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					      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
  1976 - Satellite Detection of Regional Haze
           Event over the Midwest
SMS GOES June 30 1975        Daily Haze Maps
                        Surface Visual Range Data




       Regional
                                 Hazy ‘Blobs’
        Haze

                              Lyons W.A., Husar R.B. Mon. Weather Rev. 1976
              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                                     Smoke Event

Hence, the NC & OK ozone violations can not be
due to smoke-generated excess ozone
                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; satellite
 images of the dispersing smoke; Identification of the spatial
 pattern of the affected area (the size, shape, and area of
 geographic coverage)….
‘But for’ demonstration video
  Georgia Smoke, May 2007
    Legitimate EE Flag:
The Exceedance would not Occur,
  But For the Exceptional Event
          Example EE Tool in DataFed: Anayst’sConsole
           Near-Real-Time browser of EE-relevant data




Pane 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, 2007
Pane 5,6: AirNOW Ozone, Surf. Wind – Ozone, transport pattern        May 09, 2007
                                                                     May 10, 2007
Pane 7,8: OMI satellite Total, Tropospheric NO2 – NO2 column conc.   May 11, 2007
Pane 9,10: OMI satellite Aerosol Index, Fire P-xels – Smoke, Fire    May 12, 2007
                                                                     May 13, 2007
Pane 11,12: GOCART, NAAPS Models of smoke – Smoke forecast           May 14, 2007
                                                                     May 15, 2007
     Satellites and EER: The Future


Estimation of emissions from EE sources
Determination of Policy-Relevant Background
Understanding qualitative features of events
 Estimation of emissions from EE sources
    Needed for modeling,
    Quantification of ‘but for’




                                  Sweat Water fire in S.
                                  Georgia (May 2007)




OMI Tropo
  NO2
 Estimation of emissions from EE sources
    Needed for modeling,
    Quantification of ‘but for’




                                  Sweat Water fire in S.
                                  Georgia (May 2007)




OMI Tropo
  NO2
           Kansas Agricultural Smoke, April 12, 2003

     Fire Pixels     PM25 Mass, FRM       Organics
                         65 ug/m3 max     35 ug/m3 max




Ag Fires



   SeaWiFS, Refl    SeaWiFS, AOT Col         AOT Blue
  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
Real-Time Smoke Emission Estimation:
        Local Smoke Model with Data Assimilation

                                                          Continuous
                            Fire Model                  Smoke Emissions
                             Land Vegetation
                             Emission Model

                                       Assimilated Smoke Emission for
                                                Available Data
                     Local Smoke Simulation Model
                         e..g. MM5 winds, plume model



 Assimilated Fire                       Assimilated
 Location, Energy                      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
        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
          Color Satellites: Qualitative visualizers of Ees
                Improves 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
       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
              Facilitation of a Data Sharing Network
     More effective use and reuse of data through a Data Pool
Earth Ob-                                                 Societal
servations                                                Benefit


 Monitorig                                                 Informing
 Network                                                   the Public


                                                           Protecting
 Satellite
                           Data Pool                         Health


                                                           Atmosph.
  Model                                                     Science


                                                            Global
 Emission                                                   Policies
Earth Ob-                                               Societal
servations                                 Decision
                                                        Benefit
                                           Support
                                        AIRNow-Public

 Monitorig   Data & Tool                VIEWS – RHR     Informing
 Network        Hubs                                    the Public
                                        FASTNET –EER
             HazMAP..                   …

                                                        Protecting
 Satellite   RSIG..         Data Pool                     Health
                                          Science
             GIOVANNI                      Teams
                                        AQAST           Atmosph.
             DataFed                                     Science
   Model                                TF-HTAP

             States                     Others ...
                                                         Global
  Emission                                               Policies




 AQ CoP Motto: Connecting
    and Enabling Other
  Integrating Initiatives
                         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’?
                  Fast forward 25 years

         ca. 1975                           ca. 2000
• Air quality data are sparse in      Richer AQ data from surface
  space, time, composition            network, satellites, etc.
• Qualitative satellite, visibility   Regional AQ is quantitatively
  data show synoptic AQ               observed
• Science of regional AQ poor         Science has improved …
• AQ regulations are mild             Regulations became much tighter
                         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
      Sahara Dust over Southern Europe
    Interoperability Demo through GEOSS



                         Accessible datasets for the Barcelona Demo




Sahara Dust
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
Application-Task-Centric Workspace
            Example:
        EventSpaces

                                     Specific Exceptional Event




     Catalog - Find Dataset




                                          Harvest Resources
    Temporal Signal
Decomposition and Event                     EUS Daily Average    50%-ile, 30 day
                                                                 50%-ile smoothing
      Detection

•    First, the median and average is
     obtained over a region for each
     hour/day (thin blue line)

                                          Event : Deviation > x*percentile
•    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
                                                    Deviation from %-ile
     pattern.


                                                                                Average
    • Finally, the hourly/daily deviation from the the
    smooth median is used to determine the noise (blue)
    and event (red) components                                             Median
                                                                       Mean Seasonal
                                                                       Conc.
                                                                       Median Seasonal
                                                                       Conc.
Tools/Methods for for Regional AQ – Climate Analysis

				
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