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Rich Scheffe, EPA; Rudy Husar, Washington University; Jim Szykeman, EPA, Fred Dimmick, EPA; Steve Fine, NOAA, Steve Youn

The Next Air Monitoring Strategy: Linking Agencies, Disciplines, Media and Global Communities …and the National Academy of Sciences, accountability and world peace . Rich Scheffe, EPA; Rudy Husar, Washington University; Jim Szykeman, EPA, Fred Dimmick, EPA; Steve Fine, NOAA, Steve Young, EPA.

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Rich Scheffe, EPA; Rudy Husar, Washington University; Jim Szykeman, EPA, Fred Dimmick, EPA; Steve Fine, NOAA, Steve Youn

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  1. The Next Air Monitoring Strategy: Linking Agencies, Disciplines, Media and Global Communities…and the National Academy of Sciences, accountabilityand world peace. Rich Scheffe, EPA; Rudy Husar, Washington University; Jim Szykeman, EPA, Fred Dimmick, EPA; Steve Fine, NOAA, Steve Young, EPA

  2. How Encompassing?

  3. http://aeronet.gsfc.nasa.gov/ National Ambient Air Monitoring Strategy Office of Air Quality Planning and Standards Research Triangle Park, NC December 2005 CENR/AQRS aeronet Barrow L1 Trinidad Head GAW L2 NCORE L3 Mauna Loa A. Samoa S. Pole http://www.cmdl.noaa.gov/ http://www.fz-juelich.de/icg/icg-ii/mozaic/home http://www.fz-juelich.de/icg/icg-ii/iagos/ http://www.epa.gov/ttn/amtic/monstratdoc.html GEOSS EMEP http://www.al.noaa.gov/AQRS/ reports/monitoring.html http://www.empa.ch/gaw/gawsis/ http://www.emep.int/ http://earthobservations.org/ NOAA CMDL NOAA NESDIS http://www.igospartners.org/ http://www.nesdis.noaa.gov/

  4. Sequence • Context, rationale setting • Examples • Strategic elements

  5. Context Setting • Charged by CASAC monitoring subcommittee that endorsed National Ambient Air Monitoring Strategy (NAAMS), and recommended expansion to integrate across multiple organizations and disciplines (e.g., atmospheric research, ecosystems) • Air Quality Research Subcommittee (AQRS) of the Committee for Environment Natural Resources (CENR) request to develop a broad inter-agency observation strategy

  6. Accountability assessments Linking Themes(or air quality assessment future) • Spatial scales • Hemispherical-Continental-national/regional-urban-local {global(1000km) –synoptic (100km) – meso 10km)} • Increasing importance of Global and local (near roadway phenomena) • Climate-AQ-long range transport interactions and C balances • Multiple Media • air – land/soil – water • deposition, ecosystem and environmental welfare needs elevated • Facilitates integration of observations • Composition…Multiple pollutant • chemical (compounds, aggregate groupings) • physical (size distribution) • Builds a MP assessment focus and links with transport and MM needs • Time • Current (forecasting, problem definition, strategy development basis) • Retrospective • Prospective

  7. 24-h Average PM10 Mass and Chemical Composition in Downey (Source Site) 14 Metals OC MMD ~ 0.35 um EC 12 (NH4)2OS4 g/m3) NH4NO3 10 Concentration ( m 8 6 4 MMD ~ 0.7 um 2 0 < 0.1 0.1 - 0.35 0.35 - 1.0 1 - 2.5 2.5 - 10 Challenge’ How to accommodate global and local scales? Next AQ challenges: Near Roadway and International Transport

  8. Collaboration (culture) empire building? Observation technologies {e.g., satellites} Computational power Science, talent {embodied in AQ models and young geniuses} Accountability, ↓ regulatory assessments {e.g., NAS, CASAC} Information technologies {e.g., data sharing protocols} Alignment Budgets, agency collaboration resource/program accountability Stars aligned?

  9. NPS Protect ecosystems, AQ WQ NASA Explore fundamental Earth System Properties USDA Protect/optimize Ag and forest resources EPA (Protect human health and environment) Improve air, water, ecosystem health CDC To promote health and quality of life by preventing and controlling disease, injury, and disability. NOAA To understand and predict changes in the Earth’s environment and conserve and manage coastal and marine resources to meet our nation’s economic, social and environmental needs” Example User Client Missions Apparent divergences?

  10. Or substantial convergence and optimization potential? Client Data Overlaps Common data

  11. For instance, • Health • effects/outcomes associations (PHASE) • Public health warnings/forecasting • Air program support • defining attainment/nonattainment areas (and projection, current practice) • developing emission strategies • accountability • Environmental • Ecosystem deposition assessments/support • AQ trends in National Parks • Regional haze assessments • Atmospheric science • Diagnosing emissions and models Benefit from Air quality characterizations And benefit even more from rich (t,s,c) AQ characterizations

  12. Consequently • A simple overarching goal or vision, • Strive for maximum and efficient AQ characterization in time, space and compositional terms

  13. Examples

  14. National Ambient Air Monitoring Strategy Office of Air Quality Planning and Standards Research Triangle Park, NC December 2005 CENR/AQRS Barrow L1 Trinidad Head GAW L2 NCORE L3 Mauna Loa A. Samoa S. Pole http://www.cmdl.noaa.gov/ http://www.fz-juelich.de/icg/icg-ii/mozaic/home http://www.fz-juelich.de/icg/icg-ii/iagos/ GEOSS http://www.epa.gov/ttn/amtic/monstratdoc.html http://earthobservations.org/ EMEP http://www.al.noaa.gov/AQRS/ reports/monitoring.html http://www.empa.ch/gaw/gawsis/ http://www.emep.int/ NOAA CMDL NOAA NESDIS http://www.igospartners.org/ http://www.nesdis.noaa.gov/

  15. Coordinating Earth Observing Systems Capabilities Vantage Points L1/HEO/GEO TDRSS & Commercial Satellites Far-Space Permanent LEO/MEO Commercial Satellites and Manned Spacecraft Near-Space Aircraft/Balloon Event Tracking and Campaigns Airborne Deployable Terrestrial User Community Forecasts & Predictions

  16. Monitoring Modeling Satellite Demonstrating the Power of Integrating Air Quality Information through Partnerships

  17. ` Air Quality/Public Health NTO Integrated Observed-Modeled Air Quality Fields GEOS-12 AQUA MODIS TERRA MODIS CONUS every 30 minutes ~1:30 local overpass ~10:30 local overpass Products Products Aerosol Optical Depth (MOD04_L2) Aerosol Optical Depth (GASP) NOAA NESDIS/ORA & CREST Institutes NOAA NESDIS NASA GFSC DACC NASA GFSC Science Team Products Products Algorithms/QA Algorithms/QA US EPA OAQPS/ORD/OEI RSI Gateway ? UMBC CREST Institute US EPA AQS Products Products Spatial surface Predictions Satellite/Model/Surface Data Fusion HHS CDC-EPHTN REALM SLAMS/NAMS SURACE PM2.5 Data Continuous Vertical Resolution Data EPA OAR & ORD NOAA NWS State & Local *Note: Regional East Atmospheric Lidar Mesonet (REALM) is university led federated network by UMBC and is identified as a NTO in the implementation plan. Canadian Providences State Public Health Departments Products Products Products CMAQ Assessment Data Studies and Impacts to human health CMAQ Forecast Data

  18. Future Air Quality Predictions Will Likely Benefit from Assimilation of Satellite Observations to provide Modeling Constraints and Merging of Global and Regional Chemical Transport Models Public Impact CMAQ Regional Prediction Global Assimilation Scientific Understanding Satellite Data Products Current research is being conducted with a nested global- to regional-scale meteorological and chemical modeling system for assimilating and predicting the chemical state of the atmosphere (air quality).

  19. We Take Advantage of the Strengths of Each Data Type by Analyzing the Data Together All of the data combined indicate that the increased levels of fine particles measured on July 21, 2004 in North Carolina were due to the Alaskan fire event.

  20. Improved Emissions Inventory Reduced exposure (near term) Improved forecasting system Total column depth (through Satellites) Air program accountability Vertical Profiles Health research Optimized PM2.5, O3 Emission strategy development Improved Transport Assessments AQ model results Characterizations Integrated Observation- Modeling Air Quality Process Assessments Land (Pt) AQ Monitors Benefits Products

  21. Arithmetic injustice • Greater than 95% of air pollutant mass is located above 100m, yet we focus 95 % of our characterization on the bottom 10 meters {compromises both predictive and current characterization phenomena}

  22. Why is this important? • Competing demands of representativeness (t,s,c) and truth require complimentary use of predictive and observational tools

  23. The Public Health Air Surveillance Evaluation (PHASE) Project • Collaboration between the US EPA and the Centers for Disease Control (CDC) • Fusion application: Develop and evaluate alternative air quality characterization methods for environmental public health tracking • Air Pollutants • Ozone and Particulate Matter • Health Endpoints • Asthma and Cardio Vascular Disease • Working with 3 CDC State Partners • Maine • New York • Wisconsin • Much broader spin off applications

  24. Daily PM2.5 Concentration (ug/m3) Sept. 12, 2001 EPA FRM Monitoring Data and CMAQ

  25. Strategy Elements • Inventory/cataloguing of what we have • Existing strategies (NAAMS, IGACOS, GEOSS,…) • Existing networks/obs. systems • How expansive? • Stating objectives, goals questions • Identification of integration drivers • Identification of critical information gaps • e.g., vertical profiles, rural sites • Information technology solutions • Roles of existing structures • GEOSS, CENR, EPA/STAPPA/Tribal, …., CASAC, NPS • AQ forecasting • Implementation plan • Recommendations • Schedules • roles

  26. Preliminary Suggestions • Strengthen horizontal, ground based connections • Work through CENR/AQRS to integrate across agencies and media? • Start connecting ground and total column • Build on NAAMS and IGACO structures • Similarity in key species (Level 2) • Enhance vertical profile resolution • Integrating ground based lidars, sondes • Routine aircraft (MOZAIC/IAGOS) • Additional species? • Develop data-model fusion protocols • Build on PHASE success • Incorporating routine AQ model forecasting results • Advance to true chemical data assimilation • Expand to International/hemispherical integration • NARSTO to LRTAP? • Common challenges and information sources • Organize Information Technology infrastructure • Deal with massive data quantities • Facillitate access and interpretabilit • Data-Fed, Fed. Architecure, ESIP, RSIG • Uphold positive cultural change • Up with collaboration • Down with empire building

  27. NCORE Level 2 species + + + + + Common parameters linking column totals and surface measurements

  28. Issues • Accommodating bottom –up and top-down approaches • E.g., importance of local scales for near roadway and HAPs related exposures and hemispherical scales for regional transport impacts • Stovepipes..Breaking the single project, single agency focus • Difficult to develop ownership of elements that enable substantial synergistic benefits for multiple clients • IT support; vertical chemical profiling, fusion • Complexity due to enormous scope and numerous entities

  29. NAS, CAAAC CASAC, OMB GEOSS NOAA EPA PM research NASA Eco-informatic Test beds CDC PHASE Organizations Private Sector NPS Programs AQ forecasting Risk/exposure assessments States/Tribes/RPO’s Interstate orgs. USDA Accountability/ indicators NAAQS setting DOE NARSTO Coordination Cluster Mess Enviros SIPs, nat. rules designations Academia NADP Satellite data Intensive studies IMPROVE, NCore PM monit, PAMS PM centers Supersites Data sources CASTNET Health/mort. records Other networks: SEARCH, IADN.. Lidar systems Emissions Meteorology CMAQ GEOS-CHEM

  30. I think we need an IT solution to glue this mess together…

  31. Uniform Access Data Processing Web Service Chain Info Products Reports, Websites Users Providers SciFlo Public EPA AIRNow Forecasting NASA DAACs DataFed EPA R&D Model Compliance Manager Sci. Reports Scientist Custom Processing others Other other • The info system transforms the data into info products for each user • In the first stage the heterogeneous data are prepared for uniform access • The second stage performs filtering, aggregation, fusion and other operations • The third stage prepares and delivers the needed info products Information Landscape: Info System Data Access, Processing and Products

  32. Or does the IT world create their own mess? • Data Fed • RSIG • ESIP • Federation Architecture • AQS • AIRNOW • Data Mart • Data Warehouse • VIEWS

  33. Accountabiltyfor the AQ Manager Is the air is cleaner and safer to breath? Can an integrated monitoring-modeling approach measure this? Accountability as a program vehicle to accommodate enhanced assessment change?

  34. Accountability and Indicators Pipeline Source emissions Direct NO, SO2, VOC, CO, metals, Increasing influence in confounding factors and perceived value to public policy Ambient precursors and intermediates NO, NOy, CO, VOC, SO2, metals, radicals, peroxides Ambient target species O3, PM, HAPs Secondary and deposition loads Visibility, acidification, eutrophication, metals Exposures Inhalation, digestion Increasing confidence In characterization Ecosystem + effects Defoliation, Visibility ↓ biodiversity, Metals concentration Health effects Asthma, cardio-pulmonary ↓, Cancer, death Perceived (measured?) Life quality Feedback/correction

  35. What it amounts to… • Communication, connectivity, synergism

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