Predictive analysis and risk assessment of potentially toxic elements in Beijing gas station soils using machine learning and two-dimensional Monte Carlo simulations.

Gas stations not only serve as sites for oil storage and refueling but also as locations where vehicles frequently brake, significantly enriching the surrounding soil with potentially toxic elements (PTEs). Herein, 117 topsoil samples from gas stations were collected in Beijing to explore the impact of gas stations on PTE accumulation. The analysis revealed that the average Pollution Index (PI) values for Cd, Hg, Pb, Cu, and Zn in the soil samples all exceeded 1.

Machine learning-based prediction of cadmium bioaccumulation capacity and associated analysis of driving factors in tobacco grown in Zunyi City, China.

Tobacco grown in areas with high-geochemical backgrounds exhibits considerably different cadmium (Cd) bioaccumulation abilities due to regional disparities and environmental changes. However, the impact of key factors on the Cd bioaccumulation ability of tobacco grown in the karst regions with high selenium (Se) geochemical backgrounds is unclear. Herein, 365 paired rhizospheric soil-grown tobacco samples and 321 topsoil samples were collected from typical karst tobacco-growing soil in southwestern China and analyzed for Cd and Se.

Digital mapping of soil organic carbon density in China using an ensemble model.

The soil organic carbon stock (SOCS) is considered as one of the largest carbon reservoirs in terrestrial ecosystems, and small changes in soil can cause significant changes in atmospheric CO concentration. Understanding organic carbon accumulation in soils is crucial if China is to meet its dual carbon target. In this study, the soil organic carbon density (SOCD) in China was digitally mapped using an ensemble machine learning (ML) model.

Variation in rhizosphere microbiota correlates with edaphic factor in an abandoned antimony tailing dump.

The distribution pattern of root-associated bacteria in native plant growth in tailing dumps with extreme conditions remains poorly understood and largely unexplored. Herein we chose a native plant, Bidens bipinnata, growing on both an Sb tailing dump (WKA) and adjacent normal soils (WKC) to in-depth understand the distribution pattern of root-associated bacteria and their responses on environmental factors. We found that the rhizosphere microbial diversity indices in the tailing dump were significantly different from that in the adjacent soil, and that such variation was significantly related with soil nutrients (TC, TOC, TN) and metal(loid) concentrations (Sb and As).