In 2010, nuclear weapon effects experts at Sandia National Laboratories (SNL) were asked to provide a quick reference document containing estimated prompt nuclear effects. This report is an update to the 2010 document that includes updated model assumptions. This report addresses only the prompt effects associated with a nuclear detonation (e.g., blast, thermal fluence, and prompt ionizing radiation). The potential medium- and longer-term health effects associated with nuclear fallout are not considered in this report because, in part, of the impracticality of making generic estimates given the high dependency of fallout predictions on the local meteorological conditions at the time of the event. The results included in this report also do not consider the urban environment (e.g., shielding by or collapse of structures) which may affect the extent of prompt effects. It is important to note that any operational recommendations made using the estimates in this report are limited by the generic assumptions considered in the analysis and should not replace analyses made for a specific scenario/device. Furthermore, nuclear effects experts (John Hogan, SNL, and Byron Ristvet, Defense Threat Reduction Agency (DTRA)) have indicated that the accuracy of effects predictions below 0.5 kilotons (kT) or 500 tons nuclear yield have greater uncertainty because of the limited data available for the prompt effects in this regime. The Specialized Hazard Assessment Response Capability (SHARC) effects prediction tool was used for these analyses. Specifically, the NUKE model within SHARC 2021 Version 10.2 was used. NUKE models only the prompt effects following a nuclear detonation. The algorithms for predicting range-to-output data contained within the NUKE model are primarily based on nuclear test effects data. Probits have been derived from nuclear test data and the U.S. Environmental Protection Agency (EPA) protective action guides. Probits relate the probability of a hazard (e.g., fatality or injury) caused by a given insult (e.g., overpressure, thermal fluence, dose level). Several probits have been built into SHARC to determine the fatality and injury associated with a given level of insult. Some of these probits differ with varying yield. Such probits were used to develop the tables and plots in this report.
This report documents the analysis conducted by Sandia National Laboratories on the effect of various alterations to published methodologies to calculate Derived Response Levels for Environmental Protection Agency Drinking Water Protective Action Guides. Specifically, this study sought to assess and provide recommendations on calculation of the Derived Response Level accounting for decay during the consumption period, assess the impact of decay on laboratory Minimal Detectable Concentration in water samples as compared to the Derived Response Level, make a recommendation on the calculation of Derived Response Level consistent with existing Public Protection Methods, and make a recommendation on the use of six age groups versus eight age groups based on available dose coefficients for calculation of the Derived Response Level. The authors analyzed these various factors using nominal radionuclide mixes from four scenarios and compared calculation of the Derived Response Level accounting for decay and no decay and then compared those results to the laboratory Minimal Detectable Concentrations. The authors concluded that decay should be included in the calculation of the Derived Response Level, existing Public Protection Methods should be employed to calculate the Derived Response Level, six age groups should be used versus eight, and the use of both decay and Public Protection Methods result in little to no concern for water samples meeting the Minimum Detectable Concentration requirements. The results of this study may be used in further developing and implementing a method for the Environmental Protection Agency Water Derived Response Level calculation in the Federal Radiological Monitoring and Assessment Center Assessment Manual.
Objectives: Automate the labor-intensive process of generating Analytical Action Levels (AALs) in Turbo FRMAC to shorten the timeline for planning sampling campaigns and sample analysis during a response. Make the tool output results in a format that is easily imported to RadResponder as a Mixture for use in Analysis Request Forms. Deliver training to EPA on using this new tool in Turbo FRMAC (Delayed due to COVID.
This report documents the findings of an assessment of the Turbo FRMAC software's ability to implement International Atomic Energy Agency (IAEA) guidance for calculating Operational Intervention Levels (OIL) 1 & 2 for nuclear and radiological emergencies. The IAEA OIL and U.S. Federal Radiological Monitoring and Assessment Center (FRMAC) Derived Response Level methodology and implementation in respective tools were compared, as demonstrated through benchmarking activities for a nuclear power plant source term and potential radionuclides of concern for radiological dispersal devices. This comparison revealed some shortcomings in Turbo FRMACs ability to perform IAEA OIL calculations and resulted in recommended software modifications to be considered for future development.
An interlaboratory effort has developed a probabilistic framework to characterize uncertainty in data products that are developed by the US Department of Energy Consequence Management Program in support of the Federal Radiological Monitoring and Assessment Center. The purpose of this paper is to provide an overview of the probability distributions of input variables and the statistical methods used to propagate and quantify the overall uncertainty of the derived response levels that are used as contours on data products due to the uncertainty in input parameters. Uncertainty analysis results are also presented for several study scenarios. This paper includes an example data product to illustrate the potential real-world implications of incorporating uncertainty analysis results into data products that inform protective action decisions. Data product contours that indicate areas where public protection actions may be warranted can be customized to an acceptable level of uncertainty. The investigators seek feedback from decision makers and the radiological emergency response community to determine how uncertainty information can be used to support the protective action decision-making process and how it can be presented on data products.
The goal of this project is to develop and execute methods for characterizing uncertainty in data products that are deve loped and distributed by the DOE Consequence Management (CM) Program. A global approach to this problem is necessary because multiple sources of error and uncertainty from across the CM skill sets contribute to the ultimate p roduction of CM data products. This report presents the methods used to develop a probabilistic framework to characterize this uncertainty and provides results for an uncertainty analysis for a study scenario analyzed using this framework.
This goal of this project is to address the current inability to assess the overall error and uncertainty of data products developed and distributed by DOE’s Consequence Management (CM) Program.
This goal of this project is to address the current inability to assess the overall error and uncertainty of data products developed and distributed by DOE’s Consequence Management (CM) Program. This is a widely recognized shortfall, the resolution of which would provide a great deal of value and defensibility to the analysis results, data products, and the decision making process that follows this work. A global approach to this problem is necessary because multiple sources of error and uncertainty contribute to the ultimate production of CM data products. Therefore, this project will require collaboration with subject matter experts across a wide range of FRMAC skill sets in order to quantify the types of uncertainty that each area of the CM process might contain and to understand how variations in these uncertainty sources contribute to the aggregated uncertainty present in CM data products. The ultimate goal of this project is to quantify the confidence level of CM products to ensure that appropriate public and worker protections decisions are supported by defensible analysis.