We developed an air quality forecasting system that can predict the impacts of electric generation, vehicular traffic and prescribed burning. The Decoupled Direct Method (DDM) available in CMAQ version 5.0.2 for sensitivity analysis is used to predict the impacts of targeted emission sources. This method is acknowledged to perform well especially for relatively small fluctuations around nominal emissions. However, since it is a local method, the accuracy of the modeled sensitivities depends on how close the estimated nominal values are to actual emissions.
A top-down method is used to predict weekly fluctuations in power plant and traffic emissions. Ground-level measurements of PM2.5 and MODIS AOD along with simulated concentrations and sensitivities are used in an inverse modeling framework for adjusting these emissions and reducing the bias in the air quality forecasts. The prescribed burn emissions can fluctuate on shorter time scales with larger amplitudes. Bottom-up methods are used to forecast daily prescribed burn emissions. First, the location and acreages of the burns are forecasted based on the forecasted meteorology and geographic burning patterns identified by mining a burn permit database. Then burn emissions are forecasted using satellite-based estimates of understory fuel loads (vegetation and debris on the ground) and field-measured and laboratory tested emission factors.
In this presentation, the forecasting system, its impact forecasting capability, and the top-down and bottom-up emissions forecasting methods will be described. A preliminary evaluation of the air quality and impact forecasts during the test operation in 2015 will be presented. For the prescribed burn impacts, the evaluation includes comparisons to satellite observations and ground-based accounts of fires as well as smoke-induced jumps in observed pollutant levels at network monitors. Updates to be implemented in 2016 as a result of the lessons learned from the initial evaluation will be reviewed.