A uranium bioassay program was conducted involving 103 active and retired Canadian Forces personnel. The total uranium concentrations in each of two 24-h urine collections were analyzed separately at independent commercial laboratories by inductively coupled plasma mass spectrometry (ICP-MS) and by instrumental neutron activation analysis (INAA). The mean and median concentrations were determined to be 4.5 ng L(-1) and 2.8 ng L(-1), respectively, from ICP-MS and 17 ng L(-1) and 15 ng L(-1), respectively, from INAA. The total uranium concentrations were sufficiently low so that isotopic (238U:235U ratio) assays could not be performed directly from urine samples. Isotopic assays were performed on hair samples from 19 of the veterans participating in the testing. The isotopic hair assays were scattered around the natural 238U:235U ratio of 137.8, ranging from 122 +/- 21 to 145 +/- 16 (1sigma). Due to concern expressed in the media over possible depleted uranium exposure and long-term retention in bone, a single bone sample (vertebrate bone marrow) from a deceased member of the Canadian Forces was also analyzed for total uranium content and isotopic ratio by ICP-MS. The sample was shown to have 16.0 +/- 0.3 microg kg(-1) uranium by dry weight and a 238U:238U isotopic ratio of 138 +/- 4, consistent with natural uranium.

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A neural network model was developed to predict the short-term (<150 s) concentration distributions of aerosols released from point sources over very short time periods (approximately 2 s). The model was based on data from field experiments covering a wide range of meteorological conditions. The study focused on relative dispersion about the puff centroid, with puff/cloud meander and large-scale gusts not being considered. The artificial neural network (ANN) model included explicitly a number of meteorological and turbulence parameters, and was compared with predictions from two Gaussian-based puff models to the measurements of four independent trials representing different stability conditions. The performance of the neural network model was comparable (in stable conditions) or better (in unstable and neutral conditions) than these two models when high concentration predictions were considered. Simulations of concentration distributions under different stability conditions were also generated using the developed neural network model, with the result that Gaussian distributions provided good descriptors for puff dispersion in the downwind and crosswind directions, and for particles close to the centroid in the vertical when dealing with short dispersion times.

The Gaussian distribution is a good approximation for transient (instantaneously released) puff concentration distributions within a short period of time after release. Artificial neural network (ANN) models for puff dispersion coefficients were developed, based on observations from field experiments covering a wide range of meteorological conditions (in March, May, August and November). Their average predictions were in very good agreement with measurements, having high correlation coefficients (r > 0.99). A non-linear multi-variable regression model for dispersion coefficients was also developed, under the assumption that puff dispersion coefficients increase with time, and follow power laws. Both ANN-based and multi-regression non-linear models were able to use easily measured atmospheric parameters directly, without the necessity of predefining the Pasquill stability category. Predictions of ANN-based and multi-regression-based Gaussian puff models were compared with those of Gaussian puff models using Slade's dispersion coefficients and COMBIC, a sophisticated model based on Gaussian distributions. Predictions from our two new models showed better agreement with concentration measurements than the other Gaussian puff models, by having a much higher fraction within a factor of two of measured values, and lower normalized mean square errors.