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

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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

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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.