Enhancing health risk assessment for soil heavy metal (loid)s using a copula-based monte carlo simulation method

In probabilistic health risk assessment (HRA) of multiple soil heavy metal(loid)s (HMs), Monte Carlo Simulation (MCS) typically ignores inter-element correlations during concentration simulation, potentially compromising risk estimation accuracy. In this study, a total of 109 surface soil samples collected in 2023 from a mining area in Chizhou, China, were analyzed for HM concentrations, which followed the order Zn > Cr > Cu > Pb > As > Ni > Hg > Cd. An enhanced HRA model by integrating Copula functions with MCS was employed to account for HM interdependencies. Among five tested Copula functions, the t Copula performed the best, effectively capturing the tail dependence and nonlinear associations between HMs. Risk assessment results showed children's 95th percentile hazard index (HI) increased by 9.2 % (from 3.170 to 3.461) and adult females' 95th percentile total carcinogenic risk (TCR) elevated by 11.8 % (from 1.19E-04-1.33E-04) when considering correlations, both aligning more closely with actual risk levels. Sensitivity analysis revealed that HM correlations redistribute risk contributions among elements, reducing the dominance of primary contaminants. This Copula-MCS integrated framework significantly enhances HRA reliability in polymetallic contamination scenarios, providing critical methodological support for soil risk management.