Abstract
Arsenic contamination in groundwater and rivers has become a major problem around the world, and may cause severe environment pollution and human health problems. In this study, cost-efficient adsorbent red mud porous beads (RPB), using red mud – a kind of alumina industry by-product, was synthesized for adsorptive removal of arsenic(V) from aqueous solution. Kinetic studies showed that chemisorption mainly governed the adsorption process. The experimental data were fitted well using the Langmuir isotherm, and the equilibrium adsorption capacity for arsenic of 11.758 mg/g at pH = 7 conditions. The effect of pH showed that the pHpzc of RPB was 6.0 and at pH = 6 the removal rate reached nearly 100%. The removal rate decreased from 91.3% to 79.0% with increase in the initial concentration of arsenic from 2.5 to 20 mg/L. The adsorption performance from column studies illustrated that the velocity of flow and the initial concentration influenced the breakthrough time of the column. This study would facilitate the use of red mud, which can be fabricated into RPB, acting as a valuable adsorbent for removing arsenic in aqueous solutions.
HIGHLIGHTS
RPB was used as a valuable adsorbent for arsenic adsorption.
Chemisorption governed the adsorption process.
The experimental data were described well by Langmuir isotherm.
The velocity of flow and initial concentration influenced the breakthrough time.
INTRODUCTION
Toxic arsenic presented in groundwater and rivers is considered to be a potential issue for the environment and human health (Amen et al. 2020). It is reported that long-term exposure to arsenic can induce complications in assorted organ systems of human body comprehensively, even cancer of skin, lung and urinary bladder (Mazumder 2008; Mohammed Abdul et al. 2015a). The main source of arsenic in the environment is geological or via anthropogenic activities (Mohammed Abdul et al. 2015b). At this time, it has been reported that over 19 million people are drinking from arsenic-polluted water resources in Bangladesh which have a arsenic concentrations above 50 μg/L (Saha et al. 2020). However, the safe limit for arsenic in drinking water has been already lowered to 10 μg/L set by the World Health Organization (WHO) in 2008 (WHO, 2008; Akin et al., 2012; Niazi et al., 2018a). Hence, it is of great importance to address the arsenic-polluted water resource issue.
Various processes have been implemented to remove arsenic from drinking water, such as ion exchange, photocatalysis, coagulation/flocculation, electrochemical techniques, and membrane separation (Kim et al., 2003; Bilici Baskan & Pala, 2010; Wan et al., 2011; Litter, 2015; Hao et al., 2018; Niazi et al., 2018a). Nguyen et al. (2008) investigated photocatalyst experiments with titanium dioxide (TiO2) as the photocatalyst to remove arsenic. The results showed that 98% of arsenic could be reduced by photocatalysis reaction with TiO2 from water containing 500 μg/L of arsenic. Kumar and Quaff used a commercial coagulant (ferric chloride, FeCl3) to study arsenic removal by a coagulation–flocculation method and the maximum removal efficiency of arsenic reached 69.25% at 40 mg/L concentration of FeCl3 (Kumar & Quaff 2019). The arsenic removal from aqueous solution by electrochemical means was studied, and almost 95% was removed within 5 min from the initial concentration of 10 mg/L (Lakshmipathiraj et al. 2010). However, the disadvantages of these processes are still significant, including high operational cost, strict operational requirement and insufficient removal for low-concentration arsenic-polluted water (Xu et al. 2019; Amen et al. 2020). In recent years, adsorption technology has been considered as a promising arsenic removal method for water, due to its low cost, high efficiency and no sludge production (Niazi et al. 2018b). Table 1 lists the studies on arsenic removal through adsorption. Therefore, in this research, adsorption technology was selected for arsenic removal from aqueous solutions.
Removal of arsenic by various adsorbents
Adsorbents . | Arsenic valence state . | Arsenic concentration . | Removal rate (%) . | References . |
---|---|---|---|---|
Saxaul ash | As(V) | 250 μg/L | 94.62 | Rahdar et al. (2019) |
Zeolites | As(V) | 10 mg/L | 79.13 | Kang et al. (2019) |
Biochar | As(III), As(V) | 4 mg/L | 81, 84 | Niazi et al. (2018b) |
Red mud sludge | As(III) | 2.5 mg/L | 96 | Naga Babu et al. (2021) |
Adsorbents . | Arsenic valence state . | Arsenic concentration . | Removal rate (%) . | References . |
---|---|---|---|---|
Saxaul ash | As(V) | 250 μg/L | 94.62 | Rahdar et al. (2019) |
Zeolites | As(V) | 10 mg/L | 79.13 | Kang et al. (2019) |
Biochar | As(III), As(V) | 4 mg/L | 81, 84 | Niazi et al. (2018b) |
Red mud sludge | As(III) | 2.5 mg/L | 96 | Naga Babu et al. (2021) |
Red mud is a fine-grained residue discarded in alumina production. When 1 ton of alumina is produced, approximately 1–1.5 tons of red mud residues are produced (Chen et al. 2018). At this time, the main disposal of red mud is through stack and landfill, which not only occupies a lot of land resources, but also has great potential harm to the environment. Therefore, proper disposal of red mud has attracted more attention. Red mud is applied to adsorption in many research studies, because red mud has a porous structure and its surface carries hydroxyl groups (Wu et al. 2017). Li et al. investigated the adsorptive removal of diclofenac and phosphate from aqueous solutions onto pyrrole-modified red mud by an in-situ chemical oxidative polymerization method (Li et al. 2020). Yunus et al. explored the adsorption capacity of fluoride in aqueous solution by red mud with acid modification and without modification (Cengeloglu et al. 2002). Neutralized red mud was used as an adsorbent to remove arsenic from water and the removal rate reached above 90% (Genç et al. 2003). These studies confirmed the possibility of red mud-based adsorbents.
In this research, novel red mud porous beads (RPB) were prepared by cross-linking sodium alginate with iron ions and used as an adsorbent to remove arsenic(V) from water. The major goals of this research were to: (1) study the adsorption capacity and evaluate the factors that affect the adsorption process on RPB, (2) investigate the adsorption performance and progress in column studies, and (3) investigate the mechanism of arsenic onto RPB.
MATERIALS AND METHODS
Materials
All chemicals used in this experiment were of analytical grade. The stock arsenic solution (1,000 μg/L) was prepared from a high concentration standard arsenic solution (Sinopharm Chemical Reagent, 100 mg/L). Red mud, collected from an aluminum company in Shandong province, was oven dried overnight at 60 °C and passed through a 40-mesh sieve. The chemical compositions of red mud were studied by X-ray fluorescence (XRF). The results are shown in Table 2.
Main components of red mud (%)
Fe2O3 . | Al2O3 . | SiO2 . | Na2O . | TiO2 . | CaO . |
---|---|---|---|---|---|
35.0 | 22.0 | 20.0 | 10.0 | 5.0 | 3.0 |
Fe2O3 . | Al2O3 . | SiO2 . | Na2O . | TiO2 . | CaO . |
---|---|---|---|---|---|
35.0 | 22.0 | 20.0 | 10.0 | 5.0 | 3.0 |
Preparation of adsorbents
The red mud porous beads prepared in this study referred to the method of preparing of porous beads by Peretz et al. (2015). A brief description of porous beads synthesis is given below. The aqueous mixture of 3 g sodium alginate and 6% sodium chloride (NaCl) solution (6 g NaCl and 100 mL deionized water) was placed on a magnetic stirrer at 50 °C under continuous stirring (1,000 rpm) for 20 min. After that, 5 g red mud were added in it and stirring was continued (1,000 rpm) for 20 min. Thus, a homogenous mixture was obtained and the same was added as droplets into a 2.0% (w/w) ferric trichloride (FeCl3) solution. The beads were then filtered, rinsed with deionized water 3–5 times to eliminate the surplus amount of FeCl3, and then, beads were soaked in 0.5% (w/w) FeCl3 for about 12–16 hours. The beads were washed with deionized water three times. After drying in an oven for 4 h at 60 °C, the beads were prepared successfully. The difference between the red mud porous beads (RPB) and red mud beads (RB) was whether NaCl was added. The schematic flowchart of preparation of RPB is presented in Figure 1.
Characterization of the adsorbents
The surface areas were measured via a surface area analyzer and calculated by the N2-Brunauer-Emmett-Teller (BET) method. The surface morphology of the adsorbents was determined by scanning electron microscopy (SEM) (SU8010, Hitachi, Japan). The elemental composition of RPB was evaluated by X-ray fluorescence (XRF, ZSX Primus II, Rigaku, Japan). Fourier transform infrared (FT-IR) spectra were detected using a Spectrum 2 instrument (PerkinElmer, German).
Batch experiments
Batch experiments were carried out to determine the arsenic removal capacity of RB and RPB. Briefly, 0.1 g adsorbent was added into a 100 mL conical flask, in which 1 mg/L arsenic solution was contained. The experiments were carried out at 25 °C with 50 mL arsenic solution in a thermostatic oscillator at 200 rpm for 2 h. The pH of arsenic aqueous was adjusted to 7 by using 0.1 M HCl or 0.1 M NaOH.
Kinetic and isotherm study
Batch adsorption kinetic experiments were carried out in 100-mL conical flasks containing 50 mL arsenic aqueous with 0.1 g RPB adsorbent. The conical flasks were put in a thermostatic oscillator at 200 rpm under constant temperature (25 °C). The samples were extracted, filtered and analyzed for the arsenic concentration at different time intervals (from 1 to 120 min). the sorption kinetics of arsenic were investigated using pseudo-first-order kinetic, pseudo-second-order kinetic and intra-particle diffusion models.
The sorption isotherm experiments were conducted at a constant temperature (25 °C) and at pH = 7. Accurately, 0.1 g of RPB adsorbent was added into the 100-mL conical flasks containing 50 mL arsenic aqueous, the initial concentration ranged from 1 to 20 mg/L. The experimental data were fitted with the Langmuir isotherm and Freundlich isotherm. The Langmuir isotherm and the Freundlich isotherm are based on a monolayer adsorption assumption and the multilayer adsorption condition, respectively (Zhu et al. 2016).
The RL value indicates whether the adsorption process is favorable or unfavorable at a certain concentration. When RL > 1, it presents that the adsorption process to be unfavorable; when 0 < RL < 1, the adsorption process is favorable; when RL = 0, the adsorption process is irreversible; when RL = 1, the adsorption process is linear (Shao et al. 2019).
Effect of pH and adsorbent dosage
To determine the effect of pH, the arsenic initial concentrations for 1 mg/L were adjusted to different pH levels from 3 to 12. Here, 50 ml arsenic solutions with different pH were added into a flask with 0.1 g RPB. The mixed solution was stirred for 2 h to reach the equilibrium. The point of zero charge (pHpzc) was also determined using the potentiometric mass titration method (Koh et al. 2020). The surface charge of adsorbent was negative at pH > pHpzc and positive at pH < pHpzc (Kragović et al. 2019). Briefly, 100 ml 0.01 mol/L sodium nitrate (NaNO3) was added into a flask with 1 g/L RPB. The pH of NaNO3 was adjusted to 3–12 by 0.1 M HCl or 0.1 M NaOH. The flasks were put in a thermostatic oscillator at 200 rpm for 2 h under constant temperature (25 °C). The final pH was measured and the changes of pH (ΔpH) were depicted against the initial pH. The point where the change of pH was zero was the pHpzc.
In order to evaluate the effect of adsorbent dosage, the adsorbent dosage of 1–5 g/L was added into the arsenic solution with initial concentration of 5 mg/L. Other experimental steps were the same as those in the pH study.
Column experiments
RESULTS AND DISCUSSION
Characterization of adsorbents
The SEM images of RB and RPB are shown in Figure 3(a) and 3(b), respectively. RB showed a smooth surface that was slightly crumpled, while RPB had more gills and more obvious pores on its surface. These results indicated that the RPB was a porous bead and might be a good adsorbent for wastewater treatment. The BET surfaces of RB and RPB were 6.75 and 18.52 m2/g, respectively. The result confirmed that the RPB had a larger surface than RB as the average pore size increased from 21.0 to 24.9. The FTIR spectra of RB and RPB are presented in Figure 3(c). The presence of bands at 2,920 cm−1 and 2,851 cm−1 presented in RPB were related to the symmetric stretching vibrations of aldehydic C–H groups in the adsorbent (Naga Babu et al. 2021). In addition, the bands at 3,458 and 3,425 cm−1 were attributed to the stretching vibration of O–H, indicating that RPB increased more O–H functional groups than red mud. The effect of time on adsorption of arsenic from water by RPB and RB was investigated under the initial concentration of 1 mg/L. The result is presented in Figure 3(d). The reaction between arsenic solution and adsorbents achieved equilibrium at 90 min and removal rate reached 90 and 77%, respectively. The result showed that RPB had the higher removal rate than RB. From Table 3, it can be seen that the adsorbent of RPB was composed of 73.0% of Fe2O3 and 7.6% of Al2O3. Compared with the components of red mud, the content of Fe2O3 obviously increased, confirming that iron was successfully impregnated into RPB.
Main components of RPB (%)
Fe2O3 . | Al2O3 . | TiO2 . | SiO2 . | Cl . | P2O5 . | SO3 . | Na2O . |
---|---|---|---|---|---|---|---|
73.0 | 7.6 | 6.0 | 4.8 | 3.2 | 2.4 | 2.0 | 0.2 |
Fe2O3 . | Al2O3 . | TiO2 . | SiO2 . | Cl . | P2O5 . | SO3 . | Na2O . |
---|---|---|---|---|---|---|---|
73.0 | 7.6 | 6.0 | 4.8 | 3.2 | 2.4 | 2.0 | 0.2 |
SEM images (a, b), FTIR spectra (c) and arsenic removal rate (d) of RB and RPB.
Effect of pH and dosage
The pH of the solution played an important role in the adsorption process because the surface charge was changed under different pH conditions (Rahman et al. 2020). The point of zero charge (pHpzc) and effect of the pH on adsorption process were studied. From Figure 4, it can be seen that the pHpzc is at 6.0. When the solution pH was < 6.0, the surface of the adsorbent had a positive charge, which would accelerate the adsorption process. When the pH is > 6.0, the surface of the adsorbent turned into negatively charged, which would have adverse impacts on the adsorption progress due to electrostatic repulsion (Yin et al. 2021). When pH was ≤ 6, the removal rate of the arsenic form aqueous solution reached nearly 100%. The removal rate of arsenic first increased (pH = 4) and then decreased with the increase in pH. Especially at pH of 12, arsenic adsorption performance completely deteriorated with nearly 0 of arsenic removed. The form of the arsenic under this situation was negatively charged AsO43−, while the surface of the adsorbent was also negatively charged after a pH of 6; this led to a greater repulsion between the adsorbate and adsorbent, which deteriorated the arsenic adsorption behavior.
Figure 5 depicts the effect of adsorbent dosage on arsenic removal rate and adsorption capacity. The diagram indicated that with the increase in adsorbent dosage from 1 g/L to 5 g/L, the removal rate increased from 62.6% to 92.3%. This could be explained by the increasing active sites on the RPB surface and greater surface area of RPB (Rahman et al. 2020; Yin et al. 2021). However, the adsorbent capacity reduced from 3.13 to 0.92 mg/g with the adsorbent dosage increasing from 1 to 5 g/L. The increase in adsorption capacity was due to the larger surface area and greater number of adsorption sites available that were introduced by increasing the number of particles. Considering the adsorption efficiency and economics, we chose the adsorbent dosage of 2 g/L in kinetic and isotherm studies.
Effect of the dosage of RPB on arsenic removal rate and adsorption capacity.
Adsorption kinetics
As shown in Figure 6, the effect of time was studied under initial concentrations of 1, 5 and 10 mg/L. The data were fitted by pseudo-first-order and pseudo-second-order models at optimum physicochemical parameters of pH at 7, dosage of 2 g/L, and temperature at 25 °C. The kinetic parameters determined from the rate models are presented in Figure 6 and Table 4.
Kinetic parameters of pseudo-first-order and pseudo-second-order
C0 (mg/L) . | qe (mg/g) . | Pseudo-first-order constant . | Pseudo-second-order constant . | ||||
---|---|---|---|---|---|---|---|
qe (mg/g) . | k1 (1/min) . | R2 . | qe (mg/g) . | k2 (g/mg/min) . | R2 . | ||
1 | 0.45 | 0.59 | 0.174 | 0.8769 | 0.49 | 0.208 | 0.9991 |
5 | 2.24 | 2.37 | 0.112 | 0.9698 | 2.55 | 0.024 | 0.9921 |
10 | 3.80 | 1.30 | 0.087 | 0.8674 | 3.86 | 0.104 | 0.9998 |
C0 (mg/L) . | qe (mg/g) . | Pseudo-first-order constant . | Pseudo-second-order constant . | ||||
---|---|---|---|---|---|---|---|
qe (mg/g) . | k1 (1/min) . | R2 . | qe (mg/g) . | k2 (g/mg/min) . | R2 . | ||
1 | 0.45 | 0.59 | 0.174 | 0.8769 | 0.49 | 0.208 | 0.9991 |
5 | 2.24 | 2.37 | 0.112 | 0.9698 | 2.55 | 0.024 | 0.9921 |
10 | 3.80 | 1.30 | 0.087 | 0.8674 | 3.86 | 0.104 | 0.9998 |
Pseudo-first-order and pseudo-second-order fitting with arsenic concentrations of 1, 5 and 10 mg/L.
Pseudo-first-order and pseudo-second-order fitting with arsenic concentrations of 1, 5 and 10 mg/L.
It was clearly seen that the correlation coefficient of the pseudo-second-order kinetics model was higher than that of the pseudo-first-order kinetics model, and the calculated equilibrium adsorption capacity (qe,cal) of the pseudo-second-order kinetics model was in agreement with the experimental data. Hence, the pseudo-second-order kinetics model was suitable to describe the adsorption of arsenic for the entire adsorption period. The results assumed that chemical adsorption was the controlling step and the adsorption rate was directly proportional to the square of the unoccupied adsorption sites on the surface of the adsorbent as well as the concentration of the arsenic. The equilibrium adsorption capacity increased with the increase in the initial concentration. The adequate adsorptive sites and driving force of greater concentration gradients may lead to the initial rapidly increased adsorption capacity.
Meanwhile, the intra-particle diffusion model was also applied to evaluate the rate limiting steps involved in the kinetic mechanism. The plots of qt versus t0.5 are shown in Figure 7 under three different concentrations of arsenic. The adsorption mechanism generally included three steps: (I) film diffusion (II) intra-particle diffusion or pore diffusion on the surface (III) adsorption onto interior sites. From Figure 7 it could be seen that all the three linear plots did not pass through the origin, which illustrated that intra-particle diffusion was not the only rate controlling step in the adsorption process and some other mechanisms might be referred to the process. The initial rapid removal of arsenic may be governed by boundary layer diffusion and the subsequent slow uptake was attributed to the intra-particle pore diffusion effect (Gupta & Ghosh 2009). The rate constant (Ki) and the corresponding correlation coefficients, determined from the intra-particle diffusion model at three different concentrations, are displayed in Table 5.
Intra-particle diffusion constants and correlation coefficients for adsorption of arsenic
C0 (mg/L) . | First step . | Second step . | Third step . | ||||||
---|---|---|---|---|---|---|---|---|---|
Ki (mg/g/min0.5) . | C . | R2 . | Ki (mg/g/min0.5) . | C . | R2 . | Ki (mg/g/min0.5) . | C . | R2 . | |
1 | 0.084 | −0.029 | 0.9800 | 0.053 | 0.079 | 0.9760 | 0.012 | 0.323 | 0.9131 |
5 | 0.227 | 0.094 | 0.9701 | 0.211 | 0.340 | 0.9990 | 0.030 | 1.925 | 0.9612 |
10 | 1.173 | 0.133 | 0.9920 | 0.254 | 2.193 | 0.9837 | 0.035 | 3.415 | 0.9743 |
C0 (mg/L) . | First step . | Second step . | Third step . | ||||||
---|---|---|---|---|---|---|---|---|---|
Ki (mg/g/min0.5) . | C . | R2 . | Ki (mg/g/min0.5) . | C . | R2 . | Ki (mg/g/min0.5) . | C . | R2 . | |
1 | 0.084 | −0.029 | 0.9800 | 0.053 | 0.079 | 0.9760 | 0.012 | 0.323 | 0.9131 |
5 | 0.227 | 0.094 | 0.9701 | 0.211 | 0.340 | 0.9990 | 0.030 | 1.925 | 0.9612 |
10 | 1.173 | 0.133 | 0.9920 | 0.254 | 2.193 | 0.9837 | 0.035 | 3.415 | 0.9743 |
Adsorption isotherms
The adsorption isotherm revealed the distribution of adsorbed molecules in the liquid phase and solid phase when the adsorption process achieved an equilibrium state (Peng et al. 2014). The data were fitted by the Langmuir isotherm and the Freundlich isotherm (Figure 8). The isotherm constants and R2 values for each model are given in Table 6. The R2 value of the Langmuir isotherm was higher than that of the Freundlich isotherm, which indicated that the adsorption data were fitted better with the Langmuir isotherm. It was indicated that the adsorption of arsenic onto the adsorbent surface was a monolayer sorption process. The calculated RL value found in this study was 0.6560, between zero and one, representing a favorable adsorption process of RPB. The absorption capacity of red mud is about 0.55–0.60 mg/g, which is consistent with other work done before (Li et al. 2010). In this study, the maximum adsorption capacity is 11.76 mg/g, larger than the reported value.
Langmuir and Freundlich isotherm parameters
Langmuir constant . | Freundlich constant . | |||||
---|---|---|---|---|---|---|
qmax (mg/g) . | KL (L/mg) . | R2 . | RL . | 1/n . | KF (mg/g) . | R2 . |
11.76 | 0.52 | 0.9930 | 0.6560 | 0.57 | 3.71 | 0.9583 |
Langmuir constant . | Freundlich constant . | |||||
---|---|---|---|---|---|---|
qmax (mg/g) . | KL (L/mg) . | R2 . | RL . | 1/n . | KF (mg/g) . | R2 . |
11.76 | 0.52 | 0.9930 | 0.6560 | 0.57 | 3.71 | 0.9583 |
Fixed parameters of the Thomas model and the Yoon-Nelson models
. | Thomas model . | Yoon-Nelson model . | ||||
---|---|---|---|---|---|---|
. | kth (mL/(mg min)) . | qth (mg/g) . | R2 . | kyn (min−1) . | th (min) . | R2 . |
1 mg/L 0.5 mL/min | 0.65 | 1.52 | 0.9504 | 0.65 × 10−3 | 6,092 | 0.9504 |
1 mg/L 1 mL/min | 1.44 | 1.19 | 0.8549 | 1.44 × 10−3 | 2,375 | 0.8549 |
5 mg/L 0.5 mL/min | 0.34 | 2.59 | 0.8707 | 1.72 × 10−3 | 2,052 | 0.8707 |
5 mg/L 1 mL/min | 0.39 | 2.08 | 0.8660 | 1.96 × 10−3 | 826 | 0.8660 |
. | Thomas model . | Yoon-Nelson model . | ||||
---|---|---|---|---|---|---|
. | kth (mL/(mg min)) . | qth (mg/g) . | R2 . | kyn (min−1) . | th (min) . | R2 . |
1 mg/L 0.5 mL/min | 0.65 | 1.52 | 0.9504 | 0.65 × 10−3 | 6,092 | 0.9504 |
1 mg/L 1 mL/min | 1.44 | 1.19 | 0.8549 | 1.44 × 10−3 | 2,375 | 0.8549 |
5 mg/L 0.5 mL/min | 0.34 | 2.59 | 0.8707 | 1.72 × 10−3 | 2,052 | 0.8707 |
5 mg/L 1 mL/min | 0.39 | 2.08 | 0.8660 | 1.96 × 10−3 | 826 | 0.8660 |
Effect of initial concentration
As depicted in Figure 9, the removal rate and adsorption capacity were significantly impacted by the initial concentration of arsenic. The removal rate was decreased from 91.3% to 79.0% with the increased initial concentration of arsenic from 2.5 to 20 mg/L. This might be attributed to the delayed establishment of equilibrium between the adsorbent sites and the arsenic in aqueous phase due to the increase in initial arsenic concentrations. But the adsorption capacity of the adsorbent increased with the initial concentration of arsenic. This discrepancy in behavior was due to the increase in concentration gradient of arsenate between the sorption sites and in the aqueous phase.
Column experiments
The measured experimental break through curve for the adsorption of arsenic is presented in Figure 10. The breakthrough points were 2.2, 23.8, 39.2 and 60.2 bed volume (BV) at 5 mg/L, 1 mL/min, 5 mg/L, 0.5 mL/min, 1 mg/L, 1 mL/min and 1 mg/L, 0.5 mL/min, respectively (according to C/C0 = 0.1). The breakthrough point for the 5 mg/L solution appeared more quickly than that for the 1 mg/L solution. That might be interpreted as mass transfer limitations and/or chemical kinetics. The breakthrough time for the velocity of flow of 1 mL/min was faster than that for 0.5 mL/min, because more RPB was fed to the column unit interval (Santos et al. 2019). This meant that the exposure of arsenic to the active sites was reduced with the fluid superficial velocity increasing. The initial concentration of arsenic and the different velocities of flow caused the limitations to reach the break through curve (Baek et al. 2007). The parameters of the Thomas and Yoon-Nelson models were summarized in Table 7. From Table 7, it was found that kth and qth increased for higher initial concentration and lower velocity of flow for the Thomas model. An increase in both the initial concentration and the flow rate led to an increase in kyn and a decrease in th for the Yoon-Nelson model. This is because column saturation is reached more quickly at a higher initial arsenic concentration and flow rate (Jang & Sung 2019).
CONCLUSION
In this research, the adsorption of arsenic onto the RPB was studied through batch experiments and column studies. The removal rate of arsenic onto RPB was higher than that onto RB, and reached 90 and 77% respectively. The adsorption process was governed by chemisorption. The data fitted better with the Langmuir isotherm, indicating that the adsorption of arsenic onto the adsorbent surface was a monolayer sorption process. The adsorption capacity inferred from the Langmuir isotherm was 11.758 mg/g under 25 °C. The pHpzc is at 6.0, and when pH > 6.0, the removal efficiency of arsenic decreased. When the initial concentration of arsenic was 2.5 mg/L, the removal rate of arsenic was the maximum at 95%. With the initial concentration increasing, the removal rate decreased. With the increase in adsorbent dosage from 1 g/L to 5 g/L, the removal rate increased from 62.6% to 92.3%, while the adsorbent capacity reduced from 3.13 to 0.92 mg/g. According to the column studies, increasing velocity of flow and initial concentrations significantly promoted the saturation of the adsorption column. In short, RPB showed a high arsenic adsorption capacity and can act as a facile and cost-effective adsorbent for adsorption in a future field test.
ACKNOWLEDGEMENT
This study was funded by the Major Science and Technology Innovation Project of Shandong Province (Grant No. 2018YFJH0902).
DECLARATION OF INTEREST STATEMENT
The authors declare that there is no conflict of interest regarding the publication of this paper.
DATA AVAILABILITY STATEMENT
Data cannot be made publicly available; readers should contact the corresponding author for details.