November 2006



# 2006 Report for the Western Regional Air Partnership (WRAP) Regional Modeling Center (RMC)

WGA Contract Number: 30203

Prepared for:
Western Regional Air Partnership Western Governors’ Association 1515 Cleveland Place, Suite 200 Denver, CO 80202

Prepared by:
Gail Tonnesen, Mohammad Omary, Zion Wang, and Chao-Jung Chien Bourns College of Engineering Center for Environmental Research and

Technology (CE-CERT), Universi ty of California, Riverside 1084 Columbia Avenue, Riverside, CA 92507 http://pah.cert.ucr.edu/aqm/308/
Ralph Morris, Gerry Mansell, Yiqin Jia, and Bo Wang ENVIRON Internati onal Corporation 101 Rowland Way, Suite 220, Novato, CA 94945 http://www.environcorp.com

Zac Adelman UNC-Chapel Hill, Carolina Environmental Program Bank of America Plaza, CB# 6116, 137 E. Franklin Street, Chapel Hill, NC 27599-6116 http://cf.unc.edu/cep/empd2006 Report for the Western Regional Air Part nership (WRAP) Regional Modeling Center (RMC)



## Key Findings for Regional Haze Planning

• We created historic and future emissions scenarios to support regional haze analysis and planning. The assumptions for each are listed below.

♦ The 2002 Planning Case typical-year emissions scenario, represented by case Plan02c, reflected the five-year 2000-2004 baseline monitoring period for regional haze planning. The native emissions inventories’ inputs a ssembled for this scenario represented the following:

 2002 U.S. EGU point sources with 2000-2004 average monthly and diurnal temporal profiles by state.

 2002 U.S. area, mobile, dust, biogenic, and off-shore marine sources.

 2000-2004 averaged U.S. fire sources.

 Sources in all available 2000 Cana da and 1999 Mexico inventories.

♦ The 2018 Base Case future-year emissions scenario, repres ented by case Base18b, reflected 2018 projected emissions, assuming growth and control factors “on the books” in December 2004, as a planning target for th e first regional haze planning milestone year. The native emissions inventories’ inputs assembled for this scenario represented the following:

 2018 U.S. EGU point sources (based on projec tions of emissions rates and number of sources) with the same 2000-2004 average m onthly and diurnal temporal profiles by state used in Plan02c above.

 2018 U.S. area sources for selected an thropogenic source categories that had sufficient projection data available; the rest of the area sources were held constant in relation to Plan02c, as were all biogenic, off-shore, and international (Canada and Mexico) sources.

 2018 U.S. mobile and road dust sources pr ojected with information obtained through surveys completed by each of the WRAP states.

 2000-2004 averaged U.S. fire sources, with the vast majority of fire emissions held constant in relation to Plan02c, but with ma naged fire emissions modified to reflect the application of emissions reduction techniques (ERTs).

ii2006 Report for the Western Regional Air Part nership (WRAP) Regional Modeling Center (RMC)

• We ran multiple modeling simulations using the historic and future emissions scenarios to support regional haze analysis and planning.

♦ There were three historic 2000-2004 baseline (“typical-yea r”) planning runs (cases Plan02a,b,c) performed in sequence to dia gnose emissions issues and true up the representativeness and accuracy of the emissions for planning purposes.

♦ There were two projected future 2018 base case planning runs (cas es Base18a,b), also performed in sequence to diagnose emissions issues and true up the representativeness and accuracy of the emissions for planning purposes.

• We projected the future visibility condition s using EPA default methods while awaiting completed guidance on projection techniques from EPA.

♦ We started with the observed five-year baseline of 2000-2004 using IMPROVE PM species measurements.

♦ We used 2002 and 2018 modeling results for th e 20% worst monitored days in 2002 to project the monitored 20% worst days from the 2000-2004 baseline to obtain the 2018 visibility projections. Missing data was a problem; we were unable to make 2018 visibility projections for approximately a do zen of the WRAP western U.S. Class I areas. In the future this problem will be overcome using data filling techniques.

♦ We held a number of emissions categories co nstant in the air quality/visibility modeling from 2002 to 2018 due to insufficient information on how they would change (e.g., emissions from Canada, Mexico, fires, o ff-shore marine vessels, windblown dust, and biogenics).

♦ We reconfirmed that coarse matter (CM) s hould not be projected to 2018 due to model performance problems, so it will be held constant in the future.

♦ We concluded that other five monitored PM species of visibility co uld be projected, as long as the projections were evaluated in a relative fashion, i.e., using the relative changes in the model estimates from 2000- 2004 to 2018 to scale the observed values.

♦ We found that the 2018 visibility projections at WRAP Cla ss I areas did not meet the 2018 Uniform Rate of Progress (URP) goal due to the contributions of uncontrollable PM (e.g., wildfires, dust, in ternational transport).

♦ We evaluated the visibility improvement on a species-by-species basis, confirming that little air quality or visibility change wa s noted when emissions were not markedly changed.

iii2006 Report for the Western Regional Air Part nership (WRAP) Regional Modeling Center (RMC)

• We completed SO x and NO x source apportionment modeling for WRAP-region states and other selected source regions for Plan02c and Base18b. This allowed the evaluation of the following:

♦ The contributions of sources in each scenario to overall SO 4 and NO 3 loadings at each Class I area thought to be noticeably affected by WRAP-region emissions.

♦ The change between scenarios in the above contributions.

• We developed datasets for BART exem ption screening modeling and conducted the modeling for BART sources in six WRAP-region states.

♦ Prepared numerous versions of the subject to BART modeling protocol to address issues raised by States, Federal Land Managers and others

♦ Processed 2001, 2002 and 2003 MM5 data for CALMET/CALPUFF modeling

♦ Acquired and processed surface meteor ological and precipitation data for CALMET/CALPUFF modeling

♦ Began the CALMET/CALPUFF mo deling for 6 WRAP States

• We completed a modeling study comparing two different algorithm s for representing emissions plume rise from fires.

♦ We found that the WRAP Fire Emissions Joint Forum (WF) algorithm and the SMOKEBriggs (SB) algorithm produce almost opposite re sults in allocating fire emissions to the vertical model layers; the SB algorithm dist ributes the bulk of the emissions below the mixing height, while the WF algorithm puts the bulk of the emissions in the free troposphere.

♦ In the current emissions m odeling configuration used by the RMC, using the SMOKEBriggs algorithm is impractical because of the need to calculate heat flux for each individual fire us ing the FEPS model.

♦ Without observational data on actual fire pl ume distributions, we cannot evaluate which plume rise approach is more accurate.

• A CMAQ model sensitivity simulation for 2002 using only natural emissions showed that the modeled haze levels exceeded the de fault natural haze levels specified in EPA guidance.

♦ This result indicates that it is difficult for modeling studies to show progress toward the visibility goals in areas that ha ve large natural fire emissions.

iv2006 Report for the Western Regional Air Part nership (WRAP) Regional Modeling Center (RMC)

♦ Wildfires were by far the largest source of PM 2.5 in the natural emissions. Biogenic emissions were a large source of VOC emissi ons and a precursor to secondary org aerosols. Other natural emissions were smaller and had large uncertainties.

For this sensitivity simulation we u anic

♦ sed the same GEOS-Chem boundary conditions that were used in the 2002 Base Case simu lations. While the GEOS-Chem boundary

s not

• A CMAQ model sensitivity simulation showed that small fires (fewer than 100 acres in woodlands or fewer than 300 acres in gra sslands) can contribute to visibility impairment in Class I areas. This analysis su ggests that at least so me small fires should be tracked for purposes of modeling visibility. conditions reflect both natural and anthropoge nic emissions, we included them in the clean sensitivity simulation because they re present a contribution to haze that i controllable by the WRAP states.

v2006 Report for the Western Regional Air Part nership (WRAP) Regional Modeling Center (RMC)



## Contents

Key Findings for Regional Haze Planning.................................................................................. ii

Tables ......................................................................................................................... ................... ix Figures............................................................................................................................................ x

Abbreviations .................................................................................................................. ...........xiii

1. Introduction............................................................................................................................ 1 1.1.1 Need for regional haze modeling ..................................................................... 1 1.1.2 Role of the Western Regional Air Partnership................................................. 1 1.1.3 WRAP strategic plan ........................................................................................ 3 1.1.4 The WRAP Regional Modeling Center............................................................ 4 1.2 Description of WRAP Modeling Domain ...................................................................... 6

2. 2002 Planning Case Modeling............................................................................................... 7 2.1 Emissions Input and Quality Assurance......................................................................... 7 2.1.1 Description of Plan02 emissions ...................................................................... 8 2.1.2 Data collection and preparation...................................................................... 11 2.1.3 Emissions processing ..................................................................................... 13 2.1.4 Quality assurance ........................................................................................... 13 2.1.5 Uncertainties and future work ........................................................................ 14 2.1.6 Summary ........................................................................................................ 14 2.2 Visibility Modeling and Co mparison to 2002 Base Case ............................................ 14

3. 2018 Base Case Modeling.................................................................................................... 17 3.1 Emissions Input and Quality Assurance....................................................................... 17 3.1.1 Description of Base18 emissions ................................................................... 18 3.1.2 Data collection and preparation...................................................................... 21 3.1.3 Emissions processing ..................................................................................... 21 3.1.4 Quality assurance ........................................................................................... 22 3.1.5 Uncertainties and future work ........................................................................ 22 3.1.6 Summary ........................................................................................................ 24 3.2 2018 Visibility Modeling ............................................................................................. 24 3.3 Visibility Projections.................................................................................................... 3 0 3.3.1 Calculation of Baseline Conditions................................................................ 31 3.3.2 Mapping of modeling results to the IMPROVE measurements..................... 34

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3.3.3 Using modeling results to project changes in visibility ................................. 34 3.3.4 Glide path to Natural Conditions.................................................................... 36 3.3.5 Preliminary visibility projection results ......................................................... 37 3.3.6 Alternative approaches for sele cting worst visibility days............................. 39

4. PSAT Modeling.................................................................................................................... 40 4.1 Background and Setup Information for PSAT Modeling ............................................ 41 4.1.1 CAMx/PSAT .................................................................................................. 41 4.1.2 CAMx/PSAT 2002 and 2018 setup................................................................ 42 4.2 Preparation of Emissions Data ..................................................................................... 45 4.3 Sample PSAT Results .................................................................................................. 45 4.3.1 Source apportionment spatial plots ................................................................ 46 4.3.2 Source apportionment receptor bar plots........................................................ 48 4.4 Quality Assurance and Comparison to CMAQ/TSSA ................................................. 51 4.5 Conclusions from PSAT Modeling .............................................................................. 55

5. Selecting Sources for BART Sensitivity Modeling ........................................................... 56 5.1 Background ................................................................................................................. . 56 5.2 Spreadsheets and Memo............................................................................................... 57 5.3 Colorado Case Study .................................................................................................... 57
5.4 Future Work ................................................................................................................ . 57

6. BART Exemption Screening Modeling ............................................................................. 59 6.1 Background and Setup Information for the CALMET/CALPUFF Modeling ............. 60 6.1.1 CALMET/CALPUFF model sel ection and methodology.............................. 60 6.1.2 Use of CALMM5 to extract MM5 data for CALMET/CALPUFF modeling......................................................................................................... 61 6.1.3 CALMET/CALPUFF modeling domains ...................................................... 63 6.1.4 CALMET modeling options and comp arison with regulatory default settings............................................................................................................ 68 6.1.5 CALMET performance evaluation................................................................. 69 6.2 Summary of Results from CALMM5 .......................................................................... 69 6.3 Summary of Results from CALMET/CALPUFF ........................................................ 69

7. “Clean Conditions” 2002 CMAQ Sensitivity Analysis..................................................... 71 7.1 Description of Emissions Used .................................................................................... 71 7.2 Summary of Results ..................................................................................................... 72

8. Small-Fires Sensitivity Analysis ......................................................................................... 79
8.1 Emissions Scenarios..................................................................................................... 79 8.2 Summary of Results ..................................................................................................... 80

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9. Comparison of Two Fire Plume Rise Algorithms............................................................. 83 9.1 Description of FEPS and Fire Events Used.................................................................. 83 9.2 Plume Rise Algorithms ................................................................................................ 84 9.2.1 WRAP-FEJF approach ................................................................................... 84 9.2.2 SMOKE-Briggs approach .............................................................................. 85 9.3 Emission Modeling....................................................................................................... 86
9.4 Results .................................................................................................................... ...... 86
9.5 Conclusions ................................................................................................................ .. 87

10. Data Transfer....................................................................................................................... 89
10.1 Data Transfer Procedure............................................................................................... 89 10.2 Summary of Data Transfers to Date............................................................................. 89

11. Summary of Work Completed, Lessons Learned, and Future Work ............................ 91 11.1 Summary of Work Complete d through September 2006............................................. 91 11.1.1 Visibility progress calculations us ing the Plan02 and Base18 scenarios ....... 91 11.1.2 PSAT source apportionment modeling .......................................................... 93 11.1.3 CALMM5/CALMET/CALPUFF modeling for BART exemption screening......................................................................................................... 93 11.2 Emissions Modeling Experience: Lessons Learned..................................................... 94 11.2.1 Addressing errors in the emissions................................................................. 94 11.2.2 Shortcomings of systematic QA procedures .................................................. 95 11.2.3 New technologies ........................................................................................... 95 11.2.4 Important issues.............................................................................................. 96
11.3 Future RMC Work for WRAP ..................................................................................... 98

References..................................................................................................................... ............... 99 Appendix A: WRAP 2018 Stationary-Point-S ource Inventory Corrections (Base18b)..... A-1 Appendix B: Fire Plume Rise Sensitivity Results ...................................................................B-1

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## Tables

Table 1-1. WRAP 2003 Strate gic Plan (WGA, 2003).....................................................................3 Table 3-1. Definition of IMPROVE components from measured species. ...................................32 Table 3-2. Mapping of CMAQ v4.5 modeled species concentrations to IMPROVE measured components............................................................................................................ ..34

Table 3-3. WRAP sites with da ta completeness problems. ...........................................................39 Table 4-1. Benchmarks for PSAT computational costs for each PM species. Run time is for one day (01/02/2002) on the WRAP 36-km domain. ..............................................................41

Table 4-2. WRAP 2002 CAMx /PSAT specifications....................................................................42

Table 4-3. WRAP CAMx/PSA T run-time options........................................................................43

Table 4-4. WRAP CAMx/PSAT source re gions cross-reference table. ........................................43

Table 4-5. WRAP CAMx/PSAT emissions source groups. ..........................................................44 Table 4-6. PSAT tracer source tags. ............................................................................................ ..49

Table 6-1. Definition of continental-U.S. state CALMET/CALPUFF modeling domains using the RPO LCC projection based on an origin at (-97o,40o) and true latitudes at 33o and 45o. ....................................................................................................................................64 Table 8-1. Numbers of small and large fires in July and November 2002. ...................................80

Table 8-2. Mass emissions of NO x and primary PM 2.5 from small and large fires in July and November 2002........................................................................................................................80 Table 9-1. Fire events summary (based on 2002 WRAP fire inventories)....................................84 Table 10-1. Summary of data tran sfers completed or in progress, from log used to schedule data transfers on RMC web site...............................................................................................90

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## Figures

Figure 1-1. Regional Planning Organizations engaged in regional haze modeling.........................2 Figure 1-2. Locations of the Class I areas requiring regional haze plans by December 2007 within the WRAP region......................................................................................................... ...2 Figure 2-1. Example plot showing change in annual average CMAQ-modeled organic carbon (OC) for Plan02 minus Base02. ...................................................................................16 Figure 2-2. Example plot showing change in annual average CMAQ-modeled PM 2.5 for Plan02 minus Base02............................................................................................................ ...16 Figure 3-1. Annual average aerosol sulfate (A SO4) concentration comparisons between Base18b and Plan02b. Top plot: difference between the two (Base18b – Plan02b); bottom left plot: Plan02b results; bottom right plot: Base18b results. ....................................26 Figure 3-2. Annual average aerosol nitrate (ANO3) concentration comparisons between Base18b and Plan02b. Top plot: difference between the two (Base18b – Plan02b); bottom left plot: Plan02b results; bottom right plot: Base18b results. ....................................27 Figure 3-3. Annual average PM 2.5 concentration comparis ons between Base18b and Plan02b. Top plot: difference between the two (Base18b – Plan02b); bottom left plot: Plan02b results; bottom right plot: Base18b results. ...............................................................28 Figure 3-4. Annual average deciview comp arisons between Base18b and Plan02b. Top plot: difference between the two (Base 18b – Plan02b); bottom left plot: Plan02b results; bottom right plot: Base18b results...............................................................................29 Figure 3-5. Example of URP glide path us ing IMPROVE data from the Grand Canyon National Park for the W20% days and comparis on with Base18b visibi lity projections. .......36 Figure 3-6. 2018 visibility projections at WRAP Class I areas expressed as a percent of achieving the 2018 URP goal using the Ol d and New IMPROVE equation and the WRAP Base18c CMAQ 36-km modeling results. ..................................................................38 Figure 4-1. WRAP CAMx/PSAT source region map. Table 4.4 defines the source region IDs............................................................................................................................................44 Figure 4-2. January 2002 monthly-average PSAT spatial plot for PSO4 from Arizona point sources......................................................................................................................................47 Figure 4-3. January 2002 monthly-average PSA T spatial plot for PNO3 from Arizona mobile sources. ........................................................................................................................47 Figure 4-4. PSAT bar plot for receptor site at Hance Camp in Grand Canyon National Park, AZ, showing the top contributo rs to PSO4 based on 20% best β ext days’ average for 2002........................................................................................................................... ...............49

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Figure 4-5. PSAT bar plot for receptor site at Hance Camp in Grand Canyon National Park, AZ, showing the top contributo rs to PSO4 based on 20% worst β ext days’ average for 2002........................................................................................................................... ...............50 Figure 4-6. PSAT bar plot for receptor site at Hance Camp in Grand Canyon National Park, AZ, showing the top contribu tors to PSO4 based on July 2002 monthly average. .................50 Figure 4-7. Source apportionment spatial plots for aerosol sulf ate from California mobile sources from CMAQ/TSSA (left) and CAMx/PSAT (right) for January 31, 2002, at 0000 GMT................................................................................................................................52 Figure 4-8. Source apportionment spatial plots for aerosol su lfate from California point sources from CMAQ/TSSA (left) and CAMx/PSAT (right) for January 31, 2002, at 0000 GMT................................................................................................................................52 Figure 4-9. Source apportionment spatial plots for aerosol sulf ate from California mobile sources from CMAQ/TSSA (left) and CA Mx/PSAT (right) for July 31, 2002, at 0000 GMT.........................................................................................................................................53 Figure 4-10. Source apportionment spatial plot s for aerosol sulfate from California point sources from CMAQ/TSSA (left) and CA Mx/PSAT (right) for July 31, 2002, at 0000 GMT.........................................................................................................................................53 Figure 4-11. TSSA (top panel) vs. PSAT (bo ttom panel) receptor bar plots for PSO4 for Hance Camp at Grand Canyon National Park, AZ, for the month of July 2002. ....................54 Figure 5-1. Example draft BA RT selection memorandum............................................................58 Figure 6-1. Relationship of four MM5 window do mains for the states for which the RMC is performing CALPUFF modeling (AZ, MT, NM, NV, SD, and UT) and Class I areas. .........62 Figure 6-2. CALMET/CALPUFF m odeling domains and Class I area receptors (green), observed surface meteorological sites (purple triangles), and potential BART-eligible source locations (red dots) for (a) Arizona , (b) Montana, and (c) New Mexico. ....................65 Figure 6-3. CALMET/CALPUFF m odeling domains and Class I area receptors (green), observed surface meteorological sites (purple triangles), and potential BART-eligible source locations (red dots) for (a ) South Dakota and (b) Utah................................................66 Figure 6-4. CALMET/CALPUFF m odeling domains and Class I area receptors (green), observed surface meteorological sites (purple triangles), and potential BART-eligible source locations (red dots) for (a) Neva da CALMET modeling, (b) northern Nevada CALPUFF modeling, and (c) southern Nevada CALPUFF modeling....................................67 Figure 6-5. Alaska CALMET/CA LPUFF modeling domain show ing the potential BARTeligible source locations (re d dots) and Denali and Tuxedni Class I area receptors (green)......................................................................................................................................68 Figure 7-1. Annual average model-reconstructed “clean conditions” visibility as extinction coefficient. ................................................................................................................... ............73

xi2006 Report for the Western Regional Air Part nership (WRAP) Regional Modeling Center (RMC)

Figure 7-2. Annual average modeled primar y anthropogenic OC (AORGPA) in Base02b (top) and the portion that is “controllable” primary anthropogenic OC (bottom). ..................75 Figure 7-3. Annual average modeled seconda ry anthropogenic OC (AORGA) in Base02b (top) and the portion that is “controllable” secondary anthropogenic OC (bottom)................76 Figure 7-4. Annual average modeled primary biogenic OC (AORGB) in Base02b (top) and the portion that is “controllable” pr imary biogenic OC (bottom)............................................77 Figure 8-1. Modeled effects of small fires on vi sibility (as change in deciview) for the Montana and Idaho area on November 11, 2002. Plot shows Base02 minus the sensitivity case that has small fires rem oved, so the deciview values indicate the contributions from small fires to visibility impairment. ..........................................................81 Figure 8-2. For each day in July and November 2002 at the Agua Tibia, CA, Class I area, the top plot shows the total modeled daily average light extinction in Base02b, and the bottom plot shows the modeled contribution of small fires to light extinction. In the top plot the red dashes show the total measured light extinction on the 20% worst days. ............82

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## Abbreviations

ACM ....................Asymmetric Convective Model ADEC...................Alaska Department of Environmental Conservation AIM......................Aerosol Inorganic Module AIRMoN ..............Atmospheric Integrated Research Monitoring Network AIRS ....................Aerometric Information Retrieval System AML.....................ARC Macro Language AoH......................Attribution of Haze AOI ......................area of influence AQS......................Air Quality System ARDB ..................Acid Rain Database AVHRR ...............Advanced Very High Resolution Radiometer BART...................Best Available Retrofit Technology Base02..................2002 Base Case scenario (histo rical base case emissions scenario) Base18..................2018 Base Case scenario (future- year base case emissions scenario) BC ........................boundary condition BEIS.....................Biogenic Emissions Inventory System BELD ...................Biogenic Emissions Landcover Database BM .......................Betts-Miller BRAVO................Big Bend Regional Aerosol and Visibility Observations Study CAA .....................Clean Air Act CAFO...................confined animal feed operations CAIR....................Clean Air Interstate Rule CALMET .............California Meteorological Model CALPUFF............California Puff Model CAMR..................Clean Air Mercury Rule CAMx ..................Comprehensive Air quality Model with extensions CAPE ...................convective available potential energy CARB...................California Air Resources Board CASTNet..............Clean Air Stat us and Trends Network CB-IV...................Carbon Bond IV (also referred to in the modeling community as CBIV, CB4) CCM2...................Community Climate Model 2 CCTM ..................CMAQ Chemical-Transport Model CDPHE ................Colorado Department of Health and Environment CEM ....................continuous emissions monitoring CENRAP..............Central Regional Air Planning Association CEP ......................Carolina Environmental Program CM .......................coarse mass (a term used in the cont ext of the IMPROVE network; see also PMc) CMAQ..................Community Multi scale Air Quality

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CMAS ..................Community Modeling and Analysis System CMC.....................Chemical Mechanism Compiler CMU ....................Carnegie Mellon University CNG .....................compressed natural gas CPC......................Climate Prediction Center CPU......................central processing unit CVS......................Concurrent Versions System CY02....................calendar year 2002 DDM ....................decoupled direct method DEAD ..................Dust Entrainment and Deposition dv..........................deciview DWM ...................Diagnostic Wind Model EC ........................elemental carbon EDAS ...................Eta Data Assimilation System EEA......................European Environment Agency EFIG ....................Emissions Factor s and Inventory Group EGAS ...................Economic Growth Analysis System EGU .....................electric generating unit EPA......................U.S. Environmental Protection Agency ERG......................Eastern Research Group, Inc. ERT......................emissions reduction techniques ESSC....................Earth System Science Center FAQ......................frequently asked question FDDA...................four-dimensional data assimilation FEJF .....................Fire Emissions Joint Forum FEPS ....................Fire Emission Production Simulator FIPS......................Federal Implementation Standards FLM .....................Federal Land Manager GCVTC................Grand Canyon Visibility Transport Commission GEOS ...................Goddard Earth Observing System GIS .......................geographic information system GUI ......................graphical user interface IC..........................initial condition IDA ......................Inventory Data Analyzer IDNR....................Iowa Department of Natural Resources IEH.......................Implicit-Explicit Hybrid IMPROVE............Interagency Monitoring of Protected Visual Environments IOA ......................index of agreement IPM ......................Integrated Planning Model IPR .......................Integrated Process Rates IRR.......................Integrated Reaction Rates KF ........................Kain-Fritsch LAC......................light-absorbing carbon

xiv2006 Report for the Western Regional Air Part nership (WRAP) Regional Modeling Center (RMC)

LADCO................Lake Michigan Air Directors Consortium lat-lon ...................latitude-longitude LCC......................Lambert conformal conic LCP ......................Lambert conformal projection LPG......................liquefied petroleum gas LSM .....................land-surface model LULC ...................land use/land cover M4........................Mechanism 4 MADM.................Multicomponent Aerosol Dynamics Model MADRID .............Model of Aerosol Dynamics, Reaction, Ionization, and Dissolution MANE-VU...........Mid-Atlantic/Northeast Visibility Union MARAMA ..........Mid-Atlantic Regional Air Management Association MDN ....................Mercury Deposition Network MFB .....................mean fractional bias MFE .....................mean fractional error MIMS...................Multimedia Integrated Modeling System MM5 ....................Fifth-Generation PSU/N CAR Mesoscale Model M-MRF ................modified Medium-Range Forecast MMS ...................Minerals Management Service MPE .....................model performance evaluation MRF .....................Medium-Range Forecast MRLC ..................Multi-Resolution Land Characterization MRPO ..................Midwest Regional Planning Organization MWSS..................Monday-weekday-Saturday-Sunday NADP...................National Atmospheric Deposition Program NAMS..................National Air Monitoring Stations NASS ...................National Agricultu ral Statistics Service NCAR ..................National Center for Atmospheric Research NCEP ...................National Centers for Environmental Prediction NEI.......................National Emissions I nventory (NEI96 = 1996 versi on; NEI99 = 1999 version) NIF .......................National Emissions Inve ntory (NEI) Input Format NLCD...................National Land Cover Database NNRP...................NCAR/NCEP Reanalysis Project NORTAD.............North American Tr ansportation Atlas Data NTN .....................National Trends Network NWS.....................National Weather Service OAQPS ...............Office of Air Quality and Planning Standards OC........................organic carbon OMC ....................organic matter carbon ORD .....................Office of Resear ch and Development ORL......................one record per line OSAT ...................Ozone Source Apportionment Technology PAMS...................Photochemical Assessment Monitoring Stations

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PAVE ...................Program for the Analysis and Vi sualization of Environmental data PBL ......................planetary boundary layer PDM.....................Plume Dynamics Model PinG .....................plume-in-grid Plan02 ..................2002 Planning Case scenario, the typical-year planning case emissions scenario used in this report for co mparison with 2018 Base Case, the futureyear base case emissions scenario PM........................particulate matter PM 2.5 ....................particulate matter ≤ 2.5 μm in diameter (referred to in this report as “fine particulate matter”) PM 10 .....................particulate matter ≤ 10 μm in diameter PMc......................coarse particulate matter: PM that ra nges from 2.5 to10 μm in diameter (note that PMc is also referred to as CM in the context of the IMPROVE network) POA......................primary organic aerosol PPM......................Piecewise Parabolic Method ppmv ....................parts per million by volume PSAT....................PM Source Apportionment Technology PSU ......................Pennsylvania State University PX ........................Pleim-Xiu QA........................quality assurance QAPP ...................quality assurance project plan QC........................quality control QSSA ...................Quasi Steady State Approximation RADM..................Regional Acid Deposition Model RAMS ..................Regional Atmospheric Modeling System REMSAD.............Regional Modeling System for Aerosols and Deposition RHR .....................Regional Haze Rule RMC.....................Regional Modeling Center RMSE...................root mean squared error ROG .....................reactive organic gas RPO......................Regional Planning Organization RRF......................relative response factor RRTM ..................Rapid Radiative Transfer Model Rx.........................prescribed (used with “fires”) SAPRC.................Statewide Air Pollution Research Center SCC......................source classification code SEARCH..............Southeastern Aerosol Research a nd Characterization (monitoring network) SEARCH_H.........SEARCH monitoring ne twork collecting hourly data (vs. daily [24-hour] data) SIP........................State Implementation Plans SLAMS ................State and Local Air Monitoring Stations SMOKE................Sparse Matrix Operat or Kernel Emissions SMVGEAR..........Sparse-Matrix Vectorized Gear SOA......................secondary organic aerosols SST.......................sea-surface temperature

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STATSGO............State Soil Geographic STN......................Speciated Trends Network STP.......................standard temperature and pressure TIP........................Tribal Implementation Plan TM........................thematic mapper TOG .....................total organic gas TOMS ..................Total Ozone Mapping Spectrometer TSD......................technical support document TSS.......................Technical Support System TSSA....................Tagged Species Source Apportionment UAF......................University of Alaska Fairbanks UCR .....................University of California, Riverside UNC .....................University of North Carolina URP......................uniform rate of progress USDA...................U.S. Department of Agriculture USGS ...................U.S. Geological Survey VIEWS.................Visibility Information Exchange Web System VISTAS................Visibility Improvement – State and Tribal Associations of the Southeast VMT.....................vehicle miles traveled VOC .....................volatile organic compound(s) VRSM ..................Variable Size-Resolution Model WEQ ....................Wind Erosion Equation WRAP..................Western Regional Air Partnership

xvii2006 Report for the Western Regional Air Part nership (WRAP) Regional Modeling Center (RMC)



## 1. Introduction
1.1.1 Need for regional haze modeling

The Clean Air Act (CAA) establishes special goals for visibility in many U.S. national parks, wilderness areas, and international parks. Thr ough the 1977 amendments to the Clean Air Act, Congress set a national goal for visibility as “the prevention of any future, and the remedying of any existing, impairment of vi sibility in mandatory Class I Federal areas which impairment results from manmade air pollution” (40 CFR 51.300). States are requi red to develop State Implementation Plans (SIPs) to attain visibility standards; also, tribes may opt to assume responsibility for visibility programs under 40 CFR Pa rt 49 by developing Tribal Implementation Plans (TIPs). The goal of the U.S. Environmental Protection Agency (EPA) Regional Haze Rule (RHR) is to achieve natural vi sibility conditions at 156 Fede rally mandated Class I areas by 2064. To achieve this goal, the RHR has set up milestone years of 2018, 2028, 2038, 2048, 2058, and 2064 to monitor progress to ward natural visibility c onditions. CAA Section 308 (§308) requires that the first visibility SIP/TIP be submitted to EPA by 2007-2008 to demonstrate progress toward natural visibi lity conditions in 2018 using a 2000-2004 five-year baseline.



### 1.1.2 Role of the Western Regional Air Partnership

Five Regional Planning Organiza tions (RPOs) (Figure 1-1) ar e responsible for coordinating development of SIPs and TIPs in selected areas of the U.S. to address the requirements of the RHR. These RPOs are the Western Regional Air Partnership (WRAP), th e Central Regional Air Planning Association (CENRAP), the Midwest Regional Planning Organization (MRPO), the Visibility Improvement – State a nd Tribal Associations of the Southeast (VISTAS), and the MidAtlantic/Northeast Visibility Union (MANE-VU). This report focuses on work performed for WRAP.
The WRAP region is composed of states and tribal lands loca ted within the boundaries of 14 states: Alaska, Arizona, Califor nia, Colorado, Hawaii, Idaho, Montana, New Mexico, North Dakota, Oregon, South Dakota, Utah, Washington, and Wyoming. It includes the Class I areas indicated in Figure 1-2. WRAP is a regional partnership of stat es, tribes, Federal agencies, stakeholders, and citizen groups th at was established to initiate and coordinate activities associated with the management of regional haze and other air quality issues within the WRAP states; WRAP’s role includes providing technical and policy tools to help states and tribes. The members of WRAP comm ittees and forums ( http://wrapair.org/about/orgchart.htm ) consist of representatives from these affected groups . Committee and forum members are urged to communicate freely with their peer s and professional associations so that they can adequately represent the views of that segment of the public. Each committee/forum is expected to translate technical materials into a form accessible to the public. More information about the WRAP organizational structure and activities is available at http://wrapair.org/facts/forumdescribe.html

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Figure 1-1. Regional Planning Organizatio ns engaged in regional haze modeling.

Figure 1-2. Locations of the Class I areas requiring regional haze plans by December 2007 within the WRAP region.

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Regional haze is linked to fi ne particulate matter (PM 2.5 , which is particulate matter ≤ 2.5 μm in diameter), for which EPA has a health-based standard. PM 2.5 SIPs/TIPs are to be submitted three years after designation of PM 2.5 nonattainment areas. EPA performed the final designations of these nonattainment areas in December 2004, so the PM 2.5 SIPs / TIPs are due in early 2008. Except in California, there are few PM 2.5 nonattainment areas in the WRAP region that are affected by the regional haze SIPs/TIPs due in December 2007. Because regional haze is intricately linked to PM 2.5 , regional SIPs/TIPs will c ontain analysis of both PM 2.5 and the effect on regional haze at the 118 WRAP Class I Federa l areas. EPA also designated 8-hour ozone nonattainment areas in April 2004, which makes 8-hour ozone SIPs / TIPs also due in 2007, again affecting primarily California in the WRAP region. States and trib es may use the integrated oneatmosphere WRAP base-year visi bility modeling (introduced in Section 1.1.4) as the regional component of the 8-hour ozone attain ment activities as well as for PM 2.5 and regional haze. Those decisions will be based on schedule constr aints, model performance, appropriateness of the episodes, feasibility, and need.



### 1.1.3 WRAP strategic plan

On September 29, 2003, WRAP released their st rategic plan for the years 2003-2008 (WGA, 2003): http://wrapair.org/WRAP/meetings/03101 4board/Tab_4_Strategic_Plan_Final.pdf . Prior to 2003, WRAP’s activities focused on developing the Section 309 (§309) SIPs/TIPs that nine WRAP states may elect to opt into and ther eby adopt the recommendati ons of the Grand Canyon Visibility Transport Commission (GCVTC). Thes e §309 SIPs/TIPs address visibility at the 16 Class I areas on the Colorado Plateau (see Figure 1-2). In 2003, WRAP’s efforts turned to performing the technical analyses needed to develo p the §308 SIPs/TIPs that address all 118 Class I areas in the WRAP region. The WRAP 2003-2008 stra tegic plan is broken down into two phases (Table 1-1). It will be reviewed and revised as needed in 2006-07.

Table 1-1. WRAP 2003 Strategic Plan (WGA, 2003).

Phase I 2003-2005 Phase II 2005-2007 Purpose: Dry run for Phase II. Hedge against earlier due dates. Refine and apply Phase I approaches for SIP/TIP purposes.
Scale: Regional. Regional and subregional. Apportionment: 1996 & 2002 source contributions. Areas each plan to address. 2002 source contributions. Reduction obligations. Strategies: Identify options, screen. Perform cost/benefit, select, design.
Communications: Public education. Public acceptance. Major state/tribe submittals: 2002 emissions inventories. Modeling run specifications.

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### 1.1.4 The WRAP Regional Modeling Center

The WRAP Regional Modeling Cent er (RMC) is responsible for performing air quality modeling simulations for the states and tribes in the WRAP region in order to prov ide analytical results used in developing implementation plans unde r the EPA RHR. The RMC is composed of researchers from the University of Califor nia, Riverside (UCR), ENVIRON International Corporation, and the University of North Ca rolina’s Carolina Environmental Program (UNCCEP). Dr. Gail Tonnesen of UCR is the Project Ma nager and Principal Investigator for the RMC. Mr. Ralph Morris and Mr. Zac Adelman lead the RMC efforts at ENVIRON and UNC-CEP, respectively. UCR hosts the RMC’s computer center, which includes numerous Linux computer platforms and RAID disk storage systems. In the past, training of states , tribes, and others has been performed at UCR’s facilities.
To support WRAP’s efforts to comply with th e requirements of the RH R, the RMC’s responsibilities include the following:

• meteorological modeling

• emissions processing and modeling

• air quality and visibili ty modeling simulations

• analysis, display, and repor ting of modeling results

• storage and quality assurance of the modeling input and output files
The air quality and visibility modeling will be used by the WRAP states and tribes as part of their technical support documents (TSDs) to de monstrate reasonable progress toward attaining visibility goals. Key work elements of the RMC visibility modeling include the following:

1. Evaluating the visibility model for a historic al episode—in this cas e, for calendar year 2002. Output from the model simulation is compared with ambient air quality data for the historical episode as part of a model performance evaluation (MPE).

2. Developing visibility planning scenarios for the regional haze five-y ear baseline period, 2000-2004, and for the initial regional haze futu re projection period, calendar year 2018.

3. Modeling a variety of emissions sensitivity, emissions source apportionment, and emissions control strategies to assess whether pl anned future regional emissions reductions will be sufficient to demonstrate reasonable progress toward achieving visibility goals.
This report discusses the act ivities the RMC completed fo r WRAP during 2006 (through September). To provide context, we first briefly review the modeling systems used and the efforts we completed during prior years.
The three primary modeling tools used by the RMC are

• the Fifth-Generation Pennsylvania State Un iversity/National Center for Atmospheric Research (PSU/NCAR) Mesoscale Model (MM5) meteorological modeling system,

• the Sparse Matrix Operator Kernel Emissi ons (SMOKE) emissions modeling system, and

• the Community Multiscale Air Quality (CMAQ) air quality modeling system.

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We have also performed some sensitivity tests using the Comprehensive Air Quality Model with extensions (CAMx) and the Regional Modeling System fo r Aerosols and Deposition (REMSAD). In addition, we applied MM5 and the Californi a Meteorological Model/California Puff Model (CALMET/CALPUFF) modeli ng system to Alaska.
During 2002 and 2003, the RMC focused on supporting WRAP’s development of the §309 SIPs/TIPs for those states and tribes that el ected to pursue the §309 process. We set up and completed simulations of the MM5, SMOKE, and CMAQ modeling systems for 1996 for the western states using 36-km-resolu tion MM5 data, and performed th e technical analysis needed for the SIPs/TIPs. More details can be found in the Section 309 TSD (WRAP, 2003) located at http://wrapair.org/309 /031215Final309TSD.pdf

. As 2003 progressed, the RMC turned its efforts to ward setting up the modeling tools needed to address the technical requirements of the §308 SIPs/TIPs. This included setting up the MM5, SMOKE, and CMAQ modeling syst ems for calendar year 2002 (CY02) using a 36-kmresolution grid for the continental United States and a 12-km-resolution grid for the WRAP states. During 2004 we continued setting up the m odeling infrastructure fo r CY02 to address the requirements of the WR AP strategic plan.
In 2005 the RMC developed draft versions of th e visibility modeling da tasets and completed their quality assurance (QA). Th ese datasets included emissions inventories and visibility modeling data for three a nnual modeling scenarios:

1. The 2002 base case emissions scenario, referred to in this report as “2002 Base Case” or “Base02”

2. The 2002 typical-year planning cas e emissions scenario, referr ed to as “2002 Planning Case” or “Plan02”

3. The 2018 future-year base case emissions scenar io, referred to as “2018 Base Case” or “Base18”
During 2005 we also completed final versi ons of the meteorology modeling, emissions modeling, and visibility MPE for the 2002 Base Ca se scenario for both a 36-km domain and a higher-resolution 12-km nested domain. We concl uded that both versions of the modeling had acceptable performance for use in projecting fu ture visibility, and we selected the 36-km modeling for making future projections because its performance was similar to that of the 12-km modeling but it required cons iderably fewer resources to perfor m the model runs. A final task we performed in 2005 was to complete a comparison of two visibility models, CMAQ and CAMx. We observed performance differenc es between them, with one m odel performing better than the other in various comparisons to ambient data for selected PM 2.5 species, but neither model was clearly superior to the other, so we concluded that either model could be used for future visibility projections. These results informed the decisions to (1) continue work during 2006 with the final 2002 and 2018 planning modeling using 36-km CMAQ runs, and (2) perform 2002 and 2018 source apportionment modeling using 36-km CAMx runs. The Final Report for the Western Regional Air Partnership (W RAP) 2002 Visibility Mode l Performance Evaluation (Tonnesen et al., 2006) describes each of these ac tivities in more detail; it is available on the RMC web site at http://pah.cert.ucr.edu/aqm/308/docs.shtml

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This report describes activities completed in 2006. These include development, QA, and analysis of the final versions of the Base02, Plan02, a nd Base18 CMAQ simulations (Sections 2 and 3); source apportionment modeling using CAMx (Section 4); Best Available Retrofit Technology (BART) modeling using CALMET and CALPUFF (Sec tions 5 and 6); a sensitivity simulation to evaluate “clean” visibility condi tions (Section 7); sensitivity simulations and analysis of effects and treatment of fire emissions (Sections 8 and 9); and data transfer ac tivities (Section 10).
The RMC notes and understands the importance of the guidance contained in the WRAP Tribal Template ( http://wrapair.org/WRAP/reports/Gen-Tribe-Temp.pdf ). Work products from the RMC are reviewed by the WRAP Modeling Fo rum and any interested stakeholders.



### 1.2 Description of WRAP Modeling Domain

The 36-km coarse-grid horizontal domain used for the air quality modeling was the RPO Unified Continental 36-km Modeling Grid do main (abbreviated in this repo rt as “RPO Unified domain”), which consists of the contiguous 48 U.S. states, contiguous lands and waters of southern Canada and northern Mexico, portions of the Pacific and Atlantic Oceans, most of the Gulf of Mexico, all of the Gulf of California, and the southern Hudson Bay regi on. Detailed descriptions of the modeling domain, map projection, ve rtical layer structure, grid definition, and grid nesting schemes were included in Section 1.3 of the Final Report for the West ern Regional Air Partnership (WRAP) 2002 Visibility Mo del Performance Evaluation (Tonnesen et al., 2006), which is available on the RMC web site at this link: http://pah.cert.ucr. edu/aqm/308/docs.shtml . In summary, the visibility modeli ng employs a Lambert conformal projection of 149 by 113 grid cells with a 36-km horizontal reso lution and 19 layers extending ve rtically to the lower stratosphere. The visibility modeling domain is nested within a larger MM5 domain with greater vertical resolution. Nesting procedures and paramete rs used in the grid definition are described in detail in Tonnesen et al. (2006).
Use of the high-resolution 12-km nested domain was discontinued after 2005 because it did not provide a benefit in model performance for regi onal visibility modeling. However, as noted in Tonnesen et al. (2006), we would expect a benefit if 12-km or finer-resolution domains were used for urban-scale PM 2.5 and ozone modeling.

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## 2. 2002 Planning Case Modeling

For the 2002 Base Case (Base02) visibility modeling introduced in Section 1.1.4, the RMC used a historical year to demonstrate the suitability of the modeling system for projecting future visibility and for evaluating the effects of changes in emissions on that visibi lity. That exercise and the related model performance evaluation were described in Tonnesen et al. (2006). However, the 2002 actual emissions we used in Base02 are not well suited for projecting future visibility changes because the specific emissions condition s of 2002 might not be a ppropriate to represent typical conditions. Therefore, we developed a typical-emissions scenario for planning purposes using emissions that are believed to be more t ypical of the baseline emissions during the fiveyear period from 2000 through 2004. Results for th is 2002 Planning Case (Plan02) scenario can then be compared with projected emissions and modeled visibility for the future-year 2018 Base Case (Base18) scenario (discussed in Section 3), to evaluate progress towa rd attaining visibility goals. This section discusses the data sources, emissions processing, visibility modeling, and quality assurance for the 2002 Planning Case, the typical-emissions scenario. The visibility simulations for both Plan02 and Base18 we re conducted using the CMAQ model.
We developed the Plan02 emissions data from the Base02 emissions modeling. In this document we discuss only the subset of source categor ies that underwent change s when creating Plan02 from Base02. For information about the catego ries that remained unchanged and background information related to the emissions modeling, in cluding the historical work that fed into the Plan02 emissions, general emissions modeling a pproaches, terminology, and any other technical details, please refer to Tonnesen et al. (2006).



### 2.1 Emissions Input and Quality Assurance

In this section we explain how we created the Plan02 emissions files for use in visibility modeling runs we later performed with CMAQ (discussed in Section 2.2) . The two major distinctions between the Plan02 emissions and the Base02 emissi ons are the inclusion in Plan02 of baseline (typical-year) fire emissions rather than actual-year fire emissions , and the application in Plan02 of averaged temporal profiles fo r stationary point sources.
The RMC completed emissions modeling for three iter ations (referred to here as “cases”) of the Plan02 emissions scenario, all discussed in detail below, before we settled on a final version to be used as input to CMAQ. First, we deve loped version A of the Plan02 emissions (“case Plan02a”) directly from version A of the Base 02 emissions (“case Base02 a”), which is described in Tonnesen et al. (2006). To create case Pla n02a we intended to make only two types of changes: (1) replace the actual 2002 fire emissions inventories with the baseline fire emissions inventories and (2) replace the te mporal profiles for large stati onary point sources with profiles developed from an average of several year s surrounding 2002. As we performed QA on case Base02a, however, we discovered several errors a nd issues that needed to be corrected. As a result, our case Plan02a contained not just the originally planned inventory changes but also corrections to the nonroad mobile, on-road mobile , offshore Pacific shipping lane, and WRAP oil

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and gas inventories. Then, after observing unexpected ly large differences in the stationary point emissions in non-WRAP U.S. sources between ca ses Base02a and Plan02a , we discovered that we had erroneously implemented in case Plan02a the temporal profiles that were actually intended for use with the 2018 Base Case inventorie s. After correcting this error, we combined the resulting stationary point emissions with the rest of the sectors to create version B of the Plan02 emissions (case Plan02b). Finally, the correction of an error in the a pplication of temporal and chemical profiles to the non-Federal rangeland prescribed fire emissions resulted in the creation of version C of the Plan02 emissions (case Plan02c).
This section documents all work performed to co llect, prepare, and quali ty-assure the emissions for the Plan02 annual scenario. We provide details on the sources of the i nventories and ancillary emissions data, where “ancillary data” refers to all of the SMOKE input data other than the emissions inventories. The technical content of this section begins w ith a discussion on the evolution of the Plan02 inventor ies and their origin in the Ba se02 inventories (Section 2.1.1). We then describe the details of producing CMAQ -ready emissions in Sections 2.1.2 through 2.1.5: data collection and preparation, emissions proce ssing, quality assurance, and uncertainties and future work. Finally, in Secti on 2.1.6 we summarize the work we performed to create cases Plan02a, Plan02b, and Plan02c.



### 2.1.1 Description of Plan02 emissions

The overall goal of the Plan02 emissions scenario is to represent baseline emission patterns for comparison with inventories projected to 2018. To prepare these baseline emissions, we created average- or “typical”-year emissi ons for sectors that are strong ly influenced by meteorology and thus have the most pronounced episodic trends in their emissions magnitudes and temporal variability. Wildfires are very episodi c in nature, with their locatio n, timing, and intensity correlated to optimal fire conditions. Agricultural and pres cribed fires are managed and subsequently occur during periods of less optimal wild fire conditions. Electricity use a nd the emissions from electric generating units (EGUs) are correlated with season al and daily ambient temperature patterns. As the meteorological factors influencing the emissions from these sectors are highly variable from year to year, the WRAP developed the Plan02 baseli ne emissions scenario to be representative of typical local meteorology using averaged conditions from the years surrounding 2002.

As noted above, the Plan02 emissions were initia lly intended to differ from the Base02 emissions only in the use of averaged activity data for the fi re and stationary-point-s ource sectors; the rest of the emissions sectors were to be constant between scenarios Base02 and Plan02. Using fire activity data collected between the years 2000 and 2004, Air Sciences, Inc., developed the Baseline Phase III WRAP fire inventory for use in the 2002 Planning Case and projected 2018 Base Case scenarios (WRAP-FEJF , 2006); this inventory included w ildfires, agricultural fires, prescribed fires, non-Federal prescribed rangeland fires, and wildland fire use. Regarding EGU emissions, Eastern Research Group, Inc., (ERG) and Alpine Geophysics, LLC, created historically averaged monthly temporal profiles from continuous emissions monitoring (CEM) data collected between 2000 and 2003 (Fields et al., 2005) for EGUs in the WRAP and VISTAS regions. We applied these profile s to actual 2002 inventories to create EGU emissions for the Plan02 cases.

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2.1.1.1 2002 Planning Case emi ssions, version A (case Plan02a)

We created case Plan02a from a combination of th e WRAP baseline fire inventories, actual 2002 EGU inventories with historically averaged temporal profiles, typi cal-year inventories from other RPOs, and Base02 emissions for the remaini ng emission sectors. After conducting the case Base02a emissions simulation (T onnesen et al., 2006), we gath ered several updates to the 2002 base case inventories that aff ected most regions of the modeling domain. Although they were considered components of the Base02 inventories, we applied these updated inventories initially in case Plan02a. We describe here only the upda tes to the 2002 base case inventories; for the inventories that remained constant , refer to Tonnesen et al. (2006).
The RMC implemented the following changes to th e Base02 inventories before using these data in case Plan02a:

• Stationary Area Sources: Revised inventories for Califor nia, Washington, North Dakota, Oregon, Utah, Mexico, and nonfire sources in th e VISTAS states. Additional dust sources extracted from the area-source inventory to be modeled explicitly as fugitive dust.

• Fugitive Dust Sources: Revised inventories for Califor nia, Washington, North Dakota, Oregon, Utah, Mexico, and nonfire sources in the VISTAS states. Expansion of the fugitive dust list of sources to include a broader definition of dust.

• Road Dust Sources: Revised inventory for Californi a and VISTAS states. Note: We extracted the California data from the revised stationary-a rea-source inventory.

• On-road Mobile Sources: Revised inventories for Calif ornia, Montana, Colorado, and Mexico.

• Non-road Mobile Sources: Revised inventories for Califor nia, Mexico, and the VISTAS states. Updated temporal profiles for all non-WRAP states, as developed from the NONROAD2004 model by Alpine Geophysics, LLC.

• Oil and Gas Sources: Revised inventory for stationa ry area California oil and gas sources. Note: We extracted the California da ta from the revised stationary-area-source inventory.

• Stationary Point Sources: Revised inventories for California, Alaska, Arizona, Montana, New Mexico, Oregon, Utah, Washington, Wyom ing, WRAP tribes, Mexico, and MANEVU and VISTAS states. Updated temporal pr ofiles for all non-WRAP EGU sources, as developed from the Integrated Planning Mode l (IPM) (ICF International, Inc., 2006) for 2018 emissions.

• Pacific Commercial Marine Sources: Revised inventories for Pacific shipping lanes and California on- and near-shore sources.

• Broomfield County, CO: Revised the 36-km spatial surr ogates to include Broomfield County, CO, and applied these to the stationary area, road dust, fugitive, oil and gas, and on-road mobile inventories. As these invent ories all explicitly contain emissions for Broomfield County, we revised our spatial al location approach to include surrogates for this county, as opposed to including these emissions in the surrounding counties. As the

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nonroad mobile inventories still contain Br oomfield County emissions folded into the surrounding counties, we did not change th e spatial surrogates for allocating these emissions to the model grid.
We combined these updates with the changes to the fire inventories and EGU temporal profiles to create case Plan02a. QA plots and documenta tion for Plan02a are posted on the RMC web site
at http://pah.cert.ucr.edu/aqm/308/Plan02a_36.shtml

. 2.1.1.2 2002 Planning Case emi ssions, version B (case Plan02b)

The RMC created case Plan02b to correct errors th at we discovered in th e Plan02a emissions and to include inventory updates th at we received after we had already begun the case Plan02a emissions simulation. To create case Pla n02b from Plan02a, we updated the non-WRAP stationary-point-source emissions, domainwide fugitive dust and road dust emissions, and Gulf of Mexico offshore area-source inventories. We also corrected errors in th e configuration of the Pacific shipping emissions and omissions of sources in the fugitive dust inventories used in case Plan02a.

• Updates to non-WRAP stati onary-point-source emissions: As part of the QA process for new emissions simulations, we qualitatively and quantitatively compare sequential cases to confirm that the results show the expected changes based on the incremental updates that are made between cases. Our comparis on of the Plan02a emissions results with Base02a results were consistent with the revisions outlined in Section 2.1.1.1, except for the non-WRAP stationary point sources. We obs erved differences in these emissions that were much larger than we expected, consid ering that we had only updated the temporal profiles for these sources. We discovered that the IPM-derived tempor al profiles that we used in case Plan02a for the non-WRAP stati onary point sources were intended for use only with IPM-projected 2018 inventories, no t with the 2002 inventories. The use of these profiles caused the 2002 emissions for non-WRAP EGUs to increase dramatically in case Plan02a. We replaced the IPM-deri ved temporal profiles with baseline CEM temporal profiles calculated as 2000-2003 activity averages for the VISTAS states and with actual 2002 CEM-derived temporal pr ofiles for the CENRAP, MANE-VU, and MRPO states.

• Updates to domainwide fug itive dust and road dust emissions: Recent information about dust emissions led to a revision in the approach that we use to mode l fugitive and road dust emissions. Implementing these updates in case Plan02b impacted the fugitive dust, road dust, and stationary-area-source i nventories. In May 2006 the EPA Emissions Factors and Inventory Group (EFIG) revised th e transport factors th at they recommend for scaling dust emissions downward to accoun t for scavenging by vegetation (U.S. EPA, 2006c). In implementing these updated factors, we expanded the list of source classification codes (SCCs) that we use to define th e dust sources that we extract from the stationary area inventory. This change impacts both the fugitive dust inventory, by increasing the number of sources contained in this sector, and the stationary-area-source inventory, by removing fugitive dust sources from this sector that previously were modeled as area sources.

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Another update to the dust emissions evolved from recommendations made by MRI (2005) regarding a revision to the fine fracti on of PM from dust sources. Based on these recommendations, we changed the PM 2.5 /PM 10 ratio in all of the road dust and fugitive dust inventories before modeling them thr ough SMOKE to create the Plan02b emissions.

• Updates to Gulf of Mexico o ffshore area-source inventories: We also received an updated Gulf of Mexico offshore area-source invent ory from the U.S. Minerals Management Service during the Plan02a modeling. For case Plan02b, we replaced the previous inventory for this region of the modeling domain with these updated emissions data.

• Errors in the configuration of the Pacific sh ipping emissions and omissions of sources in the fugitive dust inventories: We corrected errors in the Plan02a modeling that we discovered during the QA of these emissions. QA of the Pacific shipping emissions revealed that we made an error in converting the pre- speciated VOC emissions from tons to moles. We recalculated these emissions with the co rrect conversion factors for the affected pollutants. During the preparation of the fugitive dust inventories, we inadvertently omitted construction and mining dust sources from Arizona, Colorado, Montana, Nevada, New Mexico, South Dakota, and Wyoming. We added these sources back into the Plan02b dust inventories.
We combined the dust emissions updates, the re vised stationary-area-source inventory, the corrected stationary-point-source em issions, and updates and corrections to the rest of the sources with the unaffected sources from case Plan02a to produce case Plan02b. QA plots and documentation for Plan02b are posted on the WRAP-RMC web site at http://pah.cert.ucr.edu/aqm/308/qa_plan02b36.shtml

. 2.1.1.3 2002 Planning Case emi ssions, version C (case Plan02c)

The RMC created case Plan02c to correct one er ror that we discovered in the Plan02b emissions. To create case Plan02c from Plan02b, we upda ted the WRAP non-Federal rangeland prescribed fire emissions. When we added this inventory to the WRAP m odeling, we did not update the speciation and temporal cross-re ference file to reflect the ad dition. The result was that we incorrectly applied the default diur nal temporal profile and VOC and PM 2.5 speciation profiles to these emissions. In case Plan02c we applied the co rrect temporal and speci ation profiles to these sources. Although these fires account for only 0.5% of the total annual WRAP PM 2.5 emissions, we made this change because their episodic and localized nature concentrate the emissions in a few grid cells in short time periods, leading to a magnification of the impacts from these sources.



### 2.1.2 Data collection and preparation

This section describes the procedures that the RMC followed to collect and prepare all emissions data for the Plan02 simulations. We discuss the s ources of all inventory a nd ancillary data that changed between the Base02a emissions simu lation and the Plan02a, Plan02b, and Plan02c emissions simulations. We worked with emissi ons inventory contract ors, WRAP and other RPOs, and EPA to collect all of the data that we used in updating the Base02a emissions to create the Plan02 emissions.

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For the WRAP state and tribal inventories, Air Sc iences, Inc., provided all fire inventories; ERG, Inc., provided all stationary area and stationary point inventories and temporal profiles; and ENVIRON provided all mobile, oil a nd gas, and Pacific shipping inve ntories that we used in the Plan02 scenario emissions simulations. Alpine Geophysics, LLC, provided all inventories and temporal profiles for the non-WRAP U.S. data. ERG, Inc., provided all updated Mexican inventory data. We received all emissions data electronically; the inventories as SMOKE-ready IDA-format data, and the temporal pr ofiles as SMOKE-ready ASCII files.
The RMC received corrected U.S. data for only the updated portions of the Base02 inventories. We removed the previous inventory data for the affected states from the files that we used in the Base02a modeling and combined the remaining data with the updated information to build revised Base02 inventories. For example, we received updated California inventories for all source categories. We extracted and discarded th e California inventories from the Base02a data and replaced them with the updated inventories. We used the resulting dataset for the Plan02 scenario. This substitutio n of only the revised portions of th e inventories was a general approach that we applied to several emissions sectors. We also developed more specific approaches for preparing the fire, EGU, Pacific ship ping, dust, and Mexico inventories.

• Fires: Air Sciences, Inc., provided annual Baselin e Phase III fire inventories for each of the five fire categories (wildfires, agricu ltural fires, prescribed fires, non-Federal rangeland prescribed fires, and wildland fire use) as three-file sets for each category. Consistent with the fire inventories for Phases I and II, each fire cat egory consisted of an annual IDA file with physical fire event info rmation, a daily IDA file with daily emissions by criteria pollutant, a nd an hourly IDA file with hourly precomputed plume rise values. Upon receiving these data, we split the annual inventories into monthly files to avoid computer memory problems related to processing very larg e inventories with SMOKE. For additional information on the developm ent of these fire inventories, refer to WRAP-FEJF (2006).

• EGUs: ERG, Inc., provided SMOKE-ready temporal profiles and cross-reference files for representing baseline EGU activities in the WRAP states. We worked closely with ERG to refine the cross-references that associate the profiles with actual inventory sources. For additional information on the de velopment and application of these profiles, refer to Fields et al. (2005). Alpine Geophysics, LLC, provided SMOKE -ready temporal profiles and cross-reference files for representing baseline EGU activities for non-WRAP EGUs.

• Pacific shipping: To prepare the new Pacific sh ipping inventories for the Plan02 scenario, we converted them directly to th e CMAQ-ready format outside of SMOKE. We received the inventories as annual estimates of criteria pollutants in gridded ASCII files on the WRAP 36-km model grid. We developed a Fortran program to convert these files to the SMOKE netCDF format and then a pplied speciation and temporal adjustments using SMOKE chemical and temporal profil es, respectively, to create gridded, hourly emissions with volatile organic compound (VOC) and PM 2.5 speciation consistent with the rest of the Plan02 emissions. Because SMOKE is not set up to process pregridded data, we used Fortran programs developed in- house at the RMC to prepare these data for merging with the rest of the source categories.

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• Dust: We built the fugitive dust inventory for the entire modeling domain by extracting these sources, based on SCCs, from the statio nary-area inventories. A list of the sources that we defined as fugitive dust is provide d in U.S. EPA (2004). We received explicit road dust inventories for most of the WRAP states; for California, the non-WRAP states, and the non-U.S. sources, we built the road dust inventory by extracting the paved and unpaved road dust SCCs from the stationary -area inventory. After building the fugitive and road dust inventories, we prepared them for the Plan02 emissions modeling by applying transport factors to these da ta and re-adjusting the inventory PM 2.5 /PM 10 ratios. To apply the transport factors recommended by EPA (2006c), we built a SMOKE control file (GCNTL) that contains scalars by SCC a nd location codes. We created new, adjusted dust inventories with the factors in the GC NTL file using the SMOKE programs Cntlmat and Grwinven. The RMC developed a Fortran pr ogram to reapportion the PM inventories using the revised PM 2.5 /PM 10 ratios.

• Mexico: Entirely new inventories for the six northe rn states of Mexico for stationary area, stationary point, on-road mobile, and nonroa d mobile sources became available at the conclusion of the Plan02a modeling. ERG, Inc., completed an updated 1999 emissions inventory for northern Mexico (ERG, Inc., 2006b) and delivered these data to us in early 2006. We integrated them into Plan02 modeling in time to include them in case Plan02b.



### 2.1.3 Emissions processing

We based the Plan02 emissions processing on the a pproaches developed in previous years during the RMC preliminary 2002 modeling (Tonnesen et al., 2005) and refined during case Base02a (Tonnesen et al., 2006). We mode led the Plan02 inventories with SMOKE version 2.1 using the same 22 source categories developed for Base 02a. We developed the Plan02 SMOKE scripts directly from the Base02a scripts to maintain consistent configurations between these scenarios. As nothing significant changed in the SMOKE co nfigurations between the Base02a and Plan02 emissions simulations, readers can refer to the WRAP 2002 MPE repor t (Tonnesen et al., 2006) for details on how we conducted emissi ons processing for the Plan02 emissions.



### 2.1.4 Quality assurance

To QA the Plan02 emissions, we used the WRAP emissions modeling QA protocol (Adelman, 2004) and a suite of graphical summaries. We us ed tabulated summaries of the input data and SMOKE script settings to document the data a nd configuration of SMOK E for all three Plan02 cases. The graphical QA summaries include, for a ll emissions output species that changed from Base02a to Plan02, daily spatial plots summed across all model layers, daily time-series plots, annual time-series plots, and daily vertical profiles. The Plan02a, Plan02b, and Plan02c QA graphics are available on the RMC web site at http://pah.cert.ucr.edu/aqm/308/Plan02a_36.shtml
, http://pah.cert.ucr.edu/aqm/308/qa_plan02b36.shtml , and http://pah.cert.ucr.edu/aqm/308/qa_plan02c36.shtml , respectively.

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### 2.1.5 Uncertainties and future work

Analysis and QA of the Plan02 emi ssions revealed various issues with these data. As described in Section 2.1.1.2, we corrected one set of issues in the case Plan02a emissions simulation, which resulted in case Plan02b. After completing Pla n02b, we discovered that we did not apply the correct PM 2.5 and VOC speciation profiles or the correct diurnal tem poral profile to the WRAP non-Federal rangeland prescribed fi re emissions. We mistakenly a pplied overall default profiles to these sources in the WRAP region in the Pl an02a and Plan02b cases. As described in Section 2.1.1.3, we corrected both of this er ror to create case Plan02c.
As we analyze the CMAQ air quality modeling re sults that area based on these emissions and perform more detailed QA on the emissions themselves, we will undoubtedly discover other problems with both the data and the modeling of th ese data. Future work will focus on continued refinement of the modeling and on documenti ng known issues and problems with the Plan02 emissions modeling results.



### 2.1.6 Summary

Because the Plan02 cases evolved from the Base 02a emissions modeling, we were able to develop the Plan02 emissions using the establis hed RMC emissions modeli ng infrastructure. The incremental updates we made to case Base02a to develop the Plan02 results affected only a fraction of the 22 source categories and made perf orming QA on the Plan02 re sults quite straightforward. As we made each change in creating th e Plan02 emissions, we compared the resulting emissions back to the Base02a results, looking fo r expected trends in di fference plots and annual summaries. The three iterations of the Plan02 emissions prod uced a dataset in Plan02c that has been subjected to a significant amount of QA/QC at the levels of both the inventory data and the SMOKE output files. While there are still undis covered problems with these data, we have confidence that the Plan02c emissions are a we ll-characterized dataset and provide a solid foundation with which to compare the projec ted 2018 emissions described in Section 3.



### 2.2 Visibility Modeling and Comparison to 2002 Base Case

For each of the three Plan02 emissions datasets, we also performed annual visibility modeling using the CMAQ model. This was a key aspect ofthe QA procedure, since errors in the emissions inventories that might not be apparent during the emissions QA steps might be more readily detected in the results from the CMAQ modeling.
The setup of the CMAQ model (including science options, run scripts, simulation periods, and ancillary data) for the Plan02 cases was identical to that used in the Base02 modeling; this is described on pages 48-51 of the 2002 MPE report (Tonnesen et al., 2006). In summary, we used CMAQ v4.5 (released by EPA in October 2005) on the RPO Unified 36-km domain. We used the Carbon Bond Mechanism version 4 (CB4) w ith RADM aqueous chemistry, the SORGAM organic aerosol algorithm, and all other science algorithms identical to those in Table 4-1 of that report. Initial condition (IC) data for January 1, 2002, were developed using a 15-day spin-up period (December 16-31, 2001). Boundary condition ( BC) data were generated in an annual simulation of the global-scale GEOS-Chem mo del that was completed by Jacob et al.

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( http://www-as.harvard.e du/chemistry/trop/geos/ ) for calendar year 2002. The Plan02 CMAQ emissions simulations differed from the Base02 em issions simulations only regarding the various changes in the emissions dataset discussed in Section 2.1.
In our initial analysis of the Plan02 scenario, we prepared plots for QA purposes that compared the Plan02a CMAQ results with the Base02a CM AQ results for daily and monthly averages. After revising Plan02a to create Plan02b and Plan02c, we prepared additional QA plots comparing the CMAQ results of each revised Pl an02 case to the previous iteration. These were prepared as Program for the Analysis and Visu alization of Environmen tal data (PAVE) spatial plots showing the change in individual PM 2.5 species concentrations as daily, monthly, and annual averages. Several iterations of interim QA plots were posted on the RMC web site as these datasets were being deve loped; these plots are being re moved from the site as the QA process is completed. The final se t of analysis products that will remain on the web site are PAVE difference plots comparing the CMAQ-predic ted annual average species concentrations from the Plan02c case with those from the Base02b case. Note that these plots are not useful for visibility planning purposes, but are being provided to show the magnitudes of changes when moving from the 2002 Base Case to the 2002 Pla nning Case—in other words, from the actual emissions for the year 2002 to the “typical-year” emissions created for the final Plan02 scenario. The primary analysis “product” from the Pla n02 CMAQ modeling is the us e of its output in combination with the CMAQ output from the 2018 modeling (Section 3) to develop the visibility progress calculations and glide path plots that are described later in Section 3.3.
As example PAVE difference plots, Figures 2-1 and 2-2 show the annual average differences in organic carbon (OC) concentration and PM 2.5 concentration, respectiv ely, for the Plan02c CMAQ simulation minus the Base02b simulation. Warm colors (green to red) show regions of increased concentration in Plan02c versus Base 02b, while cool colors (shades of blue) show regions where concentrations were reduced in the Plan02 case. Because the actual 2002 data (Base02) had higher-than-average wildfire activity, the single largest difference between scenarios Plan02 and Base02 is the development of the typical baseline wi ldfire activity and the consequent changes in wildfire emissions for the Plan02 scenario. This resulted in large changes in model-predicted ambient concentrations of OC and PM 2.5 in Plan02 compared with Base02. The modeled concentrations of other species, su ch as sulfate and coarse mass, were similar between the Base02 and Plan02 scenarios.

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Figure 2-1. Example plot showing chan ge in annual average CMAQ-modeled organic carbon (OC) for Plan02 minus Base02.

Figure 2-2. Example plot showing chan ge in annual average CMAQ-modeled PM 2.5 for Plan02 minus Base02.

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## 3. 2018 Base Case Modeling

The purpose of performing visibility modeling fo r the year 2018, an RHR milestone year (see Section 1.1.1), is to compare the model-predicted visibility in 2018 to the baseline visibility represented by the typical-year modeling for 2002 (i.e ., to the visibility predicted using the Plan02 scenario described in Section 2).
The 2018 Base Case (Base18) scenario was devel oped using projected emi ssions inventories for 2018 that incorporate expected future emissions reductions resulting from technology improvements and regulatory programs, as well as emissions increases from projected economic and population growth (Section 3.1). The Base18 visibili ty modeling results were compared to the Plan02 results to evaluate progres s in attaining visibility improve ment goals, as discussed in Sections 3.2 and 3.3.



### 3.1 Emissions Input and Quality Assurance

The Base18 scenario, which represents emissions forecasted to 2018, was developed to represent baseline future-year emissions patterns for use in developing emissions control strategies and then evaluating their effects on future-year air qualit y. The first step in this process is to comparethe Base18 future emissions with the Plan02 typical-year emissions. To facilitate direct comparison, we normalized the SMOKE configuration between the Base18 and Plan02 scenarios as much as possible, varying only th e inventories and ancill ary input data. In c ontrast to the Plan02 cases, the Base18 cases used inventories and SMOKE ancillary data that we had never used before. WRAP inventory contractor s delivered 2018 inventories and temporal profiles to us in SMOKE-ready formats.
The RMC completed two iterations (cases) of the Base18 scenario before we settled on a final version for input to CMAQ for visibility mode ling. Because we developed version A of the Base18 emissions (case Base18a) using datasets that had never been modeled through SMOKE, we anticipated encountering multiple errors in th e data and incompatibilities between the data and SMOKE. As expected, during the QA of th e Base18a emissions we were notified about errors in the 2018 inventories and also discovered other problems in the modeling of these data. After receiving corrected inve ntories and fixing the errors in the modeling, we generated version B of the Base 2018 emissions (case Base18b).
This section documents all work performed to co llect, prepare, and quality assure the emissions for the Base18 annual scenario. We provide de tails on the sources of the inventories and ancillary emissions data. The technical content of this section begins with a discussion on the evolution and purpose of the Base 18 inventories (Section 3.1.1). We then describe the details of producing CMAQ-ready emissions in Secti ons 3.1.2 through 3.1.5: da ta collection and preparation, emissions processing, quality assurance, and uncertainties and future work. Finally, in Section 3.1.6 we summarize the work we pe rformed to create emissions cases Base18a and Base18b.

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### 3.1.1 Description of Base18 emissions

We derived the Base18 emissions from inve ntories projected from 2002 to 2018 by WRAP inventory contractors, the RMC, and WRAP and other RPOs. For most inventory sectors, we built the Base18 input dataset by combining (1) 2018 inventories from inventory contractors both within and outside of the WRAP region and (2 ) 2018 inventories developed at the WRAP RMC by applying projection factors to th e Plan02 inventories. Exceptions to this procedure were the fire inventories, non-U.S. sources, WRAP ammo nia, Pacific shipping, and most of the MRPO sources; these were all kept the same as in th e Plan02 scenario. The majority of the emissions sectors used temporal, spatial, a nd chemical profiles that are consis tent with the Plan02 scenario. Details on the differences between the two Base18 cases follow.

3.1.1.1 2018 Base emissions , version A (case Base18a)

The RMC created case Base18a from a combination of U.S. baseline fire inventories; 2018 stationary area, point, and mob ile inventories; and 2002 inventor ies for MRPO sources, non-U.S. sources, NH 3 emissions in the WRAP region, and P acific shipping emissions. For the WRAP, VISTAS, and MANE-VU states, we received 2018 inventories for st ationary area, point, on-road mobile, and nonroad mobile sources. For the CENRAP states, we received projection factors to “grow” the Plan02 inventories to 2018. While we kept a few emissions categories constant with respect to the Plan02 emissions —primarily due to the lack of either 2018 inventories or projection information for these sources—most of the inventories we used in case Base18a emissions simulation were new and developed by the RPOs specifically for 2018 modeling.
Breaking it down by source categories, we used th e following data to develop the case Base18a simulation:

• Stationary Area Sources: 2018 inventories for WRAP, MANE-VU, and VISTAS; 2018 projection factors (growth and control) applied to the Pla n02 inventories for MRPO and CENRAP; Mexico and Canada he ld constant with Plan02a.

• Fugitive and Road Dust Sources: 2018 inventories for WRAP, MANE-VU, and VISTAS; 2018 growth factors applied to the Plan02 inventories for CENRAP; MRPO, Mexico, and Canada held constant with Plan02a.

• Windblown Dust Sources: The entire domain held constant with Plan02a.

• Agricultural and Animal Ammonia Sources: WRAP, MANE-VU, VISTAS, Mexico, and Canada held constant with Plan02a; 2018 growth factors applied to the Plan02a inventories for CENRAP and MRPO.

• Stationary Point Sources: 2018 inventories for WRAP, MANE-VU, and VISTAS; 2018 projection factors (growth and control) applied to the Pl an02 inventories for CENRAP; MRPO, Mexico, and Canada held constant wi th Plan02a; updated temporal profiles for all non-WRAP EGU sources, as developed from the IPM for 2018 emissions.

• Fire Sources: Baseline inventories for WRAP, CE NRAP, and VISTAS; these emissions were held constant with Plan02a.

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• On-road Mobile Sources: 2018 inventories for WRAP, CENRAP, VISTAS, and MANEVU; MRPO, Mexico, and Canada held constant with Plan02a. As with the Base02 and Plan02 inventories, WRAP, Mexico, and Canada use precomputed emissions inventories; the rest of the U.S. sources use on- road mobile activity inventories.

• Non-road Mobile Sources: 2018 inventories for WRAP , CENRAP, VISTAS, and MANE-VU; MRPO, Mexico, and Canada held constant with Plan02a.

• Oil and Gas Sources: 2018 inventories for WRAP states only.

• Pacific Marine Shipping Sources: Held constant with Plan02a.

• Gulf of Mexico Offshore Area and Point Sources: 2018 growth factors applied to the Plan02a inventories.
We combined the inventory updates with the Pl an02a emissions for the sources that did not change to create case Base18a. QA plots and do cumentation for Base18a are posted on the RMC web site at http://pah.cert.ucr.e du/aqm/308/Base18a_36.shtml

.
3.1.1.2 2018 Base emissions , version B (case Base18b)

The RMC created case Base18b to correct errors that we discovered in the Base18a emissions and to include inventory updates that we r eceived after beginning the Base18a emissions simulation. These updates affected emissions both within and outside of the WRAP region. The WRAP updates included corrections to the sta tionary point inventory, revisions to the PM 2.5 emissions from fugitive and road dust sources, a correction to near-shore shipping sources in the Pacific Northwest, and updates to the fire emissions. The non-WRAP updates included the creation of new 2018 inventories for CENRAP and MR PO states with revised projection factors; fixing an error in the on-road mobile emissions for non-WRAP U.S. sources; replacing the temporal profiles for EGU sources in the CENRAP, MANE-VU, and MRPO states; and revisions to the PM 2.5 emissions from fugitive and road dust sources throughout the entire 36-km modeling domain. These revisions are all discussed below.
For the WRAP states and tribal lands, we applie d four major corrections to the inventories and emissions inputs for modeling sour ces to create case Base18b:

• ERG, Inc., the WRAP inventory contractor that prepared the WRAP 2018 stationary point inventory, notified us about corrections to apply to the Ba se18a inventory. They provided incremental updates to apply to a group of sources in the WRAP 2018 inventory. Appendix A contains a memo describing these change s as well as a tabulated summary of these updates.

• We applied the expanded list of dust sources described in Section 2.1.1.2 to develop the Base18b dust inventories. This change impact ed both the dust and st ationary-area-source inventories.

• We added on-shore and near-shore marine shipping sources for Washington and Oregon in case Base18b to correct the omi ssion of these sources in Base18a.

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• We updated the WRAP inventories for prescrib ed and agricultural fires, and corrected errors in the application of temporal and speciation profiles for non-Federal rangeland prescribed fires.

• Air Sciences, Inc., provided revisions to the Phase III prescribed and agricultural fire inventories to estimate the emissions re ductions from applying fire emissions reduction techniques (ERTs) to controllable fire emissions (Randall, 2006). They based the revised emissions on the same da ta that the RMC used in case Plan02b to illustrate the changes that resulted from cont rolling prescribed and agricultural fires between the Plan02b and Base18b cases.

• We also corrected the temporal and speci ation profiles that we applied to the nonFederal rangeland prescribed fires. By not adding the SCC for this source to the input cross-reference files in the Plan02 scenario and Base18a ca se, we mistakenly applied default temporal and speciation profiles to these emissions. We added the appropriate cross-reference for this source to the SMOKE input files in case Base18b.
For the non-WRAP areas of the modeling domain, we applied four major corrections to the inventories and emissions input s for modeling sources to prep are the case Base18b emissions simulation:

• We received updated projection factors for the CENRAP and MRPO inventories to correct inconsistencies in their file formats with the IDA-formatted inventories that we used for input to SMOKE. We received the co rrected projection data from CENRAP to apply to the CENRAP and MRPO stationary area, fugitive and road dust, agricultural and animal ammonia, and stati onary point inventories.

• We corrected an error that we discovered in our modeling of the 2018 on-road mobile activity inventories. By not resetting the SMOKE emissions factor year from 2002 to 2018, we did not apply the correct emissions factors to the non-WRAP U.S. on-road mobile emissions. We corrected this problem by setting up SMOKE to use 2018 emissions factors.

• Similar to the problem we had in the Pl an02 modeling, we discovered that the IPMderived temporal profiles th at we used in case Base18a for the non-WRAP stationary point sources produced emissions estimates th at appeared unreasona bly high, relative to the Plan02b emissions. We replaced the IPM-de rived temporal profiles with baseline CEM temporal profiles calculated as 2000-2003 activity averages for the VISTAS states, and with actual 2002 CEM-derived temporal profiles for the CENRAP, MANE-VU, and MRPO states.

• We applied the expanded list of dust sources described in Section 2.1.1.2 to develop the Base18b dust inventories. As with the WRAP states, this change impacted both the dust and stationary-area-source inventories.
We combined all of these emissions updates with the unaffected sources from Base18a to produce case Base18b. QA plots and documentati on for Base18b are posted on the RMC web site at http://pah.cert.ucr.edu/aqm/308/qa_base18b36.shtml

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### 3.1.2 Data collection and preparation

This section describes the procedur es that we followed to collect and prepare all emissions data for the Base18 emissions simulations. We discuss th e sources of all invent ory and ancillary data that changed between the Plan02 scenario a nd the Base18 scenario. The RMC worked with emissions inventory contractors, WRAP and other RPOs, and EPA to collect all of the data that we used to create the Base18 emissions inputs for SMOKE.

For the WRAP state and tribal inventories, Air Sc iences, Inc., provided all fire inventories; ERG, Inc., provided all stationary area and stationary point inventories and temporal profiles; and ENVIRON provided all mobile, oil a nd gas, and Pacific shipping inve ntories that we used in the Base18 scenario. Alpine Geophysic s, LLC, provided all inventorie s and ancillary data for the VISTAS states and tribes. CE NRAP provided the 2018 projection factors for the CENRAP and MRPO states and tribes. The Mid-Atlantic Re gional Air Management Association (MARAMA) provided 2018 inventories for the MANE-VU states and tribes. We used the Mexico and Canada emissions from the Plan02 scenario. We recei ved all emissions data electronically; the inventories in SMOKE-rea dy IDA format and the ancillary da ta as SMOKE-ready ASCII files.
To prepare the 2018 emissions data for SMOKE, we reorganized these invent ories to conform to the list of sources that we modeled in the Plan02 scenario. Most of this reorganization affected the stationary-area-source inventory. We pr epared the 2018 inventories for modeling through SMOKE in the following ways:

• Apply projection factors to applicab le CENRAP and MRPO inventories.

• Extract fugitive and road dust sources from the stationary-area inve ntories to separate inventory files. Apply tran sport factors and revised PM 2.5 /PM 10 splits to these sources.

• Extract WRAP oil and gas sources from the stationary-area invent ories to separate inventory files.

• Extract refueling emissions from the non- WRAP U.S. nonroad mobile inventory and discard; these sources are covered in the on-road mobile emissions.

• Split the fire inventories from annual to monthly files.

• For case Base18b, update the stationary-point inventories with the information in Appendix A.



### 3.1.3 Emissions processing

We based the Base18 emissions processing on the approaches develope d in previous years during the RMC preliminary 2002 modeling (Tonnese n et al., 2005) and refined during case Base02a (Tonnesen et al., 2006). We modeled the Base18 inventories with SMOKE version 2.1 using the same 22 source categories developed for Base02a. We developed the Base18 SMOKE scripts directly from the Plan02b scripts to main tain consistent configurations between these emissions simulations. As nothi ng significant changed in the SM OKE configurations between Base02a and the Base18 simulations, reader s can refer to the WRAP 2002 MPE report

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(Tonnesen et al., 2006) for details on how we conducted emissions processing for the Base18 emissions.



### 3.1.4 Quality assurance

To QA the Base18 emissions, we used the WRAP emissions modeling QA protocol (Adelman, 2004) and a suite of graphical summaries. We us ed tabulated summaries of the input data and SMOKE script settings to document the data and configuration of SM OKE for cases Base18a and Base18b. The graphical QA summaries includ e, for all emissions output species that changed from Plan02a to Base18, daily spatial pl ots summed across all model layers, daily timeseries plots, annual time-serie s plots, and daily vertical pr ofiles. The Base18a and Base18b QA graphics are available on the RMC web site at http://pah.cert.ucr.edu/aqm/308/Base18a_36.shtml and http://pah.cert.ucr.edu/aqm/308/qa_base18b36.shtml , respectively.



### 3.1.5 Uncertainties and future work

Analysis and QA of the Base18 em issions revealed a variety of issues with these data. As described earlier, we corrected one set of issu es in case Base18a, resulting in case Base18b. We discovered additional issues with the WRAP Ba se18 emissions inventories, as described in Section 3.1.5.1 through 3.1.5.4, after completing the Ba se18b emissions and air quality modeling simulations. These issues affect both the WR AP and non-WRAP states within the 36-km modeling domain. We plan to correct these i ssues in a series of forthcoming emissions simulations.
3.1.5.1 IPM temporal prof ile application errors

We discovered an error in the implementation of the temporal profiles for EGU sources in the VISTAS, MRPO, and MANE-VU regions of the U.S. modeling domain. This problem arose because the RMC emissions modelers did not ap ply the correct temporal profiles for these sources based on the assumptions that were us ed to generate the inventory estimates. The inventories for these three RPOs were generate d using the IPM to project the data to 2018. The IPM-based inventories for each RPO contain an nualized estimates for a five-month summer season and a seven-month winter season. A set of accompanying temporal profiles are used to covert these inventories to hourly data for input to air quality mode ls. When we initially received these data and applied the accompanying tem poral profiles, we observed both emissions magnitudes and temporal variability that were so different from those of the non-IPM emissions that we assumed there was an error in these data. We discontinued the use of the temporal profiles and opted to use the st andard EGU profiles with the IP M-based inventories for these three RPOs. Alpine Geophysics, LLC (the creator of these data) recently brought to our attention that it is incorrect to use these inventories without using the accompanying temporal profiles because the inventories assume the five-month and seven-month summer and winter seasonal splits, respectively.
To correct this problem we reverted back to modeling these sources with the correct IPM-based temporal profiles. To facilitate this correction, we split the st ationary point inventory between

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those sources that are not based on IPM projec tions (WRAP, CENRAP, WRAP tribes, Canada, and Mexico) and the IPM-based invent ories (VISTAS, MRPO, MANE-VU).
A three-month sensitivity case, Base18c, designed to test the effects of using the IPM profiles on the point-source emissions in the three affected RP O regions revealed significant effects from the IPM profiles on the air quality modeling results. For future 2018 emissions simulations we will have two point-source sectors: PTW (point west, the non-IPM s ources that include the WRAP, CENRAP and non-U.S. sources) an d PTE (point east, for the IP M-based sources east of the CENRAP region).

3.1.5.2 PM 2.5 speciation profile error

VISTAS modelers notified us that they discovered an error in the PM 2.5 speciation profiles for some EGU sources. The VISTAS modelers determined that a recently developed speciation profile used for point- and area-source coal co mbustion has a mass fraction estimate 10 times too high for elemental carbon. Additionally, EPA has informed us that the mass of PM 2.5 from natural-gas-fired boilers has been overestimated by approxima tely an order of magnitude, which has an additional impact on the source contribution.
To correct this problem in future emissions simu lations, we will revert back to the EPA default profiles for these sources. The revised, erroneous profile was added to the speciation database in 2003 by the EPA Emissions Factors and Inventory Group. In the future we will speciate all PM 2.5 emissions from coal combustion sources with profile number 22001.

3.1.5.3 WRAP road dust PM 2.5 omission
Starting in version B of the Base02 and Pla n02 emissions simulations , we recalculated PM 10 and PM 2.5 from dust sources using a revised split fact or for the distribution of the PM emissions between the two size fractions. We discovered an error in our applic ation of the revised splits to the WRAP road dust inventories. The program that we used to re-create these inventory values failed on the road dust files, allocating all of the PM emissions to PM 10 . Because SMOKE calculates PMC as the difference between PM 10 and PM 2.5 , the resulting model-ready emissions contained only PMC for road dust in the WRAP st ates. While no mass was lost from this error, the size distribution of the PM emissions from these sources was wrong.
To correct this error, we modified the program that recalculates the PM splits and then regenerated the 2002 road dust inventory for the WRAP states. We will use the corrected road dust inventory in future 2002 emissions simulations.
3.1.5.4 Off-road mobile tempor al profile application errors

We recently discovered a bug in the SMOKE processo r that results from duplicate entries in the ancillary input files for assigning temporal profiles to inventory sources. The result of this bug was an inconsistent assignment of temporal profiles to sources th at had duplicate entries in the temporal cross-reference input file to SMOKE . We observed on some occasions that SMOKE assigns one of the duplicate cross- reference entries to an emissions source; then, after we modify the inventories by adding new data , unrelated to the original inve ntory data (i.e., in a different region or for different sources), and rerun SMOKE, we find that the other duplicate entry gets

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assigned to the same source. Sinc e this inconsistency in assigning the profiles appears to be random, we are not comfortable with the resulting emissions results.
To correct this problem we scrubbed the SMOKE input cross-reference f iles of all duplicate entries, preserving only those en tries that represent the most r ecent or accurate data. The addition of the duplicate cross-reference entries was the result of concat enating datasets from several RPOs when preparing the inputs for the continen tal-scale modeling. We will use this corrected cross-reference file in all future SMOKE simulations.
We have also reported this potential bug to th e SMOKE developers through the CMAS Center help desk ( http://bugz.unc.edu/show_bug.cgi?id=2183 ).
As we analyze the air quality modeling result s using the Base18 emissions and perform more detailed QA on the emissions themselves, we will undoubtedly discover other problems with both the data and the modeling of these data. Futu re work will focus on continued refinement of the modeling and on documenting known issues and problems with the Base18 emissions modeling results.



### 3.1.6 Summary

With the Base18 emissions simulations evolving from the Base02 and Plan02 modeling, we had an established emissions modeling infrastructu re around which to develop the simulations. The updates that we made to the Pl an02 modeling platform in devel oping the Base18 results affected all 22 source categories and requi red a QA effort on the large scal e of that performed for the Base02 emissions. The two iterations of the Ba se18 emissions produced a dataset in Base18b that has been subjected to a significant amount of QA/QC at the levels of both the inventory data and the SMOKE output files.



### 3.2 2018 Visibility Modeling

The purpose of modeling 2018 visibility is to compare the 2018 visibility predictions to the 2002 typical-year visibility modeling results, as discussed below. We expect some improvements in visibility by 2018 because of reductions in emissi ons due to currently planned regulations and technology improvements. We briefly summarize here the comparison between the 2018 and 2002 results using annual average PAVE spatial plots. The goal of this summary is to convey the scale and spatial extent of changes in key PM 2.5 species from 2002 to 2018. For planning purposes, on the other hand, states and tribes should focus on the vi sibility projections and glide path calculations at individual Class I Areas, discussed in Section 3.3.
Figures 3-1 through 3-4 show the annual averag e concentrations for sulfate, nitrate, PM 2.5 and model-reconstructed visibility (in deciviews), respectively. In each figure, the bottom two plots show the modeled concentration or deciviews for the Plan02b and Base18b cases, while the top plot shows the change in visibility calculated as Base18b minus Plan02b. We show the Plan02b results here instead of Plan02c results because we had previously prepared these plots with version B. As the differences between Plan02b and Plan02c are extremely small, new plots prepared using Plan02c would be essentially identical to the results in Figure 3-1 through 3-4.

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In each of the top plots in the four figures, cool colors indicate areas in which model-predicted visibility improved from 2002 to 2018, while wa rm colors indicate areas where modeled visibility became worse over that period. Figure 3-1 shows that reductions in sulfate were largest in the southwest corner of the WRAP region an d in Texas and Oklahoma. This results from planned SO x emissions reductions in the CENRAP re gion. There were smaller reductions in sulfate in the Los Angeles area, western Washi ngton state, and southern Nevada. There were small increases of sulfate, mostly in Wyoming, due to growth in SO x emissions. Most regions of the WRAP domain had low concentrations of su lfate in 2002 and little ch ange in sulfate by 2018.
Figure 3-2 shows the results for nitrate. In the both 2002 and 2018, the modeled nitrate was greatest in California, and ther e were reduction in nitrate in that state in 2018 because of reductions in mobile-source NO x emissions. There were small reductions in the Phoenix area as well, also from reductions in mobile-source NO x emissions.
Figure 3-3 shows the comparison of PM 2.5 for 2002 and 2018. In most areas of the WRAP region, changes in PM 2.5 were less than 1 μ g/m 3 . Locations with increases in PM 2.5 correspond to areas of increased sulfate (see Figure 3-1) . Areas with the largest reductions in PM 2.5 were the areas in California that had large reductions in modeled nitrate in 2018 (see Figure 3-2). Results for other species that contribute to PM 2.5 are available on the RMC web site at http://pah.cert.ucr.edu/aqm/ 308/cmaq.shtml#base18bvsplan02b

. Figure 3-4 compares model-reconstructed visibil ity for 2002 and 2018. Note that these results are calculated using the modeled relative humidity (RH), so they differ from the results presented in Section 3.3 that use site-specific monthly average RH. Nonetheless, the resu lts in Figure 3-4 are indicative of the direction and magnitude of visibility changes in from 2002 to 2018. Although the largest improvements are in California and the Pacific Northwest, there were improvements throughout the WRAP region. The change in decivi ews is more dramatic than the change in PM 2.5 mass (Figure 3-3) because the visibility in deciviews is a relative metric, so small mass changes in PM 2.5 in good visibility areas can result in large relative improvements in visibility.
The discussion of results in this section is qua litative and is presente d to convey the general spatial patterns and magnitudes of changes in visi bility. Quantitative results at individual Class I Areas are described next.

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Figure 3-1. Annual average aerosol sulfate (A SO4) concentration comparisons between Base18b and Plan02b. Top plot: difference between the two (Base18b – Plan02b); bottom left plot: Plan02b results; bottom right plot: Base18b results.

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Figure 3-2. Annual average aerosol nitrate (A NO3) concentration comparisons between Base18b and Plan02b. Top plot: difference between the two (Base18b – Plan02b); bottom left plot: Plan02b results; bottom right plot: Base18b results.

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Figure 3-3. Annual average PM 2.5 concentration comparisons between Base18b and Plan02b. Top plot: difference be tween the two (Base18b – Plan02b);

bottom left plot: Plan02b results; bottom right plot: Base18b results.

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Figure 3-4. Annual average deciview comp arisons between Base18b and Plan02b. Top plot: difference between the two (Base18b – Plan02b); bottom left plot: Plan02b results; bottom right plot: Base18b results.

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### 3.3 Visibility Projections

As noted in Section 1.1.1, the RHR goals includ e achieving natural visib ility conditions at 156 Federally mandated Class I areas by 2064. In more specific terms, that RHR goal is defined as (1) visibility improvement toward natural conditions for the 20% of days that have the worst visibility (termed “20% worst,” or W20%, visibili ty days) and (2) no worsen ing in visibility for the 20% of days that have the best visibility (“ 20% best,” or B20%, visi bility days). One component of the states’ demonstrati on to EPA that they are making reasonable progress toward this 2064 goal is the comparison of modeled visibility pr ojections for the first milestone year of 2018 with what is termed a uniform rate of progress (URP) goal. As explained in detail below, the 2018 URP goal is obtained by cons tructing a “linear glid e path” (in deciviews) that has at one end the observed visibility conditions during th e mandated five-year (2000-2004) baseline period and at the other end natural visi bility conditions in 2064; the visi bility value that occurs on the glide path at year 2018 is the URP goal.
We have made preliminary WRAP 2018 visibil ity projections using the Plan02c and Base18b CMAQ 36-km modeling results discussed above , following EPA guidance that recommends applying the modeling results in a relative sense to project future-y ear visibility conditions (U.S. EPA, 2001, 2003b, 2006b). Projections are made using relative response factors (RRFs), which are defined as the ratio of the future-year modeli ng results to the current-year modeling results. The calculated RRFs are applied to the baseline obs erved visibility conditi ons to project futureyear observed visibility. These pr ojections can then be used to assess the effectiveness of the simulated emission control strategies that were included in the future-year modeling. The major features of EPA’s recommended visibility projections are as follows (U.S. EPA, 2003a,b, 2006b):

• Monitoring data should be used to define current air quality.

• Monitored concentrations of PM 10 are divided into six major components; the first five are assumed to be PM 2.5 and the sixth is PM 2.5-10
.

• SO 4 (sulfate)

• NO 3 (particulate nitrate)

• OC (organic carbon)

• EC (elemental carbon)

• OF (other fine particulate, or soil)

• CM (coarse matter).

• Models are used in a relative sense to devel op RRFs between future and current predicted concentrations of each component.

• Component-specific RRFs are multiplied by curr ent monitored values to estimate future component concentrations.

• Estimates of future component concentrations are consolidated to pr ovide an estimate of future air quality.

• Future estimated air quality is compared w ith the goal for regiona l haze to see whether the simulated control strategy woul d result in the goal being met.

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• It is acceptable to assume that all measured sulfate is in the form of ammonium sulfate [(NH 4 ) 2 SO 4 ] and all particulate nitrate is in the form of ammonium nitrate [NH 4 NO 3 ].
To facilitate tracking the progress toward visibilit y goals, two important visibility parameters are required for each Class I area:

• Baseline Conditions: “Baseline Conditions” represent visibility for the B20% and W20% days for the initial five-year baseline pe riod of the regional haze program. Baseline Conditions are calculated using monitoring data collected during the 2000-2004 five-year period and are the starting point in 2004 for th e uniform rate of progress (URP) glide path to Natural Conditions in 2064 (U.S. EPA, 2003b).

• Natural Conditions: “Natural Conditions,” the RHR goal for 2064 for the Federally mandated Class I areas, represen t estimates of natural visib ility conditions for the B20% and W20% days at a given Class I area.



### 3.3.1 Calculation of Baseline Conditions

Baseline Conditions for Class I areas are calculat ed using fine and coarse PM concentrations measured at Interagency Monitoring of Protect ed Visual Environments (IMPROVE) monitors (Malm et al., 2000). Each Class I area in the WRAP domain has an associated IMPROVE PM monitor. The IMPROVE monitors do not measure visibility directly, but instead measure speciated fine particulate (PM 2.5 ) and total PM 2.5 and PM 10 mass concentrations from which visibility is calculated using the IMPROVE aer osol extinction equation, discussed later.
Visibility conditions are estimated starti ng with the IMPROVE 24-h average PM mass measurements related to six PM components of light extinction:

• Sulfate [(NH 4 ) 2 SO 4 ]

• Particulate nitrate [NH 4 NO 3 ]

• Organic matter [OMC]

• Light-absorbing carbon [LAC] or elemental carbon [EC]

• Soil

• Coarse matter [CM]
The IMPROVE monitors do not directly measure some of these species, so assumptions are made as to how the IMPROVE measurements can be adjusted and combined to obtain these six components. For example, sulfate and particul ate nitrate are assumed to be completely neutralized by ammonium and only the fine mode (PM 2.5 ) is speciated to obtain sulfate and nitrate measurements (that is, any coarse-mode sulf ate and nitrate in the real atmosphere may be present in the IMPROVE CM measurement). Con centrations for the above six components of light extinction in the IMPROV E aerosol extinction equation ar e obtained from the IMPROVE measured species using the formulas shown in Table 3-1.

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Table 3-1. Definition of IMPROVE co mponents from measured species.

IMPROVE Component Calculation of Component from IMPROVE Measured Species

Sulfate 1.375 x (3 x S)
Nitrate 1.29 x NO 3 -
OMC 1.4 x OC
LAC EC
Soil (2.2 x Al) + (2.49 x Si) + (1.63 x Ca) + (2.42 x Fe) + (1.94 x Ti)
CM MT – MF

where

• S is elemental sulfur as determined from pr oton-induced x-ray emissions (PIXE) analysis of the IMPROVE Module A. To estima te the mass of the sulfate ion (SO 4 = ), S is multiplied by 3 to account for the presence of oxygen. If S is missing then the sulfate (SO 4 ) measured by ion chromatography analysis of Module B is used to replace (3 x S). For the IMPROVE aerosol extinction calculation, sulfate is assumed to be completely neutralized by ammonium (1.375 x SO 4 ).

• NO 3 - is the particulate nitrate measured by ion chromatography analysis of Module B. For the IMPROVE aerosol extinction calcula tion, it is assumed to be completely neutralized by ammonium (1.29 x NO 3 ).

• The IMPROVE organic carbon (OC) measur ements are multiplied by 1.4 to obtain organic matter (OMC), which adjusts the OC mass for other elements assumed to be associated with OC.

• Elemental carbon (EC) is also referr ed to as light-absorbing carbon (LAC).

• Soil is determined as a sum of the masse s of those elements (measured by PIXE) predominantly associated with soil (Al, Si, Ca, Fe, K, and Ti), adjusted to account for oxygen associated with the common oxide form s. Because K is also a product of the combustion of vegetation, it is represented in the formula by 0.6 x Fe and is not shown explicitly.

• MT and MF are total PM 10 and PM 2.5 mass, respectively.
Associated with each PM species is an extinctio n efficiency that converts concentrations (in
μ g/m 3 ) to light extinction (in inverse megameters, Mm -1 ), as listed below. Sulfate and nitrate are hygroscopic, so relative humidity (RH) adjustme nt factors, f(RH), are used to increase the particles’ extinction efficiency with increasi ng RH; this accounts for the particles’ taking on water and having greater light scattering. Note that some organic matter (OMC) compounds may also have hygroscopic properties, but the IM PROVE aerosol extincti on equation assumes OMC is nonhygroscopic.

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β Sulfate = 3 x f(RH) x [sulfate] β Nitrate = 3 x f(RH) x [nitrate] β OMC = 4 x [OMC] β EC = 10 x [EC] β Soil = 1 x [soil] β CM = 0.6 x [CM]
The total light extinction ( β ext ) is assumed to be the sum of the light extinctions due to the six PM species listed above plus Raylei gh (blue sky) background extinction ( β Ray ), which is assumed to be 10 Mm -1 . This is reflected in the IMPROVE extinction equation:
β ext = β Ray + β Sulfate + β Nitrate + β EC + β OMC + β Soil + β CM The total light extinction ( β ext ) in Mm -1 is related to visual range (VR) in kilometers using the following relationship:

VR = 3912 / β ext The RHR requires that visibility be expressed in terms of a haze i ndex (HI) in units of deciview (dv), which is calculated as follows:
HI = 10 ln( β ext /10)
The equations above, with measurements from th e associated IMPROVE monitor, are used to estimate the daily average visibility at each Cl ass I area for each IMPROVE monitored day. For each year from the 2000-2004 baseline period, these daily average visibility values are then ranked from highest to lowest. Th e “worst days” visibility for each of the five years in the baseline period is defined as the average visibili ty across the 20% worst-vi sibility days (highest deciview values); similarly, the “best days” visib ility is defined as the average visibility across the 20% best-visibility days (l owest deciview values) for each year. The Baseline Conditions for the best and worst days are defined as the five-y ear average of the B20% visibility days and of the W20% visibility days, respectively, across the five-year baseline period.
The set of equations given above for relating measur ed PM species to visibi lity (light extinction) are referred to as the “Old IMPROVE” e quation. The IMPROVE Steering Committee has developed a “New IMPROVE” equation that they believe better represents the fit between measured PM species concentrations and visibilit y impairment. Although conceptually similar to the Old IMPROVE equation, the New IMPROVE e quation includes updates to many of the parameters and the addition of extinctions due to NO 2 absorption and sea salt. We performed 2018 visibility projections and comparisons with the URP glide path goals using both the New and Old IMPROVE equations. The reader is referred elsewhere for details on the New IMPROVE extinction equati on (e.g., EPA, 2006a,b).

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### 3.3.2 Mapping of modeling results to the IMPROVE measurements

As noted above, future-year visibi lity at Class I areas is project ed by using modeling results in a relative sense to scale current ob served visibility for the B20% and W20% visibility days. This scaling is done using RRFs, th e ratios of future-year modeling results to current-year results. Each of the six components of light extincti on in the IMPROVE reconstructed mass extinction equation is scaled separately. Because the mode led species do not exactly match up with the IMPROVE measured PM species, assumptions must be made to map the modeled PM species to the IMPROVE measured species for the purpose of projecting visibility improvements. For example, in the model’s chemistry (which explic itly simulates ammonium), sulfate may or may not be fully neutralized; the IMPROVE extinction equation, on the other hand, assumes that observed sulfate is fully neutralized by ammonium. For th e CMAQ v4.5 model (September 2005 release) used in our modeling, the mapping of modeled species to IMPROVE measured PM species is listed in Table 3-2.

Table 3-2. Mapping of CMAQ v4.5 modeled species concentrations to IMPROVE measured components.

IMPROVE Component CMAQ V4.5 Species

Sulfate 1.375 x (ASO4J + ASO4I)
Nitrate 1.29 x (ANO3J + ANO3I)
OMC AORGAJ + AORGAI + AORGPAJ + AORGPAI + AORGBJ + AORGBI
LAC AECJ + AECI
Soil A25J + A25I
CM ACORS + ASEAS + ASOIL



### 3.3.3 Using modeling results to project changes in visibility

RRFs calculated from modeling results can be used to project future-year vi sibility. For the work discussed in this report, RRFs are the ratio of the 2018 modeling results to the 2002 modeling results, and are specific to each Class I area and each PM species. RRFs are applied to the Baseline Condition observed PM species levels to project future-year PM levels, which are then used with the IMPROVE extinction equation liste d above to assess visibility. The following six steps are used to project future -year visibility for the B20% a nd W20% visibility days (the discussion below is for W20% days but also applies to B20% days):

1. For each Class I area and each monitored day, daily visibility is ranked using IMPROVE data and IMPROVE extinction equation for each year from the five-year baseline period (2000-2004) to identify the W20% visibility days for each year (see Section 3.2.1 for calculation details).

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2. Use an air quality model to simulate a base -year period (ideally 2000-2004, but in reality just 2002) and a future year (e.g., 2018), then apply the resulting information to develop Class-I-area-specific RRFs for each of the six components of light extinction in the IMPROVE aerosol extinction equation.

3. Multiply the RRFs by the measured 24-h PM data for each day from the W20% days for each year from the five-year baseline period to obtain projected future-year (2018) 24-h PM concentrations for the W20% days.

4. Compute the future-year daily extinction us ing the IMPROVE aerosol extinction equation and the projected PM concentrations for each of the W20% days in the five-year baseline from Step 3.

5. For each of the W20% days within each year of the five-year baseline, convert the futureyear daily extinction to units of deciview and average the da ily deciview values within each of the five years separately to obtain five years of average deciview visibility for the W20% days.

6. Average the five years of average deciview visi bility to obtain the future-year visibility Haze Index estimate that is compar ed with the 2018 progress goal.
In calculating the RRFs, EPA draft guidance (U.S. EPA, 2001, 2006b) recommends selecting modeled PM species concentrations “near” th e monitor by taking a spatial average of PM concentrations across a grid-c ell-resolution–dependent NX by NY array of cells centered on the grid containing the monitor. For the WRAP 36-km CMAQ modeling, th e model estimates for just the grid cell containing the monitor are used (i.e., NX=NY=1).
We are analyzing various ways to calculate the model-derived RRFs. For the preliminary 2018 visibility projections in this document, results are presented only for “Method 1,” which is the recommended approach in EPA’s draft mode ling guidance documents (U.S. EPA, 2001, 2006b). In the Method 1 Average RRF Approach, an average RRF for the W20% days from 2002 (Modeled Worst Days) is obtained for the Plan02c and the Base18b CMAQ simulations by averaging the PM concentration components across the Modeled Worst Days and then calculating the (future year):(base year) ratio of the average PM concentrations. For example, if SO4 i,j is the measured sulfate concentrations at Class I area j for the i =1,..., N 20% worst visibility days in 2002, then the RRF for sulf ate on the W20% days would be obtained as:



## ∑ ∑
∑ ∑

= =

= = = = N

i ij N

i ij

N

i ij N

i ij

j SO SO

SO N SO N SORRF

1 1

1 1 )2002(4 )2018(4

)2002(4 1 )2018(4 1
)4 (

For each Class I area and each of the W20% days, the average RRF for each PM component would be applied to concentrations for the W2 0% days from the 2000-20 04 baseline period to estimate future-year PM concentrations for each of the W20% days. Extinction and HI would then be calculated to obtain the projected future -year visibility conditions using the procedures given previously.

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### 3.3.4 Glide path to Natural Conditions

The presumptive visibility targ et for 2018 is the URP goal that is obtained by constructing a linear glide path from the cu rrent Baseline Conditions to Na tural Conditions in 2064 (both expressed in deciviews). For instan ce, Figure 3-5 displays an exampl e visibility glide path for the Grand Canyon National Park (GRCA) Class I area. EPA’s default Natural Conditions value for the W20% days (U.S. EPA, 2003a), shown as the green line, is the 2064 visibility goal at GRCA of 6.95 dv. The blue diamonds at the left of the plot are the annual average current conditions, based on IMPROVE observations for the W20% days as obtained from the Visibility Information Exchange Web System (VIEWS) web site ( http://vista.cira.colostate.edu/views/ ). These annual average visibility values for the 20 % worst days allow an assessment of trends and the year-to-year variatio n in visibility. The Baseline Condi tions are the aver age of the W20% visibility from 2000-2004, which is the starting point for the glide path in 2004 (12.04 dv for GRCA). A linear URP from the Baseline Condi tions in 2004 to Natu ral Conditions in 2064 (sloping pink line with triangles) is assumed, and the value on the glide path at 2018 is the presumptive URP visibility target that the m odeled 2018 projections are compared against to judge progress. In this example, the visibi lity progress goal in 2018 would be 10.85 dv. Meeting this would require a 1.19 dv reduction in visi bility by 2018 to meet that milestone year’s visibility progress target at the Grand Canyon National Park.

Uniform Rate of Reasonable Progress Glide Path Grand Canyon NP - 20% Worst Days

12.04 11.70 10.85 10.01 9.16 8.31 7.46 6.95 11.71

0 5 10 15 20 25 30

2000 2004 2008 2012 2016 2020 2024 2028 2032 2036 2040 2044 2048 2052 2056 2060 2064 YearHaziness Index (Deciviews)

Glide Path Natural Condition (Worst Days) Observation Method 1 Prediction

Figure 3-5. Example of URP glide path usin g IMPROVE data from the Grand Canyon National Park for the W20% days and comparis on with Base18b visibility projections.

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### 3.3.5 Preliminary visib ility projection results

For all of the WRAP Class I areas, we have pe rformed preliminary 2018 visibility projections and compared them to the 2018 URP goals us ing the Plan02c and Base18b CMAQ modeling results and the Old and New IM PROVE equations. As an example, Figure 3-5 above compares the Base18b visibility projections with the URP goal based on the glide path for GRCA and the Old IMPROVE equation. To achieve the 2018 URP goal, the modeled 2018 visibility projection would have to show a 1.19 dv (=12.04-10.85) reduc tion. However, the modeled 2018 visibility projection shows only a 0.33 dv (=12.04-11.71) re duction by 2018, which indicates that the emission controls simulated in case Base18b w ould not achieve the modeled URP goal; the 2018 visibility projection achieves only 28% of the goal (28% = 100 x 0.33/1.19). Figure 3-6 displays the 2018 visibility projections for all WRAP Class I areas , using both the Old and New IMPROVE equations, expressed as a percentage of achieving the URP goal, with values of 100% or greater achieving the goal.
Using the procedures outlined in this section, none of the WRAP Class I areas are projected to achieve their URP goals. There are various reasons for this, such as the presence of W20% days that are dominated by emissions from sources that are not controllable, in cluding wildfires, dust, and/or international transport. Additional analysis of these re sults and alternative projection techniques are currently under study. At this point in time, we can draw the following conclusions about 2018 visibility proj ections at WRAP Class I areas:

• The modeled coarse mass (CM) predictions are highly uncertain and produce poor model performance when compared to the measured va lues. In particular, at sites in the desert Southwest where CM is high, the model exhib its a large underpredi ction tendency that we believe is due in part to local dust imp acts that are sub-grid-scale and not simulated by the model. Consequently, the modeled RRFs for CM are not a reliable indicator of actual changes in atmospheric CM concentrations a nd so should not be used to project future levels of CM concentrations. We are recomm ending that future visibility projections be made using an RRF for CM of 1.0.

• The RRFs for the other five co mponents of visibility (SO 4 , NO 3 , EC, OC, and soil) appear to be more reliable indicators of ch anges in PM species concentrations. They will be used in future visibility projections.

• At about a dozen of the WRAP western U.S. Class I areas, 2018 visibility projections could not be made due to incomplete data. Eith er there were insufficient data to have at least three complete years from the 2000- 2004 baseline period, as required by EPA guidance (EPA, 2003a); or there were insuffi cient days during 2002 to identify the 20% worst visibility monitored days that are need ed to develop the RRF projection factors (see Table 3-3 for details). In the future, data fil ling will have to be used to obtain sufficient data at these Class I areas to perfor m future-year visibi lity projections.

• Using the standard EPA approach for projec ting 2018 visibility, mo st if not all WRAP Class I areas will fail to meet the 2018 URP goal due to the contributions of uncontrollable sources such as wildfires, dust, and international transport.

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Method 1 predictions for Colorado Plateau and Desert Southwest sites

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%BRCA1CANY1GRCA2MEVE1SAPE1WEMI1ZION1BAND1BOAP1CHIR1GICL1GRSA1IKBA1SACR1SAGU1SIAN1WHIT1WHPE1Percent of target reduction achieved base18b old algorithm

base18b new algorithm

Colorado Plateau Desert Southwest

Method 1 predictions for North, Great Basin and Rockies sites

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%BADL1CABI1GAMO1LOST1MELA1MONT1SULA1THRO1ULBE1WICA1CRMO1JARB1SAWT1BRID1NOAB1ROMO1WHRI1YELL2Percent of target reduction achieved base18b old algorithm

base18b new algorithm

North Great Basin Rockies

Method 1 predictions for Pacific Northwest and California sites

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%HECA1KALM1MOHO1MORA1NOCA1OLYM1PASA1SNPA1STAR1THSI1WHPA1AGTI1BLIS1DOME1HOOV1JOSH1LAVO1PINN1REDW1SAGA1SAGO1YOSE1Percent of target reduction achieved base18b old algorithm

base18b new algorithm

Pacific Northwest California Figure 3-6. 2018 visibility projections at WRAP Class I areas expressed as a percent of achieving the 2018 URP goal using the Old and New IMPROVE equation and the WRAP Base18c CMAQ 36-km modeling results.

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Table 3-3. WRAP sites with data completeness problems.

State Site Name Site Abbreviation Fewer than 3 Years of Data Insufficient Days in 2002 Mt. Baldy BALD ▲ ▲
Indian Garden INGA ▲ Arizona

Tonto TONT ▲
Kaiser KAIS ▲ ▲ Point Reyes PORE ▲
San Rafael RAFA ▲ ▲ Sequoia SEQU ▲ California

Trinity TRIN ▲
Flathead FLAT ▲
Fort Peck FOPE ▲
Glacier GLAC ▲ Montana

Northern Cheyenne NOCH ▲
Utah Capitol Reef CAPI ▲ ▲ Washington North Cascades NOCA ▲



### 3.3.6 Alternative approaches for selecting worst visibility days

For the analysis above, we selected the wors t visibility days using the EPA-recommended approach based on the worst observed days during 2000 to 2004. In discussions between the RMC and WRAP, however, a concern was raised that the determination of the worst visibility days was heavily influenced by natural wildfire emissions. Because wildfire emissions are not controllable, we would expect that days dominated by those emissions in the Plan02c case would not be responsive to the pla nned anthropogenic emissions reduc tions in the Base02b case. We therefore performed additional visibility projecti on analyses using two alternate approaches for selecting the worst visibility days. We select ed the 20% worst days based on (1) the worst modeled sulfate days and (2) the worst modeled n itrate days, and then recalculated the visibility projections using these two a lternate sets of W20% days. The visibility projection results are available on the RMC web page for case Plan02b for the worst sulfate days ( http://pah.cert.ucr.edu/ aqm/308/projections/w20SO4/ ) and the worst nitrate days ( http://pah.cert.ucr.edu/ aqm/308/projections/w20NO3/ ). As expected, both of these approaches did result in larger improvements in visibility in 2018, although the improvements still were not sufficient to attain progress goals at most Class I Areas. Even though these alternate approaches do not conform to the EPA guidance for determining visibility progress, we recommend that they be considered in developing and evaluating SIPs/TIPs because they could be useful as complementary evidence.

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## 4. PSAT Modeling

Impairment of visibility in Class I areas is ca used by a combination of local air pollutants and regional pollutants that are tran sported long distances. To devel op effective visibility improvement strategies, the WRAP member states and tr ibes need to know the relative contributions of local and transported pollutants , and which emissions sources ar e significant c ontributors to visibility impairment at a given Class I area.
A variety of modeling and data analysis methods can be used to perform source apportionment of the PM observed at a given receptor site. In this section we focus on methods that employ gridded air quality models. Model sensitivity simula tions have been used in which a “base case” model simulation is performed and then a particul ar source is “zeroed out” of the emissions. The importance of that source is assessed by evaluating the change in pollutants at the receptor site, calculated as pollutant concentration in the sens itivity case minus that in the base case. This approach is known as a “brute force” sensitivity because a separate model run is required for each sensitivity.
An alternative approach is to implement a mass-tracking algorithm in the air quality model to explicitly track for a given emissions source the chemical transformations, transport, and removal of the PM that was formed from th at source. Mass tracking methods have been implemented in both the CMAQ and CAMx air qua lity models. Initial work completed by the RMC during 2004 used the CMAQ Tagged Species Source Apportionment (TSSA) method. Unfortunately, there were problems with mass conser vation in the version of CMAQ used in that study, and these affected the TSSA results. A si milar algorithm has been implemented in CAMx, the PM Source Apportionment Technology (PSAT) . Comparisons of TSSA and PSAT showed that the results were qualitatively similar, that is, the relative ranking of the most significant source contributors were similar for the two me thods. However, the total mass contributions differed. With separate funding fr om EPA, UCR has implemented a version of TSSA in the new CMAQ release (v4.5) that corrects the mass conser vation error, but because we were uncertain whether this update would be completed in tim e, we used the CAMx/PSAT source apportionment method for the WRAP mode ling analysis. We did complete a test version of CMAQ/TSSA v4.5, and then used it in simulating January a nd July of case Plan02c to compare with the CAMx/PSAT results for QA purposes.
The main objective of applying CAMx/PSAT is to evaluate the regional haze air quality for typical 2002 (Plan02c) and futu re-year 2018 (Base18b) conditions. These results are used

• to assess the contributions of different ge ographic source regions (e.g., states) and source categories to current (2002) and future (2018) visibility impairment at Class I areas, to obtain improved understanding of (1) the causes of the impairment and (2) which states are included in the area of influence (AOI) of a given Class I area; and

• to identify the source regions and emissions categories that, if controlled, would produce the greatest visibility impr ovements at a Class I area.

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### 4.1 Background and Setup Info rmation for PSAT Modeling

4.1.1 CAMx/PSAT

The PM Source Apportionment Technology performs source apportionment based on userdefined source groups. A source group is the combin ation of a geographic source region and an emissions source category. Examples of source regions include states, nonattainment areas, and counties. Examples of source ca tegories include mobile sources, biogenic sources, and elevated point sources; PSAT can even focus on individual sources. The user defines a geographic source region map to specify the source regions of intere st. He or she then i nputs each source category as separate, gridded low-level emissions and/or elevated-point-source emissions. The model then determines each source group by overlaying the source categories on the source region map. For further information, please refer to our white pa per on the features and capabilities of PSAT ( http://pah.cert.ucr.edu/aqm/308/repo rts/PSAT_White_Paper_111405_final_draft1.pdf ), with additional details available in the CAMx user’s guide (ENVIRON, 2005; http://www.camx.com ).
We performed PM source apportionme nt modeling for aerosol sulfate (SO 4 ) and aerosol nitrate (NO 3 ) and their related species (e.g., SO 2 , NO, NO 2 , HNO 3 , NH 3 , and NH 4 ). The PSAT simulations include 9 tracers, 18 source regions, and 6 source groups . The computational cost for each of these species differs because additiona l tracers must be used to track chemical conversions of precursors to the secondary PM species SO 4 , NO 3 , NH 4 , and secondary organic aerosols (SOA). Table 4-1 summarizes the comput er run time required for each species. The practical implication of this table for WRAP is th at it is much more expensive to perform PSAT simulations for NO 3 and especially for SOA than it is to perform simulations for other species.

Table 4-1. Benchmarks for PSAT comput ational costs for each PM species. Run time is for one day (01/02/200 2) on the WRAP 36-km domain.

Species No. of Species Tracers RAM Memory Disk Storage per Day Run Time with 1 CPU
SO 4 2 1.6 GB 1.1 GB 4.7 h/day
NO 3 7 1.7 GB 2.6 GB 13.2 h/day
SO 4 and NO 3 combined 9 1.9 GB 3.3 GB 16.8 h/day

SOA 14 6.8 GB Not tested Not tested Primary PM species 6 1.5 GB 3.0 GB 10.8 h/day

Two annual 36-km CAMx/PSAT model simulations were performed: one with the Plan02c typical-year baseline case and th e other with the Base18b future-year case. We expect that the states and tribes will use these results to asse ss the sources that contribute to visibility impairment at each Class I Area, and to guide the choice of emission control strategies. The RMC web site includes a full set of source apportionment spatial plots (explained in Section

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4.3.1) and receptor bar plots (discussed in S ection 4.3.2) for both Plan02b and Base18b. It is beyond the scope of this task to evaluate the resu lts for individual sites. However, we present sample plots below for the Plan02c results to illust rate the types of plots that are available to the states and tribes. In addition, the WRAP Technical Support System (TSS) has completed additional processing of these results (see http://vista.cira.colostate. edu/tss/Tools/ResultsSA.aspx ).



### 4.1.2 CAMx/PSAT 2002 and 2018 setup

PSAT source apportionment simulations for 2002 and 2018 were performed using CAMx v4.30. Table 4-2 lists overall specifica tions for the 2002 PSAT simulations . The domain setup was identical to the standard WRAP CMAQ modeling domain. The CAMx/PSAT run-time options are shown in Table 4-3. The CAMx/PSAT computationa l cost for one simulation day with source tracking for sulfate (SO 4 ) and nitrate (NO 3 ) is approximately 14.5 CPU hours with an AMD Opteron CPU. The source regions used in the PSAT simulations are shown in Figure 4-1 and Table 4-4. The six emissions sour ce groups are listed in Table 4-5. For the WRAP region, wildfires and some prescribed burni ng were considered natural emi ssions, while agricultural burning and the remaining prescribed burning were consid ered anthropogenic, so these WRAP fire emissions were treated as two separa te categories in PSAT. For non-WRAP regions, some fire emissions were provided to the RMC as elevated sour ces while other fire emissions were provided as area sources in the surface layer. CAMx/PSAT emissions are provided as separate input files, so these two categories of non-WR AP fire emissions were tracked separately in PSAT.

We divided the annual PSAT run into four season s. The initial conditions for the first season (January 1 to March 31, 2002) came from a CE NRAP annual simulation. For the other three seasons, we allowed 15 model spin-up days prio r to the beginning of each season. Based on the chosen set of source regions and groups, with ni ne tracers, and with a minimum requirement of 87,000 point sources and a horizontal domain of 148 by 112 grid cells with 19 vertical layers, the run-time memory requirement is 1.9 GB. Total di sk storage per day is approximately 3.3 GB. Although our computation nodes are equipped with dual Opteron CPUs with 2 GB of RAM and 1 GB of swap space, the high run-time memory requirements prevented us from running PSAT simulations using the OpenMP shared memory multiprocessing capability implemented in CAMx.

Table 4-2. WRAP 2002 CAMx/ PSAT specifications.
WRAP PSAT Specs Description
Model CAMx v4.30 OS/compiler Linux, pgf90 v.6.0-5 CPU type AMD Opteron with 2 GB of RAM Source region 18 source regions; see Figure 4.1 and Table 4.4 Emissions source groups Plan02b, 6 source groups; see Table 4.5
Initial conditions From CENRAP (camx.v4.30.cenrap36.omp.2001365.inst.2) Boundary conditions 3-h BC from GEOS-Chem v2

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Table 4-3. WRAP CAMx/PSAT run-time options.

WRAP PSAT specs Description
Advection solver PPM Chemistry parameters CAMx4.3.chemparam.4_CF Chemistry solver CMC Plume-in-grid Not used Probing tool PSAT Dry/wet deposition TRUE (turned on) Staggered winds TRUE (turned on)

Table 4-4. WRAP CAMx/PSAT source regions cross-reference table.

Source Region ID Source Region Description 1 Source Region ID Source Region Description 1

1 Arizona (AZ) 10 South Dakota (SD)
2 California (CA) 11 Utah (UT)
3 Colorado (CO) 12 Washington (WA)
4 Idaho (ID) 13 Wyoming (WY)
5 Montana (MT) 14 Pacific off-shore & Sea of Cortez (OF)
6 Nevada (NV) 15 CENRAP states (CE)
7 New Mexico (NM) 16 Eastern U.S., Gulf of Mexico, & Atlantic Ocean (EA)
8 North Dakota (ND) 17 Mexico (MX)
9 Oregon (OR) 18 Canada (CN)

1 The abbreviations in parentheses are used to iden tify source regions in PSAT receptor bar plots.

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-2736 -2412 -2088 -1764 -1440 -1116 -792 -468 -144 180 504 828 1152 1476 1800 2124 2448 -2088 -1872 -1656 -1440 -1224 -1008

-792 -576 -360 -144

72 288 504 720 936 1152 1368 1584 1800

2 6 9 12

4 5 8

13

11 3

1 7

15 15

16

16 16 18 18

14

17 14

10

Figure 4-1. WRAP CAMx/PSAT source region map. Table 4.4 defines the source region IDs.

Table 4-5. WRAP CAMx/PSAT emissions source groups.

Emissions Source Groups Emissions Description

1 Low-level and elevated point sour ces (including stationary off-shore)
2 Anthropogenic fires (WRAP only)
3 Total mobile (on-road, off-road, incl uding planes, trains, ships in/near port, off-shore shipping)
4 Natural emissions (natural fire in WRAP states, biogenic emissions)
5 Non-WRAP elevated wildfires
6 Everything else (area sources, all dus t, fugitive ammonia, nonelevated fire sources in other RPOs)

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### 4.2 Preparation of Emissions Data

Emissions datasets for the CAMx/PSAT simulati ons were prepared directly from the SMOKEprocessed emissions developed fo r CMAQ. A simple format convers ion was used to convert the original SMOKE output files from the I/O API fo rmat to the CAMx format. For certain source categories, SMOKE was configured to output CAMx-formatted files di rectly. In addition, CMAQ species names were changed for several of the emissions species to make them consistent with the CAMx species. Additional processing was also required for the PSAT algorithm. For each of the emissions categories th at were tracked in PSAT, we converted the intermediate SMOKE output files for those catego ries to the CAMx/PSAT format, and these files were read by the CAMx program to distinguish among the emissions source categories. We also processed the elevated-point-source CAMx emi ssions files to specify the PSAT source region individually for each point source. This was n ecessary in order to assign elevated-point-source emissions to the correct PSAT s ource region (particularly near st ate boundaries and along coastal regions), due to the relatively co arse (36-km) grid cell resolution.
No additional postprocessing QA was performed on the CAMx emissions files, since they were prepared directly from the previously quality -assured CMAQ emissions files. However, we did QA the CAMx/PSAT simulations by comparing the CAMx- and CMAQ-predicted species concentrations (as discussed in Section 4.4). After we corrected some formatting errors, the CMAQ and CAMx simulations produced similar results. 

### 4.3 Sample PSAT Results

The source apportionment algorithms implemented in CAMx generate output files in the same format as the standard modeled species concentr ations files. This typically consists of a two-dimensional, gridded dataset of hourly-average surface concentrations for each source group tracer that gives the contribution of the tracer to all the surface grid cells in the model domain for each hour of the simulation. Three-dimensional inst antaneous concentrations are also output for the last two hours of the simula tion, which are used to restar t the model. Although there are options to output hourly 3-D average tracer concentra tions, we usually output only the model’s surface layer concentrations because of the vast disk storage space needed for the 3-D file output for all the source gr oup contributions.
The source apportionment model results are typica lly presented in two ways (Sections 4.3.1 and 4.3.2):

• Spatial plots showing the area of influence of a s ource group’s PM species contributions throughout the model domain, either at a given hourly-average point in time or averaged over some time interval (e.g., monthly average).

• Receptor bar plots showing the rank order of source groupings that contribute to PM species at any given receptor si te. These plots also can be at a particular point in time or averaged over selected time intervals—for ex ample, the average source contributions for the 20% worst visibility days.

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If the 3-D tracer output files ar e saved, it is also possible to pr epare animations of PM species plumes from each of the source groups. However, these plots are less useful than the others for quantitative analysis, are expensive to produce, and require saving 3-D hourly output, which is disk-space intensive. The primary products of the WRAP PSAT modeling were receptor bar plots showing the emission source groups that co ntribute the most to the model grid cells containing each IMPROVE monitoring site a nd other receptor sites identified by WRAP.



### 4.3.1 Source apportionment spatial plots

Once the CAMx/PSAT simulations were complete d, we used software developed at UCR to calculate monthly averages for each source re gion and emissions group. The PAVE software mentioned in Section 2.2 was then used to gene rate spatial plots of the monthly-average PSAT results. For example, Figure 4-2 shows the contri butions to aerosol sulfate from Arizona pointsource emissions averaged over the entire month of January 2002. Figure 4-3 shows the contributions to aerosol nitrat e from Arizona mobile sources, also for January 2002. What is clearly evident in these plots is that different source categories in a state (e.g., elevated point vs. mobile sources) and different PM species (e.g., SO 4 vs. NO 3 ) can have quite different downwind impact regions.
Additional monthly-average spatia l plots of PSAT PM source cont ributions for the Plan02c and Base18b cases are available on the RMC web s ite under the subsection “WRAP Regional Haze Air Quality and Visibil ity Modeling Results” ( http://pah.cert.ucr.edu/aqm/308/cmaq.shtml ).

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Figure 4-2. January 2002 monthly- average PSAT spatial plot for PSO4 from Arizona point sources.

Figure 4-3. January 2002 monthly- average PSAT spatial plot for PNO3 from Arizona mobile sources.

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### 4.3.2 Source apportionment receptor bar plots

A second approach for visualizing PSAT results is the use of a receptor bar plot that shows the ranked contributions of each emissions source gr oup that contributes to each component of PM at a given receptor site. This display is more di rectly relevant to dete rmining source apportionment at a receptor site. We have postprocessing software that reads the PSAT output files, extracts data for a list of receptor sites, and cr eates bar plots and ASCII files showing the ranked largest emissions source group contributors to the individual components of PM (e.g., SO 4 and NO 3 ) at each receptor site.
Receptor sites for which we prepared PSAT bar pl ots are defined by model grid cells containing IMPROVE sites in Class I areas. The source ap portionments were calculated as the apportionment averaged over a 24-h period (to match the temporal resolution specified in the IMPROVE monitoring protocol). To create the receptor bar plots, we used postprocessing software that sorts and identifies for each receptor site the average source apportionment for the 20% best (B20%) and 20% worst (W20%) visibility days, where “wor st” and “best” can be defined using either ambient IMPROVE data or model predictions.
Figure 4-4 is an example source apportionmen t receptor bar plot. It shows the largest contributors to aerosol sulfate (PSO4) at a r eceptor site (Hance Camp at Grand Canyon National Park, AZ) based upon the 20% best β ext days’ average for 2002. The x -axis labels are explained in Tables 4-4 and 4-6. The top five contribu tors are boundary conditions and the point-source emissions from Arizona, Utah, Nevada, and Wy oming. Figure 4-5 shows the bar plot for the same receptor site and same year but based upon the 20% worst β ext days’ average. The top five contributors are now boundary conditi ons, area sources from Pacific off-shore and Sea of Cortez, and point-source emissions from Mexico, Nevada, and California. Figure 4-6 shows the bar plot for the same receptor site but based on a mont hly average (July 2002) ra ther than an annual average. The top five contribu tors are boundary conditions, point-s ource emissions from Mexico, off-shore emissions, and point-source emissi ons from Nevada and the eastern U.S.
More receptor bar plots for aerosol sulfate ( PSO4), nitrate (PNO3), and ammonium (PNH4) are available on the WRAP RMC web site at http://pah.cert.ucr. edu/aqm/308/cmaq.shtml

. When preparing the bar plots fo r the W20% days, we used th ree different approaches for determining the 20% worst visibility days (see disc ussion in Section 3.3.6). First, we selected the worst days using the EPA-recommended approa ch based on the worst observed days during 2000 to 2004. Then, because of concern that these da ys might be heavily affected by wildfires, we also selected W20% days based on the wors t modeled sulfate and worst modeled nitrate days. Complete results for these thre e approaches are available on the RMC web site for Plan02c at http://pah.cert.ucr.edu/aqm/308/cmaq.shtml#CAMxPlan02c and for Base18b at http://pah.cert.ucr.edu/aqm/308/cmaq.shtml#CAMxBase18b . Although we do not present or discuss those results here, the a lternate selections of worst days did create some changes in the major contributors to PM 2.5 at some Class I Areas, so these re sults might be useful to state or tribal staff as they review emission control strategies.

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Table 4-6. PSAT tracer source tags.

Type Source Category ID Source Types

ICON ICON Initial conditions
BCON BCON Boundary conditions MV_* On/off-road mobile sources from each source region PT_* Point sources from each source region ANF_* Anthropogenic fire sources from each source region NTWF_* Natural fire sources from each source region NWF_* Non-WRAP fire sources from each source region Emissions

AR_* Area sources from each source region

Figure 4-4. PSAT bar plot for receptor si te at Hance Camp in Grand Canyon National Park, AZ, showing the top contributors to PSO4 based on 20% best β ext days’ average for 2002.

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Figure 4-5. PSAT bar plot for receptor si te at Hance Camp in Grand Canyon National Park, AZ, showing the top contributors to PSO4 based on 20% worst β ext days’ average for 2002.

Figure 4-6. PSAT bar plot for receptor site at Hance Camp in Grand Canyon National Park, AZ, showing the top contributors to PSO4 based on July 2002 monthly average.

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### 4.4 Quality Assurance and Comparison to CMAQ/TSSA

To quality-assure the 2002 CAMx/PSAT simulati ons, we ran CMAQ with its source apportionment method, TSSA, for the months of January a nd July 2002. We expected that the results from CAMx/PSAT and CMAQ/TSSA would be similar but not identical.

Similar to CAMx/PSAT, CMAQ/TSSA uses reactive tracers to track the chemical transport and transformation of emissions from selected emi ssions source categories or regions. The algorithm includes a number of new tracers, or “tagged spec ies,” for each emissions source to be evaluated. The result of the TSSA algorithm is 3-D concen tration fields showing the transport of mass of both primary emissions and secondary products fr om the selected emissions sources. A single CMAQ Chemical-Transport Model (CCTM) simulation will include enough tagged species tracers to evaluate the contributions of as ma ny as 20 different emissions sources. The algorithm was designed to track the emissions either from a single source or from selected classes of sources, grouped by source category and/or by re gion. As noted at the beginning of Section 4, the latest version of TSSA was implemented in CMAQ v4.5.0.
We performed the CAMx/PSAT and CMAQ/TSSA m odel simulations with identical meteorology and emissions datasets so that we could more accurately evaluate th e consistency of modeling results between the two models. We also used identical source regi ons and emissions groups for both simulations. A model spin-up period was us ed in the TSSA simulations to eliminate any effects from the ICs. The BCs data, similar to CAMx/PSAT boundary conditions, were generated from the global-scale GEOS-Chem model and were postprocessed to be used in CMAQ modeling (Moon and Byun, 2004). The photolysis rates were generated by the CMAQ JPROC preprocessor. We also used day-specific TOMS oz one profiles to calculate the photolysis rates.
Figure 4-7 shows source apportionm ent spatial plots for aerosol su lfate from California mobile sources from CMAQ/TSSA (left plot) and CA Mx/PSAT (right plot) for January 31, 2002, at 0000 GMT. The maximum values are different, but both source apportionment technologies show similar spatial patterns. Figure 4-8 shows sp atial plots for aerosol sulfate from California point sources from CMAQ/TSSA and CAMx/P SAT for January 31, 2002, at 0000 GMT. The two methodologies again show similar spatia l patterns but different maximum values.
Figure 4-9 shows spatial plots similar to Figur e 4-7 but for July 31, 2002, at 0000 GMT. As in the first two figures, the spatial patterns are si milar between the two methods. Figure 4-10 shows the aerosol sulfate from California point s ources for July 31, 2002, at 0000 GMT. Although the spatial coverage of sulfate in CAMx/PSAT is s lightly wider than in CMAQ/TSSA, both methods show similar patterns.
Figure 4-11 compares CMAQ/TSSA and CAMx/PSA T receptor bar plots for Hance Camp at Grand Canyon National Park, AZ, for the month of July 2002. The top five contributors to PSO4 from the CMAQ/TSSA run (top plot) are point s ources from Nevada and Pacific off-shore, boundary conditions, and point and mobile sources from Arizona. The top five contributors from the CAMx/PSAT run are boundary conditions, poin t sources from Mexico, area sources from Pacific off-shore, and point sources from Ne vada and the eastern U. S. Although the spatial patterns from both methodologies are similar, th e receptor bar plots show differences between the two sets of top- five contributors.

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Figure 4-7. Source apportionment spatial pl ots for aerosol sulfate from California mobile sources from CMAQ/TSSA (left) and CAMx/PSAT (right) for January 31, 2002, at 0000 GMT.

Figure 4-8. Source apportionment spatial pl ots for aerosol sulfate from California point sources from CMAQ/TSSA (left) and CAMx/PSAT (right) for January 31, 2002, at 0000 GMT.

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Figure 4-9. Source apportionment spatial pl ots for aerosol sulfate from California mobile sources from CMAQ/TSSA (left) and CAMx/PSAT (right) for July 31, 2002, at 0000 GMT.

Figure 4-10. Source apportionment spatial pl ots for aerosol sulfate from California point sources from CMAQ/TSSA (left) and CAMx/PSAT (right) for July 31, 2002, at 0000 GMT.

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Figure 4-11. TSSA (top panel) vs. PSAT (bo ttom panel) receptor bar plots for PSO4 for Hance Camp at Grand Canyon National Park, AZ, for the month of July 2002.

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### 4.5 Conclusions from PSAT Modeling

The goal of the PSAT modeling was to identify the most significant emissions source categories and regions that contribute to vi sibility impairment at each Cla ss I area, as simulated by the air quality model. These results, in combination with other types of “attri bution of haze” products developed by WRAP an d its contractors (see http://www.wrapair.org/forums/aoh/index.html ), should be useful in identifyi ng and prioritizing emissions sour ces that could be reduced to achieve visibility progress. The most significant contributors are di fferent for each Class I Area, and it is beyond the scope of our work to provide detailed discussion of the results at individual sites. Instead, we expect that pl anning staff at state and tribal agencies will use the PSAT results, along with other information, to analyze the sour ces of haze at the individual sites. Below we comment on several trends that are common at most sites:

• Transported PM 2.5 from the model boundaries was the si ngle largest contributor at many sites and was among the largest contributors to haze at all sites. The boundary conditions were developed from an annual simulation of the global-scale GEOS-Chem model for calendar year 2002 that was perfor med by Jacob and coworkers (see: http://wwwas.harvard.edu/chemistry/trop/geos/ ). Thus, the modeled transport from the boundaries represents the current best available es timate of international transport of PM 2.5
.

• Other than boundary conditions, local sources (i.e., within the state or in neighboring states) were the largest contri butors to haze at any given si te. The sum of smaller sources from more distant states or regions, however, also repres ented a substantial contribution to haze at all sites. Thus, local sources ha d the greatest impact on visibility, but longrange transport from more distant sources was still significant.

• Although modeling with CMAQ/TSSA (as a CAMx/PSAT modeling QA procedure) was completed for only January and July 2002 (rather than a full year), the two models gave very similar results concerning the relative order of contributors to haze. Boundary conditions were ranked slightly lower as a c ontributor in CMAQ than in CAMx, but they were a major source in both cases.

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## 5. Selecting Sources for BART Sensitivity Modeling

An amendment to the EPA RHR requires the inst allation of Best Available Retrofit Technology (BART) on older facilities emitting pollution that states and tribes determine harms visibility in specially protected areas, such as national parks. The WRAP states and tribes are currently in the process of designating the BART sources in th eir regions. One task in the WRAP-RMC 2006 work plan is to perform sens itivity modeling studies to dete rmine how much influence BART controls on eligible WRAP sour ces would have on air quality in the region. The RMC has developed tools to assist the states a nd tribes in selecting the sources in the WRAP stationary-pointsource inventory to include in BA RT sensitivity modeling. This se ction describes the nature and development of these tools, how we will deploy them to the WRAP states and tribes, and how we will use the results for developing future emissions simulations to evaluate the effect of BART controls on the selected sources.



### 5.1 Background

The initial design of the BART sensitivity mode ling studies requires that the RMC identify the BART-eligible sources within the WRAP stati onary-point-source inventory. We are soliciting the help of the WRAP states a nd tribes to identify these sources by developing spreadsheets that cross-reference information we have about BART eligibility with the re cords contained in the emissions inventories.
As a first approach to this source selection proces s, we used SCC-based BART eligibility criteria and information provided by ERG, Inc., in their report “Identification of BART-eligible Sources in the WRAP Region” (ERG, 2006a) to flag sour ces in the WRAP 2018 point-source inventory as BART-eligible. We produced spreadsheets based on the WRAP 2018 inventory with color coding to flag the sources in the inventory that are identified as BART-e ligible and to indicate eligible sources in the ERG report that are mi ssing from the inventory. We circulated sample versions of these spreadsheets to the WRAP states and tr ibes for comment.
During the first two quarters of 2006, several WRAP states drafted letters identifying the sources that they want designated as BART-eligible fo r modeling. We will use this new information to revise the selections we made in the initial spreadsheets and resend this information to the states for review. We also received some comments b ack from the State of Colorado suggesting ways to improve the presentation of the information in these spreadsheets. We will integrate these comments and redistribute a new ro und of BART-eligibility selec tion spreadsheets to the WRAP states and tribes in preparation for the BA RT sensitivity modeling that the RMC will conduct during the next project year. Th e next section describes the fo rm in which we present the information and how we will use it in the sensitivity modeling.

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### 5.2 Spreadsheets and Memo

The RMC developed a set of spreadsheets and a draf t memo to circulate to the WRAP states and tribes to solicit fee dback about the sources within the WRAP 2018 point -source inventory that we intend to treat as BART-elig ible for modeling. The memo expl ains what we need from the recipients and contains instructions on how to use the spreadsheets (see Figure 5-1). We will distribute the final memo by e-mail and the spreadsheets by FTP.



### 5.3 Colorado Case Study

We are currently planning a case study for depl oying these spreadsheets using the State of Colorado as a test case. We will test our proced ures for using the spreadsheets to collect the information that we need to select BART-elig ible sources, and our strategy for deploying the spreadsheets and receiving comments back from the states and tribes. As not ed earlier, we have already received comments from inventory specia lists at the State of Colorado regarding the content of the spreadsheets and will integrate th ose into new versions of the spreadsheets. We will use the real-world experience that we gain from working with Colorado to improve the tools and process for collecting this informa tion from all the states and tribes.



### 5.4 Future Work

The next steps in the BART eligibility selecti on process are to complete the case study with Colorado, revise the memo (Figure 5-1) by addi ng examples from the Colorado case study to illustrate lessons learned from it, and then send the memo and BART selection spreadsheets out for review to all of the WRAP states and trib es. We will specify a closing date for receiving comments on the spreadsheets. After the closing da te, we will summarize the information in a memo that lists all of the sour ces in the WRAP region that will be treated as BART-eligible for the WRAP-RMC sensitivity modeling. We will then develop control files for SMOKE that will allow us to apply growth or c ontrol factors to the BART-eligible sources. When the details of how we are to treat these sources in th e sensitivity modeling become available from the WRAP, we will add the prescribed multipliers to the emissions from these sources using the SMOKE control files and then develop new inventories and emissions fo r input to CMAQ. Output from the resulting CMAQ runs will be used to assess how BART controls on the chosen sources would affect future air quality.

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DRAFT MEMORANDUM

To: WRAP State and Tribal Emissions Specialists
From : WRAP-RMC Subject: Selection of BART-eligible sour ces in the WRAP 2018 emissions inventories

This memo describes the procedures to follo w to confirm which sources in the WRAP 2018 stationary-point-source inventory will be treated as BART-eligible during upcoming BART sensitivity modeling. The implication of designa ting sources as BART-eligible is that the WRAPRMC will include them in the sensitivity modeling and will adjust the emissions from these sources to determine their influence on visibility in the WRAP region. Example sensitivity simulations for these sources could involve either zeroing out or d oubling their annual emissions magnitudes. The WRAP-RMC is requesting that the states and tribes review the spreadsheets we’ve prepared containing sources that we have designated as BA RT-eligible, and then provide input on whether we’ve selected the correct units within the WRAP 2018 stationary-point-source inventory. We’ve made initial BART eligibility selections based on me mos provided to the WRAP by some states and tribes: Utah, Nevada, South Dakota, and Washi ngton. For those states from which we haven’t received memos, we’ve made our selections basedon Appendix H of the 2006 ERG, Inc., BARTeligible source selection report, “Identification of BART-eligible Sources in the WRAP Region.”

If we do not receive a response to this memo by {Insert Date} , the WRAP-RMC will perform the sensitivity modeling using the default sources curre ntly selected in the spreadsheets as BARTeligible.

Instructions

1. Visit the following anonymous FTP site at UNC -CEP and download the spreadsheet for your region: ftp://ftp.unc.edu/pub/empd/wrap/2006/BART

. 2. Please review the README worksheet in the spr eadsheet for an explanation of the fields contained in the data worksheet.

3. In the data worksheet, review the selections that the WRAP-RMC has made for BART eligibility. Confirm or deny each source selection by adding a Y or N to Column C of the worksheet. We will treat each source that has a Y in Column C as BART-eligible. If you want to add sources to the spreadsheet, turn on the Excel feature “Track Changes” (in the “Tools” pull-down menu) before creating new rows that contain the additional sources.

4. For each source that you confirm as BART-eligible, please review the stack parameters listed in the data worksheet. These are the parameters that we will use to model the plume rise for these sources; they affect the dispersion of the emissions plumes and the subsequent impact on regional air quality. If you find a field that needs to be cha nged, turn on track changes (as above) and then add the new value.

5. Once you’ve completed the review of the BART-eligible sources in your region, e-mail the spreadsheet to Zac Adelman (zac@unc.edu) and Tom Moore ( mooret@cira.colostate.edu ). Thank you for your help in this process.

Figure 5-1. Example draft BART selection memorandum

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## 6. BART Exemption Screening Modeling

As discussed in Section 5, an important componen t of the RHR is the ap plication of BART controls to stationary point sources that satisfy specific requirement s and can be shown to “significantly” contribute to visi bility impairment at a Class I area. To determine whether a source contributes significantly, EPA recommends use of the CALM ET/CALPUFF modeling system; the main components of this modeling system are CALMET (a diagnostic three-dimensional meteorological model), CALPUFF (an air qual ity dispersion model), and CALPOST (a postprocessing package). Six WRAP States requested that the RMC perform BART exemption screening modeling to determine whether poten tial BART-eligible sources in their states contribute significantly to visibility impairm ent at a Class I area. That CALMET/CALPUFF modeling effort is described in this section.
To conduct the modeling, we followed the EPA BART guidelines (U.S. EPA, 2005) and the applicable CALMET/CALPUFF modeling guid ance (e.g., IWAQM, 1998; FLAG, 2000; EPA, 2003c), including EPA’s March 16, 2006, memorandum “Dispersion Coefficients for Regulatory Air Quality Modeling in CALPUFF” (Atkins on and Fox, 2006). This memo was written by the EPA Office of Air Quality and Planning Standards (OAQPS) M odel Clearinghouse Director in response to questions from EP A Region 4 on what constitutes the regulatory version of CALPUFF and recommended CALPUFF options for BART modeling in their region. The memo’s modeling recommendations are followed in our protocol for conducting the modeling ( http://pah.cert.ucr.edu/aqm/308/ba rt/WRAP_RMC_BART_Protocol_Aug15_2006.pdf ), except in the ways noted later in Section 6.1.4. Using th e procedures in this modeling protocol, we are performing initial subject-to-B ART screening analysis modeli ng to determine which sources contribute significantly to visibility impairment at Class I area. After we provide the modeling results to the affected states, it will be up to th e states to determine whether or not a potential BART-eligible source will be subject to BART requirements.
Relevant guidance (IWAQM, 1998; FLAG 2000; EPA, 2003c) suggests that the CALPUFF model is generally applicable at distances from 50 km to 200-300 km downwind of a source, and may also be used for distances less than 50 km when complex flows exist on a case-by-case basis. Class I areas in the West are generally located in comple x terrain, resulting in complex flows. Consequently, the BART screening mo deling that we conducted includes results for potential BART-eligible sources that are locate d within 50 km of a Class I area. The CALPUFF modeling was also performed for downwind dist ances greater than 200-300 km. When providing results to the states, the downwind distance betw een the BART source and the Class I area will be included, as will a recommendation from the RMC concerning the utility of applying the results for Class I areas less than 50 km or great er than 300 km from the source. The individual states will need to make their own regulatory assessment regarding the applicability of these model results from outside the 50-300–km range.

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### 6.1 Background and Setup Inform ation for the CALMET/CALPUFF Modeling

6.1.1 CALMET/CALPUFF model selection and methodology

The March 16, 2006, memorandum cited above from the OAQPS Model Clearinghouse is very clear regarding which is the re gulatory version of the mode l: “The regulatory version of CALPUFF, along with specific regulatory options, re sides on the Earth Tech website, described as the ‘EPA-Approved Version’” (Atkinson a nd Fox, 2006). This version was originally proposed for conducting the BART screening analys is. However, discussions with the Federal Land Managers (FLMs) and others revealed that this version of the model contains errors and that a newer version should be used. Consequentl y, we chose to use a more recent version (April 2006) of the CALPUFF modeli ng system, available at http://www.src.com

. We applied CALMET for separate state domains for the states of Alaska, Arizona, Nevada, New Mexico, Utah, and South Dakota. For the five con tinental-U.S. domains, we used a 4-km grid and 11 vertical layers with a Lambert conforma l conic (LCC) map projection identical to that used for the WRAP MM5 and CMAQ modeling (see Section 1.3 of th e WRAP 2002 MPE report [Tonnesen et al., 2006]; http://pah.cert.ucr.edu/aqm/308/docs.shtml ). The CALMET modeling for these five states used MM5 data at 36-km resolution and terrain and land use data at 4-km resolution, along with surface meteorological an d precipitation observations. For the WRAP Alaska CALMET/CALPUFF modeling, a 2-km gr id was used, along with 15-km-resolution MM5 data, surface meteorological and precipita tion data, and 1-km-resolution land use and terrain data. For CALMET modeli ng for the continental-U.S. states, we used MM5 meteorology data for the years 2001, 2002 and 2003. EPA r ecommends performing three years of CALMET/CALPUFF modeling when using MM5 data, and 2001 through 2003 were the most recent three years with MM5 data available at the time this modeling wa s initiated. For Alaska CALMET modeling, we used one year of data ( 2002) because only one year of historical MM5 modeling is available.
The 36-km MM5 meteorological fields for 2001, 2002, and 2003 were processed into the format expected by CALMET using the CALMM5 pr eprocessor. Wind fields in CALMET are generated by the Diagnostic Wind Model (DWM), which uses a two-step approach to calculate wind fields. In the first step, an initial-guess wind field is adjusted for diagnostic wind effects of terrain, such as channeling, blocking and deflection, and slope flows, to produce a Step 1 wind field. In Step 2, an objective anal ysis is performed to introduce obs ervational data into the Step 1 wind field. For the RMC BART screening mode ling, CALMET was operated using hourly threedimensional 36-km MM5 meteor ological fields and observe d surface meteorological and precipitation data. The initial-guess wind fields were based on hourly 36-km MM5 meteorological data for 2001, 2002, and 2003. These initi al-guess fields were adjusted to account for the diagnostic effects of 4-km terrain and land use data in Step 1 of CALMET/DWM. Note that although surface meteorological and precip itation observations were used as input to CALMET, upper-air meteorological observati ons were not, as they ar e redundant to the MM5 data and may introduce spurious artifacts in th e wind fields. In the ap plication of MM5, the twice-daily upper-air meteorologi cal observations are used as input, with the MM5 model estimates nudged to the observations as part of four-dimensional data assimilation (FDDA). This

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results in upper-air meteorology in the MM5 field that is dynamically balanced and that has higher temporal (hourly vs. 12- hour) and spatial (36 km vs. ~300 km) resolutions than do the upper-air observations. Therefor e, using the upper-air obser vations with CALMET is not necessary as they are already embedded in the MM5 data and in fact would upset the dynamic balance of the meteorological fields, potential ly producing spurious vertical velocities.



### 6.1.2 Use of CALMM5 to extract MM5 data for CALMET/CALPUFF modeling

6.1.2.1 Continental-U.S. states

We used the CALMM5 preprocessor to pro cess the 36-km MM5 data from 2001, 2002, and 2003 for the CALMET/CALPUFF modeling system for th e five continental-U.S. states listed in Section 6.1.1 (Arizona, New Mexi co, Nevada, Utah, and South Dakota). These data were also processed for Montana, which re quested that the RMC provide pr ocessed MM5 data for use in CALMET/CALPUFF modeling that th ey intended to perform themselves. The MM5 simulations for these three years were performed by different organizations, as follows:

• 2001 36-km MM5 Data: Developed by Alpine Geophysics, LLC, for EPA and used in the Clean Air Interstate Rule (CAIR) and Clean Air Mercury Rule (CAMR) modeling (McNally, 2003)

• 2002 36-km MM5 Data: Developed by the WRAP RMC for the WRAP regional modeling (Kemball-Cook et al., 2004)

• 2003 36-km MM5 Data: Developed by the Midwest RPO (MRPO/LADCO) for their regional modeling (Baker, 2004a,b)
The 2002 36-km MM5 data have been more extensively analyzed and tested by the RMC for optimal configuration for performance in the WRAP region. The 2001 and 2003 MM5 datasets have not been evaluated in as detailed a fashion for their accuracy and performance in the WRAP region.
The CALMM5-processed MM5 data for CALPUFF mode ling reside in very large files. Thus, we defined smaller domains for each of the states that need the data in order to make the file sizes and data transfers more manageable. The MM5 data must completely cover the CALMET/CALPUFF modeling domains. They need to include the state of interest, plus Class I areas in nearby states, with sufficient buffer be tween the Class I areas and the domain boundaries (e.g., 50 km) to assure that CALPUFF puffs are not eliminated due to th e centerline leaving the domain while the puff is still having a visibility impact at a Class I area. Based on the locations of the six WRAP continental-U.S. states that re quired MM5 processing assistance, we identified four MM5 extraction (window) dom ains that are shown in Figur e 6-1 and described below.

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Figure 6-1. Relationship of four MM5 window do mains for the states for which the RMC is performing CALPUFF modeling (AZ, MT, NM , NV, SD, and UT) and Class I areas.

• Arizona-New Mexico: The Arizona-New Mexico MM5 window domain is centered on the two states. It extends westward to include the Sequoia and Kings Canyon National Parks and Dome Land Wilderness in central California as well as several Southern California Class I areas. The northern bounda ry of this window domain is far enough north to include Class I areas in southern Utah and Colorado with a 50-km buffer. The eastern boundary was originally placed in a position that included the Wichita Mountains Class I area in Oklahoma, but comments receive d from the FLMs indicated that this was unnecessary. To the south we considered a boun dary placement that would have included Big Bend National Park; however, any BART source impact at Bi g Bend would likely have a much greater effect in the closer s outhern New Mexico and Arizona Class I areas (e.g., Guadalupe and Carlsbad Caverns), so th e southern boundary wa s terminated north of Big Bend National Park.

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• Nevada-Utah: The Nevada-Utah MM5 window doma in includes the two states and extends to include the following locations: (1 ) southwest to include the Southern California Class I areas; (2) northwest to include the northeastern California and some southcentral Oregon Class I areas; (3) north to include the Sawtooth Wilderness Area; (4) northeast to include the Wind Ri ver and Eton Class I areas in Wyoming; and (5) east and southeast to include Class I areas in the Colorado Rocky Mountains and the San Pedro Parks Class I area in New Mexico.

• Montana: The Montana MM5 window domain include s all of Montana plus all Class I areas in Idaho and some in eastern Wash ington and Oregon. The southern boundary was placed to include all of the Wyoming Class I areas. In the east, the Theodore Roosevelt, ND, and Wind Cave and Badlands, SD, Class I areas are included. Also, this window domain was made sufficiently large to incl ude the State of Montana’s CALPUFF modeling domain, which stretches west-east from long itude -177o to -103o and south-north from latitude 44o to 49.5o.

• South Dakota: The South Dakota MM5 window domain had to be made fairly large due to the distances between that state and Cla ss I areas in nearby states. In the west it includes eastern Montana and western Wyom ing Class I areas; the southern portion includes the Mount Zirkel Colorado Class I area; and the northern and eastern boundaries were defined to include the Voyageurs and Boundary Waters, MN, Class I areas.

6.1.2.2 Alaska
For the Alaska CALMET/CALPUFF modeling do main, separate MM5 meteorological modeling was conducted for 2002 on a 45-/15-km nested gr id. To optimize model performance, we performed numerous sensitivity tests to determine the best physics options and other parameters (e.g., time of year when sea ice forms and melts ). The Alaska MM5 modeling is described in a report by Kemball-Cook and co-workers (2005), which can be found at http://pah.cert.ucr.edu/aqm/308/docs/ alaska/Alaska_MM5_DraftReport_Sept05.pdf

. We used CALMM5 to process the Alaska 15-km MM5 results for CALMET/CALPUFF modeling of southern Alaska. Included in the MM5 extraction domain were Denali National Park and Tuxedni Wildlife Refuge Class I areas. The Be ring Sea and Simenof Wildlife Refuge Class I areas are located too far (>500-1000 km) from potent ial BART-eligible sources to be of interest.



### 6.1.3 CALMET/CALPUFF modeling domains

The RMC developed a set of CALMET/CALPUFF modeling domain s in the contiguous U.S. that focus on the following states and nearby Class I areas: Arizona, Montana, * New Mexico, Nevada, South Dakota, and Utah (Figures 6-2 through 6-4). In addition, there is an Alaska CALMET/CALPUFF modeling domain (Figure 6-5). These figures show the Class I areas, potential BART-eligible sources (when availabl e), and locations of the surface meteorological

*Originally, Montana requested only the processed MM5 data and indicated they would perform their own CALMET/CALPUFF modeling. However, more recently the RMC was requested to perform the CALMET modeling for Montana for 2001, 2002, and 2003 and provide the CALMET data to EPA for CALPUFF modeling.

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observation sites. All of the continental-U. S. CALMET/CALPUFF modeling domains are based on the same LCC map projection used in the WRAP MM5 and CMAQ modeling, as defined in Section 6.1.1. Alaska uses its own LCC projection.
The continental-U.S. CALMET/CALPUFF modeling domains are defined using the parameters in Table 6-1. These domains are quite large, and many of the source-receptor distances exceed 300 km. When the RMC provides the modeling resu lts to the states, th e distance between a potential BART-eligible source and an associated Class I area should be considered in interpreting whether a visibility impact is signifi cant. For the Nevada CALMET/CALPUFF modeling, CALMET modeling for 2001, 2002, and 2003 was c onducted for the entire 198 x 306 4-km domain depicted in Figure 6-4a. However, be cause the Nevada BART sources are clustered either near Reno in the north or Las Vegas in the south, fo r CALPUFF modeling we ran two separate modeling domains, centered on northern Nevada and southern Nevada, respectively (Figures 6-4b and 6-4c).

Table 6-1. Definition of continental-U. S. state CALMET/CALPUFF modeling domains using the RPO LCC projection based on an origin at (-97o,40o) and true latitudes at 33o and 45o.
SW and NE Corners (m LCC) Number of 4-km Grid Cells State Southwest Northeast x -Direction y -Direction No. Class I Receptors

Arizona (

– 1944,

– 900) (

– 792, 0) 288 225 12,156
Montana (

– 1656, 360) (

– 360, 1296) 324 234 10,575
New Mexico (

– 1368,

– 900) (

– 504,

– 36) 216 216 4,471
NV CALMET (

– 2088,

– 576) (

– 1296, 648) 198 306 10,254
NV CALPUFF N (

– 2088,

– 200) (

– 1460, 500) 157 175 4,900
NV CALPUFF S (

– 2000,

– 576) (

– 1300, 420) 175 249 8,183
South Dakota (

– 1080, 216) (468, 1044) 387 207 5,786
Utah (

– 1620,

– 540) (

– 792, 648) 207 297 11,365

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(a)

(b)

(c)

Figure 6-2. CALMET/CALPUFF modeling domains and Class I area receptors (green),

observed surface meteorological sites (purpl e triangles), and potential BART-eligible source locations (red dots) for (a) Ar izona, (b) Montana, and (c) New Mexico.

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(a)

(b)

Figure 6-3. CALMET/CALPUFF modeling domains and Class I area receptors (green),

observed surface meteorological sites (purpl e triangles), and potential BART-eligible source locations (red dots) for (a) South Dakota and (b) Utah.

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(b)

(a) (c)

Figure 6-4. CALMET/CALPUFF modeling domains and Class I area receptors (green),

observed surface meteorological sites (purpl e triangles), and potential BART-eligible source locations (red dots) for (a) Nevada CALMET modeling, (b) northern Nevada CALPUFF modeling, and (c) south ern Nevada CALPUFF modeling.

The CALMET/CALPUFF modeling domain for Alas ka is shown in Figure 6-5, which also shows the locations of the Denali and Tuxedni Cl ass I areas and some of the potential BARTeligible sources. The Alaska modeling used a 2-km horizontal grid resolution, surface meteorological data from five sites, upper-air meteorological data from MM5 only (i.e., no observational data), and the following grid definition:

Longitude origin ................... -151o Latitude origin ..................... 59o 1 st standard parallel ............. 30o 2 nd standard parallel ............ 60o x-origin offset ....................... -200 km y-origin offset ....................... 0 km Horizontal grid resolution ... 2 km Number of grid cells ............. 275 x 325 Domain extent ...................... 550 km x 650 km

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-200 -100 0 100 200 300 0 100 200 300 400 500 600

Figure 6-5. Alaska CALMET/CA LPUFF modeling domain showing the potential BARTeligible source locations (red dots) and Dena li and Tuxedni Class I area receptors (green).



### 6.1.4 CALMET modeling options and comparison with regulatory default settings

Detailed CALMET options and settings and a comparison with those recommended by EPA for the regulatory application of CALMET (Atkinson and Fox, 2006) can be found in Table 3-2 of the WRAP-RMC BART modeling prot ocol (Tonnesen et al., 2006; http://pah.cert.ucr.edu/aqm/308/ba rt/WRAP_RMC_BART_Protocol_Aug15_2006.pdf ). As noted at the beginning of Section 6, our screen ing analysis CALMET parameters conform with all of EPA’s recommendations for regulatory application of the CA LPUFF modeling system, except in the following ways:

1. The WRAP CALMET/CALPUFF modeling dom ains were based on the same LCC projection used in the WRAP MM5 and CM AQ modeling, rather than the EPA default LCC projection.

2. The maximum allowable mixing height (Z 1MAX) for the WRAP CALMET modeling was 4,500 m AGL, versus the EPA regulator y default of 3,000 m AGL. A 3,000-m AGL maximum mixing height may be appropriate for the eastern U.S., but in the western U.S. in the summer, mixing heights may exceed this value. In their BART modeling protocol,

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(CDPHE, 2005) the Colorado Department of Health and Environment (CDPHE) presents compelling evidence that mixing heights ex ceed 3,000 m AGL in Colorado during the summer. We would expect other areas in the we st to have higher mixing heights as well, so we adopted the 4,500 m AGL value used by CDPHE.

3. The EPA default assumes no MM5 data will be used (IPROG=0). In the RMC BART screening analysis, MM5 data were used as an initial-guess field (IPROG=14).

4. The EPA default assumes values for IEXTRP (-4) and RMIN2 (4) that are incompatible with each other. We used values that ar e consistent with each other (1 and 4, respectively). Because we used hourly 36-km MM5 data to define the upper-level winds, the extrapolation of the surface wind data aloft as recommended by EPA (IEXTRP= -4) was not needed, and in fact would upset the dynamic balance of the aloft winds, producing spurious ve rtical velocities.

5. We set IAVET to 0 to turn off spatial aver aging of the temperatur e interpolation, since the MM5 temperatures are already fairly smooth.

6. We set the beginning and ending water land use categories to 51 and 55, respectively, rather than using the EPA default (999) that assumes no water land use categories.



### 6.1.5 CALMET performance evaluation

The CALMET meteorological fields were spot-checked for reasonableness using visualization and animation tools, but no comprehensive ev aluation was undertaken. The main inputs to CALMET are the hourly 36-km MM5 data (15-km MM5 data for Alaska), surface meteorological and precipitation measurements, te rrain data, and land use data. The 36-km MM5 fields were evaluated previously (McNally, 2003; Kemball-Cook et al., 2005; Baker, 2004a,b), and the terrain and land use inputs were evalua ted by comparing spatia l plots of the inputs against topographic and land use maps. The Al aska 15-km MM5 data were also evaluated (Kemball-Cook et al., 2005).



### 6.2 Summary of Results from CALMM5

As explained earlier, the CALMM5 preprocessor was used to process the 36-km MM5 data for the four continental-U.S. window domains shown in Figure 6-1 and the 15-km MM5 data for the Alaska domain (Figure 6-5). QA/QC procedures were performed to assure that the jobs ran correctly and the data were not corrupted. The data are availa ble for download from the RMC web site at http://pah.cert.ucr. edu/aqm/308/bart.shtml

.


### 6.3 Summary of Results from CALMET/CALPUFF

We have completed CALMET modeling for the fi ve continental-U.S. states for 2001, 2002, and 2003. QA/QC has been conducted to assure that CALMET was exercised with the proper parameters and that the jobs were completed successfully. Some spot -checking of the CALMET winds, temperatures, and mixing heights has b een performed using vi sualization tools.

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Additional QA/QC will be perf ormed within the limits of available resources. The CALMET modeling for Alaska for 2002 has also b een completed and subjected to QA/QC

. As of this writing, CALPUFF m odeling of the potential BART-e ligible sources is ongoing, and results are being provided to the states. It will be up to the states to apply these results to determine which sources contribute significantly to visibility impairment and are therefore subject to the BART requirements. It is also up to the states to decide when the CALPUFF modeling results will be released to other stak eholders who might want to perform additional modeling analyses using these data.

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## 7. “Clean Conditions” 2002 CMAQ Sensitivity Analysis

There are many natural sources of ambient PM 2.5 , both direct emissions of primary PM 2.5 (such as windblown dust) and emissions of gaseous sp ecies that undergo photoc hemical transformation or condensation to form secondary PM 2.5 . Natural sources of PM 2.5 are of concern because they represent sources that cannot be controlled. Estimates of natural haze levels have been developed by EPA for visibility planning purposes and are described in Guidance for Estimating Natural Visibility Conditions Under the Regional Haze Rule (U.S. EPA, 2003a). These are the natural haze levels to be used in glide path calculations, such as thos e we performed as part of the visibility projections for 2018 (see Section 3.3). However, the natural haze levels developed by EPA for glide path calculations were based on am bient data analysis, not on visibility modeling. This question thus arises: Would modeled levels of natural haze be consis tent with the values estimated by EPA for visibility planning? If the natural haze levels calculated by the model were substantially higher than the leve ls used for planning purposes, this would make it more difficult for modeling studies to demonstrate progress in attaining visibility goals, because the model would predict haze levels that exceeded EPA’s natural haze levels even if all anthropogenic sources of PM 2.5 were removed from the modeling. The RMC explored this issue by conducting a CMAQ sensitivity “clean conditions” simulation
There are many uncertainties and unknowns regarding natural emi ssions. There have been only limited studies of natural emissions conditions. We know that there are very large uncertainties in the categories of natural emissions included in our emissions invent ories, and that some categories of natural emissions are not included at all. Also, it is difficult to know what truly natural emissions would have been like in the absence of human modifications of the environment. For example, wildfire emissions are a large source of natural emissions in our modeling, but how much larger might that source be in the absence of fire suppression efforts? For all of these reasons, we decided to describe this sensitivity simulation as a “clean conditions” scenario rather than a “natural conditions” scenario. In th is simulation, we removed all anthropogenic emissions from the inventory and in cluded only those emissions that were defined as biogenic in our 2002 base case (Base02). Thus, this model simulation does not represent true natural conditions. It indicates in stead the lowest haze levels that could be achieved in the model if all anthropogenic emi ssions were zeroed out.



### 7.1 Description of Emissions Used

We derived the emissions for the clean 2002 sens itivity case from case Base02a. Because it was a sensitivity analysis to test the impacts of natu ral emissions sources on visi bility, we refer to it as scenario Base02nt, where “nt” refers to natural. We included the following emissions categories in Base02nt:

• Biogenics: Generated in case Base 02a by BEIS3.12 using SMOKE.

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• WRAP Ammonia: The Base02a ammonia emissions for the WRAP region were developed with a GIS by ENVIRON. The fi ve emissions category modeled included three anthropogenic sources (domestic animals, livestock, and fertilizer application) and two natural sources (soils and wildlife). We used only the two natura l sources in scenario Base02nt.

• CENRAP and MRPO Ammonia: To create ammonia inventory files for only natural sources, we used a list of SCCs representi ng natural sources to extract the emissions records of these sources from the monthly inve ntory files that were used in Base02a. We found that there were no natura l ammonia sources in the MRPO monthly inventory files.

• Natural Area Sources: The Base02a area-source inventory files incl uded natural sources, such as wildfires and wild animals. We extr acted these records from the stationary-areasource inventories. Note that the WRAP ar ea-source files did not include any natural

sources.

• Natural Fires: Of the five fire categories modele d in Base02a—wildfires, wildland fire use, non-Federal rangeland prescribed fires, prescribed fires (which were split into natural and anthropogenic prescribed for this pu rpose of this sensitivity), and agricultural fires—we used the categories that represent na tural fires: wildfires, wildland fire use, and natural prescribed fires.

• Windblown Dust: We used the windblown dust inve ntory that ENVIRON and the RMC developed for use in case Base02a. Additional details on this dust i nventory are available
at http://www.cert.ucr.edu/aqm/308/wb_dust2002/wb_dust_ii_36k.shtml

. We took the biogenic and windblow n dust emissions directly from the Base02a SMOKE outputs that are stored at the RMC. For the fire (inc luding both point and area fi res), natural area, and ammonia emissions, we reprocesse d these data specifically for s cenario Base02nt using the same ancillary data (temporal, chemical, and spatial a llocation data) that we used in case Base02a. QA plots and documentation for scenario Ba se02nt are posted on the RMC web site at http://pah.cert.ucr.edu/ aqm/308/qa_Base02nt36.shtml

.


### 7.2 Summary of Results

Figure 7-1 shows the model-reconstructed light extinction in the clean emissions model simulation. Because the natural fire emissions in the WRAP states were a major component of the clean emissions, the largest visi bility impairment is in the regi ons with natural fire emissions. Contributions to light extinction from natural s ources were small in regi ons without large fire emissions, as evidenced in the eastern U.S., wher e the extinction was only slightly larger (about 2 Mm -1 ) than perfectly clean Ra yleigh conditions of 10 Mm -1

. Although there are large uncertainties in the natu ral emissions, and we also know that we are missing certain types of natural em issions, the components of the na tural inventory used in this sensitivity simulation did contribut e to relatively large visibility impairment in regions where there were large wildfires. Extinc tion coefficients as large as 90 Mm -1 were simulated in the southern Oregon and northern California regions; this was most likely a result of the large

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Biscuit fire in Oregon, plus contributions from smaller fires and other natural emissions. These visibility impairment levels exceed the natural visibility levels specifi ed in the EPA regional haze natural visibility guidance document. It w ill thus be more difficult for the modeling to demonstrate attainment of progress goals in areas of the country subject to wildfires because of their large contribution to visibility impairment that is not co ntrollable. In other regions of the country for which the inventories lacked large natural fire emissi ons, the modeled clean visibility was only slightly greater than clean Rayleigh co nditions. Note the model results may be overly optimistic in these regions because we lack a complete, accurate natural emissions inventory.

Figure 7-1. Annual average model-reconstruc ted “clean conditions” visibility as extinction coefficient.

These results are all very tentative because of the large uncertainties in natural emissions. Considerable effort would be needed to more fully investigate natural conditions in future modeling studies. It will always be difficult to determine and quantify “clean conditions” based on observations because of the pervasive influence of anthropogenic emissions.
Also as part of this sensitivity analysis, we evaluated the contri butors to organic carbon aerosols (OC) for the clean conditions scenario. The CMAQ model represents explicitly three classes of organic carbon aerosols:

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• AORGPA: Primary anthropogenic OC resulting from direct organic mass emissions, such as primary organic aerosol (POA).

• AORGA: Secondary anthropogenic OC resulting from aromatic VOCs, such as xylene, toluene, and cresols.

• AORGB: Secondary biogenic OC resulting from biogenic VOCs, such as terpenes.
Because it was not cost effective to carry out CAMx/PSAT simulations with OC, we analyzed the explicit OC results for the clean conditions case, and then compared those results to the Base02b case in an attempt to in fer the relative contributions of biogenic and anthropogenic VOCs to OC. These results are difficult to interpret for at least two reasons:

• Because of the simplified approach us ed by CMAQ and the Carbon Bond Mechanism version 4 (CB4) to represent these species, it is not possible to accurately classify all emissions into the CMAQ model as either bi ogenic or anthropogenic based simply on the species name. Thus, some biogenic OC might be included with AORGA, and some anthropogenic OC might be included in AORB.

• Some fire emissions are classified as anth ropogenic, but these emissions might include species such as terpenes that are typically considered biogenic . Using the analysis approach in which all terpenes are assume d biogenic then incorrectly causes some anthropogenic emissions to be labeled biogeni c when we use the simplified approach of analyzing OC in terms of AORGPA, AORGA and AORGB.
In spite of these difficulties, however, the result s should classify the majority of the emissions correctly as either bi ogenic or anthropogenic.
For each of the above three components of OC, we prepared plots of the annual average mass in the Base02b case, and then estimated the controllable mass as the difference between the Base02b case the Base02nt clean emissions scen ario. Figure 7-2 shows the annual average mass of OC contributed from AORGPA in case Base02b (top) and the portion of that mass attributed to controllable emissions (botto m). Comparing these two plots indi cates that in the western U.S. there is considerable AORGPA mass th at is not controllable. It is lik ely that much of this mass is from fires, since uncontrollable AORGPA mass is pr esent at the site of large fires in southern Oregon and north of Tucson, AZ.
Figure 7-3 shows the annual average mass of s econdary OC contributed from AORGA in the Base02b case (top) and the portion of that mass attr ibuted to controllable emissions (bottom). These plots indicate that virtua lly all of the AORGA mass is cont rollable, since the bottom plot is almost identical to the top plot.
Figure 7-4 shows the annual average mass of OC contributed from AORGPA in the Base02b case (top) and the portion of that mass attributed to controllable emissions (bottom). These plots indicate that although most of th e AORGB mass is not controllabl e, a significant amount of mass is controllable. It is likely that the contro llable AORGB mass results from VOC oxidation chemistry and the larger amount of biogenic mass th at is oxidized and subsequently condenses to form OC in the Base02b case. These results indicate that controlling O 3 precursor emissions is effective at reducing a small but sign ificant fraction of the biogenic OC.

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Figure 7-2. Annual average modeled primary anthropogenic OC (AORGPA) in Base02b (top) and the portion that is “controlla ble” primary anthropogenic OC (bottom).

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Figure 7-3. Annual average modeled secondary anthropogenic OC (AORGA) in Base02b (top) and the portion that is “controllable” secondary anthropogenic OC (bottom).

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Figure 7-4. Annual average modeled primary biogenic OC (AORGB) in Base02b (top) and the portion that is “controlla ble” primary biogenic OC (bottom).

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It might be difficult for the WRAP states and tribes to use these results quantitatively in developing emissions control strategies for visibility SIPs and TIPs. However, the results do provide some insight into the relative contributions of biogenic and anthropogenic OC as well as the amount of each that is controllable in the model simulations.
Finally, we note that there are uncertainties in the modeled emissions of anthropogenic VOCs, and larger uncertainties in the modeled emissions of biogenic VOCs. It is not possible to evaluate the model performance individually for bioge nic and anthropogenic OC because the OC measurements do not distinguish between those two forms. Instead, we can only compare total modeled OC to total measured OC. Therefore, even when the model achieves good performance for total OC, it is possible that the model may be overpredicting one component of total OC and underpredicting the other. The inability to ev aluate model performance for each component of OC increases the uncertainty of the results desc ribed here and illustrated in Figures 7-2 through 7-4, so caution should be used when drawing co nclusions about the sources of OC based on these results.

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## 8. Small-Fires Sensitivity Analysis

The effect of emissions from small fires on visi bility is a key area of uncertainty. Although small fires are much more frequent than large fires, only a minor fraction of total fire emissions is formed by the small fires. At the same time, cons iderable effort is required to compile emissions data for the small fires because of their large numb er. In this sensitivity study we investigated the effects on model performance of including small fires in or near Class 1 areas. If model sensitivity simulations suggest that small fires have minimal effects on visibi lity at Class I Areas, their emissions could then be neglected in future modeling studies, and state and Federal land managers could significantly reduce the effort they spend tracking small fires for development of emissions inventories. Alternatively, if model sensitivity studies indicate that small fires can affect visibility in Class I Areas, it might be nece ssary to continue recordkeeping for these fires. To address this question, WRAP tasked the RMC with performing a fire sensitivity study.

In consultation with the WRAP Fire Emissions Joint Forum (FEJF), we performed a model sensitivity simulation in which we eliminated a ll fires smaller than 100 acres in woodlands or smaller than 300 acres in grasslands. We used the larger limit for grasslands because grasslands have lower fuel density and lower emissions per acre burned comp ared to woodlands.
For this study we used the high-resolution, 12- km model domain instead of the 36-km domain because we were concerned that contributions of small fires might be difficult to model on the larger grid. Because each 36-km grid cell has an area of 1,296 km 2 , introducing emissions from a 100-ac fire into a 1,296 km 2 grid cell would create significan t artificial dilution of the plume. Using the 12-km model domain in troduced less artificial dilution and provided a more rigorous test of the visibility impacts of small fires.



### 8.1 Emissions Scenarios

The 2002 base case scenario (Base02) included all fire emissions. In the sensitivity case, we evaluated the visibility effects of small fires by performing a CMAQ sensitivity simulation in which we removed all small-fire emissions. We removed all wild and prescribed fires smaller than 100 acres in woodlands or smaller than 300 acr es in grasslands. The sensitivity scenario was produced using a list of fires provided to th e RMC by Dave Randall at Air Sciences, Inc. Agricultural burning and non-Fede ral rangeland fires were not removed in the sensitivity case (i.e., these fires were included in both the base case and the sensitivity case).
The procedure for the small-fire sensitivity test was as follows:

• Process emissions for sensitivity case by excluding small fires.

• Perform CMAQ sensitivity simulation using th e 12-km model for July and November 2002. We chose those two months because July has the greatest total fire emissions, and November has a large proportion of smaller, prescribed fires and more stagnant meteorology.

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• Graphically compare base case visibility results minus fire sensitivity visibility results to assess the effects of the small fires.
Table 8-1 summarizes the numbers of large and sma ll fires for the two mont hs studied. July had far fewer small fires (1,250) than November ( 5,207) because of the increased prescribed burningin November. Also smaller in July was the percentage of fires classified as small: 62%, compared with November’s 91%. Table 8-2 summarizes the mass emissions of NO x and primary PM 2.5 from small and large fires in July and Nove mber. This table indicates that for July the small-fire sensitivity case had 5,706 fewer tons of PM 2.5 than the base case (which translates to only 2.6% less), while for November it had 8,701 fewer tons of PM 2.5 (38.3% less).

Table 8-1. Numbers of small and large fires in July and November 2002.

Number of Fires Period All Fires Large Fires Small Fires Percent that Were Small
July 2,007 757 1,250 62%
November 5,697 490 5,207 91%

Table 8-2. Mass emissions of NO x and primary PM 2.5 from small and large fires in July and November 2002.

Mass Emissions (tons/month) Period Species Total Emissions Large Fires Small Fires Percent from Small Fires

NO x 56,434 54,959 1,475 2.6% July PM 2.5 219,337 213,631 5,706 2.6%
NO x 7,789 3,608 4,180 53.7% November PM 2.5 22,725 14,024 8,701 38.3%



### 8.2 Summary of Results

We used a variety of postprocessing tools on th e CMAQ sensitivity results to show them in different ways:

• PAVE spatial plots showed the daily or monthl y average change in visibility (an example is shown in Figure 8-1). Mont hly average plots displayed the spatial patterns of the impacts of small fires, while hourly animations showed the plumes from individual fires. The results indicated that the visibility impacts are very close to the locations of the fires.

• Stacked-bar time-series plots for each IMPROV E monitoring site showed the impacts of fires at each Class 1 area (Fi gure 8-2 is an example). These plots were prepared as the base case minus the sensitivity case to show the contribution to light extinction of the

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small-fire emissions. (These plots were ba sed only on modeling results; they did not show IMPROVE data.)
We posted on the RMC web site the monthly av erage CMAQ results showing the impact of small fires on emissions of each PM species and on model-reconstructed extinction (in deciviews). This web page, http://www.cert.ucr.edu/aqm/308/cmaq.shtml#fire2 , also includes plots of daily average model results.
We reached the following conclusions from the small-fire sensitivity study:

• Small fires are important in terms of modeled impacts on visibility.

• There is no air-quality-related reason to esta blish a de minimis fire size of 100 acres in woodlands and 300 acres in grasslands for re porting fire events. However, there may be valid administrative reasons why states/tribes will need to accept some de minimis fire size for reporting wildfire data.

• It is possible that a smaller de minimis level could be established, but we did not evaluate other de minimis levels in this study.

Figure 8-1. Modeled effects of small fires on visibility (as change in deciview) for the Montana and Idaho area on November 11, 2002. Plot shows Base02 minus the

sensitivity case that has small fires rem oved, so the deciview values indicate the contributions from small fi res to visibility impairment.

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Figure 8-2. For each day in July and November 2002 at the Agua Tibia, CA, Class I area,

the top plot shows the total modeled daily ave rage light extinction in Base02b, and the bottom plot shows the modeled contribution of small fires to light extinction. In the top

plot the red dashes show the total measured light extinction on the 20% worst days.

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## 9. Comparison of Two Fire Plume Rise Algorithms

Accurately estimating the vertical distribution of smoke plumes from fire emissions is both a challenging problem in emissions modeling and critical for ev aluating the impacts of these sources on ambient air quality. Fire emissions estimates have traditionally been modeled as an areal average (either a county average or a model-grid-cell average) in only the surface model layer. Observations of actual fire emissions, how ever, show that fire plumes actually are lofted vertically several hundreds if not thousands of meters, depend ing on the fire size, fuel conditions, and meteorology. In this task we compared two different algorithms for distributing fire emissions into the vertical la yers of an air quality model. This se ction begins with descriptions of the fire events that we modeled to test these algori thms and the two approaches we used to estimate the characteristics of the plumes from different fire events. We describe the concept of plume rise in the context of emissions modeling, and the technical details of the two algorithms we tested. Finally, we discuss the results from our analysis and our plans for future work.



### 9.1 Description of FEPS and Fire Events Used

We selected five fire events to use in evaluating the effects of the two plume rise algorithms on the vertical distribution of fire emissions estimated by SMOKE. The five events ranged in size from 850 to 9,860 acres and included wildfires, pres cribed fires, and wildland fire use events in Colorado, Oregon, and Arizona. Details on the fire events are given in Table 9-1. We used the Fire Emission Production Simulator (FEPS) (Anders on et al., 2004) to determine heat flux for each fire. FEPS provides an MS-Windows-based gr aphical user interface (GUI) through which a user specifies certain paramete rs that characterize a fire; FE PS then produces output data on emissions, heat flux, and plume rise height. Exampl es of input parameters are date, location, size, fuel type, and burn type. The FEPS program is fair ly complex. It includes default values for most parameters, but also includes menus regarding sp ecific fuel types and burn types. The user can select event-specific characteristics from those menus and/or override the default values with user-specified data.
Our goal was to use FEPS to determine heat flux data. We used default data for most parameters, except for the fire date, location, and size. We were also required to sele ct certain values from default choices within menus, such as fire time and spread patterns. We were uncertain about the values to use for these parameters and also a bout the fuels choices, so this is a source of uncertainty in our analysis. It is possible that if an expert FEPS user entered the data into the FEPS GUI, he or she might obtain results that di ffer from ours. We also note that FEPS is not suitable for use in modeling a large number of fire events because entering data via the GUI is very labor intensive.

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Table 9-1. Fire events summary (based on 2002 WRAP fire inventories).
Daily Emissions (tons/day) Fire Type 1 State Date Fire Size (ac) CO PM 2.5 NO x VOC Heat Flux (Btu/day)

WFU CO Jul 14 850 3,382.6 282.08 72.57 159.18 82,530,000,000
RX AZ Nov 7 2577 3,988.1 332.58 85.56 187.68 268,320,000,000
WF AZ Jun 30 9860 19,804.3 1,651.5 424.9 931.97 1,036,600,000,000
RX OR Sep 24 1000 173.4 14.46 3.72 8.16 300,030,000
WF OR Aug 3 7885 25,293.8 2,109.3 542.6 1,190.3 2,237,008,255,600

1 WFU= wildland fire use; RX=prescribed fire; WF=wildfire



### 9.2 Plume Rise Algorithms

Plume rise is an emissions modeling concept for simulating the vertical di stribution of emissions from large, elevated point sources. The plume rise algorithm in SMOKE is based on the equations developed by Briggs (1984), and consider s atmospheric stability, wind speeds, plume buoyancies, and the stack height relative to the vertical layer boundaries to distribute emissions plumes vertically across multiple model layers. Until recently, the default implementation of the Briggs algorithm in SMOKE was not applicable to fires because they lack conventional stack parameter information, such as stack heights a nd diameters and plume temperatures and velocities. The two plume rise approaches we compared in this sensitivity analysis circumvent this problem either by calculating the plume rise outside of SMOKE (Section 9.2.1) or by deriving the variables needed by the Briggs equation fr om fire-specific parameters (Section 9.2.2).



### 9.2.1 WRAP-FEJF approach

The WRAP Fire Emissions Joint Forum (FEJF) de veloped a work-around for the fire plume rise constraint in SMOKE using a methodology to preco mpute fire plume parameters (Air Sciences, Inc., 2004). In this method, referred to here as th e WF approach, fires are categorized into five plume classes that are defined by increasing poten tial plume heights based on a range of possible fire size classes, with respect to virtual acre si ze. Virtual fire sizes are calculated using Equation 9-1 and are used to relate fuel loadings to a representative stack diameter for fire plumes. The normalizer in Equation 9-1 is derived from th e National Fire Danger Rating System (Andrews and Bradshaw, 1997) for different types of fires.
Virtual Acre Size = Daily Actual Acre Size x (Fuel loading) -1⁄2 / Normalizer (9-1)
For the various plume classes based on the virtua l fire acreage, hourly plume top, plume bottom, and the fraction of the plume in model layer 1 are derived using Equations 9-2, 9-3, and 9-4, respectively:
Plume Top hour = (BE hour ) 2 * (BE size ) 2 * Ptop max (9-2)
Plume Bottom hour = (BE hour ) 2 * (BE size ) 2 * Pbot max (9-3) Layer 1 Fraction hour = 1 – (BE hour * BE size ) (9-4)

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where BE hour is hourly buoyancy e fficiency (unitless), BE size is a fire-size-dependent buoyancy efficiency (unitless), Ptop max is the maximum height of the plume top (meters), and Pbot max is the maximum height of the plume bottom (meters). R ecommended values as a function of fire size for these four variables are provided in the FEJF Phase II fire report (A ir Sciences, Inc., 2005). To calculate the hourly layer fractions for the emissions outside of layer 1, SMOKE uses the hourly plume top and plume bottom values and the full range of model layer heights to derive a layer distribution based on a weight ed sigma pressure coordinate. Fo r the layers that contain the plume top and plume bottom, SMOKE calculates a weighted layer fraction based on the fraction of the layer that contains the plume. For example, if the plume top falls at the exact midpoint of a layer, the weighted sigma pressure fraction is multiplied by 50% to find the fraction of emissions allocated to that model layer.
As part of applying the MF approach in this sensitivity study, we also modified SMOKE to use these precomputed hourly plume parameters in simulating fire events.



### 9.2.2 SMOKE-Briggs approach

The U.S. EPA, the United States Forest Service, and the University of North Carolina Center for Environmental Modeling for Policy Developmen t (CEMPD) collaborated in modifying the plume rise calculation in SMOKE to accommodate fire sources that do not have typical stack parameters. This modification derives a plume buoya ncy efficiency from th e daily fire heat flux to prepare the fire information for input to th e Briggs plume rise algorithm (Pouliot et al., 2005). In this project we used FEPS to calculate the daily heat flux, Q (Btu/day), as described above. To use the Briggs algorithm, we apply Equation 9-5 to relate Q to the plume buoyancy efficiency, F (m 4 /s 3 ), as follows:

F

= Q x 2.58 x 10 -6 (9-5)
Buoyant efficiency (BE size ) and fire size in acres are related using Equation 9-6:

BE size = 0.0703 x ln(fire size) + 0.03 (9-6)
The smoldering fraction ( S fract ) is then derived using Equation 9-7:

S fract = 1 – BE size (9-7)
The SMOKE-Briggs approach, herein referred to as the SB approach, uses the actual size of the fire as opposed to the virtual size used in th e WF approach, and makes calculations on a daily basis rather than an hourly basis. The smoldering fraction is used to allocate emissions to the layers below the plume bottom. All of the emissi ons from the SB approach are allocated to the model layers using a weighting based on the sigma- pressure vertical coor dinate. A key difference between the WF and SB approaches is that the SB approach uses the same predicted local hourly meteorological fields (wind, temperature, pressu re, etc., derived from MM5) to calculate plume rise and layer fractions as are used to drive the subsequent ai r quality model simulations. We expect that using MM5 data will make the SB re sults more scientifically sound than the results from the simpler WF approach.

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### 9.3 Emission Modeling

We applied the WF and SB plume rise approaches to the five fire events to compare how they each affected the vertical distribution of the fi re emissions. We used the actual emissions data provided by ARS for these fires for both the WF a nd SB approaches. It should be noted that the coefficients and formulae used for the WF appr oach were derived using Western U.S. fuel loadings (vegetation dependent) and climatological data. While we did apply event-specific fuel and heat flux information in both approaches, th e FEJF equations were validated using Western U.S. fire events and so may not be as suita ble for fires in other regions of the world.
Invoking the WF approach when using SMOKE re quires using Equations 9-1 through 9-4 toprecompute hourly plume parameters for each fire. Combined with daily estimates of the pollutant emissions from each fire, the plume data are used by SMOKE to calculate hourly layer fractions for allocating the fire emissions to th e model layers. Using the SB approach involves providing SMOKE with daily acreage and heat fl ux data along with the daily pollutant inventories. SMOKE uses these data to derive the hour ly layer fractions for distributing emissions to the vertical model layers. We configured the SMOKE program Smkreport to output hourly emissions reports by layer to compare the ve rtical distributions of the fire plumes.



### 9.4 Results

Appendix B contains figures that graphically summarize the results of the plume rise sensitivity analysis. Figures B-1 through B-10 compare the vert ical layer structures resulting from the WF and SB approaches. In both cases we configured the model to distribute the emissions into 19 vertical layers at a 36-k m horizontal grid resolution.
For each of the five fire events there are two sets of plots; each set contains a pair of plots, the top one for the WF approach and the bottom one fo r the SB approach. The first type of plot in each set (Figures B-1, -3, -5, -7, and -9) is a 3-D bar chart that plots the hourly emissions in each layer as a function of time of day and vertical layer number. The s econd type of plot in each set (Figures B-2, -4, -6, -8, and -10) is a combination of a stacked-b ar plot and a line chart showing the hourly emissions magnitudes as a function of time of day. Each stack segment represents the distribution of emissions across the model layers at each hour. The line overlaid on each of these plots is plotted on the right y -axis and illustrates the height of the maximum layer for the emission plume at each hour.
There are three plume characteristics that we us ed to compare the two plume rise approaches: (1) maximum height of the plume, (2) the fraction of emissions allocated to the first model layer, and (3) the distribution of the plume across all of the model la yers. Regarding the calculated maximum plume height, there is considerable variation between the tw o approaches. The WF approach calculates maximum plum e heights 2 to 3 times greater th an the SB approach, with the WF emissions allocated 5 to 8 laye rs higher than the SB approach.
In the layer 1 analysis, we found that the WF approach allocates a much larger percentage of emissions to layer 1 than does the SB approach. Fo r the five fires in this study, the WF approach allocated 35%-51% of the total daily emissions to the first layer, while the SB approach allocated

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only 0.2%-12% to that layer. The WF approach pr edicts that the first layer receives the single largest fraction of emissions as compared to the rest of the m odel layers. In contrast, the SB approach shows that the top m odel layer receives the single la rgest fraction of emissions.
Plume distribution across the model layers is al so vastly different between the two plume rise approaches. A notable feature from the WF appr oach is the layer discontinuity of the plume distribution between the first and elevated model layers. The figures in Appendix B show that in many of the simulation hours, laye r fractions calculated using the WF approach jump from the first model layer across several layers to the upper model layers. For example, Figure B-2 shows that in hour 14 the WF approach allocates a bout 30% of the emissions from the Colorado wildland fire use event to layer 1 and then the rest of the emissi ons to layers 14 through 18; the emissions pass completely over layers 2 through 13. The SB approach does not create this layer discontinuity. All of the layers between the groun d and the plume top receive emissions, with the allocation weighted by the sigma pressure coordinate.
The temporal and chemical distributions of th e emissions do not differ between the two plume rise approaches. The diurnal temporal consistency is illustrated in the similarity of the time series plots and the magnitudes of the emissions across the two approaches. The significant differences in the emissions modeling results are in the laye r distribution of the plum es and in the maximum layer heights at each hour, refl ected in the differences in the stack segments and the line overlays, respectively.



### 9.5 Conclusions

The two plume rise approaches we studied produce vertical plume prof iles that are vastly different. The WF approach calculates higher plumes than the SB approac h, puts the majority of the emissions in the first model layer, and shows discontinuities in plume distribution between the first model layer and the elevated layers. Th e SB approach calculates plumes that have the majority of the emissions near the top of the plume and a con tinuous distribution of emissions from the ground to the plume top. The differences in the emissions allocation to the first model layer results from the assumpti ons concerning the vertical exte nt of smoldering emissions. The WF approach assumes that all smoldering emission s remain in layer 1 and contains an explicit calculation for the fraction of emi ssions allocated to the first layer. With the SB approach, the smoldering emissions are assumed to be distribu ted across all of the la yers beneath the plume bottom. These contrasting assumptions result in the continuity differences across model layers observed in the layer fractions calculated by the two approaches.
Because the WF approach uses a look-up ta ble of maximum plume top and plume bottom heights as a function of fire si ze, the maximum height and verti cal extent of the plumes are controlled by climatological parameters that may not be applicable outside of the Western U.S. The more generally applicable SB approach us es modeled local meteorology to calculate the maximum height and vertical extent of the plumes.
We have found that the SB approach is curren tly impractical at the scale required for WRAP modeling because FEPS must be used to calculate a daily heat flux for every fire event. Without the option of running in batch mode, FEPS cannot be used to process the thousands of fire events

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we model in the WRAP region. Currently FEPS is a bottleneck in the use of this SB approach, an issue separate from its impacts on the pl ume rise of selected fire events.
The next release of SMOKE will circumvent the need for FEPS by calculating the heat flux internally. A new file format in SMOKE called the one record per line (ORL)-fire format reads in the variables used by the FEPS heat flux algor ithm and uses them to calculate heat flux internally. This facilitates the use of the SB appr oach to calculate plume rise for multiple fires at once. Many of the variables used in the heat fl ux calculation are treated as constants in the FEPS algorithm (such as the fuel heat content), and will be provided in example SMOKE input files. To use the modified SB algorithm, a new daily fuel loading variable is re quired in addition to the number of acres burned. The SMOKE release that contains the new file format became available in October 2006. We recommend reformatting the WRAP fire inventories from the IDA format to ORL format so that we can apply the SB algo rithm on an inventory-wide basis and then test the effects of this plume rise approach on air qua lity modeling results. To determine how either of these two approaches affects modeled air qual ity, CMAQ runs need to be performed using the results from this study.
The RMC is currently preparing other fire events for use in comparing the effects of the two plume rise approaches. We are al so drafting a manuscript to publish the results of our plume rise sensitivity work in a peer-reviewed journal.

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## 10. Data Transfer

The goal of the RMC data and technology transfer task is to transfer th e models, model evaluation tools, and datasets to the WRAP states and tribes so that they can perform additional visibility modeling studies. We have made availabl e by download from the RMC web site or by hard disk all of the datasets, run scripts, and analysis software . For the CMAQ and SMOKE models, the RMC web site provides links to the organiza tions that support those models. We are also providing limited support in the setup and operation of the m odels using a standard Linux operating system and installation of the modeling system.



### 10.1 Data Transfer Procedure

The RMC is providing datasets and modeling products to the states and tribes to support additional modeling studies that are needed for developing the RHR SIPs/TIPs. Priority is given to requests directly related to the RHR. However, because these datasets may be useful for other state and tribal air quality mode ling studies, they will also be provided for those requests if resources permit. WRAP states and tribes that are performing additional visibility modeling should contact the RMC to request datasets via the e-mail addresses on the RMC’s data transfer web page: http://pah.cert.ucr.edu/aqm/308/data_transfer.shtml

. When a data request is made, RMC staff log the data request on the web page along with contact information from the requesting agency, and estim ate the date that the data will ship, media format, file size, etc. The state/ tribe staff should verify that th e summary of the data transfer request on the web page is correct. For large data sets, we ship a hard drive with the requested data via FedEx. The state/tribe staff should off-load the data to their data storage system and hold the hard drive until the RMC gives them the retu rn or forwarding shipping address. To speed up the data transfer process, we will usually request that the data be copied within a few days of receiving the hard drive, and that it then be shipped directly to the next state/tribe that has requested the data.



### 10.2 Summary of Data Transfers to Date

Table 10-1 is a recent example of the data reque st log on the RMC web site. It summarizes data requests that have been completed an d those that are still in progress.

892006 Report for the Western Regional Air Partnership (WRAP) Regional Modeling Center (RMC)Table 10-1. Summary of data transfers completed or in progress,from log used to schedule data transfers on RMC web site.DeliverableAgency NameRequestDateContactInfoData RequestedExpectedDeliveryDateStatusCompatibleOSTransfer MediaTotal FileSizeUtah4/20/06PatrickBarickman12km cmaq aconc; 12km emisby sector, 12km oil&gas emisinput data4/30/06ShippedLinuxdisk/firewire cageIdaho4/20/06Rick HardyMCIP and CMAQ 12 kmoutputs (5 months of 2002)5/30/06ShippedWindowsLaCie 1TB drive(PCI-X)~660GIdaho4/20/06Rick HardyData/scripts for running CMAQwith nested grids5/30/06ShippedIdaho5/31/06Rick HardyMM5 36km/12km36kmShippedNevada4/20/06FrankForsgrenCALPUFF datasets for UT/NVdomain, 2001-2003test data byMay 15inprogressftp transfer fortest dataTest data:7.4GMontana4/20/06DianeLorenzenCALMM5 Data sets for MTsubregion, 2001-2003test data byMay 15ShippedWindowsFirewire20 GConfederated Salish& Kootenai Tribes5/03/06RandyAshleyCALMET data sets for MTsubregion, 2001-2003early JuneinprogressArizona DEQ5/18/06Jie YangMM5 36km and 12km data6/8/06ShippedLinuxdisk/firewire cage700 GArizona DEQ9/11/06Jie YangCMAQ 36km outputs andemissions datalateSeptember~860 GTRC EnvironmentalCorporation9/19/06MichaelNewmanCMAQ 36km 2002 outputs;2018baseB SMOKE output;and 2002 12km MM5 datawill beforwarded toby AZ DEQUniversity ofMarylandJeff StehrMM5 and NH3 emissions datanotscheduled902006 Report for the Western Regional Air Part nership (WRAP) Regional Modeling Center (RMC)



## 11. Summary of Work Completed, Lessons Learned, and Future Work

11.1 Summary of Work Completed through September 2006

During 2006 to date, the RMC has completed th ree major tasks for the WRAP visibility modeling:

• Visibility modeling using SMOKE and CM AQ for the 2002 Planning Case and the 2018 Base Case (Section 11.1.1). This included th ree related modeling studies: a “clean conditions” sensitivity analysis, a small-fire s sensitivity analysis, and an analysis and comparison of two fire plume rise algorithms.

• CAMx/PSAT source apportionmen t modeling (Section 11.1.2).

• BART modeling using CA LPUFF (Section 11.1.3).



### 11.1.1 Visibility progress calculatio ns using the Plan 02 and Base18 scenarios

To assist WRAP states and tribes in eval uating the progress in 2018 toward the 2064 natural conditions visibility goals for WR AP Class I areas, we completed draft and final simulations for the 2002 typical-emissions scenario designed to re present the baseline vi sibility for typical conditions during the five-year period from 2000 through 2004 (Section 2). We also completed a 2018 base case scenario that in cluded emissions reductions planne d by 2018 as well as increases in emissions due to population and economic grow th (Section 3). These simulations were completed using the SMOKE emissions processing system and the CMAQ visibility model on the 36-km grid of the RPO Unified continental domain. Draft versions of these model simulations were used to QA the datasets, and final versi ons were used in calcu lating progress toward visibility goals for 2018. The visi bility progress is presented as “glide path” calculations that show the percentage of visibility goal attain ed for each Class I area based on the emissions assumptions used in creating the Base18 scenario.
Our modeling results showed that none of the WRAP Class I areas achieved the 2018 uniform rate of progress (URP) visibility goals. The failure to achieve thes e goals can be attributed to the following factors:

• The WRAP region already has ve ry clean conditions compared to other regions of the U.S. I is difficult to show improvement wh en starting from a relatively clean baseline.

• As indicated by the PSAT modeling results and the fire sensitivity and clean emissions modeling analyses, three types of sources—international tran sport, dust, and wildfires— contribute a significan t portion of the PM 2.5 at most WRAP Class 1 Areas. The 2018

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modeling did not show much visibility progress because these sources of PM 2.5 were uncontrollable in our case Base18b simulation.

• Emissions reductions from improved technolog ies and new regulations were partially offset by population growth in the West by 2018.

• We explored alternate approaches for select ing the worst visibility days, based on worst sulfate days and worst nitrate days. The modeling results did show larger reductions in sulfate and nitrate using these approaches, but they still did not achieve progress goals because of the large contributions of internati onal transport, dust, and wildfire emissions.
Complete results from the visibility modeling ar e available in Table 1 on the RMC web site at http://www.cert.ucr.edu/aqm/308/cmaq.shtml

. We also completed other visibility-related modeling and analysis to investigate sources of PM 2.5 and uncertainty in their mode ling. These studies included

1. CMAQ modeling of a “clean” scenario that in cluded only emissions sources identified as biogenic (Section 7),

2. evaluation of the contribution of small fire s to haze at Class I areas (Section 8), and

3. assessment of the vertical distributions of emissions from the smoke plumes of fires (Section 9).
In the clean scenario modeling, we found that wild fire emissions were by far the largest source of haze, and that in many locations in the western U.S. during 2002 these natural emissions resulted in visibility levels that were consid erably worse than the na tural-conditions progressgoals given in the EPA guidance documents. This means that it will not be possible to demonstrate progress toward goals usi ng visibility modeling at those Cl ass I areas that have high haze levels from wildfires. We also concluded that the biogenic emissions inventories available were highly uncertain, which means it was not possibl e to determine whether the model accurately represented natural haze levels.
Our evaluation of the effects of small fires (f ewer than 100 acres in woodlands, fewer than 300 acres in grasslands) showed that small fires can significantly affect visibility in areas downwind of the fires. This analysis does not support the di scontinuation of record-keeping for small fires.
Analysis of the two fire plume rise height algorithms showed that the WRAP-FEJF (WF) approach and the SMOKE-Briggs (SB) approach calculate considerably different plume rise results for a given fire. The WF approach calcula tes higher plumes than the SB approach, puts the majority of the emissions in the first model layer, and shows discontinuities in plume distribution between the first model laye r and the elevated layers. The SB approach calculates plumes that have most of the emissions near the top ofthe plume and a continuous distribution of emissions from the ground to the plume top. It is diffi cult to predict what effect these differences would have on modeled visibility downwind of fires, bu t it is possible that the SB approach might have greater visibility impacts because it re tains a larger fraction of the emissions within the mixed layer.

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### 11.1.2 PSAT source appo rtionment modeling

We used the CAMx/PSAT model to perform source apportionment modeling for both the 2002 typical and the 2018 base case annual simulations (Section 4). We processed the results to produce graphics that show the largest contributors to haze at each Class I area for the average of the 20% worst and 20% best visibility days. The PSAT results include s ource apportionment for aerosol sulfate, nitrate, and a mmonium for 6 emissions source gr oups and for 18 different source regions, with each WRAP state tagged separately. We also used three alternate approaches for determining the 20% worst visibility days. First, we selected the worst days using the EPArecommended approach based on the worst obser ved days during 2000 to 2004. Then, because of concern that these days might be heavily affect ed by wildfires, we also selected W20% days based on the worst modeled sulfate and worst modeled nitrate days. Results of the source apportionment modeling are available in Table 2 on the RMC web site at http://www.cert.ucr.edu/aqm/308/cmaq.shtml . We expect that these results will be useful to the WRAP states and tribes as they explore additional emissions control strategies.
We also presented some PSAT modeling results regarding the contributio ns to organic carbon aerosols from primary anthropogenic OC, seconda ry anthropogenic OC, and secondary biogenic OC using the clean emissions scenario contrast ed with the Base02b case. These results give a qualitative assessment of the relative contribu tions of biogenic and anthropogenic VOCs to OC, but there are important uncertainties in these results.



### 11.1.3 CALMM5/CALMET/CALPUFF modeling for BART exemption screening

Using the CALPUFF modeling syst em, we are performing BART exemption screening modeling for the WRAP domain for six continental-U.S. st ates and Alaska (Sections 5 and 6). For use in CALMET modeling, we processed the meteorology data from MM5 using CALMM5 for three years (2001, 2002, and 2003) for the six continental- U.S. states and for one year (2002) for Alaska. Surface meteorological and precipitation obser vations were also processed for input into CALMET; we did not use upper-air meteorol ogical observations because the MM5 data provided a better source of upper-air data than did the relatively sparse aloft observations, and the MM5 data already have th e upper-air observations embedde d in them. For the non-Alaska states, CALMM5 was applied using four extrac tion (window) domains. We then processed the meteorology data produced by CALMM5 through CALMET for use in the CALPUFF model. The WRAP-RMC BART CALMET/CALPUFF m odeling is ongoing. We have completed the CALMET modeling and some of the CALPUFF modeling, and are in the process of providing the CALPUFF modeling results to th e states. It is up to the states to determine which potential BART-eligible sources will be subject to BART requirements, and also when the CALPUFF modeling results will be released to other stakeholder for performing additional modeling analyses. The current status of the RMC CALM ET/CALPUFF modeling and the latest results, including CALMET/CALPUFF databases, are available on the RMC BART web page at http://www.cert.ucr.edu/aqm/308/bart.shtml

.

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### 11.2 Emissions Modeling Experience: Lessons Learned
This section reviews the experience gained by the RMC in emissions modeling during the development of the Plan02 and Base18 emissions simulations. Specifically, we focus on the significant lessons learned. The purpose of this sec tion is to frame the work that we completed in the overall context of emissions modeling, and to document our experience to help both the RMC and other emissions modeling groups in the WR AP region anticipate what to expect from similar projects in the future.


### 11.2.1 Addressing errors in the emissions
A basic principle in emissions modeling is that it is inherently both inaccurate and imprecise. Emissions modeling is inaccurate because we omit act ual sources, due to data gaps or a lack of emissions process information or modeling methodol ogy; it is imprecise because we make gross simplifying assumptions about the sources that we do simulate. De spite this bleak assessment of the science of emissions modeling, the product— model-ready emissions—is arguably the single most influential input to the chem ical predictions of an air quality model. Emissions data present an important paradox in air quality modeling: th ey are both highly uncertain and highly influential. With this fundamental point in mind, we have learned that it is important to minimize the uncertainties that modelers have control over du ring the emissions simulation process. Examples of these are errors introduced in the preparation of the data, in the configuration of the emissions model, or in the production of model-ready emissions. By minimi zing these types of uncertainties, we avoid adding controllable uncertainties to the inherent uncerta inties that we cannot control and thus increasing th e overall emissions uncertainty.
Operationally, the concept of minimizing the cont rollable uncertainties in modeling is problematic when costs versus benefits are considered. In a process as complex as emissions modeling, especially in the context of a large organization such as the WRAP RMC where the number of source categories and the data sources within each category are numerous, many errors both large and small arise over the cour se of a modeling study. It is nece ssary to prioritize these errors for correction based on (1) their effect on the ai r quality modeling result s and (2) the amount of effort required by the modeling sta ff to diagnose and correct them, because resources are limited.
To further complicate the issue, the determination of error magnitude requires targeted analysis that proportions an emissions error across multiple spatial and temporal scales. An emissions source that is small relative to the entire inventory on an a nnual basis may produce all of its emissions on one day in a single loca tion that is directly upwind of a receptor area. In this case, the error would appear small on an inventory-wide scale, but could significantly affect the airquality in the receptor area on the day when the emissions are released. As an example, we discovered an error in the applica tion of temporal and chemical profiles to non- Federal rangeland prescribed fires in the WRAP region. While the em issions from this source category contribute only 0.5% of the annual PM 2.5 emissions in the WRAP states, they are so episodic that these seemingly minor emissions changes produced signi ficant changes in PM and visibility predictions in certain model grid cells. Obviously, larg e errors in the emissions modeling that have a significant effect on the modeling re sults need to be corrected, rega rdless of the effort required. It is the seemingly small errors that require scrutiny to determine whether they are either insignificant enough to live with or have a tempor al/spatial/chemical sign ature that contributes significantly to the model predictions.

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As part of our quality assurance project plan, we developed a strategy to document all emissions errors, regardless of the size, and attempt to diagnose how much each one affects air quality model performance. We attempt to distinguish between errors in the data and errors in the modeling to determine the level of effort required to correct th e errors. Significant errors are cause for developing a new version of an emissi ons simulation, as in going from case Plan02b to case Plan02c. Multiple small errors are also cause for developing new simulation versions. Developing general quantitative cr iteria for determining error “significance” is nearly impossible, because even small errors on a mass-basis can have significant results. Our approach has been to evaluate each error as it arises and determine whether/how to address it within the context of the modeling results and deliverables schedule.



### 11.2.2 Shortcomings of sy stematic QA procedures

The RMC developed the most comprehensive an d well-documented protocol for emissions QA currently available (Adelman, 2004). Our system atic approach to performing emissions QA focuses on everything from the modeling hardware and software, to how to manage modeling staff, to the input and output data. We develope d an automated system for displaying the results of the emissions simulations at multiple spatial a nd temporal scales. Despite all of our expertise in emissions modeling and exhaustive QA proce sses, however, we still make mistakes and discover errors in the modeling. We do not ach ieve perfection in our emissions modeling because perfection is unatta inable; emissions modeling is inhere ntly a very complex process that is prone to errors.
As described in the previous subsection, errors can arise in both the data and the modeling at many different levels and magnitudes. It is impossi ble for a standard set of procedures to account for every type of problem that one will encoun ter in emissions modeling. While we have minimized common problems and uncovered many others when applying the pr ocedures in our QA protocol, some errors still slip through the cracks. The detailed QA procedures that we have in place are rooted in a systematic, rigid process. Our experience has shown, however, that these procedures also require a certain level of flexibility to adapt to the changing nature of the input and output data and the schedul e and resources available for the RMC’s work for WRAP. Maintaining a systematic QA process is vital to producing reliable emi ssions modeling results. At the same time, relying solely on a systematic process to catch all erro rs in emissions modeling results ignores the reality of th e process. Emissions QA that succ eeds in threshing out errors and building confidence in the modeling results is an endless process that requ ires systematic procedures combined with informed judgment that the emissions modeler has gained both from general modeling experience and from working with the specific systems and data being used for a given project.



### 11.2.3 New technologies

The current state of emissions modeling is qui te dynamic. New technologies for representing emissions processes that until now have been cr udely represented are rapidly being developed. Research and development regarding modeling sour ces that we omitted in the past is also an advancing area in emissions modeling. Integrat ing these new emissions modeling technologies and data into the operational modeling work at the RMC is allowing us to produce innovative

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and high-quality emissions data. We have also encountered problems in using these prototype systems, however, where the basic assumptions we re not well-defined or the systems’ products were not easily integrated in to the RMC modeling process. For new emissions modeling technologies to be useful to organizations like WRAP and the RMC, they must be practical at the operational level.

The WRAP fire emissions inventories are an ex ample of a success with a new emissions modeling technology. They represent an advancement over the traditional approach of treating fire emissions on an annual, county basis and assuming th at they occur in only the first model layer. With the modeling techniques developed for WR AP, the RMC can now estimate fire emissions by event (located by latitude-longitu de coordinates) and calculate plume rise to allocate the emissions to the ver tical model layers.
Conversely, these fire emissions da ta also represent a problem that we have encountered in using new emissions modeling technologies at the RMC. During 2006 we have been testing the effects of a new fire plume-rise algorithm on the emissi ons from fires in the WRAP states. Documented in Section 9, the new SB algorithm is appealing because it uses predic ted local meteorology to calculate the layer fractions, as opposed to usi ng climatologically -derived plume characteristics. However, we have discovered in our evaluation that the level of effort re quired to use this algorithm is prohibitive on the scale required by the WRAP work, where we are modeling several thousand individual fire events during our annual simulations. To use the SB algorithm we must preprocess the emissions for every event through th e FEPS software to calculate a daily heat flux value; as FEPS cannot run batch jobs, each fire event represents a unique FEPS simulation. So, while we are encouraged by the results from th e new fire plume rise algorithm, the effort required to integrate this algorithm into our mode ling represents unreasona ble time and resource burdens. This example shows that while encouraging new technologies for modeling emissions processes are available, they must be practical to be useful in real-world modeling situations.



### 11.2.4 Important issues

Through the modeling that we have conducted for WRAP over the past five years, we have learned a great deal about key issues that we confront during the emissions modeling process. This section lists the issues that we feel are th e most important. We first list some general issues, then issues specific to source categories.

11.2.4.1 General issues

• Correct Application of SMOKE and Inventory Data : The most important issue in emissions modeling is to ensure that the modeling software and data are being used correctly, that there are no major errors in their applicati on. It is vital to have a QA process in place that will uncover major issues like missing i nventory sources, err oneous spatial surrogates or temporal profiles , and corrupted output data.

• Mixing Datasets and Mixing Assumptions : When modeling inventory data from multiple data sources, it is critical to le arn the various assumptions that are inherent in each dataset and then to design the SMOKE simulation ar ound those assumptions. A single simulation of a SMOKE processor can use only one conf iguration. When confronted with modeling

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several datasets for a single source categor y, such as RPO-specific stationary-area-source inventories, there will likely need to be a di fferent configuration for each dataset, which will result in running multiple SMOKE simulations for that source category.

• New Inventories and Ancillary Data : While this point may seem obvious, we emphasize that when running datasets through SMOKE for the first time, modelers should anticipate the existence of problems and pay close atte ntion to the messages that SMOKE writes to the log files for each program.

• Splitting Hourly Emissions Data : To circumvent computational memory issues that arise from using SMOKE to model hourly emissions data, we often split the inventories from yearly into monthly datasets. It is important when splitting th ese data to attach the last day of the previous month to each monthly inventory file. SMOKE uses Greenwich Mean Time (GMT) as its internal time zone, while hourly data are provided in local time zones. Therefore, if you are modeling a region west of the prime meridian, each month’s dataset must have hourly data that cover the time zone offset during the first few hours of a month. For example, 0000 GMT on February 1 corresponds to 1900 Eastern Standard Time on January 31. If you do not include the da ta for January 31 in the February inventory, you will lose these emissions and introduce errors into the modeling.

• Default Profile Application : If SMOKE does not find a match in the temporal/spatial/ chemical cross-reference files for an SCC to a profile, it will assign a default profile and write a message to the log file from the program that assigned the default data. The SMOKE log files need to be checked t horoughly for these messages and all default profile applications scrutini zed. Each time a new inventory is modeled in SMOKE, it is highly likely that default profiles will be e rroneously assigned because new SCCs in the inventory did not get added to the cross-refe rence files. The assignment of default profiles in SMOKE modeling is in tended only as a last resort and requires close scrutiny.

• Overlapping Inventories : With the extensive amount of inventory splitti ng and rearranging that we had to perform to prepare the Plan02 and Base18 inventories for modeling, it was important to ensure that we did not doubl e-count source categories. For example, the agricultural and animal ammonia emissions data we received were estimates derived from a GIS-based model. As these estimates overlapped with data in the stationary-area inventory, we had to remove the overlapping so urces from our existing inventory to avoid double-counting these sour ces in the modeling.
11.2.4.2 Source-specific issues

• On-road Mobile Sources (VMT Data) : The high level of detail provided in the CENRAP MOBILE6 activity inventories required us to model the on-road “vehicle miles traveled” (VMT) sources for non-WRAP states in three different emissions simulations. We split the CENRAP inventory in two and modele d MRPO, VISTAS, and MANE-VU data as a third MOBILE6 simulation. We divided the MO BILE6 processing to circumvent memory overload problems. Running out of memory when modeling sources in a SMOKE run indicates that the computer system has insu fficient memory for the dataset being modeled, so the data must be divided into spatial subr egions to conform to the available computing

resources.

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• Dust Sources : Fugitive and road dust sources requ ired the largest preprocessing effort of all the source categories beca use of the need to first extract these sources from the stationary-area-source invent ory, apply transport factors, and then recalculate the PM 2.5 /PM 10 ratios.

• Stationary Point and CEM Sources : We received CEM data in two forms over the course of the Plan02 and Base18 modeling. One fo rm was actual hourly estimates derived directly from the CEM database; the other form was aggregated annual estimates with temporal profiles derived from the CEM data . When modeling these data we had to be sure to apply the correct combination of SMOKE configuration options and temporal profiles that conformed with the conf iguration of the data we modeled.

• Nonroad Mobile Sources : Because the nonroad inventorie s consisted of a mixture of annual and seasonal/monthly data, we had to split this category into two processing streams consistent with the temporal resoluti ons of the inventories. For seasonal/monthly inventories, where the monthly variability is inherent in the invent ory, it is necessary to apply flat monthly temporal profiles. Annual inventories, on the other hand, require that monthly variability be applied through the te mporal profiles. While we could have accomplished this configuration in a single simulation by developi ng a temporal cross-reference file, the amount of work required to build this file would have been significant because of the numbers of sources in both the annual a nd seasonal/monthly inventories. We found that it was easier to distinguish between th e two types of inventories for this source category and then use a temporal cross-referen ce file that applies flat monthly profiles to the monthly/seasonal inventories.



### 11.3 Future RMC Work for WRAP

In the coming months, the RMC may perform add itional visibility modeling to evaluate the effects of several types of emi ssions reduction strategies. These could include simple, across-theboard emission reductions of select ed emissions species, or emissi on reductions in selected emissions source categories. The type and number of emissions control strategies to be evaluated will be determined by WRAP. The objectiv e of these model simulations w ould be to assist the states and tribes in identifying emission reduction stra tegies that can achieve desired progress in improving future visibility.
Additionally, we will continue to provide support a nd data transfer to the states and tribes for visibility modeling. This will in clude transfers of datasets, mo dels, and software, and basic support in installing the models a nd scripts used at the RMC for visibility modeling. The installation support will be for operating the SMOKE , CMAQ, and CAMx visibility models, and associated software and scripts, on a standard Linux operating system.
We will also finish up the BART CALMET/CA LPUFF modeling. We are providing consultation assistance to the states as they move forward in the process of determining which sources will be subject to BART requirements and when to release the modeling results.

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. ERG, Inc., 2006b. “Mexico National Emissions Inventor y, 1999: Final Report,” prepared for Secretariat of the Environment and Natural Resources and the National Institute of Ec ology of Mexico by Eastern Research Group, Inc., Sacramento, CA, October, 2006, http://www.epa.gov/ttn/chief/net/mexico/1999_mexico_nei_final_report.pdf Fields, P., M. Wolf, G. Stella. 2005. “Technical Memorandum: Development of Temporal Profiles for Electric Generating Units in the WRAP States. ” Eastern Research Group, Inc., Sacramento, California, December, 2005.

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FLAG. 2000. “Federal Land Manager’s Air Quality Re lated Values Workgroup (FLAG): Phase I Report,” U.S. Forest Service, National Park Service, U.S. Fish and Wildlife Service, December 2000. ICF International, Inc. 2006: “The Integrated Planning Model”. Website: http://www.icfi.com/Markets/Energy/energy-modeling.asp#2 IWAQM. 1998. “Interagency Workgroup on Air Quality Modeling (IWAQM) Phase 2 Summary Report and Recommendations for Modeling Long Range Transport Impacts.” EPA-454/R-98-019, December 1998. Kemball-Cook, S., Y. Jia, C. Emery and R. Morris. 2005. Alaska MM5 Modeling for the 2002 Annual Period to Support Visibility Modeling. Western Re gional Air Partnership, Regional Modeling Center. September. (http://pah.cert.ucr.edu/aqm/308/do cs/alaska/Alaska_MM5_DraftReport_Sept05.pdf) Kemball-Cook, S., Y. Jia, C. Emery, R. Morri s, Z. Wang and G. Tonnesen. 2004. 2002 Annual MM5 Simulation to Support WRAP CMAQ Visibility Modeling for the Section 308 SIP/TIP – MM5 Sensitivity Simulations to Identify a More Optima l MM5 Configuration for Simulating Meteorology in the Western United States. Western Regiona l Air Partnership, Regional Modeling Center. December 10. (http://pah.cert.ucr.edu/aqm/308/reports /mm5/MM5SensitivityRevRep_Dec_10_2004.pdf) Malm, W., M. Pitchford, M. Scruggs, J. Sisler, R. Ames, S. Copeland, K. Gebhart and D. Day. 2000. Spatial and Seasonal Patterns and Temporal Variability of Haze and Its Constituents in the United States – Report III. Cooperative Institute for Research in the Atmosphere, Fort Collins, Colorado. May. (http://vista.cira.colostate.edu/Impr ove/Publications/Rpeorts/2000/2000.htm). McNally, D.E. 2003. Annual Application of MM5 for Calendar Year 2001. Prepared for U.S. EPA, Office of Air Quality and Planning Standards. Prepared by Alpine Geophysics, Arvada, CO. Moon, N., and D. W. Byun, 2004. Simple User's Guide for "geos2cmaq" Code: Linking CMAQ with GEOS-CHEM, Version 1.0, August 22, 2004, Ins titute for Multidimensional Air Quality Studies, University of Houston, Houston, TX ( www.cert.ucr.edu/vistas/vistas2/BC_fi les/geos2cmaq_ugui de_aug22_2004.pdf ). MRI (Midwest Research Institute), 2 005: Analysis of the Fine Fraction of Particulate Matter in Fugitive Dust – Final Report, Prepared for the Western Re gional Air Partnership by MRI, Kansas City, MO. http://wrapair.org/forums/dejf/documents /fffd/Final_Fine_Fraction_Dust_Report.pdf

. Pouliot, G., T. Pierce, W. Benjey, S.M. O’Neill, a nd S.A. Ferguson, 2005: Wildfire Emissions Modeling: Integrating Blue Sky and SMOKE, in Proceedi ngs of the 14th Annual Emissions Inventory Conference, Atlanta, GA. Randall, D., 2006: Technical Memorandum: Fire Emissions Inventory – Phase III Base-Control Case, Prepared for Western Regional Air Partnership Fi re Emissions Joint Forum by Dave Randall, Air Sciences, Inc., April 28, 2006. Tonnesen, G. et al., 2006: Final Report for the WR AP 2002 Visibility Model Performance Evaluation, Prepared for the Western Governors Association by the WRAP RMC, Riverside, CA. http://pah.cert.ucr.edu/aqm/308/reports/fin al/2002_MPE_report_main_body_FINAL.pdf Tonnesen, G. et al., 2005: Final Report for the WRAP RMC for the Project Period March 1, 2004 through February 28, 2005, Prepared for the Western G overnors Association by ENVIRON International Corporation, Novato, CA. http://pah.cert.ucr.edu/aqm/308/ppt_files/em issions/nh3/Volume_I_FinalReport.3-07.pdf U.S. EPA. 2006a. Additional Regional Haze Questions. U.S. Environmental Protections Agency. August 3. ( http://www.wrapair.org/forums/iwg/docum ents/Q_and_A_for_Regional_Haze_8-03- 06.pdf#search=%22%22New%20I MPROVE%20equation%22%22 ).

1002006 Report for the Western Regional Air Part nership (WRAP) Regional Modeling Center (RMC)

U.S. EPA. 2006b. Guidance on the Use of Models and Other Analyses for Demonstrating Attainment of Air Quality Goals for Ozone, Pm2.5, and Regional Haze – Draft 3.2. U.S. Environmental Protection Agency, Office of Air Quality and Planning Sta ndards, Research Triangle Park, North Carolina. September. ( http://www.epa.gov/scram001/guidance/guide/draft_final-pm-O3-RH.pdf ). U.S. EPA, 2006c, README describing dust transport factor updates, http://www.epa.gov/ttn/chief/emch/invent/tfreadme062306.txt

. U.S. EPA. 2005. “Regional Haze Regulations and Gu idelines for Best Available Technology (BART) Determinations”. Fed. Reg./Vol. 70, No. 128/Wed. July 6, 2005, Rules and Regulations, pp. 39104- 39172. 40 CFR Part 51, FRL-7925-9, RIN AJ31. U.S. EPA, 2004: SCCs whose fugitive dust emissi ons may be adjusted for near source capture, http://www.epa.gov/ttn/chief/emch/invent/fugitive_dust_sccs.xls

. U.S. EPA. 2003a. Guidance for Estimating Natural Visibility Conditions Under the Regional Haze Rule. EPA-454/B-03-005, OAQPS, RTP, NC. U.S. EPA. 2003b. “Guidance for Tracking Progr ess under the Regional Haze Rule.” U.S. EPA, EPA454/B-03-004. September 2003. U.S. EPA. 2003c. “Revisions to the Guideline on Air Quality Models: Adoption of a Preferred Long Range Transport Model and Other Resources”; Final Rule. Fed. Reg./Vol. 68, No. 72/Tuesday April 15, 2003/Rules and Regulations. 40 CFR51.
U.S. EPA. 2001. “Guidance for Demonstrati ng Attainment of Air Quality Goals for PM 2.5 and Regional Haze”, Draft Report, U.S. Environmental Protection Agency, Research Triangle Park, NC. WGA (Western Governors’ Association). 2003. Strategic Plan 2003- 2008 of the Western Regional Air Partnership. Western Governors Associa tion, Denver, Colorado. September 29. ( http://wrapair.org/WRAP/meetings/031014b oard/Tab_4_Strategic_Plan_Final.pdf ) WRAP (Western Regional Air Partnership). 2003. Regional Technical Support Document for the Requirements of Section 309 of the Regional Haze Rule (64 Federal Regist er 35714 – July 1, 1999). Western Regional Air Partnership. December 15. ( http://wrapair.org/309/031215Final309TSD.pdf ) WRAP-FEJF (WRAP-Fire Emissions Joint Forum). 2006. WRAP Phase III & IV Fire Emission Inventories for the 2000-04 Baseline Period and 201 8 Project Year. Western Governors Association – wrapair.org website. (http://www.wrapair.org/fo rums/fejf/tasks/FEJFtask7Phase3-4.html).

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## Appendix A: WRAP 2018 Stationary-Point-Source Inventory Corrections (Base18b)2006 Report for the Western Regional Air Part nership (WRAP) Regional Modeling Center (RMC)

The following memo, circulated by Tom Moore on May 4, 2006, describes the inventory updates required for the WRAP 2018 stati onary-point-source inventory. Ta ble A-1 at the end of this appendix summarizes these changes.

To: WRAP Regional Modeling Center (Gail, Zac, Ralph, Mohammad, Gerry) From: Tom Moore, Technical Coordinator Re: Updating 2018 Base Case v1 emissions projections for “Base18b” Modeling Date: May 4, 2006

Following is a list of WRAP region emissions i nventory corrections to be addressed in the RMC’s “base18b” modeling simulation. These ch anges were identified and made due to: 1) Comments from February 1-2, 2006 SSJF meeting; 2) State reviews of ERG 2018 ba se case version 1 EI data; 3) Review and research by WRAP staff; and 4) Include errata from the ERG Final 2018 Repor t, Appendix B action items discussed on with them on various March and April 2006 calls. These changes apply only to point and area sources’ EIs.

______________________________________________________________________________ 1. Wyoming [Thomas Andretta & Lee Gribovicz, May 1, 2006]: WY’s point source inventory was revised on November 18, 2005. This revisi on was not included in the 2018 base case inventory since it came too late in the process. Response: Campbell County – Powder River Ba sin coal mines’ emissions were revised, these are negligible for the purpose of W RAP modeling, do not incl ude in projections.
2. Confederated Tribes of the Yakama Nation : Point source emissions for Yakama Forest Products were added to the 2002 inventory for this Native American Reservation after the 2002 emissions inventory had been incorporated into the 2018 base case calculations. Thus, emissions were not projected for this point source. ERG to provide as much information as is available for this source (e.g., tribal ID, 2002 emissions, multiplier for 2018 projections), and advise WRAP on how to make adjustment in 2018. Response: This facility was NOT included in the 2002 IDA file, nor was it in the 2018 IDA file. Therefore, we will not address it at all at this juncture since its emissions are so minimal (2002 = 63 tons NO x , 107 tons SO 2 ).
3. North Dakota May 2, 2006 E-Mail : Emissions for three new post-2002 facilities could not be included in the 2018 base case inventory because only hour ly emissions (and not annual emissions) were provided:

• North Border #5 Compressor Station

• Manning Compressor Station

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• Silurian Compressor Station Response: See attached spreadsheet: “ND _2018 New Sources_TPY.xls”. Note that an additional row has been added for N Border CS #5 CO.

4. Arapahoe Tribe of the Wind River Reservation : The tribal environmental agency indicated operation of a new post-2002 facility, South Pavi lion Compressor Station, although annual emissions were not provided so this so urce could not be incl uded in the 2018 base case inventory. Response: This facility’s emissions or constr uction/permitting status are unknown, we will not address it at this juncture (c ould be included in future co ntrol strategy modeling runs).
5. Oregon May 2, 2006 E-Mail [Mark Fisher]: VOC emission reductions for two wood products facilities (Boise Cascad e in White City and La Grande) were not included in the 2018 base case projections due to problems ma tching the SCCs in the 2002 inventory and information received from the State. If thes e problems were resolved, the VOC reductions (totaling approximately 273 tons) could be applied to the 2018 base case inventory.

Response: For Boise Cascade in White City, the VOC reductions (95%) should be applied to emissions unit 7/process code 2 (SCC 30700 716). This is row 1339 in the OR Point spreadsheet in Oregon's emission inventory Excel workbook.

For Boise Cascade in La Grande, the VOC reductions (95%) should be applied to emissions unit 37/process code 2 (SCC 30700703). This is row 3848 in the OR Point spreadsheet in Oregon's emission inventory Excel workbook.

However, according to my calculations, the reductions from 2002 will only be about 128 tons of VOC for these two sources, not 273 as indicated below.

6. Wyoming May 1, 2006 conference call & Washington May 1, 2006 E-Mail – Wyoming [Thomas Andretta & Lee Gribovi cz] and Washington [ORCAA – Jim Wilson]: Emissions for six new post-2002 facilities could not be included in the 2018 base case files (NIF and IDA) files due to missing county FIPS:

• Burlington Resources, Big Ho rn Wells (WY, ID 00012)

• Chevron USA Table Rock Field (WY)

• Chevron USA Whitney Canyon, Carter Creek (WY)

• ExxonMobil Corporation, Black Canyon (WY)

• Sierra Pacific Industries (W A, ID 1118) from Olympic Regional Clean Air Authority [Jim Wilson]

• Olympic Panel Products (WA, ID 2100284) from Olympic Regional Clean Air Authority [Jim Wilson] Response for WY: Line items in IDA file are as follows: Burlington Resources – Bighorn Wells (56xxx56003-00012(?)) = 56013 Fremont County

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Chevron USA – Table Rock Field (56xxxPRJFAC22) = 56037 Sweetwater County Chevron USA – Whitney Canyon/Carter Cr eek (56xxxPRJFAC23) = 56041 Uinta County ExxonMobil – Black Canyon (56xxxPRJ FAC24) = 56035 Sublette County SCC = 31000205 industrial processes, oil & gas production, natural gas production, flares Only SO2 emissions are included, and NOx is likely emitted - No NOx tracked from these flaring emissions. Response for WA: Two new WA (post-2002) so urces are included in 2018 IDA file (IDAPT_States-non-O&G-20051210.zip). Line ite ms in IDA file are as follows: Sierra Pacific Industries (53xxx1118) = 027 Sie rra Pacific Industries (WA, ID 1118) from ORCAA Olympic Panel Products (53xxx2100284) = 045 Olympic Panel Products (WA, ID 2100284) from ORCAA

7. APPLY TRANSPORT FRACTION TO THESE EMISSIONS??? - Investigate potentially overestimated PM 10 from construction/wind eros ion (SCC 2311000100) in Washington and Oregon. ERG to provide list of lines in IDA files for 2002 and 2018 that have this SCC (for all states, not just WA and OR). Response: Based on the projection spreads heets, nine states have emissions from construction wind erosion (2311000100) (AZ, ID, MT, ND, NM, NV, OR, WA, and WY). Three other states (CO, SD, and UT) and the tribes do not have construction wind erosion emissions. Unsure about CA. Of the nine stat es that do have emissions, seven states (AZ, MT, ND, NM, NV, WA, and WY) in ventoried six pollutants (PM 10

, PM 2.5

, EC 10

, EC 2.5

, OC 10

, and OC 2.5

). OR inventoried only PM 10 and PM 2.5

, while ID inventoried only PM 10 . Each of the IDA area records begins with a 16 digit ID. The first 5 digits are the state/county FIPs code and the next 10 digits are the 10-digit area source code. The list of the construction wind erosion emission records in the IDA files is as follows: AZ All records beginning with 04xxx2311000100 ID All records beginning with 16xxx2311000100 MT All records beginning with 30xxx2311000100 ND All records beginning with 38xxx2311000100 NM All records beginning with 35xxx2311000100 NV All records beginning with 32xxx2311000100 OR All records beginning with 41xxx2311000100 WA All records beginning with 53xxx2311000100 WY All records beginning with 56xxx2311000100

8. Idaho - Marty and Paula had a call with Mary an d Chris from Idaho DEQ to discuss some questions they had on their 2018 base case projecti ons. As a result, they would like to make a change to the emissions at the "P4 Production LCC" facility in Soda Springs (State Facility ID 2900001). ERG had applie d a 97% reduction in SO 2 to emission unit 10 based on

A-42006 Report for the Western Regional Air Part nership (WRAP) Regional Modeling Center (RMC)

information we gleaned from the 2004 Idaho A nnual Report (scrubbers are being installed). However, Mary and Chris have pointed out th at these are not enforceable reductions, and should not be taken as part of the base cas e inventory. The 97% reduc tion should be removed for the base case – can be put back in for the Control Strategy Run A.

Also, ID DEQ is looking over some of their SCCs that might need to be changed (i.e., these cause potentially incorrect reti rement/replacement rates to be applied). Mary/Chris will get in touch with you later about these. ERG to provide list of lines in IDA file for 2018, and adjustment that needs to be made (e.g., multiply ver. 1 2018 values by “x”). Response: Two adjustments need to be made to the Idaho IDA files:
#1 – The 97% reduction in SO 2 to Emission Unit 10 at the P4 Production LCC facility in Soda Springs (State ID# 2900001) should be eliminated. The following multiplicative fact or needs to be applied – For IDA ID# 1602902900001 (1 st field), Emission Unit ID# 10 (2 nd field), Emission Release Point ID# 10 (3 rd field)
SO 2 emissions (455.097 tpy) – Factor of 33.333 Factor only applied to SO 2 emissions. #2 – Duplicate SCCs in the retirement/rep lacement look-up table resulted in some incorrect emissions at the TASCO facility in Paul (State ID# 6700001). The following multiplicative factors need to be applied: For IDA ID# 1606706700001 (1 st field), Emission Unit ID# 10 (2 nd field), Emission Release Point ID# 10 (3 rd field), SCC 10200229 (5 th field):
NO x emissions (466.1971 tpy) – Factor of 1.1364

PM 10 emissions (83.38766 tpy) – Factor of 1.0415
SO 2 emissions (108.1761 tpy) – Factor of 0.6576 For IDA ID# 1606706700001 (1 st field), Emission Unit ID# 20 (2 nd field), Emission Release Point ID# 20 (3 rd field), SCC 10200229 (5 th field):
NO x emissions (277.9395 tpy) – Factor of 1.1364

PM 10 emissions (23.58439 tpy) – Factor of 1.0415
SO 2 emissions (73.2477 tpy) – Factor of 0.6576 Factors only applied to NO x , PM 10

, and SO 2 emissions.

9. North Dakota projected lime kiln at paper plant in ND – no action.

10. Arizona copper smelters – coding and tons correct?

Response: ERG listed lines in IDA file for which SCC needs to be changed - Commercial/institutional, boilers, NG =>Indus trial process/Primary metal/Primary CU smelting; SO 2 only).

A-52006 Report for the Western Regional Air Part nership (WRAP) Regional Modeling Center (RMC)

Response: The SCC code for the Phelps Dodge – Miami Smelter needs to be changed. For IDA ID# 040070400701391, the SCC (5 th field) needs to be changed from 10300602 (Commercial/Institutional Natural Gas External Combustion Boiler) to 30300599 (Primary Copper Smelters – Other Not Classi fied). A miscellaneous SCC was assigned because specific process/equipmen t information was not available.

Ray Complex – Smelter & Mill – Hayden, AZ Original Corrected 2018 2018 SCC 30300527 SO2 (tons) SO2 (tons) By ERG by WRAP staff

Industrial Processes Primary Metal Production Primary Copper Smelting RAY COMPLEX - SMELTER & MILL 34,490 23,000

11. North Dakota and 3 or 4 other states : 2018 area source residual a nd distillate oil and coal combustion SO2 emissions – may be overpredicted by EGAS model.

Response: Analysis by WRAP staff shows that there are roughly 25,000 tons of [possibly excess] SO2 in the 2002 WRAP region area sour ce inventory. Slightly over half are from distillate and residual fuel use. Most of the rest is from coal use. As for the coal use, the cause of the potential ND excess emission is the estimates that ca me out of the 2002 EPA NEI gap-filling for area sources, which wa s the basis of the 2018 ND area source projection EI by ERG. This may also be true in SD, MT and possibly NV. Recommendation: these states may want to l ook at this issue in 2018 control strategy modeling runs.

12. EGU 2018 projections : Check Mohave, Colstrip, Apache, and Four Corners: ERG and WRAP staff conducted analysis and QA – itemized below.

AZ – Apache: 2018 SO2 = 2,664 tons

For Apache, all estimates were also projecte d using 2002 heat input wa s grown to capacity; emissions were estimated by multiplying project ed heat input by 2004 (i.e., most recent) # per BTU emission rates.

Both Unit #2 and #3 were running near cap acity (82.8% and 79.3%, respectively), so projected heat input increased slightly.

The 2004 NO x emission rates were similar for Units #2 and #3 (0.487 and 0.437 lbs/mmBTU, respectively), as were the SO 2 emission rates (0.174 and 0.184 lbs/mmBTU, respectively).

A-62006 Report for the Western Regional Air Part nership (WRAP) Regional Modeling Center (RMC)

It is possible that some SO 2 controls were implemented between 2002 and 2004. For both units, both heat input and NO x increased 2002 to 2004, but SO 2 emissions decreased.

MT - Colstrip: 2018 SO2 = 16,056 tons

For Colstrip, all estimates were projected using 2002 heat input was grown to capacity; emissions were estimated by multiplying project ed heat input by 2004 (i.e., most recent) # per BTU emission rates.

Unit #4 was already running above capacity ( 86.6%) and Unit #1 was near capacity (82.5%). Unit #2 and #3 were considerably below cap acity (71.0% and 59.8%, respectively), so projected heat input increased c onsiderably for these two units.

The 2004 NO x emission rates were similar for Units #2, #3, and #4 (ranging from 0.391 to 0.409 lbs/mmBTU); the emission rate for Unit #1 was considerably lower (0.346 lbs/mmBTU). The 2004 SO 2 emission rates were similar for Units #1 and #2 (0.329 and 0.341 lbs/mmBTU, respectively); while the emi ssion rates for Units #3 and #4 were much lower (0.097 and 0.103 lbs/mmBTU, respectively).

It is possible that some controls were implemented between 2002 and 2004. For Unit #1, NO x and SO 2 emissions decrease from 2002 to 2004 ev en though the heat input increases. For Unit #2, the SO 2 emissions increase by less than 1%, even though the heat input increases by 18%.

CO - Commanche: 2018 SO2 [tons] = 6,936 per Xcel [3,686 from existing units plus 3,250 for the new unit per their permit

CO - Commanche: 2018 NOx [tons] = 6,143 (existing) + 2,600 for the new unit per their permit

For Comanche, all estimates were initially es timated using 2002 heat input was grown to capacity; emissions were estimat ed by multiplying projected heat input by 2004 (i.e., most recent) # per BTU emission rates. Howeve r, permit limits were applied to the NO x and SO 2 emissions from Unit #1 and #2. For Unit #1, 3,093 tpy NO x and 1,856 tpy SO 2 . For Unit #2, 3,050 tpy NO x and 1,830 tpy SO 2 . UT - Escalante 2018 SO2 = 1,796 tons

Navajo Nation (facility located in NM) Four Corners 2018 emissions: leave as is for now, apply any reductions in Control Strategy Run A

NV- Mohave: okay as projected.

13. Check SO2 and NOx growth assumptions for major non-EGU source categories

Response: address as needed in 2018 control strategy modeling runs

A-72006 Report for the Western Regional Air Partnership (WRAP) Regional Modeling Center (RMC)Table A-1. Summary of updates to the WRAP 2018 stationary point inventoryInventory ChangeLocationFIPSSCCPlantDescriptionOriginalRevisedWhite City, OR4102930700716Boise Cascade – Unit 795% reduction in VOCemissions95.24 tons (VOC)4.76 tons (VOC)La Grande, OR4106130700703Boise Cascade – Unit 3795% reduction in VOCemissions88.92 tons (VOC)4.45 tons (VOC)Soda Springs, ID1602930111299P4 Production, LLC –Unit 10Back out 97% reduction inSO2emissions455.10 tons (SO2)15169.9 tons (SO2)Minidoka Co., ID1606710200229Tasco Paul – Unit 10,Stack 10Emissions adjustment forNOx, PM10, and SO2466.20 tons (NOx)83.39 tons (PM10)108.18 tons (SO2)529.79 tons (NOx)86.85 tons (PM10)71.14 tons (SO2)Minidoka Co., ID1606710200229Tasco Paul – Unit 20,Stack 20Emissions adjustment forNOx, PM10, and SO2277.94 tons (NOx)23.58 tons (PM10)73.25 tons (SO2)315.85 tons (NOx)24.56 tons (PM10)48.17 tons (SO2)Gila Co, AZ0400710300602Phelps Dodge – MiamiSmelterSCC change1030060230300599Gila Co, AZ0400730300527Ray Complex – Unit 2Emissions adjustment for SO234,490 tons (SO2)23,000 tons (SO2)Cochise Co., AZ0400310100222AZ Electric PowerCooperative, Inc.; ApacheEGU – Unit 2Revised SO2total for Units 2and 3 = 2664 tons3721.9 tons (SO2)1438.6 tons (SO2)Cochise Co., AZ0400310100222AZ Electric PowerCooperative, Inc.;ApacheEGU – Unit 3Revised SO2total for Units 2and 3 = 2664 tons3168.6 tons (SO2)1225.4 tons (SO2)Rosebud Co., MT3008710100226Colstrip EGU – Unit 1Revised SO2total for Units 1– 4 = 16056 tons3690.2 tons (SO2)4303.0 tons (SO2)Rosebud Co., MT3008710100226Colstrip EGU – Unit 2Revised SO2total for Units 1– 4 = 16056 tons3950.3 tons (SO2)4592.0 tons (SO2)A-82006 Report for the Western Regional Air Partnership (WRAP) Regional Modeling Center (RMC)Rosebud Co., MT3008710100226Colstrip EGU – Unit 3Revised SO2total for Units 1– 4 = 16056 tons2980.7 tons (SO2)3468.1 tons (SO2)Rosebud Co., MT3008710100226Colstrip EGU – Unit 4Revised SO2total for Units 1– 4 = 16056 tons3169.0 tons (SO2)3692.9 tons (SO2)Pueblo Co., CO081011010226Public Service Co;Comanche EGU – Unit 1Emissions adjustment for units1 and 2 = 3686 tons (SO2),6143 tons (NOx)1856 tons (SO2)3093 tons (NOx)1857.7 tons (SO2)3089.9 tons (NOx)Pueblo Co., CO0810110100222Public Service Co;Comanche EGU – Unit 2Emissions adjustment for units1 and 2 = 3686 tons (SO2),6143 tons (NOx)1830 tons (SO2)3050 tons (NOx)1828 tons (SO2)3053.1 tons (NOx)Pueblo Co., CO0810110100201Public Service Co;Comanche EGU – Unit 3Emissions adjustment for unit3 = 3250 tons (SO2), 2600 tons(NOx)2513 tons (SO2)2513 tons (NOx)3250 tons (SO2)2600 tons (NOx)McKinley Co.,NM3503110100226Escalante Station – Unit 1Emissions adjustment for SO21228.7 tons (SO2)1796 tons (SO2)Fremont Co., WY31000205Burlington Resources –Bighorn WellsAdded missing FIPS codes56013Sweetwater Co.,WY31000205Chevron USA – TableRock FieldAdded missing FIPS codes56037Uinta Co., WY31000205Chevron USA – WhitneyCanyon/Carter CreekAdded missing FIPS codes56041Sublette Co., WY31000205Exxon/Mobile – BlackCanyonAdded missing FIPS codes56035Grays HarborCo., WA10200902Sierra Pacific IndustriesAdded missing FIPS codes53027Lincoln Co., WA3070071130700805Olympic Panel ProductsAdded missing FIPS codes53045A-92006 Report for the Western Regional Air Part nership (WRAP) Regional Modeling Center (RMC)



## Appendix B: Fire Plume Rise Sensitivity Results2006 Report for the Western Regional Air Part nership (WRAP) Regional Modeling Center (RMC)

(a)159131721L1L4L7L10L13L16 0 100 200 300 400 500 600CO Tons/h

Time

L a y e r CO, Wilde fire use FEJF Plume rise L1 L2 L3 L4 L5 L6 L7 L8 L9 L10 L11 L12 L13 L14 L15 L16 L17 L18

Tot

(b)1471013161922L1L3L5L7L9L11 0 100 200 300 400 500 600CO Tons/h

Time

L a y e r CO, Wilde fire use Blue Sky Plume rise

L1
L2
L3
L4
L5
L6
L7
L8
L9
L10
L11
L12

Tot

Figure B-1. Colorado wildland fire use emissions vertical distribution: (a) WF and (b) SB

B-22006 Report for the Western Regional Air Part nership (WRAP) Regional Modeling Center (RMC)

(a)

CO , Wild fire use FEJF Profile

0 50 100 150 200 250 300 350 400 450 500 550 600

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

T i me ( h) 0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000 5500 6000 6500 7000 7500 8000

L1 L2 L3 L4 L5 L6 L7 L8 L9 L10 L11 L12 L13 L14 L15 L16 L17 L18 Max LH
(b)

CO, Wild fire use Blue Sky Profile

0 50 100 150 200 250 300 350 400 450 500 550 600

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Time (h)Tons/h

0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000 5500 6000 6500 7000 7500 8000Max Layer Hieght (m)

L1 L2 L3 L4 L5 L6 L7 L8 L9 L10 L11 L12 Max LH

Figure B-2. Colorado wildland fire use em issions vertical distribution and maximum layer heights: (a) WF and (b) SB

B-32006 Report for the Western Regional Air Part nership (WRAP) Regional Modeling Center (RMC)

(a)1471013161922L1L3L5L7L9L11L13L15L17 0 100 200 300 400 500 600 700CO Tons/h

Time

L a y e r AZ, Prescribed fire

FEJF Plume rise L1
L2
L3
L4
L5
L6
L7
L8
L9
L10
L11
L12
L13
L14
L15
L16
L17
L18

Tot (b)1471013161922L1L3L5L7L9L11Tot 0 100 200 300 400 500 600 700CO Tons/h

Time

L a y e r AZ, Prescribed fire Blue Sky Plume rise

L1
L2
L3
L4
L5
L6
L7
L8
L9
L10
L11

Tot

Figure B-3. Arizona prescribed fire vertic al distribution: (a) WF and (b) SB

B-42006 Report for the Western Regional Air Part nership (WRAP) Regional Modeling Center (RMC)

(a)

AZ, Prescribed fire FEJF Profile

0 50 100 150 200 250 300 350 400 450 500 550 600 650 700

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

T i me ( h) 0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000 5500 6000 6500 7000 7500 8000

L1 L2 L3 L4 L5
L6 L7 L8 L9 L10
L11 L12 L13 L14 L15 L16 L17 L18 Max LH (b)

AZ, Prescribed fire Blue Sky Profile

0 50 100 150 200 250 300 350 400 450 500 550 600 650 700

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

T i me ( h) 0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000 5500 6000 6500 7000 7500 8000

L1 L2 L3 L4 L5 L6 L7 L8 L9 L10 L11 Max LH

Figure B-4. Arizona prescribed fire em issions vertical distribution and maximum layer heights: (a) WF and (b) SB

B-52006 Report for the Western Regional Air Part nership (WRAP) Regional Modeling Center (RMC)

(a)1471013161922L1L3L5L7L9L11L13L15L17 0 500 1000 1500 2000 2500 3000CO Tons/h

Time

L a y e r AZ, Wilde fire FEJF Plume rise L1
L2
L3
L4
L5
L6
L7
L8
L9
L10
L11
L12
L13
L14
L15
L16
L17
L18

Tot (b)

0 500 1000 1500 2000 2500 3000 3500CO Tons/h

Time

L a y e r AZ, Wilde fire Blue Sky Plume rise L1

L2

L3

L4

L5

L6

L7

L8

L9

L10

L11

L12

L13

L14

L15

Tot

Figure B-5. Arizona wildfire vertical distribution: (a) WF and (b) SB

B-62006 Report for the Western Regional Air Part nership (WRAP) Regional Modeling Center (RMC)

(a)

AZ, Wild fire FEJF Profile

0 500 1000 1500 2000 2500 3000 3500

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Time (h)Tons/h

0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000 5500 6000 6500 7000 7500 8000Max Layer Hieght (m)

L1 L2 L3 L4 L5
L6 L7 L8 L9 L10
L11 L12 L13 L14 L15
L16 L17 L18 Max LH (b)

AZ, Wild fire Blue Sky Profile

0 500 1000 1500 2000 2500 3000 3500

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Time (h)Tons/h

0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000 5500 6000 6500 7000 7500 8000Max Layer Hieght (m)

L1 L2 L3 L4 L5 L6 L7 L8 L9 L10 L11 L12 L13 L14 L15 Max LH

Figure B-6. Arizona wildfire emissi ons vertical distribution and maximum layer heights: (a) WF and (b) SB

B-72006 Report for the Western Regional Air Part nership (WRAP) Regional Modeling Center (RMC)

(a)1471013161922L1L3L5L4L9L11L13L15L17Tot 0 5 10 15 20 25 30CO Tons/h

Time L a y e r OR, Wilde fire FEJF Plume rise L1
L2
L3
L4
L5
L6
L4
L8
L9
L10
L11
L12
L13
L14
L15
L16
L17

Tot

(b)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24L1L3 0 5 10 15 20 25 30CO Tons/h

Time

L a y e r OR, Prescribed fire Blue Sky Plume rise

L1
L2
L3
L4

Tot

Figure B-7. Oregon prescribe d fire vertical distributio n: (a) WF and (b) SB

B-82006 Report for the Western Regional Air Part nership (WRAP) Regional Modeling Center (RMC)

(a)

OR, P re s c ribe d fire FEJF P ro file

0 5 10 15 20 25 30

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

T i me ( h) 0 400 800 1200 1600 2000 2400 2800 3200 3600 4000 4400 4800 5200

L1 L2 L3 L4 L5 L6
L7 L8 L9 L10 L11 L12
L13 L14 L15 L16 L17 Max LH
(b)

OR, Prescribed fire Blue Sky Profile

0 5 10 15 20 25 30

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Time (h)Tons/h

0 400 800 1200 1600 2000 2400 2800 3200 3600 4000 4400 4800 5200Max Layer Hieght (m)

L1 L2 L3 L4 Max LH

Figure B-8.Oregon prescribed fire em issions vertical distribution and maximum layer heights: (a) WF and (b) SB

B-92006 Report for the Western Regional Air Part nership (WRAP) Regional Modeling Center (RMC)

(a)1471013161922L1L4L7L10L13L16 0 500 1000 1500 2000 2500 3000 3500 4000 4500CO Tons/h

Time L a y e r OR, Wilde fire use FEJF Plume rise L1
L2
L3
L4
L5
L6
L7
L8
L9
L10
L11
L12
L13
L14
L15
L16
L17
L18

Tot (b)1357911131517192123L1L3L5L7L9L11L13L15Tot 0 500 1000 1500 2000 2500 3000 3500 4000 4500CO Tons/h

Time
L a y e r OR, Wilde fire use Blue Sky Plume rise L1
L2
L3
L4
L5
L6
L7
L8
L9
L10
L11
L12
L13
L14
L15

Tot

Figure B-9. Oregon wildfire vertical distribution: (a) WF and (b) SB

B-102006 Report for the Western Regional Air Part nership (WRAP) Regional Modeling Center (RMC)

(a)

OR, Wild fire Blue Sky Profile

0 400 800 1200 1600 2000 2400 2800 3200 3600 4000 4400

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Time (h)Tons/h

0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000 5500 6000 6500 7000 7500 8000Max Layer Hieght (m)

L1 L2 L3 L4 L5 L6 L7 L8 L9 L10 L11 L12 L13 L14 L15 L16 L17 L18 Max LH
(b)

OR, Wild fire Blue Sky Profile

0 500 1000 1500 2000 2500 3000 3500 4000 4500

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Time (h)Tons/h

0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000 5500 6000 6500 7000 7500 8000Max Layer Hieght (m)

L1 L2 L3 L4 L5 L6

L7 L8 L9 L10 L11 L12
L13 L14 L15 Max LH

Figure B-10. Oregon wildfire emis sions vertical distribution and maximum layer heights: (a) WF and (b) SB

B-11