Interpretable Graph Deep Learning Reveals Ecological Risks and Attribution Patterns of Polycyclic Aromatic Hydrocarbons in Urban Greenspace Soils across China.
- DOI
- 10.1021/acs.est.6c06741
- Published
- 2026 Aug 25
- Container
- Environmental science & technology
- Publisher
- Not recorded
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1021/acs.est.6c06741,
title = {Interpretable Graph Deep Learning Reveals Ecological Risks and Attribution Patterns of Polycyclic Aromatic Hydrocarbons in Urban Greenspace Soils across China.},
author = {Han Y and Li Z and Chen G and Wang C and Wang W and Li R and Zhang L},
year = {2026},
journal = {Environmental science \& technology},
doi = {10.1021/acs.est.6c06741},
url = {https://doi.org/10.1021/acs.est.6c06741}
}RIS
TY - JOUR TI - Interpretable Graph Deep Learning Reveals Ecological Risks and Attribution Patterns of Polycyclic Aromatic Hydrocarbons in Urban Greenspace Soils across China. AU - Han Y AU - Li Z AU - Chen G AU - Wang C AU - Wang W AU - Li R AU - Zhang L PY - 2026 JO - Environmental science & technology DO - 10.1021/acs.est.6c06741 UR - https://doi.org/10.1021/acs.est.6c06741 ER -
APA
Y, H., Z, L., G, C., C, W., W, W., R, L., & L, Z. (2026). Interpretable Graph Deep Learning Reveals Ecological Risks and Attribution Patterns of Polycyclic Aromatic Hydrocarbons in Urban Greenspace Soils across China.. Environmental science & technology. https://doi.org/10.1021/acs.est.6c06741
Source records
- pubmed · retrieved 2026-09-24T23:49:53.686Z