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

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

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 Smoke Event Data shows that O3 DEPLETION under smoke 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 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, 2007May 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

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

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

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

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

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

Real-Time Smoke Emission Estimation: Local Smoke Model with Data Assimilation Emission Model Land Vegetation Fire Model e..g. MM5 winds, plume model Local Smoke Simulation Model AOT Aer. Retrieval Satellite Smoke Visibility, AIRNOW Surface Smoke Assimilated Smoke Pattern Continuous Smoke Emissions Assimilated Smoke Emission for Available Data Fire Pixel, Field Obs Fire Loc, Energy Assimilated Fire Location, Energy 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 , first Natural Cond. SIP in 2008; SIP & Natural Condition Revisions every 10 yrs

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

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

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

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

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’? Summary

Fast forward 25 years 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 ca ca. 2000

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

Accessible datasets for the Barcelona Demo Sahara Dust over Southern Europe Interoperability Demo through GEOSS Sahara Dust

Asian Dust Cloud over N. America On April 27, the dust cloud arrived in North America. Regional average PM10 concentrations increased to 65  g/m 3 In Washington State, PM10 concentrations exceeded 100  g/m 3 Asian Dust 100  g/m 3 Hourly PM10

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

Temporal Signal Decomposition and Event Detection 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. EUS Daily Average 50%-ile, 30 day 50%-ile smoothing Deviation from %-ile Event : Deviation > x*percentile Median Seasonal Conc. Mean Seasonal Conc. Average Median Finally, the hourly/daily deviation from the the smooth median is used to determine the noise (blue) and event (red) components

Tools/Methods for for Regional AQ – Climate Analysis