Identification of soil contamination in AnShen using a geographic information system

Objectives: In a "superfund site" in Tainan City, Taiwan, soils are heavily polluted. Major pollutants include pentachlorophenol, dioxin, and mercury. The current study used a geographic information system (GIS) spatial interpolation method to analyze soil and sediment samples in this area and estimate the range and severity of pollution in areas in the An-Shun "superfund site" with respect to dioxin and mercury. Methods: Different Kriging methods were used to select the best model, based on the smallest standardized prediction (SPE) and root mean square standardized (RMSS) errors. Results: The Universal Kriging with Spherical and the Ordinary Kriging with Exponential models were shown to be the best models for estimating dioxin and mercury levels, respectively. Discussion: Our results confirmed the monitoring data generated by the Taiwan EPA and provide additional information to aid in targeting areas in need of immediate clean-up action. Better simulation results are expected as more data and better GIS methodologies become available.