1. Sierra Research January 10, 2012 CMAQ Inventory Development (2008 Base Case Inventories)

2. 2 Overview Purpose – Where the 2008 Base Case CMAQ inventories fit Discussion of Data Sources & Methods SMOKE Inventory Data Processing – Workflow issues/approaches Resulting Emissions Summaries Implications & Status

3. 3 Purpose of 2008 CMAQ Base Case Inventories Primary goal - Support CMAQ model performance analysis and validation over two 2008 Base Case modeling episodes:  Jan/Feb 2008 (1/23/08 to 2/13/08) – 22 days, daily mean temperatures from +6°F to -40°F, six-day period ≤ -28°F  Nov 2008 (11/2/08 to 11/17/08) – 16 days, daily mean temperatures from +10°F to -6°F Need to account for temporal and spatial variations over these episodes based on actual data (where available) Develop inventories at sufficient level of source detail to support sensitivity and control strategy analysis

4. 4 Data Sources & Methods – Point Sources ADEC obtained actual day-specific, hourly emissions/activity data over the 2008 episodes Data were collected from eight facilities in the non-attainment area by individual emission unit and also included:  Annual emissions  Seasonal and weekly/daily activity fractions  Stack parameters (height, diameter, exit temp, velocity/flowrate)  Location coordinates and underlying building dimensions  Information on both combustion and fugitive VOC sources  Notes identifying sources not operated during episodes

5. Summary of Fairbanks Point Source Facilities 5 Data Sources & Methods – Point Sources (cont.) 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

6. 6 Data Sources & Methods – Point Sources (cont.) Downtown Monitor

7. 7 Data Sources & Methods – Point Sources (cont.) Comparison of Key Point Source Fuel Properties HAGO – Heavy Atmospheric Gas Oil Fuel Sulfur Content (%) Ash Content (%) LPG/Natural gas~0.0010 Naphtha0.018 - 0.0240 Jet A0.083 - 0.0930 Coal0.12 – 0.347-15 Distillate Oil0.39 – 0.440 HAGO0.69 – 0.710

8. 8 Data Sources & Methods – Point Sources (cont.) Key point source data processing & validation elements:  Standardized as-received release coordinates to single datum  Used Google Earth to validate & correct release coordinates  Reviewed episodic data supplied by each facility  Most supplied hourly PM 2.5 and SO 2 emission rates  NO X and VOC rates developed by Sierra from AP-42 emission factors (where fuel use data provided) or EF ratios  UAF provided hourly fuel use data only - Sierra corrected units, developed PM 2.5, SO 2, NO X, VOC emissions from AP-42 factors  Aurora only had daily data – constant hourly emissions for each day assumed  Data request only covered 19 of 22 days in Episode 1 – emissions for 2/11/08 – 2/13/08 estimated from 2/10/08 data

9. 9 Space Heating – Key Data Sources CCHRC Household Instrumentation Study (Phase II):  30 homes instrumented from mid Dec 2010 – mid Feb 2011  Homes selected based on target sampling matrix – device usage by candidate household from preceding phone survey  $150 participation incentive, homes classified as Oil (100% oil), Wood (>80% wood), Mixed (mixed oil-wood)  Five devices sampled: 1) woodstove; 2) fireplace; 3) OWB; 4) central oil furnace and 5) direct-vent (DV) oil heater  Central oil use measured directly (furnace rating, on/off time)  DV oil and wood use measured by thermocouples (proxy) and oil or wood use logs (oil tank levels/fills, wood weighed for first 1-2 weeks of instrumentation)  Hourly ambient temperature collected from local stations  Notes kept on known instrumentation problems

10. 10 Space Heating – Key Data Sources (cont.) Hays Research Home Heating Telephone Surveys:  Random household phone surveys in 2006, 2007, 2010, 2011  First three ~ 300 households each, zip code-based sampling targets from household distributions in 2000 Census  2011 survey – 712 households, 86 cell-only (no land line) FNSB Assessor 2008 Parcel Database:  GIS layers identifying locations of residential and non-exempt commercial parcels  Separate data tables linked by parcel number that sub-divided “parent” parcels (e.g., apartment/condo complex) and provided building size info (sq ft)  Covered PM 2.5 non-attainment area, not entire Borough

11. 11 Space Heating – Home Instrumentation Data Analysis Detailed review/validation of as-received data from CCHRC:  Assembled into master spreadsheet database  Checked time series plots, recorded notes by household  Thermocouples sometimes observed to have fallen off or provided spotty results (e.g. anomalies near room temperature)  Wood log suspect/missing for two households  Discarded 4 of 30 households where data were invalid/missing  Removed selected portions for remaining households were data were incomplete (rare)  Determined 3 Wood households were Mixed fuel households

12. 12 Space Heating – Home Instrumentation Data Analysis (cont.) Processing Step 1 – Calculation of hourly BTUs by device  Central Oil devices (direct): Hourly BTUs = Time On (per hour) × Rated Fuel Use × EC  Wood/Direct-Vent oil devices (estimated from fuel log vs. thermocouple measurements):  Plotted fuel loadings vs. time from fuel log data  Overlaid with thermocouple temperature time series  Regression-based estimation of BTUs vs. Degree-Hours  Performed separately by household and device  Approach modified for DV devices with discrete fuel rates

13. 13 Space Heating – Home Instrumentation Data Analysis (cont.) Wood Stove Flue Temperature Cumulative Wood Stove BTUs vs. Flue Degree-Hours

14. 14 Space Heating – Home Instrumentation Data Analysis (cont.) CCHRC 2011 Home Heating Device Instrumentation Study Summary of Measured/Estimated Heating Energy Use by Household

15. 15 Space Heating – Home Instrumentation Data Analysis (cont.) Household Energy vs. Time - Oil Only Households

16. 16 Space Heating – Home Instrumentation Data Analysis (cont.) Household Energy vs. Time – Mixed Use Households

17. 17 Space Heating – Home Instrumentation Data Analysis (cont.) Household Energy vs. Time - Wood Households

18. 18 Space Heating – Home Instrumentation Data Analysis (cont.) Processing Step 2 – Household Energy Prediction Model  Predict HH BTUs as a function of:  Building size (sq ft)  Mix of devices (five from instrumented study)  Ambient temperature  Hour of day  Day of week (weekday/weekend)  Two-step (combined model) multivariate regression: Daily BTUs = C1 + C2*Size + C3*UseFrac dev1 + C4*UseFrac dev2 … + C7*UseFrac dev5 BTU/Hr i,j = (C1 + Ci i=2..24 ) + C25*Temp(F) + C26*weekday=0|weekend=1  Uses daily average ambient temperature  Normalize hourly model with reference temperature and day

19. 19 Space Heating – Home Instrumentation Data Analysis (cont.) Household Energy Prediction Model Scenarios

20. 20 Space Heating – Home Instrumentation Data Analysis (cont.) Household Energy Model Performance – Study Sample

21. 21 Space Heating – Episodic Emission Inventory Combined household energy model with data from 2011 Home Heating Survey (and 2007 & 2010 Surveys):  Contains info on device usage fractions (by zip code)  Used 2011 survey – large sample, includes cell-only households  Scaled energy estimates for other devices in HH survey  Certified vs. Conventional wood device splits interpolated to 2008 from 2007 and 2010 surveys

22. 22 Space Heating – Episodic Emission Inventory (cont.) Used GIS parcel database to spatially allocate households to grid cells:  Produced counts of residential and commercial units (and average sq ft) by grid cell  Masked out area served by District/Municipal steam heat  Parcel database totaled 21,383 residential units  Scaled up to match total households in FNSB reported in 2010 Census = 33,441  May re-do scaling since this is entire Borough and GIS parcel layer later learned to only extend to non-attainment area  How many households are in Borough, but outside NA area?

23. 23 Space Heating – Episodic Emission Inventory (cont.) Heating energy (BTUs) calculated by grid cell, day and hour:  Households in cell  Average household size  Zip code-specific device usage fractions (average over winter)  Ambient temperature (daily)  Household energy prediction model  Commercial parcels assumed to use 98% heating oil and 2% natural gas split (except locations provided by FNSB where waste oil also burned) Energy estimates translated to fuel use (from energy content) and combined with device-specific emission factors to calculate space heating emissions (by cell, SCC, day, hour)

24. 24 Space Heating – Episodic Emission Inventory (cont.) Assumed Energy Content and Emission Factors

25. 25 Data Sources & Methods – Other Area Sources Beyond space heating, other area sources are small in winter No fugitive dust or wildfires (summer only) Only sources assumed to contribute in winter are fugitive VOCs from gasoline storage, transfer and dispensing and combustion-pollutant emissions from structural fires Given their very minor role in winter, other area sources carried forward from 2008 EPA/ORD inventory

26. 26 Data Sources & Methods – On-Road Mobile On-road travel data (VMT, speed, fleet characteristics):  FMATS 2010 Base Year travel model runs covering non- attainment area (use 2010 Census-based socioeconomics)  Model runs include GIS layers with modeled road network  Permanent Traffic Recorder count station (~20) data from ADOT&PF used to develop monthly activity factors  Fleet characteristics from 2010 DMV registration data (and winter parking survey data for light-duty vehicles)  ADOT&PF GIS road centerline database (for Alaska), AADT estimates and posted speeds used to represent on-road activity beyond non-attainment area

27. 27 Data Sources & Methods – On-Road Mobile (cont.) Travel activity combined with MOVES-based emission factors:  SMOKE-MOVES integration tool used to develop gridded, on- road emissions for each modeling episode  MOVES runs generated in “Emission Rates” mode  SMOKE-MOVES uses spatially, temporally gridded temperature fields to calculate temperature-dependent emissions  Calculations are process-specific (e.g. running vs. starting exhaust)  SMOKE-MOVES uses gridded VMT data and Census population surrogates to spatially allocate emissions – default population “split” used

28. 28 Data Sources & Methods – Non-Road Mobile Non-road emissions based on methods used in EPA/ORD inventory NONROAD2008 model-based county-level activity and emission factors Winter-season allocations developed and applied by Sierra (e.g., 100% snow equipment, 0% lawn & garden equipment) Added rail (line-haul and yard switching) based on activity data for travel from Healy to Eielson provided by ARRC Added airport (aircraft and GSE) from data collected at Fairbanks International, Fort Wainwright and Eielson AFB Calculated emissions vertically in 21 layers up to 3,000 meters using EDMS model

29. 29 SMOKE Inventory Processing – Key Issues Episodic inventory data processed into CMAQ-ready inputs using SMOKE (Version 2.7.5 Beta):  Gridding (horizontally and vertically)  Temporal “allocation”  Speciation Sector-specific workflow to maximize highly-resolved point source and space heating emissions:  Point – Used PTHOUR/PTDAY input option to accommodate day and hour-specific point source emissions  Allowed SMOKE to perform plume rise calculations  Space Heating – made custom modifications to SMOKE source code to enable input of day and hour-specific space heating emissions via PTHOUR/PTDAY type processing

30. 30 SMOKE Inventory Processing – Key Issues (cont.) SMOKE outputs were compared to input emissions to ensure proper operation (no lost or added emissions):  SMOKE output file dumps were ported from Linux environment  Output dumps were tabulated by sector (point, space heating, on-road, etc.), vertical layer and episode day  SMOKE runs were “debugged” and eventually showed agreement with inputs within ±3% by individual sector for all pollutants  VERDI tool also used to export GIS-ready gridded estimates and verify spatial allocations  Completed above for Episode 2, being performed for Episode 1

31. 31 Episode 2, 11/14/08 PM2.5 Emissions (g/s) – Point Sources

32. 32 Episode 2, 11/14/08 PM2.5 Emissions (g/s) - Space Heating

33. 33 Episode 2, 11/14/08 PM2.5 Emissions (g/s) – On-Road Vehicles, Running

34. 34 Episode 2, 11/14/08 PM2.5 Emissions (g/s) – Non-Road (includes Rail)

35. 35 Episode 2, 11/14/08 PM2.5 Emissions (g/s) – Aircraft

36. 36 Episode 2, 11/14/08 SO 2 Emissions (moles/s) – Point Sources

37. 37 Episode 2, 11/14/08 SO 2 Emissions (moles/s) - Space Heating

38. 38 Episode 2, 11/14/08 SO 2 Emissions (moles/s) - On-Road Vehicles, Running

39. 39 Episode 2, 11/14/08 SO 2 Emissions (moles/s) – Non-Road (includes Rail)

40. 40 Episode 2, 11/14/08 SO 2 Emissions (moles/s) - Aircraft

41. 41 Resulting Emission Summaries Episode 2 Average Daily Emissions by Sector and Pollutant

42. 42 Resulting Emission Summaries (cont.) Episode 2 Average Day Emission Contributions by Source Sector

43. 43 Resulting Emission Summaries (cont.) Episode 1 (Jan-Feb) Space Heating Emissions by Day

44. 44 CMAQ Inventories – Implications & Status “Bottom-up” based methods for point and space heating area sources confirm their significance during winter episodes Pending modeling results may:  Confirm inventory/ambient concentration consistency  Indicate role of secondary formation  Identify potential need to look for additional sources Need to perform additional Q/A or revision of assumptions?  Number of households from parcel database  Comparison of instrumented data-based energy model to degree-day demand-based method used in EPA/ORD inventory  How to handle point sources in 2008 Baseline (vs. Base Case)