Increased human health risk associated with groundwater contamination from potential carbon dioxide (CO2) leakage into a potable aquifer is predicted by conducting a joint uncertainty and variability (JUV) risk assessment. The approach presented here explicitly incorporates heterogeneous flow and geochemical reactive transport in an efficient manner and is used to evaluate how differences in representation of subsurface physical heterogeneity and geochemical reactions change the calculated risk for the same hypothetical aquifer scenario where a CO2 leak induces increased lead (Pb(2+)) concentrations through dissolution of galena (PbS). A nested Monte Carlo approach was used to take Pb(2+) concentrations at a well from an ensemble of numerical reactive transport simulations (uncertainty) and sample within a population of potentially exposed individuals (variability) to calculate risk as a function of both uncertainty and variability. Pb(2+) concentrations at the well were determined with numerical reactive transport simulation ensembles using a streamline technique in a heterogeneous 3D aquifer. Three ensembles with variances of log hydraulic conductivity (σ(2)lnK) of 1, 3.61, and 16 were simulated. Under the conditions simulated, calculated risk is shown to be a function of the strength of subsurface heterogeneity, σ(2)lnK and the choice between calculating Pb(2+) concentrations in groundwater using equilibrium with galena and kinetic mineral reaction rates. Calculated risk increased with an increase in σ(2)lnK of 1 to 3.61, but decreased when σ(2)lnK was increased from 3.61 to 16 for all but the highest percentiles of uncertainty. Using a Pb(2+) concentration in equilibrium with galena under CO2 leakage conditions (PCO2 = 30 bar) resulted in lower estimated risk than the simulations where Pb(2+) concentrations were calculated using kinetic mass transfer reaction rates for galena dissolution and precipitation. This study highlights the importance of understanding both hydrologic and geochemical conditions when numerical simulations are used to perform quantitative risk calculations.