A probabilistic, risk-based performance-assessment methodology has been developed to assist designers, regulators, and stakeholders in the selection, design, and monitoring of long-term covers for contaminated subsurface sites. This report describes the method, the software tools that were developed, and an example that illustrates the probabilistic performance-assessment method using a repository site in Monticello, Utah. At the Monticello site, a long-term cover system is being used to isolate long-lived uranium mill tailings from the biosphere. Computer models were developed to simulate relevant features, events, and processes that include water flux through the cover, source-term release, vadose-zone transport, saturated-zone transport, gas transport, and exposure pathways. The component models were then integrated into a totalsystem performance-assessment model, and uncertainty distributions of important input parameters were constructed and sampled in a stochastic Monte Carlo analysis. Multiple realizations were simulated using the integrated model to produce cumulative distribution functions of the performance metrics, which were used to assess cover performance for both present-and long-term future conditions. Performance metrics for this study included the water percolation reaching the uranium mill tailings, radon gas flux at the surface, groundwater concentrations, and dose. Results from uncertainty analyses, sensitivity analyses, and alternative design comparisons are presented for each of the performance metrics. The benefits from this methodology include a quantification of uncertainty, the identification of parameters most important to performance (to prioritize site characterization and monitoring activities), and the ability to compare alternative designs using probabilistic evaluations of performance (for cost savings). (Sections 4.3.1 and 4.5) with simulation of all four performance metrics (water percolation through the cover, radon gas flux through the cover, groundwater concentration, and peak cumulative dose). · Additional uncertainty quantification for the base-case-composite and alternative-ET cover designs (Section 4.5)-illustrates how probabilistic resultscan be used to estimate probability of exceeding regulatory or performance metrics. ·Comparison between alternative designs for each scenario and performance metric (Section 4.5)-illustrates how alternative designs can be compared to minimize cost while ensuring adherence to relevant regulatory requirements and performance metrics. ·Additional results and descriptions of sensitivity analyses for each scenario, design, and performance metric (Section 4.6)-demonstrates how parameters important to long-term performance can be identified for prioritizing characterization and monitoring studies. · Description of computer modules that were added to FRAMES for stochastic analyses (Appendix A).These include the radon-gas-transport code, RAECOM, and the water-balance code, HELP.6
A probabilistic, risk-based performance-assessment methodology is being developed to assist designers, regulators, and involved stakeholders in the selection, design, and monitoring of longterm covers for contaminated subsurface sites. This report presents an example of the risk-based performance-assessment method using a repository site in Monticello, Utah. At the Monticello site, a long-term cover system is being used to isolate long-lived uranium mill tailings from the biosphere. Computer models were developed to simulate relevant features, events, and processes that include water flux through the cover, source-term release, vadose-zone transport, saturatedzone transport, gas transport, and exposure pathways. The component models were then integrated into a total-system performance-assessment model, and uncertainty distributions of important input parameters were constructed and sampled in a stochastic Monte Carlo analysis. Multiple realizations were simulated using the integrated model to produce cumulative distribution functions of the performance metrics, which were used to assess cover performance for both present-and long-term future conditions. Performance metrics for this study included the water percolation reaching the uranium mill tailings, radon flux at the surface, groundwater concentrations, and dose. Results of this study can be used to identify engineering and environmental parameters (e.g., liner properties, long-term precipitation, distribution coefficients) that require additional data to reduce uncertainty in the calculations and improve confidence in the model predictions. These results can also be used to evaluate alternative engineering designs and to identify parameters most important to long-term performance.
This report uses hypothetical decommissioning test cases to illustrate an uncertainty assessment methodology for dose assessments conducted as part of decommissioning analyses for NRC-licensed facilities. This methodology was presented previously in NUREG/CR-6656. The hypothetical test case source term and scenarios are based on an actual decommissioning case and the physical setting is based on the site of a field experiment carried out for the NRC in Arizona. The emphasis in the test case was on parameter uncertainty. The analysis is limited to the hydrologic aspects of the exposure pathway involving infiltration of water at the ground surface, leaching of contaminants, and transport of contaminants through the groundwater to a point of exposure. The methodology uses generic parameter distributions based on national or regional databases for estimating parameter uncertainty. A Bayesian updating method is used in one of the test case applications to combine site-specific information with the generic parameter distributions. Sensitivity analyses and probabilistic simulations are used to describe the impact of parameter uncertainty on predicted dose. Emphasis is placed on understanding the conceptual and computational behavior of the dose assessment codes as they are applied to the test cases. The primary code used in these applications was RESRAD v. 6.0, although DandD v.
This report uses hypothetical decommissioning test cases to illustrate an uncertainty assessment methodology for dose assessments conducted as part of decommissioning analyses for NRC-licensed facilities. This methodology was presented previously in NUREG/CR-6656. The hypothetical test case source term and scenarios are based on an actual decommissioning case and the physical setting is based on the site of a field experiment carried out for the NRC in Arizona. The emphasis in the test case was on parameter uncertainty. The analysis is limited to the hydrologic aspects of the exposure pathway involving infiltration of water at the ground surface, leaching of contaminants, and transport of contaminants through the groundwater to a point of exposure. The methodology uses generic parameter distributions based on national or regional databases for estimating parameter uncertainty. A Bayesian updating method is used in one of the test case applications to combine site-specific information with the generic parameter distributions. Sensitivity analyses and probabilistic simulations are used to describe the impact of parameter uncertainty on predicted dose. Emphasis is placed on understanding the conceptual and computational behavior of the dose assessment codes as they are applied to the test cases. The primary code used in these applications was RESRAD v. 6.0, although DandD v.
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