1. Third NOAA GOES-R Users Conference May 10, 2004 Broomfield, CO U.S. Environmental Protection Agency: A User Perspective Focused on Air Quality Assessments and Forecasts Deborah Mangis, PhD Assistant Lab Director National Exposure Research Lab

2. Research Air quality (forecast & assessment) Water quality and watershed modeling Land cover mapping Landscape indicators and ecological assessments Assessing impacts from mining activities Emergency response (e.g., oil spills, Columbia recovery effort) Monitoring regulatory compliance and enforcing permits Chesapeake Bay Watershed Landsat EMT+ Image EPA Uses of Remote Sensing Data

3. Air Quality (Chemical Weather)  Impacts  Application & Relevant Mandates  Ideal User Needs  Trace Gases/Aerosols – EPA Criteria Pollutants Additional Considerations  Water quality and watershed modeling  Fire Characterization/Emissions  Emergency Response EPA Users Needs

4. Why is Air Quality Important? Aerosols (PM 2.5 )  Induce respiratory diseases and cancer  Reduce visibility  Impacts Climate Ozone  Induces respiratory diseases (e.g., asthma)  Damages crops  Greenhouse gas Impacts of Poor Air Quality on Society  60,000 Death per annual (mean)*  $143 Billion Cost per annual (mean)** *Science 289, 2000; **American Lung Assoc. 2001

5. National Mandates related to Air Quality Regulatory NAAQS (Assessment/Monitoring): Clean Air Act - EPA Administrator required to periodically review and revise National Ambient Air Quality Standards in accordance with latest state of the science Air Quality Forecast: H.R. 4 Energy Policy Act of 2002 (Senate Amendment) EPA-NOAA Agreements: EPA-NOAA Agreements: EPA Administrator and Dept. of Commerce Deputy Secretary signed MOU/MOA for AQ forecasting May 6, 2003 Public Health Tracking: Nationwide Health Tracking Bills introduced S.2054 and H.R.4061 EPA-CDC Agreement: EPA Administrator and Dept. of HHS Secretary signed MOU related to ENPHT September 30, 2002

6. Assessment/Monitoring - Enhance Traditional Focus on Regulatory Policy  Identify, characterize, and track pollution -Data will help to develop pollution reduction strategies. -Data will help assess existing pollution control strategies Criteria Pollutant NAAQS Application of Meteorological, Trace Gas, and Aerosol Satellite Measurements

7. 4 day sequence showing transport of regional pollution event. Posts show EPA PM2.5 ground-based measuring site. Color contours are MODIS aerosol optical depth (US EPA/NASA, 2003) 0.0 0.2 0.4 0.6 0.8 1.0 0 10 20 30 40 50 60 70 0 15.5 40.5 65.5 150.5 Aerosol Optical Depth Cloud Optical Thickness PM2.5 (ug/m3) Sept 9Sept 10 Sept 11Sept 12 No EPA sites MODIS fills in

8. 0.0 0.2 0.4 0.6 0.8 1.0 0 10 20 30 40 50 60 70 0 15.5 40.5 65.5 150.5 Aerosol Optical Depth Cloud Optical Thickness PM2.5 (ug/m3) 12 Sept. 2002-A close-up of Houston shows many of the hourly PM2.5 monitors recorded 24 averages in excess of 40.5 ug/m3, (AQI>100). High AOD extends into a large portion of TX. Time Series shows agreement of hourly PM2.5 Concentrations (Surface Monitor) and Aerosol Optical Depth in Coincident MODIS pixel. Correlation Coefficient > 0.88. (U.S. EPA, 2003)

9. Current EPA research using MODIS AOD Evaluation of model performance over large spatial domains Assessment of PM transport in Eastern US Improvements in spatial predictions of surface PM concentrations

10. Air Quality Forecasting - Extending into new Air Quality Applications through EPA/NOAA Partnership  Assimilation of data to improve air quality forecast Connecting to other Science Areas  Global Climate-Regional Air Quality Connections  Public Health Tracking - Human Exposure “Applications require balanced approach of measurements and models” Application of Meteorological, Trace Gas, and Aerosol Satellite Measurements

11. “Ideal” Meteorological Parameters Needed for Air Quality Modeling Vertical profiles of state variables  Water vapor, temperature, winds, solar rad.  Vertical resolution required: 100-500 m or better - Finest resolution needed within PBL; can be coarser aloft  Horizontal resolution: 4-12 km to match model resolution  Temporal resolution: hourly or better Surface characteristics  Temperature, land-use/cover, moisture, radiative fluxes  Deposition fluxes

12. “Ideal” Meteorological Parameters Needed for Air Quality Modeling Clouds  Base, top heights  Type; optical depth  Properties Information used for :  Data assimilation during meteorological model simulation  Meteorological model evaluation as air quality driver

13. Geostationary; Hourly Observations with 4-km Resolution can resolve Urban Structure TOMS (Daily) OMI (Daily) Geostationary (Hourly) Map of Houston and surrounding area Demonstrated LEO capability: O 3, CO, NO 2, SO 2, CH 2 O and aerosols J. Fishman NASA 09/2003

14. 442 grid cells 265 grid cells 268 grid cells 259 grid cells 166 142 Domain of Interest (12 km grid) Full U.S. Domain By 2009 NE Domain starting 2004

15. Scientific Understanding Global Assimilation Regional Prediction In Situ and Satellite Observations Eventual Requirement: Capability of nested global- to regional-scale meteorological and chemical modeling for assimilating and predicting the chemical state of the atmosphere (air quality) Most Uses of Satellite Data for Air Quality Will Require Assimilation into Global and/or Regional Chemical Transport Models Public Impact

16. “Ideal” Chemical Parameters Needed for Air Quality Key Chemical species directly related to NAAQS  Ozone, NO2, HCHO, CO, SO2, NH3  Other trace gases of opportunity within spectrum  PM species: AOD at a minimum Really need chemical composition and size distribution of aerosols (PM10 - PM2.5) Resolution:  Vertical – 2 to 4 layers in troposphere with resolution of PBL  Horizontal – Spatial resolution needs to be in with high resolution regional AQ models (i.e., 4 km x 4 km)  Temporal - Hourly Information used for :  Data assimilation during air quality model simulation  Operational and diagnostic model evaluation  Emissions inventory verification  Evaluation of Policy Changes

17. Current Derived Tropospheric Observables for Air Quality PollutantCurrent SensorsMeasurement Particulate Matter (PM2.5) (as aerosol optical depth) TOMS, AVHRR, MODIS, MISR, SeaWIFS, GOME, SCIAMACHY, GOES Column Ozone (O3)*TOMS/SBUV, AIRSColumn Nitrogen dioxide (NO2)*GOME, SCIAMACHYColumn Carbon monoxide (CO)MOPITT, AIRSColumn Sulfur dioxide (SO2)GOME, SCIAMACHYColumn Formaldehyde (H2CO)GOME, SCIAMACHYColumn *requires removal of stratospheric overburden

18. Derived Tropospheric Satellite Data to Date Are from Low-Earth Orbit (LEO) Current Research Satellites (TOMS/SBUV, MODIS, MISR, MOPITT, AIRS, GOME) are LEO and have demonstrated the measurements. Next Generation Research Satellites (SCIAMACHY, TES, OMI) are also LEO and Will Provide Asynoptic Global Coverage. The measurement capability has been demonstrated from LEO and research continues.

19. Additional Pollutants Observable from Space Tropospheric NO 2 from GOME 97-01 * * Produced using GOME narrow swath mode data Source: Beirle et al., ACPD 2004

20. Considerations for Derived Tropospheric Satellite Data from Geostationary Platform LEO is good for climate and global monitoring, once per day. But air quality requires hourly observations, making Geostationary the Appropriate Platform.  Region of interest is US, and areas of transport to US (Mexico, Canada, SE asia).  Pollution is episodic, can be regional or local, but most importantly has large diurnal variability Do we have the right part of the spectrum?  EPA criteria pollutants – current state of science in UV-VIS

21. GEO provides the appropriate time resolution for air quality O 3, aerosols, & precursors change rapidly during the day. Stars indicate typical times for Low Earth Orbit (LEO) measurements Circles indicate individual GEO hourly measurements GEO LEO

22. EPA Criteria Pollutants OzoneUV-VIS, IR* SO2UV*, IR* COIR* NO2UV-VIS* H2COUV* Aerosols (PM2.5 and PM10)  DustIR*, VIS  SmokeIR, VIS  SulfateVIS  OrganicVIS  CarbonUV * High spectral resolution measurements essential

23. Current State of Science Observes O 3, NO 2, H 2 CO, SO 2 in UV-VIS Trace Gases and Aerosols Important for Air Quality CO & PM in IR

24. Additional Considerations Fire Characterization/Emissions for Air Quality  Fire and association parameters Fire location Fire radiative energy (relationship with burned biomass) Area burned Information used for :  Assimilation into model simulations (forecast & assessments)

25. Coastal Waters Quality  Red Tides  Chlorophyll  Beach water quality Information used for :  People/fisheries health Additional Considerations

26. Emergency Response  Oil spills  Flooding/water contamination Information used for :  Clean up  Health advisories Additional Considerations

27. Landscape Characterization  30 m resolution  National land cover Information used for  Water quality indices  Water models – runoff/sedimentation  Effectiveness of BMP’s  Ecological assessments  Regional vulnerability assessments

28. Advance Baseline Imager (ABI)  Additional “MODIS like” bands very useful for air quality – especially.47 microns. Hyperspectral Environmental Suite (HES)  Would like to see CO and O3 retrievals in IR (day/night).  Highest precision of state parameters possible. Additional Considerations for Instrumentations  UV/VIS Instrument for trace gas retrievals.  Instrument related to coastal and U.S. navigable waters with high spatial resolution (1km). Conclusion

29. What is possible on GOES-R in 2012? Applications Research Validation & Verification Applications Demonstration 2004- 20052006- 20082009-20122012 and beyond Operations SCIENCE Nationwide Data APPLICATIONS Research Air Quality Management Tools (mature products) Ozone, CO, Aerosol Ozone, NO 2, CO, SO 2, HCHO, Aerosol All NAAQS Criteria Pollutants (except Pb) EPA, State, Local, and Tribal Air Quality Management Organizations Air Quality Forecast Pilot Studies: Initial research Shows satellite can provide Supra-regional Observations Initial research on use in Regional & Global Air Quality Models Demonstrate use of LEO satellite data to fill data gaps in non- urban areas and for data assimilation Air Quality data assimilation and prediction MODIS AIRS OMI Ozone, PM CO data (potential) Geostationary Observatory for Tropospheric Air Chemistry Decision Tools Air Quality Forecast as Part of WRF Initiative within NASA/NOAA/EPA ESMF