Multi-Sensor Characterization of Aerosol Events

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Multi-Sensor Characterization of Aerosol Events

• R. B. Husar, R. L. Poirot and E. M. Robinson
• 2008 Fall Meeting
• San Francisco, CA, December 15-19, 2008

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Air Quality Observations as Evidence

• In the past, AQ regulatory decisions were made
  based on standard reference methods for PM2.5,
  ozone, etc.
• There was no legal pathway to use satellite-based
  measurements.
• Recently, two new regulations allowed the
  incorporation of satellite and other observations
  as evidence
• Exceptional Event Rule (for natural smoke dust)
• Regional Haze Rule (for natural background)

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Exceptional Event Rule An air quality
exceedance that would not have occurred but for
the presence of a natural event.
Transported Pollution Transported African, Asian
Dust Smoke from Mexican fires Mining dust, Ag.
Emissions
Natural Events Nat. Disasters. High Wind Events
Wild land Fires Stratospheric Ozone Prescribed
Fires
Human Activities Chemical Spills Industrial
Accidents July 4th Structural Fires Terrorist
Attack
Satellite remote sensors provide key observations
for Exceptional Events
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Regional Haze Rule Attain Natural Condition by
2064
What is Natural Condition? Natural haze is due to
natural windblown dust, biomass smoke and other
natural processes
Satellite remote sensors provide key observations
for Natural Haze
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This Talk Illustrate the use of multi-sensory
data

• Technical Challenge Characterization
• PM characterization requires many sensors,
  sampling methods and analysis tools
• Each sensor/method covers only a fraction of the
  7-Dimensional PM data space.
• Spatial dimensions (X, Y, Z)
• Temporal Dimensions (T)
• Particle size (D)
• Particle Composition ( C )
• Particle Shape (S)
• Most of the 7 Dim PM data space is extrapolated
  from sparse measured data
• Others sensors integrate over time, space,
  chemistry, size etc. .

Satellites, have high spatial resolution but
integrate over height H, size D, composition C,
particle shape
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Mexican Smoke May 98 SeaWiFS, TOMS, Surface
Visibility, Ozone

• Satellite image of color SeaWiFS data, contours
  of TOMS satellite data (green) and surface
  extinction coefficient, Bext
• The smoke plume extends from Guatemala to Hudson
  May in Canada
• The Bext values indicate that the smoke is
  present at the surface

Surface visibility is low under the smoke
Surface ozone depressed under smoke
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Kansas Agricultural Smoke, April 12, 2003
Organics 35 ug/m3 max
Fire Pixels
PM25 Mass, FRM 65 ug/m3 max
Ag Fires
SeaWiFS, Refl
SeaWiFS, AOT Col
AOT Blue
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May 2007 Georgia FiresAn actual Exceptional
Event Analysis for EPA
May 5, 2007
May 12, 2007
Observations Used
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OMI NO2 Quantifies the NO2 Emission
Sweat Water fire in S. Georgia (May 2007)
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3. Evidence Aerosol Composition
Sulfate
Organics
Measured
Sulfate
Organics
Modeled
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5. The Exceedance would not Occur, But For the
Exceptional Event
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Texas-Mexico Dust Event February 19, 2004
MODIS Rapid Response
1200
1400
1600
1800
1000
0800
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High Wind Speed Dust Spatially Correspond

• The spatial/temporal correspondence suggests that
  most visibility loss is due to locally suspended
  dust, rather than transported dust
• Alternatively, suspended dust and high winds
  travel forward at the same speed
• Wind speed animation Bext animation. (material
  for model validation?)

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Satellite Aerosol Optical Thickness
ClimatologySeaWiFS Satellite, Summer 2000 - 2003
20 Percentile
60 Percentile
99 Percentile
90 Percentile
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Summary

• There are real opportunities for supporting air
  quality
• There is an embarrassment of the riches in
  observations
• Missing skills and tools for integrating
  multi-sensory data

Collaborate?