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UNCERTAINTY AND SENSITIVITY ANALYSES FOR INTEGRATED HUMAN HEALTH AND ECOLOGICAL RISK ASSESSMENT OF HAZARDOUS WASTE DISPOSAL
Citation:
Babendreier, J E. UNCERTAINTY AND SENSITIVITY ANALYSES FOR INTEGRATED HUMAN HEALTH AND ECOLOGICAL RISK ASSESSMENT OF HAZARDOUS WASTE DISPOSAL. Presented at 2003 Resource Conservation and Recovery Act National Meeting: Putting Resource Conservation into Resource Conservation and Recovery Act, Washington, DC, August 12-15, 2003.
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:
While there is a high potential for exposure of humans and ecosystems to chemicals released from hazardous waste sites, the degree to which this potential is realized is often uncertain. Conceptually divided among parameter, model, and modeler uncertainties imparted during simulation, inaccuracy in model predictions result principally from lack of knowledge and data. In comparison, sensitivity analysis can lead to a better understanding of how models respond to variation in their inputs, which in turn can be used to better focus laboratory and field-based data collection efforts on processes and parameters that contribute most to uncertainty in outputs. Evaluating uncertainty and sensitivity in environmental models can be a difficult task, even for low-order, single-medium constructs driven by a unique set of site-specific data. Quantitative assessment of integrated, multimedia models that simulate hundreds of sites, spanning multiple geographical and ecological regions, will ultimately require a comparative approach using several techniques, coupled with sufficient computational power. Residing within the Framework for Risk Analysis in Multimedia Environmental Systems (FRAMES), the Multimedia, Multipathway, and Multireceptor Risk Assessment (3MRA) modeling system is being developed by EPA for use in assessment of hazardous waste management facilities. The 3MRA modelling system includes a set of 17 science modules that collectively simulate release, fate and transport, exposure, and risk associated with hazardous contaminants disposed of in land-based waste management units (WMU). The 3MRA model currently encompasses 922 input variables, over 185 of which are explicitly stochastic. A characteristic of uncertainty analysis (UA) and sensitivity analysis (SA) for very high order models (VHOMs) like 3MRA is their need for significant computational capacity to perform relatively redundant simulations. While UA/SA is emerging as a critical area for environmental model evaluation, Windows-based models have been limited by a lack of supercomputing capacity. Equally, higher-order UA/SA algorithms warrant investigation to determine their efficacy in establishing requisite confidence in the use of VHOMs for regulatory decision-making. Design of SuperMUSE, a 215 GHz PC-based, Windows-based Supercomputer for Model Uncertainty and Sensitivity Evaluation is described. Research is reported for an uncertainty analysis and sensitivity analysis of benzene disposal using 3MRA that describes the relative importance of various exposure pathways in driving risk levels for ecological receptors and human health, evaluating aspects of both site-scale and national scale assessments. Incorporating landfills, waste piles, aerated tanks, surface impoundments, and land application units, the site-based data used in the analysis included 201 national facilities representing 419 site-WMU combinations