Spatial interaction and risk zoning of compound pollutants in farmland soils: Insights from heavy metals and polycyclic aromatic hydrocarbons in Hezhang County, China

Abstract

The accurate identification and assessment of comprehensive risks associated with compound pollution in agricultural ecosystems remain significant challenges due to the complexity of pollution sources, soil heterogeneity, and spatial variability. In this study, bivariate local indicators of spatial association (LISA) were applied to analyze the spatial interaction between heavy metals (HMs) and polycyclic aromatic hydrocarbons (PAHs) in farmland soils in Hezhang County. The results revealed distinct clusters with elevated concentrations of both HMs and PAHs, predominantly in areas affected by long-standing lead-zinc mining and smelting activities. Positive matrix factorization (PMF) was utilized to identify mining and smelting activities, and associated coal consumption as common sources of both pollutants, contributing 53 % and 28 %, respectively. Ecological health risk assessment results indicated that the combined pollution in this area has led to particularly severe ecological and cancer risks, with the pollution coefficient (Pc) exceeding 3.0, and risk values for both adults and children surpassing the threshold of 10−4. Through the integration of advanced bivariate LISA mapping and thorough risk assessment, this study precisely delineated ecological risk zones (33.1 %) and more refined health risk zones (40.1 %) associated with combined pollution. The southwest of Hezhang was identified as a critical hotspot for combined pollution risks, primarily due to intensive mining and smelting activities in the region. Overall, this study underscores the utility of bivariate LISA as a robust approach for delineating spatial clustering patterns caused by combined pollutants. It provides crucial insights for identifying regions with heightened human health and ecological risks in rural settings.

Keywords

• Farmland soils
• Heavy metals
• PAHs
• Source apportionment
• LISA
• Risk zonation