Yongde Zhang

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.

Traditional methods for obtaining soil heavy metal content are expensive, inefficient, and limited in monitoring range. In order to meet the needs of soil environmental quality evaluation and health status assessment, visible near-infrared spectroscopy and XRF spectroscopy for monitoring heavy metal content in soil have attracted much attention, because of their rapid, nondestructive, economical, and environmentally friendly features. The use of either of these spectra alone cannot meet the accuracy requirements of traditional measurements, while the synergistic use of the two spectra can further improve the accuracy of monitoring heavy metal lead content in soil. Therefore, this study applied various spectral transformations and preprocessing to vis-NIR and XRF spectra; used the whale optimization algorithm (WOA) and competitive adaptive re-weighted sampling (CARS) algorithms to identify feature spectra; designed a combination variable model (CVM) based on multi-layer spectral data fusion, which improved the spectral preprocessing and spectral feature screening process to increase the efficiency of spectral fusion; and established a quantitative model for soil Pb concentration using partial least squares regression (PLSR). The estimation performance of three spectral fusion strategies, CVM, outer-product analysis (OPA), and Granger-Ramanathan averaging (GRA), was discussed. The results showed that the accuracy and efficiency of the CARS algorithm in the fused spectra estimation model were superior to those of the WOA algorithm, with an average coefficient of determination (R²) value of 0.9226 and an average root mean square error (RMSE) of 0.1984. The accuracy of the estimation models established, based on the different spectral types, to predict the Pb content of the soil was ranked as follows: the CVM model > the XRF spectral model > the vis-NIR spectral model. Within the CVM fusion strategy, the estimation model based on CARS and PLSR (CARS_D1+D2) performed the best, with R² and RMSE values of 0.9546 and 0.2035, respectively. Among the three spectral fusion strategies, CVM had the highest accuracy, OPA had the smallest errors, and GRA showed a more balanced performance. This study provides technical means for on-site rapid estimation of Pb content based on multi-source spectral fusion and lays the foundation for subsequent research on dynamic, real-time, and large-scale quantitative monitoring of soil heavy metal pollution using high-spectral remote sensing images.

This study focuses on using spodumene flotation tailings (SFT) to prepare phosphoric acid-activated metakaolin geopolymer, in which the replacement of metakaolin (MK) by a high percentage (up to 75 wt.%) of tailings was achieved. The compressive strength of geopolymer mortar was significantly improved with SFT as aggregates. In addition, the mechanical properties could also be enhanced by an increased concentration of phosphoric acid (H₃PO₄) solution or a decreased aggregate particle size. The optimized geopolymer mortar composite was SFT:MK = 3:1, which was activated by H₃PO₄ solution with a concentration of 51 vol%, followed by curing at 55 °C for 24 h. On the other hand, properties of the geopolymer mortar could also be affected by the morphology of the aggregates. For example, SFT as aggregates could produce more interconnected pores compared to standard sand. The major chemical structural units of geopolymer mortar were -P-O-Al- and AlPO₄, which could be spontaneously generated according to the thermodynamic calculation results. Finally, many aluminum ions and a small amount of silicon ions could be leached from the tailings under acidic conditions.