Quantitative Source Identification of Heavy Metals in Soil via Integrated Data Mining and GIS Techniques

Soil heavy metal contamination poses significant risks to ecological safety and human health, particularly in rapidly industrializing cities. Effectively identifying pollution sources is crucial for risk management and remediation. GIS coupled with data mining techniques, provide a powerful tool for quantifying and visualizing these sources. This study investigates the concentration, spatial distribution, and sources of heavy metals in urban soils of Bengbu City, an industrial and transportation hub in eastern China. A total of 139 surface soil samples from the urban core were analyzed for nine heavy metals. Using integrated GIS and PCA-APCS-MLR data mining techniques, we systematically determined their contamination characteristics and apportioned sources. The results identified widespread Hg enrichment, with concentrations exceeding background levels at all sampling sites, and a Cd exceedance rate of 28.06%, leading to a moderate ecological risk level overall. Spatial patterns revealed significant heterogeneity. Quantitative source apportionment identified four primary sources: industrial source (37.1%), which was the dominant origin of Cr, Cu, and Ni, primarily associated with precision manufacturing and metallurgical activities; mixed source (26.7%) governing the distribution of Mn, As, and Hg, mainly from coal combustion and the natural geological background; traffic source (22.3%) significantly contributing to Pb and Zn; and a specific cadmium source (13.9%) potentially originating from non-ferrous metal smelting, electroplating, and agricultural activities. These findings provide a critical scientific basis for targeted pollution control and sustainable land-use management in analogous industrial cities.