Abstract

Heavy metal contamination in water resources is on the rise in developing countries, causing negative health impact in the population. Metal factories, industrial fertilizers, and pesticides spill inorganic pollutants into water bodies. Arsenic is an inorganic pollutant that accumulates in drinking water and causes a variety of diseases such as arsenicosis including melanosis and keratosis, cancer and disruptions in the human system's various functions. Despite various pollution-control technologies, the problem continues to exist in fast-growing countries. The aim of the arsenic adsorptive studies is to encourage arsenic remediation technologies that are both cost-effective and environmentally friendly. To do so, the properties of iron dust are investigated in order to use it as an adsorbent in the arsenic adsorption phase in this study. The percentage of adsorption (89% - 68%) onto iron dust increased with an increase in the adsorptive parameters of contact time, dose, initial concentration, pH, and temperature, indicated the competence of the arsenic removal. Protonation, deprotonation, hydroxyl ion substitution, surface complexation, electrostatic attraction, electrostatic repulsion, and ion exchange were all involved in the effect of pH on arsenic adsorption behaviour. The adsorption isotherm models adequately illustrated the experimental results, implying that arsenic adsorption with Iron dust was better suited to the Freundlich model and reasonably adapted to the Temkin isotherm model in linear form, with R₂ 0.999 and 0.953, respectively. Because of the applicability of kinetics, Arsenic removal adopted the pseudo second kinetic order. Thermodynamics revealed that the arsenic adsorption process was instinctive and beneficial, with negative values ΔG^o -0.104, ΔH^o -0.295 indicating an exothermic process, and ΔS^o +90 indicating an associative mechanism at the interface. The R_L>1 revealed the arsenic metal ion onto iron dust was satisfactory. Finally, the above data indicated that the abundantly available iron dust can be treated as an adsorbent that is economically viable for removing metal ions from different sources of water.

1. Introduction

Arsenic has been described as a highly toxic factor that is abundantly available in the environment both naturally and anthropogenically ¹. Natural sources include long-term geochemical shifts such as arsenic erosion in rocks and soils. Anthropogenic sources include various industrial sewage, forestry, and agricultural applications of various chemicals ^{2, 3}. Under mildly reducing conditions, such as in ground water, As (III) is a thermodynamically stable form that exists as a non-ionic form of arsenous acid ⁴ (H3AsO3). Metal oxide adsorption is the most reliable and cost-effective method for removing arsenic out of all of them.

The investigation's final conclusion is that iron dust (iron-based) obtained from rusted devices has been shown to be a very powerful adsorbent for Arsenic adsorption ^{5, 6}. As a result, iron dust was tested for its ability to remove arsenic using batch tests, optimizing factors such as contact time, adsorbent dosage, concentration, pH of the solution, and temperature to achieve the best results. The adsorption Isotherms are more relevant for calculating experimental data in order to test parameters related to adsorption behaviour in solid or liquid systems. The adsorption isotherms investigate the distribution of metalloid molecules on the surface of metal after they have reached equilibrium. Kinetics investigates the rate at which a chemical reaction between solid and liquid phase occurs before it reaches equilibrium in a given amount of time.

The iron dust was obtained from the rusted devices for the adsorption processes because smaller particle sizes were considered to have a greater surface area per unit mass. The adsorbent was dried in the sun at 40°C for full moisture removal and then used for further demonstration. The screening of the adsorbents resulted in a 92% reduction of arsenic on iron dust. In this research study, the batch adsorption for Arsenic removal is performed by using Arsenomolybdate method.

Iron oxides (III) with a similar structure and properties as oxides make up iron dust (rust). Rust is described as a hydrous ferric oxide ⁷ with a wide and highly reactive surface area. This adsorbent has the following characteristics: (i) a large number of active sites (ion exchange sites or vacancies), (ii) low maintenance costs, (iii) good mechanical properties, (iv) environmental friendliness, (v) high performance, and (vi) simple activity ^{8, 9}. The iron dust can be used as a virtual adsorbent for arsenic adsorption because of these properties.

SEM images taken at various magnifications give the surface morphology, particle size, and texture of iron dust. Several distinct morphologies, a high density of macro pores, a rough surface, and crystalline nature can be seen in these images.

The occupation of spaces between cracks can be seen in the magnified, high-resolution pictures, as well as a decrease in the heterogeneous nature of the soil.

The iron dust/rust is scraped from the iron devices and contains many chemical compounds such as iron oxides, dust, silica, and other particles, according to XRD research. Due to multi compounds low XRD signal are observed. More impurities/scattering is seen.

According to Figure 16 and Figure 17, the involvement ofdifferent iron oxides and their crystalline nature that led to adsorption of Arsenic on Iron/Iron dust has been reduced from before to after adsorption, confirming the amorphous existence of the Arsenic adsorption to the iron dust.

The mechanism of arsenic molecule adsorption by iron dust is revealed by IR spectral studies. The major constituents of rust are hydroxides and oxides of iron. They are γ- Fe OOH, α-Fe OOH and Fe₃O₄. FTIR analysis of adsorbent behaviour before and after adsorption reveals the involvement of OH^- ions in the adsorption process. The peaks between 3000-3500 cm^-1 could be attributed to the O-H bonds stretching. The band 1632.14 cm^-1 could explain the involvement of C=C or C-H bond. The band 1057.30 cm^-1 discloses the presence of SiO₂. The Fe-O-H bonds are involved in the adsorption process, as indicated by the peaks below 1000 cm^-1 as seen in Figure 18, Figure 19 and Table 2.

2. Results and Discussion

One of the parameters that allows for a precise analysis of arsenic adsorption is contact time. Since the adsorbent, iron dust, has a greater number of sorption sites with its oxides, arsenic (III) ions appear to bind with ferric oxides due to their ionic binding existence, resulting in high Arsenic removal efficiency over contact times of 10 to 60 minutes. In this empirical investigation, the maximal removal of arsenic is observed at 30 minutes, indicating the equilibrium. Furthermore, as shown in Figure 2, the adsorption rate decreases due to total saturation of adsorption sites with Arsenic ions ¹⁰.

The removal efficiency of Arsenic is calculated in a batch experiment with a 30 minute contact period and 1 gm of iron dust in a 50 µg/l Arsenic concentration. For this analysis, sufficient quantities of adsorbent ranging from 0.5 gm to 2.5 gm were used. The high potency of arsenic removal was observed from 0.5 gm to 1.5 gm, as shown in Figure 3, due to factors such as active surface binding sites and increased ionic strength. The additional dosage had little effect on the arsenic removal performance due to the limited number of sorption sites and the accumulation of adsorbent particles. The results of removing arsenic from aqueous solution using Fe(III) loaded with pomegranate waste ¹¹ were published.

The adsorbent is made up of various ores such as hematite, magnetite, and goethite, which have a remarkable affinity for all inorganic ions found in aqueous media. However, because the focus of our research was on the percentage of arsenic removal, we used different concentrations of arsenic, ranging from 14 µg/l to 71.1 µg/l, to carry out the adsorption process and determine the efficacy of the arsenic removal using iron dust as an adsorbent. According to the analysis, arsenic concentrations above 42.6 µg/l resulted in better arsenic elimination, so the equilibrium was maintained until arsenic concentrations reached 71.1 µg/l, as seen in the Figure 4.

The pH level had a significant impact on arsenic removal. The p^H solution regulates the distribution of arsenic and the ionization of functional groups on the adsorbent surface ^{12, 13}. From pH 3 to 9, there was a steady rise in arsenic adsorption. The action of arsenic adsorption may have differed at various pH values due to the different types of arsenic (oxy anions).

• The point of zero charge is reached when the adsorbent surface charge density equals zero in the solution conditions (PZC). The optimum pH range of 6.8–9.8 is the point of zero charge of Iron dust ¹⁴ as shown in Figure 5, and the same range was previously stated in the literature by Benjamin et al ¹⁵. At lower pH, the adsorbent surface becomes positive, and Arsenic anionic species (H2AsO3 and HAsO3) dissolved in water become negative and at higher pH, the adsorbent surface becomes negative. As a result, arsenic sorption is aided by attractive electrostatic interactions with positively charged functional groups of iron particles below PZC ¹⁶. while adsorption rate is slowed by repulsive electrostatic interactions between Arsenic species and positively charged functional groups of iron particles above PZC ¹⁶.

• Arsenic adsorption could not have been appropriated in highly acidic medium due to protonation on the adsorbent surface with arsenic neutral species and poor interaction between adsorbent and H₃AsO₃. Deprotonation of Arsenic hydroxyl groups at pH 7 and 9 can result in the formation of anionic species with neutral and negative charges, respectively.

• The high efficiency of Arsenic removal may also be attributed to the replacement of the hydroxyl ions (water molecules) held in the metal ion from the adsorbent surface.

• Ion exchange, in addition to surface complexation, electrostatic attraction, and electrostatic repulsion, played a role in arsenic adsorption ^{17, 18, 19}. The iron-based layered double hydroxides, which are the best anion exchangers, are responsible for the eminence of arsenic anions removal in their interlayer space. LDHs are one of the strongest anion exchangers, according to the calibre of the interlayer space to arsenic anion removal ^{17, 18, 19}.

The removal abilities of arsenic under varying temperatures were investigated using the previously optimized parameters. The percentage of arsenic adsorption increased with an increase in temperature under different arsenic concentrations, according to the data in Figure 6. At high temperatures, more adsorption sites are created, enhancing the adsorption phenomenon ²⁰. This resulted from a stronger bond between inorganic arsenic species and iron dust particles.

The Langmuir Adsorption Isotherm and the Freundlich Adsorption Isotherm are more essential to calculate experimental data of adsorption isotherms in order to evaluate the parameters related to adsorption behaviour in solid or liquid systems. After reaching equilibrium, these adsorption isotherms investigate the distribution of arsenic molecules between the liquid and solid phases.

This Langmuir adsorption isotherm was used to investigate the adsorptive action of arsenic on iron dust particles. The Langmuir adsorption isotherm could not satisfactorily elucidate the characteristics of the adsorbent and the saturated monolayer adsorption coverage on the homogeneous surface, based on the current study taken from Figure 7. Table 3 shows that there is a minor interaction between adsorbed organisms, as the regression (R₂) values are marginally lower (0.983-0.992) than the Freundlich isotherm. As a result, the Langmuir isotherm could not be used to model arsenic adsorption on iron dust because the lines did not pass through the sources.

On a heterogeneous surface, the Freundlich isotherm predicts a rapid increase in the distribution of active sites and their energies ^{21, 22, 23, 24}. The coefficients of determination R₂ and their plots were used to compare the applicability of the Freundlich Adsorption isotherm equation and the Langmuir isotherm. Table 3 shows that the R₂ coefficient values are better than the Langmuir coefficient values, indicating that the experimental data drawn on the iron dust adsorbent was apt with the linear form of Freundlich model, and the value of 1/n provides data about surface heterogeneity and surface affinity for the solute, indicating that the iron oxide has a strong affinity for As (III) ²⁵.

With a correlation coefficient of 0.999, the Freundlich model effectively describes the sorption data. As a result, the isotherm approaches the sorption mechanism in the concentration spectrum investigated. In addition, the maximum adsorption capacity calculated from the Langmuir plot is nearly identical to the maximum adsorption capacity computed experimentally.

Figure 9 and Table 3 in this study show that moderate R₂ values ranging from 0.908 to 0.953 suggest that the heat of adsorption of arsenic molecules in the layer increases moderately in a linear form with adsorbent coverage ^{25, 26, 27}. The slopes and intercepts of the derived Temkin adsorption isotherm, which is characterized by a uniform distribution of binding energies (up to the maximum binding energy), were determined by plotting the quantity adsorbed qe against the concentration ln ce. For arsenic adsorption on iron dust, the Temkin adsorption isotherm is reasonably suitable.

Kinetics analyzes the speed of chemical reaction between adsorbate and adsorbent till it attains the equilibrium in particular time ²⁸. The results obtained from the kinetics of Arsenic adsorption onto iron oxides are studied using the following models.

Lagergren suggested the pseudo first order kinetic model, which uses the solid capacity to calculate the rate constant of the adsorption mechanism from equation ²⁸. The slope and intercepts of ln (qe-qt) versus t are used to estimate the rate constant.

The pseudo first order model (As(III) = 0.977) has lower correlation coefficients (R₂) than the pseudo second order model, and the equilibrium adsorption calibre for the pseudo first order at all temperatures qe values (0.863gm/l) is not similar to the experimental values (0.914gm/l). Despite the fact that the lines appear to be originating in Figure 10, the values of the k₁ constants did not increase as temperatures rose. These findings led to the fact that the pseudo first order kinetics model does not account for arsenic adsorption on iron dust.

The values in the pseudo second order kinetic model are theoretically closer to the experimental values at all temperatures as compared to the equation ²⁹. As shown in Table 4, the highest R₂values (0.994) investigate the characteristics of the adsorbent that contributed to a high percentage of arsenic removal, while k₁ values revealed that pseudo second order constants increased as reaction temperatures increased in addition to the values mentioned above, Figure 11. The kinetics of Arsenic with iron dust are better represented by a pseudo second order kinetic model. This suggests that chemisorption processes can control arsenic adsorption on iron dust ^{30, 31, 32}.

The Elovich model constants like α and β determine the rate of chemisorption at zero coverage, as well as the degree of surface coverage and activation energy, and they were calculated from the slope and intercept, respectively, from the plot (Figure 12), which showed nonlinear relationships with R₂ values 0.965-0.838 at all temperatures represented in Table 4. The chemisorptions are not assumed to be the rate-determining phase in this model.

Since the lines did not move through the origin as shown in Figure 13, the Weber Morris model, also known as the Intra particle diffusion model, was not the only rate determining phase in this analysis. Table 4 shows the values of constant (k_id) and (I) at various temperatures. The non-linear regressions indicate that arsenic adsorption can be controlled by multiple mechanisms, each of which is affected by pH. The same conclusions were drawn from the literature ³³.

Thermodynamics is used to determine the phase equilibrium between the arsenic metal and the adsorbent, as well as the amount of arsenic adsorbed onto the iron dust, which serves as the adsorbent, as a function of the external strain. Gibbs energy (ΔG^o), enthalpy (ΔH^o), and entropy (ΔS^o) are thermodynamic parameters that control the spontaneity of the adsorption phase, and their values are determined using Vont Hoff's equation ^{34, 35, 36}.

Negative values of ΔG^o were observed with increasing absolute temperatures of 273, 293, 313, 333, 353 K (-104.4 to -46.95KJ/mol), ^{37, 38} indicating the spontaneity and viability of the Arsenic adsorption method onto iron dust. The fact that the Gibbs energy was negative also proved that the Arsenic adsorption was purely chemical ³⁹. The ΔH^o and ΔS^o values show that the negative enthalpy (ΔH^o = -0.295 KJ/mol) indicates that the adsorption process is exothermic, and the positive entropy (ΔS^o = +90 KJ/mol) indicates that there was randomness at the fluid/liquid interface, resulting in the associative mechanism.

The dimensionless constant the equilibrium parameter (R_L) is used with the properties of the Langmuir adsorption isotherm and the expression of R_L equation to measure the affinity between adsorbate and adsorbent. The R_L >1 denotes unfavourable adsorption, while the 0 < R_L < 1 denotes a favourable process ⁴⁰. In our analysis, the R_L value was found to be between 0.100 and 0.200, indicating that arsenic adsorption onto Iron dust was satisfactory.

3. Conclusion

The findings show that arsenic can be successfully adsorbed on iron dust particles via the adsorption process. Arsenic adsorption occurs due to electrostatic interactions with positively charged functional groups of iron particles in the optimum pH range of 6.8–9.8, which is a basic field. The adsorption parameter contact time determined the best adsorption at 30min due to ionic binding presence with ferric oxides, 1.5 gm optimum dosage, 42.6 µg/l of optimum arsenic concentration. The effects of this arsenic adsorption are efficiently reported because iron ores have a higher affinity for all inorganic species in aqueous media.

The arsenic adsorption was better characterized by the Freundlich isotherm. The negative Gibbs energy values from -104.2 to -46.95 shown in the Table 5 suggested that arsenic adsorption on iron species is a natural and viable process. The negative value of enthalpy -0.295 suggests an exothermic mechanism. Since the arsenic molecules adhere to the surface of the iron dust particles with chemical forces, the arsenic adsorption is referred to as chemisorption.

The electrostatic interactions of Ferric oxides and Ferric hydroxides with arsenic species are the main role in arsenic adsorption, as shown by scanning electron microscopy, X-ray diffraction, and Fourier Transform Infrared Spectroscopy. The role of –OH in arsenic adsorption has also been discovered. As a result of its effectiveness, iron dust is chosen as a promising adsorbent for arsenic removal.

Acknowledgments

I would like to convey my thanks to Dr.B.B.V.Sailaja, the head of the Department of Inorganic and Analytical Chemistry at Andhra University in Visakhapatnam, and Dr.D.Sirisha for their advice, support services, and passion in reporting the findings of this paper, as well as St.Ann’s Degree Ann's College for Women, Mehdipatnam, Hyderabad, for offering me the lab facilities and materials.

References