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Quantifying the impact of factors on soil available arsenic using machine learning

文献类型：期刊论文

作者Han, Zhaoyang3,4; Yang, Jun3,4; Yan, Yunxian3,4; Zhao, Chen1; Wan, Xiaoming3,4; Ma, Chuang2; Shi, Huading1
刊名ENVIRONMENTAL POLLUTION
出版日期2024-10-15
卷号359页码:124572
关键词Arsenic availability Farmland soil Soil property Machine learning Quantitative assessment
DOI10.1016/j.envpol.2024.124572
产权排序1
文献子类Article
英文摘要Arsenic (As) can accumulate in edible plant parts and thus pose a serious threat to human health. Identifying the contributions of various factors to soil available As is crucial for evaluating environmental risks. However, research quantitatively assessing the importance of soil properties on available As is scarce. In this study, we utilized 442 datasets covering total As, available As, and properties of farmland soils. The five machine learning models were employed to predict soil available As content, and the model with the best predictive performance was selected to calculate the importance of soil properties on available As and interpret the model results. The Random Forest model exhibited the best predictive performance, with R-2 for the test set of dryland and paddy fields being 0.83 and 0.82 respectively, while also outperforming other machine learning models in terms of accuracy. Concurrently, evaluating the contribution of soil properties to soil available As revealed that increases in soil total arsenic, pH, organic matter (OM), and cation exchange capacity (CEC) led to higher soil available As content. Among these factors, soil total As had the greatest impact, followed by CEC. The influence of pH on soil available As was greater in dryland compared to OM, while in paddy fields, it was smaller than OM (p < 0.01). Sensitivity analysis results indicated that reducing soil total As content had the greatest effect on available As. In both dryland and paddy field soils, reducing soil total As had the most pronounced effect on available As, leading to reductions of 10.09% and 8.48%, respectively. Therefore, prioritizing the regulation of soil total As and CEC is crucial in As contamination management practices to alter As availability in farmland soils.
WOS关键词MICROBIAL COMMUNITY ; HEAVY-METALS ; PADDY SOILS ; RICE ; SPECIATION ; REDUCTION ; EH ; PH ; BIOACCESSIBILITY ; REMEDIATION
WOS研究方向Environmental Sciences & Ecology
WOS记录号WOS:001280705600001
出版者ELSEVIER SCI LTD
源URL[http://ir.igsnrr.ac.cn/handle/311030/206874]  
专题资源利用与环境修复重点实验室_外文论文
通讯作者Yan, Yunxian; Shi, Huading
作者单位1.Minist Ecol & Environm, Tech Ctr Soil Agr & Rural Ecol & Environm, Beijing 100012, Peoples R China
2.Zhengzhou Univ Light Ind, Henan Collaborat Innovat Ctr Environm Pollut Contr, Zhengzhou 45000, Peoples R China
3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Ctr Environm Remediat, Beijing 100101, Peoples R China
4.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China
推荐引用方式
GB/T 7714		 Han, Zhaoyang,Yang, Jun,Yan, Yunxian,et al. Quantifying the impact of factors on soil available arsenic using machine learning[J]. ENVIRONMENTAL POLLUTION,2024,359:124572.
APA Han, Zhaoyang.,Yang, Jun.,Yan, Yunxian.,Zhao, Chen.,Wan, Xiaoming.,...&Shi, Huading.(2024).Quantifying the impact of factors on soil available arsenic using machine learning.ENVIRONMENTAL POLLUTION,359,124572.
MLA Han, Zhaoyang,et al."Quantifying the impact of factors on soil available arsenic using machine learning".ENVIRONMENTAL POLLUTION 359(2024):124572.

入库方式： OAI收割

来源：地理科学与资源研究所

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