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Evaluating variability and uncertainty in radiological impact assessment using SYMBIOSE
M Simon-Cornu1, K Beaugelin-Seiller1, P Boyer1
1Institut de Radioprotection et de Sûreté Nucléaire (IRSN), PRP-ENV, SERIS, LM2E, Cadarache, France.
Journal of Environmental Radioactivity
|December 3, 2014
Summary
SYMBIOSE models radiological impacts, incorporating variability and uncertainty. It uses 331 Probability Distribution Functions (PDFs) derived from IAEA documents to assess environmental radionuclide fate and human doses.
Area of Science:
- Environmental Science
- Radiological Protection
- Computational Modeling
Background:
- Radiological impact assessments require accounting for parameter variability and uncertainty.
- Existing models often lack comprehensive databases for these parameters.
- International Atomic Energy Agency (IAEA) documents provide summary statistics for transfer parameters.
Purpose of the Study:
- To present the SYMBIOSE modeling platform for radiological impact assessments.
- To detail the creation and use of a database of Probability Distribution Functions (PDFs) for radionuclide transfer parameters.
- To demonstrate the separation of parametric uncertainty and inter-individual variability in dose assessments.
Main Methods:
- Development of the SYMBIOSE modeling platform.
- Derivation of 331 Probability Distribution Functions (PDFs) from summary statistics in IAEA documents.
- Application of a second-order Monte Carlo calculation in a case study.
Main Results:
- A database of 331 PDFs characterizing uncertainty in transfer parameters is established.
- Methods for deriving PDFs from incomplete statistical data are presented.
- A case study successfully illustrates the separation of uncertainty and variability.
Conclusions:
- The SYMBIOSE platform effectively incorporates variability and uncertainty in radiological assessments.
- The developed PDF database enhances the robustness of environmental radionuclide fate and dose assessments.
- The methodology allows for a clearer distinction between parametric uncertainty and inter-individual variability.
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