Transcript 071124_EE Slides2

Exceptional Air Pollution Events:
Exceedances due to Natural/Non-recurring Events
R. B. Husar, Washington U.; R.L Poirot, Vermont Dep. Env. Cons.; N. Frank, US EPA
Presented at Fall Meeting of AGU
December 13, 2007, San Francisco, CA
Exceptional Air Quality Event:
An exceedance that would not have occurred but for the natural/nonrecurring event
Evidence Needed to Flag Data as Exceptional
1.
2.
3.
4.
The event was not reasonably controllable or preventable
Would be no exceedances or violation but for the event.
The event is in excess of historical values.
Clear casual relationship of data and the event
1. 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. No exceedance/violation but for the event.
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.
.
3. The event is in excess of historical values.
Evidence from comparison of flagged data with historical values
.
Frequency Distribution
Time Series Analysis
The 'exceptional' concentration is
an outlier on the frequency.
Event data deviate from the regular
seasonal concentration pattern.
4. Clear support of event causality with data.
EE causality may come from multiple lines of observational evidence
Chemical Signature
Source & Transport
Spatial Pattern
Temporal Pattern
The EE sample shows
the fingerprints of
'exceptional‘ source.
Clear evidence of
transport from known
source region.
Unusual spatial pattern
as evidence of
Exceptional source.
Unusual concentration
spike as indication of
an Exceptional Event.
PM2.5 Exceedances: Annual, Daily (Unofficial)
Annual
2000-2002
Daily
Annual
2005-2007
Daily
• The daily PM2.5 NAAQS is more stringent than the annual
• Since 2000, the regions of PM2.5 non-compliance has decreased
EE Tools: Near-Real-Time Data Console
Near-Real-Time Data for May 11, 07 GA Smoke
Displayed on DataFed Analysts Console
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10
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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
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)
EE Analysis Community Workspace
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
October 2007 Southern California Fires
Smoke
Santa Ana
Winds
Dust
Dust
• Consoles are multi-view panels of space-time synchronized data views
• On Oct 21, note the burst of smoke, dust between 11 AM and 1:30PM
Southern California Fires
Oct 21, 2007
Oct 22, 2007
Oct 23, 2007
Oct 24, 2007
OMI/TOMS - Absorbing Aerosol Index
OMI/TOMS – Tropospheric NO2
• The hi-res OMI data provides columnar NO2 and Aerosol Index
• The difference of their spatial pattern indicates smoke age
Summary Notes
• As the NAAQS get tighter, EEs will become more important
• Evidence for EE may include any data/info (e.g. satellites)
• For EE characterization, near-real-time data are essential
• Places high demand on data access, processing tools
• Integrating multi-sensory data is technically challenging
• Both State and Federal agencies require tech support