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This report provides a technical description of the module urbanSOURCE, which is an operational implementation of an innovative sensor-driven modeling paradigm for source reconstruction. This module permits the rapid and robust estimation of the parameters of an unknown source, using a finite number of noisy concentration measurements obtained from a sensor array. The problem is solved using a Bayesian probabilistic inferential framework in which Bayesian probability theory is used to formulate the posterior distribution for the source parameters. Three different model equations have been formulated for the likelihood function, leading to three different models for the posterior distribution of the source parameters. The application of the methodology implemented in urbanSOURCE is illustrated using real dispersion data obtained from two examples (Joint Urban 2003 field experiment in Oklahoma City and European Tracer Experiment) involving contaminant dispersion in highly disturbed flows over urban and complex environments, where the idealizations of horizontal homogeneity and/ or temporal stationarity in the flow cannot be applied to simplify the problem.

This memorandum describes some initial work to construct a wind field library consisting of pre-computed building-aware mean wind and turbulence fields obtained from a high-resolution, high-fidelity computational fluid dynamics (CFD) model for a specific urban area; namely, for the downtown area of Vancouver and the surrounding environs for use in on-demand CFD in support of emergency response applications (requiring quick turnaround times) for the 2010 Winter Olympic Games. To this purpose, mean wind and turbulence field simulations were obtained for 32 wind directions using CFD over a model (computational) domain of 255 km2 (with a building-aware region of 10.4 km2 at 9-m horizontal resolution) centered on downtown Vancouver. An example illustrating the application of the wind field library for the rapid calculation of urban dispersion for a release of a hazardous material in downtown Vancouver is provided.

A Bayesian probabilistic inferential framework provides a natural and logically consistent method for source reconstruction that fully utilizes the information provided by a limited number of noisy concentration data obtained from a network (or, array) of detectors. This report addresses the application of this framework to the difficult problem of estimating the parameters of an a priori unknown number of sources, using an array of detectors. To this purpose, Bayesian probability theory is used to formulate the full joint posterior probability density function for the number of sources and the parameters (e.g., location, emission rate, activation and deactivation times) that describe each source. A simulated annealing algorithm, applied in conjunction with a reversible jump Markov chain Monte Carlo technique, is used to draw random samples from the posterior probability density function. By calculating the marginal posterior probability distribution of the number of sources from these samples, a maximum a posteriori estimate Ns for the number of sources can be obtained, and all samples of source distribution models with exactly Ns discrete sources can be used to provide best estimates for the source parameters (along with their associated uncertainties). The method is validated against a real dispersion experiment involving various combinations of multiple source releases conducted under a multinational cooperative Fusing Sensor Information from Observing Networks (FUSION) Field Trial 2007 (FFT-07) undertaken at US Army Dugway Proving Ground (DPG) in September 2007., The original document contains color images.

The relationships between various normalized higher-order concentration moments have been investigated using a large data set of concentration fluctuations obtained in a boundary-layer water channel with high-resolution laser-induced fluorescence. This data set corresponds to a series of comprehensive measurements of plume dispersion in a number of obstacle arrays (e.g., various arrays of cubical and non-cubical obstacles in aligned and staggered arrangements with uniform and random heights). A remarkably robust feature of all the concentration data was the observed collapse, of the third- and fourth-order normalized concentration moments on the second-order normalized concentration moment and of the concentration kurtosis on the concentration skewness, to a series of universal curves. These universal curves were identical to those observed previously for open-terrain plumes, and are well modeled using either a clippedgamma probability density function (PDF) or the simpler intermittent exponential PDF for the concentration. A comparison of the shape of the model probability distributions to the measured concentration data at various plume locations showed that the clipped-gamma distribution provided a good representation for the general distribution shape, whereas the simpler intermittent exponential distribution yielded a poor conformance to the measured concentration probability distribution (in spite of the fact that both of the model distributions gave a good representation for the first four concentration moments). The implication of the form of the concentration PDF, for the formulation of a probabilistic model for the response of a chemical agent detector, is investigated in the context of the source reconstruction problem. 14.

A new technique for approximating the scattering of laser light by biological cells is reported. This technique is based on a three-dimensional scalar wave equation approximation of the full Maxwell's field equations for the electromagnetic field. This scalar wave equation for describing the light scattering patterns of cells containing arbitrary morphological structure (e.g., various organelles) is solved numerically using a spectral method. The accuracy of the spectral numerical method is verified by comparison with solutions obtained from linear perturbation theory and Mie theory. Comparison with Mie theory shows that the three-dimensional scalar wave equation is a good approximation to the full Maxwell's field equations for light scattering up to moderate forward scattering angles (i.e., for scattering angles less than about 350). The approximate technique used here is capable of correctly predicting the scattered intensity patterns from biological cells over a dynamic range spanning six orders of magnitude. The new technique can be applied to calculate the light scattering either from an individual biological cell and from a sample containing an ensemble of such biological cells. The scattering intensity patterns predicted using the new technique can potentially be applied to diagnose the size and internal structure of biological cells, making it a valuable interpretative tool in flow cytometry (e.g., in the detection of rare event cells such as those resulting from a biological warfare agent attack, or for the rapid noninvasive optical assessment of tissue pathology in the detection of cancerous cells)., and Summary in English and French.

Lagrangian Stochastic (LS) particle models have proven to be a useful computational tool for the description and prediction of dispersion of pollutant releases in complex meteorological situations (e.g., space- and time-varying situations pertaining to complex flow and turbulence). However, simulating the emitted pollutant by following the trajectories of many "marked" fluid elements released from the source distribution brings up the difficulty of the correct estimation of the mean concentration of the dispersing pollutant from the particle trajectory information. Recently, the density kernel estimation method has been proposed and applied successfully to estimate mean concentrations from Lagrangian Stochastic particle models. However, the computational effort needed by this method increases as N(exp 2) (assuming the number of receptor locations N(sub r) at which the concentration is required is comparable to the number of fluid particles N(sub p) used in the trajectory simulation, so N(sub r) = N(sub p) -N) and, in consequence, the method has not been widely used because of the significant computer resources required. Here, we describe a novel algorithm for calculating the kernel estimate of the mean concentration field whose computational complexity scales only as N. The technique uses a tesselation (subdivision) of space in cubic cells of side length h (where h is the bandwidth of the kernel function), and then associates a linked-list data structure with each cell that is used as a bookkeeping device to keep track of the "marked" fluid particles in that cell. The fast approach developed here has been verified by comparing results with the direct implementation of the kernel estimator and with the conventional box-counting estimator for the mean concentration field.

Estimated human inhalation toxicity values for sarin (GB) were calculated using a new 3D, nonlinear dose response model combined with new allometric equations relating animal and human respiration. Historical animal studies of GB toxicity containing both exposure and fractional animal response data were used to test the new process. The final data set contained 6,621 animals, 762 groups, 37 studies, and 7 species. The toxicity of GB for each species was empirically related to exposure concentration (C; mg/m(exp 3)) and exposure time (T; min) through the surface function Y = b(sub 0) + b(sub 1) Log(sub 10) C + b(sub 2) Log(sub 10)T or Y = b(sub 0) + b(sub 2) Log(sub 10) C(exp n)T where Y is the PROBIT, b(sub 0), b(sub 1), and b(sub 2) are constants and n is the toxic load exponent. Between exposure times 0.17 and 30 minutes, the average value for n in 7 species was 1.35 +/- 0.15. The near parallel toxic load equations for each species and the linear relationship between minute volume/body weight ratio and the inhalation toxicity (LCt(sub 50)) for GB were used to create a pseudo-human data set and then an exposure time/toxicity surface for the human. The calculated n for the human was 1.38 +/- 0.01. The pseudo-human data had much more variability at low exposure times. Raising the lower exposure limit to 1 minute did not change the LCt(sub 50) but did result in lower variability. Raising the lower value to 2 minutes was unproductive. Based on the toxic load model for 1-30 minute exposures, the n value was 1.40 and the human GB toxicities (LCt(sub 01), LCt(sub 05), LCt(sub 50), and LCt(sub 95)) for 70 kg humans breathing 15 L/minute were estimated to be 11, 16, 36, and 83; 18, 25, 57, and 132; 24, 34, 79, and 182 mg.min/m(exp 3) for 2, 10, and 30 minute exposures, respectively. These values are recommended for general use for the total human population. The empirical relationships employed in the calculations may not be valid for exposure times > 30 minutes. (96 re7, and Executive Summary in English and French.

The Canadian C7 canister was challenged at various concentrations of chlorine to determine the breakthrough times. During the breakthrough experiments, it was found that at chlorine concentration above 10,000 ppm, the canister underwent spontaneous ignition before breakthrough occurred. The CANSLAB dense gas model developed at DRES was used to calculate the peak mean concentration and total dosage as a function of downwind distance that can be expected from the catastrophic failure of chlorine cylinders and tank cars under the worst but feasible meteorological condition. Combining the CANSLAB calculations and the breakthrough results, distances downwind of the release site where the C7 canister will ignite (spontaneous ignition distance) or allow breakthrough of chlorine (breakthrough distance) were determined. From the combined result, it could be concluded that providing the wearer is beyond the spontaneous ignition distance, the C7 canister should provide the wearer with respiratory protection and to safely evacuate from an area contaminated with chlorine.

The relationship between body weight (BW) and respiratory minute volume (Vm) was reviewed by collecting a data base from the literature of minute volume rates that encompassed species from mice at 12 g body weight to horses and a giraffe at 500 kg body weight. The data were separated into anesthetized and non-anesthetized groups and juvenile animals were removed from the non-anesthetized group. The final data set of non-anesthetized animals contained 131 studies representing 2125 animals and 18 species. The data show a power-law (allometric) relationship between the minute volume and body weight. The scaling or allometric parameters in this power-law have been estimated using a linear regression of the logarithms of the minute volume against body weight. The resulting allometric equations were; Log10Vm = -0.286+ 0.802 Log10BW or Vm = 0.518 BW0.802. From these equations a corresponding set of minute volumes were obtained for various body weights of humans eg. 15.6 L/min for a 70 kg human. The results of the analyses were compared to similar studies in the literature. The relationship is recommended for military uses because it is derived from non-anesthetized, young adult mammals which are expected to mimic the soldier.