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DataFed Support for EPA’s Exceptional Event Rule

DataFed Support for EPA’s Exceptional Event Rule. R.B. Husar Washington University in St. Louis. Presented at the workshop: Satellite and Above-Boundary Layer Observations for Air Quality Management January, 11-12, 2012, Baltimore, MD.

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DataFed Support for EPA’s Exceptional Event Rule

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  1. DataFed Support for EPA’s Exceptional Event Rule R.B. Husar Washington University in St. Louis Presented at the workshop: Satellite and Above-Boundary Layer Observations for Air Quality Management January, 11-12, 2012, Baltimore, MD

  2. 1976 - Satellite Detection of Regional Haze Event over the Midwest Daily Haze Maps Surface Visual Range Data SMS GOES June 30 1975 Hazy ‘Blobs’ Regional Haze Lyons W.A., Husar R.B. Mon. Weather Rev.1976

  3. Mexican Smoke Event, May 1998 Smoke sweeps through Eastern US TOMS, SeaWiFS, monitors show daily smoke Airports close, surface concentrations at max -------------------------- NC, OK attribute Ozone violation to smoke They request waivers for exceedances Record Smoke Impact on PM Concentrations Data shows that O3 DEPLETION under smoke Hence, the NC & OK ozone violations can not be due to smoke-generated excess ozone Smoke Event

  4. EE Rule and Satellites • The enforcement of NAAQS is normally based on standardized surface-based observations, “Federal/Equivalent Reference Methods” • The EE Rule allows multiple lines of observational evidence ..demonstrating the occurrence of the event, including: …satellite-derived pixels indicating the presence of fires; satelliteimages of the dispersing smoke; Identification of the spatial pattern of the affected area (the size, shape, and area of geographic coverage)….

  5. ‘But for’ demonstration videoGeorgia Smoke, May 2007

  6. Legitimate EE Flag: The Exceedance would not Occur, But For the Exceptional Event

  7. Example EE Tool in DataFed: Anayst’sConsoleNear-Real-Time browser of EE-relevant data 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

  8. Satellites and EER: The Future • Estimation of emissions from EE sources • Determination of Policy-Relevant Background • Understanding qualitative features of events

  9. Estimation of emissions from EE sources • Needed for modeling, • Quantification of ‘but for’ OMI Tropo NO2 Sweat Water fire in S. Georgia (May 2007)

  10. Estimation of emissions from EE sources • Needed for modeling, • Quantification of ‘but for’ OMI Tropo NO2 Sweat Water fire in S. Georgia (May 2007)

  11. Kansas Agricultural Smoke, April 12, 2003 Organics 35 ug/m3 max Fire Pixels PM25 Mass, FRM 65 ug/m3 max Ag Fires SeaWiFS, Refl SeaWiFS, AOT Col AOT Blue

  12. Kansas Grass Smoke Emission Estimation SeaWiFS AOD: April 9-11, 2003 Mass Extinction Efficiency: 5 m2/g Day 1: ~100 T/day Day 2: 1240 T/d Day 3, 87 T/day

  13. Fire Model Land Vegetation Emission Model Real-Time Smoke Emission Estimation:Local Smoke Model with Data Assimilation Continuous Smoke Emissions Assimilated Smoke Emission for Available Data Local Smoke Simulation Model e..g. MM5 winds, plume model Assimilated Fire Location, Energy Assimilated Smoke Pattern Fire Loc, Energy Satellite Smoke Surface Smoke Fire Pixel, Field Obs AOT Aer. Retrieval Visibility, AIRNOW NOAA, NASA, NFS NOAA, NASA, NFS NOAA, EPA, States

  14. EER-Relevant Background: What is Natural/Normal?? Regional Haze Rule: Natural Aerosol The goal is to attain natural conditions by 2064; Baseline during 2000-2004, first Natural Cond. SIP in 2008; SIP & Natural Condition Revisions every 10 yrs

  15. Color Satellites: Qualitative visualizers of EesImproves general understanding Mongolia China Korea On April 19, 1998 a major dust storm occurred over the Gobi Desert The dust cloud was seen by SeaWiFS, TOMS, GMS, AVHRR satellites

  16. EER Decision Support System (DSS) The Regional Haze Rule has been supported by the VIEWS DSS EER tech support was ad hoc through States (e.g. Texas), DataFed and others

  17. Facilitation of a Data Sharing NetworkMore effective use and reuse of data through a Data Pool Earth Ob-servations Societal Benefit Monitorig Network Informing the Public Protecting Health Data Pool Satellite Atmosph. Science Model Global Policies Emission

  18. Decision Support AIRNow-Public VIEWS – RHR FASTNET –EER … Earth Ob-servations Societal Benefit Data Pool Data & Tool Hubs Monitorig Network Informing the Public Science Teams HazMAP.. RSIG.. GIOVANNI DataFed States AQAST TF-HTAP Others... Protecting Health Satellite Atmosph. Science Model Global Policies Emission AQ CoP Motto: Connecting and Enabling Other Integrating Initiatives

  19. Summary • Satellites and EER • Estimation of emissions from EE sources • Determination of Policy-Relevant Background • Understanding qualitative features of events • Impediments to Satellite data use • Data access  Networking • Management/Coordination  Workgroups? ‘CoPs’?

  20. Fast forward 25 years ca. 1975 ca. 2000 • Air quality data are sparse in space, time, composition • Qualitative satellite, visibility data show synoptic AQ • Science of regional AQ poor • AQ regulations are mild Richer AQ data from surface network, satellites, etc. Regional AQ is quantitatively observed Science has improved … Regulations became much tighter

  21. EER Evolution • 1998 ‘Color’ satellite images, surface obs. offer compelling evidence of EEs, EPAs OAQPS issues memo outlining EE flagging procedure • 1998-2007 Development of the EE Rule • Development of EE flagging procedure • Guidance through detailed case studies • States, other Agencies and (RHR) Researchers analyze many EEs • 2007 - EE Rule implementation

  22. Sahara Dust over Southern EuropeInteroperability Demo through GEOSS Accessible datasets for the Barcelona Demo Sahara Dust

  23. Asian Dust Cloud over N. America Asian Dust 100 mg/m3 Hourly PM10 On April 27, the dust cloud arrived in North America. Regional average PM10 concentrations increased to 65 mg/m3 In Washington State, PM10 concentrations exceeded 100 mg/m3

  24. Application-Task-Centric Workspace Example:EventSpaces Specific Exceptional Event Catalog - Find Dataset Harvest Resources

  25. Temporal Signal Decomposition and Event Detection EUS Daily Average 50%-ile, 30 day 50%-ile smoothing • First, the median and average is obtained over a region for each hour/day (thin blue line) • Next, the data are temporally smoothed by a 30 day moving window (spatial median - red line; spatial mean – heavy blue line). These determine the seasonal pattern. Event : Deviation > x*percentile Deviation from %-ile Average • Finally, the hourly/daily deviation from the the smooth median is used to determine the noise (blue) and event (red) components Mean Seasonal Conc. Median Median Seasonal Conc.

  26. Tools/Methods for for Regional AQ – Climate Analysis

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