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Machine-Learning-Accelerated Prediction of Water Quality Criteria for Microplastics

ACS ES&T Water 2026 Score: 50 ? 0–100 AI score estimating relevance to the microplastics field. Papers below 30 are filtered from public browse.

Mengxiao Wang, Mengxiao Wang, Chenxi Wang, Christie M. Sayes, Chenxi Wang, Christie M. Sayes, Christie M. Sayes, Xiaoling Yang, Xinyu Zhao, Xiaoling Yang, Yunsong Mu, Christie M. Sayes, Xinyu Zhao, Hyeong‐Moo Shin, Yunsong Mu, Xinyu Zhao, Christie M. Sayes, Xinyu Zhao, Fengchang Wu Xinyu Zhao, Chenxi Wang, John P. Giesy, John P. Giesy, John P. Giesy, Fengchang Wu Christie M. Sayes, John P. Giesy, Fengchang Wu John P. Giesy, John P. Giesy, Fengchang Wu John P. Giesy, Fengchang Wu Fengchang Wu, John P. Giesy, Kwan-chi Leung, Fengchang Wu Fengchang Wu Fengchang Wu Fengchang Wu Kwan-chi Leung, John P. Giesy, John P. Giesy, John P. Giesy, John P. Giesy, Hyeong‐Moo Shin, Christie M. Sayes, Christie M. Sayes, Fengchang Wu Christie M. Sayes, Fengchang Wu Fengchang Wu Fengchang Wu Fengchang Wu Fengchang Wu John P. Giesy, John P. Giesy, John P. Giesy, Fengchang Wu Fengchang Wu Fengchang Wu

Summary

Researchers developed a machine learning framework to predict microplastic toxicity in aquatic organisms and derive water quality criteria for five common polymer types. The random forest model outperformed other algorithms, with particle size, density, and aquatic species group accounting for 72% of prediction variability. The study found that polystyrene and PET exhibited the greatest toxicity, and that microplastics were generally more toxic in freshwater than saltwater environments.

Polymers

PE PET PS

Study Type Environmental

Microplastics (MPs) are widely distributed in aquatic environments, raising global concerns. However, determining their toxic effects on aquatic organisms and deriving water quality criteria (WQC) for hazardous substances remain challenging due to the heterogeneity of existing toxicity data. Additionally, acquiring sufficient data requires substantial resources. This study proposes a machine learning framework to predict the aquatic toxicity of five types of MPs. Three machine learning algorithms, k-nearest neighbors (kNN), support vector machine (SVM), and random forest (RF), were used to develop quantitative structure–toxicity relationships and derive site-specific WQC from predicted toxic end points. The RF model outperformed kNN and SVM in predictive accuracy after both internal and external validation. SHAP analysis revealed that particle size, density, and aquatic group accounted for 72% of the variability in the predictions. Polystyrene and polyethylene terephthalate exhibited significant toxicity in both freshwater and saltwater, with MPs being more toxic in freshwater. These findings highlight the need for site-specific WQC to protect aquatic ecosystems and improve ecological risk assessments of emerging contaminants.

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