Enrichment of High Arsenic Groundwater Controlled by Hydrogeochemical and Physical Processes in the Hetao Basin, China

Bowl effect of irreversible primary salinization driven by geology in Hetao irrigation area, China

Agricultural irrigation areas around the world employ similar planting methods, but there are notable disparities in salinization mechanism and management strategies. Many scholars have focused on human activities as the main cause of secondary soil salinization, while neglecting the underlying issue of primary soil salinization caused by geological factors. This study takes the Hetao irrigation area in China as a case study, delving into the geological forces responsible for primary salinization. Using historical survey data on geological structure, lake evolution, and sedimentation to analyze the stratigraphic distribution and groundwater storage characteristic. Additionally, using groundwater hydrochemistry data from historical literatures to analyze the concentration, distribution patterns, and source issues of salt ions. The research results show that a novel concept called the "bowl effect" can explain the unique cause of primary salinization in Hetao irrigation area. The bowl effect effectively transforms Hetao irrigation area into an enclosed space, which significantly limits the movement of groundwater and hinders the dilution of highly saline or alkaline water. The bowl effect has broad applicability and can serve as a useful framework for studying primary salinization challenges in agricultural irrigation areas worldwide. This research provides a scientifically reference for selecting salinization control methods, and will benefit local stakeholders, government agencies, and water resource managers.

Hazards and influence factors of arsenic in the upper pleistocene aquifer, Hetao region, using machine learning modeling

The Hetao region is one of the regions with the most serious problem of the greatest measured arsenic concentrations in China. The enrichment of arsenic in groundwater may poses a great risk to the health of local residents. A comprehensive understanding of the groundwater quality, spatial distribution characteristics and hazard of the high arsenic in groundwater is indispensable for the sustainable utilization of groundwater resources and resident health. This study selected six environmental factors, climate, human activity, sedimentary environment, hydrogeology, soil, and others, as the independent input variables to the model, compared three machine learning algorithms (support vector machine, extreme gradient boosting, and random forest), and mapped unsafe arsenic to estimate the population that may be exposed to unhealthy conditions in the Hetao region. The results show that nearly half the number of the 605 sampling wells for arsenic exceeded the WHO provisional guide value for drinking water, the water chemistry of groundwater are mainly Na-HCO3-Cl or Na-Mg-HCO3-Cl type water, and the groundwater with excessive arsenic concentration is mainly concentrated in the ancient stream channel influence zone and the Yellow River crevasse splay. The results of factor importance explanation revealed that the sedimentary environment was the key factor affecting the primary high arsenic groundwater concentration, followed by climate and human activities. The random forest algorithm produced the probability distribution of high arsenic groundwater that is consistent with the observed results. The estimated area of groundwater with excessive arsenic reached 38.81 %. An estimated 940,000 people could be exposed to high arsenic in groundwater.