Spatial prediction and influencing factors identification of potential toxic element contamination in soil of different karst landform regions using integration model

Exposure to arsenic and other potentially toxic elements: health risk assessment and source analysis in the Wuming Basin, Guangxi Province, China

Guangxi, China, is one of the world's largest karst regions where potential toxic elements tend to accumulate, resulting in high soil background values. This study explores the ecological risk, elemental baseline values, and sources of potential toxic elements in karst regions, expanding the research to include 21 common elements. The significance of this research lies in its implications for the management of potential toxic element pollution, the formulation of environmental quality standards, and soil remediation in karst areas. In this study, 12,547 topsoil samples (0-20 cm) were collected in the study area. Pollution assessment and ecological risk evaluation of eight potential toxic elements (Zn, Ni, Cu, Pb, Cd, Hg, Cr, and As) were conducted using the geo-accumulation index method and potential ecological risk index method. Multivariate statistical analysis was applied to analyze the total content of 21 common elements (Zn, Ni, Cu, Pb, P, Cd, Hg, Co, Mn, Cr, V, I, S, As, pH, Se, N, CaO, Corg, Mo, and F). Additionally, the potential sources of 21 soil elements were preliminarily quantitatively analyzed using the principal component analysis-absolute principal component scores-multiple linear regression receptor model. The results showed that (1) Zn, Ni, Cu, Pb, Cd, Cr, V, and As were enriched in the research area and Ca, Cd, Mn, Mo, Hg, As, and Cu might have been influenced by human activities; (2) Cr, Pb, As, and Zn were generally lightly polluted, with Hg having a moderate potential ecological risk level; and (3) Ni and Zn have contributions of 37.99% and 35.07% from geological sources, agricultural fertilization, and pesticides. Mo, V, Cr, Se, Hg, and As exhibit contributions ranging from 39.44 to 59.22% originating from geological backgrounds and human activities. Corg, S, N, and P show contributions of 45.39% to 80.33% from surface vegetation. F, Co, Mn, and Pb have contributions ranging from 31.63 to 47.93% from acidic rocks in the soil parent material, mining activities, and transportation. Cd and CaO derive 31.67% and 40.23%, respectively, from soil parent material and industrial sources. I has 31.94% from geological background and human activities, and 31.95% from soil parent material and atmospheric sources. Cu has 30.56% from geological sources. The study results can serve as a scientific basis for element research in karst areas domestically and internationally.

A remote sensing-based strategy for mapping potentially toxic elements of soils: Temporal-spatial-spectral covariates combined with random forest

The selection of predictor variables is a crucial issue in building a digital mapping model of potentially toxic elements (PTEs) in soil. Traditionally, the predictor variables for mapping models of soil PTEs have been chosen from sets of spatial parameters or spectral parameters derived from geographical environmental data. However, the enrichment of soil PTEs exhibits significant variations in both spatial and temporal dimensions, with the temporal dimension often being overlooked in the selection of predictor variables for digital mapping models. This limitation hampers the robustness and generalizability of the models. Therefore, multi-source geographical data were used in this study to determine three temporal indices for characterizing the enrichment process of soil PTEs in temporal dimensions, and additionally to construct the temporal-spatial-spectral (TSS) covariate combinations. The random forest (RF) algorithm was used to map soil PTEs at a regional scale. Results showed that: (1) When using spatial parameters or spectral parameters as predictor variables and measured Pb content as the dependent variable, the values of the model performance indicator RPIQ (ratio of performance to inter-quartile range) were 2.66 and 2.27, respectively. After incorporating the temporal parameters into the model input, values of RPIQ for the RF model reached 3.55 (using spatial-temporal covariates) and 3.21 (using spectral-temporal covariates), representing performance improvements of 33.46% and 41.41%, respectively. (2) The RF model constructed with the temporal-spatial-spectral covariates achieved satisfactory mapping accuracy (R2 = 0.85; RMSE = 0.80 mg kg-1; RPIQ = 4.09). (3) The soil Pb content in the western and northeastern regions was relatively high, while the remaining areas exhibited lower Pb levels, mainly due to industrial activities. (4) The mapping results of Pb obtained in this study were superior to other mapping methods, such as ordinary kriging, artificial neural networks, and multivariate linear regression methods. The soil PTE mapping technique employed in this study that combined TSS covariates with the RF provided an effective methodological approach for preventing soil pollution, controlling environmental risk, and improving soil management.