Analysis of the spatial distribution of heavy metals in an area of farmland in Sichuan province, China

Abstract

To accurately visualize the spatial distribution of heavy metal pollution and provide information that assists in remediating farmland soil, this study employed GIS technology and collected 0–20 cm-depth surface soil and shallow groundwater samples from farmland in Jianxin Village, Xiba Town, Wutongqiao City, Sichuan Province, China. The soil samples were decomposed using a high-temperature closed digestion method, and the contents of Cr, Cd, Pb, As, Cu and Zn were determined using ICP-MS. A geographic semi-variogram analysis was then conducted to delineate spatial variations in the heavy metal concentrations within the soil, and a spatial distribution map of heavy metals in the soil was drawn using ArcGIS10.2 software based on a GIS Kriging interpolation spatial structure analysis. The results showed that all heavy metal element concentrations exceeded the soil background values of Sichuan Province; the exceeded rates of Cr, Cd, and Zn were 100%. All Cd samples exceeded the soil baseline value, and the pollution accumulation of Cr, Cd, and Zn was determined as being serious. Through a comparison of cross-validation methods, a theoretical optimal semi-variogram model of six heavy metal elements was obtained. Indexes C0/(C0 + C) of Cd, Pb, As, Cu, and Zn were in the range of 48–75%, which showed that their contents related to structural factors, such as the soil parent material and topography, and also to non-structural factors such as human activities. The index C0/(C0 + C) of Cr was more than 75%, which indicates that its content is mainly related to non-structural factors and human activities. The spatial correlation followed the order of Cu > Cd > Pb > Zn > As > Cr. The vertical distribution of heavy metals was greatly affected by the soil physical properties: the Cu and Pb contents of sandy soil decreased at first and then increased with depth; the Cr, Zn, and As contents first increased and then decreased with depth; and the Cd contents decreased with depth. In clay soil, there was little change in the Cu content with depth; the Cd, Zn, Pb, and As contents first decreased and then increased with depth, and the Cr content increased first and then decreased with depth. These results show that visualizing the spatial distribution of heavy metal contamination using GIS technology is significant for providing information that can be used to remediate farmland soil.