Leigh Patterson 06/15/09 M.S. Defense

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Presentation transcript:

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

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

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

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)

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

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)

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)

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)

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

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

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

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

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

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)

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

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

Spatial Distribution of fuelbeds

Levoglucosan/OC Map

Mannosan/OC Map

Galactosan/OC Map

K+/OC Map

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

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

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

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

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

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

Any Q uestions ?

Kuo et. al. 2008 Figure

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

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)

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

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)

Source Profiles Sheesley et. al. 2007