TY - JOUR
T1 - Machine Learning-Assisted Insights into Sources and Fate of Microplastics in Wastewater Treatment Plants
AU - Wu, Pengfei
AU - Wang, Bolun
AU - Lu, Yi
AU - Cao, Guodong
AU - Xie, Peisi
AU - Wang, Wei
AU - Chen, Duoli
AU - Huang, Gefei
AU - Jin, Hangbiao
AU - Yang, Zhu
AU - Cai, Zongwei
N1 - Funding Information:
This work is supported financially by the National Natural Science Foundation of China (NSFC) (22106130) and the General Research Fund (12303321) from the Research Grant Committee of Hong Kong SAR, China.
Publisher copyright:
© 2022 The Authors. Published by American Chemical Society.
PY - 2023/8/24
Y1 - 2023/8/24
N2 - 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.
AB - 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.
KW - Machine Learning
KW - Microplastic contamination
KW - Physicochemical features
KW - Sources and fate
KW - Wastewater treatment parameters
UR - http://www.scopus.com/inward/record.url?scp=85170426558&partnerID=8YFLogxK
U2 - 10.1021/acsestwater.3c00386
DO - 10.1021/acsestwater.3c00386
M3 - Journal article
SN - 2690-0637
JO - ACS Environmental Science and Technology Water
JF - ACS Environmental Science and Technology Water
ER -