The document describes the Exceptional Event Decision Support System (EE DSS), a tool to help states and EPA regions implement the EPA's Exceptional Events Rule. The EE DSS uses air quality, meteorological, and other data to screen for exceedances and flag those likely caused by exceptional events like dust storms, wildfires, or July 4th fireworks. It aims to minimize the technical hurdles of the EE rule and provide a uniform, transparent methodology. The document outlines the EE DSS's data sources and modeling, screening approach, tools for visualizing events, and provides an example demo of the system in action.

1. Exceptional Event Decision Support System (EE DSS)
Approach and Demo
Dr. Rudolf B. Husar
With
Kari Hoijarvi and Erin Robinson
Center for Air Pollution Impact and Trend Analysis (CAPITA)
Washington University, St. Louis
Webinar presented April 30, 2013
to EPA, Regional and State AQ Analysts
http://wiki.esipfed.org/index.php/EE_DSS_Webinar

2. EPA’s Exceptional Event Rule
The States and the EPA Regions encounter many hurdles implementing EE Rule
The technical hurdles can be minimized by a suitable Decision Support System
The Exceedance would not Occur, But For the Exceptional Event

3. • There is a NAAQS exceedance or violation based on FRM data
• The event concentration is in excess of the ”normal" fluctuations
• Event satisfies the definition of exceptional: The cause is not
reasonably controllable or preventable.
• There is a clear causal relationship between the exceedance and
the exceptional source
• The exceedance would not have occurred, ‘but for’ the
exceptional source
Approach for supporting EE flagging process:
Screening tool for PM2.5, PM10 and Ozone Exceedances
Data
Screening
Tool
EPA Exceptional Event Rule Conditions:

4. EE DSS Overview Video
• http://datafedwiki.wustl.edu/index.php/2013-02-15_EE-DSS_Kansas_Use_Case_EPA

5. Exceptional Event Decision Support System (EE DSS)
• EE DSS offers a uniform tool set applicable nation-wide
• Employs an open and transparent methodology for flagging
• Uses FRM and a wide array of model, satellite and other datasets
Specific aims of the EE DSS
• Aid the States identifying and documenting candidate EE samples
• Assists the EPA Regions in evaluation the flag documentations
• Supports collaborative tools development and EE analysis
The EE DSS was developed over the past 8 years with generous support from the NASA
Applications Program, by EPA and by Washington University.

6. Exceptional Event Screening Approach and Tool Set
Focus of April 30, 2013 Webinar
• The EE screening tool for supporting 2012 flag submissions by
July 1, 2013
– detects exceedances of the daily NAAQS, PM2.5, PM10, Ozone
– eliminates those that fall within normal fluctuations
– identifies those attributable to
• Windblown dust events (domestic, international)
• Smoke events (domestic, international)
• July 4th event
The EE flag documentation is to be prepared by the States by Dec 12, 2012

7. Datasets Used
• Primary Data from EPA AQS, (April 20 extraction)
– FRM PM2.5, PM10 (daily)
– FRM Ozone (hourly) – aggregated to daily 8hr-day-max
• Supporting Datasets
– Naval Res. Lab NAAPS aerosol model, dust, smoke, sulfate
– NASA satellite data from 4 sensors
– Continuous surface obs. AIRNOW,

8. EE DSS Tools: Data System Architecture
• Data are accessible from the Air Quality Data Network (ADN) by the AQ Community Catalog
• ADN is facilitated by the GEO AQ Community of Practice (GEO AQ CoP), including R. Husar’s group.
• The generic client tools (red boxes) are for processing and visualization; used in many applications
• Specialized Application Tools are dedicated to specific applications, e.g. event detection

9. Normal Variation -Excess of Normal Variation
Variation of PM2.5, PPM10 and Ozone is determined for each location
Using all observations over a 3-year period 2010-2013
Variation is quantified using nonparametric statistic – percentiles (16, 50, 84, 97)
Long-term trends are ignored but percentiles are calculated seasonally
Raw Daily Observation
(3rd-6th-day fom PM2.5, PM10)
Normal Variation
(3-yr seasonal percentiles)
Raw Daily & Normal Variation
(3-yr seasonal percentiles)
Exceedances above Normal
(values >NAAQ and and > 84th perc
Qualified Samples

10. What is the Normal Variation for PM2.5?
CA Central
Valley
Ohio River
Idaho Tennessee

11. Aggregation over what time range
A reason for the 2006-2010 EE flag decline is the
overall reduction in PM2.5 concentrations.
2000-2003 avg. 2009-2012 avg.
As a result of PM2.5 decline, the number of
>35ug/m3 samples has also declined dramatically.
No NAAQS exceedances-no EE flag.
>35 ug/m3 station count

12. O3 ‘Violation’ Trends
Number of CONUS Stations with O3>75 ppb
2010-2013
1993-2013
Apr-Oct 2012

13. EE Screening tool for a specific site, day
Seasonal
Percentiles
Station time series
Exceedances above normal
Anomaly Map
Exceedances above normal
Station Data Map
Data Contour Map
Specific
Station
Specific
Day

14. EE Samples and NAAPS Model Smoke, Dust

15. Navy Aerosol Analysis and Prediction System (NAAPS)
• Assimilates satellite data from multiple sensors
• Aerosol Optical Depth (AOD; 2-D) (MODIS and MISR)
• Extinction (3-D) (CALIPSO)
• Assimilation of previous forecast + remote sensing of aerosols
+ Land/Ocean MODIS
+ Land/Ocean MISR
Natural run + Ocean MODIS
+ land/Ocean MISR
Forecasting Dust, Smoke and Sulfate Globally
D. Westphal, NRL

16. 4D Dust, Smoke, Sulfate
Vertical Cross Section Views
Vertical dust cross sections at about 120 (surface plume) and 1000 km (elevated plume).
Knowing the vertical structure of smoke and dust plumes is critical to EE documentation
The DataFed Browser now incorporates vertical cross section views

17. NAAPS Surface Smoke – IMPROVE Total Organic Carbon Comparison
2006-2007, Purple Box Region
• Temporal correlation shows that NAAPS properly
simulates the temporal smoke variation over NW
• Spatial correlation indicates that NAAPS
reproduces the spatial distribution of smoke
• Time series of measured IMPROVE OCf and NAAPS
smoke over the cursor box shows close co-variation
Seasonal cycle of OCf and NAAPS averaged over the
cursor box and multiple years is very similar.
• The NAAPS smoke and IMPROVE organics (OCf) correlation is excellent and the here is excellent
since OCf is dominated by smoke
• At other regions of the US, OCf has biogenic and urban-industrial sources
What does this red dot represent?
Are NAAPS data presented in this
figure?

18. NAAPS Surface Smoke – IMPROVE Total Organic Carbon Comparison
2006-2007, Purple Box Region
• Temporal correlation shows that NAAPS properly
simulates the temporal smoke variation over NW
• Spatial correlation indicates that NAAPS
reproduces the spatial distribution of smoke
• Time series of measured IMPROVE OCf and NAAPS
smoke over the cursor box shows close co-variation
Seasonal cycle of OCf and NAAPS averaged over the
cursor box and multiple years is very similar.
NAAPS Smoke IMPROVE OCf
• Spatial distribution NAAPS smoke and IMPROVE Ocf
averaged over 2006-07
• For the NW States, both the temporal and the spatial correlations are excellent.
• Over the SW US, the correlation is poor since Ocf has biogenic and man-made sources

19. Back-Trajectory Tool

20. Event Console
• Consoles are spatial representations of observations, emissions and models
• All maps are synchronized spatially and temporally, and navigated by the user
• Provide rich multisensory context to illuminate complex atmospheric situations

21. Permalink for Bookmarking an EE Sample
Once you identified a candidate EE sample, you can save the settings in a
bookamark, so you can return to the EE sample ‘page’ any time.
• An EE sample bookmark will help
• ‘Remembering’ and listing the EE candidates during your exploration
• Sharing the EE sample with your colleagues in the office or elsewhere
• Communicating with the EPA Region
If you whish to discuss a specific EE sample with the EE Community (inlc. The DSS
team), send us the permalink to open a discussion topic..

22. Browser Video
http://datafedwiki.wustl.edu/index.php/2013-04-23_DataFed_Browser_Navigation

23. EE DSS Tool Demo
• PM2.5 EE Screen PM25
– Bakersfild, CA - Winter
peaked, Exceedances over the
normal variation, 84
percentile
– Also summer peaks
– Salt Lake City July 4
• Ditto , July 4th
• PM2.5 Smoke
– West Washington - Smoke
• Click Smoke Dust
• PM10 EE Screen PM10
– Click Dust Smoke
• Ozone EE Screen Ozone
– Sequence 2012-06-26, Click
Arkansas
– 2012-06-27, 28, 29, 30, 7-01
Click Charlotte
• 2011 Ozone Kansas smoke
– Click Back trajectory
• Kansas Smoke Ozone
– Back trajectory
• Kansas Smoke KMZ
http://wiki.esipfed.org/index.php/2013-04-30_EE_DSS_Webinar_Demo

24. Many Thanks:
• Kari Hoijarvi, Washington University
• Anshu Tirumali, Washington University
• Rich Poirot, State of Vermont
• Doug Westphal, Naval Research Lab
• Neil Frank, EPA
• Ali Omar, NASA Applied Sciences

25. Community Contacts
Rudy Husar, Washington University, Stl. rhusar@wustl.edu
Erin Robinson, Foundation for Earth Science, erinrobinson@esipfed.org
Neil Frank, EPA, Frank.Neil@epa.gov;
Mark Evangelista, EPA, Evangelista.Mark@epamail.epa.gov