Arsenic health risk in shallow groundwater of the alluvial plains in the lower Yellow River, China: driving mechanisms of climate change and human activities

Groundwater arsenic contamination poses a significant public health threat. The mechanisms driving high-arsenic concentrations in groundwater are highly intricate, and the interactions influencing the spatial distribution of arsenic remain insufficiently understood. In this study, we developed a robust machine learning model framework to predict the spatial variation of arsenic levels in shallow groundwater within the alluvial plains of the lower Yellow River. Additionally, we investigated the underlying factors that govern arsenic distribution in this region. The findings indicate that the improved high-arsenic probability map can accurately identify high-arsenic exposure areas. Compared with 2010, the distribution pattern of high-arsenic risk in the study area in 2020 has changed, with a decrease in risk in the north and an increase in concentrated risk in the south, and the potential population in the south exposed to health risks has increased to 2.02 million. Environmental factors such as temperature, clay-sand ratio, hydraulic gradient, precipitation, and water level change have a significant impact on arsenic release. Comprehensive analysis shows that hydrogeological conditions, human activities, and climate change work together to shape the distribution pattern of high-arsenic groundwater in the lower Yellow River. The synergistic effect of the climate factor group regulates high-arsenic to a greater extent than human activities and hydrogeological conditions. Especially under arid climate conditions, climate change and groundwater exploitation have a synergistic effect on arsenic release, controlling the distribution of high-arsenic by up to 55%. This study provides a scientific basis for predicting regional health risks, strengthening water resources management and pollution control.