Abstract
Following a malicious or accidental release in an outdoor environment (industrial or urban), first responders will ensure public safety by cordoning off and/or evacuating areas where human life may be in danger. Information on the source (strength and location) and the type of chemical agent released is needed for this to happen reasonably promptly and accurately. A simple inverse modelling technique has been developed to estimate the source strength and location of such a release using measurements of concentration from chemical sensors. The technique relies on either a fixed installation or rapid deployment of chemical sensors to gather and return data to a base station. These measurements are there used, together with meteorological information, as the input data to an inverse algorithm that attempts to make a “best” estimate of the source strength and location. The algorithm works to minimise a penalty function that measures the difference between the concentration observations and predictions based on the current estimate of the source parameters. This is an iterative procedure that should converge to a best estimate of those parameters and, in doing so, provide a measure of the uncertainty in that estimate. There is, in this, a trade-off between the desire for an early prediction and the error implicit in that prediction. Wind tunnel experiments have been used to investigate the propagation of error through the inverse modelling procedure. Firstly, very detailed dispersion measurements were made in a deep boundary layer so that an accurate dispersion model could be established. Four fast flame ionisation detectors were then used to provide long, simultaneous concentration records in the plume from a ground level point source. The output was used to study the sensitivity to sensor placement and then sample duration. Simultaneous sub-samples were taken from the main records and used with the inversion algorithm to quantify the degradation of its performance with decreasing sample duration; i.e. with increasing uncertainty in the concentration observations. Guidelines for application of the inversion technique could then be proposed. The final stage was to move from a simple Gaussian plume to an urban dispersion model, in this case a street network model.