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.

Analysis of PBPK models for risk characterization.

Adoption of a Bayesian framework for risk characterization permits the seamless integration of different kinds of information available in order to choose and parameterize risk models. It also becomes easy to disentangle uncertainty from variability, through hierarchical statistical modeling. Appropriate numerical techniques can be found, for example, in the recently developed arsenal of Markov chain, Monte Carlo simulations. The developments in this area can actually be viewed as extensions of the traditional or standard Monte Carlo methods for uncertainty analysis. Following a brief review of the techniques, examples of Bayesian analyses of physiologically-based pharmacokinetic models are presented for tetrachloroethylene and dichloromethane. The discussion touches on some open problems and perspectives for the proposed methods.

Multimedia benchmarking analysis for three risk assessment models: RESRAD, MMSOILS, and MEPAS.

This paper is one in a series that describes results of a benchmarking analysis initiated by the Department of Energy (DOE) and the United States Environmental Protection Agency (EPA). An overview of the study is provided in a companion paper by Laniak et al. presented in this journal issue. The three models used in the study--RESRAD (DOE), MMSOILS (EPA), and MEPAS (DOE)--represent analytically-based tools that are used by the respective agencies for performing human exposure and health risk assessments. Both single media and multimedia benchmarking scenarios were developed and executed. In this paper, the multimedia scenario is examined. That scenario consists of a hypothetical landfill that initially contained uranium-238 and methylene chloride. The multimedia models predict the fate of these contaminants, plus the progeny of uranium-238, through the unsaturated zone, saturated zone, surface water, and atmosphere. Carcinogenic risks are calculated from exposure to the contaminants via multiple pathways. Results of the tests show that differences in model endpoint estimates arise from both differences in the models' mathematical formulations and assumptions related to the implementation of the scenarios.

Biologically motivated models for chemical risk assessment.

Assessing the risk associated with human exposure to environmental chemicals depends to a large extent on the ability to extrapolate from a particular range of exposure conditions in the test animal species to a very different range of exposure conditions in the human. One of the more promising tools for accomplishing this extrapolation is the biologically motivated pharmacokinetic/pharmacodynamic model. In a biologically motivated model, the structure is based on the physiological and biochemical structure of the animal system being described. This paper provides an overview of the biologically motivated modeling approach. Examples of models for styrene and methylene chloride are discussed in relation to their ability to predict human kinetics for these chemicals and their use in estimating the risk of chemicals to exposed humans. Finally, the use of a biologically motivated model to analyze the mechanistic basis of chemical carcinogenesis is discussed.