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

Abstract

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.