Chen Hui, Qiao Shuo, Li Chang, Wu Yong, Li Donghao, Li Ling, Liu Jianwei
College of Resources and Environmental Sciences, Henan Agricultural University, Zhengzhou, 450046, China.
Environ Geochem Health. 2024 Jan 27;46(2):59. doi: 10.1007/s10653-023-01849-5.
Heavy metal(loid) (HM) contamination in agricultural soils, particularly in areas severely impacted by smelting industries, has attracted worldwide attention. In this study, agricultural soils were collected in a flourishing multimetal smelting area near the Yellow River in central China. By an integrated approach encompassing the positive matrix factorization model, ordinary kriging interpolation and hierarchical clustering analysis (PMF-OK-HC), a total of four major sources and their mass contributions were identified, namely, soil parent material (56.6%), industrial waste and Mo smelting (24.0%), metal smelting and traffic emissions (12.8%), and coal combustion (6.7%). On this basis, the health risk of HMs was evaluated by Monte Carlo simulations and showed that a higher risk, with a higher proportion of exceeding-thresholds risk, was observed for children than for adults in terms of both noncarcinogenic and carcinogenic risks. Exposure pathways of oral ingestion in children could result in a higher attributed risk than other pathways. Furthermore, source-oriented risk assessment (SORA) revealed that the sources of coal combustion, industrial waste and Mo smelting had the highest contributions to noncarcinogenic and carcinogenic risks. Overall, for effective environmental management in agricultural soil, the framework of SORA was verified as an effective tool in the identification of the priority control of HMs and their sources.
农业土壤中的重金属(类金属)(HM)污染,尤其是在受冶炼行业严重影响的地区,已引起全球关注。在本研究中,在中国中部黄河附近一个繁荣的多金属冶炼区采集了农业土壤。通过综合运用正定矩阵因子分解模型、普通克里金插值法和层次聚类分析(PMF-OK-HC),共识别出四个主要来源及其质量贡献,即土壤母质(56.6%)、工业废物和钼冶炼(24.0%)、金属冶炼和交通排放(12.8%)以及煤炭燃烧(6.7%)。在此基础上,通过蒙特卡洛模拟评估了重金属的健康风险,结果表明,在非致癌和致癌风险方面,儿童面临的风险更高,超过阈值风险的比例也更高。儿童经口摄入的暴露途径导致的归因风险高于其他途径。此外,源导向风险评估(SORA)表明,煤炭燃烧、工业废物和钼冶炼源对非致癌和致癌风险的贡献最大。总体而言,对于农业土壤的有效环境管理,SORA框架被验证为识别重金属及其来源优先控制的有效工具。