​Statistical-based optimization and mechanism assessments of Arsenic (III)​ adsorption by ZnO-Halloysite nanocomposite​

Abstract

Arsenic contamination in aqueous media is a serious environmental problem, especially in developing countries. In this research, the Box-Behnken response surface methodology was used to optimize the most relevant variables affecting arsenic adsorption on the ZnO-halloysite surface, including temperature, adsorbent dosage, pH, contact time, and As (III) initial concentration. The regression analysis indicated that the experimental data were appropriately fitted to a quadratic model with the adjusted R-squared value (R2) of 0.982 for As(III) adsorption capacity and a linear model with R2 of 0.931 for As(III) removal. The p-values for both adsorption capacity and removal efficiency were below 0.05, with F-values of 116.91 and 115.58, respectively, supporting the model’s validity. The optimum conditions for maximum removal of As(III) were determined through numerical and graphical optimization using the desirability function. It was found that the optimum conditions for adsorption were pH = 7.99, contact time of 3.99 h, As(III) initial concentration of 49.96 mg/L, and adsorbent dosage of 0.135 g/40 ml. The accuracy of the optimization procedure was confirmed by a confirmatory experiment, which showed a maximum arsenic removal of 91.31% and an adsorption capacity of 12.63 mg/g under optimized conditions. Moreover, XPS analysis was performed at different pH levels to investigate the As (III) adsorption mechanism. The results demonstrated that As(III) adsorption occurs at acidic and neutral pH levels. On the other hand, when pH is increased to 8, As (III) oxidizes to As (V), and then adsorption occurs.