Abstract
Contaminated sites pose a serious threat to the ecological environment and human health. Because of the presence of multiple peaks in the pollution data of some contaminated sites, as well as strong spatial heterogeneity and skewness in their distribution, the accuracy of spatial interpolation prediction is low. This study proposes a method for investigating highly skewed contaminated sites, which uses Thiessen polygons coupled with geostatistics and deterministic interpolation to optimize the spatial prediction and sampling strategy of sites. An industrial site in Luohe is used as an example to validate the proposed method. The results indicate that using 40 × 40m as the minimum initial sampling unit can obtain data that is representative of the regional pollution situation. Evaluation indexes reveal that the ordinarykriging (OK) method for interpolation prediction accuracy and the radial basisfunction_inverse distance weighted (RBF_IMQ) method for pollution scope prediction provides the best results, which can effectively improve the spatial prediction accuracy of pollution in the study area. Each accuracy indicator is enhanced by 20-70% after supplementing 11 sampling points in the suspect region, and the identification of the pollution scope approaches 95%. This method offers a novel approach for investigating highly biased contaminated sites, which can optimize the spatial prediction accuracy of pollution and reduce economic costs.
Published Version
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