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Open AccessArticle
Machine Learning Models for Predicting Bioavailability of Traditional and Emerging Aromatic Contaminants in Plant Roots
by
Siyuan Li
Siyuan Li ,
Yuting Shen
Yuting Shen ,
Meng Gao
Meng Gao ,
Huatai Song
Huatai Song ,
Zhanpeng Ge
Zhanpeng Ge ,
Qiuyue Zhang
Qiuyue Zhang ,
Jiaping Xu
Jiaping Xu ,
Yu Wang
Yu Wang
Dr. Yu Wang has been an Associate Professor at the College of Environmental Science and Engineering, [...]
Dr. Yu Wang has been an Associate Professor at the College of Environmental Science and Engineering, Nankai University, China, since 2023. He graduated from the China Agricultural University with a Bachelor's (Environmental Engineering) in 2013 and a Master's (Environmental Engineering) in 2015. He received a Ph.D. (Environmental Science) at Nankai University in 2019. He was a lecturer at Nankai University from 2019 to 2023. His research interests include environmental behaviors and the effects of emerging contaminants in air–soil–plant systems.
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Hongwen Sun
Hongwen Sun
Ms. Li is a graduate student at the College of Environmental Science and Engineering, Nankai where a [...]
Ms. Li is a graduate student at the College of Environmental Science and Engineering, Nankai University, where she started her studies in 2023. Her research interests cover a wide range of topics within environmental science, with a focus on the behavior and ecological risk assessment of contaminants.
*
MOE Key Laboratory of Pollution Processes and Environmental Criteria, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China
*
Authors to whom correspondence should be addressed.
Toxics 2024, 12(10), 737; https://doi.org/10.3390/toxics12100737 (registering DOI)
Submission received: 10 September 2024
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Revised: 8 October 2024
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Accepted: 10 October 2024
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Published: 12 October 2024
Abstract
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., phthalate acid esters, aryl organophosphate esters). Four ML algorithms were employed, trained on a unified RCF dataset comprising 878 data points, covering 6 features of soil–plant cultivation systems and 98 molecular descriptors of 55 chemicals, including 29 emerging ACs. The gradient-boosted regression tree (GBRT) model demonstrated strong predictive performance, with a coefficient of determination (R2) of 0.75, a mean absolute error (MAE) of 0.11, and a root mean square error (RMSE) of 0.22, as validated by five-fold cross-validation. Multiple explanatory analyses highlighted the significance of soil organic matter (SOM), plant protein and lipid content, exposure time, and molecular descriptors related to electronegativity distribution pattern (GATS8e) and double-ring structure (fr_bicyclic). An increase in SOM was found to decrease the overall RCF, while other variables showed strong correlations within specific ranges. This GBRT model provides an important tool for assessing the environmental behaviors of ACs in soil–plant systems, thereby supporting further investigations into their ecological and human exposure risks.
Share and Cite
MDPI and ACS Style
Li, S.; Shen, Y.; Gao, M.; Song, H.; Ge, Z.; Zhang, Q.; Xu, J.; Wang, Y.; Sun, H.
Machine Learning Models for Predicting Bioavailability of Traditional and Emerging Aromatic Contaminants in Plant Roots. Toxics 2024, 12, 737.
https://doi.org/10.3390/toxics12100737
AMA Style
Li S, Shen Y, Gao M, Song H, Ge Z, Zhang Q, Xu J, Wang Y, Sun H.
Machine Learning Models for Predicting Bioavailability of Traditional and Emerging Aromatic Contaminants in Plant Roots. Toxics. 2024; 12(10):737.
https://doi.org/10.3390/toxics12100737
Chicago/Turabian Style
Li, Siyuan, Yuting Shen, Meng Gao, Huatai Song, Zhanpeng Ge, Qiuyue Zhang, Jiaping Xu, Yu Wang, and Hongwen Sun.
2024. "Machine Learning Models for Predicting Bioavailability of Traditional and Emerging Aromatic Contaminants in Plant Roots" Toxics 12, no. 10: 737.
https://doi.org/10.3390/toxics12100737
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