Source apportionment of heavy metals via PMF-space auto-correlation and source-oriented human health risk assessment:...

Traditional assessment methods are inadequate for accurately tracing the sources of soil heavy metal pollution and implementing targeted risk management. To establish a scientific and efficient system for accurate soil pollution control, this study integrates the Positive Matrix Factorization (PMF) model with spatial autocorrelation models to analyze pollution sources and spatial differentiation patterns of heavy metals in soil. Based on source-oriented approaches, it further conducts human health risk assessments to precisely identify priority pollutants and high-risk exposed populations. In this study, the average concentrations of Cd and Pb reached 0.33 and 140.45 mg·kg^-1, respectively, with an over-limit rate (Exceeding the background value of soil in Sichuan Province) of more than 80%. Source tracing and spatial analysis indicated that agricultural activities were the primary source of As pollution (source contribution > 80%), while industrial activities were the main sources of Cd and Pb pollution (source contribution > 60%). High-pollution patches were concentrated in the northwestern part of the study area, showing clear spatial clustering characteristics. Health risk assessment implied that children are a high-risk exposure group, facing significant carcinogenic (TCR > 1 × 10^-6) and non-carcinogenic health risks (HI > 1). Source-oriented risk assessment further confirmed that As and Cd (As > Cd) released from industrial and agricultural activities were priority pollutants requiring urgent control. The research findings could provide data support for zonal management and targeted remediation of regional soil heavy metal pollution, as well as offer scientific references and technical models for precise prevention and control of similar complexly contaminated sites.