To enhance the sorption efficacy of attapulgite for heavy metals, mercapto-functionalized attapulgite (ATP-SH) was synthesized with mercaptan functional groups. When the mass-to-volume ratio of calcined attapulgite (ATP-C) to 3-Mercapropyltrimethoxysilane (MPTMS) was 1 g:0.5 mL (ATP-SH-0.5) and the pH was set to 8, a strong adsorption capacity for Cd (II) and Pb (II) was demonstrated. This indicates excellent adsorption performance for these heavy metals. ATP-SH-0.5 exhibited a maximum adsorption capacity of 43.81 mg/g and 274.83 mg/g for Cd (II) and Pb (II), respectively, in a single ion system. In a binary ion system, the maximum adsorption capacity was 31.86 mg/L and 254.45 mg/L for Cd (II) and Pb (II), respectively. Various characterizations and experiments showed that the adsorption of Cd (II) and Pb (II) onto ATP-SH-0.5 involves ion exchange reactions involving hydroxyl and thiol functional group complexation reactions. This adsorption process follows a single-molecule layer adsorption mechanism. XPS results indicate that hydroxyl and grafted thiol functional groups on the surface of mercapto-functionalized attapulgite participated in surface complexation reactions with Cd (II) and Pb (II), resulting in the formation of Cd-S and Pb-S species. Overall, this study provides a promising mercapto-functionalized modification material for the remediation of polluted water and soil.

Vis-NIR and XRF spectroscopy are widely used in monitoring heavy metals in soil due to their advantages of being fast, non-destructive, cost-effective, and non-polluting. However, when used individually, XRF and vis-NIR may not meet the accuracy requirements for Cd determination. In this study, we focused on the impact area of a non-ferrous metal smelting slag site in Gejiu City, Yunnan Province, fused the pre-selected vis-NIR and XRF spectra using the Pearson correlation coefficient (PCC), and identified the characteristic spectra using the competitive adaptive reweighted sampling (CARS) method. Based on this, a quantitative model for soil Cd concentration was established using partial least squares regression (PLSR). The results showed that among the four fusion spectral quantitative models constructed, the model combining vis-NIR spectral second-order derivative transformation and XRF spectral first-order derivative transformation (D2(vis-NIR) + D1(XRF)) had the highest coefficient of determination (R² = 0.9505) and the smallest root mean square error (RMSE = 0.1174). Compared to the estimation models built using vis-NIR and XRF spectra alone, the average computational time of the fusion models was reduced by 68.19% and 63.92%, respectively. This study provides important technical means for real-time and large-scale on-site rapid estimation of Cd content using multi-source spectral fusion.