Analysis of heavy metal contamination in topsoils across land use types within the Manghe River watershed in South Taihang and its source attribution

To investigate the characteristics of soil heavy metal pollution in the Manghe River watershed, a typical industrial and mining complex area in the Yellow River Basin, concentrations of Hg, Cr, Cu, Ni, Pb, Zn, Cd, and pH were measured in 121 topsoil samples (0–20 cm) collected from the study area. Geostatistical methods were employed to analyze the spatial distribution patterns of heavy metals. The pollution status was assessed using the pollution load index (PLI), while correlation analysis, principal component analysis (PCA), and a positive matrix factorization (PMF) model were applied to identify the sources of heavy metals. The results indicated that: (1) The concentrations of Hg, As, Ni, Cu, Pb, Zn, and Cd exceeded their respective background values, with Hg, Pb and Cd reaching 3.52, 4.85, and 46.4 times of the background levels, respectively.(2) Different elements exhibited distinct spatial distribution and diffusion patterns, revealing their respective sources and influencing factors. (3) The overall PLI was 0.785, reflecting a mild pollution level across the region, while industrial and mining lands exhibited severe pollution (PLI = 4.3). The relative contribution of each heavy metal to the pollution load was ranked as follows: Cd (30.35)> Pb (4.76)> Hg (3.62)> Zn (2.18)> As (1.77)> Cu (1.53). (4) Principal component analysis categorized the sources of heavy metals into anthropogenic activities and natural origins. Further analysis using the PMF model delineated four specific sources: coal combustion (10.87%), natural and agricultural contributions (27.37%), transportation and agricultural actives (26.81%), and industrial emissions (34.95%). Finally, the study identified the following feasible strategies for controlling heavy metal pollution: blocking and remediating industrial pollution sources; treating agricultural non-point source pollution through biological methods; and substituting traditional transportation sources with new energy alternatives. This research could support decision-making processes related to the prevention and control of heavy metal pollution in the study area, as well as regional sustainable development.