| Affiliation: | 1. Institute of Soil Science, State Key Laboratory of Soil and Sustainable Agriculture, Chinese Academy of Sciences, Nanjing, China University of Chinese Academy of Sciences, Beijing, China;2. Technical Centre for Soil, Agriculture and Rural Ecology and Environment, Ministry of Ecology and Environment, Beijing, China;3. Institute of Soil Science, State Key Laboratory of Soil and Sustainable Agriculture, Chinese Academy of Sciences, Nanjing, China |
| 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 . |
