Impact of sensor characteristics on source characterization for dispersion modeling

An accidental or intentional release of hazardous chemical, biological, radiological, or nuclear material into the atmosphere obligates responsible agencies to model its transport and dispersion in order to mitigate the effects. This modeling requires input parameters that may not be known and must therefore be estimated from sensor measurements of the resulting concentration field. The genetic algorithm (GA) method used here has been successful at back-calculating not only these source characteristics but also the meteorological parameters necessary to predict the contaminants subsequent transport and dispersion. This study assesses the impact of sensor thresholds, i.e. the sensor minimum detection limit and saturation level, on the ability of the algorithm to back-calculate modeling variables. The sensitivity of the back-calculation to these sensor constraints is analyzed in the context of an identical twin approach, where the data is simulated using the same Gaussian Puff model that is used in the back-calculation algorithm in order to analyze sensitivity in a controlled environment. The solution is optimized by the GA and further tuned with the Nelder-Mead downhill simplex algorithm. For this back-calculation to be successful, it is important that the sensor capture the maximum concentrations.