Machine Learning in Contaminated Soil Remediation: From Monitoring, Source Apportionment, and Risk Assessment to Practical Applications
- DOI
- 10.3390/toxics14100854
- Published
- 2026-09-25
- Container
- Toxics
- Publisher
- MDPI AG
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.3390/toxics14100854,
title = {Machine Learning in Contaminated Soil Remediation: From Monitoring, Source Apportionment, and Risk Assessment to Practical Applications},
author = {Chen Li and Hong Jiang and Qi Deng and Linlin Li and Yankai Huang and Boyue Liu and Cuilian Fu and Jie Li},
year = {2026},
journal = {Toxics},
doi = {10.3390/toxics14100854},
url = {https://doi.org/10.3390/toxics14100854}
}RIS
TY - JOUR TI - Machine Learning in Contaminated Soil Remediation: From Monitoring, Source Apportionment, and Risk Assessment to Practical Applications AU - Chen Li AU - Hong Jiang AU - Qi Deng AU - Linlin Li AU - Yankai Huang AU - Boyue Liu AU - Cuilian Fu AU - Jie Li PY - 2026 JO - Toxics DO - 10.3390/toxics14100854 UR - https://doi.org/10.3390/toxics14100854 ER -
APA
Li, C., Jiang, H., Deng, Q., Li, L., Huang, Y., Liu, B., Fu, C., & Li, J. (2026). Machine Learning in Contaminated Soil Remediation: From Monitoring, Source Apportionment, and Risk Assessment to Practical Applications. Toxics. https://doi.org/10.3390/toxics14100854
Source records
- crossref · retrieved 2026-11-10T10:58:41.693Z