A novel methodological framework for risk zonation and source-sink response concerning heavy-metal contamination in agroecosystems

Lian Chen; Shenglu Zhou; Cuiming Tang; Guoqing Luo; Zhenjiang Wang; Sen Lin; Jianwu Zhong; Zhiyi Li; Yuan Wang

doi:10.1016/j.scitotenv.2023.161610

A novel methodological framework for risk zonation and source-sink response concerning heavy-metal contamination in agroecosystems

Sci Total Environ. 2023 Apr 10:868:161610. doi: 10.1016/j.scitotenv.2023.161610. Epub 2023 Jan 14.

Authors

Lian Chen¹, Shenglu Zhou², Cuiming Tang³, Guoqing Luo⁴, Zhenjiang Wang⁴, Sen Lin¹, Jianwu Zhong¹, Zhiyi Li¹, Yuan Wang¹

Affiliations

¹ Sericultural & Agri-Food Research Institute, Guangdong Academy of Agricultural Sciences, Guangzhou 510610, Guangdong, China.
² School of Geography and Ocean Science, Nanjing University, 163 Xianlin Road, Nanjing 210023, Jiangsu, China. Electronic address: zhousl@nju.edu.cn.
³ Sericultural & Agri-Food Research Institute, Guangdong Academy of Agricultural Sciences, Guangzhou 510610, Guangdong, China; Key Laboratory of Urban Agriculture in South China, Ministry of Agriculture and Rural Affairs, Guangzhou 510610, Guangdong, China. Electronic address: tangcuiming@gdaas.cn.
⁴ Sericultural & Agri-Food Research Institute, Guangdong Academy of Agricultural Sciences, Guangzhou 510610, Guangdong, China; Key Laboratory of Urban Agriculture in South China, Ministry of Agriculture and Rural Affairs, Guangzhou 510610, Guangdong, China.

PMID: 36646223
DOI: 10.1016/j.scitotenv.2023.161610

Abstract

The accurate identification of high-risk zones and risk source-sink responses for heavy-metal (HM) contamination of agroecosystems remains challenging due to involving multiple environmental media such as soils, dustfall, and crops and a wide range of evaluation criteria and constraints. This study established a novel evaluation model based on the integration of Geographical Information Systems (GIS) and multi-criteria decision analysis (MCDA) to assess agroecosystem risk in the Lihe River watershed, China. Bivariate local indicators of spatial association (LISA) were adopted to explore the spatial interaction of risk sources and sinks with outputs ranging from 0.0003 (no risk) to 0.83 (high risk). Areas with moderate, considerable, and high risk constituted 67.4 % of the total land area, and only 1.8 % of the area was classed as low risk. Central urban and eastern areas around Taihu Lake were risk accumulation regions that needed more remedial attention. Risk cluster zones in the central urban area involved significant source-sink response relationships with the spatial distribution of industries, whereas eastern zones were linked to vehicular traffic distribution, accounting for 27.5 % and 16.5 % of the total area, respectively. This study provides a new methodological framework for the assessment of environmental risk, risk zonation, and risk source-sink spatial interaction in agroecosystems.

Keywords: Assessment criteria; GIS–DCMA; LISA; Risk zonation; Source–sink.