Groundwater plays an indispensable role in daily life. However, with the continuous advancement of industrialization, more attention should be paid to the quality of groundwater and the associated potential health risks in areas surrounding industrial parks. In this study, groundwater samples collected in the city of the Tibetan Plateau during the wet season (WS) and dry season (DS) were analyzed using Piper diagrams, Gibbs diagrams, and correlation analysis. The results elucidated the hydrochemical characteristics, formation mechanisms, and controlling factors of groundwater in the area. Groundwater potability was assessed using the Entropy-weighted Water Quality Index (EWQI) method. In addition, the health risk assessment model was applied to evaluate potential risks for four population groups, with NO3− and F− selected as representative groundwater pollutants. The findings revealed that groundwater in the study zone was typically moderately alkaline and characterized primarily as soft–fresh and hard–fresh. The groundwater in both seasons mainly exhibited HCO3–Ca chemical facies. Water–rock interactions involving silicate and carbonate minerals were identified as key processes controlling the hydrochemical composition in both seasons. EWQI results showed that groundwater quality for drinking purposes was excellent in the seasons. Sensitivity analysis further showed that Cl− exerted the greatest influence on the drinking water quality evaluation in both seasons. Health risk assessments revealed that the risks posed by NO3− and F− to infants, children, adult females, and adult males remained within acceptable limits (with max values of 0.63, 0.39, 0.28, and 0.33 in the WS, and 0.59, 0.36, 0.26, and 0.31 in the DS, respectively). However, infants exhibited greater susceptibility than the other groups across seasons, with a risk index approximately twice that of adults. Overall, the findings contribute valuable insights for the sustainable management and planning of groundwater resources in the study zone. Future research could refine the risk assessment model with localized data and explore mitigation strategies for elevated risks in specific seasons or regions.
Zhou et al. (Tue,) studied this question.