Adaptive Grid Modeling and Direct Sensitivity Analysis for Predicting the Air Quality Impacts of DoD Activities
Abstract
Air pollutants emitted from Department of Defense (DoD) facilities can interfere with military activities or in some cases even threaten life and property. These emissions can contribute to local concerns on the military 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. For example, prescribed burnings performed on a base primarily to save the habitat of an endangered species may have contributed 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 determine the impacts of various types of emissions from military installations on air quality. Existing air quality models have limited reliability to respond to such needs. The objective of this project was to enhance current air quality models in order to simulate the air quality impacts of military activities. Two techniques developed earlier, adaptive grid modeling and direct sensitivity analysis, have the potential to improve the ability of the models to capture source-receptor relationships between emissions at local scales and air quality at the regional scale. While adaptive grid modeling can fill the gaps between local and regional scales, the direct sensitivity analysis allows the impacts of specific sources to be discerned from cumulative effects on regional air quality. In this study, a new air quality model that incorporates the adaptive grid and sensitivity analysis techniques was developed. A code review was conducted and test simulations were performed to verify that the new model fulfills the design requirements.
Document Details
- Document Type
- Technical Report
- Publication Date
- Feb 13, 2004
- Accession Number
- ADA606913
Entities
People
- Alper Unal
- M. T. Odman
Organizations
- Georgia Tech