Revealing the sources of water-soluble PM<sub>2.5</sub> oxidative potential with explainable machine learning.
- DOI
- 10.1016/j.envpol.2025.127278
- Published
- 2025-10-15
- Container
- Environ Pollut
- Publisher
- Not recorded
- Open access
- no
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Cite this work
BibTeX
@article{allodium:10.1016/j.envpol.2025.127278,
title = {Revealing the sources of water-soluble PM\<sub\>2.5\</sub\> oxidative potential with explainable machine learning.},
author = {Zhang LY and Chen J and Yu Q and Sun YW and Xin K and Qin WH and Ahmad M and Zhai ST.},
year = {2025},
journal = {Environ Pollut},
doi = {10.1016/j.envpol.2025.127278},
url = {https://doi.org/10.1016/j.envpol.2025.127278}
}RIS
TY - JOUR TI - Revealing the sources of water-soluble PM<sub>2.5</sub> oxidative potential with explainable machine learning. AU - Zhang LY AU - Chen J AU - Yu Q AU - Sun YW AU - Xin K AU - Qin WH AU - Ahmad M AU - Zhai ST. PY - 2025 JO - Environ Pollut DO - 10.1016/j.envpol.2025.127278 UR - https://doi.org/10.1016/j.envpol.2025.127278 ER -
APA
LY, Z., J, C., Q, Y., YW, S., K, X., WH, Q., M, A., & ST., Z. (2025). Revealing the sources of water-soluble PM<sub>2.5</sub> oxidative potential with explainable machine learning.. Environ Pollut. https://doi.org/10.1016/j.envpol.2025.127278
Source records
- europe-pmc · retrieved 2026-09-27T11:55:49.099Z