Validation of a Receptor–Dispersion Model Coupled with a Genetic Algorithm Using Synthetic Data

Abstract A methodology for characterizing emission sources is presented that couples a dispersion and transport model with a pollution receptor model. This coupling allows the use of the backward (receptor) model to calibrate the forward (dispersion) model, potentially across a wide range of meteorological conditions. Moreover, by using a receptor model one can calibrate from observations taken in a multisource setting. This approach offers practical advantages over calibrating via single-source artificial release experiments. A genetic algorithm is used to optimize the source calibration factors that couple the two models. The ability of the genetic algorithm to correctly couple these two models is demonstrated for two separate source–receptor configurations using synthetic meteorological and receptor data. The calibration factors underlying the synthetic data are successfully reconstructed by this optimization process. A Monte Carlo technique is used to compute error bounds for the resulting estimates o...

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