Until the recent decade, air quality forecasts have been largely based on statistical modeling techniques. There have been significant improvements and innovations made to these statistically based air quality forecast models during past years. Forecast fidelity has improved considerably using these methods. Nonetheless, being non-physically-based models, the performance of these models can vary dramatically, both spatially and temporally. Recent strides in computational technology and the increasing speed of supercomputers, combined with scientific improvements in meteorological and air quality models has spurred the development of operational numerical air quality prediction models.