Vertical migration behavior simulation and prediction of Pb and Cd in co-contaminated soil around Pb-Zn smelting slag site.

Heavy metal migration in soil poses a serious threat to the soil and groundwater. Understanding the migration pattern of heavy metals (HMs) under different factors could provide a more reasonable position for pollution evaluation and targetoriented treatment of soil heavy metal. In this study, the migration behavior of Pb and Cd in co-contaminated soil under different pH and ionic strength (NaCl concentration) was simulated using convective dispersion equation (CDE). We predicted the migration trends of Pb and Cd in soils after 5, 10, and 20 years via PHREEQC. The results showed that the migration time of Cd in the soil column experiment was about 60 days faster than that of Pb, and the migration trend was much steeper. The CDE was proved to describe the migration behavior of Pb and Cd (R2 > 0.75) in soil. The predicted results showed that Cd migrated to 15-20 cm of soil within 7 years and Pb stayed mainly in the top 0-6 cm of soil within 5 years as the duration of irrigation increased. Overall, our study is expected to provide new insight into the migration of heavy metal in soil ecosystems and guidance for reducing risk of heavy metal in the environment.

Rapid Estimation of Soil Pb Concentration Based on Spectral Feature Screening and Multi-Strategy 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.

Contamination characteristics and source analysis of potentially toxic elements in dustfall-soil-crop systems near non-ferrous mining areas of Yunnan, southwestern China.

Potentially toxic elements (PTEs) in the dustfall-soil-crop system pose a serious threat to the ecological environment and agricultural production. However, there is still a knowledge gap in terms of better understanding the distinctive sources of PTEs by integrating various models and technologies. In this study, we comprehensively investigated the concentrations, distribution, and sources of seven PTEs in a dustfall-soil-crop system (424 samples in total) near a typical non-ferrous mining area, using absolute principal component score/multiple linear regression (APCS/MLR) combined with X-ray diffraction (XRD) and microscopy techniques. Our results showed that the mean values of As, Cd, Cr, Cu, Ni, Pb, and Zn in the soils were 211, 14, 105, 91, 65, 232, and 325 mg/kg, respectively. These values were significantly higher than the background soil values in Yunnan. Except for Ni and Cr, all elements in the soil were significantly higher than the screening values of agricultural lands in China. The spatial distribution of PTE concentrations was similar among the three media. The ACPS/MLR, XRD, and microscopy analyses further indicated that soil PTEs mainly originated from industrial activities (37 %), vehicle emissions and agricultural activities (29 %), respectively. Dustfall PTEs mainly originated from vehicle emissions and industrial activities, accounting for 40 % and 37 %, respectively. Crop PTEs mainly originated from vehicle emissions and soil (57 %), and agricultural activities (11 %), respectively. PTEs seriously threaten the safety of agricultural products and the ecological environment once they settle from the atmosphere to soil and crop leaves, further accumulate in crops, and spread through the food chain. Therefore, our study provides scientific evidence for government regulators to control PTE pollution and reduce their environmental risks in dustfall-soil-crop systems.

Discovery of novel propargylamine-modified 4-aminoalkyl imidazole substituted pyrimidinylthiourea derivatives as multifunctional agents for the treatment of Alzheimer's disease.

A series of novel propargylamine-modified pyrimidinylthiourea derivatives (1-3) were designed and synthesized as multifunctional agents for Alzheimer's disease (AD) therapy, and their potential was evaluated through various biological experiments. Among these derivatives, compound 1b displayed good selective inhibitory activity against AChE (vs BuChE, IC50 = 0.324 µM, SI > 123) and MAO-B (vs MAO-A, IC50 = 1.427 µM, SI > 35). Molecular docking study showed that the pyrimidinylthiourea moiety of 1b could bind to the catalytic active site (CAS) of AChE, and the propargylamine moiety interacted directly with the flavin adenine dinucleotide (FAD) of MAO-B. Moreover, 1b demonstrated mild antioxidant ability, good copper chelating property, effective inhibitory activity against Cu2+-induced Aß1-42 aggregation, moderate neuroprotection, low cytotoxicity, and appropriate blood-brain barrier (BBB) permeability in vitro and was capable of ameliorating scopolamine-induced cognitive impairment in mice. These results indicated that 1b has the potential to be a multifunctional candidate for the treatment of Alzheimer's disease.