Source-oriented landscape apportionment of heavy metals by using an integrated approach in typical mining urban agricultural soil

Chuanghong Su; Hongcheng Li; Shaoyang Chen; Yushu Shu; Zheyong Li; Qiang Zhang; Yong Yang; Luojing Xiang

doi:10.1016/j.envpol.2025.126719

Source-oriented landscape apportionment of heavy metals by using an integrated approach in typical mining urban agricultural soil

Environ Pollut. 2025 Jun 21:382:126719. doi: 10.1016/j.envpol.2025.126719. Online ahead of print.

Authors

Chuanghong Su¹, Hongcheng Li², Shaoyang Chen², Yushu Shu², Zheyong Li², Qiang Zhang², Yong Yang³, Luojing Xiang⁴

Affiliations

¹ Hubei Provincial Academy of Eco-Environmental Sciences, Wuhan, 430072, China; College of Resources and Environment, Huazhong Agricultural University, Wuhan, 430070, China; Hubei Key Laboratory of Soil Environment and Pollution Remediation, Wuhan, 430070, China; Key Laboratory of Arable Land Conservation (Middle and Lower Reaches of Yangtze River), Ministry of Agriculture, Wuhan, 430070, China.
² Hubei Provincial Academy of Eco-Environmental Sciences, Wuhan, 430072, China.
³ College of Resources and Environment, Huazhong Agricultural University, Wuhan, 430070, China; Hubei Key Laboratory of Soil Environment and Pollution Remediation, Wuhan, 430070, China; Key Laboratory of Arable Land Conservation (Middle and Lower Reaches of Yangtze River), Ministry of Agriculture, Wuhan, 430070, China. Electronic address: yangyong@mail.hzau.edu.cn.
⁴ Hubei Provincial Academy of Eco-Environmental Sciences, Wuhan, 430072, China. Electronic address: xlj19651717@163.com.

PMID: 40550379
DOI: 10.1016/j.envpol.2025.126719

Abstract

Heavy metals (HMs) contamination in agricultural soils presents potential environmental risks and has therefore attracted widespread public concern. Precise identification of pollution source landscapes is essential to mitigate soil pollution. In this research, the principal component analysis-absolute principal component score-multiple linear regression model, along with correlation analysis, bivariate spatial autocorrelation analysis, and the Geodetector model, was used to identify source landscapes. Pollution radius, diffusion coefficient, positive matrix factorization, and atmospheric input fluxes were used to quantify their contributions in a typical mining city. Results revealed that Cd and Cu were the most serious contaminants among eight HMs. The natural (38 %), agricultural (19 %), and industrial (22 %) sources were identified as the dominant pollution sources. Compared with existing models, the coupled source landscape apportionment model could identify the locations of source landscapes and quantify their contribution. The industrial source landscapes result indicated that Source Landscapes 2, 3, 4, 5, and 6 were the major source landscapes, which accounted for 13.96 %, 27.93 %, 27.35 %, 9.98 %, and 20.78 %, respectively. Cu-related mining and smelting activities were identified as the most important sources of anthropogenic emissions, which were the major contributors to the accumulation of Cu in SLs 2, 3, and 4. Overall, our findings provide precise location and contribution information for key source areas, which enables decision-makers to formulate highly effective regional policies for preventing and controlling soil HM-pollution.

Keywords: Agricultural soils; Heavy metals; Input fluxes; PMF model; Source landscape apportionment.