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Integration of Satellite and Surface Observations
during Exceptional Air Quality Events
R.B. Husar, Washinton UniversityN. Frank, US EPA
R. Poroit, State of VermontJ. McHenry, Baron Met.
Presented at EGU, Vienna April 17, 2008
EPA Exceptional Event Rule
• The air quality standards for PM2.5 and ozone provide for the exclusion of data when it is strongly influenced by “exceptional events" (EE), such as smoke from wildfires or windblown dust.
• For EE exclusion, States must provide appropriate documentation to support the dominance of the uncontrollable source.
• This report presents that methodology for justifying Exceptional Event exclusions
Show that the exceedance is explicitly caused by the exceptional event
Exceptional Event
The 'exceptional' concentration raises the
level above the standard. A valid EE to be flagged.
NOT Exceptional Event
Controllable sources are sufficient to cause
exceedance. Not a 'but for‘, not an EE.
NOT Exceptional Event
No exceedance, hence, there is no justification for
an EE flag.
.
Evidence Needed to Flag Data as Exceptional
1. Is there a likely exceedance?
2. Not Reasonably Controllable or Preventable
3. Clear Causal Relationship between the Data and the Event
4. The Event is in Excess of the "Normal" Values
5. The Exceedance or Violation would not Occur, But For the Exceptional Event
May 2007 Georgia FiresThe fires in S. Georgia emitted intense smoke throughout May 07.
Google Earth Video (small 50MB, large 170mb)
May 5, 2007
May 12, 2007
1. Is there a likely exceedance of NAAQS?
2. The event not reasonably controllable/ preventable
Transported Pollution
Transported African, Asian Dust; Smoke from Mexican
fires & Mining dust, Ag. Emissions
Natural Events
Nat. Disasters.; High Wind Events; Wildland Fires; Stratospheric Ozone;
Prescribed Fires
Human Activities
Chemical Spills; Industrial Accidents; July 4th; Structural
Fires; Terrorist Attack
Show that the cause is in category of uncontrollable/preventable
2. The event not reasonably controllable
OMI Aerosol Index
OMI NO2
Fire Pixels
MODIS Visible
3. Evidence: Transport
3. Evidence: Aerosol Composition
Sulfate Organics
Sulfate Organics
Measured
Modeled
3. Evidence: OMI NO2
Sweat Water fire in S. Georgia (May 2007)
3. Evidence: OMI NO2
Sweat Water fire in S. Georgia (May 2007)
Friday/Sunday RatioBiomass Burning
Sunday
Smoke
4. The Event is in Excess of the "Normal"
Values
Excess over the Median
Median Concentration
5. The Exceedance would not Occur, But For the Exceptional Event
Near-Real-Time Data for May 11, 07 GA SmokeDisplayed on DataFed Analysts Console
Pane 1,2: MODIS visible satellite images – smoke patternPane 3,4: AirNOW PM2.5, Surf. Visibility – PM surface conc.Pane 5,6: AirNOW Ozone, Surf. Wind – Ozone, transport patternPane 7,8: OMI satellite Total, Tropospheric NO2 – NO2 column conc.Pane 9,10: OMI satellite Aerosol Index, Fire P-xels – Smoke, FirePane 11,12: GOCART, NAAPS Models of smoke – Smoke forecast
1
10
2 4
5 876
3
9 1211
Console LinksMay 07, 2007, May 08, 2007May 09, 2007May 10, 2007May 11, 2007May 12, 2007May 13, 2007May 14, 2007May 15, 2007
EE Analysis Wiki
May 07 Georgia Fires:User-Supplied Qualitative Observations
Google and Technorati blog seaches yielded entries on GA Smoke.
. Smoke images, were also found searching Flickr and Google
Searching and pruning user-contributed Internet content yielded rich, but qualitative description of the May 07 Georgia Smoke Event.
Videos of smoke were found on YouTube
Visually pruned blogs, videos and images were bookmarked and tagged fore later analysis
Abstract
The air quality standards for PM2.5 and ozone in the U.S. and E.U. provide for the exclusion of data for a given day when it is strongly influenced by "exceptional events" (EE), such as smoke from wildfires or windblown dust. In order to apply for EE exclusion, organizations must provide appropriate documentation to demonstrate the dominance of uncontrollable sources on that day.
Most of the EE days are due to regional or continental-scale smoke or dust events. The availability of near real-time monitoring data from satellite remote sensing data and surface air quality data now allows the early assessment of such events. Here we report the candidate methodologies that are being developed for the quantification and documentation of EEs over the US, including:
(1) Observed/modeled pollutant transport based on trajectory and regional models;
(2) Spatial pattern of pollutant derived from surface (AIRNOW, FRM, Visibility) and satellite data (OMI, GOES, AVHRR, SEAWiFS, MODIS);
(3) Temporal pattern analysis; (4) Chemical fingerprinting and source apportionment. The characteristics and
initial climatology of EEs over the US will also be presented along with approaches to iterative reconciliation of observations, emissions and forecast models.