Transcript 080417_EGU_ExcEvents

Integration of Satellite and Surface Observations
during Exceptional Air Quality Events
R.B. Husar, Washinton University
N. Frank, US EPA
R. Poroit, State of Vermont
J. 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
NOT Exceptional Event
NOT Exceptional Event
The 'exceptional'
concentration raises the
level above the standard.
A valid EE to be flagged.
Controllable sources are
sufficient to cause
exceedance. Not a 'but
for‘, not an EE.
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 Fires
The fires in S. Georgia emitted intense smoke throughout May 07.
May 5, 2007
May 12, 2007
Google Earth Video (small 50MB, large 170mb)
1. Is there a likely exceedance of NAAQS?
2. The event not reasonably controllable/ preventable
Show that the cause is in category of uncontrollable/preventable
Transported Pollution
Natural Events
Human Activities
Transported African, Asian
Dust; Smoke from Mexican
fires & Mining dust, Ag.
Emissions
Nat. Disasters.; High Wind
Events; Wildland Fires;
Stratospheric Ozone;
Prescribed Fires
Chemical Spills; Industrial
Accidents; July 4th; Structural
Fires; Terrorist Attack
2. The event not reasonably controllable
Fire Pixels
MODIS Visible
OMI Aerosol Index
OMI NO2
3. Evidence: Transport
3. Evidence: Aerosol Composition
Sulfate
Organics
Measured
Sulfate
Organics
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)
Biomass Burning
Friday/Sunday Ratio
Sunday
Smoke
4. The Event is in
Excess of the "Normal"
Values
Median
Concentration
Excess over the
Median
5. The Exceedance would not Occur, But
For the Exceptional Event
Near-Real-Time Data for May 11, 07 GA Smoke
Displayed on DataFed Analysts Console
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2
3
4
5
6
7
8
9
10
11
12
Pane 1,2: MODIS visible satellite images – smoke pattern
Pane 3,4: AirNOW PM2.5, Surf. Visibility – PM surface conc.
Pane 5,6: AirNOW Ozone, Surf. Wind – Ozone, transport pattern
Pane 7,8: OMI satellite Total, Tropospheric NO2 – NO2 column conc.
Pane 9,10: OMI satellite Aerosol Index, Fire P-xels – Smoke, Fire
Pane 11,12: GOCART, NAAPS Models of smoke – Smoke forecast
Console Links
May 07, 2007,
May 08, 2007
May 09, 2007
May 10, 2007
May 11, 2007
May 12, 2007
May 13, 2007
May 14, 2007
May 15, 2007
EE Analysis Wiki
May 07 Georgia Fires:
User-Supplied Qualitative Observations
Searching and pruning user-contributed Internet content yielded rich, but
qualitative description of the May 07 Georgia Smoke Event.
Google and Technorati blog seaches
yielded entries on GA Smoke.
Videos of smoke were found
on YouTube
Smoke
. images, were also found
searching Flickr and Google
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.