Sierra Research January 10, 2012 Principal Components Analysis (PCA) Inventory Development

2 Overview PCA Modeling - Differences With CMAQ Episodic Inventory Key Analysis Issues Sector-Specific Approaches Resulting Emissions Summaries Improvements

3 PCA Modeling - Differences with CMAQ Inventory PCA Modeling - What it is:  Examines variations in measured, speciated ambient PM 2.5 concentrations in Fairbanks over five-year period ( )  Statistically links these variations to temporal variations in emissions and meteorology  Explain key sources/contributions to ambient PM 2.5 variations Differences with CMAQ Inventory:  Emissions required for 228 winter “PCA Days” from – speciated FRM data measured at downtown monitor  PCA modeling does not address transport and dispersion – need to indirectly address spatial relationships

4 Key Analysis/Workflow Issues Extrapolation from 38 modeling episode days to 228 PCA days  Broader five-year period, account for population/housing growth  Use ambient temperature dependence in space heating energy model and on-road modeling  On-road modeling also includes monthly activity variations  Have to develop an approach to evaluate seasonal and temperature-driven variations in point source emissions  Examine point source emissions variations in episodic data facility-by-facility Examine spatial relationships  Calculate inventories over different-sized areas, vertical layers  Use differences in statistical relationships across areas to infer spatial relationships

5 Key Analysis/Workflow Issues (cont.) PCA Inventory Analysis Areas Grid 3 Domain (Outer) PM Non-Attainment Area (Middle) Downtown Monitor Area (Inner)

6 Key Analysis/Workflow Issues (cont.) Inner PCA Inventory Analysis Areas PM Non-Attainment Area (red area) – official planning area Monitor Area (green area) – 9 x km cells around downtown monitor, represents area with no terrain blocking, reasonable proximity to monitor

Summary of Fairbanks Point Source Facilities 7 PCA Inventories – Point Sources Facility IDFacility NamePrimary Equipment/Fuels 71Flint Hills North Pole Refinery 11 crude & process heaters burning process gas/LPG (9 operated during episodes), plus 2 natural gas-fired steam generators, gas flare 109GVEA Zehnder (Illinois St) Power PlantTwo gas turbines burning HAGO, two diesel generators burning Jet A 110GVEA North Pole Power Plant Three gas turbines, two burning HAGO, one burning naphtha (plus an emergency generator and building heaters not used during episodes) 236Fort Wainwright Backup diesel boilers & generators (3 each) - none operated during episodes 264Eielson Air Force Base Over 70 combustion units - six coal-fired main boilers only operated during episodes 315Aurora Energy Chena Power Plant Four coal-fired boilers (1 large, 3 small), all exhausted through common stack 316UAF Campus Power Plant Two coal-fired, two oil-fired boilers (plus backup generators & incinerator not operated during episodes) 1121 Doyon Utilities (private Ft Wainwright units) Six coal-fired boilers

8 PCA Inventories – Point Sources (cont.) Reviewed episodic, annual (actual) emissions and seasonal throughput data supplied for each facility/emission unit Focused on three facilities having key contributions at downtown monitor from CALPUFF modeling:  GVEA North Pole – two HAGO turbines, one Naphtha unit  UAF Power Plant – two coal boilers, two oil boilers  Doyon Utilities – six coal boilers Examined daily PM 2.5, SO 2 emissions vs. temperature:  GVEA – Good correlation (R 2 =0.65), HAGO on EP1, off EP2  UAF – Good correlation for SO 2 (R 2 =0.66), PM control-baghouse  Doyon – Decent correlation (R 2 ~0.5), PM control-baghouse

9 PCA Inventories – Point Sources (cont.) Examination of Temperature Dependence - Doyon

10 PCA Inventories – Point Sources (cont.) Applied regression-based emissions vs. temperature relationships for GVEA-North Pole, UAF, Doyon Used facility averages across episodes for remaining facilities (Flint Hills Refinery, GVEA-Zehnder, Eielson, Aurora Energy) Historical population data obtained from ADLWF used to scale activity from 2008 levels across period Calculated point source emissions for all 228 PCA days Emissions estimated by emission unit for Flint Hills, GVEA- North Pole and UAF, by entire facility for other point sources

11 PCA Inventories – Space Heating Used same temperature, day of week dependence in heating energy model developed for episodic inventories Scaled 2008 households to other years ( ) using historical household counts by census block group from ADOT&PF tabulated by zip code Household data also included vacancy rates (avg 13%):  Assumed vacant houses still had space heating emission unless wood only (heat left on to avoid frozen pipes)  Wood-only household fractions by zip code from 2011 HH survey

12 PCA Inventories – Space Heating (cont.) Used trends in certified wood device penetration developed from HH surveys Developed spreadsheet macro to calculate space heating emissions by SCC, grid cell for each of 228 PCA days All space heating emissions allocated to lowest PCA vertical layer (<40 meters)

13 PCA Inventories – On-Road Mobile Sources Fleet inputs:  Used population and age distribution by vehicle type inputs from episodic runs and applied to all five calendar years ( )  Scaled 2008 VMT and populations by long-term travel growth rate developed from FMATS travel modeling runs  Applied calendar year-specific fuel and I/M properties  Used day-specific ambient temperature and relative humidity for each of the 228 PCA Day runs based on FAI met data GIS processing of VMT layer to calculate travel fractions from Grid 3 within smaller non-attainment and monitor areas Script used to run these variations through SMOKE-MOVES

14 PCA Inventories – Non-Road & Other Area Sources 2008 average winter day emissions from CMAQ inventory Scaled to calendar years using ADLWF historical populations Emissions for all sources but rail and aircraft spatially allocated to grid cells using population by census block GIS layer – to get activity in NA and Monitor areas Rail and aircraft spatially allocated to NA and Monitor areas based on separate spatial layers used in CMAQ inventory

15 PCA Inventories – Totals by Sector, Area and Layer

16 PCA Inventories – Temporal Variations PM2.5 Emissions by PCA Day ( ), Grid 3, All Sources & Layers

17 PCA Inventories – Potential Improvements Confirmation and revision of point source extrapolations from episodic actual data – follow-up meetings with facilities Possible need to consider other spatial areas?