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The threat of chemical, biological, and radiological (CBR) attacks by terrorists against defence forces and civilians has attracted the interest of CBR defence. Major defence purchase decisions in CBR defence are often supported by military operations analysis studies using computer simulations because of safety risks and high cost of field trials involving releases of hazardous CBR agents. Simulation environments such as Hazard Prediction and Assessment Capability (HPAC), developed by Defence Threat Reduction Agency (DTRA), USA, and Chemical, Biological, and Radiological Virtual Battlespace (CBR-VB), developed by Dstl, UK, allow one to simulate releases of CBR agents, forecast their spread, and estimate likely human exposure levels and casualties (Fig. 1). While these simulation packages are capable of simulating the mean concentration fields due to releases of CBR agents, they do not simulate random realisations of concentration fluctuations at present. The ability to generate not only the mean concentrations but also the concentration fluctuations in these environments is crucial if they are to be used to test and evaluate CBR data fusion algorithms, sensor models, CBR sensor network topologies, and models of protective ensembles. The availability of random realisations of concentration fluctuations may also allow more accurate estimation of human health effects of CBR agents in different threat scenarios. Figure 1. Output of an HPAC simulation The literature describes ongoing efforts at generating such random realisations using computational fluid dynamic modelling, Lagrangian particle simulation, etc. While these approaches are capable of generating random plume realisations when reliable and detailed input data is available, they are time consuming and require expert knowledge and advanced computer resources. This paper describes a simple approach we have developed to generate realistic concentration fluctuation time series for HPAC and CBR-VB simulation studies when a complete plume realisation over the whole spatial domain is not required and random concentration fluctuation time series at selected locations is sufficient. This approach uses mean concentration, concentration variance, and correlation time scale data, which are available from the simulation environment, to determine the parameters of the likely probability density function (PDF) of concentration fluctuations at a given point within a simulated plume. Assuming that the concentration fluctuations can be characterised by a left-shifted and clipped gamma distribution, the parameters of this PDF are determined by solving a system of transcendental equations. Once the PDF is determined, a numerical stochastic model is employed to generate concentration time series assuming that each time series obeys a first order Markov process and that the concentration time series are non-intermittent and stationary. These non-intermittent time series are baseline shifted and clipped to simulate concentration time series with intermittent zeros. In this paper, we discuss the theoretical basis that underpins our approach and some implementation aspects. We also outline how this method can be extended to cases where the stationarity assumption does not hold.

Generating concentration time series for simulation studies of hazardous plumes