In 1993, a manual on using Version 5 of the RESRAD code to implement the U.S. Department of Energy's (DOE's) residual radioactive material guidelines was released. Since then, as part of the RESRAD quality assurance (QA) program, the RESRAD code has undergone extensive review, benchmarking, verification, and validation. The manual and code have been used widely by DOE and its contractors, the U.S. Nuclear Regulatory Commission, U.S. Environmental Protection Agency (EPA), U.S. Army Corps of Engineers, industrial firms, universities, and foreign government agencies and institutions. New features, some in response to comments received from users, have been incorporated into the code to form RESRAD 6. These improvements have increased RESRAD's capabilities and flexibility and enabled users to interact with the code more easily. With the improvements, the code has become more realistic in terms of the models and default parameters it uses. RESRAD 6 represents the sixth major version of the RESRAD code since it was first issued in 1989. The RESRAD code can now perform uncertainty/probabilistic analyses with an improved probabilistic interface. 1 It uses a preprocessor and a postprocessor to perform probabilistic dose and risk analyses. 2 It incorporates default parameter distributions (based on national average data) for selected parameters. The code can provide analysis results as text reports, interactive output, and graphic output. The results of an uncertainty analysis can be used as a basis for determining the costeffectiveness of obtaining additional information or data on input parameters (variables). 3 The RESRAD code now allows users to calculate the time-integrated dose and risk at userspecified times. The instantaneous dose/risk can be calculated by setting the time integration point to one. The other major changes in the code are a new external exposure model, new area factor xii model for the inhalation pathway, and New Dose Conversion Factor (DCF) Editor. The DCF Editor gives users the ability to change dose conversion factors, transfer factors, and slope factors. Moreover, the RESRAD database was updated. It now includes inhalation and ingestion dose conversion factors from the EPA's Federal Guidance Report No. 11 (FGR-11), direct external exposure dose conversion factors from FGR-12, risk slope factors primarily from the latest health effects and summary tables, and radionuclide half-lives from International Commission on Radiological Protection Publication 38. The risk coefficients from FGR-13 are also available in the code. Version 6 of RESRAD incorporates many improvements made since Version 5 was released. These include the addition of seven new radionuclides: selenium-79, zirconium-93, neodymium-93m, barium-133, curium-245, curium-246, and curium-247. The code was also modified to account for radioactive decay and ingrowth during food storage times. The code now has an improved groundwater model to ensure convergence for K d calculations when water concentrations are known; has an improved radon pa...

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A number of models have recently been, or are currently being, developed to enable the assessment of radiation doses from ionising radiation to non-human species. A key component of these models is the ability to predict whole-organism activity concentrations in a wide range of wildlife. In this paper, we compare the whole-organism activity concentrations predicted by eight models participating within the IAEA Environmental Modelling for Radiation Safety programme for a range of radionuclides to terrestrial and freshwater organisms. In many instances, there was considerable variation, ranging over orders of magnitude, between the predictions of the different models. Reasons for this variability (including methodology, data source and data availability) are identified and discussed. The active participation of groups responsible for the development of key models within this exercise is a useful step forward in providing the transparency in methodology and data provenance required for models which are either currently being used for regulatory purposes or which may be used in the future. The work reported in this paper, and supported by other findings, demonstrates that the largest contribution to variability between model predictions is the parameterisation of their transfer components. There is a clear need to focus efforts and provide authoritative compilations of those data which are available.

No abstract