1. Leigh Patterson 06/15/09 M.S. Defense
Development of Wildland Smoke Marker Emissions Maps for the Conterminous United States
Leigh Patterson
06/15/09
M.S. Defense

2. Acknowledgements Advisor: Dr. Jeff Collett
Committee: Drs. Kreidenweis, Schichtel, and Rocca
FLAME Partners: Cyle Wold, Dr. Wei Min Hao, Dr. Bill Malm
Sample Analysis: Amy Sullivan, Mandy Holden
Funding: Joint Fire Science Program, National Park Service, American Meteorological Society
Friends and family

3. Outline Introduction & motivation
Fire Lab at Missoula Experiment (FLAME)
Relationship between smoke marker emissions and vegetation type
Fuel Characteristic Classification System
Smoke marker emissions maps
Biomass burning carbon apportionment

4. Fire Impacts Radiative budget Visibility Health effects
OC – reflective
EC – absorptive
Visibility
Regulated by Clean Air Act
Wildfires – natural
Prescribed fires – manmade
Health effects
Ultra-fine particles provoke alveolar inflammation (Seaton 1995)
During a fire episode in California, 117 hospital admissions for smoke reactions (Shusterman et. al., 1993)

5. Effects of fires on air pollution
Total TC – IMPROVE measurements
TC enhancement
TC, µg m-3
Park et. al, 2007
Debell et. al., 2006

6. What are smoke markers? Chemical compounds used to fingerprint smoke
Produced from loss of potassium in plant matter (Arianoutsou and Margaris, 1981)
Anhydrosugars
Includes levoglucosan, mannosan, and galactosan
Produced from combustion of cellulose and hemicellulose
Marker criteria: unique, constant, inert, and measurable (Khalil and Rasmussen, 2003)

7. Why do we need accurate source profiles?
MM-CMB models use profiles to apportion wildfire smoke
CMB models attempt to apportion 100% of the PM to various sources
If one source is incorrectly apportioned, the apportionment of other sources will be misestimated
Correct geographic profiles are most important to determine wildfire smoke contribution (Sheesley et. al., 2007)

8. Why do we need accurate source profiles?
MM-CMB models use profiles to apportion wildfire smoke
CMB models attempt to apportion 100% of the PM to various sources
If one source is incorrectly apportioned, the apportionment of other sources will be misestimated
Correct geographic profiles are most important to determine wildfire smoke contribution (Sheesley et. al., 2007)

9. FLAME Study
Burned over 33 fuels in over 100 burns in two campaigns in a burn chamber in Missoula, MT
Mostly single component burns
Measured physical, optical and chemical properties
Picture courtesy of Gavin McMeeking

10. Vegetation Relationship
Levoglucosan/OC
Cellulose dry mass
Sullivan et. al., 2008
Hoch, 2007

11. Vegetation composition & anhydrosugar relationship
Levoglucosan & Cellulose
Mannosan/Galactosan & Hemicellulose

14. Vegetation source profiles
FLAME groups
Non-FLAME groups
Separate vegetation groups
Source profile = median profile of each group
Averages have outlier problems
Problem: The FLAME study does not sample all different types of vegetation in the U.S.
Identify source profiles in lit
Take median of each study
Average the medians to calculate final source profile

15. Fuel Characteristic Classification System
Fully descriptive fuelbed model
Defines 113 fuelbeds across U.S.
Assigns characteristics for six strata in each fuelbed
Maps fuelbeds across conterminous U.S. with 1 km resolution
Ottmar et. al., 2007

16. Emissions Algorithm Canopy (3 stories): Emissionsi = Hardwood branches
Emissioni =
Emissioni =
Emissions Algorithm
Emissionsi =
Canopy (3 stories):
Hardwood branches
Softwood branches
Hardwood leaves
Softwood needles
Shrubs
Shrub branches
Shrub leaves
Non-woody vegetation
Grasses
Litter
Duff
Bj = fuel loading
CEj = combustion efficiency
eij = emissions factor (source profile)

17. Source Profiles
Litter: 5 different categories are multiplied with weightings
Duffs are assigned same source profile as litters
Litter Type Assigned by FCCS
Emissions Type Assigned
Needles
Softwood Needles
Broadleaf Deciduous
Hardwood Leaves
Broadleaf Evergreen
Shrub Leaves
Palm Frond
Saw Palmetto Leaves
Grasses

18. Duff: Calculated vs. Measured
Calculated levoglucosan yields match measured
Mannosan and galactosan are underestimated
K+ is grossly overestimated
Burn conditions
Correction factor of 2.65 is applied

20. Spatial Distribution of fuelbeds

21. Levoglucosan/OC Map

23. Mannosan/OC Map

25. Galactosan/OC Map

27. K+/OC Map

29. Can a national source profile apply?
Levoglucosan/OC
Mannosan/OC

30. Source Apportionment
Samples from IMPROVE site in Rocky Mountain National Park
Weekly: 06/28/05 – 08/16/05
Attempts to apportion carbon resulted in overestimation of biomass burning carbon concentrations
Carbon concentrations and biomass burning carbon concentration
courtesy of Amanda Holden

31. Simple Fire Model Fires identified by MODIS thermal anomalies
Seven 48 hour HYSPLIT back trajectories were calculated
Source profiles of fires within 2 degrees latitude and 2 degrees longitude of a trajectory were averaged
No accounting for fire size, distance to sampler, or dispersion

32. New Source Apportionment
Estimates using levoglucosan source profile improved
K+ and galactosan source profiles yield reasonable results
Mannosan too high
Uncertainty can be assessed

33. New Source Apportionment
Estimates using levoglucosan source profile improved
K+ and galactosan source profiles yield reasonable results
Mannosan too high
Uncertainty can be assessed

34. In summary Vegetation composition & anhydrosugar relationship
Differences in means of different vegetation types
Source profiles
Vegetation
Fuelbed
Fuelbed profile maps
Source apportionment

35. Any Q uestions ?

36. Kuo et. al Figure

37. Grouping vegetation types
Sullivan et. al. – 6 groups
Grasses
Leaves
Needles
Branches
Straws
Duffs
Shrub Leaves
Hardwood Leaves
Shrub Branches
Softwood Branches

38. Physical Fuel Loadings
Canopy:
Split into hardwoods and softwoods
Hardwoods: 84% wood, 16% leaves (Wiedenmyer et. al., 2006)
Softwoods: 79% wood, 21% needles (Wiedenmyer et. al., 2006)
Shrub:
39% wood, 61% leaves (Wiedenmyer et. al., 2006)

39. Smoke Contribution Canopy: Shrub:
Combustion efficiencies: 30% for wood, 90% for leaves/needles (Wiedenmyer et. al., 2006)
Hardwoods: 64% wood, 36% leaves
Softwoods: 56% wood, 44% needles
Shrub:
Combustion efficiency: 30% for wood, e(-.013*TCP) for leaves(Wiedenmyer et. al., 2006)
For 50% shrub coverage: 27% wood, 73% leaves

40. Combustion efficiencies
Canopy: Total trees available to fire depends on fire characteristics. Combustion efficiency: 0.9 for leaves, 0.3 for wood (Wiedenmyer et. al. 2006)
Shrub: 0.3 for wood, CE = exp(-0.013*TCP) for leaves (Wiedenmyer et. al. 2006)
Non-woody vegetation: 0.98 (Wiedenmyer et. al. ,2006)
Litter-lichen-moss: 1 (Reinhardt et. al. 2003)
Ground fuels: CE = (26.1 – * DM * DEPTH)/DEPTH (Brown et. al. 1985)

41. Source Profiles
Sheesley et. al. 2007