A combined model method was used to identify the main influencing factors of soil heavy metal pollution sources in Qian river, China

The issue of soil heavy metal contamination has garnered significant global attention, with the identification of heavy metal sources and their driving factors being crucial for the prevention and management of soil heavy metal pollution. This study introduces a comprehensive source-driver model integrating the Positive Matrix Factorization (PMF) model, Geographically Weighted Regression (GWR), and Geo-detector Model (GDM). Inductively Coupled Plasma Mass Spectrometry (ICP-MS) was employed to quantify the concentrations of eight heavy metals in the soils of the lower reaches of the Qian River. The findings revealed that: (1) The average concentrations of the 8 heavy metals did not exceed the risk screening thresholds for soil environmental quality. Specifically, the mean concentrations of Ni, Zn, As, and Cd were 42.16, 102.07, 18.23, and 0.32 mg kg^-1, respectively, which are 1.46, 1.47, 3.7, and 6.17 times higher than the background values for soil in Shaanxi Province, with Cd exhibiting a coefficient of variation of 0.58. This high degree of variation is attributed to anthropogenic activities. Spatially, each heavy metal was more heavily concentrated in the southeastern and northwestern regions of the study area. (2) The results of PMF model showed that soil heavy metals in the study area mainly came from nature, industry, agriculture, and traffic, and the contribution of each source was 19.12%, 23.42%, 36.85% and 20.61%. Notably, agricultural sources emerged as the predominant contributors to soil pollution in the region. (3) GDM and GWR results showed that distance from village, soil type and elevation were the main drivers of soil heavy metal pollution sources in the study area. This study provides a reference for the analysis of soil heavy metal sources, and the results can provide a theoretical basis for the prevention and control of soil heavy metal pollution in the study area.