Air pollutants emitted from Department of Defense (DoD) facilities can interfere with military activities such as training exercises or in some cases even threaten life and property. These emissions can contribute to local concerns on the base and neighboring communities, and regional air quality problems can occur through long-range transport and transformation processes. In some cases, operations performed for the benefit of one component of the ecosystem may have adverse effects on another. For example, prescribed burnings performed on a base primarily to save the habitat of an endangered species may contribute to the air quality problem in a nearby metropolitan area. Faced with such complex problems, DoD needs reliable tools to determine the impact of its operations on the environment. In particular, air quality simulation models are needed that can help in determining the impacts of various types of emissions from military installations.

The objective of this project was to enhance current models in order to improve their predictive capacity to simulate impacts on air quality from military activities.

Air quality model equipped with adaptive grids and sensitivity analysis to determine local impact of controlled burnings at a military base.

While adaptive grid modeling is able to improve current air quality models by filling the gaps between local and regional scales, direct sensitivity analysis can discern the impacts of specific sources from cumulative effects on regional air quality. Both techniques were incorporated into current air quality models, and simulations were conducted to determine the air quality impacts of prescribed burning operations. This entailed estimating the sensitivity of ozone levels in the Columbus, Georgia metropolitan area to nitrogen oxides and volatile organic compounds emitted from prescribed burns on Fort Benning, Georgia. The Title V Air Emissions Inventory was employed. For the prescribed burning operation, detailed inventories yielded emission factors by litter type per acres burned. A historic episode was simulated and compared to model results with observed air quality to calibrate and verify the model.

A new air quality model that incorporates the adaptive grid and sensitivity analysis techniques was developed. Test simulations were performed to verify that it fulfilled design requirements. The adaptive grid enabled better resolution of the plumes from prescribed burns at Fort Benning. The sensitivity of ozone concentrations to the fires also was better resolved in comparison to taking the difference between the two old-model simulations, one without and another with the fires. The new model captured the near-source reduction and downwind increase in ozone concentrations due to the fires. This non-linear response was overlooked by the old model. In view of these results, it was concluded that the new model, enhanced by adaptive grid and sensitivity analysis techniques, can be used for accurate assessment of the air quality impacts of most DoD activities.

This effort will directly benefit the management of prescribed burning at Fort Benning. The techniques also can be used for the general purpose of predicting the fate of air pollutant emissions from aviation, ship, or coastal operations. The products may assist site managers in responding to immediate needs, planning future emissions, and ultimately minimizing the negative impact of military activities on local and regional air quality. (Project Completed - 2004)