Biodegradation of endocrine disrupting compounds from the wastewater by the immobilized indigenous bacteria

Environmental Quality Management(2024)

引用 0|浏览2
暂无评分
摘要
Abstract4‐tert‐octyl phenol is one of the important endrocrine‐disrupting compounds and is considered a major health hazard. A total of six isolates degraded 4‐tert‐octylphenol, and the strains Bacillus velezensis LG16 and Pseudomonas aeruginosa AC20 exhibited maximum 4‐tert‐octylphenol degradation. Co‐culture of these two strains improved 4‐tert‐octylphenol degradation from the wastewater. One variable at a time approach showed that 40°C incubation temperature, pH 8.0, and an initial 4‐tert‐octylphenol (60 mg/L) influenced biodegradation. The bacterial strains were immobilized in sodium alginate beads, and improved biodegradation was achieved. The biocatalytic process mediated by the immobilized cells was optimized by a statistical approach (two‐level full factorial design and response surface methodology). In a two‐level factorial model, 4‐tert‐octylphenol degradation varied from 1.1% to 55.2%. The 4‐tert‐octylphenol degradation was maximum at pH 6, 0.01 mg/L 4‐tert‐octylphenol, and 10 mg/L glucose with 20 g beads/L. ANOVA revealed that the designed model was statistically significant (p = 0.0310). A central composite design was used to analyze the interactive effect of significant variables and to explore the optimum conditions for 4‐tert‐octylphenol degradation by immobilized bacteria. The maximum phenol degradation was observed (97.4%) at pH 7.0, 0.06 4‐tert‐octylphenol, and 13.75 g sodium alginate beads/L. ANOVA showed that the designed model was statistically significant (p = 0.0091). The designed CCD model, the correlation coefficient value, and the lack of fit value showed that the designed CCD model was significant. The immobilized bacterial cells could more effectively degrade 4‐tert‐octylphenol than free bacterial cells. The high degradation potential indicated its application in degrading 4‐tert‐octylphenol from wastewater under optimized conditions.
更多
查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要