A demonstration of coupled receptor/dispersion modeling with a genetic algorithm

A technique is presented for coupling receptor to dispersion models using a genetic algorithm to optimize the calibration factors, linking the two models. The backward-looking receptor model is based on the chemical mass balance model, but in this case, is formulated to break down pollutant contributions according to independent meteorological periods. For demonstration purposes the dispersion model is a basic Gaussian plume model, but could easily be substituted with a more refined model. The key to linking these two models is a genetic algorithm. The technique described here could prove useful for apportioning monitored pollutant to its sources, calibrating dispersion models, source position identification, monitor siting, and estimating total uncertainty.