Xia Fang, Zhao Zefang, Niu Xiang, Wang Zhenfeng
School of Life and Environmental Science, Shaoxing University, Shaoxing 312000, China; Zhejiang Provincial Key Laboratory of Watershed Sciences and Health, School of Public Health and Management, Wenzhou Medical University, Wenzhou 325035, China.
School of Life and Environmental Science, Shaoxing University, Shaoxing 312000, China.
J Hazard Mater. 2024 Mar 5;465:133215. doi: 10.1016/j.jhazmat.2023.133215. Epub 2023 Dec 9.
Given the global prevalence of soil heavy metal contamination, knowledge concerning of soil environmental quality assessment, pollution area identification and source apportionment is critical for implementation of soil pollution prevention and safe utilization strategies. In this study, soil static environmental capacity (QI) for heavy metals was selected to evaluate pollution risks in agricultural soils of Wenzhou, southeast China. Combined with geostatistical methods, the pollution area was identified along with uncertainty analysis. Potential sources were quantitatively apportioned using a positive matrix factorization model (PMF). Results showed that agricultural soils in this study were mainly contaminated by Cd and Pb based on both Nemerow and QI indices. The environmental capacity assessment found more than 90% areas were identified as polluted soils for Qi-Zn, Qi-Cd and Qi-Pb, with minor uncertain areas. Cu was identified as having a high proportion of uncertain pollution area status, which was similar to the results of the integrated environmental capacity for all metals. PMF results indicated that industrial discharge, agrochemicals and parent material accounted for 32.1%, 32.2% and 35.7% of heavy metal accumulation in soils, respectively. Implementation of strict policies to reduce anthropogenic source emissions and remediate soil pollution are crucial to minimize metal pollution inputs, improve agricultural soil quality and enhance food safety.
鉴于全球土壤重金属污染的普遍存在，有关土壤环境质量评估、污染区域识别和源解析的知识对于实施土壤污染预防和安全利用策略至关重要。本研究选取土壤重金属静态环境容量（QI）来评估中国东南部温州市农业土壤的污染风险。结合地统计方法，识别污染区域并进行不确定性分析。使用正定矩阵因子分解模型（PMF）对潜在来源进行定量解析。结果表明，基于内梅罗指数和QI指数，本研究中的农业土壤主要受镉和铅污染。环境容量评估发现，超过90%的区域被确定为锌、镉和铅的污染土壤，不确定区域较小。铜被确定为具有高比例的不确定污染区域状态，这与所有金属综合环境容量的结果相似。PMF结果表明，工业排放、农用化学品和母质分别占土壤中重金属积累的32.1%、32.2%和35.7%。实施严格政策以减少人为源排放和修复土壤污染对于减少金属污染输入、改善农业土壤质量和提高食品安全至关重要。
