The creation of emission data for an air quality forecasting model requires the efficient and accurate estimation of temporal and spatial variations in emission sources of ozone precursors. To achieve this goal, the existing emission inventory preparation and processing systems need to be streamlined and modified. The critical emission precursor pollutants for ozone are volatile organic compounds (VOCs), nitric oxides (NOx), and carbon monoxide (CO). The spatial variability and temporal behavior of these compounds are influenced both by meteorological conditions and by anthropogenic activities. The key complexities in the simulation of the temporal and spatial variations of these compounds are the biogenic sources, on-road mobile sources, and major fossil-fuel point sources. The processing of the emission for biogenic sources can be streamlined by linking the preparation of meteorological output fields for the air quality chemistry model with the calculation of biogenic emissions. The processing of temperature-dependent emissions for mobile sources can be streamlined by using the MOBILE5B (or MOBILE6) model to create simple temperature regressions to apply to normalized emission data prior to the actual forecast. The temperature/emission relationship can then be used in a very efficient calculation for the actual emission calculation. Finally, the processing of point source emission from major power plants can be streamlined by using historical CEM (Continuous Emission Monitoring) data to create temperature/emission relationships that can be used to estimate current power plant emission in an air quality forecast model. This streamlined approach will be compared in this paper to the emission processing method used in a non-operational environment.