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通过相关性分析和无监督学习探索聚氯乙烯(PVC)管道氯化过程中微塑料、溶解性有机物(DOM)和消毒副产物(DBPs)的共存情况。

Exploring the co-occurrence of microplastics, DOM and DBPs inside PVC pipes undergoing chlorination by correlation analysis and unsupervised learning.

作者信息

Maliwan Thitiwut, Do Quyen Thi Thuy, Nguyen Chi Mai, Teo Wan Kee, Hu Jiangyong

机构信息

Department of Civil and Environmental Engineering, National University of Singapore, 1 Engineering Drive 2, 117576, Singapore.

NUS Environmental Research Institute, National University of Singapore, 5A Engineering Drive 1, 117411, Singapore; Department of Environmental Engineering, Faculty of Environment, Vietnam National University Ho Chi Minh City, University of Science, 227 Nguyen Van Cu St., District 5, Ho Chi Minh City, Viet Nam.

出版信息

Chemosphere. 2025 Mar;373:144171. doi: 10.1016/j.chemosphere.2025.144171. Epub 2025 Jan 30.

Abstract

Drinking water distribution systems face a multifaceted emerging concern, including in situ microplastic (MP) generation, chemical leaching from plastic pipes, and the formation of disinfection by-products (DBPs). This study investigated the co-release of MPs and chemical leachates from polyvinyl chloride (PVC) pipes exposed to different chlorine concentrations on a lab scale, as well as the subsequent formation of DBP. Results highlighted significant evidence of PVC-derived dissolved organic matter (PVC-DOM) and microplastic (PVC-MP) leaching at higher chlorine concentrations. However, at chlorine residuals of 1 ppm, natural organic matter (NOM) retained its importance, with minimal release of PVC-DOM and PVC-MP from plastic pipes. Correlation analysis highlights the critical role of DOM, including both NOM and PVC-DOM, as a key intermediary between MPs and DBPs. This is evidenced by the strongest observed correlations within the DOM group and its significant associations with both MPs and DBPs. Conversely, the limited direct connections between MPs and DBPs further underscore the importance of DOM as the key link between these two targets. Using unsupervised learning techniques, including clustering and dimensionality reduction, further elucidated the influence of DOM in controlling the data patterns, enabling robust interpretation of complex datasets, and providing valuable insights. This study contributes to advancing understanding of the co-occurrence and behaviors of MP, DOM, and DBP within drinking water distribution systems, as well as propelling the associated risk in this intricate scenario.

摘要

饮用水分配系统面临多方面的新问题,包括原位微塑料(MP)生成、塑料管道的化学物质浸出以及消毒副产物(DBP)的形成。本研究在实验室规模上调查了暴露于不同氯浓度的聚氯乙烯(PVC)管道中MP和化学浸出物的共同释放情况,以及随后DBP的形成。结果突出显示了在较高氯浓度下PVC衍生的溶解有机物(PVC-DOM)和微塑料(PVC-MP)浸出的显著证据。然而,在氯残留量为1 ppm时,天然有机物(NOM)仍然很重要,塑料管道中PVC-DOM和PVC-MP的释放量极少。相关性分析突出了DOM(包括NOM和PVC-DOM)作为MP和DBP之间关键中介的关键作用。这一点在DOM组内观察到的最强相关性及其与MP和DBP的显著关联中得到了证明。相反,MP和DBP之间有限的直接联系进一步强调了DOM作为这两个目标之间关键联系的重要性。使用无监督学习技术,包括聚类和降维,进一步阐明了DOM在控制数据模式方面的影响,能够对复杂数据集进行有力解释,并提供有价值的见解。本研究有助于增进对饮用水分配系统中MP、DOM和DBP的共存及行为的理解,以及推动这一复杂情况下的相关风险研究。

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