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GEOSS Architecture Implementation Pilot Air Quality and Health Scenario

GEOSS Architecture Implementation Pilot Air Quality and Health Scenario. David McCabe U.S. Environmental Protection Agency McCabe.David@epa.gov. Air Quality & Health AIP Scenario. Great Lakes, 23 April 2008 MODIS Terra RGB; AQI from AIRNow. Air pollution is a serious

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GEOSS Architecture Implementation Pilot Air Quality and Health Scenario

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  1. GEOSS Architecture Implementation PilotAir Quality and Health Scenario David McCabe U.S. Environmental Protection Agency McCabe.David@epa.gov

  2. Air Quality & Health AIP Scenario Great Lakes, 23 April 2008 MODIS Terra RGB; AQI from AIRNow • Air pollution is a serious global public health problem Worldwide: hundreds of thousands of deaths from outdoor air pollution • Air pollution is influenced by processes on many scales Local, regional, intercontinental to global; times of minutes to years • To understand, forecast, and manage air pollution, diverse information needs to be brought together: • surface monitors, satellites, airborne sensors • meteorological & chemical transport models • emissions & emissions-generating activities • demographics, exposure-related behavior, health impacts • air pollution crosses political boundaries so information must too

  3. Development of the AIP Air Quality Scenario • Developed iteratively by a collaborating multi-organization group • Written on the ESIP wiki, using online forum and telecons to plan, discuss, and develop the submitted scenario • Developed the ESIP air quality Community of Practice, which is co‑evolving with the AIP Pilot and other activities Community of Practice Wiki for AQ Scenario

  4. What does the air quality scenario envision? We produced a very broad, ambitious scenario, structured around 4 societal needs: • Real-time event analysis (e.g., smoke, dust plumes) For forecasts, alerts, exceptional event rule analysis, etc. • Assessing long-range transport of air pollution Policymakers need scientific assessments, assessment requires better tools for using satellite, met, ambient data • Assimilating satellite data with models to improve AQ forecasts Satellite data will improve forecasts, particularly in sensor-poor regions; interoperabilty challenge to make sufficiently fast forecasts feasible. • Getting air quality forecasts and nowcasts to the public Allows individuals, families to adjust plans if air quality is/will be poor; allows health community, other decision-makers to plan for episodes

  5. What does the air quality scenario envision? • Data and tools needed for any one of the 4 goals… …are needed for all of them • Many data, tools already exist for each goal… …but the systems don’t readily work together We have produced a broad, ambitious scenario which does not call for specific ‘killer apps’ or demonstrations. Instead it calls for a service-oriented, interoperable, accessible, and available system of systems.

  6. Air Quality & Health Scenario: Actors • Earth Observations Providers Space agencies; met, land management, and environmental/health agencies (national, regional, local); Academics; Private sector • Air Quality Modelers, Forecasters, and Analysts Environmental/health management and met agencies (national, regional, local) Modelers and analysts in many settings • Information Management Specialists • Air Quality Management Decision-Makers Environmental, health agencies; Multi-lateral Cooperative Fora: LRTAP, EANET, Malé Declaration, Arctic Council • Other Consumers of Air Quality Informations Public, researchers, mass-media, …

  7. Many actors are ‘decision-makers’ Policymakers, sensitive individuals, modelers, parents, transit officials, air quality managers, weather forecasters, air quality analysts, environmental compliance officials, …

  8. Air Quality & Health: Starting Information • Meteorological data • Observations from ground-based networks, satellites, sondes • Forecasts from numerical models at the global and regional scales • Geographical data • Land use • Demographics • Emissions-related activity • Atmospheric Composition (Air Quality) Observations • Surface Monitoring Networks • Satellite Observations • Sondes • Ground-based remote sensors • Aircraft Measurements • Numerical Air Quality Chemical Transport Models • (at regional to global scales)

  9. Air Quality & Health Scenario: Existing Efforts • Real-Time Large-Scale Event Analysis • FASTNET, IDEA, SmogBlog • Assessment of International and Intercontinental Transport of Air Pollution • HTAP Data Network, AMET, RSIG, HemiTap • Assimilation of Observations for Air Quality Forecasting • GEMS, RAQMS • Provision of Relevant Information to the Health Community & the Public • AIRNow, PHASE

  10. Examples – pieces of the puzzle GAW: WMO Global Atmosphere Watch

  11. Examples – pieces of the puzzle GEMS: Global & regional Earth system Monitoring using Satellites (ECMWF)

  12. Examples – pieces of the puzzle Informing the public about Air Quality & Forecasts in real time: AIRNow

  13. Interoperability needed! many systems, many pipes…. we shouldn’t keep reinventing wheels!

  14. Societal needs and the actors

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