As(V) adsorption on granular activated carbon (GAC) and subsequent desorption in dH2O was modeled using the pseudo-first and pseudo-second order kinetic models. Regeneration was achieved by immersing loaded GAC in NaCl, FeCl3, CaCl2 and MgCl2 aqueous solutions. As(V) detection after desorption was highest for NaCl but subsequent adsorption was lowest. Regeneration was highest in FeCl3 solution of pH 2 followed closely by pH 3, but As(V) precipitation appeared superior at pH 3. Molar ratios of Fe, Ca and Mg to As were tested in the range of 0.75:1 to 12:1 where a logarithmic relation was found between the molar ratio and As(V) desorption as diluted in HNO3 and H2O and subsequent adsorption. Precipitation was nearly complete in FeCl3, limited in MgCl2 at a ratio of 12:1 and not observed in CaCl2. While kinetic values were lower than in previous tests, the pseudo-first and pseudo-second order models could accurately describe desorption in CaCl2 and MgCl2 but not in FeCl3 due to precipitation. Desorption in FeCl3 was most effective in precipitating As(V), being highest at a molar ratio of 6:1, but regeneration was slightly higher at a molar ratio of 12:1.

  • Regeneration of activated carbon through desorption of As(V) was combined with coprecipitation.

  • 94.7% regeneration was reached using ferric chloride solution.

  • Coprecipitation with Fe(III) was able to remove As(V) from the solution down to 0.2 mg/L.

  • Desorption of As(V) from activated carbon in water was kinetically modeled.

  • Desorption in media other than water (FeCl3, CaCl2 and MgCl2) was kinetically modeled for the first time.

Graphical Abstract

Graphical Abstract
Graphical Abstract
     
  • GAC

    Granular activated carbon

  •  
  • PFO

    Pseudo-first order

  •  
  • PSO

    Pseudo-second order

Arsenic is widely distributed throughout the Earth's environment (Mandal & Suzuki 2002). Exposure to arsenic causes a wide array of health problems including respiratory (Milton et al. 2001), hematological (Armstrong et al. 1984) and carcinogenic (Banerjee et al. 2011). Many papers report on removal of arsenic from water and soil using a variety of methods such as filtration (Waypa et al. 1997), electrocoagulation (Nidheesh & Anantha Singh 2017), precipitation and adsorption (Gupta & Chen 1978).

Adsorption is the adhesion of an adsorbate onto the surface of an adsorbent. Common adsorbents for arsenic adsorption are iron hydroxide (Pierce & Moore 1982), aluminium hydroxide (Anderson et al. 1976) and activated carbon (Lorenzen et al. 1995), but other materials can be used, such as diatomite (Davoodi et al. 2019) and walnut shell (Ashrafi et al. 2018). The surface of activated carbon is typically porous, leading to an extraordinarily large specific surface area and pore volume. The properties of the pores and the functional groups on the surface depend on the preparation of the AC and determine its affinity to certain adsorbents. Over time the adsorption goes down as the adsorbent surface becomes saturated, at which point it is disposed of or regenerated (San Miguel et al. 2001; Ania et al. 2005). This regeneration can take place thermally, which is straightforward but expensive to operate and causes loss of adsorbent (Martin & Ng 1985; Salvador & Jiménez 1996; Hong et al. 2020). Nonthermal methods include the use of solvents, surfactants and acids or base. This last method is based on a change in pH, with the final pH being unfavorable to adsorption (McLaughlin 1995). One literature review indicated that research on desorption of cadmium and lead typically focus on regeneration through use of acids such as nitric acid, EDTA and hydrochloric acid (Fouda-Mbanga et al. 2021). Chemical regeneration is a versatile option, where desorption is improved through a change in chemical polarity, hydophobicity, solubility, molecular weight, pH, boiling point or toxicity (Larasati et al. 2021). It is important to consider the environmental fate of the desorbed contaminant, ensuring continued removal from waste streams. This means that an adsorption-desorption cycle should involve either a concentration of the contaminant or its separation. One way that separation can be achieved is through precipitation.

Precipitation is a common way to remove arsenic from aqueous waste streams. This is commonly achieved with iron (Halder et al. 2018), aluminium (Pantuzzo et al. 2014), calcium (Bothe & Brown 1999a; Palfy et al. 1999) and magnesium (Park et al. 2010). The solubility of precipitates is highly dependent on the solution pH, the species of both arsenic and the metal used for precipitation. In previous work arsenate removal from aqueous solution was tested through coagulation with FeCl2, FeCl3, AlCl3, CaCl2 and MgCl2 (Moed & Ku 2022). For desorption and precipitation to simultaneously occur, it is important that favorable pH for these processes overlap. In this research, desorption was found to be optimal at an initial pH of 11, followed by a pH of 3. This excludes the use of FeCl2, with optimal precipitation taking place at pH 8.5 (Johnston & Singer 2007) and AlCl3, at pH 6 (Majzlan et al. 2018). Reported optimum precipitation of FeCl3 varies from paper to paper but is generally shown to occur in the range of pH 2–5 (Nishimura & Tozawa 1978; Robins 1987). Precipitation with CaCl2 increases with pH (Zhang et al. 2015), being optimal at pH over 12 (Bothe & Brown 1999a), whereas precipitation with Mg(II) was found to be optimal between pH 7.5 and 10.2 (Park et al. 2010).

The scope of this research is to investigate the use of metals to precipitate As(V) as it is desorbed through a shift in pH. The precipitation reduces the arsenic in the solution, which may lead to an increased desorption. The arsenic is simultaneously removed from the solution. Kinetic study will be performed for both adsorption and desorption of As(V) by and from the activated carbon used.

Materials

Granular activated carbon (GAC) of 8 × 30 mesh (First Chemical Works in Taiwan) was washed with 0.3% HCl solution for 2 h at a GAC concentration of 100 g/L. It was then rinsed repeatedly with dH2O, filtered through Advantec Grade No. 2 filter paper and dried overnight at 110 °C.

Na2HAsO4•7H2O (Panreac, Spain), NaCl (Fischer, UK), FeCl3•6H2O (Acros Organics, USA), CaCl2•2H2O (Showa Chemical, Japan) and anhydrous MgCl2 (Showa Chemical, Japan) were dissolved in dH2O to create separate stock solutions. HCl and NaOH solutions at varying concentrations were used for pH adjustment. Experiments were, unless otherwise mentioned, performed inside a thermostatic shaking bath (DK-60 by Deng Yng, Taiwan) set to a shaking speed of 150 rpm and a temperature of 25 °C. All modeling was performed nonlinearly using the application Origin Pro.

The surface characteristics were tested by Brunauer, Emmett and Teller method (BET) (BELSORP-max by BEL Japan), Field Emission Scanning Electron Microscopy with Energy Dispersive Spectrometry (FE-SEM with EDS) (JSM-7900F by JEOL) and X-ray Fluorescence (XRF) (Epsilon 1 by Malvern Panalytical).

Adsorption of As(V)

Adsorption of As(V) on the fresh GAC used in this study was previously tested and found to be optimal in a solution with pH 6 (not reported). Different concentrations (50, 100, 150 and 200 mg/L) of As(V) were tested at a GAC concentration of 20 g/L, with samples being taken at set times to be filtered and analyzed by ICP-OES (iCAP 7000 by Thermo Scientific). The adsorption isotherm was tested at different concentrations of As(V) at pH 6 in plastic tubes of 50 mL volume.

Desorption in dH2O

GAC was loaded with As(V) aqueous solution at a concentration of 200 mg/L and at pH 6. After a 20-h adsorption the GAC was filtered and dried overnight at 110 °C. Desorption of As(V) from the loaded GAC was tested in dH2O at varying initial pH, where the pH was adjusted with HCl and NaOH solution. Desorption took place induplicate in 250 mL glass laboratory flasks. Samples were taken at set intervals, filtered and analyzed by ICP-OES.

Influence of pH

The influence of pH was tested on the regeneration of As(V) loaded GAC. The selected desorption solutions were FeCl3, CaCl2 and MgCl2 at a molar concentration of Fe(III)/Ca(II)/Mg(II) equal to 1.5 times that of the As(V) loaded onto the GAC. The concentration of As(V) is calculated as the adsorption capacity after adsorption (4.00 mg/g) multiplied by the concentration of GAC in the regeneration (20 g/l) for a total of 80 mg/L. Desorption was tested in FeCl2 at initial pH (pHi) of 2.0, 2.5 and 3.0 and in CaCl2 and MgCl2, separately, at pHi 11.0, 11.5 and 12.0. NaCl was added as a comparison at pHi 2.0, 3.0, 11.0 and 12.0. Desorption took place overnight after which the GAC was filtered and dried at 110 °C. Subsequent adsorption in 200 mg/L As(V) solution of pH 6 was performed overnight to determine regeneration efficiency. The desorption was performed in triplicate, to ensure accuracy, whereas subsequent adsorption was performed in duplicate, in either case using 50 mL plastic tubes. Samples of adsorption and desorption were taken which were diluted in HNO3, filtered and measured by ICP-OES. The pH of each sample was measured after both desorption and adsorption, to test the influence of the GAC surface acidity on the desorption and adsorption media.

Influence of molar ratio

The influence of molar ratio was tested on the regeneration of As(V) loaded GAC was performed by adding GAC at a concentration of 20 g/L in aqueous solution of either Fe(III), Ca(II) or Mg(II) at molar ratios of 0.75:1, 1.5:1, 3:1, 6:1 and 12:1 of Fe(III)/Ca(II)/Mg(II):As(V). NaCl was found to be least effective in the previous test and therefore omitted from further tests. Due to a combination of factors (the As(V) desorbed, regeneration efficiency and perceived As(V) precipitation) the optimum pHi were chosen to be 3 for regeneration in FeCl3 and 11 in.CaCl2 and MgCl2. Desorption was performed over 4 h, as this was found to be sufficient to reach maximum desorption, in duplicate in 50 mL plastic tubes. Two types of samples were taken, with the first diluted in HNO3 and filtered to test the total As(V) desorption. The other type of samples was filtered prior to dilution in H2O to test As(V) precipitation. The GAC was filtered and dried at 110 °C to test subsequent adsorption, and both desorption and subsequent adsorption were performed in 50 mL plastic tubes.

Kinetic desorption studies of As(V) from loaded GAC were performed at the highest ratios of 3:1, 6:1 and 12:1 of metal to arsenic. These kinetic tests were performed in 250 mL glass laboratory flasks at 25 °C, with samples taken at timed intervals. The data was modeled using the PFO and PSO kinetic models.

Error analysis

To provide a measure of certainty for the kinetic and isotherm analyses, the sum of squared residuals (SSR) was calculated as demonstrated (Batool et al. 2018; Herbert & Kumar 2021):
formula
(1)

Adsorption

Figure 1 reveals that it takes over 1 day for adsorption to be complete. The adsorption capacities for initial As(V) concentrations of 50, 100, 150 and 200 mg/L lead to adsorption capacities of 2.13, 3.16, 3.72 and 4.00 mg/g AC, respectively. The low difference in the equilibrium adsorption between initial concentrations of 150 and 200 mg/L As(V) suggests that maximum adsorption is nearly reached. Kinetics of the adsorption were modeled according to the pseudo-first order (PFO) and the pseudo-second order (PSO) adsorption kinetics. The PFO, as proposed by Lagergren, is commonly expressed in its nonlinear form as:
formula
(2)
where qt is the adsorption capacity at a given time, generally in mg of adsorbate per g of adsorbent, qe the adsorption capacity at equilibrium, k1 the PFO rate constant and t is time (Lagergren 1898).
Figure 1

Kinetic fitting for adsorption of As(V) concentrations on GAC at 20 g/L GAC, pH 6, 25 °C and a stirring speed of 150 rpm.

Figure 1

Kinetic fitting for adsorption of As(V) concentrations on GAC at 20 g/L GAC, pH 6, 25 °C and a stirring speed of 150 rpm.

Close modal
The PSO, popularized by the analyses of Ho and McKay, in its nonlinear form, can be written as:
formula
(3)
where k2 is the PSO kinetic rate constant (Ho & McKay 1999).

The results suggest that the PSO is a more accurate fit, as confirmed in Table 1 which reveals that, at any initial As(V) concentration, the PSO is most accurate in describing the adsorption of As(V) on GAC. This is revealed from the sum of the squared residuals which are, in all cases, lower for the PSO model than for the PFO model. This is also backed by the higher adjusted R2 (over 0.96 for PSO and 0.94 for PFO) and from the comparison of the equilibrium adsorption capacity qe found experimentally to those determined by the models. In their publication of 1999, Ho and McKay concluded that for all data analyzed and previously described using other kinetic models, the PSO was more accurate. The following popularity of the model has only increased over time as the PSO has been favored in the majority of kinetic modeling performed since (Ho 2006; Sun et al. 2013; Hassan et al. 2014; Koohzad et al. 2019). It appears that this superiority is so widely accepted that some papers do not even report on the PFO kinetic model (Yao et al. 2014; Gong et al. 2015).

Table 1

Adsorption kinetics at 20 g/L GAC, pH 6, 25 °C and a stirring speed of 150 rpm

Pseudo-first order
Pseudo-second order
qeexpqek1R2RSSqek2R2SSR
50 mg/L 2.12623 2.0624 0.38532 0.94453 0.28227 2.1434 0.30906 0.96934 0.15600 
100 mg/L 3.16429 3.02294 0.52232 0.96309 0.44857 3.20262 0.23932 0.98716 0.15604 
150 mg/L 3.72720 3.56646 0.51289 0.96263 0.62941 3.77819 0.19939 0.98650 0.22743 
200 mg/L 4.00812 3.79399 0.61990 0.95944 0.80015 4.04453 0.22059 0.98092 0.37648 
Pseudo-first order
Pseudo-second order
qeexpqek1R2RSSqek2R2SSR
50 mg/L 2.12623 2.0624 0.38532 0.94453 0.28227 2.1434 0.30906 0.96934 0.15600 
100 mg/L 3.16429 3.02294 0.52232 0.96309 0.44857 3.20262 0.23932 0.98716 0.15604 
150 mg/L 3.72720 3.56646 0.51289 0.96263 0.62941 3.77819 0.19939 0.98650 0.22743 
200 mg/L 4.00812 3.79399 0.61990 0.95944 0.80015 4.04453 0.22059 0.98092 0.37648 

The adsorption capacity (Q) for GAC has been plotted against initial As(V) concentration, as revealed in Figure 2. The Freundlich, Langmuir and Temkin adsorption isotherms were tested for GAC at various initial As(V) concentrations. The Freundlich isotherm model is described, in its nonlinear form, as:
formula
(4)
where KF is the Freundlich constant, n the adsorption intensity and Ce (in mg/L) the equilibrium concentration of adsorbate (Al-Ghouti & Da'ana 2020).
Figure 2

Adsorption Isotherm modeling at 20 g/L GAC, pH 6, 25 °C and a stirring speed of 150 rpm.

Figure 2

Adsorption Isotherm modeling at 20 g/L GAC, pH 6, 25 °C and a stirring speed of 150 rpm.

Close modal
The Langmuir isotherm model can be described nonlinearly as:
formula
(5)
where qmax is the maximum adsorption capacity at complete monolayer coverage and KL is the Langmuir constant in L/g (Langmuir 1916).
The Temkin isotherm model is expressed nonlinearly as:
formula
(6)
where the constant BT, which is related to the heat adsorption, is defined by the expression BT = RT/b. In this expression, T the temperature in Kelvin, R the gas constant of 8.314 (J/mol K) and b the equilibrium constant related to the adsorption energy (in J/mol). KT is the Temkin isotherm constant in L/g (Johnson & Arnold 1995). The closest fit seems to be the Freundlich isotherm, followed by the Temkin isotherm. Table 2 confirms this with the lower SSR values and the higher adjusted R2 of the Freundlich model being 0.99455, the Temkin model 0.98878 and the Langmuir model 0.87506.
Table 2

Adsorption isotherm data at 20 g/L GAC, pH 6, 25 °C and a stirring speed of 150 rpm

Freundlich
Langmuir
Temkin
kFnR2SSRkLqmR2SSRkTBTR2SSR
1.24047 4.58804 0.99455 0.01708 0.08239 3.86594 0.87506 38.30473 6.81068 4761.045 0.98878 0.03514 
Freundlich
Langmuir
Temkin
kFnR2SSRkLqmR2SSRkTBTR2SSR
1.24047 4.58804 0.99455 0.01708 0.08239 3.86594 0.87506 38.30473 6.81068 4761.045 0.98878 0.03514 

Desorption in dH2O

Figure 3 reveals that desorption is highest at 43.8% in aqueous solution of pHi 11, followed by pHi 3. This is unsurprising, considering that adsorption was optimal at a pHi of 6. In the range of pHi 3–9, adsorption is near complete after 120 min. At pHi 11 this takes considerably longer, with equilibrium adsorption likely only measured once. Nonlinear PFO and PSO modeling was performed, where the adsorption capacity (q) of the previously used model was replaced by the As(V) concentration (C). When regenerating loaded ACCA (activated carbon-chitosan complex adsorbent) in a mixture of 1 mol/L thiourea and 2 mol/L hydrochloric acid, Ge & Fan (2011) found that 85.6% of lead and 88.2% of cadmium was desorbed. It was calculated, however, that the concentration of hydrochloric acid used was enough to lower the solution pH to below 1.8. In comparison, the solution pH within this test was closer to the pH that is favorable to adsorption. The results reveal that the PSO generally was a better fit except for desorption at pHi 11, which is likely due to desorption being incomplete. Table 3 confirms that the PSO is more accurate in describing the desorption of As(V) from loaded GAC at every pHi, with an R2 of over 0.96 for all but pHi 5 (with an R2 of 0.94884). The PFO, while less accurate than the PSO model, still shows an accuracy of over 0.95 at pHi 3, 7 and 1. Only at pHi 5 and 9 the accuracy drops to below 0.95 at 0.89337 and 0.94656, respectively. The higher accuracy of the PSO model is again backed by the lower SSR values. Previous research on the use of adsorption models to describe desorption kinetics is few. Sarici-Ozdemir (2012) found that, for desorption of methylene blue from activated carbon, the PSO was most accurate with an R2 of 0.934–0.978, depending on the methylene blue concentration. This was comparable to the PSO fitting of their adsorption, with an R2 of 0.915–0.982. The PFO and Elovich models were significantly less accurate, with R2 values of 0.756–0.929 and 0.800–0.875. Shirvani et al. (2007) tested five different models for the desorption of cadmium from the minerals palygorskite (consisting mostly of aluminium, magnesium and silicate) and sepiolite (consisting mostly of magnesium silicate). They found that, for fresh samples, the PSO model was most accurate (with an R2 of 0.996 and 0.990, respectively) with the PFO (with an R2 of 0.952 and 0.943) and Elovich (with an R2 of 0.949 and 0.976) models slightly less accurate.
Table 3

Pseudo-first and pseudo-second order modeling of desorption kinetics at 20 g/L GAC loaded with 4.00 mg/g As(V), pH 3 (FeCl3) or 11 (CaCl2 and MgCl2), 25 °C and a stirring speed of 150 rpm

Pseudo-first order
Pseudo-second order
CeexpCek1R2RSSCek2R2RSS
In H2
pH 3 20.25765 18.78496 0.03575 0.96227 14.33955 21.46236 0.002100 0.98577 5.40792 
pH 5 9.44636 8.44051 0.06495 0.89337 8.28604 9.32527 0.009580 0.94884 3.97559 
pH 7 9.16282 8.49137 0.09849 0.95556 3.57397 9.31541 0.013670 0.98961 0.83563 
pH 9 10.82045 10.25372 0.04669 0.94656 5.97330 11.48449 0.005380 0.96786 3.59449 
pH 11 35.41464 34.55503 0.01597 0.97186 30.28429 42.51215 0.000397 0.98528 15.84895 
In MeClx 
Fe 3:1 36.86 36.15810 0.01853 0.88861 119.35876 38.85048 0.000716 0.92263 82.90305 
Fe 6:1 30.34 22.45502 0.03470 0.71097 137.85227 25.20699 0.001790 0.83079 80.70293 
Fe 12:1 15.96 14.77390 0.03379 0.93831 12.26398 16.11654 0.003060 0.98200 3.57933 
Ca 3:1 31.42 33.88122 0.10394 0.97360 26.12921 35.81816 0.005080 0.97023 29.45931 
Ca 6:1 36.92 38.71987 0.09757 0.98606 18.29914 41.16995 0.003950 0.98005 26.17656 
Ca 12:1 41.62 41.96055 0.12305 0.97539 36.35085 44.48227 0.004630 0.99025 14.39595 
Mg 3:1 39.44 38.11556 0.06835 0.97132 37.10551 41.10471 0.002600 0.99644 4.60167 
Mg 6:1 45.54 41.47176 0.06648 0.95708 66.26658 45.07052 0.002200 0.99773 3.50322 
Mg 12:1 55.34 47.38945 0.06749 0.91592 168.77535 51.75767 0.001900 0.98142 37.30122 
Pseudo-first order
Pseudo-second order
CeexpCek1R2RSSCek2R2RSS
In H2
pH 3 20.25765 18.78496 0.03575 0.96227 14.33955 21.46236 0.002100 0.98577 5.40792 
pH 5 9.44636 8.44051 0.06495 0.89337 8.28604 9.32527 0.009580 0.94884 3.97559 
pH 7 9.16282 8.49137 0.09849 0.95556 3.57397 9.31541 0.013670 0.98961 0.83563 
pH 9 10.82045 10.25372 0.04669 0.94656 5.97330 11.48449 0.005380 0.96786 3.59449 
pH 11 35.41464 34.55503 0.01597 0.97186 30.28429 42.51215 0.000397 0.98528 15.84895 
In MeClx 
Fe 3:1 36.86 36.15810 0.01853 0.88861 119.35876 38.85048 0.000716 0.92263 82.90305 
Fe 6:1 30.34 22.45502 0.03470 0.71097 137.85227 25.20699 0.001790 0.83079 80.70293 
Fe 12:1 15.96 14.77390 0.03379 0.93831 12.26398 16.11654 0.003060 0.98200 3.57933 
Ca 3:1 31.42 33.88122 0.10394 0.97360 26.12921 35.81816 0.005080 0.97023 29.45931 
Ca 6:1 36.92 38.71987 0.09757 0.98606 18.29914 41.16995 0.003950 0.98005 26.17656 
Ca 12:1 41.62 41.96055 0.12305 0.97539 36.35085 44.48227 0.004630 0.99025 14.39595 
Mg 3:1 39.44 38.11556 0.06835 0.97132 37.10551 41.10471 0.002600 0.99644 4.60167 
Mg 6:1 45.54 41.47176 0.06648 0.95708 66.26658 45.07052 0.002200 0.99773 3.50322 
Mg 12:1 55.34 47.38945 0.06749 0.91592 168.77535 51.75767 0.001900 0.98142 37.30122 
Figure 3

Kinetic fitting for desorption of As(V) from loaded GAC in H2O of varying pH at 20 g/L GAC loaded with 4.00 mg/g As(V), 25 °C and a stirring speed of 150 rpm.

Figure 3

Kinetic fitting for desorption of As(V) from loaded GAC in H2O of varying pH at 20 g/L GAC loaded with 4.00 mg/g As(V), 25 °C and a stirring speed of 150 rpm.

Close modal

Influence of initial pH

Regeneration through NaCl, FeCl3, CaCl2 and MgCl2 was tested at varying initial pH (pHi). Figure 4 reveals the As(V) in the solution after desorption in the various media. Desorption in NaCl resulted in the highest As(V) concentration detected, at pHi 12. The second highest desorption was found at pHi 2, while desorption at pHi 3 and 11 is among the lowest found among all metal chlorides. An even better indicator of regeneration efficiency is the adsorption following the regeneration, as also revealed in Figure 4. It is clear that the regeneration using NaCl solution, while displaying the highest As(V) concentration after desorption, showed the lowest subsequent adsorption efficiency out of all desorption tests. It is also revealed that, while As(V) desorption is higher at pHi 3 than pHi 11, subsequent adsorption is higher after regeneration at pHi 11. Di Natale et al. (2013) found that desorption of arsenic from activated carbon was best in media of dissolved sodium salts (100% in NaOH, NaCl and NaNO3 solution), but the pHi was set at 8 for all experiments and subsequent adsorption was not tested. Therefore, while the results in this paper agree with the desorption data of Di Natale, the As(V) measured during desorption may not be the best indicator for regeneration efficiency. The pHi of desorption affects the final pH of subsequent adsorption, as revealed in Figure 5. Whereas a desorption pHi of 2 and 3 had little effect on the final pH of subsequent adsorption (with less than 0.1 difference), the difference between pHi 3 and 11 was larger, with a final pH of 5.5 and 6.2, respectively. This change in final pH is explained by Noh & Schwarz (1990), who demonstrated that the surface acidity of activated carbon adjusts to that of the media it is submersed into, the degree of which depending on the difference in pH and the concentration of activated carbon. It is likely that, while As(V) desorption was higher at very high and low pH, the resulting pH is the cause for the diminished following adsorption.
Figure 4

The influence of metal chloride and pH on the concentration of As(V) and Fe/Ca/Mg post desorption and As(V) post adsorption at 20 g/L loaded GAC, 25 °C and a stirring speed of 150 rpm.

Figure 4

The influence of metal chloride and pH on the concentration of As(V) and Fe/Ca/Mg post desorption and As(V) post adsorption at 20 g/L loaded GAC, 25 °C and a stirring speed of 150 rpm.

Close modal
Figure 5

The influence of metal chloride and pH on the final solution pH of desorption and adsorption at 20 g/L GAC loaded with 4.00 mg/g As(V), 25 °C and a stirring speed of 150 rpm.

Figure 5

The influence of metal chloride and pH on the final solution pH of desorption and adsorption at 20 g/L GAC loaded with 4.00 mg/g As(V), 25 °C and a stirring speed of 150 rpm.

Close modal

When desorbing in FeCl3 solution, a higher pHi led to a lower concentration of As(V) desorbed. The concentration of Fe(III) in the solution is also revealed in Figure 4. The pHi shows a clear effect on concentration, which is presumed to be through precipitation. The Fe(III) concentration decreases from 54.3 mg/L at pHi 2 to 46.7 and 38.3 mg/L at pHi 2.5 and 3, respectively. This corresponds to the decrease in As(V) detected at these pHi, indicating that the lower As(V) concentration is due to precipitation. The pH after desorption, as listed in Figure 5, reveals that the pH goes up slightly, likely due the GAC surface acidity. For FeCl3, an initial pH of 2.0, 2.5 and 3.0, led to a final pH of 2.1, 2.7 and 3.3, respectively. Robins (1987) examined and compared data of four studies to model the stability of scorodite, or ferric arsenate (AsFeO4), by pH. Their model suggested that optimum removal took place at pH 4.8. There seems to be some disparity between optimum pH among papers, with the data of Nishimura & Tozawa (1978) showing this pH to be at 2. Ferric iron and arsenic exist in a variety of compounds such as angelellite (Fe4(AsO4)2(OH)3) and kamarizaite (Fe3(AsO4)2(OH)3). Majzlan et al. (2018) tested the precipitation of these compounds and scorodite and found that, while scorodite solubility was lowest in the range of pH 3.5 ∼ 6.5, for angellelite and kamarizaite the optimum was narrower, being close to 6.5. This seems to be in line with the data found in this paper, with the final pH of 3.3 leading to a lower detection of As(V) and Fe(III), even when dissolving in 10% HNO3 solution. Subsequent adsorption of As(V) loaded GAC is highest following desorption in FeCl3, according to Figure 4, and is highest at a pHi of 2, followed by 3. The average adsorption is lowest at pHi 2.5, which may be due to a limited overlap of desorption, which favors a pH removed from that of optimum adsorption pH, and precipitation, which was highest at pHi 3. Considering the goal is regeneration of GAC while simultaneously removing As(V) from the solution this pHi of 3 is considered optimal.

For desorption of As(V) in CaCl2 aqueous solution Ca(II) concentration goes down with pHi while As(V) concentration increases. This can be explained through the work of Bothe & Brown (1999b) who analyzed the phases of calcium arsenates at increasing pH. It was found that, as pH increases, the ratio of As(V) to Ca(II) in the predominant phase gradually decreases from 0.8 to 0.5. The relatively low ratio in this research this likely results in a lower fraction of bound As(V). Figure 5 lists the final pH after desorption in CaCl2, where a pHi of 11.0, 11.5 and 12.0 led to a final pH of 6.4, 7.5 and 11.0, respectively. When testing precipitation of various forms of calcium arsenates, Bothe & Brown (1999a) found that, at a pH of 11.2, the arsenic concentration had dropped to 3 mg/L and a pH of 12 further reduced this less than 0.5 mg/L. Zhang et al. (2015) similarly found that within their tested pH range (3–10) arsenic content in the precipitate increased with pH, where calcium transformed from gypsum (CaSO4) to calcium arsenate (Ca3(AsO4)2). This indicates that precipitation in this experiment likely takes place at the higher pHi of 12, but is too low to be optimal. Subsequent adsorption seems to be optimal at pHi 11.5, followed by pHi 11, which lead to a final pH of 7.5 and 6.4, respectively. As(V) desorption, however, is slightly lower at pHi 11. A pHi of 12 leads to both the highest detected As(V) desorption and the lowest subsequent adsorption.

For desorption in Mg(II) solution, pHi within this range does not have a major effect on Mg(II) concentration, while As(V) concentration increases with pHi. A pHi of 11.0, 11.5 and 12.0 led to a final pH of 7.9, 8.6 and 11.2, respectively. Park et al. (2010) found that for precipitation of As(V) with Mg, the pH for removal was optimal between 7.5 and 10.2, where arsenic existed in the form of Mg3(AsO4)2. A higher pH will lead to precipitation of magnesium to Mg(OH)2, which leads to a lower removal of arsenic. Li et al. (2019) tested precipitation of arsenic with magnesium in the presence of NH4 and determined the optimum pH to be 9.5. The samples with pHi 11.0 and 11.5 both fall within a favorable pH range, with 11.5 ending up closest to the optimum found by Li et al. Regeneration in MgCl2 was slightly higher at pHi 12, measuring by subsequent As(V) adsorption. The increase in adsorption is limited, however, compared to the As(V) desorbed during regeneration. Considering that a pHi of 11 led to an only slightly lower regeneration efficiency, a better precipitation and requires less base addition it was deemed optimal.

Influence of molar ratio

The effect of molar ratio of iron, calcium and magnesium to arsenic was tested on the regeneration of As(V) loaded GAC. The concentration of As(V) when diluted in 10% HNO3 solution and H2O is revealed in Figure 6, in addition to adsorption of As(V) following regeneration. It can be observed that, when diluting in HNO3, As(V) desorption increases with metal to arsenic ratio with the exception of Fe(III) at its highest molar ratio. When increasing the ratio from 6:1 to 12:1, a slight decrease in average As(V) desorption was found. This indicates that either maximum As(V) desorption is achieved at a ratio of 6:1, or the high amount of FeCl3 might have been too much to dissolve in 10% HNO3. Except for the ratio of 12:1, Fe(III) is superior to Ca(II) and Mg(II) in desorbing As(V), whereas at this ratio desorption in Fe(III) and Mg(II) solutions result in a similar concentration. Desorption in Ca(II) solution was least effective in the assisted desorption of As(V), except for the ratio of 0.75:1, at which desorption was close to that in Mg(II). It was found that there was a logarithmic relationship between molar ratio and As(V) desorption. A basic natural logarithmic model was fitted as follows:
formula
Figure 6

The effect of molar ratio on the concentration of As(V) desorbed from GAC diluted in HNO3 and H2O and on subsequent adsorption of As(V) at 20 g/L GAC loaded with 4.00 mg/g As(V), pH 3 (FeCl3) or 11 (CaCl2 and MgCl2), 25 °C and a stirring speed of 150 rpm.

Figure 6

The effect of molar ratio on the concentration of As(V) desorbed from GAC diluted in HNO3 and H2O and on subsequent adsorption of As(V) at 20 g/L GAC loaded with 4.00 mg/g As(V), pH 3 (FeCl3) or 11 (CaCl2 and MgCl2), 25 °C and a stirring speed of 150 rpm.

Close modal

where Ce is the equilibrium concentration of As(V), the variable a is the multiplier of the ratio, Ratio is the molar ratio of Fe(III), Ca(II) and Mg(II) to As(V) and b is the concentration of As(V) at a ratio of 1. This model was fitted both for the complete set of ratios (labeled Log. All) and for the ratios from 0.75:1 to 6:1 (labeled Log. Excl.), due to discrepancy a a ratio of 12:1. It is observed in Figure 6 that, for desorption in Fe(III) aqueous solution, the fitting excluding the ratio of 12:1 showed a better fit. This is confirmed by the adjusted R2 increasing from 0.90282 to 0.99992. For desorption in Ca(II) solution, a logarithmic fit is more accurate when using all ratios, with an R2 of 0.96676 as opposed to 0.94387. For desorption in Mg(II) solution, the logarithmic fit was very accurate whether the 12:1 ratio was included or not, with an R2 of 0.99820 and 0.99596, respectively.

The concentration of As(V) with samples diluted in H2O is also revealed in Figure 6. The As(V) concentration when desorbed in FeCl3 initially increases with molar ratio from 0.75:1 to 1.5:1, then decreases with molar ratio until 6:1. At this ratio an average concentration was found of 0.15 mg/L. When further increasing the ratio to 12:1, however, the As(V) concentration rises again. A higher molar ratio generally leads to a higher As(V) desorption, but also increases the amount of iron to coprecipitate As(V) with. This explains the initial rise in As(V) concentration, as the effect of increased As(V) desorption is stronger than that on the precipitation. At higher ratios the increase in precipitation is stronger than the increase in desorption. The mechanism behind the final rise in concentration at a ratio of 12:1 is unclear, however, as no increase was observed when diluted in HNO3. The optimum molar ratio is 6:1 for both desorption of As(V) and its removal from the solution through precipitation, with near complete removal taking place (99.77% of the As(V) detected in HNO3). The final pH for each molar ratio was measured between 2.6 and 3.2. No relation was found between this divergence in pH and the results. When desorbing in MgCl2 the solubility of As(V) is unaffected by ratio in the range of 0.75:1 to 6:1, remaining nearly similar to the solubility in HNO3. When further increasing the ratio from 6:1 to 12:1, however, solubility in water decreases, with the As(V) concentration increasing by only less than 1 mg/L. In comparison, the As(V) concentration increased by nearly 10 mg/L when diluting in HNO3 solution when the molar ratio was increased from 6:1 to 12:1. The final pH went up greatly with molar ratio, being 6.9 at a ratio of 0.75:1 and 9.9 at 12:1. Li et al. (2019) showed that a pH of 9.5 was optimal, which is likely another cause for the increased precipitation at this highest ratio. When desorbing in CaCl2 solution the molar ratio did not affect solubility of As(V). As(V) desorption in CaCl2 solution behaved similarly whether dilution took place in HNO3 solution or in H2O. The final pH was relatively stable in a range of 6.8–7.1. A logarithmic fitting was tested again which was unsuitable for desorption in Fe(III) solution, reaching an adjusted R2 of 0.35636 and 0.53932 for all ratios and excluding 12:1, respectively. This is expected, as the solubility in water was greatly diminished. For desorption in Ca(II) solution the logarithmic fitting showed good accuracy of 0.97097 and 0.95613, respectively, both of which are slightly higher as compared those found for the data in Figure 6. For desorption in Mg(II) solution the logarithmic model shows a good fitting in the range of ratio 0.75:1 to 6:1, at 0.99669, but is significantly worse when including the 12:1 ratio, at 0.94130. This is in contrast to the fitting for samples diluted in HNO3, where an R2 of over 0.99 was found. This fact reinforces the previous findings that, at this highest ratio and diluting in water, maximum solubility is reached.

The subsequent adsorption of As(V) by GAC regenerated in FeCl3, CaCl2 and MgCl2 solutions is also displayed in Figure 6. While there is no significant difference between the metal chlorides at a molar ratio of 0.75:1, at all higher ratios, the regeneration in FeCl3 aqueous solution is highest. The trend for As(V) desorbed in HNO3 is reflected in subsequent As(V) adsorption, with the As(V) adsorbed 10% higher than the As(V) desorbed (with a maximum of 5.4% deviation). Similarly, a further increase in molar ratio from 6:1 to 12:1 has limited beneficial effect on the regeneration of As(V) loaded GAC, with the adsorption increasing from 73.9 to 75.9 mg/L (an increase of ∼2.7%). This increase indicates that the decrease in As(V) concentration for desorption in FeCl3 solution at a molar ratio of 12:1 is caused by precipitation even when diluting in 10% HNO3 solution. Fresh GAC reached an adsorption of 80.2 mg/L, which means that regeneration in FeCl3 aqueous solution led to a regeneration of 92.2% and 94.7% at molar ratios of 6:1 and 12:1, respectively, in comparison to adsorption of fresh GAC. From a ratio of 1.5:1 to 6:1 regeneration in MgCl2 solution is superior to that in CaCl2 but, at the ratios of 0.75:1 and 12:1, there is no significant difference. At Ca(II)/Mg(II):As(V) ratios of 12:1, regeneration in CaCl2 and MgCl2 led to a regeneration efficiency of 83.6% and 83.4%, respectively. Comparing the three metal chlorides, FeCl3 is superior to CaCl2 and MgCl2 in As(V) desorption, As(V) precipitation and As(V) adsorption following regeneration. A logarithmic model was fitted to the adsorption data in Figure 6, which was comparable to the fittings for desorption of As(V) when diluted in HNO3 solution. Regeneration in FeCl3 solution led to an adjusted R2 0.94458 when fitting all ratios and 0.99739 with the exclusion of 12:1, both of which were higher than the R2 for desorption. Adsorption following regeneration in CaCl2 was very accurate at 0.99034 when fitting all ratios and dropped slightly to 0.97955 with the exclusion of 12:1, an increase in accuracy over the data for desorption. Adsorption following regeneration in MgCl2 was most accurate regardless of whether or not the ratio of 12:1 was included, with an R2 of 0.99090 and 0.99917, respectively.

While other literature investigating regeneration of activated carbon loaded with heavy metals through coagulation was not found, other methods have been tested. Hamdaoui et al. (2005) employed ultrasound to regenerate granular activated carbon loaded with Hg(II), Cr(VI), Cu(II) and Mn(II), reaching regeneration efficiencies of 24%, 35%, 37% and 43%, respectively. The same technique was used by Jing et al. (2011) for regeneration of Cr(VI) from powdered activated carbon which reached an efficiency of 29%. When eluting Pt and Pd from activated carbon using NaCl solution, Snyders et al. (2015) found a high desorption of 99%, but this required a temperature of 95 °C. At 60 °C, efficiencies were markedly lower at 64% and 68%, respectively. Removal of Au was significantly lower, reaching ∼32% at 95 °C and being nonexistent at 60 °C. When adsorption is exothermic, higher temperature will aid in desorption (Van Deventer & Van der Merwe 1994). This higher temperature, while increasing regeneration efficiency, leads to a significant increase in operating costs. Whereas the previously mentioned research of Di Natale et al. (2013) reached complete desorption of As and a regenerated adsorption capacity of 93%, this was at a relatively low loading of 0.1 mg/g. In comparison, the arsenic removal by electrocoagulation in this study displays high removal while taking place at room temperature and high loading. For regeneration of cadmium loaded activated carbon, made from highly ligno-cellulosic jute stick, using 0.1 mol/L HNO3, Ghosh et al. (2021) found that 95.4% desorption of cadmium took place. This led to a subsequent adsorption of 96.3% of the original 73,53 mg/g. The highest regeneration of 94.4% (measured as subsequent adsorption) found in this research is very comparable, proving the feasibility of coagulation in the regeneration of activated carbon.

Elemental analysis was performed by XRF, as listed in Table 4. Surface arsenic is found to increase to 6.5% after loading and decrease to 1.4–1.6% after regeneration with the exception of Fe 6:1, which is slightly higher at 2.5%. This could be due to the relatively high iron content, which is commonly doped onto AC to improve arsenic adsorption (Lorenzen et al. 1995). Regeneration with FeCl3 leads to higher concentrations than regeneration with calcium and magnesium chloride. The same is true for calcium after regeneration with CaCl2, with the observed 2.1% the highest among samples. This is not observed for magnesium after regeneration with MgCl2, however, with the detected 22.0% being among the lowest found between samples. This is likely due to the magnesium content on the GAC already being among the highest of elements measured, making it more difficult to additional magnesium to bind to the surface. Chlorine concentration decreased after loading with As(V) and was found to be highest after regeneration with FeCl3 at a molar ratio of 3:1 and 12:1. After regeneration it was found to be the lowest after regeneration with MgCl2, which corresponds to the low adsorption found for magnesium. For future experiments, the use of EDX, CHNS or XPS may provide additional insights into deposition on the GAC.

Table 4

Surface analysis of activated carbon through XRF and BET

MgAlSiSClCaTiFeAsSurface A.Pore diam.
XRF%%%%%%%%%m2/gnm
Fresh 20.0% 10.4% 34.7% 8.1% 17.9% 1.3% 1.5% 6.1% 99.8a 946.56 2.2222 
Loaded 22.6% 11.2% 33.3% 8.8% 11.2% 1.0% 1.4% 4.0% 6.5% – – 
Fe 3:1 21.8% 9.8% 27.8% 8.2% 23.7% 0.9% 1.1% 5.1% 1.4% – – 
Fe 6:1 26.9% 7.5% 22.5% 4.4% 16.0% 0.7% 1.0% 18.6% 2.5% – – 
Fe 12:1 25.8% 8.8% 23.5% 6.8% 22.7% 0.8% 1.2% 8.9% 1.4% 956.09 2.1748 
Ca 12:1 22.8% 10.4% 30.1% 8.5% 19.9% 2.1% 1.5% 3.1% 1.6% 978.98 2.0943 
Mg 12:1 22.0% 10.8% 33.9% 8.3% 15.9% 1.1% 1.4% 5.1% 1.4% 1,080.00 2.1739 
MgAlSiSClCaTiFeAsSurface A.Pore diam.
XRF%%%%%%%%%m2/gnm
Fresh 20.0% 10.4% 34.7% 8.1% 17.9% 1.3% 1.5% 6.1% 99.8a 946.56 2.2222 
Loaded 22.6% 11.2% 33.3% 8.8% 11.2% 1.0% 1.4% 4.0% 6.5% – – 
Fe 3:1 21.8% 9.8% 27.8% 8.2% 23.7% 0.9% 1.1% 5.1% 1.4% – – 
Fe 6:1 26.9% 7.5% 22.5% 4.4% 16.0% 0.7% 1.0% 18.6% 2.5% – – 
Fe 12:1 25.8% 8.8% 23.5% 6.8% 22.7% 0.8% 1.2% 8.9% 1.4% 956.09 2.1748 
Ca 12:1 22.8% 10.4% 30.1% 8.5% 19.9% 2.1% 1.5% 3.1% 1.6% 978.98 2.0943 
Mg 12:1 22.0% 10.8% 33.9% 8.3% 15.9% 1.1% 1.4% 5.1% 1.4% 1,080.00 2.1739 

alow detection, measured in ppm.

Surface area and pore diameter were tested by BET method, also listed in Table 4. It was found that surface area increases slightly through regeneration with FeCl3 and CaCl2 and more effectively with MgCl2. In contrast, the pore diameter decreases slightly during the regeneration, most visibly for CaCl2. The effect of this seems low, however, with the regeneration of GAC as displayed in Figure 7 equally effective when using CaCl2 and MgCl2.

SEM analysis was performed but the regeneration solution showed no influence on the observed GAC surface.

Regeneration kinetics

The desorption was modeled in FeCl3, CaCl2 and MgCl2 aqueous solution through PFO and PSO fittings, as revealed in Figure 7. Unlike the data in Figure 6, for desorption in FeCl3 solution a higher molar ratio leads to a lower As(V) detection, despite the samples being similarly diluted in 10% HNO3. This is likely caused by the higher reaction volume leading to insufficient mobilization of precipitate. Table 3 reveals the fitting data for the PFO and PSO models. In all cases, the PSO was more accurate in describing the desorption of As(V) from GAC in FeCl3 solution. Due to precipitation taking place the accuracy is low, however, with only desorption at a molar ratio of 12:1 leading to an R2 over 0.95. This is confirmed by the SSR data, which are larger for the PFO model. For desorption in CaCl2 solution, as in Figure 6, a molar ratio leads to an increased As(V) desorption but the As(V) concentrations detected were lower in comparison. This is likely also caused by the use of 250 mL flasks. Both the PFO and PSO fit the data with good accuracy (R2 > 0.95) with the PFO being slightly superior at 3:1 and 6:1 ratio reaching an R2 of 0.97360 and 0.98606, respectively, and the PSO being superior at a 12:1 ratio, with an R2 of 0.99025. The SSR values reveal that the PFO model was a slightly better fit at ratios of 3:1 and 6:1, whereas the PSO was a better fit at a ratio of 12:1. For desorption in MgCl2 solution, As(V) desorption increases with molar ratio but is lower than previously found, similar to desorption in CaCl2 solution. While the PFO fitting is lower than that found for CaCl2, the PSO fitting is the highest found, exceeding 0.98 at a ratio of 12:1 and 0.99 at 3:1 and 6:1. This is confirmed by the lower values SSR values. These results match those in Section 3.2, with the PSO generally outperforming that of the PFO. This also matches the literature discussed in that section, with the PSO fitting for desorption in dH2O, CaCl2 and MgCl2 in this paper being more accurate than those by Sarici-Ozdemir (2012) but lower than those by Shirvani et al. (2007). The fitting for desorption in FeCl3 solution is significantly lower, due to precipitation taking place.
Figure 7

Pseudo-first and pseudo-second order model comparison for As(V) desorption from GAC in FeCl3, CaCl2 and MgCl2 aqueous solution at molar ratios of 3:1, 6:1 and 12:1 at 20 g/l GAC loaded with 4.00 mg/g As(V), pH 3 (FeCl3) or 11 (CaCl2 and MgCl2), 25 °C and a stirring speed of 150 rpm.

Figure 7

Pseudo-first and pseudo-second order model comparison for As(V) desorption from GAC in FeCl3, CaCl2 and MgCl2 aqueous solution at molar ratios of 3:1, 6:1 and 12:1 at 20 g/l GAC loaded with 4.00 mg/g As(V), pH 3 (FeCl3) or 11 (CaCl2 and MgCl2), 25 °C and a stirring speed of 150 rpm.

Close modal

The increase in adsorption capacity of As(V) on granular activated carbon (GAC) went down when increasing the initial As(V) concentration from 150 to 200 m/L, indicating that the GAC was near completely satisfied. The pseudo-second order (PSO) was more accurate than the pseudo-first order (PFO). The Freundlich model was most accurate followed by the Temkin model and finally the Langmuir model. Desorption of As(V) loaded GAC in dH2O was highest at pH 11 and followed by pH 3, removed from the previously found optimum pH for adsorption of 6. PSO modeling was most accurate for desorption. Desorption of As(V) from GAC was tested. Despite NaCl leading to the highest desorbed As(V) detected it displayed the lowest As(V) adsorption following regeneration. Regeneration in FeCl3 showed the highest subsequent As(V) adsorption, with the largest precipitation taking place at pH 3.0. Regeneration in CaCl2 and MgCl2 was both deemed optimal at pH 11. Regeneration of GAC and precipitation of As(V) generally increased with the molar ratio of Fe(III)/Ca(II)/Mg(II) to As(V). A logarithmic relationship between molar ratio and As(V) desorption and subsequent adsorption was found. Subsequent As(V) adsorption was highest in FeCl3 and lowest in CaCl2. Kinetic modeling was inaccurate for desorption in FeCl3. For desorption in CaCl2 the PFO was a marginally better fit at the molar ratios of 3:1 and 6:1, but the PSO was more accurate at 12:1. Desorption in MgCl2 was best described with the PSO at every ratio. In conclusion, regeneration of As(V) loaded GAC was best achieved in FeCl3 aqueous solution at a pH of 3 and a molar ratio of 12:1 when subsequent adsorption is the only goal. However, a molar ratio of 6:1 was nearly as effective while requiring half the amount of FeCl3 and removing As(V) more efficiently through precipitation. This precipitation, however, results in kinetic modeling becoming more difficult to achieve in comparison to that in CaCl2 and MgCl2.

N. M. Moed: conceptualization; data curation; formal analysis; investigation; methodology; software; validation; visualization; writing – original draft & editing; Y. Ku: funding acquisition; project administration; resources; supervision

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

The authors declare there is no conflict.

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