Predicting soil-water partition coefficients of PFAS using machine learning: Model development, interpretation, and validation
- DOI
- 10.1016/j.envpol.2026.128771
- Published
- 2026-10
- Container
- Environmental Pollution
- Publisher
- Elsevier BV
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1016/j.envpol.2026.128771,
title = {Predicting soil-water partition coefficients of PFAS using machine learning: Model development, interpretation, and validation},
author = {Yue Zhou and Hao Chen and Xi Wang and Jun Huang and Jing Wei and Liman Wei and Yu Wang and Hongwen Sun},
year = {2026},
journal = {Environmental Pollution},
doi = {10.1016/j.envpol.2026.128771},
url = {https://doi.org/10.1016/j.envpol.2026.128771}
}RIS
TY - JOUR TI - Predicting soil-water partition coefficients of PFAS using machine learning: Model development, interpretation, and validation AU - Yue Zhou AU - Hao Chen AU - Xi Wang AU - Jun Huang AU - Jing Wei AU - Liman Wei AU - Yu Wang AU - Hongwen Sun PY - 2026 JO - Environmental Pollution DO - 10.1016/j.envpol.2026.128771 UR - https://doi.org/10.1016/j.envpol.2026.128771 ER -
APA
Zhou, Y., Chen, H., Wang, X., Huang, J., Wei, J., Wei, L., Wang, Y., & Sun, H. (2026). Predicting soil-water partition coefficients of PFAS using machine learning: Model development, interpretation, and validation. Environmental Pollution. https://doi.org/10.1016/j.envpol.2026.128771
Source records
- crossref · retrieved 2026-09-26T07:36:21.386Z