Wu Jin, Ge Yinxin, Li Jiao, Lai Xiaoying, Chen Ruihui
College of Architecture and Civil Engineering, Beijing University of Technology, Beijing, 100124, China.
College of Architecture and Civil Engineering, Beijing University of Technology, Beijing, 100124, China.
Environ Res. 2023 Feb 15;219:115027. doi: 10.1016/j.envres.2022.115027. Epub 2022 Dec 9.
Identifying the contamination characteristics of trace metals in river and targeting their corresponding potential contamination sources and source-specific ecological risk are of very importance for putting forward effective river environment protection strategies. Here, a detailed investigation was conducted to recognize the contamination and ecological risk characteristics of trace metals in Le'an River. To attain this objective, a PMF-SSD model (Positive Matrix Factorization-Species Sensitivity Distribution) was proposed to evaluate the ecological risk of trace metals in Le'an River. The positive matrix factorization (PMF) was employed to identify the potential source of trace metals in surface water and their corresponding contributions. The ecological risks of the sources were quantitatively calculated by PMF-SSD. In addition, the spatial dissimilarity analysis of the source contribution distributions was also conducted in this study. Results showed that the water environment in Jiangxi were considerably contaminated by trace metals (Cd, Cr, Co, Al, Mn, Cu, Zn and Ni). The concentrations of these trace metals in surface water demonstrated significant spatial variations and the ecological risk lay in high level. Mining activities were identified as the main anthropogenic sources, which should to be strictly regulated.
识别河流中痕量金属的污染特征并确定其相应的潜在污染源以及特定污染源的生态风险,对于提出有效的河流环境保护策略至关重要。在此,对乐安河痕量金属的污染及生态风险特征进行了详细调查。为实现这一目标,提出了一种PMF-SSD模型(正定矩阵因子分解-物种敏感度分布)来评估乐安河痕量金属的生态风险。采用正定矩阵因子分解(PMF)来识别地表水中痕量金属的潜在来源及其相应贡献。通过PMF-SSD定量计算了这些来源的生态风险。此外,本研究还对源贡献分布进行了空间差异分析。结果表明,江西的水环境受到痕量金属(镉、铬、钴、铝、锰、铜、锌和镍)的严重污染。这些痕量金属在地表水中的浓度呈现出显著的空间变化,且生态风险处于较高水平。采矿活动被确定为主要的人为污染源,应予以严格监管。