RESUMO
Existing source apportionment methods for soil heavy metals fail to identify the actual landscapes related to pollutant sources and quantify their contributions to the accumulation of soil heavy metals. In this work, we propose a new source identification and apportionment approach for soil heavy metal accumulation by integrating pollution landscapes, pathways, and receptors. Datasets for soil lead (Pb) concentrations in Daye city, China, which was sampled in 2018, were used. First, based on the spatial distribution of Pb, the source landscapes were identified using GeoDetector and spatial analysis methods. Second, a source landscape apportionment model (SLAM) was developed considering both atmospheric deposition and surface runoff as diffusion pathways. Third, considering soil properties and topography as receptor attributes, ordinary least squares (OLS) and geographically weighted regression (GWR) models were employed to further adjust the soil Pb accumulation at receptor locations. The results showed that SLAM followed by the GWR model (SLAM-GWR) had the highest fitting accuracy. Then, the spatial distributions and ranges of contributions of each identified source landscape to Pb accumulation through different pathways were obtained. Finally, the advantages and disadvantages of the proposed approach were discussed.
