpm)Removal efficiency (%)SDOptimum initial ion conc.(mg/L)Removal efficiency (%)SDOptimum pHRemoval efficiency (%)SD
Pb 60 96.9 0.057 400 94.14 0.029 2.85 97.0 0.009 6.56 93.16 0.020 
Fe 60 97.3 0.006 1,100 97.8 0.025 218.9 72.96 1.119 4.6 82.56 0.312 
COD 25, 35, 40, and 60 76.8 0.128 200, 800 53.56 0.770 2,120 44.27 0.308 12 76.78 0.160 
AdsorbateEffect of temperature variation
Effect of speed variation
Effect of initial ion conc. variation
Effect of pH variation
Optimum temperature (°C)Removal efficiency (%)SDOptimum speed (rpm)Removal efficiency (%)SDOptimum initial ion conc.(mg/L)Removal efficiency (%)SDOptimum pHRemoval efficiency (%)SD
Pb 60 96.9 0.057 400 94.14 0.029 2.85 97.0 0.009 6.56 93.16 0.020 
Fe 60 97.3 0.006 1,100 97.8 0.025 218.9 72.96 1.119 4.6 82.56 0.312 
COD 25, 35, 40, and 60 76.8 0.128 200, 800 53.56 0.770 2,120 44.27 0.308 12 76.78 0.160 
Figure 13

Best-fitted isotherm models.

Figure 13

Best-fitted isotherm models.

Close modal

The Elovich isotherm model was found to be the best-fitted isotherm model for Zn, Cu, Fe, NH3-N, and COD adsorption. According to Elovich (1962), this isotherm is based on the principle of chemisorption kinetics on heterogeneous surfaces and provides information about the rates of adsorption and desorption. Farouq and Yousef (2015) stated that it assumes exponential growth in the number of adsorption sites with increasing adsorption, indicating multilayer adsorption. The equation can be used for quantitative analysis of adsorption and desorption rates. On the other hand, the Langmuir isotherm assumes the existence of a single adsorption layer on the adsorbent, as proposed by Langmuir (1916). Multilayer adsorption is described by the Freundlich isotherm, introduced by Freundlich (1906a, 1906b). The Langmuir isotherm considers surface coverage by balancing the rates of adsorption and desorption (dynamic equilibrium). Adsorption is proportional to the fraction of the open surface of the adsorbent, while desorption is proportional to the fraction of the surface covered (Gunay et al. 2007). The Langmuir adsorption isotherm assumes the following: a fixed number of accessible sites are available on the adsorbent surface, all with the same energy; adsorption is a reversible process; equilibrium is reached when the rate of adsorption equals the rate of desorption; the rate of adsorption is proportional to the driving force, which is the difference between the amount adsorbed at a particular concentration and the maximum amount that can be adsorbed at that concentration; and at equilibrium concentration, this difference is zero (Metcalf and Eddy 2003).

Sensitivity analysis

After optimizing the contact time and adsorbent dosage for each heavy metal, NH3-N, and COD, batch experiments were conducted for Pb, Fe, and COD. These experiments involved varying temperature, stirring speed, initial ion concentration, and the initial pH of the solution. Table 9 summarizes the results of the sensitivity analysis.

Temperature variation

Figure 14 illustrates the effect of temperature variation on the removal of Pb, Fe, and COD. The removal efficiencies of both Pb and Fe remained relatively constant regardless of the temperature change from 25 to 60 °C. However, the impact of temperature on the removal of COD differed from that of Pb and Fe. A removal efficiency of 76% was consistently achieved at 25, 35, 40, and 60 °C for COD. The decrease in dye adsorption with the increasing temperature can be attributed to the greater Brownian movement of molecules in the solution (Li et al. 2013). In addition, high temperatures may result in the disruption of intermolecular hydrogen bonding between different functional groups, which is a significant factor in the adsorption process (Li et al. 2013). Sazali et al. (2020) explained that an increase in temperature causes the adsorbent to swell, creating more binding sites for metal ions to interact with. Consequently, the adsorbent exhibits a higher metal ion removal capacity due to the availability of more adsorption sites. As a whole, it can be concluded that temperature has no significant effect on the adsorption of Pb and Fe on the CA. However, the adsorption of COD on the CA is temperature dependent.
Figure 14

The effect of temperature variation on removal of Pb, Fe, and COD.

Figure 14

The effect of temperature variation on removal of Pb, Fe, and COD.

Close modal

Stirring speed variation

Figure 15 depicts the variation of the removal efficiencies of Pb, Fe, and COD with the varying stirring speed. A maximum removal efficiency of 94.14% for Pb was identified at a stirring speed of 400 rpm. However, when the stirring speed increased further, the Pb removal efficiency slightly decreased. On the other hand, the Fe removal efficiency slightly increased with the increase of the stirring speed. The maximum removal efficiency for Fe was 97.8%. It was achieved at a stirring speed of 1,100 rpm. As for COD, a removal efficiency of 53.56% was observed at both 200 and 800 rpm. As a whole, it can be concluded that stirring speed has no significant effect on the adsorption of Pb and Fe on the CA. However, the adsorption of COD on the CA is dependent on the stirring speed.
Figure 15

The effect of variation of the stirring speed on removal of Pb, Fe, and COD.

Figure 15

The effect of variation of the stirring speed on removal of Pb, Fe, and COD.

Close modal

Initial adsorbate concentration variation

Figure 16 depicts the variation of the removal efficiencies of Pb, Fe, and COD against the variation of the initial adsorbate concentration. There was no significant difference between the removal efficiency of Pb and the variation of the initial adsorbate concentration. The maximum removal efficiencies of PB and Fe of 97.0 and 72.96% occurred at the initial adsorbate concentrations of 2.85 and 60.2 mg/L, respectively. According to Gebretsadik et al. (2020), as the concentration of metal ions in the solution increased, the number of collisions between the metal ions and the biosorbent also increased, leading to an overall increase in metal uptake. Therefore, when the initial ion concentration increases, adsorption also increases. However, when considering COD, a maximum removal efficiency of 44.27% was achieved at an initial COD concentration of 2,120 mg/L.
Figure 16

The effect of variation of the initial adsorbate concentration on removal of Pb, Fe, and COD.

Figure 16

The effect of variation of the initial adsorbate concentration on removal of Pb, Fe, and COD.

Close modal

Initial pH variation

Figure 17 depicts the effect of initial pH on the removal of Pb, Fe, and COD, respectively. The effect of pH variation is critical in the adsorption process. According to Shrestha (2018), the pH level of an aqueous solution can influence the chemical composition of metal ions present in it and also alter the surface properties of the adsorbent material. Consequently, the extent of metal ion adsorption can be affected by the pH level since it can impact the availability and reactivity of binding sites on the adsorbent. Hence, when considering the experimental results given for pH variation in the solution, the maximum removal efficiencies for Pb, Fe, and COD were observed at pH values of 6.56, 4.6, and 6.7, respectively. The values were 93.16, 82.56, and 76.78%, respectively. Dayanthi et al. (2023) also observed that the removal efficiency of Fe rapidly decreased with the increase of pH in the alkaline region of pH. According to explanations given by both Auta & Hameed (2011) and Al-Degs et al. (2008) on their respective adsorption experiments' results, the increase/decrease of pH of the solution greatly affect the adsorption mechanism as pH could alter the electrical charge of the functional groups of the adsorbent, resulting in electrostatic repulsion/attraction. It is crucial to emphasize that the precise behavior of these metal ions within a particular system is influenced by several factors. These factors include the specific species of metal ions involved, the pH range under consideration, the nature of contaminants present, and the overall chemistry of the solution. Consequently, the impact of pH on the behavior of Fe and Pb can exhibit variability and should be thoroughly investigated within the context of the specific application being addressed.
Figure 17

The effect of variation of pH on removal of Pb, Fe, and COD.

Figure 17

The effect of variation of pH on removal of Pb, Fe, and COD.

Close modal

The applicability of a CA, mainly composed of multiple waste materials, to treat Pb, Zn, Cu, Fe, NH3-N, and COD in landfill leachate was investigated through batch sorption experiments. The CA exhibited adsorption capacities of 0.016, 0.004, 0.005, 1.14, 4.29, and 0.016 mg/g for Pb, Zn, Cu, Fe, COD, and NH3-N, respectively, with corresponding removal efficiencies of 96.4, 92.7, 60.3, 87.1, 67.5, and 75%. To identify the adsorption mechanisms, the best-fitting kinetic models and adsorption isotherm models were determined for all the adsorbates. The pseudo-second-order kinetic model provided a good fit for all the adsorbates except COD. The Langmuir isotherm model was found to be the most suitable for Pb, with an R2 value of 0.96. The adsorption characteristics of the CA for the remaining adsorbates (Zn, Cu, Fe, NH3-N, and COD) were described by the Elovich isotherm, with R2 values of 0.9627, 0.9411, 0.9391, 0.8950, and 0.9712, respectively. The influence of variations in temperature, stirring speed, initial adsorbate concentration, and pH on Pb, Fe, and COD removal efficiencies was examined. There were no consistent relationships observed between the varying parameters and different contaminants. The influence of the aforementioned factors on COD was completely different from that of heavy metals.

This CA can effectively treat multiple contaminants due to its composition of diverse materials with various properties, which address different treatment mechanisms for multiple contaminants. Therefore, it has the potential to be used as packing media in various treatment systems, not only for landfill leachate but also for other types of wastewater. According to the cost estimation, the price of 1 kg of GAC is 9.4 times higher than the cost to be spent for preparing 1 kg of the CA. In addition, utilizing waste materials such as roofing materials, concrete, bricks and mortar waste, and DAS as ingredients for an effective adsorbent offers a solution to the associated disposal issues. The adsorption efficiency varies primarily with contact time, adsorbent dosage, initial pH, initial adsorbate concentration, temperature, and stirring speed. Thus, the adsorption efficiency of the adsorbents used in the batch sorption experiments in this research can be enhanced by optimizing these factors. Furthermore, modifying the composition of the CA can yield different performances for different parameters. The treatment efficiency of these natural materials could be increased by subjecting them to various processes to enhance their adsorption properties. Considering that the present study achieved good performance using a composite made of unprocessed low-cost materials, even better performances could be achieved after subjecting these materials to different property enhancement processes.

Understanding the durability of the CA, especially when it is utilized within a treatment system, is essential for practical applications. The durability of the packing media in a treatment system, in which the CA serves as the packing media, depends on several physical and mechanical properties such as permeability, shear strength, porosity, material wear, and more. Therefore, investigating its durability would be a valuable future advancement for this research. Batch sorption experiments serve as fundamental tests to assess the applicability of any material as an adsorbent for treatment systems. Thus, it is recommended to conduct laboratory-scale column experiments, pilot-scale experiments, and full-scale field experiments to further investigate the potential and limitations of this CA for real-world applications.

About 9 waste materials were composited into an adsorbent in order to investigate the potential of treating heavy metals, organic matter and ammonia nitrogen in landfill leachate simultaneously.

This study was conducted at the Faculty of Engineering, University of Ruhuna, and thus all resources used are acknowledged. The authors wish to thank Ms Sachnthani Warnasooriya, the chemist, and Ms D.A.M. Nimal Shanthi, the senior technical officer, for their assistance in laboratory analyses.

All relevant data are included in the paper or its Supplementary Information.

The authors declare there is no conflict.

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