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 report specifies the electronic file format that was agreed upon to be used as the file format for normalized radiological data produced by the software tool developed under this TI project. The NA-84 Technology Integration (TI) Program project (SNL17-CM-635, Normalizing Radiological Data for Analysis and Integration into Models) investigators held a teleconference on December 7, 2017 to discuss the tasks to be completed under the TI program project. During this teleconference, the TI project investigators determined that the comma-separated values (CSV) file format is the most suitable file format for the normalized radiological data that will be outputted from the normalizing tool developed under this TI project. The CSV file format was selected because it provides the requisite flexibility to manage different types of radiological data (i.e., activity concentration, exposure rate, dose rate) from other sources [e.g., Radiological Assessment and Monitoring System (RAMS), Aerial Measuring System (AMS), Monitoring and Sampling). The CSV file format also is suitable for the file format of the normalized radiological data because this normalized data can then be ingested by other software [e.g., RAMS, Visual Sampling Plan (VSP)] used by the NA-84’s Consequence Management 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 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.
This report reviews the method recommended by the U.S. Food and Drug Administration for calculating Derived Intervention Levels (DILs) and identifies potential improvements to the DIL calculation method to support more accurate ingestion pathway analyses and protective action decisions. Further, this report proposes an alternate method for use by the Federal Emergency Radiological Assessment Center (FRMAC) to calculate FRMAC Intervention Levels (FILs). The default approach of the FRMAC during an emergency response is to use the FDA recommended methods. However, FRMAC recommends implementing the FIL method because we believe it to be more technically accurate. FRMAC will only implement the FIL method when approved by the FDA representative on the Federal Advisory Team for Environment, Food, and Health.
A proposed method is considered to classify the regions in the close neighborhood of selected measurements according to the ratio of two radionuclides measured from either a radioactive plume or a deposited radionuclide mixture. The subsequent associated locations are then considered in the area of interest with a representative ratio class. This method allows for a more comprehensive and meaningful understanding of the data sampled following a radiological incident.