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130205 epa ee_presentation_subm

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130205 epa ee_presentation_subm

  1. 1. Exceptional Event Analysis for 2012 Smoke and Dust Events Exceptional Events: Past, Present, Future Data, Models, Tools: Decision Support Systems NRL NAAPS Model: Advancement in Science Rudolf B. Husar Washington University, St. Louis Seminar Presented at Environmental Protection Agency, Research Triangle Park February 5, 2013
  2. 2. Mexican Smoke over the E. U, May 1998 Standard 65 un/m3The day-max ozone is depressed under the smoke plume! Record Smoke Impact on PM Concentrations
  3. 3. Asian Dust Cloud over N. America Asian Dust 100 g/m3 Hourly PM10 On April 27, the dust cloud arrived in North America. Regional average PM10 concentrations increased to 65 g/m3 In Washington State, PM10 concentrations exceeded 100 g/m3
  4. 4. Continental/Hemispheric Dust Events over the US Gobi dust in spring Sahara in summerRegional-scale fine dust events are mainly Fine Dust Events, 1992-2003 - VIEWS ug/m3 fromintercontinental transport
  5. 5. EE Rule Features – RH Perspective• Not rigid but flexible weight of evidence approach • Not based on single FRM measurement• Encourages use of diverse data and models • Satellites, real-time data, emission-transport model etc.• There is a role of science in AQ Management • EE flagging demands science, particularly the ‘but for’ clause
  6. 6. Near-Real-Time Data for May 11, 07 GA Smoke Displayed on DataFed Analysts Console 1 2 3 4 5 6 7 8 9 10 11 12Pane 1,2: MODIS visible satellite images – smoke pattern Console Links May 07, 2007,Pane 3,4: AirNOW PM2.5, Surf. Visibility – PM surface conc. May 08, 2007Pane 5,6: AirNOW Ozone, Surf. Wind – Ozone, transport pattern May 09, 2007 May 10, 2007Pane 7,8: OMI satellite Total, Tropospheric NO2 – NO2 column conc. May 11, 2007Pane 9,10: OMI satellite Aerosol Index, Fire P-xels – Smoke, Fire May 12, 2007 May 13, 2007Pane 11,12: GOCART, NAAPS Models of smoke – Smoke forecast May 14, 2007 May 15, 2007
  7. 7. ‘But for’ demonstration: May 2007 Georgia Smoke Red back-trajectories pass through source area
  8. 8. Tropospheric NO2 form May 2008 GA Fire Atlanta Sweat Water fire in S. Georgia Jacksonville Tampa Miami
  9. 9. NO2 form Fires Evidence: OMI Sweat Water fire in S. Georgia (May 2007)
  10. 10. EE Rule Implementation, 2007-2012Exceptional Event Rule in Federal Register in 2007PM2.5 and Ozone EE flags were added to the monitoring dataNext we show the EPA-Approved EE Flags for PM2.5, 2006-2011
  11. 11. Pattern of EE flagged PM2.5 data, 2006-2012Domestic Sources Foreign Sources
  12. 12. Trend of PM2.5 Concentration 2000-2003 avg. 2009-2012 avg. >35 ug/m3 station countA reason for the 2006-2010 EE flag decline is the As a result of PM2.5 decline, the number ofoverall reduction in PM2.5 concentrations. >35ug/m3 samples has also declined dramatically. No NAAQS exceedances-no EE flag.
  13. 13. Regional Haze Rule: Natural AerosolThe 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
  14. 14. PM2.5 EE Flag Decline Expected Increase in 2012 Drought Anomaly, 2012Trend of EE flags for PM2.5between 2006 and 2012 However, in 2012 the average AOT from smokebased on the official EPA AQS database. The EE was higher than any year between 2006 andflags declined tenfold between 2007-8 and 2010-11 2012, probably due the severe drought. The EE flags for 2012 are expected to rise again in 2012.
  15. 15. Smoke Super-Events in 2012: June 20- July 10 August 1- August 20 20 Average Smoke Emission Rate Average Smoke Optical DepthAverage Smoke Surface Concentration
  16. 16. O3 ‘Violation’ TrendsNumber of CONUS Stations with O3>75 ppb 1993-2013 2010-2013 Apr-Oct 2012
  17. 17. Exceptional EventDecision Support System (EE DSS) NASA Grant: 2009-2012 NASA and NAAPS Products for Air Quality Decision Making, D. Westphal, PI, R. Husar, CoI Washington University, McDonald Academy for Global Energy and Environmental Partnership (MAGEEP) EE_CATT Screencast
  18. 18. Ozone Exceedances Apr-Sep 2011
  19. 19. October 2012 MT-KS Dust Event 121015_Montana_Dust Event
  20. 20. October 2012 Dust Event: Passage of the dust plume
  21. 21. Oklahoma Dust Plume Event over Huntsville, Alabama October 19, 2012 NSSTC Rooftop Camera, Looking East, without and with DustKevin Knupp, Michael Newchurch, Udaysankar Nair, Dustin Phillips, David Bowdle, Shih Kuang, Wesley Cantrel, University of Alabama in Huntsville Kathy Jones Chattanooga-Hamilton County Air Pollution Control Bureau Claire Aiello WHNT TV, Huntsville, Alabama Atmospheric Science Brown Bag Mini-Seminar, October 23, 2012
  22. 22. Oklahoma Dust Plume Event over Huntsville, Alabama, October 19, 2012 Monte Sano WHNT TV photographs (Jacks Camera Network, WHNT) Donegal Drive, looking west (Megan Hayes, WHNT) Monte Sano (Rebekah Bynum) Colbert Heights Mountain Fort Payne (Kim Pendergrass)Looking East, (Carter Watkins) Lake Guntersville
  23. 23. October 2012 Dust Event
  24. 24. Oklahoma Dust Plume Event over Huntsville, Alabama, October 19, 2012 Altitude in km, time in Coordinated Universal Time (CDT = UTC - 05:00), intensity in false color Peak at ~17 UTC (12 N EDT)Time-Height Cross-Section of Relative Signal Intensity from Vertically Viewing Ceilometer Mobile Integrated Profiling System (MIPS), University of Alabama in Huntsville
  25. 25. 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
  26. 26. Application-Task-Centric Workspace Example: EventSpaces Specific Exceptional Event Catalog - Find Dataset Harvest Resources
  27. 27. EE DSS Links CATT – GeneralEE_CATT ScreencastAnomaly Map 110414_Kansas_Smoke Event121015_Montana_Dust Event
  28. 28. Navy Aerosol Analysis and Prediction System NAAPS by D. Westphal et al, NRL Why I luv NAAPS:1. Assimilates satellite aerosol optical thickness and fire pixels2. Provides 4D aerosol structure for dust, smoke, sulfate, sea salt3. Open access to 10 years of global simulations (via DataFed)
  29. 29. D. Westphal Navy Aerosol Analysis and Prediction SystemSeptember 11, 2011Key:Smoke = blueDust = greenSulfate = red
  30. 30. NASA Data for NAAPS Initialization Multiple aerosol sensors are critical for assimilation.Forecasting is an initial value problem:Requires the 3-D distribution of aerosol concentration at the start of the forecast:: Assimilation of previous forecast + information from remote sensing of aerosolsCurrent capabilities: Aerosol Optical Depth (AOD; 2-D) (MODIS and MISR) Extinction (3-D) (CALIPSO) Natural run + Ocean MODIS + Land/Ocean MODIS + land/Ocean MISR + Land/Ocean MISR
  31. 31. Aerosol Optical Thickness – Aeronet The Gold Standard for Satellite Calibration
  32. 32. AERONET Sun photometer – MODIS AOD Comparison Very low bias except in blue sites Kanpur, IN
  34. 34. Goal: ‘Reconciliation’, ‘Harmonization’…’Closure’By iterative refinement of of Emissions, Observations and Models HTAP, 2010Reconciling emissions, observations and models (EOMs), has been elusiveEOMs are generally autonomous and quite separate activitiesSoftware tools are available to support EOM reconciliationBut closing the EOM loop requires “interoperability” of people and machines
  35. 35. 4D Dust, Smoke, Sulfate Vertical Cross Section ViewsVertical 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 documentationThe DataFed Browser now incorporates vertical cross section views
  36. 36. Long-Term Model Data 2006-Now Emission Surface Conc. Vertical AOTA significant fraction of the dust vertical column is in the troposphere and it is ‘global’Most of surface dust is local and highly variable in space and time
  37. 37. Satellite-Surface PM relationshipThe NAAPS helps separating local and ‘global’ dust but much work is needed
  38. 38. EPA PM2.5 – CMAQ Model PM2.5 Summer PM2.5 BIASOBSERVATION MODEL
  39. 39. EPA PM2.5 – CMAQ Model PM2.5 Bias Winter PM2.5 BIASOBSERVATION MODEL
  40. 40. EPA PM2.5 – CMAQ Model PM2.5 Bias Winter DJF MAM JJA SON Nitrate Organics Fine Dust Bio. Organics Low in DJF Low in DJF Low in MAM High MAM & JJA Add nitrate source Improved smoke by Add Sahara, local dust Reduce biogenic OCInverse modeling of combined Dust and smoke BC for Adjust source trem VIEWS Nitrate chemical, satellite, spa CMAQ – e.g. NAAPS ce-timeVIEWS NO3 DJF CMAQ NAAPS Dust, July Tool to Iteratively Reduce the Bias Actual closure to be worked out by the AQ community
  41. 41. Many Thanks:• Kari Hoijarvi• Erin Robinson• Rich Poirot• Doug Westphal• Neil Frank