1. 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

2. 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

3. 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..

4. 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

5. May 2007 Georgia Fires The fires in S. Georgia emitted intense smoke throughout May 07. Google Earth Video (small 50MB, large 170mb)small 50MBlarge 170mb May 5, 2007 May 12, 2007

6. 1. Is there a likely exceedance of NAAQS?

7. 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/preventableuncontrollable/preventable

8. 2. The event not reasonably controllable OMI Aerosol Index OMI NO2 Fire Pixels MODIS Visible

9. 3. Evidence: Transport

10. 3. Evidence: Aerosol Composition SulfateOrganics SulfateOrganics Measured Modeled

11. 3. Evidence: OMI NO2 Sweat Water fire in S. Georgia (May 2007)

12. 3. Evidence: OMI NO2 Sweat Water fire in S. Georgia (May 2007)

13. Friday/Sunday Ratio Biomass Burning Sunday Smoke

14. 4. The Event is in Excess of the "Normal" Values Excess over the Median Median Concentration

15. 5. The Exceedance would not Occur, But For the Exceptional Event

16. Near-Real-Time Data for May 11, 07 GA Smoke Displayed on DataFed Analysts Console 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 1 10 24 58 76 3 91211 Console Links May 07, 2007May 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

17. EE Analysis Wiki

18. May 07 Georgia Fires: User-Supplied Qualitative Observations Google and Technorati blog seaches yielded entries on GA Smoke.. Smoke Smoke images, were also found searching Flickr and GoogleFlickr 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 YouTubeYouTube Visually pruned blogs, videos and images were bookmarked and tagged fore later analysisvideosimagestagged

19. 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.