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Value of information analysis for Corrective Action Unit 97: Yucca Flat, Nevada Test Site, Nevada
Publication year - 1999
Publication title -
osti oai (u.s. department of energy office of scientific and technical information)
Language(s) - English
Resource type - Reports
DOI - 10.2172/756462
Subject(s) - aquifer , bayesian probability , environmental science , unit (ring theory) , statistics , geology , hydrology (agriculture) , geotechnical engineering , mathematics , groundwater , mathematics education
The value-of-information analysis evaluated data collection options for characterizing groundwater transport of contamination associated with the Yucca Flat and Climax Mine Corrective Action Units. Experts provided inputs for the evaluation of 48 characterization options, which included 27 component activities, 12 combinations of activities (subgroups), and 9 combinations of subgroups (groups). The options range from an individual study using existing data and intended to address a relatively narrow uncertainty to a 52-million dollar group of activities designed to collect and analyze new information to broadly address multiple uncertainties. A modified version of the contaminant transport component of the regional model was used to simulate contaminant transport and to estimate the maximum extent of the contaminant boundary, defined as that distance beyond which the committed effective dose equivalent from the residual radionuclides in groundwater will not exceed 4 millirem per year within 1,000 years. These simulations identified the model parameters most responsible for uncertainty over the contaminant boundary and determined weights indicating the relative importance of these parameters. Key inputs were identified through sensitivity analysis; the five selected parameters were flux for flow into Yucca Flat from the north, hydrologic source term, effective porosity and diffusion parameter for the Lower Carbonate Aquifer, and path length from the Volcanic Confining Unit to the Lower Carbonate Aquifer. Four measures were used to quantify uncertainty reduction. Using Bayesian analysis, the options were compared and ranked based on their costs and estimates of their effectiveness at reducing the key uncertainties relevant to predicting the maximum contaminant boundary

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