Li Jun, Li Xu, Tai Xi-Sheng, Tuo Xin-Ying, Zhou Fa-Yuan, Rong Yi-Jing, Zang Fei
School of Environment and Urban Construction, Lanzhou City University, Lanzhou, 730070, China.
College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou, 730020, China.
Sci Rep. 2025 May 20;15(1):17451. doi: 10.1038/s41598-025-02307-1.
The accumulation of heavy metal(loid)s (HMs) in the soils of urban parks in industrial cities has raised global concerns because of their environmental and health impacts. However, traditional deterministic assessments commonly overlook uncertainties in pollution evaluation, failing to accurately quantify source-specific contributions and associated risks. This study combines multivariate statistical methods, machine learning techniques, and positive matrix factorization (PMF) with Monte Carlo simulation to investigate HM sources, probabilistic pollution levels, source-based ecological risks, and population-specific health hazards in seven urban parks in a nickel-based mining city in China. Results showed that average concentrations of Cd (0.53 mg/kg), Cr (77.72 mg/kg), Cu (171.15 mg/kg), Hg (0.03 mg/kg), Ni (125.42 mg/kg), Pb (27.13 mg/kg), and Zn (81.97 mg/kg) exceeded their background values, except for As (11.85 mg/kg), particularly for Cd, Cu, and Ni, with exceedance rates of 98.46%, 100.00%, and 100.00%, respectively. Probabilistic assessments revealed that pollution levels were particularly high due to Cd, Cu, and Ni. Source apportionment using PMF, correlation analysis, hierarchical cluster analysis, and super-clustering of self-organizing maps identified fertilizers and pesticides (19.33%), industrial atmospheric deposition (21.13%), mining and agrochemicals (16.41%), and mining and transport activities (43.13%) as the major pollution sources. Probabilistic ecological risk assessments showed significant risks from Cd, Hg, and Cu. Non-carcinogenic risks were negligible, while carcinogenic risks were cautionary, especially for children. Mining and transportation activities were the main contributors to ecological risks, while fertilizers, pesticides, and Ni were the primary health risk factors. This study provides a robust framework to improve the accuracy of risk evaluation and offers valuable guidance for targeted interventions and sustainable management of urban soils.
工业城市中城市公园土壤中重金属（类金属）（HMs）的积累因其对环境和健康的影响而引起了全球关注。然而，传统的确定性评估通常忽略了污染评估中的不确定性，无法准确量化特定来源的贡献和相关风险。本研究将多元统计方法、机器学习技术和正定矩阵因子分解（PMF）与蒙特卡洛模拟相结合，以调查中国一个镍矿城市的七个城市公园中的重金属来源、概率污染水平、基于来源的生态风险以及特定人群的健康危害。结果表明，除砷（11.85毫克/千克）外，镉（0.53毫克/千克）、铬（77.72毫克/千克）、铜（171.15毫克/千克）、汞（0.03毫克/千克）、镍（125.42毫克/千克）、铅（27.13毫克/千克）和锌（81.97毫克/千克）的平均浓度均超过了背景值，尤其是镉、铜和镍，超标率分别为98.46%、100.00%和100.00%。概率评估表明，由于镉、铜和镍，污染水平特别高。使用PMF、相关分析、层次聚类分析和自组织映射的超聚类进行源解析，确定肥料和农药（19.33%）、工业大气沉降（21.13%）、采矿和农用化学品（16.41%）以及采矿和运输活动（43.13%）为主要污染源。概率生态风险评估表明，镉、汞和铜存在重大风险。非致癌风险可忽略不计，而致癌风险则需引起警惕，尤其是对儿童而言。采矿和运输活动是生态风险的主要贡献者，而肥料、农药和镍是主要的健康风险因素。本研究提供了一个强大的框架，以提高风险评估的准确性，并为城市土壤的针对性干预和可持续管理提供有价值的指导。
