In Southern China, the co-occurrence of arsenic (As) and antimony (Sb) contamination in soils around Sb mines presents an environmental challenge. During the flooding period of mining-impacted soils, anaerobic reduction of iron (Fe) oxides enhances the mobilization and bioavailability of Sb and As, further elevating the risk of Sb and As entering the food chain. To address this problem, activated carbon (AC) and biochar (BC) were applied to remediate flooded mining-impacted soils. Our results explored that AC can significantly decrease mobilization by 9-97 % for Sb and 9-67 % for As through inhibiting Fe(III) mineral reduction and dissolution in flooded soils. In contrast, there was no significant effect of BC. This was attributed to the strong adsorption of soil dissolved organic matter (DOM) by AC compared to BC, while DOM as electron shuttle is crucial for microbial Fe(III) reduction. Consequently, the DOM sequestration by AC effectively mitigates Sb and As leaching in contaminated mining soils.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1016/j.jhazmat.2024.134663 | DOI Listing |
Ecotoxicol Environ Saf
January 2025
SCNU Environmental Research Institute, Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety & MOE Key Laboratory of Environmental Theoretical Chemistry, South China Normal University, Guangzhou 510006, China. Electronic address:
Mining activities produce large quantities of tailings and acid mine drainage, which contain varieties of heavy metals, thereby affecting the downstream farmland soils and crops. Heavy metals could induce antibiotic resistance through co-selection pressure. However, the profiles of antibiotic resistance genes (ARGs) in the mining-affected farmland soils and crops are still unclear.
View Article and Find Full Text PDFSci Total Environ
December 2024
Department of Environmental Science & Ecological Engineering, College of Life Sciences & Biotechnology, Korea University, Seoul 02841, Republic of Korea. Electronic address:
The accurate prediction of soil heavy metal contamination is crucial for the effective environmental management of abandoned mining areas. However, conventional machine learning models (CMLMs) often fail to account for the spatial heterogeneity of soil contamination, which limits their predictive accuracy. This study evaluated the performance of geographically weighted machine learning models (GWMLMs) in predicting soil Cd and Pb concentrations in abandoned mines in the Republic of Korea.
View Article and Find Full Text PDFJ Hazard Mater
December 2024
Faculty of Science and Engineering, Southern Cross University, Lismore, NSW 2480, Australia.
Environ Geochem Health
September 2024
Federal Rural University of the Amazon, Belém, PA, 66077-530, Brazil.
Artisanal gold mining can lead to soil contamination with potentially toxic elements (PTEs), necessitating soil quality monitoring due to environmental and human health risks. However, determining PTE levels through acid digestion is time-consuming, generates chemical waste, and requires significant resources. As an alternative, portable X-ray fluorescence (pXRF) offers a faster, more cost-effective, and sustainable analysis.
View Article and Find Full Text PDFJ Hazard Mater
December 2024
State Key Laboratory of Environmental Geochemistry, Institute of Geochemistry, Chinese Academy of Sciences, Guiyang 550081, PR China. Electronic address:
The environmental risk of Cd in soils strongly depends on the mobilization of Cd in soils. However, limited knowledge exists on the redistribution of exogenic Cd inputs in soils, especially across diverse lithological regions. Herein, we aimed to investigate the fate of Cd in soils from two mining areas with contrasting lithologies (siliceous and calcareous) using stable Cd isotopes.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!