Extended Abstract (1.9M)J8.1
Adaptive Grid Modeling for Predicting the Air Quality Impacts of Biomass Burning
Alper Unal, Georgia Institute of Technology, Atlanta, GA; and M. T. Odman
The objective of this study is 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, specifically in Columbus metropolitan area. The approach taken in this study utilizes two new techniques we recently developed: 1) Adaptive grid modeling and 2) Direct sensitivity analysis. We equipped an advanced air quality model, MAQSIP, with these techniques and conducted regional scale air quality simulations. 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. This enables us to isolate the effect of emissions from a specific source such as biomass burning. So far, the events that were modeled did not show any significant impact of biomass burning at Fort Benning on ozone concentration in Columbus area. However, the signal is large enough to be detected for long distances from the source, which enabled us to verify our approach. As part of a parallel study, researchers at Georgia Tech started continuous monitoring at a nearby site as of January 2003. The data collected will help us select the episodes of interest and evaluate our model. This paper summarizes the methods used and discusses the findings to date.
Joint Session 8, Smoke Management and Air Quality
Tuesday, 18 November 2003, 1:30 PM-5:15 PM
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