Improved three‐dimensional mapping of soil chromium pollution with sparse borehole data: Incorporating multisource auxiliary data into IDW‐based interpolation

Abstract

Deriving an accurate three‐dimensional (3D) spatial distribution of heterogeneous soil pollutants at contaminated sites using traditional spatial interpolation methods, such as inverse distance weighting (IDW), is challenging; especially when only limited borehole data are available. This study presents a novel IDW‐COV method, where weighting is determined by optimizing relative importance between feature distances of covariates and spatial distances to soil sample locations. The method was tested by mapping the 3D distributions of Cr (VI) and total Cr at a Cr salt production workshop (Site A) and a legacy Cr slag stacking site (Site B). The results were compared with those of IDW, the soil land inference model (SoLIM), and the SoLIM combined with IDW method (SoLIM‐IDW). The proposed IDW‐COV method returned the highest accuracy, with R2 values of 0.83 for Cr (VI) in Site A and 0.75 for total Cr in Site B, compared with 0.65 and 0.57 for IDW, 0.16 and 0.58 for SoLIM, and 0.73 and 0.61 for SoLIM‐IDW. This study highlights the importance of considering differences in explanatory power between multidimensional covariate and geographical spatial distance when incorporating multisource auxiliary data into weighted average estimators and provides guidance for mapping 3D distributions of heterogeneous pollutants from sparse soil borehole data.

Application of Integrated Land Use Regression and Geographic Information Systems for Modeling the Spatial Distribution of Chromium in Agricultural Topsoil

“…It is widely established that using correlated influencing factors as auxiliary variables can enhance prediction accuracy [14,15]. However, these models often fail to cover different variable types within a single model framework.…”