Machine learning-based prediction of phenanthrene accumulation and toxicity in earthworms across soils.

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Bibliographic Details
Title: Machine learning-based prediction of phenanthrene accumulation and toxicity in earthworms across soils.
Authors: Li GF; State Key Laboratory of Regional and Urban Ecology, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen, 361021, China; University of Chinese Academy of Sciences, Beijing, 100049, China., Liao YK; State Key Laboratory of Regional and Urban Ecology, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen, 361021, China; University of Chinese Academy of Sciences, Beijing, 100049, China., Chi HF; State Key Laboratory of Regional and Urban Ecology, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen, 361021, China; University of Chinese Academy of Sciences, Beijing, 100049, China., Zhang YC; State Key Laboratory of Regional and Urban Ecology, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen, 361021, China., Chen CE; School of Environment, MOE Key Laboratory of Theoretical Chemistry of Environment, South China Normal University, Guangzhou, 510006, China; Environmental Research Institute, Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety, South China Normal University, Guangzhou, 510006, China. Electronic address: changer.chen@m.scnu.edu.cn., Cai C; State Key Laboratory of Regional and Urban Ecology, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen, 361021, China; University of Chinese Academy of Sciences, Beijing, 100049, China. Electronic address: ccai@iue.ac.cn.
Source: Environmental research [Environ Res] 2026 Mar 15; Vol. 293, pp. 123824. Date of Electronic Publication: 2026 Jan 19.
Publication Type: Journal Article
Journal Info: Publisher: Elsevier Country of Publication: Netherlands NLM ID: 0147621 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1096-0953 (Electronic) Linking ISSN: 00139351 NLM ISO Abbreviation: Environ Res Subsets: MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:1096-0953
DOI:10.1016/j.envres.2026.123824