Adaptive grid modeling with direct sensitivity method for predicting the air quality impacts of biomass burning

The objective of this study was to improve the ability to model the air quality impacts of biomass burning on the surrounding environment. The focus is on prescribed burning emissions from a military reservation, Fort Benning in Georgia, and their impact on local and regional air quality. The approach taken in this study is to utilize two new techniques recently developed: (1) adaptive grid modeling and (2) direct sensitivity analysis. An advanced air quality model was equipped with these techniques, and regional-scale air quality simulations were conducted. Grid adaptation reduces the grid sizes in areas that have rapid changes in concentration gradients; consequently, the results are much more accurate than those of traditional static grid models. Direct sensitivity analysis calculates the rate of change of concentrations with respect to emissions. The adaptive grid simulation estimated large variations in O ₃ concentrations within 4∈×∈4-km² cells for which the static grid estimates a single average concentration. The differences between adaptive average and static grid values of O₃ sensitivities were more pronounced. The sensitivity of O₃ to fire is difficult to estimate using the brute-force method with coarse scale (4∈×∈4 km²) static grid models.