Predicting Heavy Metal Adsorption on Soil with Machine Learning and Mapping Global Distribution of Soil Adsorption Capacities
Predicting Heavy Metal Adsorption on Soil with Machine Learning and Mapping Global Distribution of Soil Adsorption Capacities
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DOI：
10.1021/acs.est.1c02479
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发表时间：
2021-10-07
影响因子：
11.4
通讯作者：
Wang, Feier
中科院分区：
文献类型：
--
作者：
Yang, Hongrui;Huang, Kuan;Wang, Feier
研究土壤对重金属的吸附对于了解重金属的去向和正确评估相关的环境风险具有重要意义。然而，现有的实验方法和传统的定量吸附模型都是费时且低效的。在这项研究中，我们利用从150篇期刊论文中提取的1105个土壤数据点，建立了土壤对六种重金属(Cd(II)、Cr(VI)、Cu(II)、Pb(II)、Ni(II)和Zn(II))的机器学习模型。经过综合比较，我们的结果表明，在基于所有数据的组合模型中，梯度提升决策树具有最好的性能。利用Shapley加性解释方法确定特征重要性以及这些特征对吸附的影响，并在此基础上建立了6个独立的模型，以获得比组合模型更好的模型性能。利用这些独立的模型，在已知土壤性质的情况下，预测了土壤对重金属的吸附能力的全球分布。当土壤/沉积物的吸附量已知时，还使用相同的数据集建立了反向模型，包括针对所有六种金属的一个组合模型和六个独立模型，以预测水中重金属的浓度。
Studying heavy metal adsorption on soil is important for understanding the fate of heavy metals and properly assessing the related environmental risks. Existing experimental methods and traditional models for quantifying adsorption, however, are time-consuming and ineffective. In this study, we developed machine learning models for the soil adsorption of six heavy metals (Cd(II), Cr(VI), Cu(II), Pb(II), Ni(II), and Zn(II)) using 4420 data points (1105 soils) extracted from 150 journal articles. After a comprehensive comparison, our results showed that the gradient boosting decision tree had the best performance for a combined model based on all the data. The Shapley additive explanation method was used to identify the feature importance and the effects of these features on the adsorption, based on which six independent models were developed for the six metals to achieve better model performance than the combined model. Using these independent models, the global distribution of heavy metal adsorption capacities on soils was predicted with known soil properties. Reversed models, including one combined model for all the six metals and six independent models, were also built using the same data sets to predict the heavy metal concentration in water when the adsorbed amount is known for a soil/sediment.