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An Integrated Systems Solution to Air Quality Data and Decision Support on the Web

An Integrated Systems Solution to Air Quality Data and Decision Support on the Web. AIP Phase 2 Call For Participation: Sensors and Models. GEO Architecture Implementation Pilot – Phase 2 (AIP-2) Kickoff Workshop NCAR Mesa Laboratory, Boulder, Colorado September 25-26, 2008. VIEWS

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An Integrated Systems Solution to Air Quality Data and Decision Support on the Web

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  1. An Integrated Systems Solution to Air Quality Data and Decision Support on the Web AIP Phase 2 Call For Participation: Sensors and Models GEO Architecture Implementation Pilot – Phase 2 (AIP-2) Kickoff Workshop NCAR Mesa Laboratory, Boulder, Colorado September 25-26, 2008

  2. VIEWS The Visibility Information Exchange Web System http://vista.cira.colostate.edu/views • Provides integrated online access to: • Monitoringdata: from over 3 dozen networks • Modeling data: from a variety of modeling scenarios • Emissions data: inventories and summaries • Satellite data: MODIS, CALIPSO, Aqua, Terra, GOES, etc. • Serves as the primary source for: • IMPROVE Aerosol and Optical data • IMPROVE Regional Haze Rule data • IMPROVE Special Studies data • Has about 1200 registered users from over 300 organizations, institutions, universities, and companies

  3. TSS WRAP Technical Support System http://vista.cira.colostate.edu/tss • Built upon the database and software infrastructure of VIEWS • Provides consolidated online access to: • Regional technical data, planning guidance, and analysis results • Decision support for the development of SIPs and TIPs • Source apportionment and visibility projections tools • Ongoing tracking and assessment of emissions control strategies • Documents the technical methods used in implementation plans

  4. How do we compare diverse datasets? • Ground-based observations • Model results • Emissions inventories • Satellite data • Airborne samples • etc… Fine Sulfate (SO4) equivalence: SO4 = SO4 = SO4 = SO4 ?? Measured Modeled Emissions Satellite

  5. Establishing Dataset Comparability: Method Metadata • Sampling and collection methods (sensors) • Analysis methods (IC, XRF, PIXE, etc.) (sensors) • Calculation methods (EC = 1 + E2 + E3 – OP) • Processing and aggregation methods • Data handling and transformation methods (AQS to VIEWS) • Unit conversion methods • etc…

  6. Sensor Metadata: Sampling Methods

  7. Model Metadata • Initial conditions • Boundary conditions • Model parameters • Grid cell size • Algorithms and processing • Assumptions • etc…

  8. Inter-domain “Rosetta Stone”

  9. CMAQ Model Performance vs. Monitored Worst 20% Days in 2002 Model Performance Evaluation

  10. Mass source apportionment by source category and region • From regional photochemical model with comprehensive emissions inputs • Species mass for various time periods – directly comparable to monitoring data Source Apportionment

  11. Parameter: Nitrate Ion Concentrations Location: Bondville, IL Networks: IMPROVE, STN, and CASTNet Graphs: Time Series and Scatter Plot Network Inter-comparisons

  12. Method (Sensors and Models) Questions: • How do we “codify” methods (sensors and models)? • What is the minimum set of method metadata for each type of dataset? • How do we codify the degree of equivalence between methods? • How do we associate method metadata with observations and results? • How do we educate users about method considerations?

  13. The End… Thanks!

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