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Wildland Smoke Marker Emissions Maps for Conterminous US

This study focuses on the development of wildland smoke marker emissions maps for the conterminous United States. It examines the relationship between smoke marker emissions and vegetation type, and explores the use of source profiles in accurately apportioning wildfire smoke. The study also analyzes the impact of fires on air pollution and the health effects associated with smoke. The findings provide valuable information for understanding and managing wildfires.

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Wildland Smoke Marker Emissions Maps for Conterminous US

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  1. 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 • 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 • K+ • 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

  12. 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

  13. 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

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

  15. Source Profiles • Litter: 5 different categories are multiplied with weightings • Duffs are assigned same source profile as litters

  16. 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

  17. Spatial Distribution of fuelbeds

  18. Levoglucosan/OC Map

  19. Mannosan/OC Map

  20. Galactosan/OC Map

  21. K+/OC Map

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

  23. 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

  24. 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

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

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

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

  28. Any Q uestions ?

  29. Kuo et. al. 2008 Figure

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

  31. 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)

  32. Smoke Contribution • Canopy: • 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

  33. 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 – 0.225 * DM + 0.0417 * DEPTH)/DEPTH (Brown et. al. 1985)

  34. Source Profiles Sheesley et. al. 2007

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