It is often necessary to characterize the source of an airborne contaminant from remote measurements of the resulting concentration field. The genetic algorithm method used here has proven successful in back-calculating not only these source characteristics but also the meteorological parameters necessary to predict the transport and dispersion of contaminant. Previous validation studies emphasized identical twin experiments wherein the synthetic validation data were created using the same transport and dispersion models used for the back-calculations. In this research the validation process is instead continued using a time-dependent computational fluid dynamics model to create the synthetic data. Such data inherently includes time-dependent behavior unique to each contaminant episode rather than the ensemble average predicted by the transport and dispersion model used in previous studies. The genetic algorithm model then back-calculates both source characteristics and meteorological data. A realistic sensor configuration is considered and the model incorporates sensor detection limits, quantification levels, and saturation levels.