Two-dimensional Monte Carlo simulation coupled with multilinear regression modeling of source-specific health risks from groundwater.

Effective protection of groundwater requires an accurate health risk assessment of contaminants; however, the diversity of pollution sources, variability, and uncertainties in exposure parameters present significant challenges in this assessment. In this study, groundwater risk estimates associated with NO, and F, along with fourteen heavy metal(loid)s (V, Cr, Mn, Fe, Ni, Cu, As, Co, Cd, Se, Pb, Hg, Zn, and Al) in an agricultural area were optimized by implementing positive matrix factorization (PMF), multilinear regression, and two-dimensional Monte Carlo simulations to characterize source-specific health risks. Groundwater pollution was analyzed considering regional variations, including differences in elevation, land use and land cover, and soil types. Three pollution sources were identified: agricultural practices, traffic, and natural processes. Moreover, the results revealed NO from an agricultural source as the primary control contaminant. Additionally, both adults and children in the study area face significant non-carcinogenic health risks. To mitigate these risks, this study recommends maximum consumption levels of 1.44 L/day for adults and 0.35 L/day for children. Furthermore, adults weighing > 68.1 kg and children weighing > 15.9 kg are likely to be at reduced risk of experiencing adverse health effects. Compared to deterministic health risk assessment and one-dimensional Monte Carlo simulation of health risks, two-dimensional Monte Carlo simulation showed improved performance, providing better accuracy and higher precision in health risk assessment results. Thus, this research is expected to enhance the understanding of health risk assessment related to groundwater and to provide valuable guidance for managing groundwater pollution.