Spatial Autocorrelation Analysis of Soil Pollution Data in Central Taiwan

Abstract

Soil pollutant concentrations such as heavy metal Cr, Cu, Ni, and Zn were collected at 1082 sampling sites in Changhua county of Taiwan. This study applies a spatial autocorrelation analysis for identifying multiple soil pollution hotspots based on original and re-sampling data in the study area. Results show that the multiple hotspots for four heavy metals and are strongly related to the locations of industrial plants and irrigation systems in the study area. Soil pollution hotspots are clearly defined based on the LISA (local indicators of spatial association) cluster maps. The cluster maps show a clear spatial autocorrelation of soil pollutants in cLHS samples, especially for Cr. Furthermore, the maps explore the spatial patterns of hazards and capture the hotspot areas without exhaustive sampling in the study area.