Abstract An important component of protecting individuals from the hazards of radiation exposure is to be able to determine whether radionuclides have been taken into the body, and in what quantities. For example, a urine sample from an individual who works with plutonium can be analyzed by α spectroscopy or mass spectroscopy to determine if plutonium is present in the sample, and hence to make an inference about possible plutonium intakes in the individual's history. Often, the quantities of plutonium that one seeks to detect are comparable to the measurement uncertainties. Hence, statistical considerations are a very important aspect of the detection methodology.

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.

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.

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.