Distribution Assessment and Source Identification Using Multivariate Statistical Analyses and Artificial Neutral Networks for Trace Elements in Agricultural Soils in Xinzhou of Shanxi Province, China

SHANGGUAN Yuxian; HUA Xiaozan; HUO Xiaolan; ZHAO Xiufeng; CHENG Bin; ZHAO Long; HOU Hong; MA Jin; SUN Zaijin; XU Yafei; ZHAO Ruifen; ZHANG Yigong

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Distribution Assessment and Source Identification Using Multivariate Statistical Analyses and Artificial Neutral Networks for Trace Elements in Agricultural Soils in Xinzhou of Shanxi Province, China

机译：基于多元统计分析和人工神经网络的山西省忻州市农业土壤中微量元素分布评估和源识别

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Multivariate statistical analyses were used to assess the contents and distributions of trace elements in agricultural soils in Xinzhou of Shanxi Province,China,and to identify their sources.Samples with high levels of trace elements were concentrated in eastern Xinzhou,with contents declining from the east to west.Principal component and redundancy analyses revealed strong correlations among Co,Cu,Mn,Ni,Se,V,and Zn contents,suggesting that these elements were derived from similar parent materials.There were also strong correlations between the contents of these elements and soil properties.Contents of Cd and Pb were significantly higher in the agricultural soil samples than in the background soil samples (P ＜ 0.05),and were higher in areas with higher levels of gross domestic product but decreased with distance to the nearest road.Therefore,human activities appear to have a strong influence on the Cd and Pb distribution patterns.A novel artificial neural network (ANN) model,using environmental input data,was used to predict the soil Cd and Pb contents of specified test dates.The performances of the ANN model and a traditional multilinear model were compared.The ANN model could successfully predict Cd and Pb content distributions,projecting that soil Cd and Pb contents will increase by 128％ and 25％,respectively,by 2020.The results thus indicated that the economic condition of an area has a greater effect on trace element contents and distributions in the soil than the scale of the economy itself.

机译：采用多元统计分析法评估山西省忻州市农业土壤中微量元素的含量和分布，并确定其来源。微量元素含量高的样品集中在忻州东部，含量从东部下降主成分分析和冗余度分析表明Co，Cu，Mn，Ni，Se，V和Zn含量之间存在很强的相关性，这表明这些元素源自相似的母体材料。这些元素的含量之间也存在强相关性农业土壤样品中的镉和铅的含量显着高于背景土壤样品（P ＜0.05），并且在国内生产总值较高的地区较高，但随着距最近道路的距离而降低。因此，人类活动似乎对Cd和Pb的分布模式有很大的影响。一种新的人工神经网络（ANN）模型，使用envi使用基本输入数据预测特定测试日期的土壤中Cd和Pb含量。比较了ANN模型和传统的多线性模型的性能。ANN模型可以成功预测Cd和Pb含量分布，并预测土壤Cd和Pb的含量。到2020年，铅的含量将分别增加128％和25％。结果表明，该地区的经济状况对土壤中微量元素含量和分布的影响大于经济规模。

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《土壤圈（英文版）》 |2018年第3期|542-554|共13页
作者
SHANGGUAN Yuxian; HUA Xiaozan; HUO Xiaolan; ZHAO Xiufeng; CHENG Bin; ZHAO Long; HOU Hong; MA Jin; SUN Zaijin; XU Yafei; ZHAO Ruifen; ZHANG Yigong;
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作者单位

State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences,Beijing 100012(China);

Institute of Agricultural Environment and Resources, Shanxi Academy of Sciences, Key Laboratory of Soil Environment and Nutrient Resources of Shanxi Province, Taiyuan 030031(China);

Institute of Agricultural Environment and Resources, Shanxi Academy of Sciences, Key Laboratory of Soil Environment and Nutrient Resources of Shanxi Province, Taiyuan 030031(China);

Institute of Agricultural Environment and Resources, Shanxi Academy of Sciences, Key Laboratory of Soil Environment and Nutrient Resources of Shanxi Province, Taiyuan 030031(China);

Institute of Agricultural Environment and Resources, Shanxi Academy of Sciences, Key Laboratory of Soil Environment and Nutrient Resources of Shanxi Province, Taiyuan 030031(China);

Institute of Agricultural Environment and Resources, Shanxi Academy of Sciences, Key Laboratory of Soil Environment and Nutrient Resources of Shanxi Province, Taiyuan 030031(China);

State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences,Beijing 100012(China);

State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences,Beijing 100012(China);

State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences,Beijing 100012(China);

State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences,Beijing 100012(China);

State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences,Beijing 100012(China);

Institute of Agricultural Environment and Resources, Shanxi Academy of Sciences, Key Laboratory of Soil Environment and Nutrient Resources of Shanxi Province, Taiyuan 030031(China);

Institute of Agricultural Environment and Resources, Shanxi Academy of Sciences, Key Laboratory of Soil Environment and Nutrient Resources of Shanxi Province, Taiyuan 030031(China);

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收录信息中国科学引文数据库(CSCD);中国科技论文与引文数据库(CSTPCD);
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Distribution Assessment and Source Identification Using Multivariate Statistical Analyses and Artificial Neutral Networks for Trace Elements in Agricultural Soils in Xinzhou of Shanxi Province, China

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