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

Type Article
Date 2021-07
Language English
Author(s) Ben Ali Rihab1, Ben Ouada Sabrine1, 2, 3, Leboulanger Christophe3, Ammar Jihene1, Sayadi Sami2, 4, Ben Ouada Hatem1
Affiliation(s) 1 : Laboratory of Blue Biotechnology and Aquatic Bioproducts, National Institute of Marine Sciences and Technology, Monastir, Tunisia
2 : Laboratory of Environmental Bioprocesses, Center of Biotechnology of Sfax, Sfax, Tunisia
3 : MARBEC, CNRS, IFREMER, University of Montpellier, Sète, France
4 : Center for Sustainable Development, College of Arts and Sciences, Qatar University, Doha, Qatar
Source International Journal Of Phytoremediation (1522-6514) (Informa UK Limited), 2021-07 , Vol. 23 , N. 8 , P. 818-828
DOI 10.1080/15226514.2020.1859985
WOS© Times Cited 10
Keyword(s) Chlorophyta, bioremediation, optimization, bisphenol A, central composite face-centered design (CCF), removal
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.

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