Statistical analysis for remediation of As(III) ions from water using pristine and derivatized Phyllanthus emblica seed coat

Availability and affordability of pure drinking water are big challenges throughout the world. Many scientists have been working in this field to provide pure water at a low cost. A lot of techniques have been developed and implemented for water purification. The scarcity of pure or drinkable water is another challenge for all living beings (Fang et al. 2023). In this respect, research is based on the fact that how to utilize maximum water without any wastage and at low cost. A lot of natural or synthetic materials have been applied for the remediation of various contaminants and other toxic materials from water (Kumar et al. 2015; Ali et al. 2022a; Al-Jaaf et al. 2022; Jabbar et al. 2022). Optimization is another way which helps to reduce the chemicals, cost, and time of the process (Saini et al. 2019). Optimal conditions can be obtained by using optimization for the treatment or purification of water (Farhaoui & Derraz 2016). Multi-criteria decision (MCD) and multi-objective optimization (MOO) analyses have been applied for the feasibility of water treatment (Sadr et al. 2020). Support vector machine (SVM), artificial neural network (AAN), and response surface methodology (RSM) have been widely applied for data analysis of the management of water (Moni et al. 2021). The predictability of the remediation conditions of different toxic chemicals in water has been accurately evaluated by using these techniques along with a genetic algorithm. Mean squared error and coefficient of determination help to evaluate the best prediction models.

Contamination of aquatic ecosystems through non-degradable toxic materials and heavy metals has become a key global concern (Chawla et al. 2015; Ali et al. 2022b; Alismaeel et al. 2022). Solar photo-catalysis reactor has been applied as pretreatment for wastewater using UV, UV/TiO₂, and UV/H₂O₂ to control membrane fouling (Ali et al. 2022c). Ultra-filtration (UF) and photochemical degradation techniques are widely used for wastewater treatment (Hao et al. 2018; Alardhi et al. 2020; Abbood et al. 2023). Arsenic is one of the noxious metalloids that can enter into ecosystems either naturally (through volcanic ash, leaching and weathering of arsenic-containing rocks from earth's crust, and contact of geothermal fluids with ground water) or anthropogenically (through mining, industrial processes, and agricultural practices) (Pezeshki et al. 2023). Arsenic is found in both organic and inorganic forms with oxidation states ranging from +3 to +5. Trivalent arsenic (As(III)) is the most hazardous type of arsenic among these two states (Hughes et al. 2011). The prescribed standard limit of arsenic ions in water set by the World Health Organization (WHO) and US Environmental Protection Agency (EPA) is 10 and 50 μg/L, respectively (Hughes et al. 2011).

Consumption of water above the WHO and US EPA limit of arsenic have detrimental effects on the multiple organs in the human body and show numerous effects on the respiratory, neurological, cardiovascular, dermatological, and endocrine system and causes several health hazards including increased threat of tumor, adverse pregnancy outcomes, decreased women's reproductive life, in addition to impaired cognitive development in children. Exposure to arsenic above the recommended limit also leads to non-pitting foot swelling, Bowen's disease, still and preterm birth, cellular disruption black foot disease (Yu et al. 2006; Hughes et al. 2011; Rakhunde et al. 2012; Milton et al. 2017). IARC (International Agency for Research on Cancer) has categorized the availability of arsenic in drinking water as the first group carcinogen as it leads to cancer of the respiratory system, kidney/bladder cancer, and skin cancer.

The harmful and destructive effects of heavy metals on ecosystems have compelled scientists all over the world to search for or the synthesis of extremely efficient adsorbents for heavy metal ion removal from water. For the adsorption of heavy metals from ecological systems, a variety of methods have been used, including precipitation, ion exchange, membrane, sedimentation, and adsorption approaches (Kadhum et al. 2021; ALSamman et al. 2023). The adsorption approach is regarded as the best practice among the ways mentioned above for the remediation of heavy metals from water because of its straightforward process and cost-effectiveness (Saini et al. 2018). Various natural and engineered adsorbents have been employed in the past to remove arsenic ions from solutions, however, some of them have ineffective sorption and limited capacity.

Hao et al. 2018 investigated the removal of As(III) from water by combined adsorption of the UF membrane using the central composite design (CCD) method. The optimal conditions for 99.9% removal of arsenic were obtained at an adsorbent dosage of 8.1 g/L, pH of 5.1, and 1.0 mg/L of initial arsenic concentration with 0.924 of regression coefficient (R²). Optimized results were in good agreement with the experimental results. Tajernia et al. (2014) investigated the effects of various variables on the removal of arsenic from water using sugarcane bagasse (SCB) and its activated carbon (AC). Optimal removal (98%) was achieved at pH of 8.9, dosage of 23.68 g/L, initial ion concentration of 63.74 mg/L for SCB with R² = 0.990, and 89% removal at pH of 7.63, dosage of 17.55 g/L, initial ion concentration of 67.15 mg/L with R² = 0.990 for AC using the BBD method. Behera et al. (2022) investigated the removal of As(III) from water by Psidium guajava leaf powder using the CCD method. The optimal conditions for 90.88% removal of arsenic were achieved at pH of 6 and 30 mg/L of arsenic ion concentration with R² = 0.924.

The novelty of the present study is to optimize the conditions for remediation of As(III) from water on PPE and DPE adsorbents using BBD and CCD techniques in RSM. There are only a few studies where these tools have been utilized for the optimization of experimental conditions and estimation of removal for different samples. A total of 29 and 30 experiments were required for BBD and CCD methods, respectively, to get the optimum values of the combined effects. The optimized optimal results are compared with the experimental results using the same variables. These optimized optimal results help to reduce the cost of the overall method, and decrease the number of experiments. The removal of arsenic can be improved statistically by adjusting independent factors, i.e. contact time, pH, dosage, and initial As(III) ion concentration.

All the preliminary adsorption studies were conducted by changing one experimental condition and keeping others the same. It is a costly and time-consuming process to get the results of removal and adsorption efficiency of both materials under all possible conditions as many experiments will need to be conducted by taking specific values of all the variables. Experimental design using RSM has been utilized to overcome this difficulty. Experimental runs were designed using BBD and CCD methods to find the best optimal conditions of various variables for remediation of As(III) ions using RSM. Utilizing optimization approaches, the primary goal is to improve performance by minimizing experimental trials that reduce the overall cost of the experimental task (Adlnasab et al. 2019; Li et al. 2022; Tee et al. 2022).

A standard 1,000-ppm As(III) ion solution was prepared using sodium arsenite. The solutions required for a batch study were obtained from stock solutions after dilution. A batch study was conducted with blank (PPE) and derivatized (DPE) products to assess adsorption characteristics under various arsenic concentration conditions (20–100 mg/L), pH (1–9), time of contact (20–120 min), and adsorbent dosage (0.03–0.50 g/L) (Nayyar et al. 2022). Langmuir adsorption was found to be the best adsorption isotherm with a pseudo-second-order kinetic model. Disposal of used adsorbents (PPE and DPE) containing arsenic is a challenging and critical issue due to the leaching of arsenic. Regeneration of the used adsorbents is the best way to control the leaching of arsenic from the used adsorbents. Leaching of As(III) ions is generally very high in low and high pH as compared to normal pH (6–8) (Bhunia et al. 2007).

Regeneration of the used adsorbents is a very important step for sustainability and assessing its commercial applicability. Both PPE and DPE adsorbents are regenerated after batch study for reuse. 30% H₂O₂ in 0.1 M HNO₃ followed by 0.1 M NaOH solution was used for regeneration of both adsorbents (Lata et al. 2015). The desorption capacity of the arsenic on adsorbents was calculated by q_t = (C_tV × 74.92)/m, where q_t represents desorbed amount of As(III) at time t min, C_t, concentration of As(III) in desorbed solution at time t min, V is the total volume of the solution and m is the mass of adsorbent after the adsorption of As(III).78–84% desorption efficiency was observed in the first cycle followed by 87–92% efficiency in the next five cycles with 75–83% recovery of arsenite. Arsenic can be recovered by extracting it from the liquid solution after desorption (Zhang & Wang 2010).

Experiments were designed as per the BBD and CCD results given in Table 1 to get the optimum combination of input variables for As(III) ion adsorption on PPE and DPE. BBD and CCD methods were applied with following parameters, namely pH, initial As(III) ion concentration, contact time, and adsorbent dosage for As(III) ion adsorption from the solution. A total of 29 and 30 trials were run in the BBD and CCD, respectively, to get the optimum values of the combined effects (Tables 2 and 3).

RSM is a mathematical and statistical tool for creating, enhancing, and maximizing processes in terms of performance or quality requirements. Additionally, it lists the impact of significant input variables, often known as independent variables, and their interactions with one another during As(III) ion adsorption. Different response surface designs were applied and it is established that the BBD and CCD are the most appropriate designs and, therefore, chosen for the present study.

Four-factor BBD and CCD matrix, experimental results, and predicted analysis for remediation of As(III) using PPE and DPE are shown in Tables 2 and 3, respectively. BBD and CCD runs were employed using Design-Expert^® 7.0.0 software to fit second-order polynomial model. Experiments were designed with various variables, i.e. contact time, initial As(III) concentration, pH, and dose of PPE and DPE. The second-order polynomial equation represents the removal of As(III) ions (Y) as a function of the pH of the aqueous solution (A), initial concentration of As(III) ions (B), time of contact (C), and adsorbent dosage (D). A quadratic model using coded parameters and adsorption capacity (q_e) can be expressed as

• (1)
  BBD

  

  (3)

  

  (4)
• (2)
  CCD

  

  (5)

  

  (6)

This equation demonstrates the effect of various variables (quadratic) on the adsorption of As(III) ions from an aqueous solution. The approximate functions are utilized to assess the predicted values by means of experimental values for a specific run. The value of F and p demonstrates the significance of the model. The statistical significance of the model is indicated by a high value of F and a low value of p. The result obtained by the analysis of variance (ANOVA) reveals the statistical significance of the model at an F-value of 162.77 for PPE and 1,238.25 for DPE in BBD (Supplementary material, Table S1) and 29.84 for PPE and 33.66 for DPE in the CCD implies the model is significant (Supplementary material, Table S2). The competence of the model was verified by the summary statistics and the sum of squares of the sequential model.

The equation also reveals the effects of experimental conditions on the adsorption of As(III) ions by PPE and DPE. The current models displayed strong regression coefficients and a good fit for the quadratic design. Quadratic models showed high regression coefficients (R²) (0.9938; 0.9653) on PPE and (0.9991; 0.9691) on DPE in the BBD and CCD, respectively (Supplementary material, Table S3).

The ‘Pred R-Squared’ (0.9641, 0.8200) for PPE and (0.9878, 0.9329) for DPE are in rational agreement with the ‘Adj R-Squared’ of (0.9878, 0.9329) for PPE and (0.998, 0.940) for DPE in the BBD and CCD, respectively.

A, B, C, D, AC, BD, A², B², C², D² and A, B, C, D, AB, AC, AD, BC, BD, CD, A², B², C², D² are significant terms for PPE and DPE respectively in BBD model. However, B, B² and B, D, B² are the only significant terms for PPE and DPE respectively in CCD model.

The signal-to-noise ratio is measured by ‘Adeq Precision’. A ratio greater than 4 is preferred. A ratio of 46.03 for PPE 125.14 for DPE in BBD, and 22.97 for PPE and 24.00 for DPE in the CCD suggested that the signals are adequate. This paradigm is useful for navigating the design space. The competence of the model was further evaluated by the coefficient of variation (CV) and the CV was found to be 4.45 for PPE and 1.64 for DPE in BBD, and 8.09 for PPE and 7.18 for DPE in the CCD indicating the accuracy and reliability of the experiments.

Figure 4(a) represents the effect of pH and contact time between the sample and sorbent on q_e at a constant initial concentration (98.82 mg/L) and adsorbent dosage (DPE) (0.12 g/L). It gives the response in terms of the value of q_e with simultaneous variation of pH and contact time. Response is also good near pH 7.31 and contact time near to 126.99 min under fixed conditions of initial concentration (98.82 mg/L) and adsorbent dosage (0.12 g/L). However, a response can also be predicted after choosing different conditions of dosages and initial concentration as per the requirement for the water sample to be treated. Figure 4(b) represents a consequence of pH and initial concentration of target ion of the sample on q_e at a constant contact time (126.99 min) and adsorbent dosage (0.12 g/L). It is clear that at an approximate pH of 7.31 and an initial concentration near 98.82 mg/L maximum q_e value can be obtained. However, a response can also be predicted after choosing different conditions of dosages and initial concentrations as per the requirement of the water sample to be treated. Similarly, Figure 4(c)–4(f) gives significant information about the response under different conditions of chosen variables in the desired range.

Figure 6(a) represents the effect of pH and contact time between the sample and sorbent on q_e at a constant initial concentration (99.99 mg/L) and adsorbent dosage (DPE) (0.08 g/L). It gives the response in terms of the value of q_e with simultaneous variation of pH and contact time. Response is also good near pH 7.5 and contact time near 100 min under fixed conditions of initial concentration (99.99 mg/L) and adsorbent dosage (0.08 g/L). However, a response can also be predicted after choosing different conditions of dosages and initial concentrations as per the requirement for the water sample to be treated. Figure 6(b) represents a consequence of pH and initial concentration of target ion in the sample on q_e at a constant contact time (100 min) and adsorbent dosage (0.08 g/L). It is clear that at an approximate pH of 7.5 and an initial concentration near 98.82 mg/L, the maximum q_e value can be obtained. However, the response can also be predicted after choosing different conditions of dosages and initial concentration as per the requirement for the water sample to be treated. Similarly, Figure 6(c)–6(f) gives significant information about the response under different conditions of chosen variables in the desired range.

Synthetic membranes, i.e. UF membrane, and natural adsorbents, i.e. SCB, AC of SCB, and Psidium guajava leaf powder have been widely applied for remediation of arsenic using optimized multivariable conditions. PPE and DPE are effective adsorbents for remediation of As(III) ions from water using the BBD method having a high regression coefficient (close to 1) compared to other synthetic and natural adsorbents (Tajernia et al. 2014; Hao et al. 2018; Behera et al. 2022; Khadim et al. 2022).

BBD and CCD statistical methods were effectively applied to determine and optimize the impacts of multivariable parameters on the remediation of As(III) ions by PPE and DPE adsorbents. The regression coefficient (R²) obtained by the BBD method (R² = 0.993 (PPE) and R² = 0.999 (DPE)) is much closer to experimental R² values (R² = 0.998 (PPE) and R² = 0.999 (DPE)) with a high value of desirability factor. Thus, the regression coefficient and desirability factor indicated that the BBD is a better technique for the optimization of the arsenic remediation process using PPE and DPE as adsorbents. These statistical tools can also be used for the prediction of the adsorption capacity of adsorbents when used in different real scenario conditions for the remediation of contaminants from water samples.

This study was supported by the management of Manav Rachna International Institute of Research and Studies Faridabad, Haryana, India.

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

The authors declare there is no conflict.