1. Georgia Institute of Technology
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
My co-authors are Prof. Ted Russell and Dr. Jim Boylan who used to be our student. Now, he works for the Georgia DNR.
Georgia Institute of Technology

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

3. Georgia Institute of Technology
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. Georgia Institute of Technology
Motivation
VOCs PM
NOx O3
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. Computer Simulation with Air Quality Model Impact to Downwind City
Objectives
Computer Simulation with Air Quality Model
Controlled Burning
at Military Base
Adaptive Grid
Sensitivity Analysis
Impact to Downwind City
Strategy
Design
To improve the ability to model the air quality impacts of biomass burning on the surrounding environment
To develop and apply modeling techniques that allow urban-to-regional scale modeling for small area sources (Adaptive Grid Modeling)
To develop and apply techniques that enable to quantify the impact of biomass burning on regional ozone problem (Direct Sensitivity)
Georgia Institute of Technology

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

8. Georgia Institute of Technology
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
Another technique we applied to find the impact of fire emissions is DDM. The alternative would have been the brute-force approach where the model is run twice: once with the fire emissions and the second time without them. The difference in ozone concentrations would yield the impact of the fire. However, the NOx emissions from the fire are a very small fraction of the total NOx emissions. Therefore the difference between the two model runs may be small and it may fall within the range of model errors. On the other hand DDM calculates the sensitivity which is defines as the derivative of concentration with respect to emissions. By definition, it is designed to capture the impact of infinitesimal changes in emissions. The sensitivities are calculated by solving an additional set of equations in the model.
Georgia Institute of Technology

12. Air Quality Simulations
Selected Episode: August 15-18, 2000 (Hugh Fort Benning provided the fire data)
Meteorology Data: MM5 (FAQS)
Base Emissions: FAQS-2000 Inventory
Biomass Burning Emissions: FOFEM V5 + Battye and Battye (2002)
NET99, CEM, BEIS3 with BELD3 data, MOBILE6
Georgia Institute of Technology

13. Georgia Institute of Technology
Fire Tracer: Adaptive Grid
Georgia Institute of Technology

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

15. Georgia Institute of Technology
O3 Sensitivity to FIRE: Static + Brute Force
At night, there is Ozone titration!
Georgia Institute of Technology

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

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

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

19. Georgia Institute of Technology
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. 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
Georgia Institute of Technology

21. 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.
Georgia Institute of Technology