Prediction of Soil Heavy Metal Distribution Using Geographically Weighted Regression Kriging.

Abstract

Both soil heavy metals and the influencing factors are related to spatial location and are spatially heterogeneous. However, the global linear regression model assumes the regression coefficients to be spatially stationary throughout the study region and is unable to account for the spatially varying relationships between soil heavy metals and influencing factors. Thus, the objectives of this study were to estimate the spatial distribution of soil heavy metals using a geographically weighted regression kriging (GWRK) approach, and compare the GWRK results with those obtained from ordinary kriging (OK) and regression kriging (RK). A dataset of soil lead (Pb) concentrations in Daye city, China, that was sampled in 2019 was used. According to the results of spatial smoothness, variability, and interpolation accuracy, GWRK was the best method and could provide the most reasonable spatial distribution pattern and the highest spatial interpolation accuracy in comparison with OK and RK.