Insights into Contaminant Composition Variations and Reactomics during Wastewater Treatment Processes Based on Nontargeted Analysis and Paired Mass Distance

流出物 污水处理 化学 环境化学 废水 污染 转化(遗传学) 污染 作文(语言) 污染物 生物转化 去甲基化 工业废水处理 活性污泥 生态毒理学 制浆造纸工业 化学成分 环境污染
作者
Keng Tu,Yun Liu,Xichao Chen,Wei Li,Zhao Xu,Chen Qia,Yunjiang Yu
出处
期刊:Environmental Science & Technology [American Chemical Society]
卷期号:60 (6): 5086-5096
标识
DOI:10.1021/acs.est.5c14774
摘要

Understanding contaminant transformation during wastewater treatment is essential for pollution reduction and environmental risk mitigation. However, the highly diverse molecular composition of wastewater, arising from various emission sources, presents a major challenge to deciphering the underlying biochemical reactions. Herein, nontargeted analysis and paired mass distance (PMD) analysis were conducted in paired influent-effluent samples from 11 wastewater treatment plants to elucidate contaminant diversity and transformation reactions across different treatment processes. In total, 418 structural subclasses of contaminants were classified, among which amino acids, peptides, and analogues (AAPAs) were the most common. Although the total number of features, subclasses, and overall abundance decreased in effluent samples, chemical diversity did not show a consistent reduction. This finding was primarily due to the removal of contaminants such as AAPAs and the persistence of aromatic compounds such as benzenoids. Transformation reactions were highly conserved across biological processes, with methylation and demethylation occurring the most frequently. High-frequency PMDs were strongly associated with carbon-related transformations, which substantially altered the polarity of transformation products. Furthermore, transformation reactions were characterized by small mass shifts and intrastructural reactions, indicating that most contaminants did not undergo extensive structural transformations. This study provides insight into common patterns of contaminant removal across wastewater treatment processes, benefiting transformation product prediction and wastewater treatment process optimization.
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