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

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

作者机构：State Key Laboratory of Environmental Criteria and Risk Assessment Chinese Research Academy of Environmental SciencesBeijing 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) 

出 版 物：《Pedosphere》 (土壤圈（英文版）)

年 卷 期：2018年第28卷第3期

页      面：542-554页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 09[农学] 0903[农学-农业资源与环境] 0835[工学-软件工程] 0811[工学-控制科学与工程] 090301[农学-土壤学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by the National High Technology Research and Development Program of China (863 Program) (Nos. 2012AA100601 and 2012AA101401) the National Natural Science Foundation of China (Nos. 41271338 and 41301342) 

主　　题：contamination enrickment factor keavy metal prediction principal component analysis redundancy analysis 

摘      要：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.