An interpolation method incorporating the pollution diffusion characteristics for soil heavy metals - taking a coke plant as an example

Abstract

The existing spatial interpolation methods in the prediction of soil heavy metal distribution are generally based on spatial auto correlation theory, rarely considering the pollution patterns. By contrast, in polluted sites, heavy metals have a strong heterogeneity even within a very small area, which is not exactly in line with auto correlation theory. This contradiction may lead to inaccuracy in spatial prediction. Atmospheric diffusion and deposition are one of the main sources of soil heavy metal pollution caused by coal-related production activities. To improve the prediction accuracy, the diffusion patterns of pollutants were considered in this paper by integrating Geodetector, Co-Kriging (COK), and partition interpolation. Geodetector was used to identify the main driving factors of soil pollution, based on which, the main driving factors were used as covariates introduced into the interpolation method (COK). Specifically, the amount of particulate matter deposition obtained by a pollutant diffusion model (AERMOD) was used as a covariate. For comparison, the distances to quenching, coke oven, and ammonium sulfate section were also used as covariates. Compared with the Ordinary Kriging method, the method COK-AERMOD established here decreased the root mean square error values of As (2.05 reduced to 1.89), Cd (0.18 reduced to 0.16), Cr (19.07 reduced to 12.97), Cu (6.92 reduced to 4.72), Hg (0.32 reduced to 0.28), Ni (16.92 reduced to 16.10), Pb (18.29 reduced to 16.62), and Zn (159.68 reduced to 153.66). This method in this paper is informative for the interpolation of soil elements in contaminated areas with known pollution source and diffusion patterns.