Machine Learning Models for Predicting Bioavailability of Traditional and Emerging Aromatic Contaminants in Plant Roots

Machine Learning Models for Predicting Bioavailability of Traditional and Emerging Aromatic Contaminants in Plant Roots

To predict the behavior of aromatic contaminants (ACs) in complex soil–plant systems, this study developed machine learning (ML) models to estimate the root concentration factor (RCF) of both traditional (e.g., polycyclic aromatic hydrocarbons, polychlorinated biphenyls) and emerging ACs (e.g., phth...

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Bibliographic Details
Main Authors: Siyuan Li, Yuting Shen, Meng Gao, Huatai Song, Zhanpeng Ge, Qiuyue Zhang, Jiaping Xu, Yu Wang, Hongwen Sun
Format: Article
Language:English
Published: MDPI AG 2024-10-01
Series:Toxics
Subjects:
aromatic contaminants
root uptake
root concentration factor
RCF
GBRT
molecular descriptors
Online Access:https://www.mdpi.com/2305-6304/12/10/737

Internet

https://www.mdpi.com/2305-6304/12/10/737

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