Sensitivity of Concentration and Risk Predictions in the PRESTO and MMSOILS Multimedia Models: Regression Technique Assessment

This paper describes the application of two multimedia models, PRESTO and MMSOILS, to predict contaminant migration from a landfill that contains an organic chemical (methylene chloride) and a radionuclide (uranium‐238). Exposure point concentrations and human health risks are predicted, and distributions of those predictions are generated using Monte Carlo techniques. Analysis of exposure point concentrations shows that predictions of uranium‐238 in groundwater differ by more than one order of magnitude between models. These differences occur mainly because PRESTO simulates uranium‐238 transport through the groundwater using a one‐dimensional algorithm and vertically mixes the plume over an effective mixing depth, whereas MMSOILS uses a three‐dimensional algorithm and simulates a plume that resides near the surface of the aquifer. A sensitivity analysis, using stepwise multiple linear regression, is performed to evaluate which of the random variables are most important in producing the predicted distributions of exposure point concentrations and health risks. The sensitivity analysis shows that the predicted distributions can be accurately reproduced using a small subset of the random variables. Simple regression techniques are applied, for comparison, to the same scenarios, and results are similar. The practical implication of this analysis is the ability to distinguish between important versus unimportant random variables in terms of their sensitivity to selected endpoints.