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), University 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 International 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/empd
2006 Report for the Western Regional Air Partnership (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 assembled 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 Canada and 1999 Mexico inventories. 

♦ The 2018 Base Case future-year emissions scenario, represented by case Base18b, 

reflected 2018 projected emissions, assuming growth and control factors “on the books” 

in December 2004, as a planning target for the 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 projections of emissions rates and number of 

sources) with the same 2000-2004 average monthly and diurnal temporal profiles by 

state used in Plan02c above. 

 2018 U.S. area sources for selected anthropogenic 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 projected 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 managed fire emissions modified to reflect 

the application of emissions reduction techniques (ERTs). 

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2006 Report for the Western Regional Air Partnership (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-year”) planning runs (cases 

Plan02a,b,c) performed in sequence to diagnose 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 (cases 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 conditions 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 the 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 dozen 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 constant 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, off-shore marine vessels, windblown dust, and 

biogenics). 

♦ We reconfirmed that coarse matter (CM) should 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 could 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 Class I areas did not meet the 

2018 Uniform Rate of Progress (URP) goal due to the contributions of uncontrollable PM 

(e.g., wildfires, dust, international transport). 

♦ We evaluated the visibility improvement on a species-by-species basis, confirming that 

little air quality or visibility change was noted when emissions were not markedly 

changed. 

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

• We completed SOx and NOx 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 SO4 and NO3 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 exemption 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 meteorological and precipitation data for 

CALMET/CALPUFF modeling 

♦ Began the CALMET/CALPUFF modeling for 6 WRAP States 

• We completed a modeling study comparing two different algorithms 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 results in allocating fire emissions to the 

vertical model layers; the SB algorithm distributes 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 modeling configuration used by the RMC, using the SMOKEBriggs algorithm is impractical because of the need to calculate heat flux for each 

individual fire using the FEPS model. 

♦ Without observational data on actual fire plume 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 default 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 have large natural fire emissions. 

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

♦ Wildfires were by far the largest source of PM2.5 in the natural emissions. Biogenic 

emissions were a large source of VOC emissions 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 simulations. 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 grasslands) can contribute to visibility 

impairment in Class I areas. This analysis suggests that at least some small fires should 

be tracked for purposes of modeling visibility. 

conditions reflect both natural and anthropogenic emissions, we included them in the 

clean sensitivity simulation because they represent a contribution to haze that i

controllable by the WRAP states. 

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2006 Report for the Western Regional Air Partnership (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 Comparison 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.................................................................................................... 30 

3.3.1 Calculation of Baseline Conditions................................................................ 31 

3.3.2 Mapping of modeling results to the IMPROVE measurements..................... 34 

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

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 selecting 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 selection 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 comparison 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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2006 Report for the Western Regional Air Partnership (WRAP) Regional Modeling Center (RMC) 

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 Completed through September 2006............................................. 91 

11.1.1 Visibility progress calculations using 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-Source Inventory Corrections (Base18b)..... A-1 

Appendix B: Fire Plume Rise Sensitivity Results ...................................................................B-1 

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

Tables 

Table 1-1. WRAP 2003 Strategic 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 data 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/PSAT run-time options........................................................................43 

Table 4-4. WRAP CAMx/PSAT source regions 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 NOx and primary PM2.5 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 transfers completed or in progress, from log used to schedule 

data transfers on RMC web site...............................................................................................90 

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

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 PM2.5 for 

Plan02 minus Base02...............................................................................................................16 

Figure 3-1. Annual average aerosol sulfate (ASO4) 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 PM2.5 concentration comparisons 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 comparisons between Base18b and Plan02b. Top 

plot: difference between the two (Base18b – Plan02b); bottom left plot: Plan02b 

results; bottom right plot: Base18b results...............................................................................29 

Figure 3-5. Example of URP glide path using IMPROVE data from the Grand Canyon 

National Park for the W20% days and comparison with Base18b visibility 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 Old 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 PSAT 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 contributors to PSO4 based on 20% best βext days’ average for 

2002..........................................................................................................................................49 

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

Figure 4-5. PSAT bar plot for receptor site at Hance Camp in Grand Canyon National Park, 

AZ, showing the top contributors to PSO4 based on 20% worst βext 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 contributors to PSO4 based on July 2002 monthly average. .................50 

Figure 4-7. Source apportionment spatial plots for aerosol sulfate 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 sulfate 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 sulfate from California mobile 

sources from CMAQ/TSSA (left) and CAMx/PSAT (right) for July 31, 2002, at 0000 

GMT.........................................................................................................................................53 

Figure 4-10. Source apportionment spatial plots for aerosol sulfate from California point 

sources from CMAQ/TSSA (left) and CAMx/PSAT (right) for July 31, 2002, at 0000 

GMT.........................................................................................................................................53 

Figure 4-11. TSSA (top panel) vs. PSAT (bottom 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 BART selection memorandum............................................................58 

Figure 6-1. Relationship of four MM5 window domains 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 modeling 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 modeling 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 modeling domains and Class I area receptors (green), 

observed surface meteorological sites (purple triangles), and potential BART-eligible 

source locations (red dots) for (a) Nevada CALMET modeling, (b) northern Nevada 

CALPUFF modeling, and (c) southern Nevada CALPUFF modeling....................................67 

Figure 6-5. Alaska CALMET/CALPUFF modeling domain showing the potential BARTeligible source locations (red 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 

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

Figure 7-2. Annual average modeled primary anthropogenic OC (AORGPA) in Base02b 

(top) and the portion that is “controllable” primary anthropogenic OC (bottom). ..................75 

Figure 7-3. Annual average modeled secondary 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” primary biogenic OC (bottom)............................................77 

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

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 (historical 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 Status 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 context of the IMPROVE network; see also 

PMc) 

CMAQ..................Community Multiscale Air Quality 

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

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 Factors 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 

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2006 Report for the Western Regional Air Partnership (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/NCAR 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 Agricultural Statistics Service 

NCAR ..................National Center for Atmospheric Research 

NCEP ...................National Centers for Environmental Prediction 

NEI.......................National Emissions Inventory (NEI96 = 1996 version; NEI99 = 1999 version) 

NIF .......................National Emissions Inventory (NEI) Input Format 

NLCD...................National Land Cover Database 

NNRP...................NCAR/NCEP Reanalysis Project 

NORTAD.............North American Transportation 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 Research and Development 

ORL......................one record per line

OSAT ...................Ozone Source Apportionment Technology 

PAMS...................Photochemical Assessment Monitoring Stations 

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

PAVE ...................Program for the Analysis and Visualization 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 comparison with 2018 Base Case, the futureyear base case emissions scenario 

PM........................particulate matter 

PM2.5 ....................particulate matter ≤2.5 μm in diameter (referred to in this report as “fine 

particulate matter”)

PM10 .....................particulate matter ≤10 μm in diameter

PMc......................coarse particulate matter: PM that ranges 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 and Characterization (monitoring network) 

SEARCH_H.........SEARCH monitoring network collecting hourly data (vs. daily [24-hour] data) 

SIP........................State Implementation Plans 

SLAMS ................State and Local Air Monitoring Stations 

SMOKE................Sparse Matrix Operator Kernel Emissions 

SMVGEAR..........Sparse-Matrix Vectorized Gear 

SOA......................secondary organic aerosols 

SST.......................sea-surface temperature 

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

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 

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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. Through 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 visibility in mandatory Class I Federal areas which impairment 

results from manmade air pollution” (40 CFR 51.300). States are required to develop State 

Implementation Plans (SIPs) to attain visibility standards; also, tribes may opt to assume responsibility for visibility programs under 40 CFR Part 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 visibility conditions at 156 Federally 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 toward natural visibility conditions. CAA Section 308 (§308) 

requires that the first visibility SIP/TIP be submitted to EPA by 2007-2008 to demonstrate 

progress toward natural visibility conditions in 2018 using a 2000-2004 five-year baseline. 

1.1.2 Role of the Western Regional Air Partnership 

Five Regional Planning Organizations (RPOs) (Figure 1-1) are 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), the Central Regional Air 

Planning Association (CENRAP), the Midwest Regional Planning Organization (MRPO), the 

Visibility Improvement – State and 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 located within the boundaries of 14 

states: Alaska, Arizona, California, 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 states, tribes, Federal agencies, 

stakeholders, and citizen groups that 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 committees 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 peers 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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2006 Report for the Western Regional Air Partnership (WRAP) Regional Modeling Center (RMC) 

Figure 1-1. Regional Planning Organizations 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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2006 Report for the Western Regional Air Partnership (WRAP) Regional Modeling Center (RMC) 

Regional haze is linked to fine particulate matter (PM2.5, which is particulate matter ≤2.5 μm in 

diameter), for which EPA has a health-based standard. PM2.5 SIPs/TIPs are to be submitted three 

years after designation of PM2.5 nonattainment areas. EPA performed the final designations of 

these nonattainment areas in December 2004, so the PM2.5 SIPs/TIPs are due in early 2008. 

Except in California, there are few PM2.5 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 PM2.5,regional SIPs/TIPs will contain analysis of both PM2.5 and the effect 

on regional haze at the 118 WRAP Class I Federal 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 tribes may use the integrated oneatmosphere WRAP base-year visibility modeling (introduced in Section 1.1.4) as the regional 

component of the 8-hour ozone attainment activities as well as for PM2.5 and regional haze. 

Those decisions will be based on schedule constraints, model performance, appropriateness of 

the episodes, feasibility, and need. 

1.1.3 WRAP strategic plan 

On September 29, 2003, WRAP released their strategic plan for the years 2003-2008 (WGA, 

2003): http://wrapair.org/WRAP/meetings/031014board/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 thereby adopt the recommendations of the Grand Canyon 

Visibility Transport Commission (GCVTC). These §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 develop the §308 SIPs/TIPs that address all 118 Class I 

areas in the WRAP region. The WRAP 2003-2008 strategic 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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2006 Report for the Western Regional Air Partnership (WRAP) Regional Modeling Center (RMC) 

1.1.4 The WRAP Regional Modeling Center 

The WRAP Regional Modeling Center (RMC) is responsible for performing air quality modeling 

simulations for the states and tribes in the WRAP region in order to provide analytical results 

used in developing implementation plans under the EPA RHR. The RMC is composed of 

researchers from the University of California, Riverside (UCR), ENVIRON International 

Corporation, and the University of North Carolina’s Carolina Environmental Program (UNCCEP). Dr. Gail Tonnesen of UCR is the Project Manager 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 the requirements of the RHR, the RMC’s responsibilities include the following: 

• meteorological modeling 

• emissions processing and modeling 

• air quality and visibility modeling simulations 

• analysis, display, and reporting 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 demonstrate reasonable progress toward attaining 

visibility goals. Key work elements of the RMC visibility modeling include the following: 

1. Evaluating the visibility model for a historical episode—in this case, 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-year baseline period, 

2000-2004, and for the initial regional haze future projection period, calendar year 2018. 

3. Modeling a variety of emissions sensitivity, emissions source apportionment, and emissions control strategies to assess whether planned future regional emissions reductions 

will be sufficient to demonstrate reasonable progress toward achieving visibility goals. 

This report discusses the activities the RMC completed for 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 University/National Center for Atmospheric 

Research (PSU/NCAR) Mesoscale Model (MM5) meteorological modeling system, 

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

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

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

We have also performed some sensitivity tests using the Comprehensive Air Quality Model with 

extensions (CAMx) and the Regional Modeling System for Aerosols and Deposition (REMSAD). 

In addition, we applied MM5 and the California Meteorological Model/California Puff Model 

(CALMET/CALPUFF) modeling 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 elected 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-resolution MM5 data, and performed the 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 toward 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 systems 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 modeling infrastructure for CY02 to address the 

requirements of the WRAP strategic plan. 

In 2005 the RMC developed draft versions of the visibility modeling datasets and completed 

their quality assurance (QA). These datasets included emissions inventories and visibility 

modeling data for three annual 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 case emissions scenario, referred to as “2002 Planning 

Case” or “Plan02” 

3. The 2018 future-year base case emissions scenario, referred to as “2018 Base Case” or 

“Base18”

During 2005 we also completed final versions of the meteorology modeling, emissions 

modeling, and visibility MPE for the 2002 Base Case scenario for both a 36-km domain and a 

higher-resolution 12-km nested domain. We concluded that both versions of the modeling had 

acceptable performance for use in projecting future 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 considerably fewer resources to perform 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 differences between them, with one model performing better than the 

other in various comparisons to ambient data for selected PM2.5 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 (WRAP) 2002 Visibility Model Performance Evaluation (Tonnesen et 

al., 2006) describes each of these activities 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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2006 Report for the Western Regional Air Partnership (WRAP) Regional Modeling Center (RMC) 

This report describes activities completed in 2006. These include development, QA, and analysis 

of the final versions of the Base02, Plan02, and Base18 CMAQ simulations (Sections 2 and 3); 

source apportionment modeling using CAMx (Section 4); Best Available Retrofit Technology 

(BART) modeling using CALMET and CALPUFF (Sections 5 and 6); a sensitivity simulation to 

evaluate “clean” visibility conditions (Section 7); sensitivity simulations and analysis of effects 

and treatment of fire emissions (Sections 8 and 9); and data transfer activities (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 Forum 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 domain (abbreviated in this report 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 region. Detailed descriptions of the 

modeling domain, map projection, vertical layer structure, grid definition, and grid nesting 

schemes were included in Section 1.3 of the Final Report for the Western Regional Air Partnership (WRAP) 2002 Visibility Model 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 modeling employs a Lambert conformal projection of 149 by 113 grid 

cells with a 36-km horizontal resolution and 19 layers extending vertically to the lower stratosphere. The visibility modeling domain is nested within a larger MM5 domain with greater 

vertical resolution. Nesting procedures and parameters 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 regional 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 PM2.5 and ozone modeling. 

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

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 visibility. 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 conditions of 2002 might not be appropriate to represent 

typical conditions. Therefore, we developed a typical-emissions scenario for planning purposes 

using emissions that are believed to be more typical of the baseline emissions during the fiveyear period from 2000 through 2004. Results for this 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 toward 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 were 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 categories that underwent changes when creating Plan02 

from Base02. For information about the categories that remained unchanged and background 

information related to the emissions modeling, including the historical work that fed into the 

Plan02 emissions, general emissions modeling approaches, 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 emissions 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 for stationary point sources. 

The RMC completed emissions modeling for three iterations (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 developed version A of the Plan02 emissions (“case 

Plan02a”) directly from version A of the Base02 emissions (“case Base02a”), which is described 

in Tonnesen et al. (2006). To create case Plan02a 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 temporal profiles for large stationary point sources with profiles 

developed from an average of several years surrounding 2002. As we performed QA on case 

Base02a, however, we discovered several errors and 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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2006 Report for the Western Regional Air Partnership (WRAP) Regional Modeling Center (RMC) 

and gas inventories. Then, after observing unexpectedly large differences in the stationary point 

emissions in non-WRAP U.S. sources between cases 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 inventories. 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 application 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 collect, prepare, and quality-assure the emissions 

for the Plan02 annual scenario. We provide details on the sources of the inventories 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 with a discussion on the 

evolution of the Plan02 inventories and their origin in the Base02 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 processing, quality assurance, and uncertainties and 

future work. Finally, in Section 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 emissions for sectors that are strongly influenced by meteorology and 

thus have the most pronounced episodic trends in their emissions magnitudes and temporal variability. Wildfires are very episodic in nature, with their location, timing, and intensity correlated 

to optimal fire conditions. Agricultural and prescribed fires are managed and subsequently occur 

during periods of less optimal wildfire conditions. Electricity use and the emissions from electric 

generating units (EGUs) are correlated with seasonal 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 baseline emissions scenario to be representative of 

typical local meteorology using averaged conditions from the years surrounding 2002. 

As noted above, the Plan02 emissions were initially intended to differ from the Base02 emissions 

only in the use of averaged activity data for the fire and stationary-point-source 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 wildfires, 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 profiles to actual 2002 inventories to create EGU emissions for the 

Plan02 cases. 

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

2.1.1.1 2002 Planning Case emissions, version A (case Plan02a) 

We created case Plan02a from a combination of the WRAP baseline fire inventories, actual 2002 

EGU inventories with historically averaged temporal profiles, typical-year inventories from other 

RPOs, and Base02 emissions for the remaining emission sectors. After conducting the case 

Base02a emissions simulation (Tonnesen et al., 2006), we gathered several updates to the 2002 

base case inventories that affected 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 updates 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 the Base02 inventories before using these data 

in case Plan02a: 

• Stationary Area Sources: Revised inventories for California, Washington, North Dakota, 

Oregon, Utah, Mexico, and nonfire sources in the VISTAS states. Additional dust sources 

extracted from the area-source inventory to be modeled explicitly as fugitive dust. 

• Fugitive Dust Sources: Revised inventories for California, 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 California and VISTAS states. Note: We 

extracted the California data from the revised stationary-area-source inventory. 

• On-road Mobile Sources: Revised inventories for California, Montana, Colorado, and 

Mexico. 

• Non-road Mobile Sources: Revised inventories for California, 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 stationary area California oil and gas 

sources. Note: We extracted the California data from the revised stationary-area-source 

inventory. 

• Stationary Point Sources: Revised inventories for California, Alaska, Arizona, Montana, 

New Mexico, Oregon, Utah, Washington, Wyoming, WRAP tribes, Mexico, and MANEVU and VISTAS states. Updated temporal profiles for all non-WRAP EGU sources, as 

developed from the Integrated Planning Model (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 surrogates 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 inventories all explicitly contain emissions for 

Broomfield County, we revised our spatial allocation approach to include surrogates for 

this county, as opposed to including these emissions in the surrounding counties. As the 

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

nonroad mobile inventories still contain Broomfield County emissions folded into the 

surrounding counties, we did not change the 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 documentation 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 emissions, version B (case Plan02b) 

The RMC created case Plan02b to correct errors that we discovered in the Plan02a emissions and 

to include inventory updates that we received after we had already begun the case Plan02a 

emissions simulation. To create case Plan02b 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 the configuration of the 

Pacific shipping emissions and omissions of sources in the fugitive dust inventories used in case 

Plan02a. 

• Updates to non-WRAP stationary-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 comparison 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 observed differences in these emissions that 

were much larger than we expected, considering that we had only updated the temporal 

profiles for these sources. We discovered that the IPM-derived temporal profiles that we 

used in case Plan02a for the non-WRAP stationary point sources were intended for use 

only with IPM-projected 2018 inventories, not 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-derived 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. 

• Updates to domainwide fugitive dust and road dust emissions: Recent information about 

dust emissions led to a revision in the approach that we use to model fugitive and road 

dust emissions. Implementing these updates in case Plan02b impacted the fugitive dust, 

road dust, and stationary-area-source inventories. In May 2006 the EPA Emissions 

Factors and Inventory Group (EFIG) revised the transport factors that they recommend 

for scaling dust emissions downward to account 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 the 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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2006 Report for the Western Regional Air Partnership (WRAP) Regional Modeling Center (RMC) 

Another update to the dust emissions evolved from recommendations made by MRI 

(2005) regarding a revision to the fine fraction of PM from dust sources. Based on these 

recommendations, we changed the PM2.5/PM10 ratio in all of the road dust and fugitive 

dust inventories before modeling them through SMOKE to create the Plan02b emissions. 

• Updates to Gulf of Mexico offshore area-source inventories: We also received an updated 

Gulf of Mexico offshore area-source inventory 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 shipping 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 correct 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 revised stationary-area-source inventory, the corrected stationary-point-source emissions, 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 emissions, version C (case Plan02c) 

The RMC created case Plan02c to correct one error that we discovered in the Plan02b emissions. 

To create case Plan02c from Plan02b, we updated the WRAP non-Federal rangeland prescribed 

fire emissions. When we added this inventory to the WRAP modeling, we did not update the 

speciation and temporal cross-reference file to reflect the addition. The result was that we 

incorrectly applied the default diurnal temporal profile and VOC and PM2.5 speciation profiles to 

these emissions. In case Plan02c we applied the correct temporal and speciation profiles to these 

sources. Although these fires account for only 0.5% of the total annual WRAP PM2.5 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 sources of all inventory and ancillary data that 

changed between the Base02a emissions simulation and the Plan02a, Plan02b, and Plan02c 

emissions simulations. We worked with emissions inventory contractors, 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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2006 Report for the Western Regional Air Partnership (WRAP) Regional Modeling Center (RMC) 

For the WRAP state and tribal inventories, Air Sciences, Inc., provided all fire inventories; ERG, 

Inc., provided all stationary area and stationary point inventories and temporal profiles; and 

ENVIRON provided all mobile, oil and gas, and Pacific shipping inventories 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 profiles 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 the California inventories from the Base02a data 

and replaced them with the updated inventories. We used the resulting dataset for the Plan02 

scenario. This substitution of only the revised portions of the inventories was a general approach 

that we applied to several emissions sectors. We also developed more specific approaches for 

preparing the fire, EGU, Pacific shipping, dust, and Mexico inventories. 

• Fires: Air Sciences, Inc., provided annual Baseline Phase III fire inventories for each of 

the five fire categories (wildfires, agricultural 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 category consisted of an 

annual IDA file with physical fire event information, a daily IDA file with daily emissions by criteria pollutant, and 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 large inventories with 

SMOKE. For additional information on the development 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 development 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 shipping inventories for the Plan02 

scenario, we converted them directly to the 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 applied speciation and temporal adjustments 

using SMOKE chemical and temporal profiles, respectively, to create gridded, hourly 

emissions with volatile organic compound (VOC) and PM2.5 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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2006 Report for the Western Regional Air Partnership (WRAP) Regional Modeling Center (RMC) 

• Dust: We built the fugitive dust inventory for the entire modeling domain by extracting 

these sources, based on SCCs, from the stationary-area inventories. A list of the sources 

that we defined as fugitive dust is provided 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 data and re-adjusting the inventory PM2.5/PM10 ratios. 

To apply the transport factors recommended by EPA (2006c), we built a SMOKE control 

file (GCNTL) that contains scalars by SCC and location codes. We created new, adjusted 

dust inventories with the factors in the GCNTL file using the SMOKE programs Cntlmat 

and Grwinven. The RMC developed a Fortran program to reapportion the PM inventories 

using the revised PM2.5/PM10 ratios. 

• Mexico: Entirely new inventories for the six northern states of Mexico for stationary area, 

stationary point, on-road mobile, and nonroad 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 approaches developed in previous years during 

the RMC preliminary 2002 modeling (Tonnesen et al., 2005) and refined during case Base02a 

(Tonnesen et al., 2006). We modeled the Plan02 inventories with SMOKE version 2.1 using the 

same 22 source categories developed for Base02a. 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 configurations between the Base02a and Plan02 

emissions simulations, readers can refer to the WRAP 2002 MPE report (Tonnesen et al., 2006) 

for details on how we conducted emissions 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 used tabulated summaries of the input data and 

SMOKE script settings to document the data and configuration of SMOKE for all three Plan02 

cases. The graphical QA summaries include, for all 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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2006 Report for the Western Regional Air Partnership (WRAP) Regional Modeling Center (RMC) 

2.1.5 Uncertainties and future work 

Analysis and QA of the Plan02 emissions 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 Plan02b, we discovered that we did not apply the 

correct PM2.5 and VOC speciation profiles or the correct diurnal temporal profile to the WRAP 

non-Federal rangeland prescribed fire emissions. We mistakenly applied overall default profiles 

to these sources in the WRAP region in the Plan02a and Plan02b cases. As described in Section 

2.1.1.3, we corrected both of this error to create case Plan02c. 

As we analyze the CMAQ air quality modeling results 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 these data. Future work will focus on continued 

refinement of the modeling and on documenting known issues and problems with the Plan02 

emissions modeling results. 

2.1.6 Summary 

Because the Plan02 cases evolved from the Base02a emissions modeling, we were able to 

develop the Plan02 emissions using the established RMC emissions modeling 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 performing QA on the Plan02 results quite straightforward. As we made each change in creating the Plan02 emissions, we compared the resulting 

emissions back to the Base02a results, looking for expected trends in difference plots and annual 

summaries. The three iterations of the Plan02 emissions produced 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 undiscovered problems with these data, we have 

confidence that the Plan02c emissions are a well-characterized dataset and provide a solid 

foundation with which to compare the projected 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 of the 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) with 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 model that was completed by Jacob et al. 

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

(http://www-as.harvard.edu/chemistry/trop/geos/) for calendar year 2002. The Plan02 CMAQ 

emissions simulations differed from the Base02 emissions 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 CMAQ 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 Plan02 case to the previous iteration. These were 

prepared as Program for the Analysis and Visualization of Environmental data (PAVE) spatial 

plots showing the change in individual PM2.5 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 developed; these plots are being removed from the site as the QA 

process is completed. The final set of analysis products that will remain on the web site are 

PAVE difference plots comparing the CMAQ-predicted 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 Planning 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 Plan02 CMAQ modeling is the use 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 PM2.5 concentration, respectively, for the Plan02c 

CMAQ simulation minus the Base02b simulation. Warm colors (green to red) show regions of 

increased concentration in Plan02c versus Base02b, 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 wildfire 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 PM2.5 in Plan02 compared with Base02. 

The modeled concentrations of other species, such as sulfate and coarse mass, were similar 

between the Base02 and Plan02 scenarios. 

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

Figure 2-1. Example plot showing change in annual average CMAQ-modeled 

organic carbon (OC) for Plan02 minus Base02. 

Figure 2-2. Example plot showing change in annual average CMAQ-modeled 

PM2.5 for Plan02 minus Base02. 

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

3. 2018 Base Case Modeling 

The purpose of performing visibility modeling for 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 developed using projected emissions 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 visibility modeling results were compared to the 

Plan02 results to evaluate progress in attaining visibility improvement 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 quality. The first step in this process is to compare 

the 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 the inventories and ancillary input data. In contrast to the Plan02 

cases, the Base18 cases used inventories and SMOKE ancillary data that we had never used 

before. WRAP inventory contractors 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 modeling. 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 the data and incompatibilities between the data 

and SMOKE. As expected, during the QA of the 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 inventories 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 collect, prepare, and quality assure the emissions 

for the Base18 annual scenario. We provide details 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 Base18 inventories (Section 3.1.1). We then describe the details of 

producing CMAQ-ready emissions in Sections 3.1.2 through 3.1.5: data collection and 

preparation, emissions processing, quality assurance, and uncertainties and future work. Finally, 

in Section 3.1.6 we summarize the work we performed to create emissions cases Base18a and 

Base18b. 

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

3.1.1 Description of Base18 emissions 

We derived the Base18 emissions from inventories 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 the Plan02 inventories. Exceptions to this procedure were the 

fire inventories, non-U.S. sources, WRAP ammonia, Pacific shipping, and most of the MRPO 

sources; these were all kept the same as in the Plan02 scenario. The majority of the emissions 

sectors used temporal, spatial, and chemical profiles that are consistent 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 mobile inventories; and 2002 inventories for MRPO sources, non-U.S. 

sources, NH3 emissions in the WRAP region, and Pacific shipping emissions. For the WRAP, 

VISTAS, and MANE-VU states, we received 2018 inventories for stationary 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 the 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 Plan02 inventories for MRPO and 

CENRAP; Mexico and Canada held 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 Plan02 inventories for CENRAP; 

MRPO, Mexico, and Canada held constant with Plan02a; updated temporal profiles for 

all non-WRAP EGU sources, as developed from the IPM for 2018 emissions. 

• Fire Sources: Baseline inventories for WRAP, CENRAP, and VISTAS; these emissions 

were held constant with Plan02a. 

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

• 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 Plan02a emissions for the sources that did not 

change to create case Base18a. QA plots and documentation for Base18a are posted on the RMC 

web site at http://pah.cert.ucr.edu/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 received 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 stationary point inventory, revisions to the PM2.5

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 MRPO 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 PM2.5 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 applied four major corrections to the inventories and 

emissions inputs for modeling sources 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 Base18a inventory. They 

provided incremental updates to apply to a group of sources in the WRAP 2018 

inventory. Appendix A contains a memo describing these changes 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 impacted both the dust and stationary-area-source 

inventories. 

• We added on-shore and near-shore marine shipping sources for Washington and Oregon 

in case Base18b to correct the omission of these sources in Base18a. 

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

• We updated the WRAP inventories for prescribed 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 reductions from applying fire emissions 

reduction techniques (ERTs) to controllable fire emissions (Randall, 2006). They 

based the revised emissions on the same data that the RMC used in case Plan02b to 

illustrate the changes that resulted from controlling prescribed and agricultural fires 

between the Plan02b and Base18b cases. 

• We also corrected the temporal and speciation 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 case, 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 inputs for modeling sources to prepare 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 corrected projection data from CENRAP to 

apply to the CENRAP and MRPO stationary area, fugitive and road dust, agricultural and 

animal ammonia, and stationary 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 Plan02 modeling, we discovered that the IPMderived temporal profiles that we used in case Base18a for the non-WRAP stationary 

point sources produced emissions estimates that appeared unreasonably high, relative to 

the Plan02b emissions. We replaced the IPM-derived 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 documentation for Base18b are posted on the RMC web 

site at http://pah.cert.ucr.edu/aqm/308/qa_base18b36.shtml. 

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

3.1.2 Data collection and preparation 

This section describes the procedures that we followed to collect and prepare all emissions data 

for the Base18 emissions simulations. We discuss the sources of all inventory and ancillary data 

that changed between the Plan02 scenario and 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 Sciences, Inc., provided all fire inventories; ERG, 

Inc., provided all stationary area and stationary point inventories and temporal profiles; and 

ENVIRON provided all mobile, oil and gas, and Pacific shipping inventories that we used in the 

Base18 scenario. Alpine Geophysics, LLC, provided all inventories and ancillary data for the 

VISTAS states and tribes. CENRAP provided the 2018 projection factors for the CENRAP and 

MRPO states and tribes. The Mid-Atlantic Regional 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 received all emissions data electronically; the 

inventories in SMOKE-ready IDA format and the ancillary data as SMOKE-ready ASCII files. 

To prepare the 2018 emissions data for SMOKE, we reorganized these inventories 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 prepared the 2018 inventories for modeling through 

SMOKE in the following ways: 

• Apply projection factors to applicable CENRAP and MRPO inventories. 

• Extract fugitive and road dust sources from the stationary-area inventories to separate 

inventory files. Apply transport factors and revised PM2.5/PM10 splits to these sources. 

• Extract WRAP oil and gas sources from the stationary-area inventories 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 developed in previous years 

during the RMC preliminary 2002 modeling (Tonnesen 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 maintain consistent configurations between these 

emissions simulations. As nothing significant changed in the SMOKE configurations between 

Base02a and the Base18 simulations, readers can refer to the WRAP 2002 MPE report 

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

(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 used tabulated summaries of the input data and 

SMOKE script settings to document the data and configuration of SMOKE for cases Base18a 

and Base18b. The graphical QA summaries include, for all emissions output species that 

changed from Plan02a to Base18, daily spatial plots summed across all model layers, daily timeseries plots, annual time-series plots, and daily vertical profiles. 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 emissions revealed a variety of issues with these data. As 

described earlier, we corrected one set of issues in case Base18a, resulting in case Base18b. We 

discovered additional issues with the WRAP Base18 emissions inventories, as described in 

Section 3.1.5.1 through 3.1.5.4, after completing the Base18b emissions and air quality modeling 

simulations. These issues affect both the WRAP and non-WRAP states within the 36-km 

modeling domain. We plan to correct these issues in a series of forthcoming emissions 

simulations. 

3.1.5.1 IPM temporal profile 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 apply the correct temporal profiles for these 

sources based on the assumptions that were used to generate the inventory estimates. The 

inventories for these three RPOs were generated using the IPM to project the data to 2018. The 

IPM-based inventories for each RPO contain annualized 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 models. When we initially received 

these data and applied the accompanying temporal 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 standard EGU profiles with the IPM-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 stationary point inventory between 

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

those sources that are not based on IPM projections (WRAP, CENRAP, WRAP tribes, Canada, 

and Mexico) and the IPM-based inventories (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 RPO 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 sources that include the WRAP, 

CENRAP and non-U.S. sources) and PTE (point east, for the IPM-based sources east of the 

CENRAP region). 

3.1.5.2 PM2.5 speciation profile error 

VISTAS modelers notified us that they discovered an error in the PM2.5 speciation profiles for 

some EGU sources. The VISTAS modelers determined that a recently developed speciation 

profile used for point- and area-source coal combustion has a mass fraction estimate 10 times too 

high for elemental carbon. Additionally, EPA has informed us that the mass of PM2.5 from 

natural-gas-fired boilers has been overestimated by approximately an order of magnitude, which 

has an additional impact on the source contribution. 

To correct this problem in future emissions simulations, 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 PM2.5

emissions from coal combustion sources with profile number 22001. 

3.1.5.3 WRAP road dust PM2.5 omission 

Starting in version B of the Base02 and Plan02 emissions simulations, we recalculated PM10 and 

PM2.5 from dust sources using a revised split factor for the distribution of the PM emissions 

between the two size fractions. We discovered an error in our application 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 PM10. Because SMOKE 

calculates PMC as the difference between PM10 and PM2.5, the resulting model-ready emissions 

contained only PMC for road dust in the WRAP states. 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 temporal profile application errors 

We recently discovered a bug in the SMOKE processor 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 that 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 inventory data (i.e., in a different 

region or for different sources), and rerun SMOKE, we find that the other duplicate entry gets 

 23
2006 Report for the Western Regional Air Partnership (WRAP) Regional Modeling Center (RMC) 

assigned to the same source. Since 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 files of all duplicate 

entries, preserving only those entries that represent the most recent or accurate data. The addition 

of the duplicate cross-reference entries was the result of concatenating datasets from several 

RPOs when preparing the inputs for the continental-scale modeling. We will use this corrected 

cross-reference file in all future SMOKE simulations. 

We have also reported this potential bug to the SMOKE developers through the CMAS Center 

help desk (http://bugz.unc.edu/show_bug.cgi?id=2183). 

As we analyze the air quality modeling results 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. Future 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 infrastructure around which to develop the simulations. The 

updates that we made to the Plan02 modeling platform in developing the Base18 results affected 

all 22 source categories and required a QA effort on the large scale of that performed for the 

Base02 emissions. The two iterations of the Base18 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 emissions 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 PM2.5 species from 2002 to 2018. For planning 

purposes, on the other hand, states and tribes should focus on the visibility projections and glide 

path calculations at individual Class I Areas, discussed in Section 3.3. 

Figures 3-1 through 3-4 show the annual average concentrations for sulfate, nitrate, PM2.5 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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2006 Report for the Western Regional Air Partnership (WRAP) Regional Modeling Center (RMC) 

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 warm 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 and in Texas and Oklahoma. This results from 

planned SOx emissions reductions in the CENRAP region. There were smaller reductions in 

sulfate in the Los Angeles area, western Washington state, and southern Nevada. There were 

small increases of sulfate, mostly in Wyoming, due to growth in SOx emissions. Most regions of 

the WRAP domain had low concentrations of sulfate in 2002 and little change 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 there were reduction in nitrate in that state in 2018 because of 

reductions in mobile-source NOx emissions. There were small reductions in the Phoenix area as 

well, also from reductions in mobile-source NOx emissions. 

Figure 3-3 shows the comparison of PM2.5 for 2002 and 2018. In most areas of the WRAP 

region, changes in PM2.5 were less than 1 μg/m3

. Locations with increases in PM2.5 correspond to 

areas of increased sulfate (see Figure 3-1). Areas with the largest reductions in PM2.5 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 PM2.5 are available on the RMC web site at 

http://pah.cert.ucr.edu/aqm/308/cmaq.shtml#base18bvsplan02b. 

Figure 3-4 compares model-reconstructed visibility 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 results 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 deciviews is more dramatic than the change in 

PM2.5 mass (Figure 3-3) because the visibility in deciviews is a relative metric, so small mass 

changes in PM2.5 in good visibility areas can result in large relative improvements in visibility. 

The discussion of results in this section is qualitative and is presented to convey the general 

spatial patterns and magnitudes of changes in visibility. Quantitative results at individual Class I 

Areas are described next. 

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

Figure 3-1. Annual average aerosol sulfate (ASO4) 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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2006 Report for the Western Regional Air Partnership (WRAP) Regional Modeling Center (RMC) 

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. 

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

Figure 3-3. Annual average PM2.5 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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2006 Report for the Western Regional Air Partnership (WRAP) Regional Modeling Center (RMC) 

Figure 3-4. Annual average deciview 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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2006 Report for the Western Regional Air Partnership (WRAP) Regional Modeling Center (RMC) 

3.3 Visibility Projections 

As noted in Section 1.1.1, the RHR goals include achieving natural visibility 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%, visibility days) and (2) no worsening in visibility for 

the 20% of days that have the best visibility (“20% best,” or B20%, visibility days). One component of the states’ demonstration to EPA that they are making reasonable progress toward this 

2064 goal is the comparison of modeled visibility projections 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 constructing a “linear glide path” (in deciviews) that has at one 

end the observed visibility conditions during the mandated five-year (2000-2004) baseline period 

and at the other end natural visibility conditions in 2064; the visibility value that occurs on the 

glide path at year 2018 is the URP goal. 

We have made preliminary WRAP 2018 visibility 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-year 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 modeling results to the current-year modeling results. 

The calculated RRFs are applied to the baseline observed visibility conditions to project futureyear observed visibility. These projections 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 PM10 are divided into six major components; the first five 

are assumed to be PM2.5 and the sixth is PM2.5-10. 

• SO4 (sulfate) 

• NO3 (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 develop RRFs between future and current predicted 

concentrations of each component. 

• Component-specific RRFs are multiplied by current monitored values to estimate future 

component concentrations. 

• Estimates of future component concentrations are consolidated to provide an estimate of 

future air quality. 

• Future estimated air quality is compared with the goal for regional haze to see whether 

the simulated control strategy would result in the goal being met. 

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

• It is acceptable to assume that all measured sulfate is in the form of ammonium sulfate 

[(NH4)2SO4] and all particulate nitrate is in the form of ammonium nitrate [NH4NO3]. 

To facilitate tracking the progress toward visibility 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 period 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 the 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, represent estimates of natural visibility 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 calculated using fine and coarse PM concentrations 

measured at Interagency Monitoring of Protected 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 (PM2.5) and total PM2.5 and PM10 mass concentrations from which 

visibility is calculated using the IMPROVE aerosol extinction equation, discussed later. 

Visibility conditions are estimated starting with the IMPROVE 24-h average PM mass 

measurements related to six PM components of light extinction: 

• Sulfate [(NH4)2SO4] 

• Particulate nitrate [NH4NO3] 

• 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 particulate nitrate are assumed to be completely 

neutralized by ammonium and only the fine mode (PM2.5) is speciated to obtain sulfate and 

nitrate measurements (that is, any coarse-mode sulfate and nitrate in the real atmosphere may be 

present in the IMPROVE CM measurement). Concentrations for the above six components of 

light extinction in the IMPROVE aerosol extinction equation are obtained from the IMPROVE 

measured species using the formulas shown in Table 3-1. 

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

Table 3-1. Definition of IMPROVE components from measured species. 

IMPROVE 

Component Calculation of Component from IMPROVE Measured Species 

Sulfate 1.375 x (3 x S) 

Nitrate 1.29 x NO3

-

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 proton-induced x-ray emissions (PIXE) analysis 

of the IMPROVE Module A. To estimate the mass of the sulfate ion (SO4

=

), S is 

multiplied by 3 to account for the presence of oxygen. If S is missing then the sulfate 

(SO4) 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 SO4). 

• NO3

- is the particulate nitrate measured by ion chromatography analysis of Module B. 

For the IMPROVE aerosol extinction calculation, it is assumed to be completely 

neutralized by ammonium (1.29 x NO3). 

• The IMPROVE organic carbon (OC) measurements 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 referred to as light-absorbing carbon (LAC). 

• Soil is determined as a sum of the masses 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 forms. 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 PM10 and PM2.5 mass, respectively. 

Associated with each PM species is an extinction efficiency that converts concentrations (in 

μg/m3) to light extinction (in inverse megameters, Mm-1), as listed below. Sulfate and nitrate are 

hygroscopic, so relative humidity (RH) adjustment factors, f(RH), are used to increase the 

particles’ extinction efficiency with increasing 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 IMPROVE aerosol extinction equation assumes OMC 

is nonhygroscopic. 

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

βSulfate = 3 x f(RH) x [sulfate] 

βNitrate = 3 x f(RH) x [nitrate] 

βOMC = 4 x [OMC] 

βEC = 10 x [EC] 

βSoil = 1 x [soil] 

βCM = 0.6 x [CM] 

The total light extinction (βext) is assumed to be the sum of the light extinctions due to the six PM 

species listed above plus Rayleigh (blue sky) background extinction (βRay), which is assumed to 

be 10 Mm-1. This is reflected in the IMPROVE extinction equation: 

βext = βRay + βSulfate + βNitrate + βEC +βOMC + βSoil + βCM

The total light extinction (βext) in Mm-1 is related to visual range (VR) in kilometers using the 

following relationship: 

VR = 3912 / βext

The RHR requires that visibility be expressed in terms of a haze index (HI) in units of deciview 

(dv), which is calculated as follows: 

HI = 10 ln(βext/10) 

The equations above, with measurements from the associated IMPROVE monitor, are used to 

estimate the daily average visibility at each Class 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. The “worst days” visibility for each of the five years in the 

baseline period is defined as the average visibility across the 20% worst-visibility days (highest 

deciview values); similarly, the “best days” visibility is defined as the average visibility across 

the 20% best-visibility days (lowest deciview values) for each year. The Baseline Conditions for 

the best and worst days are defined as the five-year 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 measured PM species to visibility (light extinction) 

are referred to as the “Old IMPROVE” equation. The IMPROVE Steering Committee has 

developed a “New IMPROVE” equation that they believe better represents the fit between 

measured PM species concentrations and visibility impairment. Although conceptually similar to 

the Old IMPROVE equation, the New IMPROVE equation includes updates to many of the 

parameters and the addition of extinctions due to NO2 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 equation (e.g., EPA, 2006a,b).

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

3.3.2 Mapping of modeling results to the IMPROVE measurements 

As noted above, future-year visibility at Class I areas is projected by using modeling results in a 

relative sense to scale current observed visibility for the B20% and W20% visibility days. This 

scaling is done using RRFs, the ratios of future-year modeling results to current-year results. 

Each of the six components of light extinction in the IMPROVE reconstructed mass extinction 

equation is scaled separately. Because the modeled 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 explicitly 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 the 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 visibility. 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 listed above to assess visibility. The following six 

steps are used to project future-year visibility for the B20% and 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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2006 Report for the Western Regional Air Partnership (WRAP) Regional Modeling Center (RMC) 

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 using 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 daily 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 visibility to obtain the future-year visibility 

Haze Index estimate that is compared with the 2018 progress goal. 

In calculating the RRFs, EPA draft guidance (U.S. EPA, 2001, 2006b) recommends selecting 

modeled PM species concentrations “near” the monitor by taking a spatial average of PM 

concentrations across a grid-cell-resolution–dependent NX by NY array of cells centered on the 

grid containing the monitor. For the WRAP 36-km CMAQ modeling, the 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 modeling 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 

SO4i,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 sulfate 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 W20% days from the 2000-2004 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. 

 35
2006 Report for the Western Regional Air Partnership (WRAP) Regional Modeling Center (RMC) 

3.3.4 Glide path to Natural Conditions 

The presumptive visibility target for 2018 is the URP goal that is obtained by constructing a 

linear glide path from the current Baseline Conditions to Natural Conditions in 2064 (both 

expressed in deciviews). For instance, Figure 3-5 displays an example 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 variation in visibility. The Baseline Conditions are the average 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 Conditions in 2004 to Natural 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 modeled 2018 projections are compared against to 

judge progress. In this example, the visibility progress goal in 2018 would be 10.85 dv. Meeting 

this would require a 1.19 dv reduction in visibility 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

Year

Haziness Index (Deciviews)

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

Figure 3-5. Example of URP glide path using IMPROVE data from the Grand Canyon 

National Park for the W20% days and comparison with Base18b visibility projections. 

 36
2006 Report for the Western Regional Air Partnership (WRAP) Regional Modeling Center (RMC) 

3.3.5 Preliminary visibility projection results 

For all of the WRAP Class I areas, we have performed preliminary 2018 visibility projections 

and compared them to the 2018 URP goals using the Plan02c and Base18b CMAQ modeling 

results and the Old and New IMPROVE 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) reduction. However, the modeled 2018 visibility 

projection shows only a 0.33 dv (=12.04-11.71) reduction by 2018, which indicates that the 

emission controls simulated in case Base18b would 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, including wildfires, dust, 

and/or international transport. Additional analysis of these results and alternative projection 

techniques are currently under study. At this point in time, we can draw the following 

conclusions about 2018 visibility projections at WRAP Class I areas: 

• The modeled coarse mass (CM) predictions are highly uncertain and produce poor model 

performance when compared to the measured values. In particular, at sites in the desert 

Southwest where CM is high, the model exhibits a large underprediction tendency that 

we believe is due in part to local dust impacts 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 and so should not be used to project future 

levels of CM concentrations. We are recommending that future visibility projections be 

made using an RRF for CM of 1.0. 

• The RRFs for the other five components of visibility (SO4, NO3, EC, OC, and soil) 

appear to be more reliable indicators of changes 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. Either 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 insufficient days during 2002 to identify the 20% 

worst visibility monitored days that are needed to develop the RRF projection factors (see 

Table 3-3 for details). In the future, data filling will have to be used to obtain sufficient 

data at these Class I areas to perform future-year visibility projections. 

• Using the standard EPA approach for projecting 2018 visibility, most 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. 

 37
2006 Report for the Western Regional Air Partnership (WRAP) Regional Modeling Center (RMC) 

Method 1 predictions for Colorado Plateau and Desert Southwest sites

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

BRCA1

CANY1

GRCA2

MEVE1

SAPE1

WEMI1

ZION1

BAND1BOAP1

CHIR1

GICL1

GRSA1

IKBA1

SACR1

SAGU1

SIAN1

WHIT1

WHPE1

Percent 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%

BADL1

CABI1

GAMO1

LOST1

MELA1

MONT1

SULA1

THRO1

ULBE1

WICA1

CRMO1

JARB1

SAWT1

BRID1

NOAB1

ROMO1

WHRI1

YELL2

Percent 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%

HECA1

KALM1

MOHO1

MORA1

NOCA1

OLYM1

PASA1

SNPA1

STAR1

THSI1

WHPA1

AGTI1

BLIS1

DOME1

HOOV1

JOSH1LAVO1

PINN1

REDW1

SAGA1

SAGO1

YOSE1

Percent 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. 

 38
2006 Report for the Western Regional Air Partnership (WRAP) Regional Modeling Center (RMC) 

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 worst 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 planned anthropogenic emissions reductions in the Base02b case. We 

therefore performed additional visibility projection analyses using two alternate approaches for 

selecting the worst visibility days. We selected the 20% worst days based on (1) the worst 

modeled sulfate days and (2) the worst modeled nitrate days, and then recalculated the visibility 

projections using these two alternate 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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2006 Report for the Western Regional Air Partnership (WRAP) Regional Modeling Center (RMC) 

4. PSAT Modeling 

Impairment of visibility in Class I areas is caused by a combination of local air pollutants and 

regional pollutants that are transported long distances. To develop effective visibility improvement strategies, the WRAP member states and tribes need to know the relative contributions of 

local and transported pollutants, and which emissions sources are significant contributors 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 simulations have been used in which a “base case” 

model simulation is performed and then a particular 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 sensitivity 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 that source. Mass tracking methods have been 

implemented in both the CMAQ and CAMx air quality models. Initial work completed by the 

RMC during 2004 used the CMAQ Tagged Species Source Apportionment (TSSA) method. 

Unfortunately, there were problems with mass conservation in the version of CMAQ used in that 

study, and these affected the TSSA results. A similar 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 methods. However, the total mass contributions 

differed. With separate funding from EPA, UCR has implemented a version of TSSA in the new 

CMAQ release (v4.5) that corrects the mass conservation error, but because we were uncertain 

whether this update would be completed in time, we used the CAMx/PSAT source apportionment method for the WRAP modeling analysis. We did complete a test version of CMAQ/TSSA 

v4.5, and then used it in simulating January and 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 future-year 2018 (Base18b) conditions. These results are used 

• to assess the contributions of different geographic 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 improvements at a Class I area. 

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

4.1 Background and Setup Information 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 combination of a geographic source region and an 

emissions source category. Examples of source regions include states, nonattainment areas, and 

counties. Examples of source categories 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 interest. He or she then inputs 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 paper on the features and capabilities of PSAT 

(http://pah.cert.ucr.edu/aqm/308/reports/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 apportionment modeling for aerosol sulfate (SO4) and aerosol nitrate 

(NO3) and their related species (e.g., SO2, NO, NO2, HNO3, NH3, and NH4). The PSAT 

simulations include 9 tracers, 18 source regions, and 6 source groups. The computational cost for 

each of these species differs because additional tracers must be used to track chemical 

conversions of precursors to the secondary PM species SO4, NO3, NH4, and secondary organic 

aerosols (SOA). Table 4-1 summarizes the computer run time required for each species. The 

practical implication of this table for WRAP is that it is much more expensive to perform PSAT 

simulations for NO3 and especially for SOA than it is to perform simulations for other species. 

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. 

Species No. of Species 

Tracers 

RAM 

Memory 

Disk Storage 

per Day 

Run Time with 

1 CPU 

SO4 2 1.6 GB 1.1 GB 4.7 h/day 

NO3 7 1.7 GB 2.6 GB 13.2 h/day 

SO4 and NO3

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 the other with the Base18b future-year case. We expect that the 

states and tribes will use these results to assess 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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2006 Report for the Western Regional Air Partnership (WRAP) Regional Modeling Center (RMC) 

4.3.1) and receptor bar plots (discussed in Section 4.3.2) for both Plan02b and Base18b. It is 

beyond the scope of this task to evaluate the results for individual sites. However, we present 

sample plots below for the Plan02c results to illustrate 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 specifications 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 computational cost for one simulation day with source 

tracking for sulfate (SO4) and nitrate (NO3) 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 source groups are listed in Table 4-5. For the WRAP region, wildfires and some prescribed burning were considered natural emissions, while agricultural burning 

and the remaining prescribed burning were considered anthropogenic, so these WRAP fire emissions were treated as two separate categories in PSAT. For non-WRAP regions, some fire emissions were provided to the RMC as elevated sources 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-WRAP fire emissions were tracked separately in PSAT. 

We divided the annual PSAT run into four seasons. The initial conditions for the first season 

(January 1 to March 31, 2002) came from a CENRAP annual simulation. For the other three 

seasons, we allowed 15 model spin-up days prior to the beginning of each season. Based on the 

chosen set of source regions and groups, with nine 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 disk 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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2006 Report for the Western Regional Air Partnership (WRAP) Regional Modeling Center (RMC) 

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 

Description1

Source 

Region ID 

Source Region 

Description1

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 identify source regions in PSAT receptor bar plots. 

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

-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 sources (including stationary off-shore) 

2 Anthropogenic fires (WRAP only) 

3 Total mobile (on-road, off-road, including 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 dust, fugitive ammonia, nonelevated fire 

sources in other RPOs) 

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

4.2 Preparation of Emissions Data 

Emissions datasets for the CAMx/PSAT simulations were prepared directly from the SMOKEprocessed emissions developed for CMAQ. A simple format conversion was used to convert the 

original SMOKE output files from the I/O API format to the CAMx format. For certain source 

categories, SMOKE was configured to output CAMx-formatted files directly. 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 that were tracked in PSAT, we converted the 

intermediate SMOKE output files for those categories 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 emissions files to specify the PSAT source region 

individually for each point source. This was necessary in order to assign elevated-point-source 

emissions to the correct PSAT source region (particularly near state boundaries and along coastal 

regions), due to the relatively coarse (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 concentrations 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 instantaneous concentrations are also output for 

the last two hours of the simulation, which are used to restart the model. Although there are 

options to output hourly 3-D average tracer concentrations, 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 group contributions. 

The source apportionment model results are typically presented in two ways (Sections 4.3.1 and 

4.3.2): 

• Spatial plots showing the area of influence of a source 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 site. These plots also can be at a particular point in time or 

averaged over selected time intervals—for example, the average source contributions for 

the 20% worst visibility days. 

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

If the 3-D tracer output files are saved, it is also possible to prepare 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 contribute the most to the model grid cells 

containing each IMPROVE monitoring site and other receptor sites identified by WRAP. 

4.3.1 Source apportionment spatial plots 

Once the CAMx/PSAT simulations were completed, we used software developed at UCR to 

calculate monthly averages for each source region and emissions group. The PAVE software 

mentioned in Section 2.2 was then used to generate spatial plots of the monthly-average PSAT 

results. For example, Figure 4-2 shows the contributions to aerosol sulfate from Arizona pointsource emissions averaged over the entire month of January 2002. Figure 4-3 shows the 

contributions to aerosol nitrate 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., SO4 vs. NO3) can have quite different downwind 

impact regions. 

Additional monthly-average spatial plots of PSAT PM source contributions for the Plan02c and 

Base18b cases are available on the RMC web site under the subsection “WRAP Regional Haze 

Air Quality and Visibility Modeling Results” (http://pah.cert.ucr.edu/aqm/308/cmaq.shtml). 

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

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

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 group that contributes to each component of PM 

at a given receptor site. This display is more directly relevant to determining 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 creates bar plots and ASCII files showing the ranked 

largest emissions source group contributors to the individual components of PM (e.g., SO4 and 

NO3) at each receptor site. 

Receptor sites for which we prepared PSAT bar plots are defined by model grid cells containing 

IMPROVE sites in Class I areas. The source apportionments 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 “worst” and “best” can be defined using either 

ambient IMPROVE data or model predictions. 

Figure 4-4 is an example source apportionment receptor bar plot. It shows the largest 

contributors to aerosol sulfate (PSO4) at a receptor site (Hance Camp at Grand Canyon National 

Park, AZ) based upon the 20% best βext days’ average for 2002. The x-axis labels are explained 

in Tables 4-4 and 4-6. The top five contributors are boundary conditions and the point-source 

emissions from Arizona, Utah, Nevada, and Wyoming. Figure 4-5 shows the bar plot for the 

same receptor site and same year but based upon the 20% worst βext days’ average. The top five 

contributors are now boundary conditions, 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 monthly average (July 2002) rather than an annual 

average. The top five contributors are boundary conditions, point-source emissions from Mexico, 

off-shore emissions, and point-source emissions 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 for the W20% days, we used three different approaches for 

determining the 20% worst visibility days (see discussion in Section 3.3.6). First, we selected the 

worst days using the EPA-recommended approach based on the worst observed days during 

2000 to 2004. Then, because of concern that these days might be heavily affected by wildfires, 

we also selected W20% days based on the worst modeled sulfate and worst modeled nitrate days. 

Complete results for these three 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 alternate selections of worst days did create some changes in the 

major contributors to PM2.5 at some Class I Areas, so these results might be useful to state or 

tribal staff as they review emission control strategies.

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

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 site at Hance Camp in Grand Canyon 

National Park, AZ, showing the top contributors to PSO4 based 

on 20% best βext days’ average for 2002. 

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

Figure 4-5. PSAT bar plot for receptor site at Hance Camp in Grand Canyon 

National Park, AZ, showing the top contributors to PSO4 based 

on 20% worst βext 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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2006 Report for the Western Regional Air Partnership (WRAP) Regional Modeling Center (RMC) 

4.4 Quality Assurance and Comparison to CMAQ/TSSA 

To quality-assure the 2002 CAMx/PSAT simulations, we ran CMAQ with its source apportionment method, TSSA, for the months of January and 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 emissions source categories or regions. The algorithm 

includes a number of new tracers, or “tagged species,” for each emissions source to be evaluated. 

The result of the TSSA algorithm is 3-D concentration fields showing the transport of mass of 

both primary emissions and secondary products from the selected emissions sources. A single 

CMAQ Chemical-Transport Model (CCTM) simulation will include enough tagged species 

tracers to evaluate the contributions of as many 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 region. 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 model simulations with identical meteorology and emissions datasets so that we could more accurately evaluate the consistency of modeling results between the two models. We also used identical source regions and emissions groups 

for both simulations. A model spin-up period was used 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 ozone profiles to calculate the photolysis rates. 

Figure 4-7 shows source apportionment spatial plots for aerosol sulfate from California mobile 

sources from CMAQ/TSSA (left plot) and CAMx/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 spatial plots for aerosol sulfate from California 

point sources from CMAQ/TSSA and CAMx/PSAT for January 31, 2002, at 0000 GMT. The 

two methodologies again show similar spatial patterns but different maximum values. 

Figure 4-9 shows spatial plots similar to Figure 4-7 but for July 31, 2002, at 0000 GMT. As in 

the first two figures, the spatial patterns are similar between the two methods. Figure 4-10 shows 

the aerosol sulfate from California point sources for July 31, 2002, at 0000 GMT. Although the 

spatial coverage of sulfate in CAMx/PSAT is slightly wider than in CMAQ/TSSA, both methods 

show similar patterns. 

Figure 4-11 compares CMAQ/TSSA and CAMx/PSAT 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 sources 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, point sources from Mexico, area sources from 

Pacific off-shore, and point sources from Nevada and the eastern U.S. Although the spatial 

patterns from both methodologies are similar, the receptor bar plots show differences between 

the two sets of top-five contributors. 

 51
2006 Report for the Western Regional Air Partnership (WRAP) Regional Modeling Center (RMC) 

Figure 4-7. Source apportionment spatial plots 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 plots 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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2006 Report for the Western Regional Air Partnership (WRAP) Regional Modeling Center (RMC) 

Figure 4-9. Source apportionment spatial plots 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 plots 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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2006 Report for the Western Regional Air Partnership (WRAP) Regional Modeling Center (RMC) 

Figure 4-11. TSSA (top panel) vs. PSAT (bottom 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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2006 Report for the Western Regional Air Partnership (WRAP) Regional Modeling Center (RMC) 

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 visibility impairment at each Class I area, as simulated by the air 

quality model. These results, in combination with other types of “attribution of haze” products 

developed by WRAP and its contractors (see http://www.wrapair.org/forums/aoh/index.html), 

should be useful in identifying and prioritizing emissions sources that could be reduced to 

achieve visibility progress. The most significant contributors are different 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 planning staff at state and tribal agencies will use the PSAT results, 

along with other information, to analyze the sources of haze at the individual sites. Below we 

comment on several trends that are common at most sites: 

• Transported PM2.5 from the model boundaries was the single 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 performed by Jacob and coworkers (see: http://wwwas.harvard.edu/chemistry/trop/geos/). Thus, the modeled transport from the boundaries 

represents the current best available estimate of international transport of PM2.5. 

• Other than boundary conditions, local sources (i.e., within the state or in neighboring 

states) were the largest contributors to haze at any given site. The sum of smaller sources 

from more distant states or regions, however, also represented a substantial contribution 

to haze at all sites. Thus, local sources had 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 contributor in CMAQ than in CAMx, but they 

were a major source in both cases. 

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

5. Selecting Sources for BART Sensitivity Modeling 

An amendment to the EPA RHR requires the installation 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 their regions. One task in the WRAP-RMC 2006 

work plan is to perform sensitivity modeling studies to determine how much influence BART 

controls on eligible WRAP sources would have on air quality in the region. The RMC has developed tools to assist the states and tribes in selecting the sources in the WRAP stationary-pointsource inventory to include in BART sensitivity modeling. This section 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 modeling studies requires that the RMC identify the 

BART-eligible sources within the WRAP stationary-point-source inventory. We are soliciting 

the help of the WRAP states and tribes to identify these sources by developing spreadsheets that 

cross-reference information we have about BART eligibility with the records contained in the 

emissions inventories. 

As a first approach to this source selection process, 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 sources 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-eligible and to indicate 

eligible sources in the ERG report that are missing from the inventory. We circulated sample 

versions of these spreadsheets to the WRAP states and tribes for comment. 

During the first two quarters of 2006, several WRAP states drafted letters identifying the sources 

that they want designated as BART-eligible for 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 back 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 round of BART-eligibility selection spreadsheets to the WRAP 

states and tribes in preparation for the BART sensitivity modeling that the RMC will conduct 

during the next project year. The next section describes the form in which we present the 

information and how we will use it in the sensitivity modeling. 

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

5.2 Spreadsheets and Memo 

The RMC developed a set of spreadsheets and a draft memo to circulate to the WRAP states and 

tribes to solicit feedback about the sources within the WRAP 2018 point-source inventory that 

we intend to treat as BART-eligible for modeling. The memo explains 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 deploying these spreadsheets using the State of 

Colorado as a test case. We will test our procedures for using the spreadsheets to collect the 

information that we need to select BART-eligible sources, and our strategy for deploying the 

spreadsheets and receiving comments back from the states and tribes. As noted earlier, we have 

already received comments from inventory specialists at the State of Colorado regarding the 

content of the spreadsheets and will integrate those 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 information from all the states and tribes. 

5.4 Future Work 

The next steps in the BART eligibility selection process are to complete the case study with 

Colorado, revise the memo (Figure 5-1) by adding 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 tribes. We will specify a closing date for receiving 

comments on the spreadsheets. After the closing date, we will summarize the information in a 

memo that lists all of the sources 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 control factors to the BART-eligible sources. When the details of 

how we are to treat these sources in the 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 for 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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2006 Report for the Western Regional Air Partnership (WRAP) Regional Modeling Center (RMC) 

DRAFT MEMORANDUM 

To: WRAP State and Tribal Emissions Specialists 

From : WRAP-RMC 

Subject: Selection of BART-eligible sources in the WRAP 2018 emissions inventories 

This memo describes the procedures to follow 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 designating 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 doubling 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 BART-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 memos provided to the WRAP by some states and 

tribes: Utah, Nevada, South Dakota, and Washington. For those states from which we haven’t 

received memos, we’ve made our selections based on 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 currently 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 spreadsheet 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 changed, 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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2006 Report for the Western Regional Air Partnership (WRAP) Regional Modeling Center (RMC) 

6. BART Exemption Screening Modeling 

As discussed in Section 5, an important component of the RHR is the application of BART controls to stationary point sources that satisfy specific requirements and can be shown to “significantly” contribute to visibility impairment at a Class I area. To determine whether a source 

contributes significantly, EPA recommends use of the CALMET/CALPUFF modeling system; 

the main components of this modeling system are CALMET (a diagnostic three-dimensional 

meteorological model), CALPUFF (an air quality dispersion model), and CALPOST (a postprocessing package). Six WRAP States requested that the RMC perform BART exemption 

screening modeling to determine whether potential BART-eligible sources in their states 

contribute significantly to visibility impairment 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 guidance (e.g., IWAQM, 1998; FLAG, 2000; EPA, 

2003c), including EPA’s March 16, 2006, memorandum “Dispersion Coefficients for Regulatory 

Air Quality Modeling in CALPUFF” (Atkinson and Fox, 2006). This memo was written by the 

EPA Office of Air Quality and Planning Standards (OAQPS) Model Clearinghouse Director in 

response to questions from EPA 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/bart/WRAP_RMC_BART_Protocol_Aug15_2006.pdf), except 

in the ways noted later in Section 6.1.4. Using the procedures in this modeling protocol, we are 

performing initial subject-to-BART screening analysis modeling 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 the 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 complex terrain, resulting in complex 

flows. Consequently, the BART screening modeling that we conducted includes results for 

potential BART-eligible sources that are located within 50 km of a Class I area. The CALPUFF 

modeling was also performed for downwind distances greater than 200-300 km. When providing 

results to the states, the downwind distance between 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 greater 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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2006 Report for the Western Regional Air Partnership (WRAP) Regional Modeling Center (RMC) 

6.1 Background and Setup Information 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 regulatory version of the model: “The regulatory version of 

CALPUFF, along with specific regulatory options, resides on the Earth Tech website, described 

as the ‘EPA-Approved Version’” (Atkinson and Fox, 2006). This version was originally 

proposed for conducting the BART screening analysis. 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. Consequently, we chose to use a more recent version (April 

2006) of the CALPUFF modeling 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 continental-U.S. domains, we used a 4-km grid 

and 11 vertical layers with a Lambert conformal conic (LCC) map projection identical to that 

used for the WRAP MM5 and CMAQ modeling (see Section 1.3 of the 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 and precipitation observations. For the WRAP 

Alaska CALMET/CALPUFF modeling, a 2-km grid was used, along with 15-km-resolution 

MM5 data, surface meteorological and precipitation data, and 1-km-resolution land use and 

terrain data. For CALMET modeling for the continental-U.S. states, we used MM5 meteorology 

data for the years 2001, 2002 and 2003. EPA recommends 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 was 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 preprocessor. 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 analysis is performed to introduce observational data into the Step 1 

wind field. For the RMC BART screening modeling, CALMET was operated using hourly threedimensional 36-km MM5 meteorological fields and observed 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 initial-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 precipitation observations were used as input to 

CALMET, upper-air meteorological observations were not, as they are redundant to the MM5 

data and may introduce spurious artifacts in the wind fields. In the application of MM5, the 

twice-daily upper-air meteorological 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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2006 Report for the Western Regional Air Partnership (WRAP) Regional Modeling Center (RMC) 

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. Therefore, using the upper-air observations 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, potentially 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 process the 36-km MM5 data from 2001, 2002, and 

2003 for the CALMET/CALPUFF modeling system for the five continental-U.S. states listed in 

Section 6.1.1 (Arizona, New Mexico, Nevada, Utah, and South Dakota). These data were also 

processed for Montana, which requested that the RMC provide processed MM5 data for use in 

CALMET/CALPUFF modeling that they 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 modeling 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 between the Class I areas and the domain boundaries 

(e.g., 50 km) to assure that CALPUFF puffs are not eliminated due to the 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 required MM5 processing assistance, we identified 

four MM5 extraction (window) domains that are shown in Figure 6-1 and described below. 

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

Figure 6-1. Relationship of four MM5 window domains 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 boundary 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 received from the FLMs indicated that this was 

unnecessary. To the south we considered a boundary placement that would have included 

Big Bend National Park; however, any BART source impact at Big Bend would likely 

have a much greater effect in the closer southern New Mexico and Arizona Class I areas 

(e.g., Guadalupe and Carlsbad Caverns), so the southern boundary was terminated north 

of Big Bend National Park. 

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

• Nevada-Utah: The Nevada-Utah MM5 window domain 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 River 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 includes all of Montana plus all Class I 

areas in Idaho and some in eastern Washington 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 include the State of Montana’s CALPUFF modeling domain, which stretches west-east from longitude -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 Class I areas in nearby states. In the west it 

includes eastern Montana and western Wyoming 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 domain, separate MM5 meteorological modeling 

was conducted for 2002 on a 45-/15-km nested grid. 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 Bering Sea and Simenof Wildlife Refuge Class I 

areas are located too far (>500-1000 km) from potential BART-eligible sources to be of interest. 

6.1.3 CALMET/CALPUFF modeling domains 

The RMC developed a set of CALMET/CALPUFF modeling domains 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 available), 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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2006 Report for the Western Regional Air Partnership (WRAP) Regional Modeling Center (RMC) 

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 results to the states, the distance between a 

potential BART-eligible source and an associated Class I area should be considered in interpreting whether a visibility impact is significant. For the Nevada CALMET/CALPUFF modeling, 

CALMET modeling for 2001, 2002, and 2003 was conducted for the entire 198 x 306 4-km 

domain depicted in Figure 6-4a. However, because the Nevada BART sources are clustered 

either near Reno in the north or Las Vegas in the south, for 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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2006 Report for the Western Regional Air Partnership (WRAP) Regional Modeling Center (RMC) 

(a) 

(b) 

(c) 

Figure 6-2. CALMET/CALPUFF modeling 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. 

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

(a) 

(b) 

Figure 6-3. CALMET/CALPUFF modeling 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. 

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

(b) 

(a) (c) 

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

observed surface meteorological sites (purple triangles), and potential BART-eligible 

source locations (red dots) for (a) Nevada CALMET modeling, (b) northern Nevada 

CALPUFF modeling, and (c) southern Nevada CALPUFF modeling. 

The CALMET/CALPUFF modeling domain for Alaska is shown in Figure 6-5, which also 

shows the locations of the Denali and Tuxedni Class 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 

1st standard parallel............. 30o 

2nd 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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2006 Report for the Western Regional Air Partnership (WRAP) Regional Modeling Center (RMC) 

-200 -100 0 100 200 300

0

100

200

300

400

500

600

Figure 6-5. Alaska CALMET/CALPUFF modeling domain showing the potential BARTeligible source locations (red dots) and Denali 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 protocol (Tonnesen et al., 2006; 

http://pah.cert.ucr.edu/aqm/308/bart/WRAP_RMC_BART_Protocol_Aug15_2006.pdf). As 

noted at the beginning of Section 6, our screening analysis CALMET parameters conform with 

all of EPA’s recommendations for regulatory application of the CALPUFF modeling system, 

except in the following ways:

1. The WRAP CALMET/CALPUFF modeling domains were based on the same LCC 

projection used in the WRAP MM5 and CMAQ modeling, rather than the EPA default 

LCC projection. 

2. The maximum allowable mixing height (Z1MAX) for the WRAP CALMET modeling 

was 4,500 m AGL, versus the EPA regulatory 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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2006 Report for the Western Regional Air Partnership (WRAP) Regional Modeling Center (RMC) 

(CDPHE, 2005) the Colorado Department of Health and Environment (CDPHE) presents 

compelling evidence that mixing heights exceed 3,000 m AGL in Colorado during the 

summer. We would expect other areas in the west 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 are 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 vertical velocities. 

5. We set IAVET to 0 to turn off spatial averaging of the temperature 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 evaluation 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, terrain 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 evaluated by comparing spatial plots of the inputs 

against topographic and land use maps. The Alaska 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 available 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 five 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 been performed using visualization tools. 

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

Additional QA/QC will be performed within the limits of available resources. The CALMET 

modeling for Alaska for 2002 has also been completed and subjected to QA/QC. 

As of this writing, CALPUFF modeling of the potential BART-eligible 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 stakeholders who might want to perform additional 

modeling analyses using these data.

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

7. “Clean Conditions” 2002 CMAQ Sensitivity Analysis 

There are many natural sources of ambient PM2.5, both direct emissions of primary PM2.5 (such 

as windblown dust) and emissions of gaseous species that undergo photochemical transformation 

or condensation to form secondary PM2.5. Natural sources of PM2.5 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 those 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 ambient data analysis, not on visibility modeling. 

This question thus arises: Would modeled levels of natural haze be consistent with the values 

estimated by EPA for visibility planning? If the natural haze levels calculated by the model were 

substantially higher than the levels 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 PM2.5 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 emissions. 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 inventories, 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 this simulation, we removed all 

anthropogenic emissions from the inventory and included 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 instead the lowest haze levels that could be achieved in the model 

if all anthropogenic emissions were zeroed out. 

7.1 Description of Emissions Used 

We derived the emissions for the clean 2002 sensitivity case from case Base02a. Because it was 

a sensitivity analysis to test the impacts of natural emissions sources on visibility, we refer to it 

as scenario Base02nt, where “nt” refers to natural. We included the following emissions 

categories in Base02nt: 

• Biogenics: Generated in case Base02a 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 five 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 natural sources in scenario 

Base02nt. 

• CENRAP and MRPO Ammonia: To create ammonia inventory files for only natural 

sources, we used a list of SCCs representing natural sources to extract the emissions 

records of these sources from the monthly inventory files that were used in Base02a. We 

found that there were no natural ammonia sources in the MRPO monthly inventory files. 

• Natural Area Sources: The Base02a area-source inventory files included natural sources, 

such as wildfires and wild animals. We extracted these records from the stationary-areasource inventories. Note that the WRAP area-source files did not include any natural 

sources. 

• Natural Fires: Of the five fire categories modeled in Base02a—wildfires, wildland fire 

use, non-Federal rangeland prescribed fires, prescribed fires (which were split into 

natural and anthropogenic prescribed for this purpose of this sensitivity), and agricultural 

fires—we used the categories that represent natural fires: wildfires, wildland fire use, and 

natural prescribed fires. 

• Windblown Dust: We used the windblown dust inventory that ENVIRON and the RMC 

developed for use in case Base02a. Additional details on this dust inventory are available 

at http://www.cert.ucr.edu/aqm/308/wb_dust2002/wb_dust_ii_36k.shtml. 

We took the biogenic and windblown dust emissions directly from the Base02a SMOKE outputs 

that are stored at the RMC. For the fire (including both point and area fires), natural area, and 

ammonia emissions, we reprocessed these data specifically for scenario Base02nt using the same 

ancillary data (temporal, chemical, and spatial allocation data) that we used in case Base02a. QA 

plots and documentation for scenario Base02nt 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 visibility impairment is in the regions with natural fire emissions. 

Contributions to light extinction from natural sources were small in regions without large fire 

emissions, as evidenced in the eastern U.S., where the extinction was only slightly larger (about 

2 Mm-1) than perfectly clean Rayleigh conditions of 10 Mm-1. 

Although there are large uncertainties in the natural emissions, and we also know that we are 

missing certain types of natural emissions, the components of the natural inventory used in this 

sensitivity simulation did contribute to relatively large visibility impairment in regions where 

there were large wildfires. Extinction 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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2006 Report for the Western Regional Air Partnership (WRAP) Regional Modeling Center (RMC) 

Biscuit fire in Oregon, plus contributions from smaller fires and other natural emissions. These 

visibility impairment levels exceed the natural visibility levels specified in the EPA regional 

haze natural visibility guidance document. It will 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 controllable. In other regions of the 

country for which the inventories lacked large natural fire emissions, the modeled clean visibility 

was only slightly greater than clean Rayleigh conditions. 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-reconstructed “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 contributors 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 infer 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 used 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 biogenic 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 anthropogenic, but these emissions might include 

species such as terpenes that are typically considered biogenic. Using the analysis 

approach in which all terpenes are assumed biogenic then incorrectly causes some 

anthropogenic emissions to be labeled biogenic when we use the simplified approach of 

analyzing OC in terms of AORGPA, AORGA and AORGB. 

In spite of these difficulties, however, the results should classify the majority of the emissions 

correctly as either biogenic 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 scenario. 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 (bottom). Comparing these two plots indicates that in the western U.S. 

there is considerable AORGPA mass that is not controllable. It is likely that much of this mass is 

from fires, since uncontrollable AORGPA mass is present at the site of large fires in southern 

Oregon and north of Tucson, AZ. 

Figure 7-3 shows the annual average mass of secondary OC contributed from AORGA in the 

Base02b case (top) and the portion of that mass attributed to controllable emissions (bottom). 

These plots indicate that virtually all of the AORGA mass is controllable, 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 the AORGB mass is not controllable, a significant amount of mass 

is controllable. It is likely that the controllable AORGB mass results from VOC oxidation 

chemistry and the larger amount of biogenic mass that is oxidized and subsequently condenses to 

form OC in the Base02b case. These results indicate that controlling O3 precursor emissions is 

effective at reducing a small but significant 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 “controllable” 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 “controllable” 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 biogenic 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 evaluate model performance for each component of 

OC increases the uncertainty of the results described here and illustrated in Figures 7-2 through 

7-4, so caution should be used when drawing conclusions 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 visibility 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, considerable effort is required to compile emissions 

data for the small fires because of their large number. 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 visibility 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 necessary 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 all 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 compared 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 km2, introducing emissions from a 

100-ac fire into a 1,296 km2 grid cell would create significant artificial dilution of the plume. 

Using the 12-km model domain introduced 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 acres in grasslands. The sensitivity scenario was 

produced using a list of fires provided to the RMC by Dave Randall at Air Sciences, Inc. 

Agricultural burning and non-Federal 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 the 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 small fires for the two months studied. July had 

far fewer small fires (1,250) than November (5,207) because of the increased prescribed burning 

in 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 NOx and primary 

PM2.5 from small and large fires in July and November. This table indicates that for July the 

small-fire sensitivity case had 5,706 fewer tons of PM2.5 than the base case (which translates to 

only 2.6% less), while for November it had 8,701 fewer tons of PM2.5 (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 NOx and primary PM2.5 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 

NOx 56,434 54,959 1,475 2.6% July PM2.5 219,337 213,631 5,706 2.6% 

NOx 7,789 3,608 4,180 53.7% 

November 

PM2.5 22,725 14,024 8,701 38.3% 

8.2 Summary of Results 

We used a variety of postprocessing tools on the CMAQ sensitivity results to show them in 

different ways: 

• PAVE spatial plots showed the daily or monthly average change in visibility (an example 

is shown in Figure 8-1). Monthly 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 IMPROVE monitoring site showed the impacts of 

fires at each Class 1 area (Figure 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 based only on modeling results; they did not 

show IMPROVE data.) 

We posted on the RMC web site the monthly average 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 establish a de minimis fire size of 100 acres in 

woodlands and 300 acres in grasslands for reporting 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 removed, so the deciview values indicate 

the contributions from small fires 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 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. 

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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 evaluating 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, however, show that fire plumes actually are lofted 

vertically several hundreds if not thousands of meters, depending on the fire size, fuel conditions, 

and meteorology. In this task we compared two different algorithms for distributing fire emissions into the vertical layers of an air quality model. This section begins with descriptions of the 

fire events that we modeled to test these algorithms 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, prescribed 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) (Anderson et al., 2004) to determine heat flux for 

each fire. FEPS provides an MS-Windows-based graphical user interface (GUI) through which a 

user specifies certain parameters that characterize a fire; FEPS then produces output data on 

emissions, heat flux, and plume rise height. Examples of input parameters are date, location, size, 

fuel type, and burn type. The FEPS program is fairly complex. It includes default values for most 

parameters, but also includes menus regarding specific 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 select 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 about 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 differ 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). 

Fire Daily Emissions (tons/day) 

Type1 State Date Fire Size (ac) CO PM2.5 NOx 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 distribution of emissions 

from large, elevated point sources. The plume rise algorithm in SMOKE is based on the equations developed by Briggs (1984), and considers 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 and 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 from fire-specific parameters (Section 9.2.2). 

9.2.1 WRAP-FEJF approach 

The WRAP Fire Emissions Joint Forum (FEJF) developed a work-around for the fire plume rise 

constraint in SMOKE using a methodology to precompute fire plume parameters (Air Sciences, 

Inc., 2004). In this method, referred to here as the WF approach, fires are categorized into five 

plume classes that are defined by increasing potential plume heights based on a range of possible 

fire size classes, with respect to virtual acre size. 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 the 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 virtual 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 Tophour = (BEhour)

2

 * (BEsize)

2

 * Ptopmax (9-2) 

Plume Bottomhour = (BEhour)

2 * (BEsize)2 * Pbotmax (9-3) 

Layer 1 Fractionhour = 1 – (BEhour * BEsize) (9-4) 

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where BEhour is hourly buoyancy efficiency (unitless), BEsize is a fire-size-dependent buoyancy 

efficiency (unitless), Ptopmax is the maximum height of the plume top (meters), and Pbotmax is the 

maximum height of the plume bottom (meters). Recommended values as a function of fire size 

for these four variables are provided in the FEJF Phase II fire report (Air 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 weighted sigma pressure coordinate. For 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 Development (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 buoyancy efficiency from the daily fire heat flux 

to prepare the fire information for input to the 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

(m4/s3), as follows: 

F = Q x 2.58 x 10-6 (9-5) 

Buoyant efficiency (BEsize) and fire size in acres are related using Equation 9-6: 

BEsize = 0.0703 x ln(fire size) + 0.03 (9-6) 

The smoldering fraction (Sfract) is then derived using Equation 9-7: 

Sfract = 1 – BEsize (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 the 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 emissions from the SB approach are allocated to the 

model layers using a weighting based on the sigma-pressure vertical coordinate. A key difference 

between the WF and SB approaches is that the SB approach uses the same predicted local hourly 

meteorological fields (wind, temperature, pressure, etc., derived from MM5) to calculate plume 

rise and layer fractions as are used to drive the subsequent air quality model simulations. We 

expect that using MM5 data will make the SB results 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 fire emissions. We used the actual emissions data 

provided by ARS for these fires for both the WF and SB approaches. It should be noted that the 

coefficients and formulae used for the WF approach 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, the FEJF equations were validated using Western 

U.S. fire events and so may not be as suitable for fires in other regions of the world. 

Invoking the WF approach when using SMOKE requires using Equations 9-1 through 9-4 to 

precompute 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 the model layers. Using the SB approach involves 

providing SMOKE with daily acreage and heat flux data along with the daily pollutant inventories. SMOKE uses these data to derive the hourly 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 vertical 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 vertical 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-km 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 for 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 second type of plot in each set 

(Figures B-2, -4, -6, -8, and -10) is a combination of a stacked-bar 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 used 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 layers. Regarding the calculated 

maximum plume height, there is considerable variation between the two approaches. The WF 

approach calculates maximum plume heights 2 to 3 times greater than the SB approach, with the 

WF emissions allocated 5 to 8 layers 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. For 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 predicts that the first layer receives the single 

largest fraction of emissions as compared to the rest of the model layers. In contrast, the SB 

approach shows that the top model layer receives the single largest fraction of emissions. 

Plume distribution across the model layers is also vastly different between the two plume rise 

approaches. A notable feature from the WF approach 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, layer 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 about 30% of the emissions from the Colorado 

wildland fire use event to layer 1 and then the rest of the emissions 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 ground and the plume top receive emissions, with the 

allocation weighted by the sigma pressure coordinate. 

The temporal and chemical distributions of the 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 layer distribution of the plumes and in the maximum 

layer heights at each hour, reflected 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 profiles that are vastly 

different. The WF approach calculates 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 layer and the elevated layers. The SB approach calculates plumes that have the 

majority of the emissions near the top of the plume and a continuous distribution of emissions 

from the ground to the plume top. The differences in the emissions allocation to the first model 

layer results from the assumptions concerning the vertical extent of smoldering emissions. The 

WF approach assumes that all smoldering emissions remain in layer 1 and contains an explicit 

calculation for the fraction of emissions allocated to the first layer. With the SB approach, the 

smoldering emissions are assumed to be distributed across all of the layers 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 table of maximum plume top and plume bottom 

heights as a function of fire size, the maximum height and vertical 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 uses modeled local meteorology to calculate the 

maximum height and vertical extent of the plumes. 

We have found that the SB approach is currently 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 plume 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 algorithm and uses them to calculate heat flux 

internally. This facilitates the use of the SB approach to calculate plume rise for multiple fires at 

once. Many of the variables used in the heat flux 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 required 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 algorithm on an inventory-wide basis and then test 

the effects of this plume rise approach on air quality modeling results. To determine how either 

of these two approaches affects modeled air quality, 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 also 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 the models, model evaluation tools, and datasets to the WRAP states and tribes so that they can perform additional visibility modeling studies. We have made available 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 organizations that support those models. We are also 

providing limited support in the setup and operation of the models 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 modeling 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 estimate the date that the data will ship, media 

format, file size, etc. The state/tribe staff should verify that the summary of the data transfer 

request on the web page is correct. For large datasets, 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 return 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 request log on the RMC web site. It summarizes data 

requests that have been completed and those that are still in progress. 

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Table 10-1. Summary of data transfers completed or in progress, from log used to schedule data transfer

Deliverabl

Agency Name Request Date Contact Info Data Requested Expected Delivery Date Status Compatible OS Trans

Utah 4/20/06 Patrick Barickman 12km cmaq aconc; 12km emis by sector, 12km oil&gas emis input data 4/30/06 Shipped Linux disk/firIdaho 4/20/06 Rick Hardy MCIP and CMAQ 12 km outputs (5 months of 2002) 5/30/06 Shipped Windows LaCie 

(PCI-X

Idaho 4/20/06 Rick Hardy Data/scripts for running CMAQ with nested grids 5/30/06 Shipped Idaho 5/31/06 Rick Hardy MM5 36km/12km 36km Shipped Nevada 4/20/06 Frank Forsgren CALPUFF datasets for UT/NV domain, 2001-2003 test data by May 15 in progress ftp tran

test dat

Montana 4/20/06 Diane Lorenzen CALMM5 Data sets for MT subregion, 2001-2003 test data by May 15 Shipped Windows FirewirConfederated Salish & Kootenai Tribes 5/03/06 Randy Ashley CALMET data sets for MT subregion, 2001-2003 early June in progress Arizona DEQ 5/18/06 Jie Yang MM5 36km and 12km data 6/8/06 Shipped Linux disk/firArizona DEQ 9/11/06 Jie Yang CMAQ 36km outputs and emissions data late September TRC Environmental Corporation 9/19/06 Michael Newman CMAQ 36km 2002 outputs; 2018baseB SMOKE output; and 2002 12km MM5 data will be forwarded to by AZ DEQ University of Maryland Jeff Stehr MM5 and NH3 emissions data not scheduled 

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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 three major tasks for the WRAP visibility 

modeling: 

• Visibility modeling using SMOKE and CMAQ for the 2002 Planning Case and the 2018 

Base Case (Section 11.1.1). This included three related modeling studies: a “clean 

conditions” sensitivity analysis, a small-fires sensitivity analysis, and an analysis and 

comparison of two fire plume rise algorithms. 

• CAMx/PSAT source apportionment modeling (Section 11.1.2). 

• BART modeling using CALPUFF (Section 11.1.3). 

11.1.1 Visibility progress calculations using the Plan02 and Base18 

scenarios 

To assist WRAP states and tribes in evaluating the progress in 2018 toward the 2064 natural 

conditions visibility goals for WRAP Class I areas, we completed draft and final simulations for 

the 2002 typical-emissions scenario designed to represent the baseline visibility for typical 

conditions during the five-year period from 2000 through 2004 (Section 2). We also completed a 

2018 base case scenario that included emissions reductions planned by 2018 as well as increases 

in emissions due to population and economic growth (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 versions were used in calculating progress toward 

visibility goals for 2018. The visibility progress is presented as “glide path” calculations that 

show the percentage of visibility goal attained 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 these goals can be attributed to the 

following factors: 

• The WRAP region already has very clean conditions compared to other regions of the 

U.S. I is difficult to show improvement when 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 transport, dust, and wildfires—

contribute a significant portion of the PM2.5 at most WRAP Class 1 Areas. The 2018 

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modeling did not show much visibility progress because these sources of PM2.5 were 

uncontrollable in our case Base18b simulation. 

• Emissions reductions from improved technologies and new regulations were partially 

offset by population growth in the West by 2018. 

• We explored alternate approaches for selecting 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 international transport, dust, and wildfire emissions. 

Complete results from the visibility modeling are 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 PM2.5

and uncertainty in their modeling. These studies included 

1. CMAQ modeling of a “clean” scenario that included only emissions sources identified as 

biogenic (Section 7), 

2. evaluation of the contribution of small fires 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 wildfire 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 considerably worse than the natural-conditions progress 

goals given in the EPA guidance documents. This means that it will not be possible to demonstrate progress toward goals using visibility modeling at those Class 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 possible to determine whether the model accurately 

represented natural haze levels. 

Our evaluation of the effects of small fires (fewer 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 discontinuation 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 calculates 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 layer and the elevated layers. The SB approach calculates plumes 

that have most of the emissions near the top of the plume and a continuous distribution of emissions from the ground to the plume top. It is difficult to predict what effect these differences 

would have on modeled visibility downwind of fires, but it is possible that the SB approach 

might have greater visibility impacts because it retains a larger fraction of the emissions within 

the mixed layer. 

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11.1.2 PSAT source apportionment 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 source apportionment for 

aerosol sulfate, nitrate, and ammonium for 6 emissions source groups 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 observed days during 2000 to 2004. Then, because of 

concern that these days might be heavily affected 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 contributions to organic carbon 

aerosols from primary anthropogenic OC, secondary anthropogenic OC, and secondary biogenic 

OC using the clean emissions scenario contrasted with the Base02b case. These results give a 

qualitative assessment of the relative contributions 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 system, we are performing BART exemption screening modeling 

for the WRAP domain for six continental-U.S. states 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 observations were also processed for input into 

CALMET; we did not use upper-air meteorological 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 the upper-air observations embedded in them. For the non-Alaska 

states, CALMM5 was applied using four extraction (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 modeling 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 the 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 CALMET/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 section 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 WRAP 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 actual sources, due to data gaps or a lack of 

emissions process information or modeling methodology; it is imprecise because we make gross 

simplifying assumptions about the sources that we do simulate. Despite this bleak assessment of 

the science of emissions modeling, the product—model-ready emissions—is arguably the single 

most influential input to the chemical predictions of an air quality model. Emissions data present 

an important paradox in air quality modeling: they 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 during 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 minimizing these types of uncertainties, we avoid adding controllable uncertainties to the inherent uncertainties that we cannot 

control and thus increasing the overall emissions uncertainty. 

Operationally, the concept of minimizing the controllable 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 course of a modeling study. It is necessary to prioritize these errors 

for correction based on (1) their effect on the air quality modeling results and (2) the amount of 

effort required by the modeling staff 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 annual basis may produce all of its 

emissions on one day in a single location 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 air 

quality in the receptor area on the day when the emissions are released. As an example, we discovered an error in the application of temporal and chemical profiles to non-Federal rangeland 

prescribed fires in the WRAP region. While the emissions from this source category contribute 

only 0.5% of the annual PM2.5 emissions in the WRAP states, they are so episodic that these 

seemingly minor emissions changes produced significant changes in PM and visibility predictions in certain model grid cells. Obviously, large errors in the emissions modeling that have a 

significant effect on the modeling results need to be corrected, regardless 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 temporal/spatial/chemical signature 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 the errors. Significant errors are 

cause for developing a new version of an emissions simulation, as in going from case Plan02b to 

case Plan02c. Multiple small errors are also cause for developing new simulation versions. 

Developing general quantitative criteria 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 systematic QA procedures 

The RMC developed the most comprehensive and well-documented protocol for emissions QA 

currently available (Adelman, 2004). Our systematic 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 developed an automated system for displaying the results 

of the emissions simulations at multiple spatial and temporal scales. Despite all of our expertise 

in emissions modeling and exhaustive QA processes, however, we still make mistakes and 

discover errors in the modeling. We do not achieve perfection in our emissions modeling 

because perfection is unattainable; emissions modeling is inherently 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 impossible for a standard set of procedures to account 

for every type of problem that one will encounter in emissions modeling. While we have minimized common problems and uncovered many others when applying the procedures 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 schedule and resources available for the RMC’s work for WRAP. 

Maintaining a systematic QA process is vital to producing reliable emissions modeling results. 

At the same time, relying solely on a systematic process to catch all errors in emissions modeling 

results ignores the reality of the process. Emissions QA that succeeds in threshing out errors and 

building confidence in the modeling results is an endless process that requires 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 quite dynamic. New technologies for representing 

emissions processes that until now have been crudely represented are rapidly being developed. 

Research and development regarding modeling sources that we omitted in the past is also an 

advancing area in emissions modeling. Integrating 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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2006 Report for the Western Regional Air Partnership (WRAP) Regional Modeling Center (RMC) 

and high-quality emissions data. We have also encountered problems in using these prototype 

systems, however, where the basic assumptions were not well-defined or the systems’ products 

were not easily integrated into 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 example 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 that they occur in only the first model layer. 

With the modeling techniques developed for WRAP, the RMC can now estimate fire emissions 

by event (located by latitude-longitude coordinates) and calculate plume rise to allocate the 

emissions to the vertical model layers. 

Conversely, these fire emissions data 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 emissions from fires in the WRAP states. Documented 

in Section 9, the new SB algorithm is appealing because it uses predicted local meteorology to 

calculate the layer fractions, as opposed to using climatologically-derived plume characteristics. 

However, we have discovered in our evaluation that the level of effort required 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 the 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 the new fire plume rise algorithm, the effort 

required to integrate this algorithm into our modeling represents unreasonable 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 the 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 application. It is vital to have a QA process in place 

that will uncover major issues like missing inventory sources, erroneous 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 learn the various assumptions that are inherent in each dataset 

and then to design the SMOKE simulation around those assumptions. A single simulation 

of a SMOKE processor can use only one configuration. When confronted with modeling 

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several datasets for a single source category, such as RPO-specific stationary-area-source 

inventories, there will likely need to be a different 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 attention 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 these 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 data 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 thoroughly for these messages and all default 

profile applications scrutinized. Each time a new inventory is modeled in SMOKE, it is 

highly likely that default profiles will be erroneously assigned because new SCCs in the 

inventory did not get added to the cross-reference files. The assignment of default profiles in SMOKE modeling is intended only as a last resort and requires close scrutiny. 

• Overlapping Inventories: With the extensive amount of inventory splitting 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 double-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 sources from our existing inventory to avoid 

double-counting these sources 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 modeled MRPO, VISTAS, and MANE-VU data as a 

third MOBILE6 simulation. We divided the MOBILE6 processing to circumvent memory 

overload problems. Running out of memory when modeling sources in a SMOKE run 

indicates that the computer system has insufficient memory for the dataset being modeled, 

so the data must be divided into spatial subregions to conform to the available computing 

resources. 

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• Dust Sources: Fugitive and road dust sources required the largest preprocessing effort 

of all the source categories because of the need to first extract these sources from the 

stationary-area-source inventory, apply transport factors, and then recalculate the 

PM2.5/PM10 ratios. 

• Stationary Point and CEM Sources: We received CEM data in two forms over the course 

of the Plan02 and Base18 modeling. One form 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 configuration of the data we modeled. 

• Nonroad Mobile Sources: Because the nonroad inventories consisted of a mixture of 

annual and seasonal/monthly data, we had to split this category into two processing 

streams consistent with the temporal resolutions of the inventories. For seasonal/monthly 

inventories, where the monthly variability is inherent in the inventory, it is necessary to 

apply flat monthly temporal profiles. Annual inventories, on the other hand, require that 

monthly variability be applied through the temporal profiles. While we could have accomplished this configuration in a single simulation by developing 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 and seasonal/monthly inventories. We found 

that it was easier to distinguish between the two types of inventories for this source 

category and then use a temporal cross-reference 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 additional visibility modeling to evaluate the 

effects of several types of emissions reduction strategies. These could include simple, across-theboard emission reductions of selected emissions species, or emission reductions in selected emissions source categories. The type and number of emissions control strategies to be evaluated will 

be determined by WRAP. The objective of these model simulations would be to assist the states 

and tribes in identifying emission reduction strategies that can achieve desired progress in 

improving future visibility. 

Additionally, we will continue to provide support and data transfer to the states and tribes for 

visibility modeling. This will include transfers of datasets, models, and software, and basic 

support in installing the models and 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/CALPUFF 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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Anderson, G.K., D.V. Sandberg, R.A. Norheim, 2004. Fire Emissions Production Simulator User's Guide 

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for the Western Regional Air Partnership by ERG, Inc., Sacramento, CA. April 4, 2006 

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Eastern Research Group, Inc., Sacramento, CA, October, 2006, 

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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 Related Values Workgroup (FLAG): Phase I Report,” 

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1998. 

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States – Report III. Cooperative Institute for Research in the Atmosphere, Fort Collins, Colorado. 

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GEOS-CHEM, Version 1.0, August 22, 2004, Institute for Multidimensional Air Quality Studies, 

University of Houston, Houston, TX 

(www.cert.ucr.edu/vistas/vistas2/BC_files/geos2cmaq_uguide_aug22_2004.pdf). 

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Dust – Final Report, Prepared for the Western Regional Air Partnership by MRI, Kansas City, MO. 

http://wrapair.org/forums/dejf/documents/fffd/Final_Fine_Fraction_Dust_Report.pdf. 

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Integrating Blue Sky and SMOKE, in Proceedings of the 14th Annual Emissions Inventory 

Conference, Atlanta, GA. 

Randall, D., 2006: Technical Memorandum: Fire Emissions Inventory – Phase III Base-Control Case, 

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Sciences, Inc., April 28, 2006. 

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06.pdf#search=%22%22New%20IMPROVE%20equation%22%22). 

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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 

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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. 

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wrapair.org website. (http://www.wrapair.org/forums/fejf/tasks/FEJFtask7Phase3-4.html). 

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

Appendix A: 

WRAP 2018 Stationary-Point-Source 

Inventory Corrections (Base18b)
2006 Report for the Western Regional Air Partnership (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 stationary-point-source inventory. Table 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 inventory corrections to be addressed in the 

RMC’s “base18b” modeling simulation. These changes were identified and made due to: 

1) Comments from February 1-2, 2006 SSJF meeting; 

2) State reviews of ERG 2018 base case version 1 EI data; 

3) Review and research by WRAP staff; and 

4) Include errata from the ERG Final 2018 Report, 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 revision was not included in the 2018 base case 

inventory since it came too late in the process.

Response: Campbell County – Powder River Basin coal mines’ emissions were revised, 

these are negligible for the purpose of WRAP modeling, do not include 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 NOx, 107 tons SO2). 

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 hourly emissions (and not annual 

emissions) were provided: 

• North Border #5 Compressor Station 

• Manning Compressor Station 

 A-2 
2006 Report for the Western Regional Air Partnership (WRAP) Regional Modeling Center (RMC)

• 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 Pavilion Compressor Station, although 

annual emissions were not provided so this source could not be included in the 2018 base 

case inventory. 

Response: This facility’s emissions or construction/permitting status are unknown, we will 

not address it at this juncture (could be included in future control strategy modeling runs).

5. Oregon May 2, 2006 E-Mail [Mark Fisher]: VOC emission reductions for two wood 

products facilities (Boise Cascade in White City and La Grande) were not included in the 

2018 base case projections due to problems matching the SCCs in the 2002 inventory and 

information received from the State. If these 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 30700716). 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 Gribovicz] 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 Horn 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 (WA, 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 

 A-3 
2006 Report for the Western Regional Air Partnership (WRAP) Regional Modeling Center (RMC)

Chevron USA – Table Rock Field (56xxxPRJFAC22) = 56037 Sweetwater County 

Chevron USA – Whitney Canyon/Carter Creek (56xxxPRJFAC23) = 56041 Uinta County 

ExxonMobil – Black Canyon (56xxxPRJFAC24) = 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) sources are included in 2018 IDA file (IDAPT_States-non-O&G-20051210.zip). Line items in IDA file are as follows: 

Sierra Pacific Industries (53xxx1118) = 027 Sierra 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 PM10 from construction/wind erosion (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 spreadsheets, 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 states that do have emissions, seven states (AZ, 

MT, ND, NM, NV, WA, and WY) inventoried six pollutants (PM10, PM2.5, EC10, EC2.5, 

OC10, and OC2.5). OR inventoried only PM10 and PM2.5, while ID inventoried only PM10. 

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 and Chris from Idaho DEQ to discuss some 

questions they had on their 2018 base case projections. 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 applied a 97% reduction in SO2 to emission unit 10 based on 

 A-4 
2006 Report for the Western Regional Air Partnership (WRAP) Regional Modeling Center (RMC)

information we gleaned from the 2004 Idaho Annual Report (scrubbers are being installed). 

However, Mary and Chris have pointed out that these are not enforceable reductions, and 

should not be taken as part of the base case inventory. The 97% reduction 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 retirement/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 SO2 to Emission Unit 10 at the P4 Production LCC facility in 

Soda Springs (State ID# 2900001) should be eliminated. 

The following multiplicative factor needs to be applied – 

For IDA ID# 1602902900001 (1st field), Emission Unit ID# 10 (2nd field), Emission 

Release Point ID# 10 (3rd field) 

SO2 emissions (455.097 tpy) – Factor of 33.333 

Factor only applied to SO2 emissions.

#2 – Duplicate SCCs in the retirement/replacement 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 (1st field), Emission Unit ID# 10 (2nd field), Emission 

Release Point ID# 10 (3rd field), SCC 10200229 (5th field): 

NOx emissions (466.1971 tpy) – Factor of 1.1364 

PM10 emissions (83.38766 tpy) – Factor of 1.0415 

SO2 emissions (108.1761 tpy) – Factor of 0.6576 

For IDA ID# 1606706700001 (1st field), Emission Unit ID# 20 (2nd field), Emission 

Release Point ID# 20 (3rd field), SCC 10200229 (5th field): 

NOx emissions (277.9395 tpy) – Factor of 1.1364 

PM10 emissions (23.58439 tpy) – Factor of 1.0415 

SO2 emissions (73.2477 tpy) – Factor of 0.6576 

Factors only applied to NOx, PM10, and SO2 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 =>Industrial process/Primary metal/Primary CU 

smelting; SO2 only). 

 A-5 
2006 Report for the Western Regional Air Partnership (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 (5th field) needs to be changed from 10300602 

(Commercial/Institutional Natural Gas External Combustion Boiler) to 30300599 

(Primary Copper Smelters – Other Not Classified). A miscellaneous SCC was assigned 

because specific process/equipment 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 and 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 source 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 came out of the 2002 EPA 

NEI gap-filling for area sources, which was 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 look 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 projected using 2002 heat input was grown to capacity; 

emissions were estimated by multiplying projected heat input by 2004 (i.e., most recent) # 

per BTU emission rates. 

Both Unit #2 and #3 were running near capacity (82.8% and 79.3%, respectively), so 

projected heat input increased slightly. 

The 2004 NOx emission rates were similar for Units #2 and #3 (0.487 and 0.437 

lbs/mmBTU, respectively), as were the SO2 emission rates (0.174 and 0.184 lbs/mmBTU, 

respectively). 

 A-6 
2006 Report for the Western Regional Air Partnership (WRAP) Regional Modeling Center (RMC)

It is possible that some SO2 controls were implemented between 2002 and 2004. For both 

units, both heat input and NOx increased 2002 to 2004, but SO2 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 projected 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 capacity (71.0% and 59.8%, respectively), so 

projected heat input increased considerably for these two units. 

The 2004 NOx 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 SO2 emission rates were similar for Units #1 and #2 (0.329 and 

0.341 lbs/mmBTU, respectively); while the emission 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, 

NOx and SO2 emissions decrease from 2002 to 2004 even though the heat input increases. 

For Unit #2, the SO2 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 estimated using 2002 heat input was grown to 

capacity; emissions were estimated by multiplying projected heat input by 2004 (i.e., most 

recent) # per BTU emission rates. However, permit limits were applied to the NOx and SO2 

emissions from Unit #1 and #2. For Unit #1, 3,093 tpy NOx and 1,856 tpy SO2. For Unit #2, 

3,050 tpy NOx and 1,830 tpy SO2. 

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-7 
2006 Report for the Western Regional Air Partnership (WRAP) Regional Modeling Center (RMC)

Table A-1. Summary of updates to the WRAP 2018 stationary point inventory 

Invent

Location FIPS SCC Plant Description 

Original 

White City, OR 41029 30700716 Boise Cascade – Unit 7 95% reduction in VOC 

emissions 

95.24 tons (VOC) 

La Grande, OR 41061 30700703 Boise Cascade – Unit 37 95% reduction in VOC 

emissions 

88.92 tons (VOC) 

Soda Springs, ID 16029 30111299 P4 Production, LLC – 

Unit 10 

Back out 97% reduction in SO2 emissions 

455.10 tons (SO2) 

Minidoka Co., ID 16067 10200229 Tasco Paul – Unit 10, 

Stack 10 

Emissions adjustment for NOx, PM10, and SO2

466.20 tons (NOx) 

83.39 tons (PM10) 108.18 tons (SO2) 

Minidoka Co., ID 16067 10200229 Tasco Paul – Unit 20, 

Stack 20 

Emissions adjustment for NOx, PM10, and SO2

277.94 tons (NOx) 

23.58 tons (PM10

73.25 tons (SO2) 

Gila Co, AZ 04007 10300602 Phelps Dodge – Miami 

Smelter 

SCC change 10300602 

Gila Co, AZ 04007 30300527 Ray Complex – Unit 2 Emissions adjustment for SO2 34,490 tons (SO2) Cochise Co., AZ 04003 10100222 AZ Electric Power 

Cooperative, Inc.; Apache EGU – Unit 2 

Revised SO2 total for Units 2 

and 3 = 2664 tons 

3721.9 tons (SO2

Cochise Co., AZ 04003 10100222 AZ Electric Power 

Cooperative, Inc.;Apache EGU – Unit 3 

Revised SO2 total for Units 2 

and 3 = 2664 tons 

3168.6 tons (SO2

Rosebud Co., MT 30087 10100226 Colstrip EGU – Unit 1 Revised SO2 total for Units 1 

– 4 = 16056 tons 

3690.2 tons (SO2

Rosebud Co., MT 30087 10100226 Colstrip EGU – Unit 2 Revised SO2 total for Units 1 

– 4 = 16056 tons 

3950.3 tons (SO2

 A-8 
2006 Report for the Western Regional Air Partnership (WRAP) Regional Modeling Center (RMC)

Rosebud Co., MT 30087 10100226 Colstrip EGU – Unit 3 Revised SO2 total for Units 1 

– 4 = 16056 tons 

2980.7 tons (SO2

Rosebud Co., MT 30087 10100226 Colstrip EGU – Unit 4 Revised SO2 total for Units 1 

– 4 = 16056 tons 

3169.0 tons (SO2

Pueblo Co., CO 08101 1010226 Public Service Co; 

Comanche EGU – Unit 1 

Emissions adjustment for units 1 and 2 = 3686 tons (SO2), 

6143 tons (NOx) 

1856 tons (SO2) 

3093 tons (NOx) 

Pueblo Co., CO 08101 10100222 Public Service Co; 

Comanche EGU – Unit 2 

Emissions adjustment for units 1 and 2 = 3686 tons (SO2), 

6143 tons (NOx) 

1830 tons (SO2) 

3050 tons (NOx) 

Pueblo Co., CO 08101 10100201 Public Service Co; 

Comanche EGU – Unit 3 

Emissions adjustment for unit 3 = 3250 tons (SO2), 2600 tons 

(NOx) 

2513 tons (SO2) 

2513 tons (NOx) 

McKinley Co., NM 

35031 10100226 Escalante Station – Unit 1 Emissions adjustment for SO2 1228.7 tons (SO2) 

Fremont Co., WY 31000205 Burlington Resources – 

Bighorn Wells 

Added missing FIPS codes 

Sweetwater Co., WY 

31000205 Chevron USA – Table 

Rock Field 

Added missing FIPS codes 

Uinta Co., WY 31000205 Chevron USA – Whitney 

Canyon/Carter Creek 

Added missing FIPS codes 

Sublette Co., WY 31000205 Exxon/Mobile – Black 

Canyon 

Added missing FIPS codes 

Grays Harbor Co., WA 

10200902 Sierra Pacific Industries Added missing FIPS codes 

Lincoln Co., WA 30700711 

30700805 

Olympic Panel Products Added missing FIPS codes 

 A-9 
2006 Report for the Western Regional Air Partnership (WRAP) Regional Modeling Center (RMC) 

 

Appendix B: 

Fire Plume Rise Sensitivity Results 
2006 Report for the Western Regional Air Partnership (WRAP) Regional Modeling Center (RMC)

(a) 

1

5

9

13

17

21

L1

L4

L7

L10

L13

L16

0

100

200

300

400

500

600

CO Tons/h

Time

Layer

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) 

1

4

7

10

13

16

19

22

L1

L3

L5

L7

L9

L11

0

100

200

300

400

500

600

CO Tons/h

Time

Layer

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-2 
2006 Report for the Western Regional Air Partnership (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

8000

Max Layer Hieght (m)

L1 L2 L3 L4 L5

L6 L7 L8 L9 L10

L11 L12 Max LH

Figure B-2. Colorado wildland fire use emissions vertical distribution and 

maximum layer heights: (a) WF and (b) SB

 B-3 
2006 Report for the Western Regional Air Partnership (WRAP) Regional Modeling Center (RMC)

(a) 

1

4

7

10

13

16

19

22

L1

L3

L5

L7

L9

L11

L13

L15

L17

0

100

200

300

400

500

600

700

CO Tons/h

Time

Layer

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) 

1

4

7

10

13

16

19

22

L1

L3

L5

L7

L9

L11

Tot

0

100

200

300

400

500

600

700

CO Tons/h

Time

Layer

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 vertical distribution: (a) WF and (b) SB 

 B-4 
2006 Report for the Western Regional Air Partnership (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 emissions vertical distribution and 

maximum layer heights: (a) WF and (b) SB 

 B-5 
2006 Report for the Western Regional Air Partnership (WRAP) Regional Modeling Center (RMC)

(a) 

1

4

7

10

13

16

19

22

L1

L3

L5

L7

L9

L11

L13

L15

L17

0

500

1000

1500

2000

2500

3000

CO Tons/h

Time

Layer

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

3500

CO Tons/h

Time

Layer

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-6 
2006 Report for the Western Regional Air Partnership (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

8000

Max 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

8000

Max 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 emissions vertical distribution and 

maximum layer heights: (a) WF and (b) SB 

 B-7 
2006 Report for the Western Regional Air Partnership (WRAP) Regional Modeling Center (RMC)

(a) 

1

4

7

10

13

16

19

22

L1

L3

L5

L4

L9

L11

L13

L15

L17

Tot

0

5

10

15

20

25

30

CO Tons/h

Time

Layer

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 24

L1

L3

0

5

10

15

20

25

30

CO Tons/h

Time

Layer

OR, Prescribed fire

Blue Sky Plume rise

L1

L2

L3

L4

Tot

Figure B-7. Oregon prescribed fire vertical distribution: (a) WF and (b) SB 

 B-8 
2006 Report for the Western Regional Air Partnership (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

5200

Max Layer Hieght (m)

L1 L2 L3 L4 Max LH

Figure B-8.Oregon prescribed fire emissions vertical distribution and 

maximum layer heights: (a) WF and (b) SB 

 B-9 
2006 Report for the Western Regional Air Partnership (WRAP) Regional Modeling Center (RMC)

(a) 

1

4

7

10

13

16

19

22

L1

L4

L7

L10

L13

L16

0

500

1000

1500

2000

2500

3000

3500

4000

4500

CO Tons/h

Time

Layer

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) 

1

3

5

7

9

11

13

15

17

19

21

23

L1

L3

L5

L7

L9

L11

L13

L15

Tot

0

500

1000

1500

2000

2500

3000

3500

4000

4500

CO Tons/h

Time

Layer

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-10 
2006 Report for the Western Regional Air Partnership (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

8000

Max 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

8000

Max 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 emissions vertical distribution and 

maximum layer heights: (a) WF and (b) SB 

 B-11