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Adaptive Grid Modeling for Predicting the Air Quality Impacts of Biomass Burning

Adaptive Grid Modeling for Predicting the Air Quality Impacts of Biomass Burning. Alper Unal, Talat Odman, Ted Russell School of Civil & Environmental Engineering Georgia Institute of Technology

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Adaptive Grid Modeling for Predicting the Air Quality Impacts of Biomass Burning

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  1. Adaptive Grid Modeling for Predicting the Air Quality Impacts of Biomass Burning Alper Unal, Talat Odman, Ted Russell School of Civil & Environmental Engineering Georgia Institute of Technology Biomass Burning Impacts on Air Quality, Human Exposure and Health- Exchanging Information for Future Initiatives -Workshop at GIT, ES&T Bldg, L1114 January 22, 2004 Georgia Institute of Technology

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

  3. Motivation • 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. Georgia Institute of Technology

  4. O3 VOCs NOx PM Motivation Georgia Institute of Technology

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

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

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

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

  9. 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. Georgia Institute of Technology

  10. Adaptive Grid Modeling Georgia Institute of Technology

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

  12. Air Quality Simulations • 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) Georgia Institute of Technology

  13. Fire Tracer: Adaptive Grid Georgia Institute of Technology

  14. O3 Concentration: Adaptive vs. Static GridAugust 15, 21:00 (GMT) Georgia Institute of Technology

  15. O3 Sensitivity to FIRE: Static + Brute Force Georgia Institute of Technology

  16. O3 Sensitivity to FIRE: Adaptive + DDM Georgia Institute of Technology

  17. O3 Sensitivity: Adaptive + DDM vs. Static + BF Georgia Institute of Technology

  18. O3 Sensitivity: Adaptive + DDM vs. Static + BFAugust 15, 20:00 GMT Georgia Institute of Technology

  19. Conclusions • Adaptive Grid Modeling and Direct Sensitivity Analysis were successfully implemented to determine the impact of biomass burning on the surrounding environment • The impact of fires at Fort Benning ranged from 16 ppb reduction to 7 ppb increase in O3 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 Georgia Institute of Technology

  20. 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 Georgia Institute of Technology

  21. 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. Georgia Institute of Technology

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