用于预测传统和新兴芳香族污染物在植物根茎中生物利用率的机器学习模型。

IF 3.9 3区 环境科学与生态学 Q2 ENVIRONMENTAL SCIENCES
Toxics Pub Date : 2024-10-12 DOI:10.3390/toxics12100737
Siyuan Li, Yuting Shen, Meng Gao, Huatai Song, Zhanpeng Ge, Qiuyue Zhang, Jiaping Xu, Yu Wang, Hongwen Sun
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引用次数: 0

摘要

为了预测芳香族污染物(ACs)在复杂土壤-植物系统中的行为,本研究开发了机器学习(ML)模型来估算传统芳香族污染物(如多环芳烃、多氯联苯)和新兴芳香族污染物(如邻苯二甲酸酯、芳基有机磷酸酯)的根浓度因子(RCF)。采用了四种 ML 算法,在统一的 RCF 数据集上进行了训练,该数据集包含 878 个数据点,涵盖土壤-植物栽培系统的 6 个特征和 55 种化学品(包括 29 种新出现的 ACs)的 98 个分子描述符。经五倍交叉验证,梯度增强回归树(GBRT)模型具有很强的预测性能,其判定系数(R2)为 0.75,平均绝对误差(MAE)为 0.11,均方根误差(RMSE)为 0.22。多重解释分析强调了土壤有机质(SOM)、植物蛋白和脂质含量、暴露时间以及与电负性分布模式(GATS8e)和双环结构(fr_bicyclic)相关的分子描述符的重要性。研究发现,SOM 的增加会降低总体 RCF,而其他变量在特定范围内显示出很强的相关性。该 GBRT 模型为评估土壤-植物系统中的 AC 环境行为提供了一个重要工具,从而支持了对其生态和人类暴露风险的进一步研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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., 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.

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来源期刊
Toxics
Toxics Chemical Engineering-Chemical Health and Safety
CiteScore
4.50
自引率
10.90%
发文量
681
审稿时长
6 weeks
期刊介绍: The Journal accepts papers describing work that furthers our understanding of the exposure, effects, and risks of chemicals and materials in humans and the natural environment as well as approaches to assess and/or manage the toxicological and ecotoxicological risks of chemicals and materials. The journal covers a wide range of toxic substances, including metals, pesticides, pharmaceuticals, biocides, nanomaterials, and polymers such as micro- and mesoplastics. Toxics accepts papers covering: The occurrence, transport, and fate of chemicals and materials in different systems (e.g., food, air, water, soil); Exposure of humans and the environment to toxic chemicals and materials as well as modelling and experimental approaches for characterizing the exposure in, e.g., water, air, soil, food, and consumer products; Uptake, metabolism, and effects of chemicals and materials in a wide range of systems including in-vitro toxicological assays, aquatic and terrestrial organisms and ecosystems, model mammalian systems, and humans; Approaches to assess the risks of chemicals and materials to humans and the environment; Methodologies to eliminate or reduce the exposure of humans and the environment to toxic chemicals and materials.
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