Machine Learning-Driven Cross-Species Toxicity Prediction for Advancing Ecologically Relevant PFAS Water Quality Criteria

Traditional toxicity testing cannot keep pace with the rapid growth of synthetic chemicals, creating major data gaps that hinder the development of water quality criteria (WQC) for emerging contaminants. This study developed a machine learning model integrating compound- and organism-related feature...

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Xehetasun bibliografikoak
Egile nagusia:	Weigang Liang (734333) (author)
Beste egile batzuk:	Jingya Li (309241) (author), Xiaolei Wang (139592) (author), John P. Giesy (302766) (author), Xiaoli Zhao (118708) (author)
Argitaratua:	2025
Gaiak:	Ecology Inorganic Chemistry Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Chemical Sciences not elsewhere classified potentially reflecting inter 30 representative per water quality criteria informed species selection 5 </ sub species toxicity prediction species prediction species sensitivity existing criteria acute toxicity synthetic chemicals study developed related features regional differences regional comparisons rapid growth polyfluoroalkyl substances perfluorooctanoic acid perfluorooctanesulfonic acid north america model predicted machine learning hazardous concentrations emerging contaminants dominant predictors
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Machine Learning-Driven Cross-Species Toxicity Prediction for Advancing Ecologically Relevant PFAS Water Quality Criteria

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