Pan Yongxing, Li Xueling, Chen Meng, Wang Xiaotong, Leng Yangyang
College of Environmental Science and Engineering, Guilin University of Technology, Guilin 541006, China.
Department of Transport and Municipal Engineering, Sichuan College of Architectural Technology, Chengdu 610399, China.
J Contam Hydrol. 2025 Sep;274:104673. doi: 10.1016/j.jconhyd.2025.104673. Epub 2025 Jul 10.
Heavy metal (HM) contamination in reservoir-adjacent soils is influenced by complex environmental factors and poses ecological and health risks. This study analyzed eight HMs (As, Cd, Cr, Cu, Hg, Ni, Pb, Zn) in 181 soil samples from Zhijin County, China, with mean concentrations exceeding regional background values by 1.06 to 7.00 times (except As, which was 91.4 % of the Guizhou background value). This study aimed to identify HM sources and associated risks by integrating GeoDetector (GD) and Positive Matrix Factorization (PMF) models in Zhijin County, China. The GD analysis revealed that river proximity (q = 0.544) had the strongest explanatory power for Cd spatial variation, while soil organic matter (q = 0.281) had the greatest influence on Pb distribution. The PMF identified five sources with quantified contributions and their dominant associated metals: a mining-agricultural mix (28.16 %; mainly Cd, Cu, Ni, and Zn), atmospheric deposition (20.39 %; mainly Hg), soil parent material (19.86 %; mainly Cr and Ni), agricultural activities (16.98 %; mainly As), and a transportation-mining mix (14.61 %; mainly Pb). Risk prioritization showed that mining and agricultural sources contributed 38.7 % of the ecological risk, while agricultural activities accounted for 41.2 % of children's non-carcinogenic risk, with ingestion contributing 89.7 % of non-carcinogenic exposure. The integrated GD-PMF framework improved the source resolution accuracy by 22-35 % compared to conventional methods. These results offer a novel source-oriented framework for prioritizing the control of Cd and Hg, which exhibited the highest ecological and health risks. Meanwhile, the framework also provides scientific support for the differentiated management of other heavy metals such as Pb and As, based on their source characteristics and spatial patterns.
水库周边土壤中的重金属（HM）污染受复杂环境因素影响，会带来生态和健康风险。本研究分析了中国织金县181份土壤样品中的8种重金属（砷、镉、铬、铜、汞、镍、铅、锌），平均浓度超过区域背景值1.06至7.00倍（砷除外，为贵州背景值的91.4%）。本研究旨在通过整合地理探测器（GD）和正定矩阵因子分解（PMF）模型，识别中国织金县的重金属来源及相关风险。地理探测器分析表明，靠近河流（q = 0.544）对镉的空间变异具有最强的解释力，而土壤有机质（q = 0.281）对铅的分布影响最大。正定矩阵因子分解识别出五个有量化贡献的来源及其主要相关金属：采矿 - 农业混合源（28.16%；主要为镉、铜、镍和锌）、大气沉降源（20.39%；主要为汞）、土壤母质源（19.86%；主要为铬和镍）、农业活动源（16.98%；主要为砷）以及交通 - 采矿混合源（14.61%；主要为铅）。风险优先级显示，采矿和农业源占生态风险的38.7%，而农业活动占儿童非致癌风险的41.2%，其中摄入占非致癌暴露的89.7%。与传统方法相比，集成的地理探测器 - 正定矩阵因子分解框架将源解析精度提高了22 - 35%。这些结果提供了一个以源为导向的新框架，用于优先控制镉和汞，它们呈现出最高的生态和健康风险。同时，该框架还基于铅和砷等其他重金属的来源特征和空间模式，为其差异化管理提供了科学支持。
