ArticleEnvironmental science & technology2025
Predicting Micropollutant Removal in Wastewater Treatment Based on Molecular Structure: Benchmark Data and Models.
Article in Environmental science & technology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Persistence Assessment of Chemicals: Trajectories toward New Approach Methodologies (P-NAMs).Environmental science & technology · 2026Review
- Confidently Uncertain: Probabilistic Machine Learning to Predict Soil Biotransformation Half-Lives.Environmental science & technology · 2026Article
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Authors and funding
5 authors.
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Abstract
Models to predict the environmental fate of micropollutants are needed for alternatives assessment and safe-by-design efforts. Wastewater treatment plants (WWTPs) are the main barrier to prevent micropollutants from entering receiving water bodies, and WWTP breakthrough is an important indicator of chemical persistence. State-of-the-art models to predict breakthrough are limited by their need for first-order degradation rate constants, a metric that is often unavailable. Here, we build models that predict removal in conventional treatment directly from the chemical structure using data from field-scale monitoring for over 1000 chemicals. The best predictions were achieved using substructure-based fingerprints (i.e., MACCS) and random forests, and identified influential substructures agree with structural moieties relevant for biotransformation. We show that our models are more reliable than existing process-based models used in EU and US regulatory contexts, making them important contributions to the
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