Interpretable machine learning for spatially differentiated nonlinear relationships between PM2.5 and landscape patterns in an urban-rural integrated zone in China.
- DOI
- 10.1016/j.jenvman.2026.130981
- Published
- 2026 Oct 1
- Container
- Journal of environmental management
- Publisher
- Not recorded
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1016/j.jenvman.2026.130981,
title = {Interpretable machine learning for spatially differentiated nonlinear relationships between PM2.5 and landscape patterns in an urban-rural integrated zone in China.},
author = {He L and Tao J and Deng H and Wang C and Li C and Yan M},
year = {2026},
journal = {Journal of environmental management},
doi = {10.1016/j.jenvman.2026.130981},
url = {https://doi.org/10.1016/j.jenvman.2026.130981}
}RIS
TY - JOUR TI - Interpretable machine learning for spatially differentiated nonlinear relationships between PM2.5 and landscape patterns in an urban-rural integrated zone in China. AU - He L AU - Tao J AU - Deng H AU - Wang C AU - Li C AU - Yan M PY - 2026 JO - Journal of environmental management DO - 10.1016/j.jenvman.2026.130981 UR - https://doi.org/10.1016/j.jenvman.2026.130981 ER -
APA
L, H., J, T., H, D., C, W., C, L., & M, Y. (2026). Interpretable machine learning for spatially differentiated nonlinear relationships between PM2.5 and landscape patterns in an urban-rural integrated zone in China.. Journal of environmental management. https://doi.org/10.1016/j.jenvman.2026.130981
Source records
- pubmed · retrieved 2026-11-10T11:04:44.220Z