徐祖奔,伍艳,赵越,等. 黄河下游典型滩区土壤重金属污染特征及来源解析[J]. 农业工程学报,2023,39(15):200-207. DOI: 10.11975/j.issn.1002-6819.202302055
    引用本文: 徐祖奔,伍艳,赵越,等. 黄河下游典型滩区土壤重金属污染特征及来源解析[J]. 农业工程学报,2023,39(15):200-207. DOI: 10.11975/j.issn.1002-6819.202302055
    XU Zuben, WU Yan, ZHAO Yue, et al. Source apportionment and pollution of soil heavy metals in typical floodplain in the Lower Reaches of the Yellow River[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2023, 39(15): 200-207. DOI: 10.11975/j.issn.1002-6819.202302055
    Citation: XU Zuben, WU Yan, ZHAO Yue, et al. Source apportionment and pollution of soil heavy metals in typical floodplain in the Lower Reaches of the Yellow River[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2023, 39(15): 200-207. DOI: 10.11975/j.issn.1002-6819.202302055

    黄河下游典型滩区土壤重金属污染特征及来源解析

    Source apportionment and pollution of soil heavy metals in typical floodplain in the Lower Reaches of the Yellow River

    • 摘要: 针对黄河滩区土壤重金属污染、来源复杂的问题,该研究以原阳滩区复合污染土壤为例,结合土壤重金属含量空间分布和正定矩阵因子分解(positive matrix factorization, PMF)模型,探讨滩区土壤重金属富集特征及来源。源解析结果表明,原阳滩区土壤重金属污染受工业源、交通源、自然来源、燃煤污染源和农业源影响,农业源占主导,相对贡献率达23.5%,其次为工业源、自然来源、交通源、和燃煤污染源。该研究能够准确解析多金属复合土壤复杂成因,可为黄河滩区多金属复合污染土壤的污染源识别提供参考。

       

      Abstract: Soil heavy metal pollution and complex sources have posed the serious risks on the ecological system in recent years. In this study, the positive matrix factorization (PMF) model was established to explore the spatial distribution and sources of heavy metals in soil. The composite polluted soil was taken from the Yuanyang floodplain of the lower Yellow River. 81 surface soil (0-20 cm) samples were collected to identify the soil heavy metal, such as Pb, Cu, Cd, Hg Cr, Ni, Zn and As. A systematic analysis was then made to determine the pollution content and spatial distribution of heavy metals. The geographical accumulation index and potential ecological risk index were used to assess the pollution degree of heavy metals and their ecology risk. The contents of Pb, Cu, Cd, Hg, Cr, Ni, Zn, and As in the soil of the study area were 27.77, 27.05, 0.31, 0.24, 73.55, 25.38, 88.25, and 29.00 mg/kg, respectively. Among them, Hg pollution was the most severe with an average content nearly 7 times of the soil background value, indicating a strong ecological risk, followed by Cd pollution with a strong ecological risk. An island -like pattern was observed in the spatial distribution of heavy metal concentration in the Yuanyang Beach District. The Cu, Ni, Pb, and Cd pollution were widely polluted with the high value in the central Jiangzhuang Township of the study area. An overlapping space was found on the high value distribution of Zn, Cr, As, and Hg in the surface soil, indicating the point source pollution. The geographical accumulation index also showed that the Hg pollution was the most serious, followed by As and Cd. The intensity of potential ecological risk was ranked in the descending order of Hg>Cd>As>Pb>Cu>Ni>Cr>Zn. The heavy metal of Hg presented the very strong ecological hazards, while Cd was the strong ecological hazards, and the rest were belonged to the slight ecological hazards. The potential ecological risk index (RI) was 459.31 on average among the eight heavy metals, indicating the level of strong ecological hazards. The contribution rates of Hg and Cd were 61.66% and 27.71%, respectively. The spatial interpolation was performed on the comprehensive potential ecological risk index of heavy metals. The most severe pollution of heavy metals was distributed in Xibianqiao North Township and middle Jiangzhuang Township, indicating a strong ecological risk level. The Hg and Cd pollution were relatively sereve and posed the greatest ecological harm to the environment. The cluster and source analysis showed that the heavy metal pollution was attributed to the industrial, transportation, natural, coal-burning, and agricultural sources. Agricultural sources were dominated in the soil heavy metal pollution, with the relative contribution rate of 23.5%. By contrast, the relative contribution rate of industrial, transportation, natural and coal-burning sources were 16.2%, 22.1%, 19.4% and 18.9%, respectively. The finding can provide a strong reference to identify the pollution sources of polymetallic composite polluted soil. The scientific basis and data support can greatly contribute to the "scientific pollution control" and "precise pollution control" of the soil for the better Ecological protection and high-quality development in the Yellow River Basin.

       

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