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COMPARING THE UTILITY OF MULTIMEDIA MODELS FOR HUMAN AND ECOLOGICAL EXPOSURE ANALYSIS: TWO CASES
Citation:
RECKHOW, K. H., J. E. BABENDREIER, R. T. DI GUILIO, AND D. A. VALLERO. COMPARING THE UTILITY OF MULTIMEDIA MODELS FOR HUMAN AND ECOLOGICAL EXPOSURE ANALYSIS: TWO CASES. Presented at International Conference on Environmental Epidemiology and Exposure, Paris, FRANCE, September 02 - 06, 2006.
Impact/Purpose:
The primary goals are to: (1) Construct a 400-node PC-based supercomputing cluster supporting Windows and Linux computer operating systems (i.e. SuperMUSE: Supercomputer for Model Uncertainty and Sensitivity Evaluation); (2) Develop platform-independent system software for the management of SuperMUSE and parallelization of EPA models and modeling systems for implementation on SuperMUSE (and other PC-based clusters); (3) Conduct uncertainty and sensitivity analyses of the 3MRA modeling system; (4) Develop advanced algorithmic software for advanced statistical sampling methods, and screening, localized, and global sensitivity analyses; and (5) Provide customer-oriented model applications for probabilistic risk assessment supporting quality assurance in multimedia decision-making.
Description:
A number of models are available for exposure assessment; however, few are used as tools for both human and ecosystem risks. This discussion will consider two modeling frameworks that have recently been used to support human and ecological decision making. The study will compare and contrast a Bayesian approach for pooling pre-implementation model forecasts with post-implementation measurements to assess compliance with the relevant water quality standard with an integrated multimedia national-scale study of land application of arsenic-bearing wastes (the latter is presented separately at this conference). A specific application of each model will provide a case study. The first case will address the U.S. EPA?s Multimedia, Multipathway, and Multireceptor Risk Assessment (3MRA) model from the standpoint of determining a variety of exposure profiles for receptor classes of concern for land-based arsenic disposal scenarios. This approach will be compared to a Bayesian process for updating model output using subsequent monitoring data. The updated results are a combination of our understanding of the system (reflected in model results) and the information in the data. The Bayesian SPARROW model was used to predict mean and standard deviation of the nitrogen loading as the basis for constructing the prior distribution of the nitrogen concentrations in the Neuse River Basin, North Carolina. The comparison will consist of a judgment by the presenters of the advantages and disadvantages of these model applications for ecological and exposure assessments.