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.