High-resolution risk mapping of heavy metals in soil with an integrated static-dynamic interaction model: A case study in an industrial agglomeration area in China

Yi Shiyi; Li Xiaonuo; Chen Weiping

doi:10.1016/j.jhazmat.2023.131650

High-resolution risk mapping of heavy metals in soil with an integrated static-dynamic interaction model: A case study in an industrial agglomeration area in China

J Hazard Mater. 2023 Aug 5:455:131650. doi: 10.1016/j.jhazmat.2023.131650. Epub 2023 May 16.

Authors

Yi Shiyi¹, Li Xiaonuo², Chen Weiping¹

Affiliations

¹ Laboratory of Soil Environmental Science and Technology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China; College of Resource and Environment, University of Chinese Academy of Sciences, Beijing 100049, China.
² Laboratory of Soil Environmental Science and Technology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China. Electronic address: xnli@rcees.ac.cn.

PMID: 37229828
DOI: 10.1016/j.jhazmat.2023.131650

Abstract

Heavy metal pollution of soils in industrial agglomeration areas is an increasing concern worldwide. In this study, we traced the sources of heavy metal emissions using a positive matrix factorization (PMF) model. Accordingly, we proposed a novel static-dynamic risk interaction model incorporating multiple risk-related factors to quantify the spatial interaction of emission sources and the probability of accumulation of heavy metals on a large scale. This model was further classified using the Jenks optimization technique to predict the spatial distribution of high-risk hotspots. Our results determined four primary emission sources of heavy metals: industrial (35.01 %), natural (28.61 %), agricultural (26.07 %), and traffic (10.31 %) sources. Five levels were classified by the integrated risk coefficient (IRC), namely, from extremely high to extremely low risk. The extremely high- and high-risk hotspots constituting 41.52 % of the total area of the Zhenhai District, with IRC values ranging from 0.221 to 0.413, were mainly generated by multiple sources linked to PMF-based factors. This quantitative evaluation framework can generate a high-resolution spatially distributed pollution risk map at the grid scale (1 km), which can provide a relatively precise basis for policymaking for point-to-point soil pollution management.

Keywords: Heavy metals pollution; Mass balance theory; Risk prediction; Soil management; Source apportionment.