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2013-04-30 EE DSS Approach and Demo

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2013-04-30 EE DSS Approach and Demo

  1. 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. 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. 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. 4. EE DSS Overview Video • http://datafedwiki.wustl.edu/index.php/2013-02-15_EE-DSS_Kansas_Use_Case_EPA
  5. 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. 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. 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. 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. 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. 10. What is the Normal Variation for PM2.5? CA Central Valley Ohio River Idaho Tennessee
  11. 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. 12. O3 ‘Violation’ Trends Number of CONUS Stations with O3>75 ppb 2010-2013 1993-2013 Apr-Oct 2012
  13. 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. 14. EE Samples and NAAPS Model Smoke, Dust
  15. 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. 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. 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. 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. 19. Back-Trajectory Tool
  20. 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. 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. 22. Browser Video http://datafedwiki.wustl.edu/index.php/2013-04-23_DataFed_Browser_Navigation
  23. 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. 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. 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

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