Arsenic removal from groundwater using an aerated electrocoagulation reactor with 3D Al electrodes in the presence of anions

Co-occurrence of arsenic and anions in groundwater causes a severe health problems and combine effects of these pollutants significantly affect performance of treatment process. Thus, this study has been conducted to examine the combine effects of anions on arsenic removal using aerated electrocoagulation (EC) reactor with 3D Al electrodes in groundwater. A 3-level, six factors Box-Behnken experimental design (BBD) was applied to investigate the individual and combine effect of anions and operating time: phosphate (x(1): 1-10 mg L-1), silica (x(2): 20-80 mg L-1), bicarbonate (x(3): 130-670 mg L-1), fluoride (x(4): 2-10 mg L-1), boron (x(5): 5-10 mg L-1), and operating time (x(6): 8-22 min) on desired responses. The specified responses were effluent arsenic concentration (C-f,C-As), removal efficiency of arsenic (R-e), consumptions of energy and electrode (ENC and ELC), operational cost (OC), and adsorption capacity (q(e)). The optimum operating parameters predicted using BBD were found to be x(1): 1.0 mg L-1, x(2): 26.0 mg L-1, x(3): 651.5 mg L-1, x(4): 2.0 mg L-1, x(5): 9.9 mg L-1, and x(6): 10.5 min considering highest removal efficiency of arsenic and lowest operational cost. Under these operating conditions, the experimental values of C-f,C-As, R-e, ENC, ELC, OC, and q(e) were found to be 2.82 mu g L-1, 98.6%, 0.411 kWh m(-3), 0.0124 kg m(-3), 0.098 $ m(-3), and 17.65 mu g As (mg Al)(-1), respectively. Furthermore, mathematical modelling was conducted using quadratic regression model and response surface analysis was performed to understand the relationship between independent parameters and responses. (C) 2020 Elsevier Ltd. All rights reserved.

Automated summary

This study investigated the combined effects of anions on arsenic removal in groundwater using aerated electrocoagulation technology. Optimal operating parameters were determined through experimental design and mathematical modeling, revealing the relationship between independent parameters and treatment efficiency.