使用机器学习和绘制土壤吸附能力的全球分布预测土壤上的重金属吸附,Environmental Science & Technology

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使用机器学习和绘制土壤吸附能力的全球分布预测土壤上的重金属吸附
Environmental Science & Technology ( IF 10.8 ) Pub Date : 2021-10-07 , DOI: 10.1021/acs.est.1c02479
Hongrui Yang ₁ , Kuan Huang ₂ , Kai Zhang ₂ , Qin Weng ₁ , Huichun Zhang ₂ , Feier Wang ₁

Affiliation

研究土壤重金属吸附对于了解重金属的归宿和正确评估相关环境风险具有重要意义。然而，现有的用于量化吸附的实验方法和传统模型既费时又无效。在这项研究中，我们开发了机器学习模型，用于六种重金属（Cd(II)、Cr(VI)、Cu(II)、Pb(II)、Ni(II) 和 Zn(II)）的土壤吸附，使用从 150 篇期刊文章中提取的 4420 个数据点（1105 个土壤）。经过综合比较，我们的结果表明梯度提升决策树对于基于所有数据的组合模型具有最佳性能。Shapley 加性解释方法用于识别特征重要性和这些特征对吸附的影响，在此基础上为六种金属开发了六个独立模型，以实现比组合模型更好的模型性能。使用这些独立模型，可以根据已知的土壤特性预测土壤上重金属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，以预测水中的重金属浓度。

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Predicting Heavy Metal Adsorption on Soil with Machine Learning and Mapping Global Distribution of Soil Adsorption Capacities

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.

更新日期：2021-10-19

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