Bisphenol A removal by the Chlorophyta Picocystis sp.: optimization and kinetic study

Abstract

The Chlorophyta Picocystis sp. isolated from a Tunisian household sewage pond appears promising for effective removal of Bisphenol A (BPA). Efficient and cost-effective technology for contaminants remediation relies on a tradeoff between several parameters such as removal efficiency, microorganism growth, and its tolerance to contaminant toxicity. This article demonstrates the optimum conditions achieving the highest removal rates and the minimal growth inhibition in batch cultures of Picocystis using response surface methodology. A central composite face-centered (CCF) design was used to determine the effects on removal and growth inhibition of four operating parameters: temperature, inoculum cell density, light intensity, and initial BPA concentration. Results showed that the maximal BPA removal was 91.36%, reached the optimal culture conditions of 30.7 °C, 25 × 105 cells ml−1 inoculum density, 80.6 µmol photons m−2 s−1 light intensity, and initial BPA concentration of 10 mg l−1. Various substrate inhibition models were used to fit the experimental data, and robustness analysis highlighted the Tessier model as more efficient to account for the interaction between Picocystis and BPA and predict removal efficiency. These results revealed how Picocystis respond to BPA contamination and suggest that optimization of experimental conditions can be effectively used to maximize BPA removal in the treatment process. Highlights Surface response methodology was applied for optimization of BPA removal by the Chlorophyta Picocystis sp. Temperature, light intensity, inoculum cell density and initial BPA concentration were selected as factors that may affect BPA removal and microalgae growth. The optimal conditions for the maximum BPA removal and minimum growth inhibition were 30.7 °C; 80.6 µmol photons m−2 s−1; 25 × 105 cells ml−1 and 10 mg l−1 BPA. Teissier model was selected to fit the kinetic of BPA removal by Picocystis with R 2 = 0.92.