Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; State Key Laboratory of Environmental Geochemistry, Institute of Geochemistry, Chinese Academy of Sciences, Guiyang 550081, China.
School of Geographic and Environmental Sciences, Tianjin Normal University, Tianjin 300387, China.
Sci Total Environ. 2023 Jul 15;882:163555. doi: 10.1016/j.scitotenv.2023.163555. Epub 2023 Apr 18.
The study combined multiple models to provide a deeper understanding to soil heavy metal contamination and source information, which are essential for controlling pollution and reducing human health risks. In this study, the agricultural soils were collected from the Qingyuan City of China as an example. The multiple models (APCS/MLR, PMF, and GDM) were used to identify and quantitatively apportion the main sources of heavy metal pollution in the area. The results showed that Cu (56.4 %), Ni (70.9 %), B (44.5 %), and Cr (72.8 %) were associated with natural sources, such as soil parent material and soil-forming processes. However, Pb (41.2 %), Zn (61.8 %), Hg (67.0 %), and Cd (69.6 %) were associated with agricultural activities, atmospheric deposition, vehicle exhaust emissions, and vehicle tires, while Mo, Se, and Mn were possibly derived from natural sources, including rock weathering and soil parent materials. Additionally, the network of environmental analysis revealed that soil microbes are far more sensitive to soil heavy metal pollution than herbivores, vegetation, and carnivores. This study can serve as a guideline for reducing the ecological and health risks associated with heavy metals in soil by controlling their preferential sources. Environmental implication Combining multiple models is more effective approach to wide understanding of heavy metal contamination and source information, which is essential for controlling pollution and reducing human health risks. Based on multiple models (APCS/MLR, PMF, and GDM) and network environ analysis, a comprehensive method for apportioning soil heavy metal sources and assessing ecological risk had been provided. Further, the present study can be a guideline for reducing ecological and health risks by heavy metals in soil by controlling preferential sources.
该研究结合多种模型,深入了解土壤重金属污染和来源信息,这对于控制污染和降低人类健康风险至关重要。本研究以中国清远市的农业土壤为例,利用多种模型(APCS/MLR、PMF 和 GDM)识别和定量分配该地区重金属污染的主要来源。结果表明,Cu(56.4%)、Ni(70.9%)、B(44.5%)和 Cr(72.8%)与自然来源有关,如土壤母质和土壤形成过程。然而,Pb(41.2%)、Zn(61.8%)、Hg(67.0%)和 Cd(69.6%)与农业活动、大气沉降、车辆排放和车辆轮胎有关,而 Mo、Se 和 Mn 可能来自自然来源,包括岩石风化和土壤母质。此外,环境分析网络表明,土壤微生物对土壤重金属污染的敏感性远高于草食动物、植被和肉食动物。本研究可为通过控制优先污染源来降低土壤重金属的生态和健康风险提供指导。