The heavy metals (HMs) usually have high natural background levels in lead-zinc mines. Strengthening the ecological risk assessment and accurate identification sources of HMs is an important component of land resource utilization and food security. A total of 795 soil samples (with a depth of 0~20 cm) were collected in Hezhang County, an area of typical high background levels of HMs with more than 18 large lead-zinc deposits. In this study, inductively coupled plasma emission spectrometry (ICP-OES), inductively coupled plasma mass spectrometry (ICP-MS), atomic fluorescence spectrometry (AFS), and the potentiometric method (POT) were used to test the total concentration of HMs and pH values. The geographic information system was used to model the concentration distribution of HMs, and the ecological risk was assessed according to the source analysis results. The results suggest that: (1) the mean concentration of As, Cd, Cr, Cu, Hg, Ni, Pb, and Zn were 24.55, 2.25, 176.4, 89.6, 0.19, 64.2, 102, and 257 mg·kg⁻¹, respectively, which exceeded the average background value of soils in Guizhou Province by 1.23, 3.41, 1.84, 2.80, 1.73, 1.64, 2.90, and 2.58 times, respectively, and the average concentrations of Cd, Cr, Cu, Ni, Pb and Zn exceed the screening values specified in soil contamination risk in agricultural land; (2) the results of enrichment factor (EF) showed that 29.73% of Cd are moderately polluted, and other HMs were slightly polluted in a small area or near the baseline value (EF ≈ 1); and (3) PMF analysis showed that there are five main sources, pyrite, lead-zinc ore, basalt, carbonate rock, and agricultural production, with the risk contribution ratios of 5.25%, 27.37%, 28.94%, 17.91%, and 20.53%, respectively.

The environmental risk posed by heavy metals in agricultural soil is primarily influenced by their sources, bioavailability, and geochemical transfer behavior. This study focused on Weining County, a region in Guizhou province, Southwest China, with a high geological background and long-term impact from artisanal Zn smelting. Vertical soil profiles, crop, and rhizospheric soil samples were collected and analyzed for heavy metal concentration (As, Cd, Co, Cr, Cu, Hg, Ni, Pb, V, and Zn) including the total concentration and chemical fraction. The results revealed elevated concentrations of Cd (range: 0.7–6.9 mg·kg⁻¹), Co (range: 19.3–120.0 mg·kg⁻¹), Cu (range: 71.6–386.0 mg·kg⁻¹), Ni (range: 51.0–121.0 mg·kg⁻¹), and V (range: 310.0–721.0 mg·kg⁻¹) in all soil samples compared to the background values of Guizhou Province. Chemical fractionation analysis indicated that Cr, Ni, As, Cu, and Zn were predominantly present in the residual fraction, while Hg and Pb were predominantly found in the potentially bioavailable fraction. Cd exhibited the highest bioavailability, accounting for 58.5% of its total concentration. Enrichment factor analysis suggested that artisanal Zn smelting activities were the main sources of Cd, Pb, and Zn contamination. Furthermore, Cd, Pb, and Zn were found to be highly accumulated in the surface soil layer (0–20 cm). Notably, 90.0% of potato and 9.4% of maize grain samples exceeded the food hygiene standards for Cd concentration, posing potential health risks to consumers. The bioconcentration factor (soil-to-root) and translocation factor (root-to-grain) analyses indicated that maize roots had a higher tendency to accumulate Cd from the soil, while Zn and Cu showed a significant transferability from roots to maize grains. These findings offer valuable insights for devising heavy metal remediation strategies in similar areas.

Heavy metal pollution not only causes detrimental effects on the environment but also poses threats to human health; thus, it is crucial to monitor the heavy metal content in the soil. Hyperspectral technology, characterized by high spectral resolution, rapid response, and non-destructive detection, is widely employed in soil composition monitoring. This study aims to investigate the effects of dimensionality reduction methods on the performance of hyperspectral inversion. To this end, 56 soil samples were collected in Daye, with the corresponding hyperspectral data acquired by the advanced ASD Fieldspec4 instrument. We employed the linear dimensionality reduction method, i.e., the principal component analysis (PCA), and non-linear method in terms of kernel PCA (KPCA) with polynomial, radial basis function (RBF), and sigmoid kernels to reduce the dimensionalities of original spectral reflectance and that processed by first-derivative transformation (FDT). Building upon this foundation, we applied the Adaptive Boosting (AdaBoost) algorithm for inverting the soil copper (Cu) content. The performance of each inversion model was evaluated by evaluation indices in terms of the coefficient of determination (R²), root-mean-square error (RMSE), and residual prediction deviation (RPD). The results revealed that the KPCA with polynomial kernel function applied to the FDT-based spectra could yield the optimal inversion accuracy, with corresponding R², RMSE, and RPD being 0.86, 21.47 mg·kg⁻¹, and 2.72, respectively. This study demonstrates that applying the FDT with KPCA processing can significantly improve the accuracy of the hyperspectral inversion for soil Cu content, providing a potential approach for monitoring heavy metal pollution using hyperspectral technology.

Soil arsenic (As) contamination presents serious threats to ecosystems and human health, necessitating the development of accurate and efficient monitoring techniques. This study introduces a novel multi-source data fusion approach to enhance the hyperspectral inversion of soil arsenic concentrations by integrating dimensionality-reduced spectral data with soil components significantly correlated with arsenic (e.g., Cd, Cr, Cu, Ni, Pb, Zn, S, and total Fe₂O₃(T-Fe₂O₃)). Principal Component Analysis (PCA) was utilized to reduce the dimensionality of hyperspectral data, effectively addressing issues of collinearity and redundancy while preserving critical spectral information. The performances of three models, namely Partial Least Squares Regression (PLSR), Artificial Neural Networks (ANN), and Random Forest (RF), were assessed under four input variable combinations: (1) original spectral data, (2) original spectral data with soil components, (3) PCA dimensionality-reduced spectral data, and (4) PCA dimensionality-reduced spectral data combined with soil components. The results demonstrated that the RF model, when applied to the multi-source data of PCA-reduced spectra and soil components, achieved the highest inversion accuracy with an R² value of 0.86, significantly outperforming the PLSR model (R² = 0.75). This study underscores the effectiveness of enhancing model performance and highlights the superior capability of the RF model in handling complex, high-dimensional datasets. The findings of soil arsenic estimation provide theoretical foundation for optimizing hyperspectral remote sensing technology in monitoring soil heavy metal contamination and establishing a robust framework for future research and practical applications in environmental science.