To Mitigate The Lnt Model's Unintended Consequences-A Proposed Stopping Point For As Low As Reasonably Achievable.

While the debate over the linear no-threshold model continues, there's a relatively straightforward step that can be taken to mitigate the unintended consequences of the linear no-threshold model and the application of the as low as reasonably achievable principle-enact a stopping point for as low as reasonably achievable. The National Council on Radiation Protection and Measurements defined the negligible individual dose in 1993 as having a value of 0.01 mSv y. Radiation safety professionals overwhelmingly agree that applying the as low as reasonably achievable principle at very low doses, such as those consistent with background radiation levels, is not improving radiation safety of the public or radiation workers. To the contrary, this practice has significant financial and social consequences, and it severely inhibits public communication of radiation risks. To move forward, the National Council on Radiation Protection and Measurements should increase the negligible individual dose to a more practical value of 0.1 mSv y-the new as low as reasonably achievable stopping point. While radiation research in radiation biology and epidemiology are needed to better understand low-dose health effects below 100 mSv, in the meantime we should apply what we know-i.e., that radiation protection should not include trying to protect people from radiation doses that are consistent with variations in background radiation.

Synopsis of the Oak Ridge Radiation Protection Research Needs Workshop.

Radiation protection is foundational to harnessing the societal benefits of radiation in nuclear energy, security, and medicine applications. Significant challenges in radiation protection remain unaddressed for the nuclear fuel cycle, nuclear medicine, emergency response, national defense, and space exploration, as the United States is lacking a coherent research strategy prioritizing radiation protection mission needs and gaps in scientific knowledge to meet these needs. Research and development in the field of radiation protection calls for cooperation among governmental agencies, emergency responders, research organizations, and the academic community. Amidst atrophying national expertise in radiation protection, the Radiation Protection Research Needs Workshop was spearheaded by the Oak Ridge Associated Universities, Oak Ridge National Laboratory, and the Health Physics Society. This workshop facilitated critical dialogue among radiation stakeholders in the governmental and scientific communities, including national laboratories, academic institutions, and industry partners. The workshop featured presentations representing 12 federal agencies and breakout sessions involving the identification of scientific drivers by subject matter experts in each of the following areas: new fuel cycles/reactors, dosimetry, medical physics, instrumentation and operations, decontamination and decommissioning, space radiation, national defense, emergency response, environmental modeling, and low-dose effects. The goal of this workshop was to seek stakeholder input toward the development of a national strategic research agenda in the field of radiation protection. Consequently, the Health Physics Society has established a Special Task Force on Health Physics Research Needs, tasked with the prioritization of scientific drivers in radiation protection for the development of a national strategic research agenda.

Scan MDCs for multiple radionuclides in Class 1 areas.

The MultiAgency Radiation Survey and Site Investigation Manual (MARSSIM), published in December 1997, has been used for designing final status surveys at a number of D&D sites to date. One of the more challenging aspects of the MARSSIM survey design is the strategy employed when multiple radionuclides are present, particularly in Class 1 survey units. The MARSSIM recommends that potential hot spots in a Class 1 survey unit--that could exceed the derived concentration guideline levels for small elevated areas of radioactivity (i.e., DCGL(EMC))--have a reasonably good probability of being detected. Soil sampling on a specified grid size, in conjunction with gamma radiation surface scanning, is used to obtain an adequate assurance level that these hot spots are not missed during the final status survey. Therefore, it is sometimes necessary to supplement the sample size required by the statistical test to provide additional assurance that potential hot spots of concern are detected.

An approach for addressing hard-to-detect hot spots.

The Multi-Agency Radiation Survey and Site Investigation Manual (MARSSIM) survey approach is comprised of systematic random sampling coupled with radiation scanning to assess acceptability of potential hot spots. Hot spot identification for some radionuclides may not be possible due to the very weak gamma or x-ray radiation they emit-these hard-to-detect nuclides are unlikely to be identified by field scans. Similarly, scanning technology is not yet available for chemical contamination. For both hard-to-detect nuclides and chemical contamination, hot spots are only identified via volumetric sampling. The remedial investigation and cleanup of sites under the Comprehensive Environmental Response, Compensation, and Liability Act typically includes the collection of samples over relatively large exposure units, and concentration limits are applied assuming the contamination is more or less uniformly distributed. However, data collected from contaminated sites demonstrate contamination is often highly localized. These highly localized areas, or hot spots, will only be identified if sample densities are high or if the environmental characterization program happens to sample directly from the hot spot footprint. This paper describes a Bayesian approach for addressing hard-to-detect nuclides and chemical hot spots. The approach begins using available data (e.g., as collected using the standard approach) to predict the probability that an unacceptable hot spot is present somewhere in the exposure unit. This Bayesian approach may even be coupled with the graded sampling approach to optimize hot spot characterization. Once the investigator concludes that the presence of hot spots is likely, then the surveyor should use the data quality objectives process to generate an appropriate sample campaign that optimizes the identification of risk-relevant hot spots.