Post on 30-Jan-2016
transcript
Georgia Institute of Technology
Adaptive Grid Modeling for Adaptive Grid Modeling for Predicting the Predicting the
Air Quality Impacts of Biomass Air Quality Impacts of Biomass BurningBurning
Alper Unal, Talat OdmanSchool of Civil & Environmental Engineering
Georgia Institute of Technology
2nd International Wildland Fire Ecology and Fire Management Congress, Orlando, FL
16-20 November 2003
Georgia Institute of Technology
Endangered Species Act Clean Air Act
Motivation
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•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
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VOCs
PM
NOx
O3
Motivation
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Gridded Daily Maximum Hourly Averaged Surface Ozone Concentrations for 12-km grid (left) and 4-km grid (right).
Motivation
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0 1E+07 2E+07 3E+07 4E+070
1E+07
2E+07
3E+07
4E+07
X - Axis (cm)
Y-Axis(cm) j
Ei
c
ijs
Computer Simulation with
Air Quality Model
Controlled Burningat Military Base
Adaptive Grid Sensitivity Analysis
Impact to Downwind City
StrategyDesign
Objectives
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Study Area: Fort Benning, GA
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Methodology
• Adaptive Grid Modeling
• Direct Sensitivity Analysis
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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.
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Adaptive Grid Modeling
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• Define first order sensitivities as
• Take derivatives of
• Solve sensitivity equations simultaneously
• Approximate response as
11
11
4ji3ji
ij1ji5jj
iji
)(ij
)(ij
)(ij
CC
ER
SSt
S
)~
(+)~(
~+++)(+)(
2
)1(111
Ku
SJKu
jiij ECS /)1(
Sensitivity Analysis with Decoupled Direct Method (DDM)
jiji ESC )1(
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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)
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Fire Tracer: Adaptive Grid
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O3 Sensitivity to FIRE Static + Brute Force
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O3 Sensitivity to FIRE Adaptive + Direct Sensitivity
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O3 Sensitivity to FIRE
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O3 Sensitivity to FIRE
-15
-10
-5
0
5
10
0 1 2 3 4 5 6 7 8 9 10
Grid Number
O3
(pp
m)
Adaptive + DDM
Static + Brute Force
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 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
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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
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.