Occurrence, geochemical characteristics, and Monte Carlo simulations-based health risk assessment of uranium in groundwater in parts of upper Ganga Basin, India.

Uranium (U) contamination in groundwater presents a serious global health hazard, driven by its radioactive nature and long-term persistence in aquatic systems. This study focuses on U contamination, its fate, mobility, speciation, and the associated health risks through ingestion and dermal pathways. Monte Carlo Simulations (MCS) were employed to develop a probabilistic framework, addressing variability and uncertainty in conventional Health Risk Assessments (HRA). A total of 72 groundwater samples were systematically collected in November 2023 from alluvial aquifers in western Uttar Pradesh, India. The mobilization of U is likely driven by oxidative dissolution, resulting from its limited adsorption to Fe-bearing minerals. PHREEQC-based speciation modeling indicates U mineral phases are undersaturated, favoring dissolution and mobility as stable hexavalent uranyl complexes. Negatively charged carbonate species, such as UO(CO) and UO(CO), exhibits high activity coefficients, indicating the role of carbonate complexation in U mobility under neutral to near-alkaline pH. HRA, employing MCS, revealed significant dual threats of radiological (carcinogenic) and chemical toxicity (non-carcinogenic) risks for males, females and children. Hazard index (HI) values exceeded safety thresholds (HI > 1) in 98% of samples for children, 92% for males, and 88% for females, with children being particularly vulnerable due to their lower body weight. Sensitivity analysis identified U concentrations as the most influential variable impacting risk analysis, followed by exposure frequency across all age groups. Infants and males exhibited higher annual ingestion doses (Da) of U, exceeding the WHO recommended limit of 100 µSv/year. This research highlights the critical need for effective groundwater management in U-endemic regions by highlighting probabilistic HRA as a key tool to reduce uncertainty.