Drivers of wind and water erosion for river sediments in a typical coarse sandy area in the middle reaches of the Yellow River.

Warmer temperatures and the combined effects of wind and water erosion leads to serious soil loss. Identifying the contribution of different drivers to wind and water compound erosion can improve soil erosion management in the watershed. Accordingly, we calculated the erosion energy based on energy theory and applied the mutation test and trend analysis to explore environmental drivers, runoff, and sediment changes for the Kuyehe River Basin.

Heavy metal immobilization and radish growth improvement using Ca(OH)-treated cypress biochar in contaminated soil.

Heavy metal contamination poses a significant threat to soil quality, plant growth, and food safety, and directly affects multiple UN SDGs. Addressing this issue and offering a remediation solution are vital for human health. One effective approach for immobilizing heavy metals involves impregnating cypress chips with calcium hydroxide (Ca(OH)) to enhance the chemical adsorption capacity of the resulting woody charcoal.

Relationship between nitrate, heavy metal, and sterols contents in Japanese agricultural soils with risk of groundwater pollution.

In Japanese agricultural lands, nitrate-nitrogen contamination of soil and groundwater often occurs due to the application of livestock excrements and compost. Therefore, rural soils in Japan were sampled and analyzed for nitrate-nitrogen leaching, heavy metal content, and sterols associated with livestock excrement and compost to calculate contamination risk indicators. The results were analyzed using self-organizing maps and cluster analysis.

Interpretable machine learning for predicting the fate and transport of pentachlorophenol in groundwater.

Pentachlorophenol (PCP) is a commonly found recalcitrant and toxic groundwater contaminant that resists degradation, bioaccumulates, and has a potential for long-range environmental transport. Taking proper actions to deal with the pollutant accounting for the life cycle consequences requires a better understanding of its behavior in the subsurface. We recognize the huge potential for enhancing decision-making at contaminated groundwater sites with the arrival of machine learning (ML) techniques in environmental applications.