Title: Multi-Sensor Characterization of Aerosol Events
1Multi-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
2Air 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)
3Exceptional 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
4Regional 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
5This 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
6Mexican 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
7Kansas 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
8(No Transcript)
9May 2007 Georgia FiresAn actual Exceptional
Event Analysis for EPA
May 5, 2007
May 12, 2007
Observations Used
10OMI NO2 Quantifies the NO2 Emission
Sweat Water fire in S. Georgia (May 2007)
113. Evidence Aerosol Composition
Sulfate
Organics
Measured
Sulfate
Organics
Modeled
125. The Exceedance would not Occur, But For the
Exceptional Event
13Texas-Mexico Dust Event February 19, 2004
MODIS Rapid Response
1200
1400
1600
1800
1000
0800
14High 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?)
15Satellite Aerosol Optical Thickness
ClimatologySeaWiFS Satellite, Summer 2000 - 2003
20 Percentile
60 Percentile
99 Percentile
90 Percentile
16Summary
- 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?