The large amounts of tritium produced at the Savannah River Site (SRS) coupled with the current dose reconstruction study at the facility emphasize the importance of ensuring accurate and efficient prediction of tritium doses to the public.
Presently, dose estimates to the general population in the site vicinity are calculated annually using a five year meteorological database.
Determining whether detailed monthly dose estimates are necessary or whether annual averaged data is sufficient offers the potential for more efficient dose prediction.
In this study, off site collective committed doses and maximum individual doses due to atmospheric tritium releases were calculated according to the methods outlined in the U.S. Nuclear Regulatory Commission's Regulatory Guide 1.109 and compared using monthly versus five-year meteorological data and source terms.
Site-specific variables not currently utilized at SRS for annual dose estimates also have been included.
In addition, the range of predicted doses, based on the distribution in model parameters given in the literature, were estimated.
Finally, a sensitivity analysis was performed in order to determine the influence of model inputs on dose estimates.
Results corroborate previous studies by indicating that the primary contributor to infant tritium dose is the ingestion of milk, while for all other age groups, the most important pathway is the ingestion of vegetation. (...)
Mots-clés Pascal : Etats Unis, Amérique du Nord, Amérique, Usine Savannah River, Pollution air, Pollution radioactive, Hydrogène Isotope, Tritium, Devenir polluant, Transport, Dispersion atmosphérique, Teneur air ambiant, Variation temporelle, Variation annuelle, Variation interannuelle, Radiocontamination, Dosimétrie, Condition météorologique, Modélisation, Modèle prévision, Analyse sensibilité
Mots-clés Pascal anglais : United States, North America, America, Savannah River plant, Air pollution, Radioactive pollution, Hydrogen Isotopes, Tritium, Pollutant behavior, Transport, Atmospheric dispersion, Ambient air concentration, Time variation, Annual variation, Interannual variation, Radioactive contamination, Dosimetry, Atmospheric condition, Modeling, Forecast model, Sensitivity analysis
Notice produite par :
Inist-CNRS - Institut de l'Information Scientifique et Technique
Cote : 98-0485536
Code Inist : 001D16C04D. Création : 19/02/1999.