Statistical optimization of arsenic biosorption by microbial enzyme via Ca-alginate beads

ABSTRACT

Bioremediation of arsenic using green technology via microbial enzymes has attracted scientists due to its simplicity and cost effectiveness. Statistical optimization of arsenate bioremediation was conducted by the enzyme arsenate reductase extracted from arsenic tolerant bacterium Pseudomonas alcaligenes. Response surface methodology based on Box–Behnken design matrix was performed to determine the optimal operational conditions of a multivariable system and their interactive effects on the bioremediation process. The highest biosorptive activity of 96.2 µg gm⁻¹ of beads was achieved under optimized conditions (pH = 7.0; As (V) concentration = 1000 ppb; time = 2 h). SEM analysis showed the morphological changes on the surface of enzyme immobilized gluteraldehyde crosslinked Ca-alginate beads. The immobilized enzyme retained its activity for 8 cycles. ANOVA with a high correlation coefficient (R² > 0.99) and lower “Prob > F”value (<0.0001) corroborated the second-order polynomial model for the biosorption process. This study on the adsorptive removal of As (V) by enzyme-loaded biosorbent revealed a possible way of its application in large scale treatment of As (V)-contaminated water bodies.

KEYWORDS:

• Arsenate
• arsenate reductase
• biosorption
• statistical optimization
• response surface methodology
• box–behnken design