Machine Learning-Assisted Insights into Sources and Fate of Microplastics in Wastewater Treatment Plants

Pengfei Wu, Bolun Wang, Yi Lu, Guodong Cao, Peisi Xie, Wei Wang, Duoli Chen, Gefei Huang, Hangbiao Jin, Zhu Yang, Zongwei Cai*

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

2 Citations (Scopus)

Abstract

Wastewater treatment plants (WWTPs) converge multiple sourced microplastics (MPs) and serve as a temporary repository in the case of releasing them into the environment. The process involves two critical scientific problems, including the source composition of MPs and their fate in WWTPs. Therefore, this study conducted a full-scale investigation in each stage of four WWTPs in Hong Kong, with the results showing that the fate of MPs was mainly affected by their physicochemical characteristics and WWTP parameters. Moreover, three conventional machine learning (ML) methods, namely the multilabel decision tree, random forests, and support vector machine, were also applied for figuring out the source compositions of MPs. The results demonstrated that the sources of MPs were mainly composed of domestic (57.3–59.9%), industrial (21.1–21.7%), coastal (11.2–12.7%), domestic/medical (4.6–5.1%), and domestic/agricultural (2.6–3.8%) sources, respectively. The discovery of domestic/medical-sourced MPs should draw the public’s attention to the insufficient management of used face masks. This study was a novel attempt to utilize ML to explore the fate and sources of MPs in environmental compartments, which provided new insights into developing the MP source tracing approaches from the source management of plastic contaminants.
Original languageEnglish
Pages (from-to)1107–1118
Number of pages12
JournalACS Environmental Science and Technology Water
Volume4
Issue number3
Early online date24 Aug 2023
DOIs
Publication statusPublished - 8 Mar 2024

Scopus Subject Areas

  • Water Science and Technology
  • Chemical Engineering (miscellaneous)
  • Chemistry (miscellaneous)
  • Environmental Chemistry

User-Defined Keywords

  • Machine Learning
  • Microplastic contamination
  • Physicochemical features
  • Sources and fate
  • Wastewater treatment parameters

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