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

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 of the calibration factors. By creating synthetic data with random noise, it is possible to quantify the robustness of the model's results in the face of variability. When white noise is incorporated into the synthetic pollutant signal at the receptors, the genetic algorithm is still able to compute the calibration factors of the coupled model up to a signal-to-noise ratio of about 2. Beyond that level of noise, the average of many coupled model optimization runs still provides a reasonable estimate of the calibration factor until the noise is an order of magnitude greater than the signal. The calibration factor linking the dispersion to the receptor model provides an estimate of the uncertainty in the combined monitoring and modeling process. This approach recognizes the mismatch between the ensemble average dispersion modeling technology and matching a single realization time of monitored data.