A hybrid model for ozone forecasting

Abstract

Significant uncertainties in the prediction of pollutant transport and dispersion limit the accuracy of air quality in areas with complex terrain, such as along the California coastline, which suffers from elevated air pollutant concentrations. Typical Lagrangian air quality models treat the dispersion of plumes better than Eulerian models but the chemical interactions induced by the mixing of intersecting plumes are ignored. In contrast, Eulerian models treat the emissions as well mixed within each grid box. To address these limitations, an air quality model with in-line chemistry and meteorology that combines the advantages of the Eulerian and Lagrangian approach to air quality modeling has been developed. In order to evaluate the model, simulation results of ozone concentrations were compared against a commonly used photochemical model (CAMx) and with airborne data from a field study made in the San Diego area of southwestern California.