2008-06-08 HTAP Aerosol Science Review
- 1. Atmospheric Aerosols R.B. Husar, Washinton University HTAP Meeting, June 10, 2008, Washington DC
- 2. Dimensions of Gaseous Pollutants: X, Y, Z, T Dimensions of Aerosols: Particle Size, Composition, Shape
- 3. Bad News: Aerosol Characterization is Challenging PM is characterized by many sensors, sampling methods and tools Each sensor covers only a fraction of the 6-Dim PM data space . The 6 D data space is extrapolated from sparse measured data Or deconvoluted from integral measurements For integral sensors, the integral samples need to be separated into components Good News Once the aerosol is characterized, opportunities exists for extracting information about the aerosol sources, transformations, etc from the data directly. Satellite-Integral
- 4. Phase II HTAP Goal: Integration of Emissions, Models and Observations
- 5. Aerosols are Indicators of Many Earth System Processes Including Human-induced Perturbations Volcanoes Dust storms Fires Anthropogenic Pollution EPA NAAQS PM2.5, O3 Exceptional Event Rule Exclusion of data when it is strongly influenced by “exceptional events" (EE), such as smoke from wildfires or windblown dust or LRTP.
- 6. Visibility from Ships, 1938 Ship observations cataloged in 1938 indicate qualitatively similar pattern to the 1990 AVHRR values McDonald 1938
- 7. AVHRR satellite optical depth data over the oceans Husar et al, 1997. The oceanic aerosol pattern is highly regional and and seasonal The highest oceanic aerosol optical thickness (AOT, 1989-91) is over the tropical regions The oceanic AOT around N. America, Europe and E. Asia is small compared to Africa and Asia
- 8. Continental Surface Visibility (7000+ Human Observers) Low Visibility High Visibility
- 9. Continental Surface Extinction Coefficient Climatology Dec, Jan, Feb Jun, Jul, Aug Sep, Oct, Nov Mar, Apr, May India Husar et al, 2000
- 10. Fusion of Satellite and Surface Visibility Data Needed: Reconciliation with Models, Emissions
- 11. Vertical Distribution of Aerosols – Space-borne Lidar Long rang transport occurs mostly in elevated layers Elevated layers mix with BL air Cloud interaction is clearly discernable Winker et., al. 1995
- 12. Everglades, FL Big Bend, TX G. Smoky Mtn. Sahara Dust - July Mex. Smoke-May
- 13. Emission: Green Modeling: Green Observations: Green
- 14. Emission: Yellow Modeling: Yellow Observations: Yellow
- 15. Seasonal Pattern of Fires over N. America The number of satellite-fire pixels Jul-Aug (1997-99) The daily fire counts shows significant day to day fluctuation
- 16. Central American Smoke Plume Surface PM2.5 Ozone
- 17. Kansas Agricultural Smoke, April 12, 2003 Fire Pixels Organics 35 ug/m3 max Ag Fires SeaWiFS, Refl Smoke Emission April 11: 87 T/day April 10: 1240 T/d Assuming Mass Extinction Efficiency: 5 m 2 /g
- 18. Emission: Red Modeling: Yellow Observations: Yellow
- 19. Aerosol Nitrate Anomaly – Every 3 Days
- 20. Seasonal PM25 by Region Sulfate-driven Jul-Aug peak Feb-Mar peak, of unknown origin
- 21. Seasonal Average Fine Dust (VIEWS database, 1992-2002) Fine soil concentration is highest in the summer over Mississippi Valley, lowest in the winter In the spring, high concentrations also exists in the arid Southwest (Arizona and Texas) Evidently, the summer Mississippi Valley peak is Sahara dust while the Spring peak is from local (and Asian) sources
- 22. Origin of Fine Dust Events over the US Gobi dust in spring Sahara in summer Fine dust events over the US are mainly from intercontinental transport
- 24. Supporting Evidence: Transport Analysis Satellite data (e.g. SeaWiFS) show Sahara Dust reaching Gulf of Mexico and entering the continent. The air masses arrive to Big Bend, TX form the east (July) and from the west (April)
- 25. Sahara PM Events over the Eastern US PM10 July 5, 1992 PM10 June 21, 1997 PM10 June 30 1993 Sahara Dust Sahara Dust TOMS, July Aerosol Index
- 26. Asian Dust Cloud over N. America On April 27, the dust cloud arrived in North America. Regional average PM10 concentrations increased to 65 g/m 3 In Washington State, PM10 concentrations exceeded 100 g/m 3 Asian Dust 100 g/m 3 Hourly PM10
- 27. ~50% of the variability in springtime PM 2.5 in the Western U.S. can be explained by changes in Asian dust (Fischer et al., 2008)
- 28. EPISODIC EPISODIC EPISODIC … episodic emissions require emission-observation-model integration…
- 29. Integration New Opportunities: Open flow and harvesting of existing data and knowledge Faster learning through scientific ‘value-chains’ More opportunities to create societal benefits