Coupled Receptor/Dispersion Modeling with a Genetic Algorithm

A mass balance receptor model is coupled with a basic Gaussian dispersion model using a genetic algorithm (GA). The receptor model begins with monitored data and looks backward to apportion pollutant to its sources. In contrast, the dispersion model begins with emission data and computes downwind dispersion to compute the predicted concentration at a receptor. This project combines the strengths of these two types of models through optimizing the calibration factor that ties them together. Using data from various time periods, the calibrating factors are optimized using a genetic algorithm. This coupled model is different from a standard receptor model since it includes known information on meteorological data and computes transport and dispersion of the pollutant to the receptor. When the calibration factor is multiplied by the computed dispersed emissions, it denotes the amount of pollutant from a particular source. The computed calibration factor can also be interpreted as the combined error of the input data plus the two coupled models.

The coupled receptor/dispersion model is tested on data from Cache Valley Utah. The first test is done with synthetic data created from known conditions. We find that the coupled model calibrates well to the computed data at the receptor. This test assures us that the methodology works.

The second test of the coupled model is with actual data from a Utah Department of Air Quality monitor in downtown Logan, Utah. This monitor is filter-based and collected on a three day cycle. Emissions data is prorated from annual average emission rates for 16 sources in the surrounding area. Meteorological data are hourly averages from a continuous meteorological station in the immediate vicinity. To fit 16 sources, we use 16, 32, and 48 periods of data and compare results for the various fitting periods.