The present research aimed to analyse the impact of economical Fe impregnated polyethylene terephthalate (PET) char (PETC-Fe) for adsorption of As (III) through series of column experiments. For an inlet arsenite concentration of 1,000 μg/L, PETC-Fe exhibits excellent uptake capacity of 1,892 μg/g. Central composite design (CCD) in response surface methodology (RSM) was used to evaluate the influence of various process variables on the response function (breakthrough time) for optimization and assessment of interaction effects. The breakthrough time is more responsive to influent As (III) concentration and bed height than inlet flow rate, according to the perturbation plot. Adams–Bohart, Bed Depth Service Time (BDST) model, and Thomas models were used to model the dynamics of the adsorption system. The BDST model suited the experimental data well in the early part of the breakthrough curve, but there were minor variations over the breakpoints. Despite the fact that the experimental values and the data sets estimated using the Adams–Bohart model followed a similar pattern, they differed slightly. The PETC-Fe was found to be a sustainable and highly economical adsorbent, with a desorption performance of more than 97%, indicating the adsorbent's reusability. This adsorbent's excellent As (III) uptake capacity and regeneration performance imply that it might be used in industrial/domestic applications, and the information obtained could aid in future scaling up of the adsorption system.

  • Fe loaded PET char is a viable alternate adsorbent for the adsorption of As (III).

  • PETC-Fe exhibits excellent uptake capacity of 1,892 μg/g.

  • The breakthrough time is more responsive to influent As (III) concentration and bed height than inlet flow rate.

  • The BDST model suited the experimental data well.

  • The regeneration and persistence of the PETC-Fe in subsequent cycles were also justified.

Graphical Abstract

Graphical Abstract
Graphical Abstract

Arsenic (As) has become a major environmental pollutant across the globe. The most common inorganic arsenic sources in the aquatic system include erosion of arsenic laden minerals, industrial effluent, weed control, and drugs (Singh et al. 2022). In water bodies, inorganic arsenic is extensively dissolved and mobilised, and its behaviour is influenced by redox state and pH (Sawood & Gupta 2018). As (III) and arsenate As (V) are the two most common inorganic arsenic forms present in groundwater and industrial effluent. It has a pH range of neutral to slightly acidic or alkaline (Weerasundara et al. 2021). It's been proven that arsenic species are hazardous, with arsenite having a greater negative impact on human health than arsenate (Lee et al. 2022). Arsenic groundwater pollution is a potential issue around the globe due to its high prospective toxicity and carcinogen nature. As a result, the World Health Organisation establishes a maximum permissible arsenic concentration of 10 ppb for potable water as a normative standard (Dettori et al. 2022).

Purification/desalination of potable aquatic system, and facilities related to treatment of waste water, particularly in poor nations, must be modified immediately to address this complex situation (Palansooriya et al. 2020). Highly effective and economic techniques for mitigating arsenic from aquatic aquifers are highly suggested for this application. Numerous methods for mitigation arsenic from the aquatic system have been developed over the last few decades, comprising oxidation, coagulation/flocculation, filtration, precipitation, and adsorption, ion exchange, and membrane separation (Pal et al. 2021). Because of its lower cost, ease of operation, and low disposal of wastes, adsorption is one of the most effective methods among them (Sawood & Gupta 2020a). Despite several shortcomings such as hydrophilic nature and blocking of pore, carbon based adsorbents have been considered a suitable strong material owing to their relatively low cost.

Due to the obvious constraints of the existing adsorbents, it was suggested that plastic generated chars can be used for contemporaneous arsenic adsorption. Amongst all the other solid wastes produced, plastics have proven to be a devastating concern to control and eradicate from the ecosystem (Foglia 2021). The expanding consumption of plastics has resulted in an annual increase in waste generation. Considering the fact that a large portion of plastic trash is repurposed and energy is recovered, some of it will undoubtedly be buried in landfills, polluting aquifers and emitting ozone-depleting chemicals into the air. As a result, it is not an adequate method for dealing with these contaminants (Hopewell et al. 2009). Furthermore, the scarcity of available land for dumping plastic garbage, as well as its harmful repercussions, necessitates the development of an additional strategy for managing waste material. As a result, the current study proposed an efficient approach for controlling and redirecting them from landfills for use as superior arsenic sorption. An approach for recycling polyethylene terephthalate (PET) trash by processing it into cross-linked hydrogels by PET aminolysis has been suggested. When evaluated as sorbent materials for an anionic dye like Congo Red, PET-derived hydrogels rich in amino groups and aromatic compounds were reported to have a 500 mg/g dye uptake capacity (Chan & Zinchenko 2021). The extraction of EBT dye from secondary effluent has been made possible by the development of metal-organic framework adsorbent from recycled PET bottles. The developed PET-derived MOF is characterized as a credible, added-value substance that serves as a long-lasting adsorbent for organic dyes observed in wastewater (Bool et al. 2022). In terms of uptake rate and capacity, the PET-based activated carbon display superior adsorption behaviour toward p-nitrophenol compared to commercial activated carbon (Mendoza-Carrasco et al. 2016).

The majority of the published research on arsenic removal by adsorption focuses on arsenate with only a few investigations on arsenite mitigation in fixed bed columns (Maia et al. 2021; Razzak et al. 2021). Researchers developed a variety of adsorbents for the removal of arsenite from aqueous system, with varying degrees of effectiveness. Arsenite mitigation by coconut husk char (Yeo et al. 2021), manganese oxide/sand matrix (Bajpai & Chaudhuri 1999), basic C3O9Y2 (Wasay et al. 1996), and granules of TiO2 (Bang et al. 2005). Metal oxides, such as ferric ox-hydroxide, Al2O3, and MnOx, have proven to have excellent arsenic uptake capacities on their respective surfaces. Because of their higher affinity for inorganic arsenic compounds, ferric ox-hydroxide is extensively used as adsorbent materials. Adsorption of arsenate and arsenite by ferric ox-hydroxide has been reported (Goldberg 2002). They observed that these materials are more effective in mitigating arsenite then adsorbing arsenate in natural aquatic system. Manganese oxides have gained wide acceptance as oxidizers for the adsorption of arsenite (Islam et al. 2018). Mn (4+) is converted to Mn (3+), which is then reduced to Mn (2+) in the entire oxidation-reduction reaction process. Natural Fe2O3 as well as Fe-abundunt soil (lateritic) have been found to be effective in adsorbing As (III) from aqueous solution. These minerals are available in large quantities and considerably cheaper (Aredes et al. 2013). The uptake of aqueous arsenic was enhanced in Fe-loaded biochar (qmax = 2,160 μg/g), which was prepared by stirring a solution of Fe salt with biochar. The resulting matrix, on the other hand, has very little magnetization, making it difficult to collect after sorption for regeneration (He et al. 2018). Nevertheless, the majority of As (III) removal investigations have been carried in batch mode, but every adsorbent has advantages and disadvantages with respect of reuse and regeneration, material strength, uptake capacity, pressure drop through fixed bed runs, and so on. Batch reactors are simple to use in a lab setup, but they are impractical in the field operations. Furthermore, because precise scale-up results for column arrangements cannot be acquired from batch findings, the adsorbent's real time applicability should be determined in fixed bed column experiments. There are distinct advantages of adsorption on columns operations. It's easy to use, produces large yields, and can simply be scaled up from a lab setting.

The current research focused on the utilisation of plastic carbon based, Fe loaded matrix as a cost-effective material for As (III) removal, as determined by mild carbonization of plastic trash. Even in extremely small concentration, it is reported to have devastating effects on the human health and environment. Although a variety of adsorbents have been employed to reduce As (III) from polluted areas, the application of chars derived from such waste plastics (PET) is a novel subject of concern. Because of As (III) affinity for Fe, a complex material (Fe2O3-imprignated PET char (PETC-FE)) was employed as an adsorbent to investigate its column effectiveness for As(III) removal under up flow circumstances in a column mode after encouraging findings in batch experiments. Breakthrough experiments were conducted to determine the influence of process parameters (influent flow rate, bed height, and inlet concentration) on the breakthrough curve. The adsorption tests were quantitatively modelled employing central composite design (CCD) for sensitivity studies and optimization capacities. Individuals and the cumulative influence of various variables influencing the adsorption mechanism have been studied using the model. Various kinetic models, such as BDST model, Thomas model, and Adam-Bohrat model, were used to simulate the dynamics characteristics of the adsorption. Furthermore, this would be a major boon because it would address both the challenges of waste disposal and heavy metal adsorption utilising a previously existent resource. These can be efficiently used on practical application for the treatment of other heavy metals based on the conclusions gained from examining the prevalent processes and sorption performance of the PET char.

A comprehensive study has been conducted to find an affordable, simple, and environmentally sustainable resolution to disastrous As (III) contamination that may be adopted in poor countries. The findings of this research are presented and discussed.

Materials

All the used chemicals were of reagent grade. The sample containers and glassware were cleansed with a soap solution followed by washing using tap water. They were soaked for a minimum 24 h in 15% HNO3, and then rinsed thrice with deionised water. The aqueous solutions used were prepared with distilled water. As (III) stock solutions were prepared with As2O3 (arsenic trioxide) and NaOH, both procured from Merck. 1.3 g of arsenic trioxide As2O3 was dissolved in a minimum amount of 20% sodium hydroxide to make As (III) standard solution. It was then diluted to 1 L after being neutralised with HNO3 (Giri 2019).

Synthesis of modified plastic char

Waste PET (polyethylene terephthalate) has been used as a precursor to synthesise plastic chars. Pyrolysis has been done to transform the materials into plastic char after they were prepared with feedstock. Pyrolysis was conducted in a lab scale arrangement consisting of specimen holder, condenser, fluid collectors, muffle furnace, flow meter. In every test run, sample (100 g) was loaded in the reaction zone of muffle furnace and heated to obtain the char. Pyrolysis was carried out under inert conditions at temperatures of 500, 600, and 700 °C, with an 10 °C/min heating rate. Subsequently, the synthesised chars were sieved to get material having particle size in 400–800 μm range. In an Erlenmeyer flask, 10 g PETC was stirred with 1 L 0.1 M FeCl2 procured from Merck India, with adjusted pH between 3 and 5 (Asghar et al. 2015). The flask was then stirred for a day at 25 °C temperature in a shaker at 70 rpm. The extract was then filtered and the remnant was rinsed numerous times with double distilled water to remove any metal salts and residual precipitates attached to the exterior surface of the PETC-Fe material.

Analytical procedure and characterization

The characterization of synthesized PETC-Fe was done for analysing decomposition characteristics, ultimate and proximate analysis, etc. A thermo-gravimetric analyser (STA 8000 Perkin Elmer) was used to perform thermogravimetric analysis (TGA) on the prepared adsorbent. The CHN/O elemental analyser was being used to examine the elemental compositions. The structural properties of PETC-Fe were carried out using D8 Focus X-Ray diffraction (XRD) equipment. Energy dispersive X-ray diffraction (EDX) coupled scanning electron microscopy (SEM) (Carl Zeiss model-EVO-50) was used to examine the morphology PETC-Fe. A Stenner-85 peristaltic pump was employed to provide a constant supply of As (III) solution to the fixed bed column. An inductively coupled plasma mass spectrometry (ICPMS) (Agilent-7900) was used to measure As (III) from the test solution, and a pH meter was used to measure the pH of the solution (model Hanna-HI2020). MEXA-6000FT model Fourier transform infrared (FTIR) were used to investigate the functional groups present in the adsorbent.

Experimental setup

Fixed-bed column experiments were performed utilizing 2 cm internal diameter and 60 cm long borosilicate glass columns. For the purpose of produce varying bed heights, the column was loaded with various amounts of PETC-Fe within two fixed glass wool (0.5 cm) layers. The supporting layers of glass wool were provided to avoid the floatage of PETC-Fe. Using central composite design in response surface methodology (RSM), a column analysis was carried to investigate the impacts of operational variables (influent As (III) concentration, bed height, and influent flow rate on the response function (breakthrough time) of As (III) adsorption. As (III) solutions with the required influent concentration were regulated to the appropriate pH for maximum As (III) uptake and injected into the column in down-flow mode at the specified flow rate through the bed. The flow rates were selected to provide enough runoff per 45 min to allow for realistic analysis of arsenic levels. When the effluent As (III) concentration surpassed 99% of the influent concentration, the column's operation was halted. All tests were carried out in triplicate at room temperature with a 3% experimental error limit and mean results reported.

Breakthrough curves modeling

The breakthrough curve's shape generated from plotting Cf/Ci against time t, where Cf and Ci is the outlet As (III) and influent concentrations in μg/L, respectively, can be used to evaluate the efficiency of a column. This is necessary to ascertain a fixed bed column's operation and dynamic responsiveness (Chowdhury et al. 2015). For a given influent flow rate and inlet concentration, the mass of As (III) adsorbed by PETC-Fe in the fixed bed, viz qoverall (μg), is equivalent to the area underneath the curve of the removed adsorbate concentration, Ca (Ci-Cf) (μg/L) against t (min), and is represented by equation:
formula
(1)
where Q represents flow rate (volumetric) and t is flow time (min).
Equation (2) is used to compute the experimental adsorption capacity, qe (μg/g), where W is the overall dry mass of As (III) in the bed (g)
formula
(2)
Equation (3) is used to calculate the total quantity of As (III) loaded into the fixed bed column (mg)
formula
(3)

BDST, Thomas, and Adams–Bohart are mathematical models used to examine the breakthrough curves, in the present study.

Adams–Bohart model

According to the Adams–Bohart model, rate of adsorption is proportional to both the concentration of the adsorbate and the adsorbent's leftover capacity. The first half of the breakthrough curve is described using Adams–Bohart model (Burdzy et al. 2022).
formula
(4)
where k represents constant of kinetic (L/μg-min), u is the linear velocity determined by taking ratio of the volumetric flow rate and column's area, h is the bed height (cm) of column, and No is the maximum uptake capacity (μg/L). A graph of ln (Cf/Ci) against t can be used to determine the values of constant of kinetic and No.

The Thomas model

The Thomas model assumes the operation is based on Langmuir model without any axial mixing in column uptake, as the deriving force for rate is based on second-order kinetics of reaction. The Thomas model in linear form can be represented as follows:
formula
(5)
where kth represents model constant (mL/min-μg), q0 is the uptake capacity (μg/q) of adsorbent at equilibrium, m represents mass (g) of PETC-Fe, and Q represents volumetric flow rate.

BDST model

In fixed-bed column study, the BDST is a straightforward semi-empirical model that allows for the quickest forecast of adsorbent efficiency. The BDST model assumes that adsorption rate is governed by the strong interaction between the As (III) and the PETC-Fe's non-utilized capacity (Igwegbe et al. 2021). This model can forecast the link between the fixed bed column's bed height and service time. The below mentioned equation gives a linearized relation between bed height and service time.
formula
(6)
where k represents the BDST model's rate constant (L/mg-min), N0 represents the (μg/L), and Z denotes the height of the packed bed (cm).

Design of experimental and optimization by RSM-CCD

RSM is a technique which can be used to model experimental procedures. This process relies on quantitative regression model, which includes finding an optimum model to minimize remaining variances. RSM was originally designed to identify the optimal process variables in the process industry, but it is today employed in a wide range of domains and applications, including not just physical sciences and engineering, but also physiological, medical, and sociology fields (Dixit & Yadav 2019). Presently, a quantitative optimization model has been designed using the RSM based on the (Design-Expert 6.0.8 software) for investigating the combined impact of the process variables (influent As (III) concentration, bed height and flow rate) on the response function (breakthrough time). The statistical model is generated using the CCD programme in terms of uptake of As (III), as it uses the smallest number of samples necessary to optimise the observed process parameters while obtaining the highest breakthrough time.

TGA was used to examine the decomposition characteristics of the raw PET. The ultimate, proximate, and Brunauer-Emmett-Teller (BET) analyses of the synthesised PET chars were also used for detailed characterization (Table 1). Furthermore, the dominant processes and the adsorption effectiveness of the investigated PETC-Fe were investigated. In the following sections, the outcomes of all of these have been reported and discussed.

Table 1

PA, UA and BET analysis results

Proximate analysisUltimate analysisBET analyses
Moisture content 1.98 Carbon (C) 71.4  PETC PETC-F 
Fixed carbon 9.31 Oxygen (O) 23.8 Surface area (SBET) (m2/g) 81.4 33.6 
Volatile matter 85.6 Hydrogen(H) 4.3 Pore size (Å) 4.73 3.55 
Ash content 6.14 Sulphur(S) 0.5 Pore volume (cc/g) 3.94 3.12 
Proximate analysisUltimate analysisBET analyses
Moisture content 1.98 Carbon (C) 71.4  PETC PETC-F 
Fixed carbon 9.31 Oxygen (O) 23.8 Surface area (SBET) (m2/g) 81.4 33.6 
Volatile matter 85.6 Hydrogen(H) 4.3 Pore size (Å) 4.73 3.55 
Ash content 6.14 Sulphur(S) 0.5 Pore volume (cc/g) 3.94 3.12 

The moisture content of the PET sample was reported as 1.98 in proximate analysis. Pyrolysis of PET yielded 85.6% volatile matter, suggesting that solid compounds such as ash content and fixed are formed less frequently. The specimen had no N but high C content, according to elemental analyses. PETC had fewer amounts of fixed carbon, 9.31%, but the high amount of C, 71.4%, indicating that while the degree of char produced will be smaller, but the quality will be superior due to the high C content.

SEM imaging is used to analyse the surface morphological properties of the PETC and PETC-Fe (Figure 1(a) and 1(b)). After modification, the PET surface is relatively smooth, however, considerable number of pores and heterogeneity is visible in PETC, indicating that it possesses high porosity, which may have resulted in a huge surface area. PETC-Fe surface is coated by numerous particles, indicating a coating of Fe2O3 molecules bonded to the PETC surface. The BET pore size, and pore volume of PETC-Fe are shown in Table 1. After Fe loading, the BET parameters corresponding to PETC-Fe reduces. This reduction could be attributed to pore occlusion caused by Fe coating in the PETC samples. Due to iron doping, a similar trend of reduction in BET parameters has previously been observed in literature (Sawood et al. 2021a). PETC-Fe has an average pore size of 5–15 Å, indicating that it is microporous in nature.
Figure 1

SEM micrographs of PET (a) PET precursor and (b) PETC-Fe.

Figure 1

SEM micrographs of PET (a) PET precursor and (b) PETC-Fe.

Close modal
Figure 2 demonstrates the X-ray diffraction analysis (XRD) findings of PETC and PETC-Fe. Two diffraction crests at 27.42° and 60.12° have been allotted to the IP 118 and 626 of silicon dioxide a diffraction peak at 39.38° and 68.92° to the indexed planes 109 and 7-4-2 of Ca2O4Si, and a diffraction peak at 49.78° to the IP (128) represents CaCO3. Fe was considered to be present on the surface of PETC-Fe, as reflected by Fe2O3, γ-Fe2O3 (36.8° and 65.6°) and Fe2O3 (34.23° and 41.52°) peaks (PDF42-0718) (Gong et al. 2016). It's noteworthy that silicon dioxide, Ca2O4Si, and CaCO3 seem to be non-visible in the X-ray diffraction pattern of PETC-Fe, which could be due to the lower concentration of these constituents after Fe was impregnated. Fe2O3/silica was claimed to have the potential to extract aqueous As (III) (Tajik et al. 2021). The increase in uptake of adsorbate owing to Fe inoculation outweighed the decrease in uptake due to the loss of silicon dioxide, Ca2O4Si, and CaCO3 in this study.
Figure 2

XRD patterns of PETC (lowermost), PETC-Fe (middle), PETC-Fe-As (upmost).

Figure 2

XRD patterns of PETC (lowermost), PETC-Fe (middle), PETC-Fe-As (upmost).

Close modal
Figure 3 depicts the variations in FTIR analysis of the PETC, PETC-Fe, and PETC-Fe-As composites. At wavelengths of 1,578, 1,390, 876, and 864 cm−1, four peaks were observed for PETC, that were attributed to the vibrations of the carbonyl, cyclic esters, aromatic H2, and C, respectively (Ahmed et al. 2016). Five peaks were identified with PETC-Fe: 3,300 cm−1 (hydroxyl), 1,578 cm−1 (carbonyl), 1,390 cm−1 (cyclic esters), 876 cm−1 (aromatic H2), and 864 cm−1 (methyl). For PETC-Fe, a new peak was found at 3,300 cm−1, probably corresponded to hydroxyl. In addition, the carbonyl (1,576 cm−1) intensity increased (Tang et al. 2016). More O2 laden groups (hydroxyl and carbonyl) were detected for PETC-Fe, implying that iron loading affected the functional groups on the exterior of the PETC.
Figure 3

FTIR spectra of PETC-Fe and PETC-Fe-As.

Figure 3

FTIR spectra of PETC-Fe and PETC-Fe-As.

Close modal
The PETC-Fe exhibited ferromagnetic characteristics having significant magnetization (saturation) and was rapidly recollected by a magnetic force; however, the PETC exhibited no sensitivity and distributed well in the solution. The PETC-Fe was still usually attracted by a magnet after a week of stirring at room temperature and 90 rpm. These results indicated that the magnetic properties were present in the PETC-Fe. The magnetic behaviour of PETC-Fe was determined using a VSM at 25 °C temperature, as illustrated in Figure 4. The PETC-Fe exhibits ferromagnetic characteristics at 298 K, with a saturation magnetization (SM) of 68.4 emu/g. The theoretical SM of bulk – iron (III) oxide (76 emu/g) was comparable to this value.
Figure 4

Magnetization plot of PETC-Fe.

Figure 4

Magnetization plot of PETC-Fe.

Close modal
Binding energy modifications for Fe, As, C, and O were discovered using XPS analysis to further elucidate the mechanisms of As (III) uptake PETC-Fe. Figure 5 demonstrates that the outer layer of precursor is solely dominated by C, O groups, whereas Fe, and As emerged in PETC-Fe after Fe impregnation and adsorption, respectively. Both pre- and post-As (III) sorption, two distinct Fe peaks related to Fe2p3, and Fe2p1 were detected on the surface of PETC-Fe. After adsorption of As (III), a noticeable peak related to As3d was observed on the surface of PETC-Fe. After Fe loading, the atomic % of C dropped significantly, whereas that of O increased substantially. The O/C atomic ratios pre- and post-As (III) uptake were reported as 0.91 and 0.64 for PETC-Fe and 0.18 for precursor, respectively. This explains that after Fe loading, the O functional groups enhanced, but after As (III) adsorption, they reduced.
Figure 5

The XPS spectra of PETC-Fe before and after As(III) adsorption.

Figure 5

The XPS spectra of PETC-Fe before and after As(III) adsorption.

Close modal

Response surface methodology for optimization:

RSM has become extremely prevalent for designing, enhancing, and optimising complicated processes, as well as determining the significance of different operating variables (Mondal et al. 2019). For a set of three independent variables, namely, influent As (III) concentration, inlet flow rate, and bed depth, the most efficient CCD in RSM was employed to study their effects on the breakthrough time for fixed-bed continuous of As (III) by PETC-Fe. Table 2 lists the variables' testing limits, as well as their notation and unit in central composite design.

Table 2

Testing levels and ranges of operational parameters

FactorNameUnitsMinimumMaximumCoded lowCoded high
Influent As(III) concentration μg/L 300.00 1,500.00 −1 ↔ 300.00 +1 ↔ 1,500.00 
Influent flow rate mL/min 3.00 9.00 −1 ↔ 3.00 +1 ↔ 9.00 
Bed height cm 5.00 15.00 −1 ↔ 5.00 +1 ↔ 15.00 
FactorNameUnitsMinimumMaximumCoded lowCoded high
Influent As(III) concentration μg/L 300.00 1,500.00 −1 ↔ 300.00 +1 ↔ 1,500.00 
Influent flow rate mL/min 3.00 9.00 −1 ↔ 3.00 +1 ↔ 9.00 
Bed height cm 5.00 15.00 −1 ↔ 5.00 +1 ↔ 15.00 

With the help of the Design Expert (6.0.8), a 23 complete factorial central composite design was obtained. A total of 20 tests in duplicate were used to the central composite design matrix according to this approach, as shown in Table 3.

Table 3

CCD for independent operational variables and the recorded response for As (III)

StdRunFactor 1Factor 2Factor 3Response breakthrough time
A: Influent As(III) concentrationB: Influent flow rateC: Bed depthExperimental valuesRSM predicted
μg/LmL/mincmmin
10 1500.00 6.00 10.00 412 417 
300.00 9.00 15.00 858 861 
109.08 6.00 10.00 496 501 
1500.00 9.00 5.00 232 238 
300.00 3.00 15.00 934 941 
16 900.00 6.00 10.00 468 463 
14 900.00 6.00 15.00 778 782 
17 900.00 6.00 10.00 468 463 
18 900.00 6.00 10.00 468 463 
10 300.00 9.00 5.00 281 277 
11 1500.00 3.00 5.00 298 291 
13 12 900.00 6.00 5.00 245 240 
13 1500.00 9.00 15.00 741 735 
14 1500.00 3.00 15.00 792 789 
11 15 900.00 6.00 10.00 468 463 
20 16 900.00 6.00 10.00 468 463 
19 17 900.00 6.00 10.00 468 463 
12 18 900.00 11.05 10.00 425 419 
19 300.00 3.00 5.00 312 318 
15 20 900.00 6.00 10.00 468 463 
StdRunFactor 1Factor 2Factor 3Response breakthrough time
A: Influent As(III) concentrationB: Influent flow rateC: Bed depthExperimental valuesRSM predicted
μg/LmL/mincmmin
10 1500.00 6.00 10.00 412 417 
300.00 9.00 15.00 858 861 
109.08 6.00 10.00 496 501 
1500.00 9.00 5.00 232 238 
300.00 3.00 15.00 934 941 
16 900.00 6.00 10.00 468 463 
14 900.00 6.00 15.00 778 782 
17 900.00 6.00 10.00 468 463 
18 900.00 6.00 10.00 468 463 
10 300.00 9.00 5.00 281 277 
11 1500.00 3.00 5.00 298 291 
13 12 900.00 6.00 5.00 245 240 
13 1500.00 9.00 15.00 741 735 
14 1500.00 3.00 15.00 792 789 
11 15 900.00 6.00 10.00 468 463 
20 16 900.00 6.00 10.00 468 463 
19 17 900.00 6.00 10.00 468 463 
12 18 900.00 11.05 10.00 425 419 
19 300.00 3.00 5.00 312 318 
15 20 900.00 6.00 10.00 468 463 
Table 4

Sum of squares sequential model

SourceSum of squaresDFMean squareF valueProb > F
Mean 5.091E + 006 5.091E + 006    
Linear 7.755E + 005 2.585E + 005 95.91 < 0.0001   
2FI 4,948.30 1,649.43 0.56 0.6497  
Quadratic 34,824.08 3 11,608.03 34.63 < 0.0001 Suggested 
Cubic 3,352.06 838.01 6.366E + 007 < 0.0001 Aliased 
Residual 0.000 0.000    
Total 5.910E + 006 20 2.955E + 005    
SourceSum of squaresDFMean squareF valueProb > F
Mean 5.091E + 006 5.091E + 006    
Linear 7.755E + 005 2.585E + 005 95.91 < 0.0001   
2FI 4,948.30 1,649.43 0.56 0.6497  
Quadratic 34,824.08 3 11,608.03 34.63 < 0.0001 Suggested 
Cubic 3,352.06 838.01 6.366E + 007 < 0.0001 Aliased 
Residual 0.000 0.000    
Total 5.910E + 006 20 2.955E + 005    

ANOVA and model assessment

Analysis of variance (ANOVA) can be used to further examine the model's feasibility and validity (Tables 4 & 5). F-value (Fisher variation ratio), lack of fit, p-value (probability value), adequate precision (AP), R2d (coefficient of determination), R2Adj (adjusted coefficient of determination), R2Pred, were some of the evidences. AP corresponds to signal to noise ratio, which predicts contrasts, the range of expected values at nodes to the prediction error. Model selectivity is adequate when the ratios are higher than four (Zhou et al. 2011; Mishra et al. 2021; Sawood et al. 2021b).

Table 5

ANOVA for response surface quadratic model

SourceSum of squaresdfMean squareF-valuep-value
Model 8.153E + 005 90,587.12 270.24 <0.0001 significant 
A-Influent As(V) concentration 15,612.35 1 15,612.35 46.58 <0.0001  
B-Inlet flow rate 8,273.95 1 8,273.95 24.68 0.0006  
C-Bed height 7.484E+005 1 7.484E+005 2,232.51 <0.0001  
A2 27.66 1 27.66 0.083 0.7798  
B2 983.96 1 983.96 2.94 0.1174  
C2 18,866.96 1 18,866.96 56.28 <0.0001  
AB 13.78 1 13.78 0.041 0.8434  
AC 4,816.71 1 4,816.71 14.37 0.0035  
BC 117.81 1 117.81 0.35 0.5665  
Residual 3,352.06 10 335.21    
Lack of fit 3,352.06 4 838.01    
Pure error 0.000 6 0.000    
Cor total 8.186E + 005 19     
SourceSum of squaresdfMean squareF-valuep-value
Model 8.153E + 005 90,587.12 270.24 <0.0001 significant 
A-Influent As(V) concentration 15,612.35 1 15,612.35 46.58 <0.0001  
B-Inlet flow rate 8,273.95 1 8,273.95 24.68 0.0006  
C-Bed height 7.484E+005 1 7.484E+005 2,232.51 <0.0001  
A2 27.66 1 27.66 0.083 0.7798  
B2 983.96 1 983.96 2.94 0.1174  
C2 18,866.96 1 18,866.96 56.28 <0.0001  
AB 13.78 1 13.78 0.041 0.8434  
AC 4,816.71 1 4,816.71 14.37 0.0035  
BC 117.81 1 117.81 0.35 0.5665  
Residual 3,352.06 10 335.21    
Lack of fit 3,352.06 4 838.01    
Pure error 0.000 6 0.000    
Cor total 8.186E + 005 19     

The preferred quadratic method was verified using ANOVA, which revealed high Fisher variation ratio value, extremely low probability value (<0.0001), not significant lack of fit value, and coefficient of determination, R2Pred, and AP. Furthermore, Figure 6 displays the actual vs expected values of As (III) uptake for the continuous column investigation, revealing that the predicted and actual values are in great agreement.
Figure 6

Predicted vs actual response for fixed bed column operation.

Figure 6

Predicted vs actual response for fixed bed column operation.

Close modal
The empirical relationship between the breakthrough time and the investigated independent process variables, represented in terms of dimensionless regression coefficient by the chosen model, is as follows:
formula
where A (influent As (III) concentration), B (influent flow rate), and C (bed height) are factors in code.

Variable interaction

Contour plots were used to investigate the interaction between the various independent factors and their effect on the response (Figures 7,89). The contour plot is a 2D representation of the response surface. This study clarifies the impact of factors and their interactions on the response as a function of two variables simultaneously.
Figure 7

Contour plot depicting the synergetic impact of influent As (III) concentration and influent flow rate on the response for fixed bed operation of As (III) uptake by PETC-Fe; bed height: 15.0 cm.

Figure 7

Contour plot depicting the synergetic impact of influent As (III) concentration and influent flow rate on the response for fixed bed operation of As (III) uptake by PETC-Fe; bed height: 15.0 cm.

Close modal
Figure 8

Contour plot depicting the synergetic impact of bed height and influent As (III) concentration on the response for fixed bed operation of As (III) uptake by PETC-Fe; influent flow rate: 3.0 mL/min.

Figure 8

Contour plot depicting the synergetic impact of bed height and influent As (III) concentration on the response for fixed bed operation of As (III) uptake by PETC-Fe; influent flow rate: 3.0 mL/min.

Close modal
Figure 9

Contour plot depicting the synergetic impact of bed height and influent flow rate on the response for fixed bed operation of As (III) uptake by PETC-Fe; and influent As (III) concentration: 900.0 μg/L/min.

Figure 9

Contour plot depicting the synergetic impact of bed height and influent flow rate on the response for fixed bed operation of As (III) uptake by PETC-Fe; and influent As (III) concentration: 900.0 μg/L/min.

Close modal

The contour plot of Figure 7 shows the synergetic impact of inlet As (III) concentration and influent flow rate on response (breakthrough time). Within the range of observations, the breakthrough time reduces as the inlet As (III) concentration and influent flow rate rise. This can be elucidated by the fact that PETC-Fe have a finite number of active sites that will be occupied by the adsorbate at a particular concentration (Roy et al. 2017; Zhu et al. 2018). The active sites on the surface of the adsorbent are now more promptly saturated as the influent As (III) concentration rises, reducing breakthrough time. Uptake capacity was also decreased at increased flow rates due to secant As (III) residence time in the fixed bed and solute diffusion into the adsorbent's pores. As a result of leaving the bed before equilibrium has been established, a lesser breakthrough time is obtained (Mohan et al. 2017; Sawood & Gupta 2020b).

The interaction impact of influent As (III) concentration and bed height on breakthrough time is displayed in Figure 8. The breakthrough time reduces with increasing influent As (III) concentration and escalates with increasing bed height within the given range of experiment. The obtained pattern could be explained by the fact that enhancement in bed height generates a higher number of available active adsorption sites resulted from enhanced surface area of adsorbent. As a result, the adsorbate has sufficient time to interact with the adsorbent, giving in a prolonged breakthrough (Patel 2020; Yassin et al. 2021).

The synergetic impact of bed height and influent flow rate on the response function for fixed bed column studies of As (III) uptake by Fe–SCC is shown in Figure 9. The breakthrough time is greatly influenced by the interaction between bed height and influent flow rate. The breakthrough time reduces as the influent flow rate increases, but it increases as the bed height increases. As previously stated, such a breakthrough trend can be justified by inadequate residence time of adsorbate in the fixed bed column as well as the presence of active sites.

Thus, contour plots demonstrate that the response function (breakthrough time), increases when concentration of adsorbate reduces, bed height increases, and influent flow rate reduces. At 9.0 cm bed height, 3 mL/min influent flow rate, and 300.0 μg/L adsorbate concentration, the absorption of 1,892 μg/g for arsenic was recorded at the maximum breakthrough time.

According to a survey of the literature, only little works on fixed bed column analysis have been documented; otherwise, the majority of As (III) mitigation research has been undertaken in batch mode. The results collected under batch operations, on the other hand, are often not suitable in real treatment operations, because the residence duration is insufficient to achieve equilibrium. As a result, equilibrium investigations utilising columns are required. Column reactors have a higher equilibrium absorption capacity of As (III), making the adsorbent appealing for As (III) mitigation filter system. Hence, the adsorption capacity of PETC-Fe under fixed bed column operation is contrasted to that of earlier reported different adsorbents (Table 6). Even though it is hard to accurately correlate the PETC-Fe to other adsorbents due to the diverse test conditions used, still the As (III) absorption capacity of PETC-Fe is found to be relatively considerable and comparable to other adsorbents used for As (III) removal in column operations. The findings suggest that PETC-Fe may be efficiently utilised in a continuous fixed bed operations to mitigate As (III) from aquatic environment.

Table 6

Comparison of the uptake capacities of char based adsorbents for As (III) mitigation by fixed-bed operation

AdsorbentOperating conditionsUptake capacity (μg/g)References
Thioglycolated sugarcane carbon 6.0 g dosage, 3.0 mL/min flow rate, and 1,500 μg/L influent concentration 85.01 Roy et al. (2013)  
Iron oxide-coated cement 10–20 cm bed height, 4.3–12 mL/min flow rate and 500–2,700 μg/L initial As(III) concentrations 600.53 Kundu & Gupta (2007)  
Multi walled CNTs 10–20 cm bed height, 30.0 mL/min flow rate, and 500 μg/L influent concentration 13.5 Ali (2018)  
Fe2O3 impregnated aspergillus niger biomass 6.925 g dosage, 2.5 mL/min flow rate, and 100 μg/L influent concentration 88 Pokhrel & Viraraghavan (2008)  
 Fe2O3 nanoneedle array- impregnated biochar fibers 2.0 g dosage, 2,500 breakthrough volume, and 275 μg/L influent concentration – Wei et al. (2019)  
Non-immobilized sorghum char 150.0 g dosage, 2,500 10.0 mL/min flow rate, and 500 μg/L influent concentration
0.18–1.4 mm particle size 
276.5 Carneiro et al. (2021)  
Modified calcined bauxite 10 cm bed height, 5.0 mL/min flow rate and 1,000 μg/L initial As(III) concentrations, 0.212 mm particle size 490 Ayoob et al. (2007)  
PETC-Fe 15 cm bed height, 3.0 mL/min flow rate 1,892 Current study 
AdsorbentOperating conditionsUptake capacity (μg/g)References
Thioglycolated sugarcane carbon 6.0 g dosage, 3.0 mL/min flow rate, and 1,500 μg/L influent concentration 85.01 Roy et al. (2013)  
Iron oxide-coated cement 10–20 cm bed height, 4.3–12 mL/min flow rate and 500–2,700 μg/L initial As(III) concentrations 600.53 Kundu & Gupta (2007)  
Multi walled CNTs 10–20 cm bed height, 30.0 mL/min flow rate, and 500 μg/L influent concentration 13.5 Ali (2018)  
Fe2O3 impregnated aspergillus niger biomass 6.925 g dosage, 2.5 mL/min flow rate, and 100 μg/L influent concentration 88 Pokhrel & Viraraghavan (2008)  
 Fe2O3 nanoneedle array- impregnated biochar fibers 2.0 g dosage, 2,500 breakthrough volume, and 275 μg/L influent concentration – Wei et al. (2019)  
Non-immobilized sorghum char 150.0 g dosage, 2,500 10.0 mL/min flow rate, and 500 μg/L influent concentration
0.18–1.4 mm particle size 
276.5 Carneiro et al. (2021)  
Modified calcined bauxite 10 cm bed height, 5.0 mL/min flow rate and 1,000 μg/L initial As(III) concentrations, 0.212 mm particle size 490 Ayoob et al. (2007)  
PETC-Fe 15 cm bed height, 3.0 mL/min flow rate 1,892 Current study 

Perturbation analysis

On the response function, the influence of bed height, inlet As (III) concentration and influent flow rate were investigated. A perturbation plot was used to determine the singular impacts of independent process variables viz A (influent As (III) concentration), B (influent flow rate), and C (bed height). The impact of interactions is not used in a perturbation analysis, which is similar to single factor experiments. Figure 10 shows a perturbation plot that may be used to examine the performance of all independent process variables at a certain location in the given design space. Only one factor is altered over the response's range while the remaining factors remain unaltered. A component with a high slope indicates its sensitivity towards the response (Zhang et al. 2016). A flattish line indicates that the factor is relatively non-sensitive to variation (Alchouron et al. 2020). Breakthrough time (response factor) is highly sensitive towards bed height, influent As (III) concentration rather than influent flow rate, according to the current findings.
Figure 10

Perturbation plot depicting the influence of the tested factors on the response is depicted.

Figure 10

Perturbation plot depicting the influence of the tested factors on the response is depicted.

Close modal

Optimization by desirability parameters

Target, minimum, maximum, in range, and set to a precise value are all conceivable goals in the software. The intended aim was favoured for every variable and response in numerical optimization (Amini et al. 2008). The desirability analysis of an optimization process are shown in a bar plot (Figure 11) in which the standard was set to max for influent As (III), min for bed height, in range for influent flow rate, and the target was fixed to ‘maximum’ to analyse commercially sustainable optimum condition. The goal of this procedure was to discover the greatest breakthrough time while operating with the least bed height possible. Independent factors' desirability values range from 0.894 to 1, whereas the aggregate of all variables' desire value is 0.99.
Figure 11

Bar graph representing optimization technique.

Figure 11

Bar graph representing optimization technique.

Close modal

When the independent variables were kept at 10.20 cm bed height, 1,500.0 μg/L of influent As (III) concentrations, and 3.0 mL/min of influent flow rate at a highest desirability point of 0.998, the top maximum response factor (breakthrough time) was found to be 480.58 min.

Finally, identical validation column experiments were carried out under ideal conditions for their confirmation. The experimental test data, which is consistent with the optimised RSM-central composite design result, implies that PETC-Fe could be an effective and cost-effective adsorbent for As (III) mitigation in the aqueous environment.

The optimal parameters for PETC-Fe to adsorb the arsenic were determined and are shown in Table 7.

Table 7

Optimum conditions selected for the maximum possible COD removal percentage

NumbersInfluent As(III) concentrationInlet flow rateBed heightBreakthrough timeDesirability
300.07 3.00 15.00 941.828 0.993 
NumbersInfluent As(III) concentrationInlet flow rateBed heightBreakthrough timeDesirability
300.07 3.00 15.00 941.828 0.993 

Modeling of breakthrough curve

Adams–Bohart and Thomas models were utilised to develop a kinetic model in the column and determine the breakthrough curves to explain the fixed-bed column behaviour.

Adams–Bohart model

The Adams–Bohart model for adsorption of As (III) was used to describe the initial section of the breakthrough curve using experimental. The respective values of kAB (kinetic constant) and No (maximum uptake capacity) were computed using linear regression analysis on all breakthrough curves and are reported in Table 8. At any flow rates, there is no significant difference in the values of the kAB and the No. As the bed depth deepens, the values of kAB and No reduce. Furthermore, as the initial As (III) concentration rises, the value of kAB falls and No rises. The model is described well by theory of surface reaction and posits that equilibrium attainment is not quick; as a result, the rate of uptake is in good accordance with both, the adsorbent's residual capacity and the concentration. Furthermore, the Adams–Bohart model is used in lesser concentration areas and where mass transfer limits the rate of uptake (López-Cervantes et al. 2018). According to Table 8, the actual breakthrough curves are not near to those expected by Adams–Bohart model, and the coefficient of regression (R2) values are low. As a result, in the range of utilised operating conditions and with present adsorbent, the model cannot be used to predict the experimental findings.

Table 8

Adams–Bohart parameters at various operating conditions using linear regression analysis

Ci (μg/L)h (cm)Q (mL/min)KN0R2
300 0.075 185.22 0.69 
300 10 0.054 167.83 0.74 
300 15 0.041 126.53 0.80 
300 15 0.040 124.71 0.76 
300 15 0.041 126.42 0.72 
900 15 0.018 39.44 0.63 
1,500 15 0.015 37.21 0.61 
Ci (μg/L)h (cm)Q (mL/min)KN0R2
300 0.075 185.22 0.69 
300 10 0.054 167.83 0.74 
300 15 0.041 126.53 0.80 
300 15 0.040 124.71 0.76 
300 15 0.041 126.42 0.72 
900 15 0.018 39.44 0.63 
1,500 15 0.015 37.21 0.61 

Thomas model

The two unknown variables of the Thomas equation, constant of Thomas model and equilibrium uptake capacity, were determined using the plot ln (Co/Ci 1) against t (linear system form of the Thomas model). Table 3 shows the calculated values of the various parameters of the model, as well as the R2. According to Table 9, the values of uptake capacity grow as the inlet As (III) concentration and bed height rise, while the values of constant of Thomas model fall, whereas the values of qo and kTh grew when the flow rate increased. Despite the fact that the Thomas model shows certain reasonable modifications for removal conditions, there is no clear connection in the breaking curve prediction. This can be seen in the disparities among experimental results and model-calculated uptake capacity estimates. Although the Thomas model is among the most frequently used to define the behaviour of biosorption process in column operations, its primary constraint is that it is rooted on second-order kinetics and assumes that biosorption is controlled by interfacial mass transfer rather than the chemical reaction. When this approach is employed to represent biosorption systems under specific circumstances, this mismatch can lead to inaccuracies (López-Cervantes et al. 2018).

Table 9

Thomas model parameters of As (III) uptake on comparison fixed bed column

Ci (μg/L)h (cm)Q (ml/min)Kthq0R2
300 54.88 896.45 0.71 
300 10 41.76 1,145.56 0.76 
300 15 38.50 1,746.20 0.83 
300 15 44.55 1,971.26 0.74 
300 15 47.81 2,245.35 0.70 
900 15 17.45 3,917.52 0.59 
1,500 15 11.28 6,125.47 0.55 
Ci (μg/L)h (cm)Q (ml/min)Kthq0R2
300 54.88 896.45 0.71 
300 10 41.76 1,145.56 0.76 
300 15 38.50 1,746.20 0.83 
300 15 44.55 1,971.26 0.74 
300 15 47.81 2,245.35 0.70 
900 15 17.45 3,917.52 0.59 
1,500 15 11.28 6,125.47 0.55 

BDST model

Plotting the service time t against bed height at Ci/Co equal to 0.15, 0.30, and 0.45 yielded the uptake capacity and rate constant from the BDST model. The tested and expected service times for different bed heights (15, 10, and 6 cm) were estimated using a influent flow rate of 3 mL/min and an influent concentration of 500 μg/L. Table 10 presents the results. The BDST model's projected service time values are consistent with the test service time values. As a result, the BDST model may be suitable for this configuration; also, the significant R2 values derived from data support the BDST model's applicability for the current fixed-bed column operations.

Table 10

BDST model parameters for As(III) adsorption onto PETC-Fe

ci/ceKN0Service Time (predicted)Service Time (experimental)R2
0.15 844 282.45 346 357 0.989 
0.30 21.40 1,541.76 1,341 1,339 0.996 
0.45 −9.45 5,985.25 881 817 0.981 
ci/ceKN0Service Time (predicted)Service Time (experimental)R2
0.15 844 282.45 346 357 0.989 
0.30 21.40 1,541.76 1,341 1,339 0.996 
0.45 −9.45 5,985.25 881 817 0.981 

Regeneration and reuse

On PETC-Fe, adsorption-desorption experiments cycle for As (III) were performed. The As (III) ions were recovered with 0.050 L of H3PO4 (0.1 M). The breakthrough curves for arsenite restoration at 3 mL/min influent flow rate, 15 cm bed height revealed no typical changes through multiple cycles, as illustrated in Figure 12. This shows that PETC-Fe can efficiently withstand repeated use for up to three cycles. For three subsequent sorption/desorption cycles, the regeneration performance was reported to be 99.14, 98.71, and 99.50%, respectively.
Figure 12

Effect of consecutive desorption on breakthrough for mitigation of As (III).

Figure 12

Effect of consecutive desorption on breakthrough for mitigation of As (III).

Close modal
The column was cleansed with moderately lukewarm distilled water post-regeneration to clear residual traces of sodium hydroxide and reduce the pH, as adsorption is less effective in the range of basicity. After drying, the column was allowed to operate in subsequent adsorption cycle. Figure 13 depicts three successive sorption cycles, demonstrating that the second cycle's removal effectiveness is reduced by around 8%, followed by depression of another 5% in the third cycle. As can be seen in Figure 13, PETC-Fe offers slightly varying As (III) uptake capacities over the three successive cycles, and the regeneration and reuse of PETC-Fe offers an affordable way to As (III) removal from aqueous system.
Figure 13

Capacities of PETC-Fe for As (III) adsorption during three consecutive cycles.

Figure 13

Capacities of PETC-Fe for As (III) adsorption during three consecutive cycles.

Close modal

Fixed bed column adsorption investigations have been used to evaluate the performance of Fe impregnated chars made from pyrolysis of waste PET for As (III) removal. The impact of independent process variables (bed height, flow rate and concentration) on the breakthrough were designed by CCD. ANOVA confirmed the quadratic model proposed by RSM-CCD. The experimental results were observed to be quite close to the model's predicted values. Contour and perturbation charts were used to predict the impact of the independent variables and their interactions on response function (breakthrough time). The breakthrough time was shown to be highly influenced by the process variables. With a raise in inlet As (III) concentration, the total amount of As (III) adsorbed rose, while the removal percentage reduced. When lesser flow rates and larger bed heights were used, the PETC-Fe column's performance enhanced. Owing to the complicated adsorption mechanism, the BDST approach of breakthrough data estimations slightly differed from the test findings over breakpoint. The results from the column studies did not have a satisfactory correlation with the results from the mass transfer model. According to the optimised CCD result, the PETC-Fe adsorbent has proven to be a sustainable and highly effective adsorbent. Consequently, it can be claimed that use of statistical model is an efficient approach for optimising and design of As (III) removal process. The information reported in this research can be used to design and implement an effective As (III) removal approach even while treating any potable water contamination. For three consecutive adsorption-desorption cycles, the PETC-Fe's regeneration efficiency was observed to be 99.14, 98.71, and 99.50 percent, respectively. This means that the PETC-Fe is capable of being used repeatedly for at least three cycles. The current investigation shows that PET char based adsorbent functions well, indicating that they should be used on a bigger field ranges. As a result, PET char based adsorbent can be regarded as sustainable and novel option for As (III) removal and management of plastic waste.

The authors would like to thank PGRL, IIT Kanpur, for providing assistance with SEM, XRD, XPS, VSM, BET and FTIR analysis.

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

The authors declare there is no conflict.

Alchouron
J.
,
Navarathna
C.
,
Chludil
H. D.
,
Dewage
N. B.
,
Perez
F.
,
Pittman Jr
C. U.
,
Vega
A. S.
&
Mlsna
T. E.
2020
Assessing South American Guadua chacoensis bamboo biochar and Fe3O4 nanoparticle dispersed analogues for aqueous arsenic (V) remediation
.
Science of The Total Environment
706
,
135943
.
Amini
M.
,
Younesi
H.
,
Bahramifar
N.
,
Lorestani
A. A. Z.
,
Ghorbani
F.
,
Daneshi
A.
&
Sharifzadeh
M.
2008
Application of response surface methodology for optimization of lead biosorption in an aqueous solution by Aspergillus niger
.
Journal of Hazardous Materials
154
(
1–3
),
694
702
.
Aredes
S.
,
Klein
B.
&
Pawlik
M.
2013
The removal of arsenic from water using natural iron oxide minerals
.
Journal of Cleaner Production
60
,
71
76
.
Asghar
A.
,
Khan
Z.
,
Maqbool
N.
,
Qazi
I. A.
&
Awan
M. A.
2015
Comparison of adsorption capability of activated carbon and metal doped TiO2 for geosmin and 2-MIB removal from water
.
Journal of Nanomaterials
2015
, 1–11 .
Ayoob
S.
,
Gupta
A.
&
Bhakat
P.
2007
Performance evaluation of modified calcined bauxite in the sorptive removal of arsenic (III) from aqueous environment
.
Colloids and Surfaces A: Physicochemical and Engineering Aspects
293
(
1–3
),
247
254
.
Bajpai
S.
&
Chaudhuri
M.
1999
Removal of arsenic from ground water by manganese dioxide–coated sand
.
Journal of Environmental Engineering
125
(
8
),
782
784
.
Bang
S.
,
Patel
M.
,
Lippincott
L.
&
Meng
X.
2005
Removal of arsenic from groundwater by granular titanium dioxide adsorbent
.
Chemosphere
60
(
3
),
389
397
.
Bool
R. J. A.
,
Luwalhati
G. C.
,
Tan
N. E. Y.
,
Aquino
A. P.
&
Maalihan
R. D.
2022
On the use of metal-organic framework-based adsorbent from recycled PET bottles for Eriochrome Black T removal
.
Materials Today: Proceedings
65 (8), 3312–3320.
Burdzy
K.
,
Aurich
A.
,
Hunger
S.
,
Jastrząb
R.
,
Zabiszak
M.
&
Kołodyńska
D.
2022
Green citric acid in the sorption process of rare earth elements
.
Chemical Engineering Journal
437,
135366
.
Carneiro
M. A.
,
Pintor
A.
,
Boaventura
R. A.
&
Botelho
C.
2021
Current trends of arsenic adsorption in continuous mode: literature review and future perspectives
.
Sustainability
13
(
3
),
1186
.
Chan
K.
&
Zinchenko
A.
2021
Conversion of waste bottles’ PET to a hydrogel adsorbent via PET aminolysis
.
Journal of Environmental Chemical Engineering
9
(
5
),
106129
.
Chowdhury
Z. Z.
,
Abd Hamid
S. B.
&
Zain
S. M.
2015
Evaluating design parameters for breakthrough curve analysis and kinetics of fixed bed columns for Cu (II) cations using lignocellulosic wastes
.
BioResources
10
(
1
),
732
749
.
Foglia
A.
2021
Closing the Plastic Waste Material Cycle in the Power Industry
.
University of South-Eastern Norway, Telemark, Norway
.
Giri
A. K.
2019
Bioaccumulation potential and toxicity of arsenite using rooted-submerged vallisneria spiralis in a hydroponic culture and its characterization studies
.
J. Adv. Sci. Res
10
,
17
22
.
Goldberg
S.
2002
Competitive adsorption of arsenate and arsenite on oxides and clay minerals
.
Soil Science Society of America Journal
66
(
2
),
413
421
.
Gong
Y.
,
Wang
L.
,
Liu
J.
,
Tang
J.
&
Zhao
D.
2016
Removal of aqueous perfluorooctanoic acid (PFOA) using starch-stabilized magnetite nanoparticles
.
Science of the Total Environment
562
,
191
200
.
He
R.
,
Peng
Z.
,
Lyu
H.
,
Huang
H.
,
Nan
Q.
&
Tang
J.
2018
Synthesis and characterization of an iron-impregnated biochar for aqueous arsenic removal
.
Science of the Total Environment
612
,
1177
1186
.
Hopewell
J.
,
Dvorak
R.
&
Kosior
E.
2009
Plastics recycling: challenges and opportunities
.
Philosophical Transactions of the Royal Society B: Biological Sciences
364
(
1526
),
2115
2126
.
Igwegbe
C. A.
,
Ighalo
J. O.
,
Ghosh
S.
,
Ahmadi
S.
&
Ugonabo
V. I.
2021
Pistachio (Pistacia vera) waste as adsorbent for wastewater treatment: a review
.
Biomass Conversion and Biorefinery
739 (9),
1
19
.
Islam
M. A.
,
Morton
D. W.
,
Johnson
B. B.
,
Mainali
B.
&
Angove
M. J.
2018
Manganese oxides and their application to metal ion and contaminant removal from wastewater
.
Journal of Water Process Engineering
26
,
264
280
.
Lee
S. Y.
,
Chang
B.
,
Kim
Y.
,
Jang
H.
&
Lee
Y. J.
2022
Characterization of arsenite (As (III)) and arsenate (As (V)) sorption on synthetic siderite spherules under anoxic conditions: different sorption behaviors with crystal size and arsenic species
.
Journal of Colloid and Interface Science
613, 499–514.
López-Cervantes
J.
,
Sánchez-Machado
D. I.
,
Sánchez-Duarte
R. G.
&
Correa-Murrieta
M. A.
2018
Study of a fixed-bed column in the adsorption of an azo dye from an aqueous medium using a chitosan–glutaraldehyde biosorbent
.
Adsorption Science & Technology
36
(
1–2
),
215
232
.
Maia
L. C.
,
Soares
L. C.
&
Gurgel
L. V. A.
2021
A review on the use of lignocellulosic materials for arsenic adsorption
.
Journal of Environmental Management
288
,
112397
.
Mendoza-Carrasco
R.
,
Cuerda-Correa
E. M.
,
Alexandre-Franco
M. F.
,
Fernández-González
C.
&
Gómez-Serrano
V.
2016
Preparation of high-quality activated carbon from polyethyleneterephthalate (PET) bottle waste. Its use in the removal of pollutants in aqueous solution
.
Journal of Environmental Management
181
,
522
535
.
Mondal
N. K.
,
Samanta
A.
,
Roy
P.
&
Das
B.
2019
Optimization study of adsorption parameters for removal of Cr (VI) using magnolia leaf biomass by response surface methodology
.
Sustainable Water Resources Management
5
(
4
),
1627
1639
.
Pal
D. B.
,
Tiwari
A. K.
&
Giri
D. D.
2021
Various purification techniques of groundwater
. In:
Groundwater Geochemistry: Pollution and Remediation Methods
(S. Madhav & P. Singh, eds.). Wiley & Sons, Hoboken, NJ, USA.
Palansooriya
K. N.
,
Yang
Y.
,
Tsang
Y. F.
,
Sarkar
B.
,
Hou
D.
,
Cao
X.
,
Meers
E.
,
Rinklebe
J.
,
Kim
K.-H.
&
Ok
Y. S.
2020
Occurrence of contaminants in drinking water sources and the potential of biochar for water quality improvement: a review
.
Critical Reviews in Environmental Science and Technology
50
(
6
),
549
611
.
Roy
P.
,
Dey
U.
,
Chattoraj
S.
,
Mukhopadhyay
D.
&
Mondal
N. K.
2013
Removal of arsenic (III) and arsenic (V) on chemically modified low-cost adsorbent: batch and column operations
.
Applied Water Science
3
(
1
),
293
309
.
Roy
P.
,
Mondal
N. K.
,
Bhattacharya
S.
,
Das
B.
&
Das
K.
2017
Modeling of the adsorptive removal of arsenic (III) using plant biomass: a bioremedial approach
.
Applied Water Science
7
(
3
),
1307
1321
.
Sawood
G.
&
Gupta
S.
2018
Arsenic remediation of the waste water using adsorbent: a review
.
International Journal of Engineering, Technology, Science and Research
5
,
1054
1070
.
Sawood
G. M.
,
Dixit
S.
,
Mishra
G.
&
Gupta
S.
2021a
Selective As (v) capture by a novel magnetic green Fe-biochar composite in a packed column: an application of central composite design
.
Environmental Science: Water Research & Technology
7
(
11
),
2129
2144
.
Singh
A.
,
Chauhan
S.
,
Varjani
S.
,
Pandey
A.
&
Bhargava
P. C.
2022
Integrated approaches to mitigate threats from emerging potentially toxic elements: a way forward for sustainable environmental management
.
Environmental Research
209,
112844
.
Tang, J., Huang, Y., Gong, Y., Lyu, H., Wang, Q. & Ma, J.
2016
Preparation of a novel graphene oxide/Fe-Mn composite and its application for aqueous Hg (II) removal
.
Journal of Hazardous Materials
316
,
151
158
.
Wasay
S. A.
,
Haron
M. J.
,
Uchiumi
A.
&
Tokunaga
S.
1996
Removal of arsenite and arsenate ions from aqueous solution by basic yttrium carbonate
.
Water Research
30
(
5
),
1143
1148
.
Weerasundara
L.
,
Ok
Y.-S.
&
Bundschuh
J.
2021
Selective removal of arsenic in water: a critical review
.
Environmental Pollution
268
,
115668
.
Yeo
K. F. H.
,
Li
C.
,
Zhang
H.
,
Chen
J.
,
Wang
W.
&
Dong
Y.
2021
Arsenic removal from contaminated water using natural adsorbents: a review
.
Coatings
11
(
11
),
1407
.
Zhang
L.
,
Zeng
Y.
&
Cheng
Z.
2016
Removal of heavy metal ions using chitosan and modified chitosan: a review
.
Journal of Molecular Liquids
214
,
175
191
.
Zhou
J.
,
Yu
X.
,
Ding
C.
,
Wang
Z.
,
Zhou
Q.
,
Pao
H.
&
Cai
W.
2011
Optimization of phenol degradation by candida tropicalis Z-04 using plackett-burman design and response surface methodology
.
Journal of Environmental Sciences
23
(
1
),
22
30
.
Zhu
N.
,
Zhang
J.
,
Tang
J.
,
Zhu
Y.
&
Wu
Y.
2018
Arsenic removal by periphytic biofilm and its application combined with biochar
.
Bioresource Technology
248
,
49
55
.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence (CC BY 4.0), which permits copying, adaptation and redistribution, provided the original work is properly cited (http://creativecommons.org/licenses/by/4.0/).