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首页> 外文期刊>Journal of soil & sediments >Identification of sources of heavy metals in agricultural soils using multivariate analysis and GIS
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Identification of sources of heavy metals in agricultural soils using multivariate analysis and GIS

机译：利用多元分析和GIS识别农业土壤中的重金属来源

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摘要

Purpose Heavy metals in agricultural soils readily enter the food chain when taken up by plants, but there have been few investigations of heavy metal pressure in farming areas with low background concentrations. This study was carried out in a cultivation area of Northeast China that has undergone decades of intensive farming, with the aim of identifying the sources of accumulated heavy metals in agricultural soils using multivariate analysis and geographic information system (GIS). Materials and methods In 2011, concentrations of total iron (Fe), manganese (Mn), copper (Cu), nickel (Ni), lead (Pb), zinc (Zn), cadmium (Cd), chromium (Cr) and cobalt (Co), as well as soil pH and organic matter, were measured at 149 sites in arable soils in the study area. The principal component analysis (PCA) was employed to extract hidden subsets from the raw dataset in order to detect possible sources. Metal contents in soils from various croplands were further investigated using analysis of variance. With the Kriging interpolation method, GIS was used to display the PCA results spatially to explore the influence of land use on heavy metal accumulation. Results and discussion Most of the studied metals in arable soils of the study area were shown to have low concentrations, except for Cd (0.241 mgkg~(-1)). According to the results of the PCA analysis, Fe, Mn, Pb, Zn, Cd, and Co formed the first component (PCI) explaining 40.1 % of the total variance. The source of these metals was attributed to fanning practices ("anthropogenic" factor). Cu, Ni, and Cr fell into the second component (PC2), heavy metals that derived from parent rock materials ("lithogetic" factor). This component describes 24.6 % of the total variance. Compared to paddy lands, soils in drylands had greater accumulations of all the metals in PCI, which can be explained by a higher rate of phosphorus fertilizer application and a longer farming history. Conclusions Owing to the natural low backgrounds, soils in the study area were safe from heavy metal pollution with a contamination risk of Cd the only exception. Multivariate analysis and GIS were effective means in helping to identify the sources of soil metals and addressing the land use influence on soil metals accumulation. This work can support the development of strategy and policies to aid in the prevention of widespread heavy metal contamination in area with characteristics similar to those of the study area.
机译：目的农业土壤中的重金属在被植物吸收后很容易进入食物链，但是很少有人研究背景浓度低的耕地中的重金属压力。这项研究是在经过数十年精耕细作的中国东北一个耕种地区进行的，目的是使用多元分析和地理信息系统（GIS）识别农业土壤中积累的重金属来源。材料和方法2011年的总铁（Fe），锰（Mn），铜（Cu），镍（Ni），铅（Pb），锌（Zn），镉（Cd），铬（Cr）和钴的浓度在研究区域的149个土壤中测量了Co（Co）以及土壤的pH值和有机质。为了检测可能的来源，采用主成分分析（PCA）从原始数据集中提取隐藏的子集。使用方差分析进一步调查了各种农田土壤中的金属含量。通过克里格插值法，使用GIS在空间上显示PCA结果，以探索土地利用对重金属积累的影响。结果与讨论研究区耕地土壤中的大多数研究金属都显示出低浓度，除了镉（0.241 mgkg〜（-1））。根据PCA分析的结果，Fe，Mn，Pb，Zn，Cd和Co形成第一成分（PCI），占总方差的40.1％。这些金属的来源归因于扇形操作（“人为”因素）。 Cu，Ni和Cr属于第二成分（PC2），它们是源自母岩材料的重金属（“岩性”因子）。该组件描述了总方差的24.6％。与稻田相比，干旱地区的土壤中PCI中所有金属的积累量更大，这可以用较高的磷肥施用率和较长的耕作历史来解释。结论由于自然背景低，研究区域的土壤不受重金属污染的影响，唯一的例外是镉的污染风险。多元分析和地理信息系统是帮助识别土壤金属来源和解决土地利用对土壤金属累积影响的有效手段。这项工作可以支持战略和政策的制定，以帮助防止与研究区域具有相似特征的区域中广泛的重金属污染。

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  • 来源
    《Journal of soil & sediments》 |2013年第4期|720-729|共10页
  • 作者

    Yushu Shan; Mats Tysklind; Fanghua Hao; Wei Ouyang; Siyang Chen; Chunye Lin;

  • 作者单位

    State Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University, Beijing 100875, People's Republic of China Department of Chemistry, Umea University, 901 87 Umea, Sweden;

    Department of Chemistry, Umea University, 901 87 Umea, Sweden;

    State Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University, Beijing 100875, People's Republic of China;

    State Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University, Beijing 100875, People's Republic of China;

    State Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University, Beijing 100875, People's Republic of China;

    State Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University, Beijing 100875, People's Republic of China;

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  • 正文语种 eng
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  • 关键词

    arable soils; geographic information system (GIS); heavy metal; principal component analysis (PCA); sources identification;

    机译：耕地地理信息系统（GIS）;重金属;主成分分析（PCA）;来源识别;

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