Benchmarking of Resrad-Offsite : Transition From Resrad (Onsite) Toresrad-Offsite and Comparison of the Resrad-Offsite Predictions With Peercodes.

The main purpose of this report is to document the benchmarking results and verification of the RESRAD-OFFSITE code as part of the quality assurance requirements of the RESRAD development program. This documentation will enable the U.S. Department of Energy (DOE) and its contractors, and the U.S. Nuclear Regulatory Commission (NRC) and its licensees and other stakeholders to use the quality-assured version of the code to perform dose analysis in a risk-informed and technically defensible manner to demonstrate compliance with the NRC's License Termination Rule, Title 10, Part 20, Subpart E, of the Code of Federal Regulations (10 CFR Part 20, Subpart E); DOE's 10 CFR Part 834, Order 5400.5, ''Radiation Protection of the Public and the Environment''; and other Federal and State regulatory requirements as appropriate. The other purpose of this report is to document the differences and similarities between the RESRAD (onsite) and RESRAD-OFFSITE codes so that users (dose analysts and risk assessors) can make a smooth transition from use of the RESRAD (onsite) code to use of the RESRAD-OFFSITE code for performing both onsite and offsite dose analyses. The evolution of the RESRAD-OFFSITE code from the RESRAD (onsite) code is described in Chapter 1 to help the dose analyst and risk assessor make a smooth conceptual transition from the use of one code to that of the other. Chapter 2 provides a comparison of the predictions of RESRAD (onsite) and RESRAD-OFFSITE for an onsite exposure scenario. Chapter 3 documents the results of benchmarking RESRAD-OFFSITE's atmospheric transport and dispersion submodel against the U.S. Environmental Protection Agency's (EPA's) CAP88-PC (Clean Air Act Assessment Package-1988) and ISCLT3 (Industrial Source Complex-Long Term) models. Chapter 4 documents the comparison results of the predictions of the RESRAD-OFFSITE code and its submodels with the predictions of peer models. This report was prepared by Argonne National Laboratory's (Argonne's) Environmental Science Division. This work is jointly sponsored by the NRC's Office of Nuclear Regulatory Research and DOE's Office of Environment, Safety and Health and Office of Environmental Management. The approaches and or methods described in this report are provided for information only. Use of product or trade names is for identification purposes only and does not constitute endorsement either by DOE, the NRC, or Argonne