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2005-06-03 Aerosol Characterization and the Supporting Information Infrastructures


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2005-06-03 Aerosol Characterization and the Supporting Information Infrastructures

  1. 1. Aerosol Characterization and the Supporting Information Infrastructures R.B. Husar Washington University in St. Louis
  2. 2. Regional Haze Rule: Natural Background by 2064 <ul><li>The goal is to attain natural conditions by 2064; </li></ul><ul><li>The baseline is established during 2000-2004 </li></ul><ul><li>The first SIP & Natural Condition estimate in 2008; </li></ul><ul><li>SIP & Natural Condition Revisions every 10 yrs </li></ul>Natural haze is due to natural windblown dust, biomass smoke and other natural processes Man-made haze is due industrial activities AND man-perturbed smoke and dust emissions A fraction of the man-perturbed smoke and dust is assigned to natural by policy decisions
  3. 3. NAAMS: National Ambient Air Monitoring Strategy and NCore Applications
  4. 4. FASTNET and DataFed pursues several NAAMS recommendations: <ul><li>Insightful Measurements </li></ul><ul><ul><li>Enhanced real-time data delivery to public </li></ul></ul><ul><ul><li>Increase capacity for hazardous air pollutant measurements </li></ul></ul><ul><ul><li>Increase in continuous PM measurements </li></ul></ul><ul><ul><li>Support for research grade/technology transfer sites </li></ul></ul><ul><ul><li>Auxiliary [non-EPA] data support </li></ul></ul><ul><li>Multiple pollutant monitoring </li></ul><ul><ul><li>Integration of sources, processes, effects </li></ul></ul><ul><li>Incorporate technological advances </li></ul><ul><ul><li>Information transfer technologies </li></ul></ul><ul><ul><li>Continuous PM monitors </li></ul></ul><ul><ul><li>High sensitivity instruments </li></ul></ul><ul><ul><li>Model-monitor integration </li></ul></ul>
  5. 5. FASTNET and DataFed FASTNET (Fast Aerosol Sensing Tools for Natural Event Tracking) an open communal information sharing facility to study aerosol events , including detection, tracking and impact on PM and haze. The main asset of FASTNET is the community of data analysts, modelers, managers participating in the production of actionable knowledge from data and models The community is supported by a non-intrusive data integration infrastructure based on Internet standards (web services) and a set of web-tools evolving under the federated data system, DataFed DataFed is supported by its community and is under the umbrella of the interagency Earth Science Information Partners, ESIP (NASA, NOAA and EPA)
  6. 6. Aerosol Characterization <ul><li>Atmospheric aerosol system has three extra dimensions (red), compared to gases (blue) : </li></ul><ul><ul><li>Spatial dimensions (X, Y, Z) </li></ul></ul><ul><ul><li>Temporal Dimensions (T) </li></ul></ul><ul><ul><li>Particle size (D) </li></ul></ul><ul><ul><li>Particle Composition ( C ) </li></ul></ul><ul><ul><li>Particle Shape (S) </li></ul></ul><ul><li>Bad news: The mere characterization of the 7Dim aerosol system is a challenge </li></ul><ul><ul><li>Spatially dense network -X, Y, Z(??) </li></ul></ul><ul><ul><li>Continuous monitoring (T) </li></ul></ul><ul><ul><li>Size segregated sampling (D) </li></ul></ul><ul><ul><li>Speciated analysis ( C ) </li></ul></ul><ul><ul><li>Shape (??) </li></ul></ul><ul><li>Good news: The aerosol system is self-describing. </li></ul><ul><ul><li>Once the aerosol is characterized (Speciated monitoring) and multidimensional aerosol data are organized, (see RPO VIEWS effort), unique opportunities exists for extracting information about the aerosol system (sources, transformations) from the data directly. </li></ul></ul><ul><li>Analysts challenge: Deciphering the handwriting contained in the data </li></ul><ul><ul><li>Chemical fingerprinting/source apportionment </li></ul></ul><ul><ul><li>Meteorological back-trajectory analysis </li></ul></ul><ul><ul><li>Dynamic forward and inverse modeling </li></ul></ul>
  7. 7. Technical Challenge: Characterization <ul><li>PM characterization requires many sensors, sampling methods and analysis tools </li></ul><ul><li>Each sensor/method covers only a fraction of the 8-D PM data space . </li></ul><ul><li>Most of the 7-8 Dim PM data space is extrapolated from sparse measured data </li></ul><ul><li>Others sensors integrate over time, space, chemistry, size etc. . </li></ul><ul><li>Example: Satellites, have high spatial resolution but integrate over height H, size D, composition C, shape, and mixture dimensions; these data need de-convolution of the integral measures. </li></ul><ul><li>For these sensors, the integral samples need to be separated into components </li></ul>Satellite-Integral
  8. 8. Satellite Detection of Fire and Smoke
  9. 9. <ul><li>FIRE and Norm. Diff. Veg. Index, NDVI </li></ul><ul><li>The ‘ Northern ’ zone from Alaska to Newfoundland has large fire ‘patches’, evidence of large, contiguous fires. </li></ul><ul><li>The ‘ Northwestern ’ zone (W. Canada, ID, MT, CA) is a mixture of large and small fires </li></ul><ul><li>The ‘ Southeastern ’ fire zone (TX–NC–FL) has a moderate density of uniformly distributed small fires. </li></ul><ul><li>The ‘ Mexican ’ zone over low elevation C America is the most intense fire zone, sharply separated from arid and the lush regions. </li></ul><ul><li>Fires are absent in arid low-vegetation areas (yellow). </li></ul>Fire Zones of North America
  10. 10. Seasonality of Fire <ul><li>Dec, Jan, Feb is generally fire-free except in Mexico, and W. Canada </li></ul><ul><li>Mar, Apr, May is the peak fire season in Mexico and Cuba; fires occur also in Alberta-Manitoba and in OK-MO region </li></ul><ul><li>Jun, Jul, Aug is the peak fire season in N. Canada, Alaska and the NW US. </li></ul><ul><li>Sep, Oct, Nov is fire over the ‘Northwest’ and the “Southeast’ </li></ul>
  11. 11. Seasonal Pattern of Fires over N. America <ul><li>The number of ATSR satellite-observed fires peaks in warm season </li></ul><ul><li>Fires are random; onset and smoke amount is unpredictable </li></ul>
  12. 12. MISR Seasonal AOT (MISR Team) <ul><li>Major smoke emission regions by season </li></ul>
  13. 13. Smoke types: blue, yellow, white <ul><ul><li>Smoke from major fires comes in different colors, e.g. blue, yellow. </li></ul></ul><ul><ul><li>The chemical, physical and optical characteristics of smokes are not known </li></ul></ul><ul><ul><li>Can the reflectance color be used to classify smokes? </li></ul></ul><ul><ul><li>Can column AOT be retrieved for optically thick smoke? Multiple scattering, absoption? </li></ul></ul>California Smoke 1999 Quebec Smoke 2002
  14. 14. SeaWiFS, TOMS, Surface Visibility, May 98 Surface ozone depressed under smoke <ul><li>Satellite image of color SeaWiFS data, contours of TOMS satellite data (green) and surface extinction coefficient, Bext </li></ul><ul><li>The smoke plume extends from Guatemala to Hudson May in Canada </li></ul><ul><li>The Bext values indicate that the smoke is present at the surface </li></ul>
  15. 15. Hourly PM10 During the Smoke Event Hourly PM10 concentration pattern at six eastern US locations during May 1998.
  16. 17. Fire Pixels from MODIS , June 25-July 6, 2002 <ul><li>Several satellite sensor ( MODIS , GOES , AVHRR , ATSR …..) detect the location of most fires - DAILY </li></ul><ul><li>These ‘fire pixels’ can be used as sensor-based inputs to regional/global models, e.g. NAAPS </li></ul><ul><li>However, the quantity of smoke emitted from the from the ‘fire pixels’ can not be estimated well . </li></ul><ul><li>Hence, real-time model simulation of smoke transport is limited by the smoke emission estimates </li></ul>Quebec Fires Note pixel clusters due to larger fires Manitoba – Sask. Fires Note pixel clusters due to larger fires SE US Fires Random pixels from small fires
  17. 18. MODIS: The Fine-Scale Picture The Fires and the Smoke Transport of Smoke from N. Quebec to SE Canada and NE US. <ul><li>020705MODIS </li></ul>020706 MODIS MODIS Land Rapid Response System 020707 MODIS
  18. 19. Haze WebCams <ul><li>020706 10:00 Normal bluish haze </li></ul>CamNet -Webcam 020707 10:00 Yellow smoke Hartford, CT New York, NY Boston, MA
  19. 20. GOES 8 Animation July 6 animation: low-resolution , high resolution July 7 animation: low resolution , high resolution
  20. 21. GOES 8 and ASOS Visibility July 6, 2002 8:15, 12:15, 16:15 EST GOES8 20020706_1315 UTC GOES8 20020706_1315 GOES8 20020706_1715 UTC GOES8 20020706_2115 UTC The largest circles correspond to > 100 ug/m3 PM2.5
  21. 22. Smoke Events: Community Websites <ul><li>er </li></ul>
  22. 23. Smoke Plumes over the Southeast <ul><li>Satellite detection yields the origin and location is the shape of smoke plumes </li></ul>R 0.68  m G 0.55  m B 0.41  m 0.41  m 0.87  m <ul><li>The influence of the smoke is to increase the reflectance ant short wavelength (0.4  m ) </li></ul><ul><li>At longer wavelength, the aerosol reflectance is insignificant. </li></ul>
  23. 24. Cumulative Seasonal PM2.5 Composition <ul><li>PM2.5 chemical components were calculated based on the CIRA methodology </li></ul><ul><li>In addition, the the organics were (tentatively) further separated as Primary Smoke Organics ( red ) and Remainder organics ( purple ) </li></ul><ul><li>PSO = 20*(K - 0.15*Si – 0.02* Na) </li></ul><ul><li> Remainder Org = Organics - PSO </li></ul><ul><li>Also, the ‘ Unknown ’ mass (white area) is the difference between the gravimetrically measured and the chemically reconstructed PM2.5. </li></ul><ul><li>The daily chemical composition was aggregated over the available IMPROVE data range (1988-99) to retain the seasonal structure. </li></ul><ul><li>I order to reduce the noise the daily data were smoothed by a 15-day moving average filter. </li></ul>Shenandoah
  24. 25. Peripheral Sites: Chemical Mass Balance <ul><li>Eastern N. America is surrounded by aerosol source regions such as Sahara and Central America. </li></ul><ul><li>As a consequence, the PM concentration at the ‘edges’ ranges between 4-15 ug/m3; much of it originating outside. </li></ul><ul><li>The chemical composition of the inflow varies by location and season. </li></ul><ul><li>At the Everglades, organics, ‘smoke organics’ and LAC dominate over sulfate and fine dust </li></ul><ul><li>Sahara dust, and smoke from Central America and W. US/Canada are the main contributions to Everglades, FL, and Big Bend, TX. </li></ul>Badlands (scale 0-15 ug/m3) Big Bend (scale 0-15 ug/m3) Voyageurs (scale 0-15 ug/m3) Acadia Everglades
  25. 26. Peripheral Sites: Carbonaceous Mass Balance <ul><li>At the northern peripheral sites, Badlands, Voyageurs and Acadia, the organics range from 1.5 to 4  g/m 3 </li></ul><ul><li>At Big Bend the organics show a spring peak, with a majority of ‘smoke organics’. This indicates biomass smoke origin. </li></ul><ul><li>At the Everglades, the fall peak is due to organics, while ‘smoke organics’ light absorption is present throughout the year. </li></ul>Badlands (scale 0-15 ug/m3) Big Bend (scale 0-15 ug/m3) Voyageurs (scale 0-15 ug/m3) Acadia (scale 0-15 ug/m3) Everglades (scale 0-15 ug/m3)
  26. 27. Possible Smoke Emission Estimation: Local Smoke Model with Data Assimilation e..g. MM5 winds, plume model Local Smoke Simulation Model AOT Aer. Retrieval Satellite Smoke Visibility, AIRNOW Surface Smoke Assimilated Smoke Pattern Continuous Smoke Emissions Assimilated Smoke Emission for Available Data Fire Pixel, Field Obs Fire Location Assimilated Fire Location Emission Model Land Vegetation Fire Model
  27. 28. Kansas Agricultural Smoke, April 12, 2003 Fire Pixels PM25 Mass, FRM 65 ug/m3 max Organics 35 ug/m3 max Ag Fires SeaWiFS, Refl SeaWiFS, AOT Col AOT Blue
  28. 30. ASOS Visibility Monitoring System (1200 Sites) <ul><li>The Automated Surface Observing System, ASOS; weather every minute. </li></ul><ul><li>The forward scattering (30-50 0 ) visibility sensor has a range 17 ft to 30 miles. </li></ul><ul><li>The synoptic visibility data are truncated (<1/4, 1/4,..10+ miles) </li></ul><ul><li>For smoke and haze events (vis. < 10 mile) truncation not a problem </li></ul>
  29. 31. Diurnal Cycle – Surface Bext, April 12, 2003 00 02 04 06 08 10 12 14 16 18 20 22 Night Day Night High Night Bext Low Day Bext Smoke
  30. 32. FASTNET Event Report: 040705July4Haze, July 6, 2004 July 4, 2004 Aerosol Pulse Event Summary by the FASTNET Community Please send PPT slides or comments to Erin Robinson or Rudy Husar , CAPITA Visit the event discussion forum
  31. 33. July 4, 2004 Aerosol Pulse <ul><li>The US-avg. AIRNOW PM25 shows a 3 hr. spike at midnight </li></ul><ul><li>In the (airport) ASOS the July 4 spike is conspicuously absent </li></ul><ul><li>Thus, the US spike is due to the urban sites affected by smoke </li></ul>00:00 04:00 08:00 20:00 AIRNOW PM25 AIRNOW PM25 US Hourly Average ASOS Bext US Hourly Average Pulse No Pulse
  32. 34. Previous work: The July 4th Potassium Spike (Poirot 1998) <ul><li>Potassium nitrate is a major component of all fireworks (provides the bang!). </li></ul><ul><li>Fine particle K for all IMPROVE data (1988-1997) were averaged for each day of year </li></ul><ul><li>The potassium spike on July 5 is 120 ng/m3 compared to 40-60 during the year </li></ul><ul><li>The corresponding IMPROVE-average daily fine mass did not show the spike </li></ul><ul><li>The K spike is clearly something to consider (and perhaps screen out) in conducting any analyses using K data </li></ul>
  33. 35. D U S T Update <ul><li>Global & Local Dust Over N. America </li></ul>
  34. 36. Sahara PM10 Events over Eastern US <ul><li>The highest July, Eastern US, 90 th percentile PM10 occurs over the Gulf Coast ( > 80 ug/m3) </li></ul><ul><li>Sahara dust is the dominant contributor to peak July PM10 levels . </li></ul>Much previous work by Prospero, Cahill, Malm, Scanning the AIRS PM10 and IMPROVE chemical databases several regional-scale PM10 episodes over the Gulf Coast (> 80 ug/m3) that can be attributed to Sahara. June 30, 1993 July 5, 1992 June 21 1997
  35. 37. 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
  36. 38. MODIS Rapid Response FASTNET Event Report: 040219TexMexDust Texas-Mexico Dust Event February 19, 2004 Contributed by the FASNET Community Correspondence to R Poirot , R Husar
  37. 39. High Wind Speed – Dust Spatially Correspond <ul><li>The spatial/temporal correspondence suggests that most visibility loss is due to locally suspended dust, rather than transported dust </li></ul><ul><li>Alternatively, suspended dust and ‘high winds’ travel forward at the same speed </li></ul><ul><li>Wind speed animation ; Bext animation . (material for model validation?) </li></ul>
  38. 40. PM10 > 10 x PM25 During the passage of the dust cloud over El Paso, the PM10 concentration was more than 10 times higher than the PM2.5 <ul><li>AIRNOW PM10 and Pm25 data </li></ul>Schematic Link to dust modelers for faster collective learning?
  39. 41. Monte Carlo simulation of dust transport using surface winds (just a toy, 3D winds are essential!) <ul><li>See animation Note, how sensitive the transport direction is to the source location (according to this toy) </li></ul>