1. Integrated Decision Support: A Tale of Two Systems “It was the best of times, it was the worst of times…” Charles Dickens, A Tale of Two Cities, 1859 “Actually, right now, I’d say the times are just OK…” Shawn McClure, EPA Data Summit, 2008 “On second thought, the times must be pretty damn good. After all, we’re at a data summit convened by the EPA to talk about data systems. Woo-hoo!!…” Shawn McClure, EPA Data Summit, 2008

2. VIEWS The Visibility Information Exchange Web System http://vista.cira.colostate.edu/views  Developed by the five Regional Planning Organizations (RPOs) in 2002  Provides easy online access to a wide variety of air quality data  Offers user-friendly tools for exploring, visualizing, and analyzing data  The primary source for IMPROVE Regional Haze Rule data  Over 1100 registered users  Over 300 organizations, institutions, and companies represented  Developed for RPOs, States, Tribes, Federal Land Managers, and local agencies  Used by researchers, analysts, planners, regulators, stakeholders, and students

3. TSS WRAP Technical Support System http://vista.cira.colostate.edu/tss  Built upon the database and software infrastructure of VIEWS  Developed initially for Western States, Tribes, and air quality agencies  Provides a access point for regional technical data, guidance, and results  Offers tools and support for the development of SIPs and TIPs  Documents the technical methods used in implementation plans  Helps assess the impact of other areas on local Class I Areas  Provides ongoing tracking and assessment of emissions control strategies  Will adapt to and serve the future regional technical needs of WRAP members

6. VIEWS/TSS Data Value Chain

8. VIEWS/TSS Data Inventory

12. VIEWS/TSS Data Processing: Import & Validation

13. VIEWS/TSS Data Processing: Integration

14. OLTP: Functions as the “back-end” database Fully relational and in 3 rd normal form Used for data import, validation, and management Technologies: Microsoft SQL Server Data Warehouse Generation System: Extracts data from the OLTP De-normalizes and transforms data Loads data into the Data Warehouse Builds table indexes Archives “snapshots” of the database Technologies: VB, stored procedures Data Warehouse: Functions as the “front-end” database Uses a de-normalized “star schema” Used for querying and archiving data Automatically generated from the OLTP Technologies: Microsoft SQL Server

17. VIEWS/TSS Analysis Tools: Dynamic Contour Maps

18. VIEWS/TSS Analysis Tools: Dynamic Data Maps

19. VIEWS/TSS Analysis Tools: Network Inter-comparison Parameter: Nitrate Ion Concentrations Location: Bondville, IL Networks: IMPROVE, STN, and CASTNet Graphs: Time Series and Scatter Plot

20. VIEWS/TSS Tools: Model Performance Evaluation CMAQ Model Performance vs. Monitored Worst 20% Days in 2002

21. VIEWS/TSS Analysis Tools: Source Apportionment  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

22. VIEWS/TSS Analysis Tools: Glide Slope

23. VIEWS/TSS Analysis Tools: Multidimensional Analysis  Multiple NAAQS indicators, multiple time periods, multiple locations  Displayed with consistent, comparable graphics in the same tool

24. VIEWS/TSS Analysis Tools: Emissions Review Tool Available Parameters: –Sulfur Dioxide –Sulfur Oxides (gas and particulate) –Nitrogen Oxides (gas) –Nitrogen Oxides (gas and particulate) –Other species from SMOKE Multiple dimensions: –Parameters –Emissions Scenarios –Source Categories –Regions of Interest Future plans: –Display regional summaries as well as state-level emissions

25. VIEWS/TSS Tools: Raw Data

26. VIEWS/TSS Tools: Connections to Other Systems

27. VIEWS/TSS Interoperability  Import and output data in common formats, by standard protocols  K.I.S.S. – Because users like to do odd things with data  Practice a “Service Oriented Architecture” approach  Develop reusable, “component” tools  Make interoperability an important goal, while resisting the urge to develop the “Ultimate All-Things-to-All Users” type of system  Adhere to sound software and system architecture principles  Allow direct, read-only access to our database  Support and promote an open data and software architecture

29. VIEWS/TSS Decisions: Benefactors & Benefits Federal Land Managers: Better air quality evaluation More accurate source apportionment More meaningful impact assessment Informed permitting and regulation Improved air quality in ecosystems EPA and RPOs: Effective emissions control evaluation Practical and equitable standards Leveraging of existing efforts Improved synergy with States and Tribes Valuable case studies and precedents More effective application of resources States, Tribes, and Local Agencies: Effective Implementation Plans Defensible and achievable control strategies Uniform and reasonable progress Better air quality in Class I Areas General Air Quality Community: Better insight into the decision process Real life evidence of successes & failures Incorporation of feedback into next generation products Increasing expertise in developing decision support tools

30. VIEWS/TSS Team: Future Plans  Add satellite data in conjunction with NASA ROSES contract  Add additional NAAQS (ozone, for example) and global data  Develop more tools that offer region- and locale-specific analyses  Develop appropriate new functionality as web services  Refine decision tools for making interpretations and recommendations  Expand and refine guidance documentation  Establish case studies from the user community as guidance  Allow users to upload and immediately work with their data  Develop modular, reusable, components for other developers  Establish reusable precedents for air quality decision support  Have a few beers every once (or twice) in a while…

31. Decisions (interpretations, conclusions, guidelines, regulations Science (knowledge, examples, expertise, theory) Analysis (Tools, reanalysis, modeling, inputs from other systems) Processed data (calculated, RHR, normalized, value- added) Raw data (observations, methods)

32. TheEnd… Thanks!