Decoding microplastic pollution in China freshwater: interpretable machine learning insights into composition-specific distribution and associated phthalate ester leaching risk

Xizhe Wan

doi:10.1016/j.watres.2026.126663

Decoding microplastic pollution in China freshwater: interpretable machine learning insights into composition-specific distribution and associated phthalate ester leaching risk

2026-08-08

0

PRE

AI

X

Xizhe Wan

Q

Qiong Guo

Z

Zhenfei Han

T

Tianqi Jiang

G

Guangshuo Chai

童

童银栋 (Yindong Tong) *

H

Hongyang Cui *

X

Xiaoyu Cui *

DOI：10.1016/j.watres.2026.126663

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Abstract

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• A national, composition-resolved freshwater MP database was established. • An interpretable multi-output model synchronously quantified five dominant MPs. • Aquatic MPs are strongly associated with climate and anthropogenic factors. • Composition-dependent risk pinpointed DEHP hotspots in Beijing, Tianjin, Taiwan rivers. • A region-system-polymer strategy supports a transferable plastic governance.

Decoding microplastic pollution in China freshwater: interpretable machine learning insights into composition-specific distribution and associated phthalate ester leaching risk

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