HR: 1340h
AN: H13A-0389    [Abstracts]
TI: Environmental Modeling and Bayesian Analysis for Assessing Human Health Impacts from Radioactive Waste Disposal
AU: * Stockton, T
EM: stockton@neptuneinc.org
AF: Neptune and Company, Inc., 1505 15th Street, Los Alamos, NM 87544 United States
AU: Black, P
AF: Neptune and Company, Inc., 1505 15th Street, Los Alamos, NM 87544 United States
AU: Tauxe, J
AF: Neptune and Company, Inc., 1505 15th Street, Los Alamos, NM 87544 United States
AU: Catlett, K
AF: Neptune and Company, Inc., 1505 15th Street, Los Alamos, NM 87544 United States
AB: Bayesian decision analysis provides a unified framework for coherent decision-making. Two key components of Bayesian decision analysis are probability distributions and utility functions. Calculating posterior distributions and performing decision analysis can be computationally challenging, especially for complex environmental models. In addition, probability distributions and utility functions for environmental models must be specified through expert elicitation, stakeholder consensus, or data collection, all of which have their own set of technical and political challenges. Nevertheless, a grand appeal of the Bayesian approach for environmental decision- making is the explicit treatment of uncertainty, including expert judgment. The impact of expert judgment on the environmental decision process, though integral, goes largely unassessed. Regulations and orders of the Environmental Protection Agency, Department Of Energy, and Nuclear Regulatory Agency orders require assessing the impact on human health of radioactive waste contamination over periods of up to ten thousand years. Towards this end complex environmental simulation models are used to assess "risk" to human and ecological health from migration of radioactive waste. As the computational burden of environmental modeling is continually reduced probabilistic process modeling using Monte Carlo simulation is becoming routinely used to propagate uncertainty from model inputs through model predictions. The utility of a Bayesian approach to environmental decision-making is discussed within the context of a buried radioactive waste example. This example highlights the desirability and difficulties of merging the cost of monitoring, the cost of the decision analysis, the cost and viability of clean up, and the probability of human health impacts within a rigorous decision framework.
DE: 6309 Decision making under uncertainty
DE: 6344 System operation and management
DE: 3210 Modeling
DE: 1832 Groundwater transport
DE: 1875 Unsaturated zone
SC: Hydrology [H]
MN: 2004 AGU Fall Meeting