Li Jun, Lu Jia-Yi, Tuo Xin-Ying, Wang Chao, Liu Jun-Zhuo, Gao Zhan-Dong, Yu Cun-Hao, Zang Fei
School of Environment and Urban Construction, Lanzhou City University, Lanzhou 730070, China.
School of Environment and Urban Construction, Lanzhou City University, Lanzhou 730070, China.
Ecotoxicol Environ Saf. 2025 Sep 1;302:118714. doi: 10.1016/j.ecoenv.2025.118714. Epub 2025 Jul 21.
Global urbanization has significantly contributed to soil contamination by heavy metals (HMs), posing serious ecological risks, particularly within urban green spaces (UGS). This study focused on UGS soils in Lanzhou, a major river-valley city in China. Multiple pollution indices, including geo-accumulation index (I), enrichment factor (EF), and Nemerow integrated enrichment factor (NIEF), were combined with Monte Carlo simulations (MCS) to assess probabilistic contamination levels. Machine learning methods, including SOM super-clustering and random forest (RF), were integrated with positive matrix factorization (PMF) to quantify the sources of soil HMs. Ecological risk index (RI) was combined with MCS analysis and PMF model to apportion the source-oriented probabilistic ecological risks. Results showed that the average concentrations of Cd (0.38 mg kg), Cu (35.51 mg kg), Hg (0.07 mg kg), Pb (34.59 mg kg), and Zn (130.58 mg kg) exceeded local soil background values, except for As (8.56 mg kg), Cr (62.77 mg kg), and Ni (27.68 mg kg). Notably, exceedance rates for Cd, Hg, Pb, and Zn were 90.91 %, 94.95 %, 80.81 %, and 87.88 %, respectively. Elevated concentrations, particularly of Zn, Cd, Pb, and Hg, displayed distinct spatial patterns linked to industrial activities and urban development. Overall contamination reached moderate levels, primarily driven by Cd and Hg. Source apportionment identified traffic emissions, industrial activities, and coal combustion as the principal HM sources. Probabilistic ecological risk assessment confirmed that Cd and Hg pose the greatest ecological risks, primarily stemming from industrial activities and coal combustion. These findings provide important insights for developing source-specific remediation to mitigate and manage HM pollution in urban green spaces.
全球城市化极大地加剧了重金属对土壤的污染，带来了严重的生态风险，尤其是在城市绿地（UGS）中。本研究聚焦于中国主要河谷城市兰州的城市绿地土壤。将包括地累积指数（I）、富集因子（EF）和内梅罗综合富集因子（NIEF）在内的多种污染指数与蒙特卡罗模拟（MCS）相结合，以评估概率污染水平。将包括自组织映射超聚类和随机森林（RF）在内的机器学习方法与正定矩阵因子分解（PMF）相结合，以量化土壤重金属的来源。将生态风险指数（RI）与MCS分析和PMF模型相结合，以分配以源为导向的概率生态风险。结果表明，除砷（8.56毫克/千克）、铬（62.77毫克/千克）和镍（27.68毫克/千克）外，镉（0.38毫克/千克）、铜（35.51毫克/千克）、汞（0.07毫克/千克）、铅（34.59毫克/千克）和锌（130.58毫克/千克）的平均浓度均超过当地土壤背景值。值得注意的是，镉、汞、铅和锌的超标率分别为90.91%、94.95%、80.81%和87.88%。浓度升高，尤其是锌、镉、铅和汞的浓度升高，呈现出与工业活动和城市发展相关的明显空间格局。总体污染达到中等水平，主要由镉和汞驱动。源解析确定交通排放、工业活动和煤炭燃烧是主要的重金属来源。概率生态风险评估证实，镉和汞带来的生态风险最大，主要源于工业活动和煤炭燃烧。这些发现为制定针对性的源修复措施以减轻和管理城市绿地中的重金属污染提供了重要见解。
