Comparison of dose estimation from occupational exposure to 239Pu using different modelling approaches.

Several approaches are available for bioassay interpretation when assigning Pu doses to Mayak workers. First, a conventional approach is to apply ICRP models per se. An alternative method involves individualised fitting of bioassay data using Bayesian statistical methods. A third approach is to develop an independent dosimetry system for Mayak workers by adapting ICRP models using a dataset of available bioassay measurements for this population. Thus, a dataset of 42 former Mayak workers, who died of non-radiation effects, with both urine bioassay and post-mortem tissue data was used to test these three approaches. All three approaches proved to be adequate for bioassay and tissue interpretation, and thus for Pu dose reconstruction purposes. However, large discrepancies are observed in the resulting quantitative dose estimates. These discrepancies can, in large part, be explained by differences in the interpretation of Pu behaviour in the lungs in the context of ICRP lung model. Thus, a careful validation of Pu lung dosimetry model is needed in Mayak worker dosimetry systems.

Analyzing bioassay data using Bayesian methods--a primer.

The classical statistics approach used in health physics for the interpretation of measurements is deficient in that it does not take into account "needle in a haystack" effects, that is, correct identification of events that are rare in a population. This is often the case in health physics measurements, and the false positive fraction (the fraction of results measuring positive that are actually zero) is often very large using the prescriptions of classical statistics. Bayesian statistics provides a methodology to minimize the number of incorrect decisions (wrong calls): false positives and false negatives. We present the basic method and a heuristic discussion. Examples are given using numerically generated and real bioassay data for tritium. Various analytical models are used to fit the prior probability distribution in order to test the sensitivity to choice of model. Parametric studies show that for typical situations involving rare events the normalized Bayesian decision level k(alpha) = Lc/sigma0, where sigma0 is the measurement uncertainty for zero true amount, is in the range of 3 to 5 depending on the true positive rate. Four times sigma0 rather than approximately two times sigma0, as in classical statistics, would seem a better choice for the decision level in these situations.

Internal dosimetry of uranium isotopes using Bayesian inference methods.

A group of personnel at Los Alamos National Laboratory is routinely monitored for the presence of uranium isotopes by urine bioassay. Samples are analysed by alpha spectroscopy, and the results are examined for evidence of an intake of uranium. Because the measurement uncertainties are often comparable to the quantities of material we wish to detect, statistical considerations are crucial for the proper interpretation of the data. The problem is further complicated by the significant, but highly non-uniform, presence of uranium in local drinking water and, in some cases, food supply. Software originally developed for internal dosimetry of plutonium has been adapted to the problem of uranium dosimetry. The software uses an unfolding algorithm to calculate an approximate Bayesian solution to the problem of characterising any intakes which may have occurred, given the history of urine bioassay results for each individual in the monitored population. The program uses biokinetic models from ICRP Publications 68 and later, and a prior probability distribution derived empirically from the body of uranium bioassay data collected at Los Alamos over the operating history of the laboratory. For each individual, the software creates a posterior probability distribution of intake quantity and solubility type as a function of time. From this distribution, estimates are made of the cumulative committed dose (CEDE) to each individual. Results of the method are compared with those obtained using an earlier classical (non-Bayesian) algorithm for uranium dosimetry. We also discuss the problem of distinguishing occupational intakes from intake of environmental uranium, within a Bayesian framework.

Bayesian internal dosimetry calculations using Markov Chain Monte Carlo.

A new numerical method for solving the inverse problem of internal dosimetry is described. The new method uses Markov Chain Monte Carlo and the Metropolis algorithm. Multiple intake amounts, biokinetic types, and times of intake are determined from bioassay data by integrating over the Bayesian posterior distribution. The method appears definitive, but its application requires a large amount of computing time.

Bayesian prior probability distributions for internal dosimetry.

The problem of choosing a prior distribution for the Bayesian interpretation of measurements (specifically internal dosimetry measurements) is considered using a theoretical analysis and by examining historical tritium and plutonium urine bioassay data from Los Alamos. Two models for the prior probability distribution are proposed: (1) the log-normal distribution, when there is some additional information to determine the scale of the true result, and (2) the 'alpha' distribution (a simplified variant of the gamma distribution) when there is not. These models have been incorporated into version 3 of the Bayesian internal dosimetry code in use at Los Alamos (downloadable from our web site). Plutonium internal dosimetry at Los Alamos is now being done using prior probability distribution parameters determined self-consistently from population averages of Los Alamos data.

Internal dosimetry for inhalation of hafnium tritide aerosols.

Metal tritides with low dissolution rates may have residence times in the lungs which are considerably longer than the biological half-time normally associated with tritium in body water, resulting in long-term irradiation of the lungs by low energy beta particles and bremsstrahlung X rays. Samples of hafnium tritide were placed in a lung simulant fluid to determine approximate lung dissolution rates. Hafnium hydride samples were analysed for particle size distribution with a scanning electron microscope. Lung simulant data indicated a biological dissolution half-time for hafnium tritide on the order of 10(5) d. Hafnium hydride particle sizes ranged between 2 and 10 microns, corresponding to activity median aerodynamic diameters of 5 to 25 microns. Review of in vitro dissolution data, development of a biokinetic model, and determination of secondary limits for 1 micron AMAD particles are presented and discussed.