A study was conducted to investigate the performance of biojarosite as a catalyst in Fenton oxidation with and without the presence of the chelating agent ethylene diamine tetra acetic acid (EDTA). The addition of EDTA resulted in increased iron dissolution, confirming the role of EDTA as a chelating agent. The heterogeneous catalytic properties of jarosite were confirmed by the removal efficiencies of 93.5 and 83.4%, with and without EDTA, respectively. The Box–Behnken method was employed as a Design of Experiments tool to identify suitable experimental runs. Both Fenton oxidation and EDTA-based Fenton oxidation were examined separately, with PCP removal being the response variable. In the case of Fenton oxidation, the optimization process resulted in the selection of 0.1–1 g/L of iron catalyst, 100–1,000 mg/L of H2O2, and a pH range of 2.5–3.5. On the other hand, for EDTA-based Fenton oxidation, the optimal conditions were determined to be 0.1–1 g/L of iron catalyst, 100–1,000 mg/L of H2O2, and a pH range of 6.5–7.5. ANOVA was conducted to analyze the results, and the model fit was examined.

  • Application of Box–Behnken design procedure for Fenton's oxidation of pentachlorophenol.

  • Use of biojarosite-based catalyst for Fenton's oxidation.

  • EDTA-based Fenton's oxidation at near neutral pH.

  • Comparison of the efficiency of the process with or without EDTA as a chelating agent.

The use of pesticides has increased dramatically over the past few decades due to their efficacy in pest control and insect management (Kawahigashi 2009). Pentachlorophenol (PCP), a member of the pentachlorobenzene family of organochlorine pesticides, is composed of an aromatic ring with five chloro groups in place of phenol. As a pesticide and wood preservative, PCP has been widely used in agriculture for many years. However, when PCP leaches out of wood or is carried away by agricultural runoff, it interacts with organic matter in the soil, leading to a decline in fertility (Martınez et al. 2000). Depending on the pH-value and presence of solvents, PCP can cause surface and groundwater contamination (Tanjore & Viraraghavan 2016). The inability to trace the non-point sources of pesticide pollution has become a significant environmental concern (Nitschke & Schossler 1998). Due to its toxicity, PCP has been included in the Environmental Protection Agency's list of pollutants (Rodrigues & Kyuma 2013).

Various treatment methods, such as activated carbon adsorption, biological degradation, and advanced oxidation processes, have been extensively explored over the years for the degradation of PCP. Among these techniques, Fenton oxidation has emerged as the most efficient method (Liou et al. 2004; Mecozzi et al. 2006; Zimbron & Reardon 2009; Rodrigues & Kyuma 2013).

Microporous and mesoporous substances are known for their versatile adsorption. Microporous materials such as zeolites and metal-organic frameworks have pores with sizes on the angstrom order (less than 2 nm). These small pores make them highly selective for the adsorption of molecules of specific size and shape while mesoporous materials with pore sizes between 2 and 50 nm provide a large internal surface area. This large surface area is beneficial for adsorption as it gives the molecules more opportunity to interact with the material (Atiyah et al. 2022b, 2022a; Khadim et al. 2022).

Fenton oxidation involves the generation of highly reactive hydroxyl radicals, which attack the aliphatic and aromatic components of organic compounds, leading to their decomposition. Numerous researchers have investigated the degradation of PCP using Fenton oxidation (Brown et al. 1986; Weavers & Hoffmann 2000; Wolski et al. 2006).

In Fenton oxidation, iron, a divalent cation, acts as a catalyst to facilitate the reaction. However, due to cost considerations, alternative catalysts have replaced commercial iron in the treatment process (Bhaskar et al. 2019, 2021, 2022b; Bhaskar et al. 2020; Rashmishree et al. 2022). Mecozzi et al. (2006) explored the use of PCP EAF dust as a catalyst for Fenton degradation (Mecozzi et al. 2006). In the Fenton oxidation process, Karale et al. (2013) introduced an alternative catalyst by substituting chemically leached laterite iron (Karale et al. 2013). Similarly, Bhaskar et al. (2021) aimed to replace commercial iron with bleached laterite iron in the Fenton oxidation process (Bhaskar et al. 2021). Successful attempts have also been made to use biojarosite, an iron hydroxy sulfate, as a catalyst, both in the form of laterite and as a synthesized bioleached laterite nano iron catalyst (Bhaskar et al. 2021, 2022a).

The utilization of statistical response surface methodology for process optimization is a recognized approach for developing prediction models. In the context of PCP removal, factorial design has been employed in optimization efforts (Mathialagan & Viraraghavan 2010; Hassen et al. 2021). Specifically, A 22 factorial design known as Box–Behnken design has been utilized, featuring three interlocking points on the surface sphere surrounding the center, to explore and optimize the impact of key process parameters as independent variables on the response, which serves as the dependent variable (Tripathi et al. 2009; Tak et al. 2015; Shokri et al. 2019). The Box–Behnken design ensures that all design points fall within the selected safe operating zone, providing reliable coefficient estimates close to the center of the design space and eliminating the need for axial points. This design method also enables the identification of significant factors and interactions between variables.

The current study investigates the role of EDTA, acting as a chelating agent, on the dissolution of iron biologically synthesized jarosite in the degradation of pentachloride via Fenton's oxidation process, also evaluating the role of biojarosite as a catalyst in the Fenton's oxidation of PCP at near neutral and acidic pH-value. A comparison is made between this approach and the conventional Fenton oxidation of pentachloride. The regression model is constructed using catalyst loading, H2O2 dosage, and pH-value as independent variables, with the removal efficiency of PCP is considered as the response variable.

Biojarosite catalyzed Fenton oxidation of PCP

The synthesis and characterization of biojarosite were conducted by employing a novel acidophilic strain known as Acidithiobacillus ferrooxidans (Accession No. MG271840), following the procedure outlined in a previous study (Bhaskar et al. 2019). To investigate the Fenton degradation of PCP catalyzed by biojarosite, two separate experiments were performed with initial target compound concentrations of 10 mg/L, one with the addition of EDTA and the other without EDTA. In each case, biojarosite was gradually added to the Erlenmeyer flask containing the PCP solution, and the pH-value was adjusted to the desired value with 0.5 M H2SO4 or 1 M NaOH. The study encompassed a range of experimental conditions, including different dosages of biojarosite (ranging from 0.1 to 1 g/L) and H2O2 (ranging from 100 to 1,000 mg/L), separately for the sets with and without EDTA (Bhaskar et al. 2022b). Samples were collected at regular intervals for analysis. The concentration of PCP was determined using an HPLC Agilent 1200 system equipped with a C18 reverse phase column (pore size 3.5 μm, 100 × 0.46 cm) and a diode array detector (DAD) (Bhaskar et al. 2019). The chemical oxygen demand (COD) was measured using the APHA Method 4500-F: 1992. pH measurements were taken using a digital pH meter (Model – edge, HANNA make). The ferric iron content was determined using the potassium thiocyanate method (Woods & Mellon 1941), while the H2O2 concentration was measured using a UV-spectrophotometer (Eisenberg 1943).

Fenton oxidation of PCP using biojarosite as a catalyst – design of experiment

This study utilized the Box–Behnken design of response surface methodology. The independent variables were chosen at three levels (−1, 0, +1), while the removal of PCP (pentachlorophenol) served as the dependent variable (Table 1). A total of 15 experimental runs were conducted. The variable parameters were optimized within specific ranges: 0.1–1.0 g/L for biojarosite, 100–1,000 mg/L for H2O2 dosage, and pH-value ranging from 2.5 to 3.5 without EDTA, and 6.5 to 7.5 with 100 mM EDTA. The focus was on PCP removal, which was considered a countermeasure. Experimental data were used to develop a regression model based on the response, and 3D contour graphs were employed to assess the influential parameters (Bhaskar et al. 2022b).

Biojarosite catalyzed Fenton oxidation and EDTA-based Fenton oxidation of PCP

Characterization of biojarosite was discussed in the previous work by Bhaskar and team (2019). The synthesized biojarosite is seen as agglomerated particles resembling rose petals with a smooth interface on scanning electron microscopy and exhibits characteristic peaks on X-ray diffraction (Bhaskar et al. 2019). BET surface area observed with biojarosite is 1.62 m2/g which is consistent (Bhaskar et al. 2019; Ali et al. 2022a). The removal of PCP was significantly influenced by Fenton oxidation. High removal efficiencies of 93.5 and 83.4% were achieved when using a biojar to H2O2 ratio of 1:1 for Fenton oxidation with EDTA and without EDTA, respectively. In the case of Fenton oxidation without EDTA, a degradation rate of about 73.6% was observed within 40 min of treatment. The degradation process continued for 120 min with a rate constant of 0.0142/min, using a catalyst loading of 0.5 g/L. The rate constant exhibited an increase from 0.00594/min to 0.0142/min as the H2O2 dosage increased from 100 to 500 mg/L, indicating a higher dissociation of H2O2. However, a further increase in H2O2 dosage beyond 500 mg/L resulted in a decline in the reaction rate, remaining constant at 0.0050/min, indicating a scavenging effect. When using a catalyst load of 0.1 g/L, a degradation of 62% was observed, while a catalyst load of 0.2 g/L resulted in a degradation of 77.9% within 120 min of treatment. Wang and co-workers (2013) demonstrated that schwertmannite, an iron oxide mineral like jarosite exhibits higher catalytic activity compared to other iron catalysts (Wang et al. 2013). Ali and team (2022) conducted the study on the treatment of municipal wastewater using UV-based titanium dioxide catalyst that achieved 95% of turbidity removal (Ali et al. 2022b). The highest degradation percentage of 79.5% was achieved with a catalyst load of 1 g/L (Andrades et al. 2021) and is illustrated in Figures 1 and 2.
Figure 1

Degradation of PCP on biojarosite loading without EDTA: (a) 0.1 g/L biojarosite, (b) 0.2 g/L biojarosite, (c) 0.5 g/L biojarosite, and (d) 1.0 g/L biojarosite.

Figure 1

Degradation of PCP on biojarosite loading without EDTA: (a) 0.1 g/L biojarosite, (b) 0.2 g/L biojarosite, (c) 0.5 g/L biojarosite, and (d) 1.0 g/L biojarosite.

Close modal
Figure 2

Degradation of PCP on biojarosite loading with EDTA: (a) 0.1 g/L biojarosite, (b) 0.2 g/L biojarosite, (c) 0.5 g/L biojarosite, and (d) 1.0 g/L biojarosite.

Figure 2

Degradation of PCP on biojarosite loading with EDTA: (a) 0.1 g/L biojarosite, (b) 0.2 g/L biojarosite, (c) 0.5 g/L biojarosite, and (d) 1.0 g/L biojarosite.

Close modal
A degradation rate of about 93.5% was achieved for Fenton oxidation with the addition of EDTA, using a catalyst load of 0.5 g/L and an EDTA concentration of 100 mM. The corresponding rate constant was measured at 0.0249/min. It was noted that an increase in degradation occurred with higher H2O2 dosages, reaching maximum degradation at an H2O2 dosage of 1,000 mg/L, with a rate constant ranging from 0.0126/min to 0.0169/min. EDTA, acting as a chelating agent, possesses carboxylic and amine functional groups that facilitate the complex formation with iron when used in conjunction with an iron catalyst. This interaction aids in the dissociation of iron oxide minerals (Zimbron & Reardon 2009). In the present study, the addition of EDTA accelerated the dissolution of biojarosite, an iron hydroxy sulfate compound. Figure 3 demonstrates that the degradation of PCP increased with the addition of EDTA. Increasing EDTA concentrations from 10, 20, 50, to 100 mM resulted in degradation efficiencies of 66, 78, 80, and 93%, respectively. Previous research also supported the identification of tetrachloro-hydroquinone and dichloromaleic acid as major intermediate compounds in PCP degradation (Zimbron & Reardon 2009). Hu and team (2018) demonstrated that EDTA with iron catalyst helps in the better dissolution of iron thus improving iron reactivity and claimed complete degradation of malachite green dye with EDTA-Iron catalyst (Hu et al. 2018).
Figure 3

Effect of EDTA on the Fenton oxidation of PCP with 0.5 g/L of biojarosite loading and 500 mg/L of H2O2 dosage.

Figure 3

Effect of EDTA on the Fenton oxidation of PCP with 0.5 g/L of biojarosite loading and 500 mg/L of H2O2 dosage.

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Figure 4 presents the variation of iron levels during Fenton oxidation. In the investigation without EDTA addition, approximately 189.50 mg/L of iron leached out from biojarosite and participated in the Fenton oxidation process. On the other hand, in the investigation with EDTA addition, a higher amount of total iron, around 257.87 mg/L, leached out and participated in the oxidation.
Figure 4

Variation of iron during the Fenton oxidation of PCP (a) without EDTA and (b) with EDTA.

Figure 4

Variation of iron during the Fenton oxidation of PCP (a) without EDTA and (b) with EDTA.

Close modal

Throughout the study, the pH-value remained slightly acidic, ranging from 3.0 to 4.7, in the absence of EDTA. This acidic pH range favored the oxidation process by promoting the formation of hydroxyl radicals (Kang & Hwang 2000; Burbano et al. 2005). The maximum reduction in chemical oxygen demand (COD) observed was 61.23% without EDTA addition and 87.61% with EDTA addition, indicating optimal removal. The adsorption process likely contributed to the removal of organic contaminants, leading to a decrease in COD (Masomboon et al. 2009). The leaching of ferrous iron from jarosite directly influenced the oxidation rate. Maximum degradation occurred within the initial 60 min of treatment, after which the process slowed down. The amount of catalyst added also played a role in the heterogeneous Fenton oxidation process, indicating the involvement of adsorption, which contributed to the enhanced removal of target compounds (Boussahel et al. 2007; Kang et al. 2016; Chen et al. 2017). It should be noted that the rate of oxidation in the Fenton process depends on the rate of dissolution of the metallic iron released from the catalyst. The amount of catalyst increases the degradation efficiency due to the adsorption on the surface of the jarosite catalyst particles, making the reaction process heterogeneous (Boussahel et al. 2007). Bhaskar et al. (2022b) studied the EDTA-based catalytic degradation of dyes using biojarosite, achieving complete dye removal.

According to Fukushima & Tatsumi (2001), tetrachlorocatechol and tetrachloro-hydroquinone were identified as the two major byproducts of Fenton oxidation in the case of PCP. It was observed that at a pH value higher than 5, octachlorodibenzodioxin was formed, while this compound was not detected as an intermediate at pH value 3. The study suggests that the formation of coupling compounds resulting from Fenton oxidation binds with the humic acid used in the experiment, thereby suppressing the formation of octachlorobenzodioxin (Fukushima & Tatsumi 2001).

In another study conducted by Ye and their team (2019), eight intermediates were identified: tetrachlorophenol, trichlorophenol, trichlorohydroquinone, tetrachloro-hydroquinone, hexachlorodibenzobenzofuran, pentachlorodibenzo-p-dioxin, hexachlorodibenzo-p-dioxin, and octachlorodibenzo-p-dioxin. It was proposed that the attack of hydroxyl radicals (OH•) on aromatic rings led to the formation of hydroxylated products, such as tetrachlorophenol, trichlorophenol, and trichlorohydroquinone. Direct attack of OH• radicals on the Cl–Cl bond resulted in tetrachloroquinone formation. Furthermore, the coupling reaction between PCP radicals formed during Fenton oxidation could lead to the generation of hexachlorodibenzofuran and octachlorodibenzo-p-dioxin through further dichlorination processes, resulting in the production of hexachlorodibenzo-p-dioxin and pentachlorodibenzo-p-dioxin (Ye et al. 2019). Thus, mineralization and the formation of dimers from intermediates are significant factors in the degradation of PCP.

Table 1

Experimental levels of the variables

Dependent variablesLow level (−1)Mid-level (0)High level (+1)
Fenton's oxidation of pentachlorophenol 
Biojarosite (g/L) 0.1 0.55 1.0 
H2O2 (mg/L) 100 550 1,000 
pH 2.5 3.0 3.5 
EDTA-based Fenton's oxidation of pentachlorophenol 
Biojarosite (g/L) 0.1 0.55 1.0 
H2O2 (mg/L) 100 550 1,000 
pH 6.5 7.0 7.5 
Dependent variablesLow level (−1)Mid-level (0)High level (+1)
Fenton's oxidation of pentachlorophenol 
Biojarosite (g/L) 0.1 0.55 1.0 
H2O2 (mg/L) 100 550 1,000 
pH 2.5 3.0 3.5 
EDTA-based Fenton's oxidation of pentachlorophenol 
Biojarosite (g/L) 0.1 0.55 1.0 
H2O2 (mg/L) 100 550 1,000 
pH 6.5 7.0 7.5 

Table 2 provides an overview of Fenton degradation, summarizing the key findings. Figure 5 illustrates the linear fit for both Fenton oxidation with and without EDTA. It is observed that both reactions follow pseudo-first-order kinetics. The reuse of catalysts in the degradation of PCP for Fenton oxidation and EDTA-based Fenton oxidation is presented in Table 3.
Table 2

Overview of Fenton's and EDTA-based Fenton's oxidation of pentachlorophenol optimized

MethodBiojarosite:H2O2pHPCP removal (%)COD removal (%)
Fenton's oxidation 1:1 3.0 83.4 61.23 
EDTA-based Fenton's oxidation 1:1 7.0 93.5 87.61 
MethodBiojarosite:H2O2pHPCP removal (%)COD removal (%)
Fenton's oxidation 1:1 3.0 83.4 61.23 
EDTA-based Fenton's oxidation 1:1 7.0 93.5 87.61 
Table 3

Reusability of catalyst on Fenton's and EDTA-based Fenton's oxidation of pentachlorophenol

CycleFenton's oxidation
EDTA-based Fenton's oxidation
PCP removal (%)COD removal (%)PCP removal (%)COD removal (%)
81.5 60.0 89.7 85.5 
77.0 57.7 79.7 71.2 
71.5 55.5 72.3 56.5 
68.6 51.0 69.8 51.7 
CycleFenton's oxidation
EDTA-based Fenton's oxidation
PCP removal (%)COD removal (%)PCP removal (%)COD removal (%)
81.5 60.0 89.7 85.5 
77.0 57.7 79.7 71.2 
71.5 55.5 72.3 56.5 
68.6 51.0 69.8 51.7 
Table 4

ANOVA for the quadratic response surface model fitting for Fenton's oxidation and EDTA-based Fenton's oxidation of pentachlorophenol

SSDfMSF-valueP-valueRemarks
Fenton's oxidation of pentachlorophenol 
Residual model 2,008.06 223.12 42.57 0.0003 Significant 
Lack of fit 24.76 8.25 11.41 0.0816 Not Significant 
Pure error 1.45 0.7233    
Total correlation 2,034.27 14     
R2 = 0.9871
Adjusted R2 = 0.9639
Predicted R2 = 0.8034 
EDTA-based Fenton's oxidation of pentachlorophenol 
Residual model 2,387.40 265.27 47.77 <0.0003 Significant 
Lack of fit 12.32 4.11 0.5316 0.7045 Not Significant 
Pure error 15.45 7.72    
Total correlation 2145.16 14     
R2 = 0.9885
Adjusted R2 = 0.9678
Predicted R2 = 0.9040 
SSDfMSF-valueP-valueRemarks
Fenton's oxidation of pentachlorophenol 
Residual model 2,008.06 223.12 42.57 0.0003 Significant 
Lack of fit 24.76 8.25 11.41 0.0816 Not Significant 
Pure error 1.45 0.7233    
Total correlation 2,034.27 14     
R2 = 0.9871
Adjusted R2 = 0.9639
Predicted R2 = 0.8034 
EDTA-based Fenton's oxidation of pentachlorophenol 
Residual model 2,387.40 265.27 47.77 <0.0003 Significant 
Lack of fit 12.32 4.11 0.5316 0.7045 Not Significant 
Pure error 15.45 7.72    
Total correlation 2145.16 14     
R2 = 0.9885
Adjusted R2 = 0.9678
Predicted R2 = 0.9040 
Table 5

Coefficient of the model for Fenton's oxidation of pentachlorophenol

FactorCoefficient estimateStandard errorF-valueP-valueRemarks
X 82.57 1.32 42.57 0.0003 Significant 
A 2.60 0.8094 10.32 0.0237 Significant 
B 5.05 0.8094 38.93 0.0015 Significant 
C −2.35 0.8094 8.43 0.0337 Significant 
AB 0.5 1.14 0.1908 0.6805 Not significant 
AC −1.10 1.14 0.1908 0.3804 Not significant 
BC 0.15 1.14 0.0172 0.9001 Not significant 
A2 −18.41 1.19 238.72 <0.0001 Significant 
B2 −8.01 1.19 45.18 0.0011 Significant 
C2 −10.66 1.19 80.23 0.0003 Significant 
FactorCoefficient estimateStandard errorF-valueP-valueRemarks
X 82.57 1.32 42.57 0.0003 Significant 
A 2.60 0.8094 10.32 0.0237 Significant 
B 5.05 0.8094 38.93 0.0015 Significant 
C −2.35 0.8094 8.43 0.0337 Significant 
AB 0.5 1.14 0.1908 0.6805 Not significant 
AC −1.10 1.14 0.1908 0.3804 Not significant 
BC 0.15 1.14 0.0172 0.9001 Not significant 
A2 −18.41 1.19 238.72 <0.0001 Significant 
B2 −8.01 1.19 45.18 0.0011 Significant 
C2 −10.66 1.19 80.23 0.0003 Significant 
Table 6

Coefficient of the model for EDTA-based Fenton's oxidation of pentachlorophenol

FactorCoefficient estimateStandard errorF-valueP-valueRemarks
X 90.33 1.36 47.77 0.0003 Significant 
A 3.46 0.8331 17.27 0.0089 Significant 
B 14.22 0.8331 291.53 <0.0001 Significant 
C 1.94 0.8331 4.35 0.0914 Significant 
AB −2.00 1.18 2.88 0.1504 Not significant 
AC −1.38 1.18 1.36 0.2958 Not significant 
BC −0.9 1.18 0.5835 0.4794 Not significant 
A2 −9.10 1.23 55.11 0.0007 Significant 
B2 −9.73 1.23 62.94 0.0005 Significant 
C2 −3.20 1.23 6.83 0.0475 Significant 
FactorCoefficient estimateStandard errorF-valueP-valueRemarks
X 90.33 1.36 47.77 0.0003 Significant 
A 3.46 0.8331 17.27 0.0089 Significant 
B 14.22 0.8331 291.53 <0.0001 Significant 
C 1.94 0.8331 4.35 0.0914 Significant 
AB −2.00 1.18 2.88 0.1504 Not significant 
AC −1.38 1.18 1.36 0.2958 Not significant 
BC −0.9 1.18 0.5835 0.4794 Not significant 
A2 −9.10 1.23 55.11 0.0007 Significant 
B2 −9.73 1.23 62.94 0.0005 Significant 
C2 −3.20 1.23 6.83 0.0475 Significant 
Figure 5

Linear fit for Fenton oxidation of PCP with and without EDTA.

Figure 5

Linear fit for Fenton oxidation of PCP with and without EDTA.

Close modal

During the first cycle of reuse, degradation efficiencies of 81.5 and 89.7% were achieved, with corresponding COD removal rates of 60 and 85.5% for Fenton oxidation and EDTA-based Fenton oxidation, respectively. In the second cycle of reuse, the efficiency decreased to 77 and 79.7%, with COD removal rates of 57.7 and 71.2%, respectively. For the third cycle of reuse, degradation efficiencies were measured at 71.5 and 72.3%, with COD removal rates of 55.5 and 56.5%. In the fourth cycle, the degradation efficiencies dropped to 68.6 and 69.8%, with COD removal rates of 51 and 51.7%, respectively. The reuse of the catalyst resulted in a reduction in efficiency by 14.8 and 23.7% for Fenton oxidation and EDTA-based Fenton oxidation of PCP, respectively.

Fenton oxidation and EDTA-based Fenton oxidation of PCP – design optimization

The significance of PCP removal through Fenton oxidation is indicated by the F-value of the model, which is 42.57, with a noise level of 0.03. The model is highly significant, as evidenced by a probability value of <0.0001. The R-squared value obtained, 0.9871, indicates a strong correlation between the predicted and observed values, suggesting that 98.71% of the variance can be attributed to the variables, highlighting their high significance in the Fenton oxidation of PCP. The model accounts for 98.71% of the total variance, leaving only 1.29% unexplained. The low probability (p < 0.0001) of the F-test, along with the ANOVA quadratic regression model, demonstrates that the model is highly significant for the Fenton oxidation of PCP and shows no lack of fit. The lack of fit F-value of 11.41 implies that the lack of fit is not significantly related to pure error. There is an 8.16% chance that the lack of fit F-value could be attributed to noise. The predicted R-squared values of 0.8037 are reasonably close to the adjusted R-squared value of 0.9639, with a difference of less than 0.2, indicating good agreement between them (Tables 4 and 5).

The significance of PCP removal through EDTA-based Fenton oxidation is indicated by the F-value of the model, which is 47.77, with a noise level of 0.03 (Table 6). The model is highly significant, as evidenced by a probability value of <0.0001. The R-squared value obtained, 0.9885, indicates a high degree of correlation between the predicted and observed values, suggesting that 98.85% of the variance can be attributed to the variables, highlighting their high significance in the EDTA-based Fenton oxidation of PCP. The model accounts for 98.85% of the total variance, leaving only 1.15% unexplained. The ANOVA quadratic regression model demonstrates that the model is highly significant for the EDTA-based Fenton oxidation of PCP, with a low probability (p < 0.0001) for the F-test, and it shows no lack of fit. The lack of fit F-value of 0.53 implies that the lack of fit is not significantly related to pure error. There is a 70.45% chance that the lack of fit F-value could be attributed to noise. The predicted R-squared values of 0.9040 show good alignment with the adjusted R-squared values of 0.9678, indicating that the difference between them is less than 0.2.

The coefficient of variance obtained in the Fenton oxidation model is 3.65%, indicating the model's reproducibility. In the case of EDTA-based Fenton oxidation, the coefficient of variance obtained in the model is 3.0%, suggesting possible reproducibility. These low coefficient of variance values demonstrate the high precision and reliability of the experiments conducted in both Fenton oxidation and EDTA-based Fenton oxidation. The p-values of the regression coefficients indicate that the linear, quadratic, and interaction effects of the independent variables significantly influence both Fenton oxidation of PCP and EDTA-based Fenton oxidation of PCP. These effects are highly significant, further supporting the robustness of the models.

In the present study, the model terms used for Fenton oxidation of PCP were represented by A, B, C, A2, B2, and C2. The statistical analysis conducted on the experimental data revealed that catalyst loading, H2O2 dosage, and pH-value had a significant impact on Fenton oxidation. Specifically, jarosite loading and H2O2 dosage exhibited a more linear influence on Fenton oxidation, indicating their contribution to PCP removal. Previous research has also reported a higher positive linear effect of iron dosage compared to H2O2 in Fenton oxidation. Moreover, H2O2 dosage demonstrated the highest positive linear influence (with a high coefficient) among the variables, surpassing the influences of biojarosite and pH-value. The analysis revealed that the independent variables acted in a quadratic manner, influencing the Fenton oxidation of PCP.

The equation representing the linear regression quadratic model fit is described as follows.
formula

The model terms used for the EDTA-based Fenton oxidation of PCP include A (Biojarosite dosage), B (H2O2 dosage), C (pH), A2, B2, and C2. Analysis of the experimental data consistently revealed that both jarosite loading and EDTA addition significantly influenced the UV-Fenton oxidation process. The EDTA-based Fenton oxidation showed a positive correlation with jarosite loading and H2O2 dosage, indicating that the removal of PCP was enhanced by increasing jarosite loading and H2O2 dosage. Among the variables, H2O2 dosage exhibited the highest positive linear influence (coefficient of 14.22), followed by jarosite loading (coefficient of 3.46) and pH-value (coefficient of 1.74). However, the quadratic effect of the independent variables had a negative impact on the oxidation of pentachlorophenol in the EDTA-based Fenton reaction.

The linear regression quadratic equation of the model fit is explained by
formula
In both Fenton oxidation and EDTA-based Fenton oxidation, there is a strong correlation between the actual and predicted responses. The interaction plots for PCP removal by Fenton oxidation and EDTA-based Fenton oxidation are presented in Figures 6 and 7, respectively. It can be observed from Figure 8 that Fenton oxidation for PCP removal operates within a narrow range of biojarosite and H2O2 dosages, while EDTA-based Fenton oxidation has a relatively broader range, taking pH-value into consideration. Figure 9 depicts a 3D response surface plot illustrating the Fenton oxidation and EDTA-based Fenton oxidation of PCP.
Figure 6

Interaction plot for PCP removal by Fenton oxidation of PCP.

Figure 6

Interaction plot for PCP removal by Fenton oxidation of PCP.

Close modal
Figure 7

Interaction plot for PCP removal by EDTA-based Fenton oxidation of PCP.

Figure 7

Interaction plot for PCP removal by EDTA-based Fenton oxidation of PCP.

Close modal
Figure 8

Contour plot for PCP removal by (a) Fenton oxidation and (b) EDTA-based Fenton oxidation.

Figure 8

Contour plot for PCP removal by (a) Fenton oxidation and (b) EDTA-based Fenton oxidation.

Close modal
Figure 9

Response surface 3D plot for PCP removal by (a) Fenton oxidation and (b) EDTA-based Fenton oxidation.

Figure 9

Response surface 3D plot for PCP removal by (a) Fenton oxidation and (b) EDTA-based Fenton oxidation.

Close modal
The impact of independent variables on dependent variables was examined through a Pareto chart, as illustrated in Figure 10. For Fenton oxidation, variables B and A exhibited significant effects, while for EDTA-based Fenton oxidation, variables A, B, and C demonstrated significant effects. Specifically, in Fenton oxidation, H2O2 dosage was identified as having the greatest influence, whereas in EDTA-based Fenton oxidation, biojarosite loading was found to have the highest effect.
Figure 10

Pareto chart for PCP removal by (a) Fenton oxidation and (b) EDTA-based Fenton oxidation.

Figure 10

Pareto chart for PCP removal by (a) Fenton oxidation and (b) EDTA-based Fenton oxidation.

Close modal

The catalytic efficacy of biojarosite, synthesized from Acidithiobacillus ferrooxidans, was assessed in the presence and absence of the chelating agent EDTA, confirming its role. The results indicated that PCP degradation through Fenton oxidation with biojarosite was more pronounced at acidic pH-levels, while at neutral pH-value, the addition of EDTA facilitated the dissolution of iron from biojarosite, enhancing its catalytic activity in PCP degradation. Significant removal efficiencies of 93.5 and 83.4% were observed with and without EDTA, respectively. The degradation of PCP followed a pseudo-first-order reaction with R-square values of 0.9607 and 0.9688 for the reactions with and without EDTA, respectively. The efficiency of PCP degradation was further enhanced by increasing the catalyst dosage and H2O2 concentration. Both models exhibited excellent fit between the actual and predicted responses, demonstrating the high precision of the Box–Behnken method. The influential parameters were discussed, and quadratic equations were derived for both models. These findings suggest that the utilization of biojarosite as a replacement for commercial iron can enhance the stability of Fenton degradation for organic compounds.

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