1. Georgia Institute of Technology Adaptive Grid Modeling for Predicting the Air Quality Impacts of Biomass Burning Alper Unal, Talat Odman School of Civil & Environmental Engineering Georgia Institute of Technology 2 nd International Wildland Fire Ecology and Fire Management Congress, Orlando, FL 16-20 November 2003

2. Georgia Institute of Technology Endangered Species Act Clean Air Act Motivation

3. Georgia Institute of Technology The endangered Red Cockaded Woodpecker (RCW) resides only in the mature long-leaf pine forests. Most of the forests are on federal and military lands. These ecosystems require periodic burning to maintain health. Prescribed burning is a safe and effective alternative to natural fire regimes. Motivation

4. Georgia Institute of Technology VOCs PM NOx O3O3 Motivation

5. Georgia Institute of Technology Gridded Daily Maximum Hourly Averaged Surface Ozone Concentrations for 12-km grid (left) and 4-km grid (right). Motivation

6. Georgia Institute of Technology Computer Simulation with Air Quality Model Controlled Burning at Military Base Adaptive GridSensitivity Analysis Impact to Downwind City Strategy Design Objectives

7. Georgia Institute of Technology Study Area: Fort Benning, GA

8. Georgia Institute of Technology Methodology Adaptive Grid Modeling Direct Sensitivity Analysis

9. Georgia Institute of Technology Adaptive Grid Modeling Inadequate grid resolution -- Important source of uncertainty in air quality models. Adaptive grids offer an effective and efficient solution. Our adaptive grid technique is a mesh refinement algorithm where the number of grid cells remains constant and the structure (topology) of the grid is preserved. A weight function controls the movement of the grid nodes according to user-defined criteria. It automatically clusters the nodes where resolution is most needed. Grid nodes move continuously during the simulation. Grid cells are automatically refined/coarsened to reduce the solution error.

10. Georgia Institute of Technology Adaptive Grid Modeling

11. Georgia Institute of Technology Define first order sensitivities as Take derivatives of Solve sensitivity equations simultaneously Approximate response as Sensitivity Analysis with Decoupled Direct Method (DDM)

12. Georgia Institute of Technology Data Preparation Selected Episode: August 15-18, 2000 (Hugh Westburry @ Fort Benning provided the fire data) Meteorology Data: MM5 (FAQS) Base Emissions: FAQS-2000 Inventory Biomass Burning Emissions: FOFEM V5 + Battye and Battye (2002)

13. Georgia Institute of Technology Fire Tracer: Adaptive Grid

14. Georgia Institute of Technology O 3 Sensitivity to FIRE Static + Brute Force

15. Georgia Institute of Technology O 3 Sensitivity to FIRE Adaptive + Direct Sensitivity

16. Georgia Institute of Technology O 3 Sensitivity to FIRE

17. Georgia Institute of Technology O 3 Sensitivity to FIRE

18. Georgia Institute of Technology Conclusions Adaptive Grid Modeling with Direct Sensitivity Methods were successfully implemented to determine the impact of biomass burning on the surrounding environment The impact of fires ranged from 16 ppb reduction to 7 ppb increase in O 3 concentrations. Impact on Columbus area is minimal due to wind directions Concentration gradients were better resolved by Adaptive Grid Direct Sensitivity compared to Brute Force, better differentiated near and far field impacts

19. Georgia Institute of Technology Future Work Emissions Inventory: –Better emissions estimation for biomass burning –Plume Rise calculations Comparison with Monitoring Data: – “Prediction of Air Quality Impacts from Prescribed Burning: Model Optimization and Validation by Detailed Emissions Characterization “ with Dr. Karsten Baumann

20. Georgia Institute of Technology Acknowledgements Strategic Environmental Research & Development Program (SERDP): Project CP-1249 Study of Air Quality Impacts Resulting from Prescribed Burning on Military Facilities" sponsored by the DOA/CERL in support of the DOD/EPA Region 4 Pollution Prevention Partnership.