2. Background  Prior to EPA/ORD effort, no temporally spatially resolved inventory prepared for Fairbanks (focus was on CO and spatial resolution not needed/addressed)  ORD defined the modeling domain, grid structure and 2007/2008 episodes  Assessor database provided GIS information on building size (ft 2 )  FMATS travel model provided GIS information road links  Telephone surveys conducted in 2006, 2007, 2010 and 2011 provide perspective on zip code specific residential space heating fuel use shifts over time  ORD allowed us to define data gaps and options for collecting information needed to project emissions within defined modeling domain

3. Data Collection Activities  Initial focus on collecting activity data for selected Jan/Feb and Nov 2008 Base Case modeling episodes for each source category  Testing programs undertaken to quantify on-road and space heating emission rates  Completion of PCA requires development of inventories for each day between 2006 – 2010 with speciation measurements (total of 228 days)  Year to year trends become an issue for first time  Access to activity data becomes more problematic  Forecast to 2014 requires identification of surrogates for both growth and control as well as operating constraints and permit limits

4. Temporal/Spatial Allocation Issues  Numerous SMOKE limitations encountered in processing source specific estimates into gridded temperature dependent emissions – lots of new ground  MOVES integration tool not initially available  Space heating temporal resolution not accepted for each episode day  Airports represented as point sources  Data collected at different levels of geographic resolution (e.g., GIS, zip code, county, census tract, etc.), methods developed to integrate disparate data sources  Different approaches taken for spatial resolution of emissions for CMAQ versus PCA

5. Key Issues to be Discussed  Inventory development methods/assumptions – CMAQ episodes  Inventory development methods/assumptions – PCA calibration  Inventory sensitivity testing  Inventory forecast assumptions  Space heating energy/fuel use