Identification of the potential risk areas for soil heavy metal pollution based on the source-sink theory

Xiaolin Jia; Tingting Fu; Bifeng Hu; Zhou Shi; Lianqing Zhou; Youwei Zhu

doi:10.1016/j.jhazmat.2020.122424

Identification of the potential risk areas for soil heavy metal pollution based on the source-sink theory

J Hazard Mater. 2020 Jul 5:393:122424. doi: 10.1016/j.jhazmat.2020.122424. Epub 2020 Feb 28.

Authors

Xiaolin Jia¹, Tingting Fu², Bifeng Hu³, Zhou Shi⁴, Lianqing Zhou⁵, Youwei Zhu⁶

Affiliations

¹ Key Laboratory of Environment Remediation and Ecological Health, Ministry of Education, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, Zhejiang, 31005, China; Institute of Agricultural Remote Sensing & Information Technology Application, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, Zhejiang, 31005, China. Electronic address: 11814013@zju.edu.cn.
² Institute of Agricultural Remote Sensing & Information Technology Application, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, Zhejiang, 31005, China. Electronic address: ftt321@zju.edu.cn.
³ Unité De Recherche En Science Du Sol, INRA, Orléans 45075, France; Sciences De La Terre Et De L'Univers, Orléans University, Orléans 45067, France. Electronic address: bifeng.hu@inra.fr.
⁴ Key Laboratory of Environment Remediation and Ecological Health, Ministry of Education, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, Zhejiang, 31005, China; Institute of Agricultural Remote Sensing & Information Technology Application, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, Zhejiang, 31005, China. Electronic address: shizhou@zju.edu.cn.
⁵ Institute of Agricultural Remote Sensing & Information Technology Application, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, Zhejiang, 31005, China. Electronic address: lianqing@zju.edu.cn.
⁶ Protection and Monitoring Station of Agricultural Environment, Bureau of Agriculture, Hangzhou, Zhejiang, 310020, China. Electronic address: 13018941333@163.com.

PMID: 32143165
DOI: 10.1016/j.jhazmat.2020.122424

Abstract

From the perspective of the mechanism of soil pollution, it is difficult to explain the process of predicting the spatial distributions of soil heavy metal pollution using traditional geostatistical methods at a regional scale. Furthermore, few methods are available to proactively identify potential risk areas for preventing soil contamination. In this study, we selected 13 environmental factors related to the accumulation of soil heavy metals based on the source-sink theory. Then, the fuzzy k-means method in combination with the random forest (RF) method was used to classify potential risk areas. The concentrations and spatial distributions of the heavy metals were well predicted by RF, and the average values of the root mean square error of the prediction and R² were 4.84 mg kg^-1 and 0.57, respectively. The results indicated that the soil pH, fine particulate matter, and proximity to polluting enterprises significantly influenced the heavy metal pollution in soils, and the environmental variables varied significantly across the identified subregions. This study provides a theoretical basis for the sustainable management and control of soil pollution at the regional scale.

Keywords: Cluster analysis; Heavy metal pollution; Machine learning methods; Potential risk areas; Source-Sink theory.

Publication types

Research Support, Non-U.S. Gov't