Appendix K 

Recommended Improvements 
Appendix K - 1
Appendix K - 2

The Regional Haze Rule requires states to conduct a mid course review in 2012 of progress 

toward reasonable progress goals for 2018. There are several technical improvements that are 

recommended in the emissions inventory and air quality models that are used to support 

regulatory decisions for regional haze. These technical improvements are also important for 

states’ efforts to demonstration attainment of the ozone and PM2.5 health standards. 

1) Emissions inventory Continued improvements in emission inventories for regulatory sources, 

particularly emissions profiles for the composition of PM2.5 emissions is necessary. 

2) Continued improvements are needed in the integrated one-atmosphere air quality models that 

are used to project air quality responses to emissions reductions. The interactions among 

primary and secondary pollutants in the atmosphere affect the levels of ozone, PM2.5, and 

haze in the atmosphere. As understanding of partitioning between gaseous and aerosol 

phases improves, models reflecting this understanding need to be developed. 

 

3) Sulfate performance for the CMAQ regional air quality model is good overall. Sulfate 

deposition is frequently overestimated in the models, particularly in the summer months. 

Overestimation of precipitation volume from convective storms is one possible explanation. 

Improvements in treatment of convective systems in the meteorological models would 

improve projections of pollutant deposition concentrations. At the coastal sites, when winds 

are blowing from the Gulf of Mexico or Atlantic Ocean, CMAQ under estimates measured 

sulfate at the monitors. Improvements to emissions inventories for commercial shipping in 

the Gulf and Atlantic were made in 2006; additional refinements to emissions estimates are 

needed. As well CMAQ’s processes should be reviewed for sulfate formation over water, 

including availability of precursor species (hydrogen peroxide and ozone), dispersal patterns 

for moving and fixed SO2 emission sources, and vertical and horizontal within clouds over 

water. The accuracy of meteorological modeling of flux at the land-water interface is also an 

area for additional attention. 

4) Nitrate is overestimated by the CMAQ model in the winter and underestimated in the 

summer, although summer monitored values of nitrate are very low. In the southeastern US, 

nitrate formation is driven largely by level of ammonia available in the atmosphere. Current 

understanding of seasonal variation in ammonia emissions from agricultural sources has 

improved; additional improvements in seasonal allocation of annual inventories would 

further improve model estimates of ammonium nitrate formation. 

5) Organic carbon is generally underestimated in the CMAQ in the summer months. 

Improvement in the characterization of primary carbon emissions and partitioning between 

gas and aerosol phase and formation of secondary organic aerosols is needed. 

6) Better tools for organic carbon source apportionment need to be developed. In aged aerosol 

most of primary aerosol has reacted to form some secondary organic aerosol. Improved 

emissions profiles and stable molecular makers for anthropogenic and biogenic sources and 

fossil and modern carbon are critical. 
Appendix K - 3

7) Consistent measurement techniques are needed between rural and urban monitoring networks 

so that concentrations of organic carbon and elemental carbon can be compared between 

sites. 

8) To improve understanding of the contribution of fire from natural forest fires, prescribed 

burning, land clearing, and agricultural burning, states need to improve record keeping. 

Records should include acres burned, dates, location (latitude/longitude), fuel type, and 

intensity of burn. 

9) Additional improvements to international emissions inventory are needed to improve 

understanding of boundary conditions for modeling domains and of the contributions from 

international emissions to pollutant concentrations at the VISTAS Class I areas. Currently 

monthly average inventories are used for non-US sources. Even when highly resolved 

meteorological data are used, these monthly average inventories do not reflect the episodic 

variation in emissions, particularly for fire events, that are important at the receptors. In the 

current modeling we cannot distinguish US from non-US sources of pollutant concentrations 

at the boundaries of the model domain. States and RPOs need to improve their ability to 

trace emissions that originate within US as they are transported outside modeling grids so 

that when they re-circulate back into the modeling domain they retain their identify as US 

emissions rather than non-US boundary conditions. 

10) The effect of meteorological model performance on air quality model performance needs 

additional attention. 

11) Values of continuous monitoring need to be captured by evaluating model for the diurnal and 

spatial patterns seen in the monitored data. 

12) Confidence in trajectory model for periods greater than 72 hours also needs to be improved. 

Currently trajectories of longer duration have such large bands of uncertainty as to reduce 

any conclusions than can be drawn for transport greater than 72 hours. 
 International Corporation Air Sciences

Final Report

Technical Support Document for VISTAS Emissions 

and Air Quality Modeling to Support Regional Haze 

State Implementation Plans

Prepared for: 

Pat Brewer 

VISTAS Technical Coordinator 

2090 U.S. 70 Highway 

Swannanoa, NC 28778 

Prepared by: 

Ralph E. Morris 

Bonyoung Koo 

Piti Piyachaturawat 

ENVIRON International Corporation 

773 San Marin Drive, Suite 2115 

Novato, CA 94998 

Gregory Stella 

Dennis McNally 

Cyndi Loomis 

Alpine Geophysics, LLC 

7341 Poppy Way 

Arvada, CO 80007 

Cha-Jung Chien 

Gail Tonnesen 

University of California at Riverside 

College of Engineering Center for 

Environmental Research and Technology 

Riverside, Ca 92517 

March 11, 2009 

 773 San Marin Drive, Suite 2115, Novato, CA 94998 415.899.0700 
 March 2009 

vi 

TABLE OF CONTENTS 

Page 

ACCRONYMS ............................................................................................................................... i 

1. INTRODUCTION................................................................................................................ 1-1 

1.1 Background...................................................................................................................... 1-1 

1.2 VISTAS Modeling Approach .......................................................................................... 1-3 

 1.2.2 VISTAS Two-Phased Approach.......................................................................... 1-3 

1.3 Overview of 2002 Annual Emissions and Air Quality 

 Modeling Approach ................................................................................................... 1-6 

 1.3.1 Modeling Protocol ............................................................................................... 1-6 

 1.3.2 Quality Assurance Project Plan (QAPP).............................................................. 1-7 

 1.3.3 Model Selection ................................................................................................... 1-7 

 1.3.4 Horizontal Modeling Domains .......................................................................... 1-18 

 1.3.5 Vertical Domain Definition ............................................................................... 1-23 

 1.3.6 2002 Calendar Year Selection ........................................................................... 1-24 

 1.3.7 Initial Concentrations and Boundary Conditions............................................... 1-25 

 1.3.8 Emissions Input Preparation .............................................................................. 1-26 

 1.3.9 Meteorological Input Preparation ...................................................................... 1-27 

1.4 Known Errors in the Base G2 and G4 Modeling........................................................... 1-33 

2. DEVELOPMENT OF EMISSION INPUTS..................................................................... 2-1 

2.1 Emissions Modeling Methodology.................................................................................. 2-2 

2.2 Set-up of SMOKE Over the VISTAS Domain................................................................ 2-2 

 2.2.1 Processing of Point Source Emissions................................................................. 2-5 

 2.2.2 Processing of Area and Non-Road Source Emissions ......................................... 2-6 

 2.2.3 Processing of On-Road Mobile Source Emissions .............................................. 2-7 

 2.2.4 Processing of Biogenic Source Emissions........................................................... 2-8 

 2.2.5 Processing of Wildfires and Prescribed Burns..................................................... 2-8 

 2.2.6 Windblown Dust .................................................................................................. 2-9 

 2.2.7 Sea Salt................................................................................................................. 2-9 

2.3 SMOKE-Specific Processing Characteristics.................................................................. 2-9 

 2.3.1 Temporal Allocation .......................................................................................... 2-10 

 2.3.2 Speciation........................................................................................................... 2-27 

 2.3.3 Spatial Allocation................................................................................................2.27 

2.4 Development of Modeling Input Inventories/Run 

 Scripts/ASSIGNS Files............................................................................................ 2-29 

2.5 Products of the Emissions Inventory Development 

 Process for QA......................................................................................................... 2-29 

2.6 2009 and 2018 Base G4 Best and Final Inventories................................................ 2-32 

2.7 Uncertainties and Caveats........................................................................................ 2-36 

 2.7.1 Updated Inventories........................................................................................... 2-36 

 2.7.2 Low Level Fires ................................................................................................. 2-36 

 2.7.3 MMS Offshore Shipping.................................................................................... 2-36 

 2.7.4 Incorrect Location Parameters ........................................................................... 2-37 

 2.7.5 SMOKE/IOAPI Coordinate Transformation Error............................................ 2-37 
 March 2009 

vii 

3. MODEL PERFORMANCE EVALUATION.................................................................... 3-1 

3.1 Introduction...................................................................................................................... 3-1 

3.2 CMAQ Evaluation Methodology..................................................................................... 3-2 

 3.2.1 Evaluation Approach ........................................................................................... 3-2 

 3.2.2 Particulate Matter and Component Species ......................................................... 3-3 

 3.2.3 Ambient Air Quality Data for VISTAS Model Evaluation ................................. 3-3 

3.3 Model Performance Statistics and Goals ......................................................................... 3-5 

3.4 Regional Model Performance Evaluation........................................................................ 3-6 

 3.4.1 Sulfate (SO4) Model Performance....................................................................... 3-6 

 3.4.2 Nitrate (NO3) Modeling Performance............................................................... 3-10 

 3.4.3 Organic Carbon Mass (OCM) Performance ...................................................... 3-13 

 3.4.4 Elemental Carbon (EC) Performance ................................................................ 3-16 

 3.4.5 Other PM2.5 or Soil Performance ....................................................................... 3-19 

 3.4.6 Coarse Mass (CM) Performance........................................................................ 3-22 

 3.4.7 Fine Particulate (PM2.5) Performance ................................................................ 3-25 

3.5 Evaluation at VISTAS and Nearby Class I Areas ......................................................... 3-28 

 3.5.1 Example Spatial Distribution Model Performance............................................ 3-28 

 3.5.2 Sipsey (SIPS), Alabama Best and Worst 20 

 Percent Days Performance................................................................................. 3-33 

 3.5.3 Chassahowitzka (CHAS), Florida Best and Worst 20 

 Percent Days Performance................................................................................. 3-33 

 3.5.4 Everglades (EVER), Florida Best and Worst 20 Percent 

 Days Performance.............................................................................................. 3-33 

 3.5.5 St. Marks (SAMA), Florida Best and Worst 20 Percent 

 Days Performance.............................................................................................. 3-40 

 3.5.6 Cohutta (COHU), Georgia Best and Worst 20 Percent 

 Days Performance.............................................................................................. 3-40 

 3.5.7 Okefenokee (OKEF), Georgia Best and Worst 20 Percent 

 Days Performance.............................................................................................. 3-40 

 3.5.8 Mammoth Cave (MACA), Kentucky Best and Worst 20 

 Percent Days Performance]................................................................................ 3-40 

 3.5.9 Linville Gorge (LIGO), North Carolina Best and Worst 20 

 Percent Days Performance................................................................................. 3-49 

 3.5.10 Shining Rock (SHRO), North Carolina Best and Worst 20 

 Percent Days Performance................................................................................. 3-49 

 3.5.11 Swanquarter (SWAN), North Carolina Best and Worst 20 

 Percent Days Performance................................................................................. 3-49 

 3.5.12 Cape Romain (ROMA), South Carolina Best and Worst 20 

 Percent Days Performance..................................................................................3.49 

 3.5.13 Great Smoky Mountains (GRSM), Tennessee-North Carolina 

 Best and Worst 20 Percent Days Performance .................................................. 3-49 

 3.5.14 James River Face Wilderness Area (JARI), Virginia 

 Best and Worst 20 Percent Days Performance .................................................. 3-60 

 3.5.15 Shenandoah (SHEN), Virginia Best and Worst 20 

 Percent Days Performance................................................................................. 3-60 

 3.5.16 Dolly Sods (DOSO), West Virginia Best and Worst 20 

 Percent Days Performance................................................................................. 3-60 
 March 2009 

viii 

 3.5.17 Breton Island (BRET), Louisiana Best and Worst 20 

 Percent Days Performance................................................................................. 3-60 

3.5.18 Caney Creek (CACR) and Upper Buffalo (UPBU), 

 Arkansas Best and Worst 20 Percent Days Performance .................................. 3-60 

 3.5.19 Mingo (MING) and Hercules Glade (HEGL), 

 Missouri Best and Worst 20 Percent Days Performance................................... 3-73 

 3.5.20 Brigantine (BRIG), New Jersey Best and Worst 20 

 Percent Days Performance................................................................................. 3-73 

3.6 Conclusions of Model Performance Evaluation ............................................................ 3-80 

4. VISIBILITY PROJECTIONS............................................................................................ 4-1 

 4.1 Guidance for Visibility Projections ................................................................................. 4-1 

 4.2 Calculation of Visibility and 2018 Uniform Rate of Progress 

 from IMPROVE Measurements ................................................................................ 4-2 

 4.2.1 Calculation of Visibility from IMPROVE PM Measurements............................ 4-3 

 4.2.2 Calculation of the Baseline Conditions................................................................ 4-8 

 4.2.3 Data Filling for Sites with Insufficient Valid Data to 

 Calculate Baseline Conditions ............................................................................. 4-9 

 4.2.4 Natural Conditions............................................................................................... 4-9 

 4.2.5 2018 Uniform Rate of Progress ........................................................................... 4-9 

 4.3 EPA Default Approach for Visibility Projections ......................................................... 4-10 

 4.3.3 Mapping of Modeling Results to the 

 IMPROVE Measurements ................................................................................. 4-10 

 4.3.4 Using Modeling Results to Project Changes in Visibility ................................. 4-11 

 4.4 EPA Default 2018 Visibility Projections at VISTAS and 

 Nearby Class I areas and Comparisons to 2018 

 Uniform Rate of Progress ........................................................................................ 4-12 

 4.4.1 Example 2018 Base G2 Visibility Projections for 

 Worst 20% Days Sipsey, Alabama .................................................................... 4-13 

 4.4.2 Summary 2018 Visibility Projections for Worst 20 

 Percent Days across Class I Areas..................................................................... 4-17 

 4.4.3 2018 Visibility Projections for the Best 20 Percent Days.................................. 4-23 

 4.5 2018 Visibility Projections using the 2018 Best and Final Emissions .......................... 4-29 

5. ADDITIONAL SUPPORTING ANALYSIS ......................................................................5.1 

 5.1 Comparison of VISTAS 2018 Visibility Projections with Other Groups........................ 5-1 

 5.1.1 Comparison of CENRAP, VISTAS, MRPO and 

 MANE-VU Visibility Projections........................................................................ 5-1 

 5.2 Alternative 2018 Visibility Projection Software.............................................................. 5-3 

 5.3 Alternative Visibility Projection Procedures ................................................................... 5-5 

 5.3.1 Use of Original IMPROVE Equation .................................................................. 5-5 

 5.3.2 Treatment of Coarse Mass and Soil as Natural.................................................... 5-8 

 5.3.3 Accounting for Model Performance in Visibility Projections ........................... 5-10 

 5.3.4 Use of Classification and Regression Tree (CART) Weighted RRFs ............... 5-13 

 5.4 Effects of International Transport on 2018 Visibility Projections................................. 5-15 

 5.5 Visibility Trends ............................................................................................................ 5-24 

 5.6 Geographic Areas of Influence of Visibility Impairment for Class I Areas.................. 5-42 
 March 2009 

ix 

 5.6.1 Back Trajectory Analyses..................................................................................... 5-42 

 5.6.2 Residence Time Plots............................................................................................ 5-43 

 5.6.3 SO2 Areas of Influence...................................................................................... 5-44 

 5.6.4 Emissions Sources within SO2 Areas of Influence ........................................... 5-45 

 5.6.5 Specific Source Types in the Areas of Influence............................................... 5-46 

 5.6.6 Evaluating the Four Statutory Factors for Specific SO2 Emissions 

 Sources in Each Area of Influence..................................................................... 5-49 

 5.7 Area of Influence Analyses Results for VISTAS and Nearby Class I Areas ................ 5-52 

6. REFERENCES..................................................................................................................... 6-1 

APPENDICES 

Appendix A: Emission Processing Selected QA Summary Reports and Figures 

Appendix B: Air Quality Model Performance Evaluation: Regional Model 

Evaluation across the VISTAS Region 

Appendix C: Air Quality Model Performance Evaluation: Evaluation at 

VISTAS and Nearby Class I Areas 

Appendix D: Air Quality Model Performance Evaluation: Spatial 

Maps of Model Performance 

 on the Best and Worst 20 Percent Days at VISTAS 

and Nearby Class I Areas 

Appendix E: 2018 Visibility Projects at VISTAS and Nearby Class I 

Areas for the Worst 20 Percent Days using EPA Default 

Approach and Comparison with 2018 Uniform Rate of Progress 

Appendix F: 2018 Visibility Projections at VISTAS and Nearby Class I 

Areas for the Best 20 Percent Days using EPA Default Approach 

Appendix G: Evaluation of the 2002 GEOS-CHEM Global Chemistry Model 

Used for Boundary Conditions in the VISTAS Regional Haze Modeling 

Appendix H: Based on “VISTAS Area of Influence Analyses for Sulfur 

 Dioxide” by Archuleta, Adhloch, Mansell, Stella and 

 Brewer (February 28, 2007) and “Procedures for Developing 

 and Displaying AoI Back-trajectories Residence Time GIS 

 Data by Mansell (August 29, 2007) 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Accronyms4.doc i

ACCRONYMS 

ACM Asymmetric Convective Mixing 

AIRS Aerometric Information Retrieval System 

Ap Accuracy of paired peak 

AQS Air Quality System 

ARS Air Resource Specialists 

ASCII American Standard Code for Information Interchange 

ASIP Association of the Southeast for Integrated Planning 

BART Best Available Retrofit Technology 

Base02a 2002 Base Case, version a 

BCs Boundary Conditions 

BEIS3 Biogenic Emission Inventory System, version 3 

BELD Biogenic Emissions Landcover Database 

bext Extinction coefficient 

BF Bias Factor 

BRAVO Big Bend Regional Aerosol and Visibility Observations Study 

BRET Breton Island National Wildlife Refuge 

BRIG Brigantine Wildlife Refuge 

CAAA Clean Air Act Amendments 

CACR Caney Creek Wilderness Area 

CAIR Clean Air Interstate Rule 

CAMx Comprehensive Air Quality Model with extensions 

CARB California Air Resources Board 

CASTNet Clean Air Status and Trends Network 

CB-IV Carbon Bond IV 

CBM-IV Carbon Bond Mechanism IV 

CCRS+CPRM Coarse matter (coarse crustal & coarse primary) 

CEM Continuous Emissions Monitoring Data 

CENRAP Central Regional Air Planning Association 

CFR Code of Federal Regulations 

CHAS Chassahowitzka Wildlife Refuge 

CHIEF Clearinghouse for Inventories and Emissions Factors 

CM Coarse Mass 

CMAQ Community Multiscale Air Quality modeling system 

CMU Carnegie Mellon University 

CNG Compressed Natural Gas 

CO Carbon Monoxide 

COHU Cohutta Wilderness Area 

DDM Decoupled direct method 

DOSO Dolly Sods Wilderness Area 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Accronyms4.doc ii

dv deciview 

EBI Euler Backward Iterative 

EC Elemental Carbon 

EFIG Emissions Factors and Inventory Group 

EGAS Economic Growth Analysis System 

EGUs Electrical Generating Units 

EMFAC California Air Resources Board mobile source emissions model 

EPA U.S. Environmental Protection Agency 

EPM Emission Production Model 

EPRI Electric Power Research Institute 

ERG Energy Resources Group 

Eta Eta model - a hydrostatic mesoscale model. 

EVER Everglades National Park 

FCRS+FPRM Fine Particulate Matter (fine crustal & fine primary) 

FDDA Four Dimensional Data Assimilation 

FE Fractional Gross Error 

FIP Federal Implementation Plan 

FLM Federal Land Managers 

GA DNR Georgia Department of Natural Resources 

GE Goddard Earth Observing System 

GEOS-CHEM Goddard Earth Observing Systems – Chemistry model 

GIS Geographic Information System 

GMAO Global Modeling and Assimilation Office 

GMT Greenwich Mean Time 

GRSM Great Smoky Mountains National Park 

HEGL Hercules-Glades Wilderness Area 

HI Haze index 

HNO3 Nitric acid 

ICs Initial concentrations 

IDA Inventory Data Analyzer 

IMPROVE Interagency Monitoring of Protected Visual Environments 

IPM Integrated Planning Model 

ISHALLO= No Shallow Convection 

ISORROPIA ISORROPIA aerosol equilibrium model. 

JARI James River Face Wilderness 

JPROC Models 3 Photolysis Rates processors 

Kh Horizontal diffusivity coefficient 

km Kilometer 

Kzmin Minimum vertical diffusivity coefficient 

LAC Light Absorbing Carbon 

LIGO Linville Gorge Wilderness Area 

LITTLE_R MM5 meteorology processor 

LPG Liquefied petroleum gas 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Accronyms4.doc iii

LSM Land-surface model 

MACA Mammoth Cave National Park 

MAGE Mean Absolute Gross Error 

MANE-VU Mid-Atlantic/Northeast Visibility Union 

MARAMA Mid-Atlantic Air Management Association 

MATS EPA’s Modeled Attainment Test Software 

MB Mean Bias 

MCIP Meteorological Chemistry Interface Processor 

MEGAN Model of emissions of gases and aerosols from nature 

MFB Mean Fractionalized Bias 

MIMS Multimedia Integrated Modeling System 

MING Mingo Wilderness Area 

Mm-1 Inverse megameters 

MM5 Mesoscale Meteorological Model 

MMS Minerals Management Service 

MNB Mean Normalized Bias 

MNGE Mean Normalized Gross Error 

MOBILE6 EPA’s latest computer program for compiling emissions from mobile sources 

MPE Model performance evaluation 

MPI Message passing interface 

MRPO Midwest Regional Planning Organization 

MWSS Monday-weekday-Saturday-Sunday 

NAAQS National Ambient Air Quality Standards 

NADP National Atmospheric Deposition Program 

NARSTO North American Research Strategy for Tropospheric Ozone 

NCEP National Centers for Environmental Prediction 

NEI National Emissions Inventory 

NH3 Ammonia 

NIA New IMPROVE Algorithm 

NIF National Emission Inventory Input Format 

NO3 Nitrate 

NMB Normalized Mean Bias 

NME Normalized Mean Error 

NO Nitrogen Oxide 

NO2 Nitrogen dioxide 

NOAA National Oceanic & Atmospheric Administration 

NOx Nitrogen Oxides 

O3 Ozone 

OAQPS Office of Air Quality and Planning Standards 

OIA Old IMPROVE Algorithm 

OKEF Okefenokee Wildlife Refuge 

OMC Organic mass carbon 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Accronyms4.doc iv

PA Process Analysis 

PBL Planetary Boundary Layer 

PEC Primary elemental carbon 

PinG Plume-in-Grid 

PM Particulate matter 

PM2.5 Particulate matter of 2.5 microns and less 

PMC Particulate Matter Coarse 

PNO3 Particulate nitrate 

POA Primary Organic Aerosol 

POC Primary Organic carbon 

POG Policy Oversight Group 

PPM Piecewise Parabolic Method 

PSAT PM Source Apportionment Technology 

PSO4 Particulate sulfate 

PSU/NCAR Pennsylvania State University/National Center for Atmospheric Research 

P-X Pleim-Xiu 

QA Quality Assurance 

QAPP Quality Assurance Project Plan 

r2 Coefficient of determination 

RADM Regional Acid Deposition Model 

RAMS Regional Atmospheric Modeling System 

RHR Regional Haze Rule 

RICE MACT 

Reciprocating Internal Combustion Engine Maximum Available Control 

Technology 

RMSE Root Mean Square Error 

ROG Reactive Organic Gas 

ROMA Cape Romain Wildlife Refuge 

RPGs Reasonable Progress Goals 

RPOs Regional Planning Organizations 

RRFs Relative Response Factors 

RRTM Rapid Radiative Transfer Model 

SA Source Apportionment 

SAMA Saint Marks Wildlife Refuge 

SCC Source Classification Code 

SEARCH Southeastern Aerosol Research and Characterization 

SECA Sulphur Emissions Control Area 

SHEN Shenandoah National Park 

SHRO Shining Rock Wilderness Area 

SIC Source Industrial Classification 

SIP State Implementation Plan 

SIPS Sipsey Wilderness Area 

SMOKE Sparse Matrix Operator Kernel Emissions 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Accronyms4.doc v

SOA Secondary Organic Aerosol 

SOAA Secondary Organic Aerosol from Anthropogenic Sources 

SOAB Secondary organic Aaerosol from biogenic sources 

SORGAM Secondary Organic Aerosol Model 

SST Sea Surface Temperature 

STI Sonoma Technology, Inc. 

STN Speciation Trends Network 

SO4 Sulfate 

SWAN Swanquarter Wildlife Refuge 

TIPs Tribal Implementation Plans 

TOG Total Organic Gas 

TOMS Total Ozone Mapping Spectrometer 

TPY Tons Per Year 

TSD Technical Support Document 

TSS Technical Support System 

TUV Tropospheric Ultraviolet and Visible Radiation Model 

Typ02G Base G Typical Version 2 

UCR University of California at Riverside 

UH University of Houston 

UPBU Upper Buffalo Wilderness Area 

URP Uniform Rate of Progress 

VIEWS Visibility Information Exchange Web Site 

VISTAS Visibility Improvements State and Tribal Association of the Southeast 

VMT Vehicle Miles Traveled 

VOC Volatile Organic Compounds 

VR Visual Range 

WRAP Western Regional Air Partnership 

μg/m3 Micrograms per cubic meter (concentration) 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chap_1_Intro3_updated.doc 1-1 

1.0 INTRODUCTION 

This is the final version of the Technical Support Document (TSD) for the Visibility 

Improvements for States and Tribal Associations in the Southeast (VISTAS) regional emissions 

and air quality modeling to support the southeastern states Regional Haze State Implementation 

Plans (SIPs). The VISTAS 2002 annual emissions and air quality modeling was performed by 

the contractor team of ENVIRON International Corporation (ENVIRON), Alpine Geophysics, 

LLC (Alpine) and the University of California at Riverside (UCR). This final version of the 

TSD updates the previous draft TSD dated November 2007 through inclusion of 2018 visibility 

projections for the 2018 Base G4 Best and Final (BaF) emissions scenario. 

1.1 Background 

The 1977 Clean Air Act Amendments (CAAA) added a new Section 169A for the protection of 

visibility in federally mandatory Class I areas (specific national parks, wilderness areas and 

wildlife refuges). Section 169A(a)(1) of the CAAA established the national goal for visibility 

protection: “Congress hereby declares as a national goal 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.” The CAAA requires States to submit State 

Implementation Plans (SIPs) containing emission limits, schedule of compliance and 

“promulgate regulations to assure reasonable progress toward meeting the national goal” 

(Section 169A(a)(4)). In response to these mandates EPA promulgated the Regional Haze Rule 

(RHR) on July 1, 1999 that requires States to “establish goals (expressed in deciviews) that 

provide for reasonable progress towards achieving natural visibility conditions” at Class I areas. 

The States’ RHR SIPs were due December 17, 2007 and an important component of the SIP are 

the 2018 Reasonable Progress Goals toward achieving natural conditions in 2064. Regional air 

quality models are used to project visibility to 2018 to determine the level of visibility 

improvement that is expected to be achieved that can be used by the states to assist in setting 

their 2018 Reasonable Progress Goals. 

VISTAS is one of five Regional Planning Organizations (RPOs) that have responsibility for 

coordinating development of State Implementation Plans (SIPs) and Tribal Implementation Plans 

(TIPs) in selected areas of the U.S. to address the requirements of the RHR. VISTAS is a 

regional partnership of states, tribes, federal agencies, stakeholders and citizen groups 

established to initiate and coordinate activities associated with the management of regional haze 

and other air quality issues within the VISTAS region. The VISTAS region includes the states 

of Alabama, Florida, Georgia, Kentucky, Mississippi, North Carolina, South Carolina, 

Tennessee, Virginia and West Virginia. The VISTAS Emissions and Air Quality Modeling 

Team is comprised of staff from ENVIRON, Alpine and UCR, with assistance and coordination 

from the VISTAS states, federal agencies and stakeholders. The VISTAS Modeling Team 

performs the emissions and air quality modeling simulations for the VISTAS region, providing 

analytical results used in developing state implementation plans under the RHR. Figure 1-1 

identifies the states included in each of the five Regional Planning Organizations in the U.S, 

including VISTAS. The VISTAS States and Class I areas within the VISTAS region are shown 

in Figure 1-2. 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chap_1_Intro3_updated.doc 1-2 

VISTAS is performing emissions and air quality modeling to project visibility to 2018 to 

determine the level of visibility improvements expected in 2018 under various emission 

strategies to assist states to determine their 2018 RPG toward achieving natural conditions in 

2064. 

The VISTAS Modeling Team is funded through the Southeastern States Air Resource Managers 

(SESARM) and has implemented a regional air quality planning process to provide the necessary 

technical and policy tools needed by states to comply with the Section 308 of the RHR 

requirements. In March 2003, VISTAS contracted with ENVIRON, with Alpine and UCR as 

Subcontractors, to be the VISTAS Modeling Team to perform the emissions and air quality 

modeling needed to develop the technical basis for the RHR SIPs. 

Figure 1-1. Regional Planning Organizations engaged in regional haze modeling. 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chap_1_Intro3_updated.doc 1-3 

Figure 1-2. VISTAS states and Class I areas within the VISTAS region. 

1.2 VISTAS Modeling Approach 

The VISTAS Emissions and Air Quality Modeling Team performs regional haze analyses by 

operating regional scale, three-dimensional air quality models that simulate the emissions, 

chemical transformations, and transport of gaseous and particulate matter (PM) species and 

consequently effects on visibility in Class I Areas in the southeastern U.S. A key element of this 

work includes the integration of emissions inventories and models with regional transport 

models. The general services provided by the VISTAS Emissions and Air Quality Modeling 

Team include, but are not limited to: 

• Emissions processing and modeling; 

• Air quality and visibility modeling simulations; 

• Analysis, display, and reporting of modeling results; and 

• Storage/quality assurance of the modeling input and output files. 

1.2.2 VISTAS Two-Phased Approach 

The VISTAS Emissions and Air Quality Modeling activities were performed in two Phases. 

Phase I, which occurred primarily during the 2003 calendar year, consisted of emissions and 

regional haze modeling for three episodes to identify the optimal model configuration(s) for 

simulating regional haze in the southeastern U.S. Phase II, initiated in 2004, consists of 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chap_1_Intro3_updated.doc 1-4 

operating the emissions and air quality models for the 2002 calendar year to develop the regional 

haze modeling databases needed to address the requirements of the Section 308 RHR SIPs. 

1.2.2.1 VISTAS Phase I 

The objective of VISTAS Phase I was to determine the optimal modeling configuration for use 

in the subsequent Phase II visibility assessment. Accordingly, Phase I entailed a comprehensive 

literature review of recent relevant visibility studies using various photochemical/aerosol 

modeling platforms in order to assess and identify appropriate model configurations, data bases, 

and model testing methodologies that were appropriate for use in conducting the VISTAS Phase 

I emissions and PM modeling assessment. Key elements of Phase I included: 

• Review all relevant air quality model simulations that have been completed related to 

regional haze and PM2.5 modeling and document the relevant sensitivity analyses, 

model configuration testing, and performance evaluations that have been performed 

(ENVIRON, 2003b); 

• Review the current science in regional emissions modeling (e.g., EPS, EMS and 

SMOKE) and PM air quality modeling (e.g., CMAQ, CMAQ-MADRID, CMAQ-AIM, 

REMSAD, UAM-V/PM, CAMx and PMCAMx) to determine the most appropriate 

model(s) for use by VISTAS (ENVIRON, 2003b); 

• Review available ambient data for evaluating one-atmosphere PM/ozone models 

(ENVIRON, 2003c); 

• Develop and implement a plan or Modeling Protocol for testing and evaluating 

alternative science configurations of the recommended Phase I model(s) and document 

the results (ENVIRON, 2003a); and 

• Prepare a Task 6 report prescribing the model set-up, data base development, 

performance testing, and control strategy evaluation procedures to be implemented in 

VISTAS Phase II (ENVIRON, 2004a). 

The VISTAS Technical Analysis Workgroup provided oversight for the comprehensive model 

evaluation. In Phase II the modeling system (models and databases) identified and tested in 

Phase I was implemented following the procedures set forth in the Phase II Modeling Protocol 

(ENVIRON, 2004a). 

For the meteorological component of the Phase I modeling, SESARM contracted with Baron 

Advanced Meteorological Systems (BAMS) to apply the PSU/NCAR Mesoscale Model (MM5) 

in multiple configurations and to evaluate its performance against surface and aloft 

meteorological observations (Olerud, 2003a-f). The emissions modeling component of VISTAS 

Phase I was carried out by the research team of ENVIRON/Alpine/UCR with staff at Alpine 

Geophysics taking the lead role in setting up, testing, and applying the emissions modeling 

system. The air quality modeling component was performed by the team at the 

ENVIRON/Alpine/UCR modeling centers. A dominant theme during Phase I was the exchange 

of modeling codes, databases, and evaluation software between the three modeling centers as the 

air quality modeling was carried out. 
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1.2.2.2 VISTAS Phase II 

The VISTAS Phase II modeling includes annual PM/regional haze simulations with the objective 

of demonstrating model performance for the selected modeling year, 2002. After detailed model 

review, testing and performance evaluation, the modeling system was exercised with a variety of 

emissions scenarios to enable VISTAS to assess the effects of future year emissions on visibility 

impairment at Class I areas in the VISTAS and nearby states. The modeling system allows 

VISTAS to estimate 2018 visibility to assist in tracking progress toward regional haze goals. 

The VISTAS Phase II program applied the SMOKE emissions and CMAQ air quality modeling 

systems for calendar year 2002 over the same 36/12 km horizontal grid system used in Phase I to 

estimate 2018 visibility improvements at VISTAS and nearby Class I areas. A number of annual 

model simulations were performed that included the following: 

3⁄4 2002 Initial Annual “Actual” Emissions Runs. Initial 2002 annual model simulations 

and performance evaluations using the 2002 inventory for VISTAS and non-VISTAS 

states, Canada and Mexico were performed to confirm the appropriateness of the model 

science configuration(s) recommended by the Phase I work, to evaluate updates to the 

model and model inputs and to refine model performance. The 2002 Actual emission 

scenarios used day-specific fire emissions and emissions from Electric Generating Units 

(EGUs) using continuous emissions monitoring (CEM) data. The initial CMAQ 2002 

36/12 km Base A Actual base case simulation used CMAQ Version 4.4beta and was 

performed during the Summer of 2004. The results and model performance evaluation 

are documented in Morris et al., (2004a). 

 

3⁄4 2002 Revised Annual “Actual” Emissions Runs. The 2002 emissions inventories for 

VISTAS and non-VISTAS states, Canada and Mexico were updated with improved 

assumptions (MACTEC, 2006). The CMAQ model was updated to version 4.5 and a 

modified CMAQ module for Secondary Organics Aerosols (SOA) was developed and 

tested. Additional annual 2002 simulations were performed using the revised 2002 

modeling inventories and revised CMAQ configurations to evaluate model performance . 

The model performance evaluation for the final VISTAS 2002 Actual base case CMAQ 

simulation for the Base G2 emissions is discussed in Chapter 3 and Appendices B, C and 

D. 

3⁄4 2002 Annual Run with “Typical Year” EGU/Fire Inventory. Typical year emissions 

inventories were developed for EGU and fire to avoid atypical EGU outages or atypical 

fire activity in the 2002 modeling year from influencing the assumptions used in the 

projection year. Continuous emissions monitoring (CEM) data for the 2000-2004 

baseline period for EGU were used to develop the 2002 typical EGU inventories. 

Available fire activity data in the period 2000-2004 was used to develop 2002 typical fire 

emissions. All other sectors used the same 2002 actual inventory for the 2002 typical 

inventory The 2002 typical inventories provide the baseline modeled air quality 

condition against which future year modeling runs were compared to develop relative 

response factors (RRFs) for each pollutant species used in the visibility projections. This 

is further discussed in Chapter 4. 
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3⁄4 Future Year Annual Base Case Runs. Future year simulations for 2018 were based on 

were performed that included a projected base case inventory of typical EGU emissions 

and current year baseline typical fire emissions for 2018. The objective of the future year 

model base case runs was to project 2018 visibility conditions under conditions of growth 

and current (on-the-books) regulated control measures. These 2018 projections were 

compared to the Uniform Rate of Progress for 2018 (see Chapter 4) as one part of 

VISTAS states’ determination of reasonable progress goals for 2018. The 2018 visibility 

projections provided the modeled air quality basis against which alternative emissions 

assumptions were evaluated. 

3⁄4 Future Year Emission Sensitivity Simulations. Future year sensitivity simulations 

were performed to assess the effects alternative future year emission assumptions and 

potential control measures would have on visibility in Class I areas. 

This report documents the 2002 and 2018 emissions and air quality modeling methods, 2002 

model performance, and 2018 model projections for the Base G2 emissions inventory. The 

VISTAS 2018 Best and Final was delivered by the VISTAS states to the modeling team on 

October 1, 2007 (MACTEC, 2008). Final 2018 emissions and air quality modeling for the Best 

and Final inventory were provided in fall 2007 and the 2018 visibility projections using the 2018 

Base G4 Best and Final inventories are presented at the end of Chapter 4. 

1.3 Overview of 2002 Annual Emissions and Air Quality Modeling Approach 

The VISTAS Phase II annual 2002 emissions and air quality modeling was initiated in 2004 and 

involved the preparation of numerous databases, model simulations, presentations and reports. 

Much of the modeling analyses have been posted to the VISTAS modeling website at: 

http://pah.cert.ucr.edu/vistas/. There were numerous versions and iterations of the modeling with 

interim results. The results presented in this TSD focus on the final Base G2 modeling results, 

for key findings in their development the reader is referred to the modeling website for interim 

products. 

1.3.1 Modeling Protocol 

A Modeling Protocol was prepared at the outset of the study to serve as a road map for 

performing the VISTAS emissions and air quality and a form of communication of the modeling 

plans to the VISTAS participants (ENVIRON, Alpine, UCR and UCD, 2004). The Modeling 

Protocol was prepared following EPA guidance at the time it was prepared (EPA, 1991; 1999, 

2001) and the modeling needs of the RHR SIPs. The first version of the Modeling Protocol was 

released in March 2004. Based on comments received from VISTAS, the Modeling Protocol 

was updated to the current version that was dated May 6, 2004. This Modeling Protocol can be 

found on the VISTAS modeling website at: 

 http://pah.cert.ucr.edu/vistas/vistas2/reports/vistasII_Final_Protocol_05_10_2004.pdf

Under a separate EPA grant and contract for the Association for Southeastern Integrated 

Planning (ASIP), the modeling team is also performing emissions and air quality modeling for 

2009 to support southeastern states’ demonstration of attainment of the ozone and PM2.5 national 
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ambient air quality standards. The same model configuration and methods and the 2002 

modeling results that are used for VISTAS are also being used for the 2009 ASIP modeling. The 

ASIP Modeling Protocol dated January 31, 2006 (Morris et al., 2006) updates some of the 

methods described in the VISTAS Modeling Protocol. 

1.3.2 Quality Assurance Project Plan (QAPP) 

A Quality Assurance Project Plan (QAPP) was prepared for the VISTAS annual emissions and 

air quality modeling study that described the quality management functions performed by the 

modeling team. The QAPP was finalized November 17, 2004 (Morris, Tesche and Tonnesen, 

2004; see: http://pah.cert.ucr.edu/vistas/vistas2/reports/VISTAS_QAPP_Nov_17_2004.pdf). 

The QAPP was based on the national consensus standards for quality assurance (ANSI/ASQC, 

1994), followed EPA’s guidelines for quality assurance project plans for modeling (EPA, 2002), 

for QAPPs (EPA, 2001) and EPA modeling guidelines (EPA, 1991; 1999; 2001; 2007) and took 

into account the recommendations from the North American Research Strategy for Tropospheric 

Ozone (NARSTO) Quality Handbook for modeling projects (NARSTO, 1998). The EPA and 

NARSTO guidance documents were developed specifically for modeling projects, which have 

different quality assurance concerns than environmental monitoring data collection projects. The 

work performed in this project involves modeling at the basic research level and for 

regulatory/policy applications. In order to utilize model outputs for these purposes, it must be 

established that each model is scientifically sound, robust, and defensible. This is accomplished 

by following a project planning process that incorporates the following elements as described in 

the EPA guidance document for modeling: 

• A systematic planning process including identification of assessments and related 

performance criteria; 

• Peer reviewed theory and equations; 

• A carefully designed life-cycle development process that minimizes errors; 

• Documentation of any changes from original plans; 

• Clear documentation of assumptions, theory, and parameterization that is detailed 

enough so others can understand the model output; 

• Input data and parameters that are accurate and appropriate for the problem; and 

• Output data that can be used to help inform decision makers. 

A key component of the VISTAS emissions and air quality modeling QAPP was the graphical 

display of model inputs and outputs and multiple peer-review of each step of the modeling 

process. This was accomplished through use of the VISTAS modeling Website that posted 

displays of products (e.g., emissions plots, model outputs, etc.) for review by the VISTAS 

modeling team, workgroups and others. This Website can be found at: 

http://pah.cert.ucr.edu/vistas/vistas2/index.shtml. 

1.3.3 Model Selection 

The selection of the meteorological, emissions and air quality models for the VISTAS annual 

regional haze modeling was based on EPA guidance (EPA, 1991; 1999; 2001; 2005b; 2007), a 

review of previous modeling studies and the VISTAS Phase I episodic model testing and 

evaluation (ENVIRON, Alpine and UCR, 2003a,b,c; 2004). The VISTAS annual emissions and 
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air quality Modeling Protocol (Morris, Tesche and Tonnesen, 2004) provides details on the 

justification for model selection and the formulation of the different models. Based on the 

VISTAS Phase I comprehensive model testing and evaluation and other work, VISTAS selected 

the following models for use in modeling particulate matter (PM) and regional haze in the central 

states: 

3⁄4 MM5: The Pennsylvania State University/National Center for Atmospheric Research 

(PSU/NCAR) Mesoscale Meteorological Model (MM5 Version 3.6 MPP) is a nonhydrostatic, prognostic meteorological model routinely used for urban- and regional-scale 

photochemical, fine particulate, and regional haze regulatory modeling studies (Anthes 

and Warner, 1978; Chen and Dudhia, 2001; Stauffer and Seaman, 1990, 1991; Xiu and 

Pleim, 2000). 

3⁄4 SMOKE: The Sparse Matrix Operator Kernel Emissions (SMOKE) modeling system is 

an emissions modeling system that generates hourly gridded speciated emission inputs of 

mobile, non-road, area, point, fire and biogenic emission sources for photochemical grid 

models. (Coats, 1995; Houyoux and Vukovich, 1999). As with most ‘emissions models’, 

SMOKE is principally an emission processing system and not a true emissions modeling 

system in which emissions estimates are simulated from ‘first principles’. This means 

that, with the exception of mobile and biogenic sources, its purpose is to provide an 

efficient, modern tool for converting an existing base emissions inventory data into the 

hourly gridded speciated formatted emission files required by an air quality simulation 

model. 

3⁄4 CMAQ: EPA’s Models-3/Community Multiscale Air Quality (CMAQ) modeling 

system is a ‘One-Atmosphere’ photochemical grid model capable of addressing ozone, 

particulate matter (PM), visibility and acid deposition at regional scale for periods up to 

one year (Dennis, et al., 1996; Byun et al., 1998a; Byun and Ching, 1999, Pleim et al., 

2003). 

1.3.3.1 MM5 Meteorological Model Configuration for VISTAS Annual Modeling 

Application of the MM5 for the 2002 annual modeling on a 36 km grid for the continental U.S. 

and a 12 km grid for the eastern U.S. was performed by Barons Advanced Meteorological 

Systems (BAMS). As part of Phase I of VISTAS, BAMS performed numerous MM5 

simulations to identify the optimal configuration for simulating meteorology to support regional 

PM and visibility modeling of the eastern U.S. (Olerud, 2003a,b,c,d). Details of the 2002 36/12 

km MM5 model application and evaluation are available on the BAMS VISTAS project website 

(www.baronams.com/projects/VISTAS). 

Based on the extensive sensitivity testing carried out by Olerud and Sims (2003) as part of 

VISTAS, the MM5 (Version 3.6, MMP) configuration used by BAMS modelers for the VISTAS 

Phase II modeling consisted of the following (see Table 1-1 or 

www.baronams.com/projects/VISTAS for more details): 

• Nested 36/12 km grids, with 34 vertical layers; 

• Two way nesting, no feedback; 

• Initialization and boundary conditions from Eta analysis fields; 

• Pleim-Xiu (P-X) land soil model (LSM); 
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• Asymmetric Convective Mixing (ACM) planetary boundary layer (PBL) model; 

• Kain-Fritsch 2 cumulus parameterization; 

• Mixed phase (Reisner 1) cloud microphysics; 

• Raptid Radiative Transfer Model (RRTM) radiation; 

• Snow effect turned on; 

• ETA model sea surface temperature; 

• 24-category USGS vegetation data sets; 

• Thermal roughness by the Garratt method; and 

• Standard FDDA analysis nudging on 36 km and 12 km grid nests. 

The emissions and air quality modeling team also performed their own independent evaluation 

and quality assurance (Morris et al., 2004b) to assure that the MM5 modeling fields prepared by 

BAMS were transferred and processed correctly for input into CMAQ using the MeteorologicalChemical transport model Interface Program (MCIP). MCIP Version 3.0 was used in the 

processing which invoked the Pleim M3DRY dry deposition scheme in CMAQ that uses the 

MM5 P-X LSM parameters. MCIP Version 2 included a significant update from previous 

versions in the calculation of layer collapsing. MCIP was also run using the option to specify 

land-use dependent minimum vertical turbulent exchange coefficients (Kz) that ranged from 0.1 

to 2.0 m2

/s. 

Table 1-1. MM5 Meteorological Model Configuration for VISTAS (Olerud, 2003a,b,c,d).

Science Options Configuration Details/Comments 

Model Code MM5 Version 3.6 (MPP) Grell et al., 1994 

Horizontal Grid Mesh 36/12 km 

 36 km grid 164 x 128 cells Continental U.S. 

 12 km grid 180 x 189 cells Eastern U.S. 

Vertical Grid Mesh 34 layers Vertically varying; sigma pressure coord. 

Grid Interaction No Feedback IFEED=0 

Initialization Eta first guess fields/LittleR 

Boundary Conditions Eta first guess fields/LittleR 

Microphysics Reisner I Mixed Ice Look up table 

Cumulus Scheme Kain-Fritsch 2 On both 36/12 Grids 

Planetary Boundary Layer ACM PBL 

Radiation RRTM 

Vegetation Data USGS 24 Category Scheme 

Land Surface Model P-X Land Surface Model (LSM) Cold restart every 5.5 days 

Shallow Convection None 

Sea Surface Temperature Eta Skin Spatially varying 

Thermal Roughness Garratt 

Snow Cover Effects None 

4D Data Assimilation Analysis Nudging on 36/12 

Integration Time Step 90 seconds 

Simulation Periods Annual 2002 

Platform Linux Cluster Done at BAMS 
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1.3.3.2 SMOKE Emissions Model Configuration for VISTAS Annual Modeling 

SMOKE supports area, mobile, fire and point source emission processing and also includes 

biogenic emissions modeling through a rewrite of the Biogenic Emission Inventory System, 

version 3 (BEIS3) (see, http://www.epa.gov/ttn/chief/software.html#pcbeis). SMOKE has been 

available since 1996, and it has been used for emissions processing in a number of regional air 

quality modeling applications. In 1998 and 1999, SMOKE was redesigned and improved with 

the support of the U.S. Environmental Protection Agency (EPA), for use with EPA's Models3/CMAQ (http://www.epa.gov/asmdnerl/models3) and is currently maintained and available 

from the CMAS center (www.cmascenter.org). 

As an emissions processing system, SMOKE has far fewer ‘science configuration’ options 

compared with the MM5 and CMAQ models. Table 1-2 summarizes the version of the SMOKE 

system to be used and the sources of data to be employed in constructing the required modeling 

inventories. Emissions for the VISTAS states for processing by SMOKE were provided by 

MACTEC (2006; 2008). Details on the SMOKE emissions modeling for the VISTAS 2002 

annual modeling is presented in Chapter 2 with emissions summary reports available in 

Appendix A. 
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Table 1-2. SMOKE Emissions Model Configuration for VISTAS.

Emissions Component Configuration Details/Comments 

Emissions Model SMOKE Version 2.1 

Errors in SMOKE Version 2.1 conversion of lat/lon 

coordinates to Lambert Conformal modeling grid resulted in 

displacement of sources and receptors. Discovered in Aug 

2007. Receptor locations corrected for Base G2 results. 

Horizontal Grid Mesh 36/12 km 

 36 km grid 148 x 112 cells RPO Unified Grid 

 12 km grid 168 x 177 cells VISTAS/ASIP eastern US 12 km grid 

Area Source Emissions 

VISTAS Domain: VISTAS 

State 2002 EI Updated '02 developed by VISTAS states 

 

Other States: EPA '02 NEI 

augmented with other 2002 

Generated from EPA NEI02 v.1 and RPO interaction 

MRPO Base K, CENRAP Base F, MANE-VU Version 

3.1 and WRAP Plan02b 

 Mexico/Canada Emissions: 

Same as Phase I w/ revisions based on available 

updates 

On-Road Mobile Sources 

VISTAS Domain: VISTAS 

State 2002 EI Updated '02 developed by VISTAS states 

 

Other States: EPA '02 NEI 

augmented with other 2002 

Generated from EPA NEI02 v.1 and RPO interaction 

MRPO Base K, CENRAP Base F, MANE-VU Version 

3.1 and WRAP Plan02b 

 Mexico/Canada Emissions: 1999 Mexico from BRAVO. 2000 Canada 

Point Sources 

VISTAS Domain: VISTAS 

State 2002 EI 

Updated '02 developed by VISTAS states and 

stakeholders Last update Base G2. 

 

Other States: EPA '02 NEI 

augmented with other 2002 

Generated from EPA NEI02 v.1 and RPO interaction 

MRPO Base K, CENRAP Base F, MANE-VU Version 

3.1 and WRAP Plan02b 

 Mexico/Canada Emissions: 

Same as Phase I w/ revisions based on available 

updates 

Off-Road Mobile Sources 

VISTAS Domain: VISTAS 

State 2002 EI 

Updated '02 developed by VISTAS states Last 

update Base F 

 

Other States: EPA '02 NEI 

augmented with other 2002 

Generated from EPA NEI02 v.1 and RPO interaction 

MRPO Base K, CENRAP Base F, MANE-VU Version 

3.1 and WRAP Plan02b 

 Mexico/Canada Emissions: 

Same as Phase I w/ revisions based on available 

updates 

Biogenic Sources 

VISTAS Domain: VISTAS 

State 2002 EI Updated '02 developed by VISTAS states 

 

Other States: EPA '02 NEI 

augmented with other 2002 Generated from EPA NEI02 v.1 and RPO interaction 

 Mexico/Canada Emissions: 

Same as Phase I w/ revisions based on available 

updates 

Temporal Adjustments Seasonal, day, hour 

Based on latest collected information and CEMbased profiles 

Chemical Speciation 

Revised CB4 Chemical 

Speciation Revised in 2004 

Gridding 

Revised EPA Spatial 

Surrogates Used 

Gridding of surrogates from 

http://www.epa.gov/ttn/chief/emch/spatial/ 

Growth and Controls MACTEC (2006) 

MRPO Base K, CENRAP Base F, MANE-VU Version 

3.1 and WRAP Plan02b 

Quality Assurance QA Tools in SMOKE 2.1 Independent QA with AG's Established Protocol 

Simulation Periods Annual 2002 

Platform Dual Athelon 2600 \+ 

Local 1.8 terabyte Ultra 320 RAID 5 system 

processing and storage 
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1.3.3.3 CMAQ Air Quality Model Configuration for VISTAS Annual Modeling 

VISTAS used Version 4.51 of the Community Multi-scale Air Quality (CMAQ) modeling 

system with an enhanced secondary organic aerosol (SOA) module (SOAmods) using the model 

configuration shown in Table 1-3. The model was set up and exercised on the 36 km grid 

continental U.S. Inter-RPO modeling domain that was also used by WRAP and CENRAP. 

VISTAS also performed annual 2002 12 km modeling for an eastern U.S. modeling domain that 

included all of the VISTAS states as well as adjacent states to the west (CENRAP), in the 

Midwest (MRPO) and the Northeast (MANE-VU). 

Initial CMAQ 2002 simulations performed by VISTAS found that the model greatly 

underestimate Organic Carbon Mass (OCM) concentrations, especially in the summer (Morris et 

al., 2004b). A review of the CMAQ formulation found that it failed to treat Secondary Organic 

Aerosol (SOA) formation from sesquiterpenes and isoprene and also failed to account for 

polymerization of SOA so that it is no longer volatile and stays in the particle form. The 

standard versions of CMAQ V4.51 assume that SOA is volatile so that once an aerosol is formed 

it can evaporate from particle to gaseous form depending on atmospheric conditions (e.g., 

temperature and humidity). After a detailed literature review, VISTAS updated the CMAQ SOA 

module to include these missing processes and found that the updated CMAQ V4.5 SOAmods 

produced much better OCM model performance in the summer (Morris et al., 2006c). 

1.3.3.3.1 CMAQ V4.5 SOAmods Enhancement 

The formulation of the CMAQ standard SOA module is described in Binkowski and Roselle 

(2003). In the CMAQ standard SOA module, SOA is formed primarily from aromatic VOCs 

and biogenic terpenes. The biogenic SOA precursor emissions were modeled with the Biogenic 

Emissions Information System – Version 3 (BEIS3) model (Pierce et al., 2002). BEIS3 

generates three biogenic VOC species: isoprene (ISOP), monoterpenes (TERP) and other 

biogenic VOC (OVOC). After testing of alternative gas-phase chemical mechanisms available 

in CMAQ (SAPRC99 and CBM-IV; ENVIRON, Alpine and UCR, 2003c)), VISTAS selected 

the Carbon Bond IV photochemical mechanism (Gery et al., 1987) for use in the annual air 

quality modeling since it produced essentially identical model performance as SAPRC99 and 

was more computationally efficient. CBM-IV represents VOC compounds based on their carbon 

bond structure. The BEIS3 ISOP, TERP and OVOC species are speciated into the CB4 species 

for photochemical modeling in CMAQ as follows (molar speciation): 

• ISOP = ISOP (isoprene is an explicit species) 

• ALD2 = 1.5 x TERP 

• OLE = 0.5 x TERP 

• PAR = 6.0 x TERP 

• NR = 0.5 x OVOC 

• OLE = 0.5 x OVOC 

• PAR = 8.5 x OVOC 

• TERPB = TERP 
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Here, ALD2, OLE, PAR and NR are the CBM-IV chemical mechanism representations of the 

biogenic VOC emissions as high molecular weight aldehydes, olefinic carbon bond, paraffin 

carbon bond and non-reactive functional groups. In CMAQ, the TERPB species is specified in 

the emissions inputs, along with its CBM-IV representation of ALD2, OLE and PAR, but does 

not participate in the photochemical mechanism and is only used in the SOA formation module. 

The TERPB species forms a SGTOT species based on oxidation parameters extracted from the 

photochemical module. SGTOT consists of the combined gaseous condensable gas (CG) plus 

particle SOA that are assumed to be in equilibrium. CMAQ transports the SGTOT species and 

splits it to a CG gaseous and particle SOA for output. 

 

The CMAQ TERB SOA formation rate is based on a two-product fit to smog chamber data 

collected at the California Institute of Technology for several biogenic monoterpene species 

(Binkowski and Roselle, 2003). A review of recent literature of biogenic SOA measurements 

identified several processes that may be important to biogenic SOA formation that are not treated 

by the BEIS3 biogenic emissions model and the CMAQ SOA module: 

Polymerization: Recent measurements indicate that some SOA species may polymerize, 

resulting in species that are no longer volatile and cannot evaporate back to a CG. In this 

case, the equilibrium assumption between the CG and SOA will understate the amount of 

particle SOA present in the atmosphere (Kalberer et al., 2004; Jang et al., 2002). 

Sesquiterpenes: Sesquiterpenes are not accounted for in the BEIS3/CMAQ SOA 

modeling system (Guenther et al., 2000; Vizuete et al., 2004). 

Isoprene: More recent evidence suggests that isoprene can also form particle SOA 

compounds that are not accounted for in CMAQ (Claeys et al., 2004; Matsunaga et al., 

2003; 2005). 

Acid Catalyzation: Recent literature also suggests that some SOA formation may have 

acid catalyzed reactions (Claeys et al., 2004; Jang et al., 2005). 

Heterogeneous Reactions: Recent evidence suggests that some SOA formation may 

occur during heterogeneous aqueous-phase chemical reactions (Yu et al., 2005). 

An enhanced SOA module was added to CMAQ that accounted for the first three processes 

listed above. The last two processes were not included in this work because there are not enough 

quantitative experimental data yet to establish a parameterization. Modules were added to the 

CMAQ SOA module under the following constraints: 

• The existing CMAQ SOA module for monoterpenes would remain unchanged; 

• The same CMAQ model inputs would be used; and 

• The basic CMAQ model formulation would remain unchanged, modules would be added 

to account for polymerization and SOA from sesquiterpenes and isoprene. 
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F:\VISTAS Phase II\TSD\draft#4\Chap_1_Intro3_updated.doc 1-14 

Figure 1-3 displays how the enhanced SOA module represented the new processes that were 

added to the CMAQSOA module to represent SOA polymerization and SOA formation from 

sesquiterpenes and isoprene along with the existing CMAQ SOA module structure and inputs. 

The new components of the SOA module are indicated in bold italic, whereas the existing 

CMAQ SOA components (Binkowski and Roselle, 2002) use a regular font. There are several 

parameters that must be defined in the new elements of the enhanced SOA module: emission 

factors (EF), canopy escape efficiencies for gases (EEG) and aerosols (EEA) and SOA yields 

(Y). Based on an analysis of recent measurements, primarily from a recent biogenic emissions 

field study in Duke Forest, North Carolina (Stroud et al., 2005; Matsunaga et al., 2005), a range 

of values for the factors in Figure 1-3 were developed as shown in Table 1-4. The enhanced 

SOA module used the mid-point of the range values for the factors from the measurements 

(Table 1-4), which represent the best estimates of these parameters given the information when 

they were developed. No attempt was made to optimize the parameters in Table 1-5 for 

OC/TCM model performance. 

The emission factors, EF1 and EF2, relate the monoterpene emissions estimated by BEIS3 to 

emissions of monoterpenes, EF1 (e.g., α-pinene), and sesquiterpenes (EF2). Table 1-4 displays 

the range of EF1 and EF2 factors based on recent field study data (Stroud et al., 2005). Using 

the midpoint of the range results in emission factors of 0.7 for EF1 and 0.4 for EF2. EF1 is 

assigned a value of 0.7 based on field observations that indicate that the BEIS3 terpene emission 

factors are likely overestimated due to a tendency of earlier measurements approaches to 

artificially increase the emissions due to disturbance when leaves were enclosed in the 

measurement system. Sesquiterpene emissions were included using an EF2 value of 0.4 based 

on the ratio of the observed sesquiterpene emission from the Duke Forest field study to terpene 

emissions that are provided by BEIS2 TERP species (Stroud et al, 2005). The net result is that 

BEIS3 TERP emissions are increased by 10% and split 64% as monoterpenes and 36% as 

sesquiterpenes. The CG yields from the sesquiterpenes are assumed to partly condense into a 

non-volatile SOA particle that is modeled in CMAQ using the new secondary organic carbon 

species (SOC2) species and only some of the gas and aerosol species associated with 

sesquiterpenes are assumed to escape from the canopy using the mid-range of the Escape 

Efficiencies (EE) estimated by Stroud et al. (2005). The fraction of BEIS3 TERP emissions that 

are assumed to be monoterpenes (i.e., 64% of the emissions) are treated with the standard 

CMAQ two-product SOA module (Binkowski and Roselle, 2003) assuming equilibrium between 

the CG and SOA with the SOA output in the standard AORGB species (Binkowski and Roselle, 

2003). The isoprene SOA formation pathway forms a CG using the mid-point yield rate based 

on the range of recent measurements (Stroud et al., 2005) and a CG/SOA partitioning rate based 

on the mid-point of measurements from Matsunaga et al. (2003, 2005) (Table 1-4). The isoprene 

SOA is assumed to be volatile and is modeled as a new secondary organic carbon species in 

CMAQ SOAmods (SOC3). Finally, all SOA species, with the exception of the already nonvolatile SOC1 (polymerized SOA) and SOC2 (sesquiterpene product) species, are assumed to 

partially polymerize into non-volatile particles that are stored in the SOC1 species. The 

polymerization rate is based on the results of Kalberer et al (2004) who found that 50% of the 

SOA polymerized in 20 hours. 

Several levels of Quality Assurance and Quality Control of the enhanced SOAmods module in 

the CMAQ model were conducted as follows. 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chap_1_Intro3_updated.doc 1-15 

QA/QC of SOAmods Coding: The SOAmods implementation was conducted at 

ENVIRON. Staff at the University of California at Riverside performed independent 

QA/QC of the SOAmods code implementation and independent testing and evaluation. 

QA of SOAmods Formulation: The new processes being added to the CMAQ SOA 

module were discussed with researchers at EPA’s Office of Research and Development 

(ORD). Although they have not completed all the laboratory tests, the inclusion of SOA 

from sesquiterpene and isoprene has been observed and are supported by their 

preliminary measurements. 

Peer Review of SOAmods: The formulation of the SOAmods enhancement to the 

CMAQ SOA module was documented, reviewed by many parties and comments were 

provided. The results were also published in a peer-reviewed journal (Morris et al., 

2006c). 

Model Performance Evaluation of SOAmods: The final level of QA of the SOAmods 

was comparisons of CMAQ V4.4 model performance with and without including the 

SOAmods enhancement. Table 1-5 displays fractional bias error for Organic Carbon 

Mass (OCM) IMPROVE and STN monitoring sites in the VISTAS, MRPO, MANE-VU 

and CENRAP states using the standard CMAQ Version 4.4 (V4.4) and then CMAQ V4.4 

with the SOAmods enhancement. Here the original IMPROVE equation was used to 

adjust the IMPROVE measured OC to OCM (i.e., OCM = 1.4 x OC). Whereas the 

standard CMAQ V4.4 underestimates OCM across IMPROVE sites from –76% (MRPO) 

to –102% (VISTAS), with the SOAmods enhancement the fractional biases centered on 

zero and ranged from –14% to +8%. Similar results are seen for OCM fractional bias 

across the more urban STN sites where the CMAQ V4.4 exhibits an underestimation bias 

of –67% to –105%, when using SOAmods the under-prediction bias is –27% to –44%. 

Note that the continued underestimation of OCM across the urban STN sites is likely due 

to missing primary OCM emissions and uncertainties in the STN OCM measurements. 

Also note that a 1.4 multiple was used to convert the measured OC to OCM, if a 1.8 

factors was used for such conversion, as given in the new IMPROVE equation, than the 

OCM underprediction bias would be even greater. 

With the release of CMAQ Versions 4.5 in October 2005, the SOAmods enhancement was added 

to the AERO3 aerosol module in CMAQ Version 4.5 that was compared against the standard 

CMAQ Versions 4.5 and SOAmods was found to produce similar improvements in OCM model 

performance as seen with CMAQ Versions 4.4. In March 2006 Version 4.51 of CMAQ was 

released that included active Sea Salt chemistry and Sea Salt emissions. Given that many of the 

VISTAS Class I areas are located in the coastal environment then Sea Salt may be important. 

Consequently, the SOAmods enhancement was implemented in the AERO4 (AE4) aerosol 

module in CMAQ V4.51 and that was the version used in the final 2002 and 2018 Base G2 

modeling. 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chap_1_Intro3_updated.doc 1-16 

Table 1-3. CMAQ Air Quality Model Configuration for VISTAS.

Science Options Configuration Details/Comments 

Model Code 

CMAQ Version 4.51 with 

SOAmods enhancement Pleim et al., (2005); Morris et al., (2006c) 

Horizontal Grid Mesh 36/12 km 

36 km covering cont. U.S; 12 km covering 

eastern US 

 36 km grid 148 x 112 cells RPO National Grid 

 12 km grid 168 x 177 cells VISTAS 12 km EUSA grid 

Vertical Grid Mesh 19 Layers First 17 layers sync'd w/ MM5 

Grid Interaction One-way nesting 

Initial Conditions ~15 days full spin-up Separately run 4 quarters of 2002 

Boundary Conditions 

2002 3-hourly GEOS-CHEM 

annual run 

Day-specific 3-hour BCs from Global 

Climate Model 

Emissions 

 Baseline Emissions Processing SMOKE (Ver 2.2) MM5 Meteorology input to SMOKE, CMAQ 

 Dust Transport Fraction 

Applied in emissions before 

SMOKE Tom Pace updates 

 NH3 Inventory Adjustment 

Applied in emissions before 

SMOKE 

 Sub-grid-scale Plumes No Plume-in-Grid (PinG) 

Chemistry 

 Gas Phase Chemistry CBM-IV 

 Aerosol Chemistry AE4/ISORROPIA Includes active Sea Salt 

 Secondary Organic Aerosols 

Secondary Organic Aerosol 

Model (SORGAM) w/ 

SOAmods enhancement Schell et al., (2001); Morris et al, (2006c) 

 Aerosol Mass Conservation 

Patch Yes Georgia Institute of Technology Update 

 Cloud Chemistry RADM-type aqueous chemistry Includes subgrid cloud processes 

N2O5 Reaction Probability 0.01 – 0.001 

Meteorological Processor MCIP Version 3.0 

Includes dry deposition for Sea Salt and 

percent urban minimum Kz (PURB) 

Horizontal Transport 

 Eddy Diffusivity Scheme 

K-theory with Kh grid size 

dependence Multiscale Smagorinsky (1963) approach 

Vertical Transport 

 Advection Scheme Yamertino V4.5 Mass Conservation (Yamertino) 

 Eddy Diffusivity Scheme K-theory 

 Diffusivity Lower Limit Kzmin = 0.1 to 2.0 PURB option in Ver 4.5 

Planetary Boundary Layer No Patch 

Deposition Scheme M3dry 

Directly linked to Pleim-Xiu Land Surface 

Model parameters 

Numerics 

 Gas Phase Chemistry Solver 

Euler Backward Iterative (EBI) 

solver 

Hertel et al (1993) EBI solver ~ 2x faster 

than MEBI 

 Horizontal Advection Scheme 

Piecewise Parabolic Method 

(PPM) scheme 

Simulation Periods Annual 2002 With~15 day spin-up in December 2001 

Integration Time Step Determined by met conditions 15 minute coupling time step 

Platform Athlon MP 2600+ MPI using 6 processors per quarter 
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F:\VISTAS Phase II\TSD\draft#4\Chap_1_Intro3_updated.doc 1-17 

Figure 1-3. Schematic describing the addition of new SOAmods processes (bold italic) to the 

existing CMAQ SOA module (regular font) to treat polymerization and SOA formation from 

sesquiterpenes and isoprene (see Table 1-4 for parameters).

Table 1-4. Parameters use in SOAmods module to enhance CMAQ (see Figure 1-3). 

Parameter Mid-Point Range 

EF1 0.7 0.4 ~ 1.0 

EF2 0.4 0.2 ~ 0.6 

EEG1 0.325 0.2 ~ 0.45 

EEA1 0.2 0.05 ~ 0.35 

Y2 0.875 0.75 ~ 1.0 

Y1 0.11 0.06 ~ 0.16 

P1 0.45 0.15 ~ 0.75 

EF1 = emission factor of monoterpenes to the TERP 

emissions estimated by BEIS3 

EF2 = emission factor of sesquiterpenes relative to 

the TERP emissions estimated by BEIS3 

EEG1 = escape efficiency of gas phase precursor of 

sesquiterpenes from canopy 

EEA1 = escape efficiency of SOA from sesquiterpenes 

from canopy 

Y1 = SOA yield of oxidated isoprenes 

Y2 = SOA yield of sesquiterpenes 

Biogenic emissions

TERP

ISOP ISPD Y1

P1

PAR

OLE

ALD2

TERPRXN

A_TRP1

G_TRP1

A_TRP2

G_TRP2

ISPRXN SGTOT_ISP

G_ISP

A_ISP

EF2 * ( EEG1 * Y2 + EEA1 )

AORGB

ASOC2

ASOC3

* Y3

*Y4

O

O

O: Oxidation reaction with OH, O3, and NO3

ISPRXN: The amount of reacted ISPD with oxidants

TERPRXN: The amount of reacted TERP with oxidants

Dotted box: Not implemented in the initial test

Bold italic: New processes

ASOC1 Polymerization All SOAs except SOC1 & SOC2 FPOLY

EF1

Biogenic emissions

TERP

ISOP

OVOC

PAR

OLE

NR

ISPD Y1

P1

PAR

OLE

ALD2

SGTOT

_TRP1

TERPRXN SGTOT

_TRP2

ISPRXN SGTOT_ISP

G_ISP

A_ISP

EF2 * ( EEG1 * Y2 + EEA1 ) ASOC2

ASOC3

*

*

O O

O

O: Oxidation reaction with OH, O3, and NO3

ISPRXN: The amount of reacted ISPD with oxidants

TERPRXN: The amount of reacted TERP with oxidants

Dotted box: Not implemented in the initial test

Bold italic: New processes

ASOC1 Polymerization All SOAs except SOC1 & SOC2 FPOLY

EF1
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F:\VISTAS Phase II\TSD\draft#4\Chap_1_Intro3_updated.doc 1-18 

Table 1-5. Results of initial evaluation of the SOAmods enhancement comparison of fractional 

bias performance metric for Organic Carbon Mass (OCM) using the standard CMAQ Version 

4.4 (V4.4) and CMAQ V4.4 with the SOAmods enhancement (observed OCM = 1.4 x OC). 

July 2002 IMPROVE OCM STN OCM 

Fractional Bias V4.4 SOAmods V4.4 SOAmods 

Southeastern U.S. -102% -2% -105% -32% 

Midwestern U.S. -76% +12% -67% -24% 

Northeast U.S. -82% -14% -95% -44% 

Central U.S. -98% +8% -81% -27% 

1.3.4 Horizontal Modeling Domains 

VISTAS used the same 36 km continental U.S. and 12 km eastern U.S. modeling domains in the 

Phase I and II modeling. The 36 km Inter-RPO coarse grid continental United States horizontal 

modeling domain is the same domain used for CENRAP and WRAP modeling. The CMAQ 

domain is nested in the MM5 domain. The selection of the MM5 domain is described in the 

VISTAS MM5 modeling protocol (Olerud, 2003a,b,c,d). Figure 1-4 displays the MM5 horizontal 

domain as the outer bluest grid. Also shown in Figure 1-4 is the CMAQ 36 km domain nested in 

the MM5 domain. To achieve finer spatial resolution in the eastern U.S., including the VISTAS 

region, VISTAS also used a one-way nested higher resolution grid with a 12 km grid resolution. 

Figure 1-5 displays the 36 km CMAQ continental grid and the high resolution, nested 12 km grid 

for the eastern U.S. Figure 1-6 shows in more detail the 12 km grid for the eastern U.S. region 

that is the focus of VISTAS. 

Both MM5 and CMAQ employ the Regional Planning Organization (RPO) unified grid 

definition for the 36 km continental domain. The RPO unified grid consists of a LambertConformal map projection using the map projections parameters listed in Table 1-6. 

Table 1-6. RPO Lambert Conformal Projection (LCP) unified grid definition. 

PARAMETER VALUE 

projection Lambert-conformal 

alpha 33 degrees 

beta 45 degrees 

x center 97 degrees 

y center 40 degrees 

The MM5 36 km grid includes 164 cells in the east-west dimension and by 128 cells in the northsouth dimension. The CMAQ 36 km grid includes 148 cells in the east-west dimension and 112 

cells in the north-south dimension. Because the MM5 model is also nested in the Eta model, 

there is a possibility of boundary effects near the MM5 boundary that occur as the Eta 

meteorological variables are being simulated by MM5 and must come into dynamic balance with 

MM5’s algorithms. Thus, a larger MM5 domain was selected to provide a buffer of 8 to 9 grid 

cells around each boundary of the CMAQ 36 km domain. This is designed to eliminate any 

errors in the meteorology from boundary effects in the MM5 simulation at the interface of the 

MM5 and Eta models. The buffer region used here exceeds the EPA suggestion of at least 5 grid 

cell buffer at each boundary. 
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F:\VISTAS Phase II\TSD\draft#4\Chap_1_Intro3_updated.doc 1-19 

Table 1-7 lists the number of rows and columns and the definition of the X and Y origin (i.e., the 

southwest corner) for the 36 km and 12 km grids for both MM5 and CMAQ. Note that the 

CMAQ grid is rotated 90 degrees relative to the MM5 grid, so rows and columns are reversed. In 

Table 4-2 “Dot” refers to the grid mesh defined at the vertices of the grid cells while “cross” 

refers to the grid mesh defined by the grid cell centers. Thus, the dimension of the dot mesh is 

equal to the cross mesh plus one. Finally, we note that the grid definition for the CMAQ 

Meteorology Chemistry Interface Processor (MCIP), SMOKE emissions model and CMAQ 

Chemical Transport Model (CCTM) are identical. 

Table 1-7. Grid definitions for MM5 and CMAQ using the RPO LCP unified grid coordinate 

system (Table 1-7).

MODEL 

COLUMNS 

DOT(CROSS) 

ROWS 

DOT(CROSS) XORIGIN YORIGIN 

MM5 36km 129 (128) 165 (164) -2952000 -2304000 

CMAQ 36km 149 (148) 113 (112) -2736000 -2088000 

MM5 12km 190 (189) 181 (180) 7200 -1656000 

CMAQ 12km 169 (168) 178 (177) 108000 -1620000 

In Aug 2007 it was discovered that the SMOKE version 2.1 provided incorrect projections from 

lat/long coordinates to Lambert Conformal coordinates. This error results in a displacement of 

stack locations of approximately 2-3 km, so point sources in the Base G2 2002 and 2018 

modeling inventories could be displaced spatially by one 12 km or 36 km grid cell to another. In 

general, this displacement was of a few kilometers so had a minimal effect on the location of grid 

cells for the point sources in the VISTAS 36/12 km modeling. This error has not been corrected. 

The location of the receptor monitors has been corrected in the Base G2 2018 visibility 

projections. 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chap_1_Intro3_updated.doc 1-20 

Figure 1-4. Nesting of VISTAS 36-km CMAQ grid in the MM5 36-km grid. 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chap_1_Intro3_updated.doc 1-21 

. 

Figure 1-5. Nesting of CMAQ 12-km grid in the CMAQ 36 km grid.
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chap_1_Intro3_updated.doc 1-22 

Figure 1-6. Domain definition for the VISTAS higher resolution CMAQ 12 km eastern U.S. grid
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chap_1_Intro3_updated.doc 1-23 

1.3.5 Vertical Domain Definition 

The CMAQ vertical layer structure is primarily defined by the vertical grid used in the MM5 

modeling. The MM5 model employed a terrain following coordinate system defined by pressure, 

using 34 layers that extend from the surface to the 100 mb. Table 1-8 lists the layer definitions 

for both MM5 and for CMAQ. A layer averaging scheme is adopted for CMAQ to reduce the 

computational cost of the CMAQ simulations. The effects of layer averaging were evaluated in 

the VISTAS Phase I modeling effort and found to have a relatively minor effect on the model 

performance metrics when both the 34 layer and a 19 layer CMAQ models were compared to 

ambient monitoring data (ENVIRON, Alpine and UCR, 2003c). 

Table 1-8. Vertical layer definition for the VISTAS MM5 simulations (left most columns) using 

34 layers and approach for reducing CMAQ layers by collapsing multiple MM5 layers (right 

columns).

MM5 CMAQ 19L 

Layer Sigma Pressure 

(mb) 

Height

(m) 

Depth

(m) 

Layer Sigma Pressure 

(mb) 

Height

(m) 

Depth

(m) 

34 0.000 100 14662 1841 19 0.000 100 14662 6536 

33 0.050 145 12822 1466 0.050 145 

32 0.100 190 11356 1228 0.100 190 

31 0.150 235 10127 1062 0.150 235 

30 0.200 280 9066 939 0.200 280 

29 0.250 325 8127 843 18 0.250 325 8127 2966 

28 0.300 370 7284 767 0.300 370 

27 0.350 415 6517 704 0.350 415 

26 0.400 460 5812 652 0.400 460 

25 0.450 505 5160 607 17 0.450 505 5160 1712 

24 0.500 550 4553 569 0.500 550 

23 0.550 595 3984 536 0.550 595 

22 0.600 640 3448 506 16 0.600 640 3448 986 

21 0.650 685 2942 480 0.650 685 

20 0.700 730 2462 367 15 0.700 730 2462 633 

19 0.740 766 2095 266 0.740 766 

18 0.770 793 1828 259 14 0.770 793 1828 428 

17 0.800 820 1569 169 0.800 820 

16 0.820 838 1400 166 13 0.820 838 1400 329 

15 0.840 856 1235 163 0.840 856 

14 0.860 874 1071 160 12 0.860 874 1071 160 

13 0.880 892 911 158 11 0.880 892 911 158 

12 0.900 910 753 78 10 0.900 910 753 155 

11 0.910 919 675 77 0.910 919 

10 0.920 928 598 77 9 0.920 928 598 153 

9 0.930 937 521 76 0.930 937 

8 0.940 946 445 76 8 0.940 946 445 76 

7 0.950 955 369 75 7 0.950 955 369 75 

6 0.960 964 294 74 6 0.960 964 294 74 

5 0.970 973 220 74 5 0.970 973 220 74 

4 0.980 982 146 37 4 0.980 982 146 37 

3 0.985 986.5 109 37 3 0.985 986.5 109 37 

2 0.990 991 73 36 2 0.990 991 73 36 

1 0.995 995.5 36 36 1 0.995 995.5 36 36 

0 1.000 1000 0 0 0 1.000 1000 0 0 
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F:\VISTAS Phase II\TSD\draft#4\Chap_1_Intro3_updated.doc 1-24 

1.3.6 2002 Calendar Year Selection 

The calendar year 2002 was selected for VISTAS regional haze annual modeling as described in 

the VISTAS Modeling Protocol prepared in 2003 (ENVIRON, Alpine and UCR, 2003d). EPA’s 

applicable guidance on PM2.5/Regional Haze modeling at that time (EPA, 2001) identifies 

specific goals to consider when selecting modeling periods for use in demonstrating reasonable 

progress in attaining the regional haze goals. However, since there is much in common with the 

goals for selecting episodes for annual and episodic PM2.5 attainment demonstrations as well as 

regional haze, EPA’s guidance addresses all three in a common document. At the time of the 

modeling period selection, EPA had also published an updated summary of PM2.5 and Regional 

Haze Modeling Guidance (Timin, 2002) that served as in interim placeholder until the final 

guidance was published in April 2007 (EPA, 2007a) as part of the PM2.5/regional haze NAAQS 

implementation process. The interim EPA modeling guidance for episode selection (EPA, 

2001;Timin, 2002) was consistent with the final guidance (EPA, 2007a). 

EPA recommends that the selection of a modeling period derive from three principal criteria: 

• A variety of meteorological conditions should be covered that includes the types of 

meteorological conditions that produce the Worst 20% and Best 20% visibility days at 

Class I areas in the VISTAS states during the 2000-2004 baseline period; 

• To the extent possible, the modeling data base should include days for which enhanced 

data bases (i.e. beyond routine aerometric and emissions monitoring) are available; and 

• Sufficient days should be available such that relative response factors (RRFs) can be 

based on several (i.e., > 15) days. 

For regional haze modeling, the guidance goes further by suggesting that the preferred approach 

is to model a full, representative year (EPA, 2001, pg. 188). Moreover, the required RRF values 

should be based on model results averaged over the 20% worst and 20% best visibility days 

determined for each Class I are based on monitoring data from the 2000 – 2004 baseline period. 

Additional EPA guidance (Timin, 2002) suggests that states should model at least 10 worst and 

10 best visibility days at each Class 1 area. EPA also lists several ‘other considerations’ to bear 

in mind when choosing potential PM/regional haze episodes including: (a) choose periods which 

have already been modeled, (b) choose periods which are drawn from the years upon which the 

current design values are based, (c) include weekend days among those chosen, and (d) choose 

modeling periods that meet as many episode selection criteria as possible in the maximum 

number of nonattainment or Class I areas as possible. 

VISTAS elected to model a complete single Calendar Year (CY) to assure that at least one year 

of worst 20% and best 20% visibility days would be available at each VISTAS Class I area (i.e., 

24 worst 20% and 24 best 20% days assuming full IMPROVE data capture for the selected year). 

The RHR uses the five-year baseline of 2000-2004 as the starting point for projecting future-year 

visibility. Thus, the modeling year should be selected from this five-year baseline period. The 

2002 calendar year, which lies in the middle of the 2000-2004 Baseline, was selected for the 

following reasons: 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chap_1_Intro3_updated.doc 1-25 

• Based on available information, appears to be a fairly typical year in terms of 

meteorology for the 5-year Baseline period of 2000-2004; 

• 2003 appeared to be colder and wetter than typical in the eastern US; 

• The enhanced IMPROVE and IMPROVE Protocol and Supersite PM monitoring data 

were fully operational by 2002 with much less IMPROVE monitoring data available 

during 2000-2001; 

• IMPROVE data for 2003 and 2004 were not yet available at the time the VISTAS annual 

modeling was initiated; and 

• 2002 was being considered by the other RPOs. 

1.3.7 Initial Concentrations and Boundary Conditions 

The CMAQ model was operated separately for each of four quarters of the 2002 year using a ~15 

day spin up period (i.e., the model was started approximately 15 days before the first day of 

interest in each quarter in order to limit the influence of the assumed initial concentrations, e.g., 

start June 15 for quarter 3 whose first day of interest is July 1). Sensitivity simulations 

demonstrated that with ~15 initialization days, the influence of initial concentrations (ICs) was 

minimal using the 36 km Inter-RPO continental U.S. modeling domain. Consequently, clean ICs 

were specified in the CMAQ modeling using a ~15 day spin up period. 

Boundary Conditions (BCs) (i.e., the assumed concentrations along the later edges of the 36 km 

modeling domain, see Figure 1-4) were based on a 2002 simulation by the GEOS-CHEM global 

circulation/chemistry model. GEOS-CHEM is a three-dimensional global chemistry model 

driven by assimilated meteorological observations from the Goddard Earth Observing System 

(GEOS) of the NASA Global Modeling and Assimilation Office. It is applied by research groups 

around the world to a wide range of atmospheric composition problems, including future 

climates and planetary atmospheres using general circulation model meteorology to drive the 

model. Central management and support of the model is provided by the Atmospheric Chemistry 

Modeling Group at Harvard University. 

A joint RPO study was performed coordinated by VISTAS in which Harvard University applied 

the GEOS-CHEM global model for the 2002 calendar year (Jacob, Park and Logan, 2005). The 

University of Houston (UH) was retained to process the 2002 GEOS-CHEM output into BCs for 

the CMAQ model (Byun, 2004). The GEOS-CHEM simulations for the RPOs used GEOS 

meteorological observations for the year 2002. These were obtained from GMAO as a 6-hourly 

archive (3-hour for surface quantities such as mixing depths). The data through August 2002 

were from the GEOS-3 assimilation, with horizontal resolution of 1o

x1o and 55 vertical layers. 

The data after August 2002 were from the updated GEOS-4 assimilation, with horizontal 

resolution of 1ox1.25o and 48 vertical layers (note 1o latitude is equally to approximately 110 

km). The horizontal resolution was 4°x5°. The emissions used in GEOS-CHEM included U.S. 

anthropogenic emissions from the EPA NEI 1999 inventory, except for ammonia; international 

emissions for the most recent year available, and forest fire information specific to 2002. 

Emissions were aggregated to monthly average in acknowledgement that greater accuracy is not 

feasible for international emissions. This GEOS-CHEM run included the secondary organic 

aerosol (SOA) formation mechanism from Chung and Seinfeld (2002), prototype soil dust and 

sea salt simulations, and application of surface emissions and dry deposition to the entire mixed 

layer column rather than to just the surface layer of the model. 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chap_1_Intro3_updated.doc 1-26 

The GEOS-CHEM output was processed by mapping the GEOS-CHEM chemical compounds to 

the species in the CBM-IV chemical mechanism used by CMAQ and mapping the GEOS-CHEM 

vertical layers to the 19 layer vertical layer structure used by CMAQ in the VISTAS modeling 

(Byun, 2004). The results were day-specific three-hourly BC inputs for the CMAQ model. 

The BCs generated from the 2002 GEOS-CHEM were subjected to QA/QC. The first QA/QC 

check was a range check to assure reasonable values. The BCs were compared against the 

GEOS-CHEM outputs to assure the mapping and interpolation was performed correctly. The 

code used to map the GEOS-CHEM output to the CMAQ BC format was obtained from UH, 

reviewed and the BC generation duplicated for several time periods during 2002. 

1.3.8 Emissions Input Preparation 

Emissions for the CMAQ model were prepared using the SMOKE emissions modeling system 

with the configuration given in Table 1-2. Four types of emissions inventories were prepared: 

• 2002 Actual Base Case; 

• 2002 Typical Base Case; 

• 2018 Base Case; and 

• 2018 Sensitivity Case. 

The differences between the 2002 Actual and Typical Base Cases were emissions from Electrical 

Generating Units (EGUs) and fires. For the 2002 Actual Base Case day-specific emissions for 

the EGUs based on measurements from continuous emissions monitoring (CEM) data and dayspecific fire emissions were used. For the 2002 Typical Base Case the EGU and fire emissions 

are representative of the 2000-2004 5-year Baseline period. The 2002 Actual Base Case was 

used when comparing the CMAQ modeling results to the 2002 measurements in the model 

performance evaluation, whereas the 2002 Typical Base Case results were used with the 2018 

modeling results to make visibility projections. 

The base emissions for the VISTAS states were provided by the VISTAS emissions contractor 

(MACTEC, 2006; 2008). Emissions for Non-VISTAS states were obtained from the other RPOs 

during late 2006. These data were either provided in the SMOKE IDA format or in the case of 

the MRPO converted to the IDA format for input into SMOKE. Emissions for Mexico were 

based on the 1999 BRAVO inventory and a year 2000 inventory was used for Canada. 

Emissions for stationary point, area, on-road mobile, non-road mobile, fires and biogenic sources 

were processed. Day-specific biogenic emissions were generated using the SMOKE-BEIS3 

program and the hourly 2002 36/12 km MM5 data discussed previously. In order to account for 

seasonal and day-of-week effects, on-road mobile sources were simulated for a week from each 

month of 2002 using the SMOKE-MOBILE6 module and the MM5 meteorological data as input. 

Holidays were modeled as a Sunday. Similarly, area and non-road emissions were modeled for a 

Thursday, Friday, Saturday, Sunday and Monday from each month with emissions for Thursday 

used to represent Tuesday and Wednesday and Holidays used Sunday emissions. More details 

on the emissions modeling is provided in Chapter 2. 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chap_1_Intro3_updated.doc 1-27 

1.3.9 Meteorological Input Preparation 

The VISTAS 2002 36/12 km MM5 modeling was conducted by Barons Advanced 

Meteorological Systems (BAMS) as described previously (Olerud, 2003a,b,c,d). The VISTAS 

annual emissions and air quality modeling team processed the 2002 36/12 km MM5 data using 

the MCIP processor version 3.0 for input into CMAQ and conducted QA/QC on the CMAQ 

meteorological input files. 

1.3.10 Photolysis Rates Model Inputs 

Several chemical reactions in the atmosphere are initiated by the photo dissociation of various 

trace gases. To accurately represent the complex chemical transformations in the atmosphere, 

accurate estimates of these photodissociation rates must be made. The Models-3 CMAQ system 

includes the JPROC processor, which calculates a table of clear-sky photolysis rates (or J-values) 

for a specific date. JPROC uses default values for total aerosol loading and provides the option to 

use default ozone column data or to use measured total ozone column data. . These data come 

from the Total Ozone Mapping Spectrometer (TOMS) satellite data. TOMS data that is available 

at 24-hour averages and was obtained from http://toms.gsfc.nasa.gov/eptoms/ep.html. Dayspecific TOMS data was used in the CMAQ radiation model (JPROC) to calculate photolysis 

rates. The TOMS data were missing or bad for several periods in 2002: August 2-12; June 10; 

and November 18-19. Thus, the TOMS data for August 1, 2002 was used for August 2-7 and 

TOMS data for August 13 was used for August 8-12. Similarly, TOMS data for June 9 was used 

for June 10 and data for August 17 was used for August 18-19. Note that the total column of 

ozone in the atmosphere is dominated by stratospheric ozone which has very little day-to-day 

variability so the use of TOMS data within a week or two of an actual day introduces minimal 

uncertainties in the modeling analysis. 

JPROC produces a "look-up" table that provides photolysis rates as a function of latitude, 

altitude, and time (in terms of the number of hours of deviation from local noon, or hour angle). 

In the current CMAQ implementation, the J-values are calculated for six latitudinal bands (10o, 

20o, 30o, 40o, 50o, and 60o N), seven altitudes (0 km, 1 km, 2 km, 3 km, 4 km, 5 km, and 10 km), 

and hourly values up to 8 hours of deviation from local noon. During model calculations, 

photolysis rates for each model grid cell are estimated by first interpolating the clear-sky 

photolysis rates from the look-up table using the grid cell latitude, altitude, and hour angle, 

followed by applying a cloud correction (attenuation) factor based on the cloud inputs from 

MM5. 

The photolysis rates input file was prepared as separate look-up tables for each simulation day. 

Photolysis files are ASCII files that were visually checked for selected days to verify that 

photolysis are within the expected ranges. 

1.3.11 Air Quality Input Preparation 

Air quality data used with the CMAQ modeling system include: (1) Initial Concentrations (ICs) 

that are the assumed three-dimensional concentrations through the modeling domain at the very 

start of the simulation; (2) the Boundary Conditions (BCs) that are the concentrations assumed 

along the lateral edges of the 36 km inter-RPO continental U.S. modeling domain; and (3) air 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chap_1_Intro3_updated.doc 1-28 

quality observations that are used in the model performance evaluation discussed in Section 3 of 

this TSD. 

The CMAQ default clean Initial Concentrations (ICs) were used along with an approximately 15 

day spin up (initialization) period to eliminate any significant influence of the ICs on the 

modeled concentrations for the days of interest. That is, the model is started approximately 15 

days prior to the first day of interest (e.g., started on around December 16, 2001 for January 1, 

2002) to allow concentrations to build up in the model to real levels in the atmosphere. CMAQ 

was run for each quarter of the year so was initialized 15 days prior to the first day of interest 

(e.g., for quarter 3, Jul-Aug-Sep, the model was initialized on June 15, 2002 with the first 

modeling day of interest July 1, 2002). 

The CMAQ Boundary Conditions (BCs) for the Inter-RPO 36 km continental U.S. grid were 

based on day-specific 3-hour averages from the output of the GEOS-CHEM global simulation 

model of 2002 (Jacob, Park and Logan, 2005). The 2002 GEOS-CHEM output was mapped to 

the species and vertical layer structure of CMAQ and interpolated to the lateral boundaries of the 

36 km grid shown in Figure 1-4 (Byun, 2004). 

1.3.12 2002 Base Case Modeling and Model Performance Evaluation 

The CMAQ model was evaluated against ambient measurements of PM species, gas-phase 

species and wet deposition within and outside of the VISTAS region. Figure 1-7 displays the 

locations of monitoring sites within and nearby the VISTAS region. Ambient measurements 

from the IMPROVE, STN, CASTNet, NADP, SEARCH and AQS monitoring networks were 

used in the VISTAS CMAQ model performance evaluation (note that the locations of the AQS 

sites are not shown in Figure 1-7 because their shear number would obscure the figure). In 

general, the model performance of the CMAQ model for sulfate (SO4) and elemental carbon 

(EC) was good, performance for nitrate (NO3) was variable with a summer underestimation and 

winter overestimation bias. Model performance for organic carbon mass (OCM) was also 

variable, whereas performance for Soil and coarse mass (CM) was generally poor. More details 

on the model performance evaluation can be found in Chapter 3. 
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F:\VISTAS Phase II\TSD\draft#4\Chap_1_Intro3_updated.doc 1-29 

Figure 1-7. Locations of monitoring sites within and nearby the VISTAS region (locations of AQS 

sites not shown). 

1.3.13 2018 Modeling and Visibility Projections 

Emissions for 2018 base case were generated following the procedures discussed in Chapter 2 

and in the Emissions Inventory report by MACTEC (2008). 2018 emissions for Electrical 

Generating Units (EGUs) were based on simulations of the Integrated Planning Model (IPM) 

that took into account the effects of the Clean Air Interstate Rule (CAIR) on emissions from 

EGUs in CAIR states using an IPM realization of a CAIR cap and trade program. In some cases 

the IPM projected EGU emissions did not agree with the state projections so those EGU 

emissions were adjusted to match state expectations (MACTEC, 2008). Emissions for on-road 

and non-road mobile sources were based on activity growth and emissions factors from the EPA 

MOBILE6 and NONROAD models, respectively. Area sources and non-EGU point sources 

were grown to 2018 levels using assumptions from EPA and DOE. The Canadian year 2000 

emissions inventory was replaced by a Canadian 2020 emissions inventory for the 2018 CMAQ 

simulations and emissions in Mexico were held constant. The following controls were assumed 

in the 2018 Base G modeling scenarios: 

250 500 750 1000 1250 1500 1750 2000

-1500

-1250

-1000

-750

-500

-250

0

250

500

IMPROVE

CASTNet

SEARCH

STN

NADP

AQS
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F:\VISTAS Phase II\TSD\draft#4\Chap_1_Intro3_updated.doc 1-30 

• Clean Air Interstate Rule (CAIR) and Clean Air Mercury Rule (CAMR) for EGUs using 

IPM output. 

• NOx SIP Call. 

• North Carolina Clean Smokestack Act. 

• Various Consent Agreements (e.g., TECO, VEPCO, Gulf Power Crist 7). 

• 1-Hour Ozone Sips (Atlanta / Birmingham / Northern Kentucky). 

• NOx RACT. 

• Heavy Duty Diesel Rule. 

• Tier 2 Tailpipe Rule. 

• Large Spark Ignition and Recreational Vehicle Nonroad Rule. 

• Nonroad Diesel Rule. 

• Industrial Boiler/Process Heater/RICE MACT. 

• Combustion Turbine MACT. 

• VOC 2-, 4-, and 10-year MACT Standards. 

The following sources were assumed to remain unchanged between the 2002 and 2018 base case 

simulations: 

• Biogenic VOC and NOx emissions from the BEIS3 biogenic emissions model; 

• Off-shore emissions associated with off-shore marine and oil and gas production 

activities; 

• Emissions from wildfires; 

• Emissions from Mexico; and 

• Global transport (i.e., emissions due to BCs from the 2002 GEOS-CHEM global 

chemistry model. 

The results from the 2002 Typical Base G2 and 2018 Base G2a CMAQ simulations were used to 

project 2018 PM levels from which 2018 visibility estimates were obtained. The 2002 and 2018 

modeling results were used in a relative sense to scale the observed PM concentrations from the 

2000-2004 Baseline and the IMPROVE monitoring network to obtain the 2018 PM projections. 

The 2002/2018 modeled scaling factors are called Relative Response Factors (RRFs) and consist 

of the ratio of modeling results of the 2018 model simulation to the 2002 model simulation. The 

two important regional haze metrics are the average visibility for the worst 20% (W20%) and 

best 20% (B20%) days from the 2000-2004 five-year Baseline. For the 2018 W20% days 

projections, EPA guidance recommends developing RRFs specific to each PM component and 

each Class I area based on the ratio of the average modeled PM species concentrations for the 

2018 to 2002 model simulations. The results of the VISTAS 2018 visibility projections 

following EPA guidance procedures are provided in Chapter 4. VISTAS has also developed 

alternative procedures for visibility projections that are discussed in Chapter 5. 

The 2018 visibility projections for the W20% days are compared against the 2018 Uniform Rate 

of Progress. The 2018 Uniform Rate of Progress is obtained by constructing a linear visibility 

Glidepath in deciview from the observed 2000-2004 Baseline for the W20% days to Natural 

Conditions in 2064. Where the linear Glidepath crosses the year 2018 is the 2018 Uniform Rate 

of Progress. States evaluate the modeled 2018 visibility projection compare to the 2018 Uniform 

Rate of Progress as one part of their evaluation to define Reasonable Progress Goals for their 

RHR SIPs. However, as noted in EPA’s RPG guidance “The glidepath is not a presumptive 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chap_1_Intro3_updated.doc 1-31 

target, and States may establish a RPG that provides for greater, lesser, or equivalent visibility 

improvement as that described by the glidepath” (EPA, 2007b). 

Figure 1-8 displays the VISTAS 2018 Base G2 visibility projections using the EPA guidance 

(EPA, 2007a), the new IMPROVE equation for converting PM concentrations to visibility 

extinction and the VISTAS 2002 Typical and 2018 BaseG2a 12 km CMAQ modeling results. 

Class I areas in the interior of the VISTAS region, including those in the Appalachian Mountains 

as well as Sipsey and Mammoth Cave, achieve the 2018 Uniform Rate of Progress by a fair 

margin. The Swanquarter and Cape Romain Class I areas on the coast of South and North 

Carolina, respectively, also achieve the 2018 Uniform Rate of Progress. The St. Marks and 

Chassahowitzka, Florida Class I areas are near the 2018 Uniform Rate of Progress, whereas 

Okefenokee Georgia and Everglades Florida fall slightly less than the 2018 Uniform Rate of 

Progress. 

For the Class I areas near the VISTAS states, the four CENRAP Class I areas in Missouri and 

Arkansas (CACR, UPBU, MING and HEGL) and the Brigantine Class I area on the coast of 

New Jersey north of the VISTAS region are projected to meet the 2018 Uniform Rate of 

Progress. The Breton Island Class I area off the southern coast of Louisiana is projected to fall 

short of the 2018 Uniform Rate of Progress. 

CMAQ 2018g2b/2002gt2a Method 1 predictions for VISTAS+ sites

0%

20%

40%

60%

80%

100%

120%

140%

160%

180%

200%

COHU1

DOSO1

GRSM1

JARI1

LIGO1

CADI1

MACA1

SHEN1

SHRO1

SIPS1

CHAS1

EVER1

OKEF1

ROMA1

SAMA1

SWAN1

BRET1

BRIG1

CACR1

HEGL1

MING1

UPBU1

Percent of target reduction achieved

CMAQ New IMPROVE Algorithm

VISTAS non-VISTAS

Figure 1-8. 2018 visibility projections expressed as a percent of achieving the 2018 URP goal for 

the 2018 BaseG2a CMAQ 12 km base case simulation using the EPA default (EPA, 2007) visibility 

projection procedures and the new IMPROVE equation. 
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F:\VISTAS Phase II\TSD\draft#4\Chap_1_Intro3_updated.doc 1-32 

The 2018 visibility projections in Figure 1-8 were based on the Base G2 emissions inventory that 

was released in July 2007 (MACTEC, 2008). Since then, several VISTAS States have updated 

their point source emissions to reflect improved knowledge of EGU emissions and controls in 

2018. Some of the VISTAS States have also specified changes to non-EGU point sources to 

reflect new information on anticipated controls and shutdowns. The new 2018 inventory is 

referred to as the 2018 Best and Final (BaF) inventory, which is also called the 2018 Base G4 

inventory. There were both increases and decreases in emissions between the 2018 Base G2 and 

Base G4 inventories with a net change of -1% and +1% for, respectively, SO2 and NOx emission 

across the VISTAS States. Figure 1-9 compares the 2018 visibility projections using the 2018 

G2a and 2018 Base G4/BaF 12 km CMAQ modeling results. 

CMAQ 2018g4a vs 2018g2b Method1 Prediction at VISTAS+ sites on W20% Days

0%

20%

40%

60%

80%

100%

120%

140%

160%

180%

200%

220%

240%

COHU1DOSO1

GRSM1

JARI1

LIGO1

CADI1

MACA1

SHEN1

SHRO1

SIPS1

CHAS1

EVER1

OKEF1

ROMA1

SAMA1

SWAN1

BRET1

BRIG1

CACR1

HEGL1

MING1

UPBU1

Percent of target reduction achieved

2018g4a 12k

2018g2b 12k

VISTAS non-VISTAS

Figure 1-9. 2018 visibility projections expressed as a percent of achieving the 2018 URP goal 

for the 2018 BaseG2a and 2018 Base G4 (BaF) CMAQ 12 km base case simulation using the 

EPA default (EPA, 2007) visibility projection procedures and the new and original (old) 

IMPROVE equation.
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F:\VISTAS Phase II\TSD\draft#4\Chap_1_Intro3_updated.doc 1-33 

1.3.14 Additional Supporting Analysis 

VISTAS perform additional supporting analysis of its modeling results and visibility projections 

to help verify the validity of the 2018 visibility projections that are discussed in Chapter 5 and 

included the following: 

• Verification of the VISTAS 2018 visibility projections using the new EPA Modeled 

Attainment Test Software (MATS). 

• Comparison of the VISTAS 2018 visibility projections with those from CENRAP, 

MRPO and MANE-VU. 

• Analysis of alternative visibility projections: 

o Use of the original IMPROVE equation. 

o Sensitivity analysis assuming Coarse Mass (CM) and CM and Soil are natural. 

o Model projections accounting for NO3 and SO4 performance issues. 

o Use of CART weights in the develop of RRFs. 

o Accounting for International Transport in the projections. 

• Analysis of observed trends in visibility across the VISTAS and nearby Class I areas for 

the worst 20 percent days, best 20 percent days and all IMPROVE sampled days. 

• Area of Influence Analysis to examine SO2 sources most likely to contribute to elevated 

SO4 concentrations at a Class I area during the worst 20 percent days. 

1.4 Known Errors in the Base G2 and G4 Modeling 

In addition to the uncertainties associated with the emissions, meteorological and air quality 

modeling and associated procedures for projecting future year visibility discussed throughout 

this document, there are several known errors in the Base G2 and Base G4 (BaF) modeling that 

are listed below most of which are discussed in more detailed in Section 2.7: 

• Errors in Point Source Location Coordinate Transformations: The SMOKE Version 2.1 

emissions modeling system used in the VISTAS modeling uses an I/O API routine for 

converting point source latitude/longitude coordinates to the Lambert Conformal 

Projection (LCP) coordinate system used by MM5/CMAQ in the VISTAS modeling. 

Although SMOKE calls the I/O API routine with the correct parameters to use the 

MM5/CMAQ LCP coordinate system, the I/O API coordinate transformation routine 

ignores the correct LCP parameters and uses its default parameters. The result is that the 

locations of point sources are off by approximately 2-3 km in the Base G2 modeling. In 

most cases this would not have an effect on the modeling as the point source would be in 

the same 36 km or 12 km grid cell, but in some cases it could cause a point source 

location to be located in an adjacent grid cell to the cell it should reside. 

 
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F:\VISTAS Phase II\TSD\draft#4\Chap_1_Intro3_updated.doc 1-34 

• Incorrect Point Source Location Parameters: As part of the continuing quality assurance 

of the Base G2 modeling, point sources were screened to see whether they were within 

100 km of the centroid of the county where they were suppose be located. A number of 

point sources were so identified and many of them were valid locations within larger 

counties. Some of the points so identified did have invalid coordinates; however these 

turned out to be small point sources with emissions of any single pollutant less than 80 

tons per year. 

• Missing Low Level Fires: During the Base G2 emissions modeling emissions from a few 

low level fires were inadvertently dropped. These dropped fire emissions were for a few 

prescribed burns in six counties in Kentucky and one county each in Tennessee and West 

Virginia. 

• Double Counting Off-Shore Marin Emissions in the Gulf of Mexico: VISTAS developed 

an off-shore marine vessel emissions inventory for the Pacific and Atlantic Oceans and 

the Gulf of Mexico. When updating the emissions from Base D to Base G, VISTAS 

received a new off-shore oil and gas production inventory for the Gulf of Mexico from 

the MMS. Previously inventories from MMS included emissions from platform oil and 

gas production and support vessels and aircraft. However, this time the MMS inventory 

also included off-shore marine vessels which resulted in the double counting of emissions 

from off-shore marine vessels in the oil and gas development zones in the Gulf of 

Mexico. 

• 2018 Gasoline RVP Error: The Summer/Winter MOBILE6 emission inputs were 

inadvertently switched for the 2018 SMOKE-MOBILE6 on-road mobile sources 

emissions modeling resulting in higher gasoline RVP in the summer than there should 

which produces higher VOC and CO emissions from on-road gasoline vehicles. To 

investigate this, VISTAS reran the SMOKE-MOBILE6 and CMAQ models for the 2018 

emissions scenario using the corrected gasoline RVP for July only and compared the 

2018 visibility projections for the original Base G2 or Base G2a simulation to the one 

with the corrected RVP in July (BaseG2b). The 2018 visibility projections for the two 

runs with the different gasoline RVP were identical. 

 
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F:\VISTAS Phase II\TSD\draft#4\Chap_2_Emissions4.doc 2-1 

2.0 DEVELOPMENT OF EMISSION INPUTS 

The base year emissions inventory for Base G2 of the VISTAS modeling is founded on revised 

2002 emissions developed by VISTAS emission inventory contractors in NEI Input Format 

(NIF) 3.0. These revised BaseG/G2 emissions were reviewed by VISTAS stakeholders and 

considered complete in March of 2007. Control assumptions, growth rates, and other inputs used 

to develop the 2018 forecasts were initially developed in 2004, many of them carried forward 

into these documented inventories. Additional growth and control modifications have since been 

identified or analyzed by VISTAS and its participating States or contractors, but for reasons of 

maintaining consistency between the various scenarios, have not been included in these 

documented files and associated runs. The development of the 2002 Base G2 inventory is 

provided in MACTEC (2008). 

Non-VISTAS state emissions for the 2002 episode year are based on inventories obtained by the 

Study Team during late 2006. Emission inventories for 2002 and 2018 for each of the other four 

RPOs (MANE-VU, MRPO, CENRAP, and WRAP) were directly acquired from the RPOs and 

either converted from their native format into structures or temporal basis consistent with 

VISTAS modeling or used directly in the SMOKE IDA formats provided (see Table 1-2 for 

versions). The exception was for MRPO’s 2018 base case, which was not available in an annual 

format, and therefore was prepared using the latest available 2002 base year data (Base K) and 

growth and control factors (Round IV) as provided by MRPO staff. 

Mexican and Canadian emissions used for 2002 are based on the latest available inventories 

obtainable by the Study Team in formats lending themselves to emissions modeling. Mexican 

emissions are based on the EPA Phase II 1999 Mexican National Emission Inventory (MNEI)1

and 2000 and 2020 (representing 2018) Canadian emissions are based on EPA releases of 

Environment Canada inventories in SMOKE IDA or preprocessed CMAQ formats2. These 

inventories were collected in the December 2006 timeframe. 

For purposes of air quality model validation, actual 2002 calendar year emissions for EGU and 

fire activity were used within the VISTAS domain, while during strategy and future year 

emission runs, “typical year” emissions for these categories were processed. Outside of the 

VISTAS RPO, 2002 emissions obtained from the non-VISTAS RPOs were used in an “as-is” 

format with no modification made for actual or typical operation. 

All emissions were converted to Inventory Data Analyzer (IDA) format and the data were 

processed for air quality modeling using Version 2.1 (except for elevated fires where Version 2.0 

was used) of the Sparse Matrix Operating Kernel Emissions (SMOKE) model. Included in these 

runs is the temporal, spatial, and speciation profiles and cross-reference data currently provided 

with the 2.0 release of the model augmented with recommended and approved emission profile 

data provided by the VISTAS stakeholders or emissions inventory contractor or obtained from 

EPA prior to initial emissions modeling. The processing was adjusted for each run to account for 

the specific air quality model (AQM) input required by CMAQ. 

 

1 http://www.epa.gov/ttn/chief/net/mexico.html 

2 http://www.epa.gov/ttn/chief/net/canada.html 
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F:\VISTAS Phase II\TSD\draft#4\Chap_2_Emissions4.doc 2-2 

2.1 Emissions Modeling Methodology 

Emissions inventory development for photochemical modeling must address several source 

categories including: (a) stationary point sources, (b) area sources, (c) on-road mobile sources, 

(d) non-road mobile sources, and (e) biogenic sources. For this analysis, inventories were 

developed for 2002 actual, 2002 typical, and 2018. 

Development of an emissions inventory customized for the VISTAS region requires a merging 

of: (a) the most recent pertinent regional inventory and (b) available high-resolution, localespecific emissions estimated by local, state, and regional agencies in the VISTAS region. Local 

air regulatory and transportation planning agencies are generally the best sources of domain 

specific activity and control factors to use in developing the base year emissions. Often, these 

local emissions data sets come from a variety of sources, frequently in different formats. 

Contacts with VISTAS’ emission inventory contractors and the RPOs were established and 

formal requests made for inventory corrections, updates and ancillary data pertinent to the 

modeling of emissions in their jurisdictions. Where feasible and consistent with project 

resources and schedule, these updated data sets were acquired and used to create day-specific 

modeling inventories specific to the VISTAS domain for the base year modeled. 

2.2 Set-up of SMOKE Over the VISTAS Domain 

SMOKE was configured to generate point, area, nonroad, highway, and biogenic source 

emissions. In addition, certain subcategories, such as fires and EGUs were maintained in separate 

source category files in order to allow maximum flexibility in producing alternate strategies. 

Settings for each of the source categories are discussed in relevant sections below. With the 

exception of biogenic and highway mobile source emissions that are generated using the, 

respectively, BEIS and MOBILE6 modules in SMOKE, pre-computed annual emissions were 

processed using the month, day, and hour specific temporal (for point sources with CEM data) 

profiles of the SMOKE model. 

To produce an emissions inventory to support annual modeling, representative time periods were 

selected and modeled. Area, nonroad, and point sources were modeled as a block of Thursday, 

Friday, Saturday, Sunday, Monday one per month (total of 60 days modeled). On-Road motor 

vehicles were represented by an entire single week for each month. This selection criterion 

allows for the representation of day-of-the-week variability in the on-road motor vehicles, and 

models a representation of the meteorological variability in each month. EGU sources which 

mapped to continuous emissions monitoring (CEM) data were processed using CEM-based 

temporal profiles. Holidays were modeled as Sundays. A list of modeled holidays is provided in 

Table 2-1. The biogenic emissions were modeled on a day specific basis (365 days). 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chap_2_Emissions4.doc 2-3 

Table 2-1. SMOKE Modeled Holidays. 

Population was used as a gridding default for all source categories when the assigned surrogate 

would cause SMOKE to drop emissions. This can be a case when the county-level emission 

inventories are prepared using surrogates other than those available for modeling purposes. 

The domain for the Phase II episode is based on the EPA’s 36-km national CMAQ domain, 

illustrated in Figure 2-1 below. 

Figure 2-1. EPA 36-km National CMAQ Domain. 

Date Julian Day Holiday Description 

January 1, 2002 2002001 New Year's Day 

March 29, 2002 2002089 Good Friday 

May 27, 2002 2002147 Memorial Day 

July 4, 2002 2002185 July 4th 

September 2, 2002 2002245 Labor Day 

November 28, 2002 2002332 Thanksgiving Thurs 

November 29, 2002 2002333 Thanksgiving Fri 

December 24, 2002 2002358 Christmas Eve 

December 25, 2002 2002359 Christmas Day 
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F:\VISTAS Phase II\TSD\draft#4\Chap_2_Emissions4.doc 2-4 

The parameters for the SMOKE runs are as follows: 

Episodes: 

2002 Calendar Base Year (Actual and Typical) 

Future Year: 

2018. 

Output Time Zone: 

Greenwich Mean Time (zone 0) 

Projection: 

Lambert Conformal with Alpha=33, Beta=45, Gamma=-97, and center at (-97,40). 

Domain: 

36 Kilometer Grid: Origin at (-2736, -2088) kilometers with 148 rows by 112 columns 

and 36-km square grid cells. 

12 Kilometer Grid: Origin at (108, -1620) kilometers with 168 rows by 177 columns and 

12-km square grid cells. 

Layer Structure: 

The CMAQ layer structure is 19 layers, with specific layer positions defined in the 

meteorology files (see Table 1-8). 

CMAQ Model Species: 

The CMAQ initial configuration is for the CB-IV chemical mechanism with PM. The 

model species in the emission input files are: CO, NO, NO2, ALD2, ETH, FORM, ISOP, 

NR, OLE, PAR, TERPB, TOL, XYL, NH3, SO2, SULF, PEC, PMFINE, PNO3, POA, 

PSO4, and PMC. 

Meteorology Data: 

Daily (25-hour). SMOKE requires the following five types of MCIP outputs: (1) Grid 

cross 2-d, (2) Grid cross 3-d, (3) Met cross 2-d, (4) Met cross 3-d, and (5), Met dot 3-d. 

These files need to match the grid projection and overlap with the emissions modeling 

region but can be larger in the horizontal directions than the modeling region shown in 

Figure 2-1. Therefore, the data files for the 36 Kilometer grid domain were at least 90 

columns by 132 rows 

Elevated Sources: 

All sources were treated by SMOKE as potentially elevated. No plume-in-grid sources 

were modeled. Where day specific and location specific wildfire data was provided by 

states and Federal Land Managers, wildfire emissions were handled as point sources. 

Producing 365 day-specific input files for all source categories places a burden on available 

computing facilities, data management systems, and would have adversely affected the modeling 

schedule. Selecting representative model days for some or all of the source categories reduces 
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F:\VISTAS Phase II\TSD\draft#4\Chap_2_Emissions4.doc 2-5 

the processing and file handling requirements to a more manageable level and in most cases does 

not compromise the accuracy of the emissions files. 

Other current or recent projects undertaken by EPA, WRAP and LADCo have used a selection 

approach for all of the source categories (except biogenics) that use a representative 

weekday/Saturday/Sunday either for each month or each season to model all of the emissions 

files. In an attempt to better represent the level of temporal and spatial detail available for each 

source category, we have developed and implemented a more detailed strategy. 

Biogenic emissions were modeled for each episode day, using the daily meteorology. Point 

sources, including CEM and elevated fire emissions were modeled for each episode day to take 

advantage of the available day-specific emissions and meteorology. Area sources, including 

non-road mobile, low level fires and dust emissions do not utilize meteorological data, and are 

temporally allocated by monthly, daily and hourly profiles. Reviewing these profiles indicate 

that maximum temporal definition can be achieved by selecting representative Thursday, Friday, 

Saturday, Sunday, and Monday profiles for each month. 

Motor vehicle emissions are influenced by meteorological variability, but the processing 

requirements for daily motor vehicle emissions were determined to be prohibitive under the 

current schedule. Rather than utilizing averaged meteorological data or pre-calculated motor 

vehicle emissions, a single week per month was selected for modeling. This week was selected 

from mid-month, to try to best represent the average temperature ranges for the month, and also 

adjusted to exclude holidays that would require atypical processing. The area source modeling 

dates were also selected from these ranges to simplify data handling procedures. 

On-Road Mobile Sources Represented by the Following Weeks: 

 January 13-19 

 February 10-16 

 March 10-16 

 April14-20 

 May 12-18 

 June 9-15 

 July 14-20 

August 11-17 

September 15-21 

October 13-19 

November 10-16 

December 15-21 

2.2.1 Processing of Point Source Emissions 

Stack parameters are often more important to the reliability of the air quality modeling results 

than the emissions rates themselves. Stack parameter data are frequently incorrect, especially in 

some of the current regional modeling inventories and careful QA is required to assure that the 

point source emissions are properly located both horizontally and vertically on the modeling 

grid. To screen for simple, but potentially serious inventory errors such as these, the study team 
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F:\VISTAS Phase II\TSD\draft#4\Chap_2_Emissions4.doc 2-6 

has modified procedures originally developed by EPA3 to quality assure, augment, and where 

necessary, revise, stack parameters to examine the accuracy of the point source emissions, as 

well as standardize procedures to identify and correct stack data errors. These procedures were 

implemented in the NIF to IDA conversion step of the inventory development. Additionally, 

SMOKE has a number of built-in QA procedures designed to catch missing or out-of-range stack 

parameters. These procedures were also invoked in the processing of the point source data. 

For the final baseline modeling, we separated the point source emissions into EGU and non-EGU 

categories. The non-EGU category was not processed using any day or hour-specific emissions 

inputs. All non-EGU point source emissions were temporally allocated to month, day, and hours 

using annual emissions and source category code (SCC) based allocation factors. These factors 

were based on the cross-reference and profile data supplied with the utilized SMOKE version 

and were supplemented with relevant data provided to the study team by VISTAS and its 

contractors. 

For EGU sources with EPA reported continuous emissions monitoring (CEM) data for 2000-

2004 or with hourly emissions provided by stakeholders, actual hourly data were used. For those 

sources where EPA CEM data are utilized, NOx, SO2, and heat input-based hour-specific 

profiles were developed and applied to NOx, SO2, and all other emissions, respectively. This 

ensured that the annual emission values provided by the EI contractor were maintained, but 

distributed using hourly to annual profiles. For sources providing hour-specific data and where 

they were approved by the State in which they operated, those data were substituted for EPA 

CEM-based emissions and distributions. 

To temporally allocate the remaining EGU point sources (those which do not report under the 

CEM program), the NOx, SO2, and heat input data were collected from the 2000-2004 CEM 

datasets, and used to develop unit-level typical temporal distributions. CEM data from 2002 were 

used to develop comparable profiles and emission distributions during the actual 2002 model 

validation runs. The hour, day of week, and monthly specific temporal profiles were used in 

conjunction with the EI supplied emissions data to calculate hourly EGU emissions by unit. 

All point sources were spatially allocated in the domain based on the stationary source 

geographic coordinates. If a point source was missing its latitude/longitude coordinates and data 

could not be found to properly site the unit within the domain, the source was placed in the 

center of its reported county. 

2.2.2 Processing of Area and Non-Road Source Emissions 

All area and non-road source emissions were temporally allocated to month, day, and hours 

using annual emissions and source category code (SCC) based allocation factors. These factors 

were based on the cross-reference and profile data supplied with the utilized SMOKE version 

and were supplemented with relevant data provided to the study team by VISTAS or its 

contractors. Area and non-road sources were spatially allocated in the domain based on SCC-

 

3 

ftp://ftp.epa.gov/EmisInventory/2002finalnei/documentation/point/augmentation_point/2002nei_qa_augmentation_r

eport0206.pdf 
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F:\VISTAS Phase II\TSD\draft#4\Chap_2_Emissions4.doc 2-7 

matched spatial allocation factor files. If an area or non-road source SCC did not have an 

existing cross-reference profile assigned to it, the county-level emissions were allocated by 

population density in the respective county. 

A county-specific crustal PM transport factor was applied to fugitive dust emission sources that 

had been identified in U.S. EPA modeling to have only a portion of its mass transportable from 

the source of the emission generation. These EPA’s studies4 indicate that 60 to 90 percent of PM 

emissions from fugitive dust sources do not reach an elevated level necessary to be transported 

or modeled in an episodic simulation. For this reason, the modeling team applied county-specific 

fugitive dust emissions transport factors to these sources to adjust PM emissions during the 

conversion of emission input files from the raw mass emissions. These adjusted PM emissions 

are reported in summary emissions tables. 

2.2.3 Processing of On-Road Mobile Source Emissions 

The MOBILE6 module of SMOKE was used to develop the base year on-road mobile source 

emissions estimates for CO, NH3 NOx, PM and VOC emissions. The MOBILE6 parameters, 

vehicle fleet descriptions, and VMT estimates were combined with gridded, episode-specific 

temperature data to calculate the gridded, temporalized emission estimates. Of note, whereas the 

on-network emissions estimates are spatially allocated based on link location and subsequently 

summed to the grid cell level, the off-network emissions estimates are spatially allocated based 

on a combination of the FHWA version 2.0 highway networks and population. For the VISTAS 

36/12 km modeling, no link based data was used. The MOBILE6 emissions factors are based on 

episode-specific temperatures predicted by the meteorological model. Further, the MOBILE6 

emissions factors model accounts for the following: 

• Hourly and daily minimum/maximum temperatures; 

• Facility speeds; 

• Locale-specific inspection/maintenance (I/M) control programs, if any; 

• Adjustments for running losses; 

• Splitting of evaporative and exhaust emissions into separate source categories; 

• VMT, fleet turnover, and changes in fuel composition and Reid vapor pressure (RVP). 

The primary input to MOBILE6 is the MOBILE shell file. The MOBILE shell contains the 

various options (e.g. type of inspection and maintenance program in effect, type of oxygenated 

fuel program in effect, alternative vehicle mix profiles, RVP of in-use fuel, operating mode) that 

direct the calculation of the MOBILE6 emissions factors. 

For the production of the MOBILE6 emission factors SMOKE was run using the hourly episodespecific meteorological data for temperature and humidity. SMOKE produces emissions factors 

for state and county groups that are selected for regional similarities and consistent MOBILE6 

option requirements (i.e. I/M programs, RVP, fuel programs). The hourly average temperature 

and humidity are calculated from the hourly temperatures in each grid cell in the state/county 

groups.

 

4 http://www.epa.gov/ttn/chief/emch/invent/index.html#dust 
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F:\VISTAS Phase II\TSD\draft#4\Chap_2_Emissions4.doc 2-8 

SMOKE was run using the daily average speed option for all states except North Carolina. The 

daily average speed was provided based on state, county, and roadway type. North Carolina 

provided hourly average speeds, also based on state, county and roadway type. 

2.2.4 Processing of Biogenic Source Emissions 

A revised version of a commonly used biogenic emissions model, the Biogenic Emissions 

Inventory System (BEIS), was developed and tested by EPA over two separate modeling 

domains/episodes. This version of the model (BEIS-3, v.0.9) contains several changes over 

BEIS-2, including the following: 

• Vegetation input data -- are now based on a 1-km Biogenic Emissions Landuse Database 

(BELD3) vegetation data base, 

• Emission factors – many updates including some recent NARSTO modifications, 

• Environmental algorithm -- includes a sunlit/shaded leaf solar radiation model. 

A series of sensitivity modeling simulations has been completed and concluded that the more 

recent BEIS-3 methodology will impact base case model ozone predictions in most parts of the 

U.S. The preliminary tests have also shown that the newer biogenic emissions do not appear to 

have a large effect on: 1) the control signal response, 2) relative reduction factors resulting from 

a projected emissions change, or 3) overall regional model performance in the eastern U.S. 

For this particular application of BEIS-3, version 0.9 as currently incorporated in the SMOKE 

processor was used. This means that: 1) soil NO emissions shall be prepared without the input of 

specific soil moisture and precipitation data and 2) MEOH emissions will not be modeled 

explicitly. 

The BELD-3 landuse data on a Lambert conformal grid at 1-km resolution have already been 

developed, are available, and were used to estimate biogenic emissions in this study. The BEIS 

model also requires as input hourly, gridded temperature and solar radiation data to estimate 

biogenic emissions, and these data were derived from the MM5 predictions. 

2.2.5 Processing of Wildfires and Prescribed Burns 

Wildfire, agricultural, and prescribed burn emissions were handled separately from the standard 

area source input files. We used day specific or monthly estimates of fire emissions from 

VISTAS, which include burn acreage and biomass loading information for the VISTAS states. 

Depending on the completeness and quality of the data received, VISTAS-specific calculations 

were made for spatial and temporal distributions of the fire emissions, rather than relying on 

standard distribution profiles. We calculated vertical distribution of the fire emissions, based on 

fire size and biomass involvement. SMOKE v2.0 can model fire plume rise when provided with 

the following variables: 

• PTOP – Top of the fire plume profile (meters above ground level) 

• PBOT – Bottom of the fire plume profile (meters above ground level) 
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F:\VISTAS Phase II\TSD\draft#4\Chap_2_Emissions4.doc 2-9 

• Lay1 – The percent of the emissions entrained in the first modeling layer 

For those elevated fires having the necessary data elements to site these files with distinct time 

and space coordinates, these variables were prepared and included in the modeling files used to 

process this emission source type. For low level fires where these discrete spatial and temporal 

data were not available, we distributed emissions in the lowest modeled vertical layer using the 

month, day of week and diurnal temporal profiles calculated from the total ASIP state fire 

distribution files. 

The WRAP Fire Emissions Joint Forum Emissions Inventory Report5 has documented an 

approach for calculating these plume descriptors. In this method, which was used in VISTAS 

modeling, the fires are assigned to one of 5 size categories, based on the total burn acreage, and 

the biomass fuel loading. These categories are then used to calculate representative hourly 

plume profiles. These profiles are then used by SMOKE 2.0 to distribute the vertical emissions 

for the fires. To successfully model fires as elevated point sources, the data included both the 

day or days on which the fire occurs, and a spatial identifier of the fire location. 

2.2.6 Windblown Dust 

PM10 and PM2.5 emissions from wind erosion of natural geogenic sources (SCCs 2730100000 

[total] and 2730100001 [dust devils]) were excluded from the resulting modeling files as these 

meteorological and episode specific categories could not be accurately reflected using the 

precalculated emission estimates and temporal profiles available at the time of the modeling. 

2.2.7 Sea Salt 

In March 2006 Version 4.51 of CMAQ was released that includes Sea Salt emissions and active 

Sea Salt chemistry. Given the number of coastal VISTAS Class I areas this could be important 

so VISTAS updated the CMAQ V4.51 with the SOAmods enhancement and it was used for the 

final Base G2 simulations. CMAQ V4.51 internally generates Sea Salt emissions using a user 

supplied OCEAN file. Thus, Sea Salt emissions were not addressed in the SMOKE processing. 

2.3 SMOKE-Specific Processing Characteristics 

The Sparse Matrix Operator Kernel Emissions (SMOKE) modeling system is an emissions 

modeling system that generates hourly gridded speciated emission inputs of mobile, nonroad 

mobile, area, point, fire and biogenic emission sources for photochemical grid models. SMOKE 

is one of the fastest emissions processing tools currently available to the air quality modeling 

community. The sparse matrix approach utilized throughout SMOKE permits both rapid and 

flexible processing of emissions data. The processing is rapid because SMOKE utilizes a series 

of matrix calculations instead of less efficient algorithms used in previous systems. The 

processing is flexible because the processing steps of temporal projection, controls, chemical 

speciation, temporal allocation, and spatial allocation have been separated into independent 

 

5 http://www.wrapair.org/forums/fejf/documents/WRAP_2002_PhII_EI_Report_20050722.pdf 
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F:\VISTAS Phase II\TSD\draft#4\Chap_2_Emissions4.doc 2-10 

operations wherever possible. The results from these steps are merged together at a final stage of 

processing. Each of these emissions processing steps is detailed below. 

2.3.1 Temporal Allocation 

VISTAS 2002 and 2018 annual emissions modeling were configured to generate point, area, 

nonroad mobile, on-road mobile, and biogenic source emissions. In addition, certain 

subcategories, such as fires and EGUs were maintained in separate source category files in order 

to allow maximum flexibility in producing alternate strategies. With the exception of biogenic 

and on-road mobile source emissions that are generated using the BEIS and MOBILE6 modules 

in SMOKE, pre-computed annual emissions were processed using the month, day, and hour 

specific temporal profiles of the SMOKE model. Area and nonroad sources were modeled as a 

block of Thursday, Friday, Saturday, Sunday, and Monday one per month (total of 60 days 

modeled). On-road motor vehicle sources were modeled for one seven-day week per month. 

Point sources and biogenics were modeled for each day of the annual period. 

VISTAS based its temporal profiles and source category cross-reference files on the EPA 

CAIR/CAMR/CAVR modeling platform with files located on EPA’s CAIR file transfer 

website6

. Modifications were made to reflect State specific profiles or updated state of 

knowledge application of these profiles. Some of these changes included the reallocation of 

North Carolina NONROAD generated emission categories to a regional set of temporal profiles 

more consistent with the operation of these source types in the State. Additionally, EGU CEMbased temporal profiles and onroad emissions modeling were prepared in manners deviating 

from EPA’s original CAIR platform. 

As noted previously, on-road mobile modeling in SMOKE was done for selected weeks (seven 

days) of each month - using these days as a “representative week” of the entire month. This 

selection allows for the representation of day-of-the-week variability in the on-road motor 

vehicles, and models a representation of the meteorological variability in each month. VISTAS 

executed sensitivity tests to examine this “representative week” methodology versus an everyday 

on-road mobile modeling method7

. VISTAS determined that the use of representative week onroad mobile emissions produced ozone and particulate matter concentrations (and thus regional 

haze) that were nearly indistinguishable from the “everyday” mobile method. VISTAS 

determined that the difference in the modeled air quality - resulting from the on-road mobile 

modeling methods - was insignificant. 

2.3.1.1 CEM-Based Temporal Profile Development and Application 

Two sets of monthly profiles were developed for processing EGU emissions with CEM data: 

1. Profiles based solely on actual 2002 CEM-based data at the state level. The 2002-only 

profiles are intended to be used by VISTAS in developing model performance evaluation 

metrics necessary for configuring air quality models in attainment demonstration analyses. 

 

6 ftp://www.airmodelingftp.com/ 

7 http://www.epa.gov/ttn/chief/conference/ei15/session9/abraczinskas.pdf 
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F:\VISTAS Phase II\TSD\draft#4\Chap_2_Emissions4.doc 2-11 

2. Profiles based on historical averages of 2000 through 2004 CEM-based data. These historical 

2000-2004 average profiles were developed and are recommended to be used to represent 

consistent “typical” operating conditions at EGUs in the VISTAS domain for the base year 

and future year emission estimates. 

Analyses conducted by the modeling team8 indicate an added benefit to the modeling results with 

the application of CEM-based day-of-week and diurnal profiles, in addition to the monthly 

profiles for each state. For the majority of EGU units processed in the VISTAS domain, unit 

specific monthly, weekly, and diurnal profiles were developed and applied using historical CEM 

averages of heat input and emissions and as further described below. As an additional part of this 

analysis, specific day-of-week (Monday, Tuesday, Wednesday, etc.) and diurnal profiles were 

also developed for each month and State to better represent operating conditions at units within a 

State. The State specific day of week and diurnal profiles were developed from averages of 

CEM-based emissions and heat input activity occurring on that day-of-week or during that hourof-day across all units within that State. These profiles were then applied to units were CEM 

specific matches could not be made to VISTAS emission inventories. 

Data Obtained

Five years (2000 through 2004) of hourly CEM information from EPA=s CAMD website were 

obtained for each unit in the VISTAS states9. The “Prepackaged Data” option allows the 

download of files containing emissions data for a specific state, quarter or month, and year. Each 

prepackaged data file is in .csv (comma delimited) format and contains the following fields: 

State, Facility Name, Facility ID (ORISPL), Unit ID, Date, Hour, SO2 Emissions (lbs), CO2

Emissions (tons), NOx Emissions Rate (lb/mmBtu), NOx Emissions (lbs), Heat Input (mmBtu), 

Operating Time (hours), Gross Load (MW), and Steam Load (1000 lb/hr). For this analysis, we 

obtained the prepackaged monthly unit-level hourly emissions data by state and year. Using 

these data, we reformatted the files and quality assured for applicability to this analysis. 

File Contents

The reformatted files were prepared as identified in Table 2-2. 

Quality Control / Quality Assurance

Each file was reviewed to determine if NOx, SO2 and heat input values were represented for 

each hour of every day for each unit in the obtained data. Zero values were considered to be 

valid if operating time identifiers indicated no operation during that hour (e.g., data value of zero 

but operating hours greater than zero). 

Using the measurement flags and field values in the reformatted files, numerous spot checks 

were made of anomalous or missing variable data to ensure that data corruption was not 

impacting the statistical analyses. Additionally, each year’s hourly total of NOx, SO2, and heat 

input (per state) were summed and compared to EPA annual summaries of the same data 

elements. 

 

8 http://www.epa.gov/ttn/chief/conference/ei14/session11/stella.pdf 

9 http://cfpub.epa.gov/gdm/index.cfm?fuseaction=prepackaged.select
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F:\VISTAS Phase II\TSD\draft#4\Chap_2_Emissions4.doc 2-12 

When there were facilities or units with no emissions data or unit characteristics, we verified that 

these sources are not required to report emissions data or had not yet reported emissions data to 

EPA. In some cases, certain months or quarters of the year were blank for individual units or 

facilities and using EPA data caveat reports, we verified these units were not in operation during 

those times. 

Inventory Matching

Prior to the development of the unit-specific SO2, NOx, and heat input ratios for each hour, the 

step of matching CEM units to the VISTAS 2002 modeling inventory started. Because naming 

convention and facility or unit numbering can be unique at the Federal, State, local, or facility 

level, the step of matching existing units from an emissions inventory to the CEM data base 

proved to be more complicated than anticipated. 

The VISTAS EGU emission inventory accounted for approximately 3.7 million tons of SO2 and 

1.5 million tons of NOx in calendar year 2002. There were 861 units reporting to the CEM 

database in 2002 for the ten VISTAS States. The primary objective of the inventory matching 

steps was to account for as many units and tons as possible allowing for the unit-specific 

application of hourly temporal distribution profiles. 

Table 2-2. CEM data file format.

Column Description 

State State in which the facility is located. 

Facility Name The name given by the owners and operators to a facility. 

Facility ID (ORISPL) The unique six-digit facility identification number, also called an ORISPL, assigned by the 

Energy Information Administration, a component of the Department of Energy. 

Unit ID 

Each unit at a facility has a unique identification number. It is alphanumeric and may be from 

one to six characters in length. For utility units and other units that generate energy for sale, 

the unit ID used for Part 75 reporting is the same unit ID that appears in the National 

Allowance Database (NADB) (for Acid Rain Program units) or in the State's allowance 

allocation list. 

Day Day on which a unit was operating. 

Hour Hour on which a unit was operating. 

Operating Hours Percent of hour in which a unit was operating. 

Gross Load (MW) Gross load is the output of the unit as measured in megawatts. 

Steam Load (1000 lb/hr) Steam load is the output of the unit as measured in 1000 lb/hr of steam. 

SO2 Mass (lbs) SO2 released for the hour in pounds. 

SO2 Mass Measurement Flag Indicates whether the value for SO2 mass was measured or derived due to missing data. 

SO2 Rate (lbs/mmBtu) SO2 emissions rate in pounds per million British thermal units (lbs/mmBtu). 

SO2 Rate Measurement Flag Indicates whether the value for SO2 rate was measured or derived due to missing data. 

NOx Rate (lb/mmBtu) NOx emissions rate in pounds per million British thermal units (lbs/mmBtu). 

NOx Rate Measurement Flag Indicates whether the value for NOx rate was measured or derived due to missing data. 

NOx Mass (lbs) NOx released for the hour in pounds. 

NOx Mass Measurement Flag Indicates whether the value for NOx mass was measured or derived due to missing data. 

CO2 Mass (lbs) CO2 released for the hour in pounds. 

CO2 Mass Measurement Flag Indicates whether the value for CO2 mass was measured or derived due to missing data. 

CO2 Rate (lbs/mmBtu) CO2 emissions rate in pounds per million British thermal units (lbs/mmBtu). 

CO2 Rate Measurement Flag Indicates whether the value for CO2 rate was measured or derived due to missing data. 

Heat Input (mmBtu) Heat per hour as calculated by multiplying the quantity of fuel by the fuel's heat content. 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chap_2_Emissions4.doc 2-13 

Under the direction of VISTAS, emissions inventory contract staff prepared comparisons of the 

VISTAS 2002 emission inventory of EGU sources to that of CEM-based emissions, heat input, 

and operating characteristics. For each unit identified as an EGU source in the VISTAS 

inventory, an attempt was made to match it to a CEM unit and associated data. 

Automated facility (ORIS) and unit identification was made for a majority of units who 

maintained the same numbering and nomenclature between the two data sets. This first 

computerized step captured the majority of emissions by matching some of the largest units in 

the VISTAS domain. The remaining steps were followed in order to match the outstanding 

facilities and emissions as reported by VISTAS States in the 2002 emission inventory. 

Inventory contractors developed county-level reports of the remaining unmatched facilities and 

units from the VISTAS inventory and made comparisons of annual emissions of SO2 and NOx 

to the CEM-based SO2 and NOx for sources also identified within the same county. This step of 

the matching process allowed an incremental amount of emissions and units to be accounted for 

and assigned unit-specific profiles for model performance evaluation. 

Finally, remaining VISTAS inventory and CEM sources were manually compared to each other 

in an effort to determine if reporting errors in State or county codes or facility or unit 

identification codes accounted for this reminder of unmatched sources. These manual matches 

were confirmed or revised with VISTAS State and stakeholder participation and input. With this 

step, a few sources were identified to have facility identification changes or misreported county 

codes preventing automated matching from occurring and corrected for the final application of 

factors. 

Once all methods of comparison were exhausted, the remaining unmatched VISTAS emission 

inventory of EGU sources was excluded from the unit-specific profile assignment steps and was 

allocated more generalized facility or State temporal profiles as described in the following 

section. 

This inventory comparison process allowed for the match of over 650 of the 861 CEM identified 

units (76%) to the VISTAS EGU emission inventory for 2002. More importantly, however, was 

the match of 99.95 percent of the SO2 emissions and over 99.4 percent of the NOx emissions 

from these sources in the VISTAS domain. 

Profile Calculations

Two sets of profile types have been developed for modeling EGU emissions within the VISTAS 

domain. The first set are to be applied to individual units able to be matched to CEM data, the 

second are to be applied to EGU sources within the VISTAS domain where CEM-based matches 

could not be identified. 

The first set of temporal profiles have been developed for specific hour-of-date periods based on 

historical actual 2002 or average NOx, SO2, and heat input data for sources reporting under 

EPA’s CEM program between 2000 and 2004. These profiles are based on the actual or 

statistical average of the CEM data variables (NOx, SO2, and heat input) for each hour-of-date 

(e.g., Hour 12 of March 3) during the year. In the typical profile calculation, variables are 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chap_2_Emissions4.doc 2-14 

calculated for each hour when the operating time of the CEM is greater than 0 (e.g., the unit is in 

operation during that hour). In the case of 2002-only calculations, all reported NOx, SO2, and 

heat input data were used in the averaging, including those identified as non-operating hours. 

This allowed for the best representation of actual 2002 conditions for the expected use of these 

profiles for model validation studies. 

In the second set of profiles, NOx, SO2, and heat input values were averaged over each unit to 

allow for the calculation of State level monthly, day-of-week, and diurnal profiles for VISTAS 

States. 

For the 2000-2004 averaging period, representation of typical operating conditions was desired, 

so in the averaging calculation only valid operating hour NOx, SO2, and heat input values were 

used. This prevented the introduction of equipment shutdown because of power outages, control 

installation, or planned maintenance into the temporal profile calculation. 

Actual 2002 Profiles

Through the EPA’s Clean Air Market’s Data and Maps website, quarterly unit-level hourly 

emissions data by State and calendar year 2002 were obtained for purposes of developing 

temporal allocation factors applicable to EGU sources within the VISTAS domain. Key elements 

in these data sets include the State where the unit is located, facility name, facility identification 

(ORISPL) code (assigned by the Department of Energy at the Energy Information 

Administration), unit identification code, date of record, hour of record, SO2, CO2, and NOx 

mass (in lbs per hour), heat input (million British thermal units [MMBtu]), and NOx emission 

rate (lbs/MMBtu). 

SO2 and NOx mass and heat input values were summed for each unit to an annual level to allow 

for the calculation of an hour of date-to-annual ratio estimation. Equation 2-1 provides this 

calculation for heat input. Table 2-2 provides an example result of the ratio calculation. 

Equation (2-1) = ∑

1

31

, , , /

Jan

Dec

hiratio hr date hihr date hi

 where hi = heat input (MMBtu) 

Since it was assumed that all sources in the VISTAS EGU inventory would not be matched to 

individual CEM-based units, the same calculations were performed for each State so that a 

hierarchical application of ratios (unit first, State second) could be assigned as necessary. Table 

2-3 shows example ratios calculated for each month by State. Table 2-4 reflects an example of 

the State-month-day of week ratio calculation and Table 2-5 shows a State-month-diurnal ratio 

calculation example. Each of these ratios were calculated for each State in the VISTAS domain 

and used in instances where CEM unit matches could not be made to the VISTAS base year 

emissions inventory. 

Three parameter values (SO2 mass, NOx mass, heat input) were calculated at each aggregation 

as NOx and SO2 emissions vary due to fuel blend, sulfur content, or seasonal control and are not 

necessarily representative of the other variables’ seasonal, daily, or even hourly variation. As 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chap_2_Emissions4.doc 2-15 

seen in Figure 2-2, when viewed on a VISTAS-domain total, the monthly variation in relative 

distribution of SO2, NOx, and heat input differs enough to justify calculating each parameter 

value set of temporal profiles with CEM data. 

Table 2-3. Application of Calculated Ratios for Actual 2002 by Unit. 

Table 2-4. Application of Calculated Ratios for Actual 2002 by Example State and Month. 

Actual Reported Values [2002] Calculated Ratios 

ORISPL UnitID Date Hour SO2 Mass 

NOx 

Mass 

Heat 

Input SO2 NOx 

Heat 

Input 

3 2 01-01-2002 0 15.563 10.294 13.3 1.417E-06 1.915E-06 1.372E-06 

3 2 01-01-2002 1 14.977 8.338 12.5 1.364E-06 1.551E-06 1.289E-06 

3 2 01-01-2002 2 14.93 9.286 12.6 1.360E-06 1.728E-06 1.300E-06 

3 2 01-01-2002 3 14.774 9.677 12.8 1.346E-06 1.800E-06 1.320E-06 

... ... ... ... ... ... ... ... ... ... 

3 2 07-01-2002 0 1084.017 717.467 995.1 9.873E-05 1.335E-04 1.026E-04 

3 2 07-01-2002 1 1102.47 750.04 1012.2 1.004E-04 1.395E-04 1.044E-04 

3 2 07-01-2002 2 1109.41 768.55 1016.6 1.010E-04 1.430E-04 1.049E-04 

3 2 07-01-2002 3 1102.598 772.614 1012.6 1.004E-04 1.437E-04 1.044E-04 

3 2 07-01-2002 4 1087.909 736.967 998.6 9.909E-05 1.371E-04 1.030E-04 

3 2 07-01-2002 5 1099.375 731.888 1009.5 1.001E-04 1.362E-04 1.041E-04 

3 2 07-01-2002 6 1127.007 693.779 1026.3 1.026E-04 1.291E-04 1.059E-04 

3 2 07-01-2002 7 1203.814 644.008 1114.2 1.096E-04 1.198E-04 1.149E-04 

... ... ... ... ... ... ... ... ... ... 

3 2 12-31-2002 21 712.26 503.505 835 6.487E-05 9.367E-05 8.612E-05 

3 2 12-31-2002 22 716.983 587.419 850.1 6.530E-05 1.093E-04 8.768E-05 

3 2 12-31-2002 23 521.311 430.787 647.8 4.748E-05 8.014E-05 6.681E-05 

3954 3 

Annual 

Sum 10979533.36 5375215.80 9695608.12 1.000 1.000 1.000 

Actual Reported Values [2002] Calculated Ratios

State Month SO2 Mass NOx Mass Heat Input SO2 NOx Heat Input 

FL Jan 67,755,539 42,004,513 113,531,981 0.0726 0.0813 0.0733 

FL Feb 56,516,278 34,145,451 91,969,840 0.0605 0.0661 0.0594 

FL Mar 69,997,283 39,244,669 107,685,763 0.0750 0.0759 0.0695 

FL Apr 73,678,638 40,824,242 118,170,997 0.0789 0.0790 0.0763 

FL May 88,889,603 48,974,695 142,351,045 0.0952 0.0948 0.0919 

FL Jun 79,736,153 44,027,147 138,648,667 0.0854 0.0852 0.0895 

FL Jul 94,401,559 50,007,339 157,075,598 0.1011 0.0968 0.1014 

FL Aug 93,041,423 50,077,048 160,601,359 0.0996 0.0969 0.1037 

FL Sep 93,349,234 49,183,990 155,433,110 0.1000 0.0952 0.1003 

FL Oct 84,214,449 46,837,495 146,347,289 0.0902 0.0906 0.0945 

FL Nov 60,374,969 33,098,684 105,854,682 0.0647 0.0641 0.0683 

FL Dec 71,853,245 38,331,463 111,702,695 0.0769 0.0742 0.0721 

FL Total 933,808,373 516,756,735 1,549,373,024 1.0000 1.0000 1.0000 
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F:\VISTAS Phase II\TSD\draft#4\Chap_2_Emissions4.doc 2-16 

Table 2-5. Application of Calculated Ratios for Actual 2002 by Example State and Month and 

Day of Week. 

Table 2-6. Application of Calculated Ratios for Actual 2002 by Example State and Month and 

Hour of Day. 

Actual Reported Values [2002] Calculated Ratios 

State Month 

Day of 

Week SO2 Mass NOx Mass Heat Input SO2 NOx Heat Input

GA Mar Sun 13,057,959 4,005,097 10,089,522 0.1467 0.1437 0.1458 

GA Mar Mon 11,937,355 3,841,172 9,564,295 0.1341 0.1378 0.1382 

GA Mar Tue 11,860,749 3,766,317 9,351,652 0.1332 0.1351 0.1352 

GA Mar Wed 12,020,458 3,764,653 9,232,574 0.1350 0.1351 0.1334 

GA Mar Thu 11,560,778 3,677,100 9,056,011 0.1299 0.1319 0.1309 

GA Mar Fri 14,572,757 4,616,042 11,368,579 0.1637 0.1656 0.1643 

GA Mar Sat 14,005,730 4,197,929 10,522,180 0.1573 0.1506 0.1521 

GA Mar Total 89,015,786 27,868,311 69,184,812 1.0000 1.0000 1.0000 

Actual Reported Values [2002] Calculated Ratios 

State Month Hour SO2 Mass NOx Mass Heat Input SO2 NOx 

Heat 

Input 

SC Dec 0 1,356,270 556,321 1,309,476 0.0399 0.0399 0.0398 

SC Dec 1 1,332,499 540,268 1,279,485 0.0392 0.0387 0.0389 

SC Dec 2 1,324,618 536,330 1,275,732 0.0389 0.0384 0.0388 

SC Dec 3 1,330,924 538,908 1,284,514 0.0391 0.0386 0.0391 

SC Dec 4 1,335,158 545,819 1,296,880 0.0392 0.0391 0.0394 

SC Dec 5 1,385,906 565,695 1,340,759 0.0407 0.0405 0.0408 

SC Dec 6 1,436,829 586,536 1,387,329 0.0422 0.0420 0.0422 

SC Dec 7 1,488,961 611,648 1,440,753 0.0438 0.0438 0.0438 

SC Dec 8 1,491,509 613,176 1,444,956 0.0438 0.0440 0.0440 

SC Dec 9 1,501,425 618,516 1,447,916 0.0441 0.0443 0.0440 

SC Dec 10 1,484,685 610,879 1,431,441 0.0436 0.0438 0.0435 

SC Dec 11 1,459,697 593,638 1,395,938 0.0429 0.0426 0.0425 

SC Dec 12 1,423,246 578,669 1,365,957 0.0418 0.0415 0.0415 

SC Dec 13 1,391,851 570,939 1,345,091 0.0409 0.0409 0.0409 

SC Dec 14 1,352,161 557,078 1,319,068 0.0397 0.0399 0.0401 

SC Dec 15 1,344,643 551,497 1,312,670 0.0395 0.0395 0.0399 

SC Dec 16 1,369,024 559,569 1,333,589 0.0402 0.0401 0.0406 

SC Dec 17 1,449,587 595,765 1,398,917 0.0426 0.0427 0.0426 

SC Dec 18 1,493,742 621,423 1,438,833 0.0439 0.0445 0.0438 

SC Dec 19 1,473,502 611,050 1,427,712 0.0433 0.0438 0.0434 

SC Dec 20 1,479,504 608,223 1,424,664 0.0435 0.0436 0.0433 

SC Dec 21 1,475,680 608,049 1,421,202 0.0434 0.0436 0.0432 

SC Dec 22 1,450,119 597,906 1,401,208 0.0426 0.0429 0.0426 

SC Dec 23 1,391,087 573,310 1,351,539 0.0409 0.0411 0.0411 

SC Dec Daily 34,022,628 13,951,210 32,875,627 1.0000 1.0000 1.0000 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chap_2_Emissions4.doc 2-17 

0.060

0.065

0.070

0.075

0.080

0.085

0.090

0.095

0.100

0.105

0.110

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Relative Distribution

Heat Input NOx SO2

Figure 2-2. Monthly variation in 2002 of CEM reported heat input, NOx mass, and SO2 mass 

for VISTAS domain. 

When viewed on a State by State basis, the differences in monthly variation are even more 

pronounced as individual facilities within each State may be affected during any calendar year by 

extreme temperature variation, shutdowns, or regular maintenance or installation of equipment. 

As an example, Figure 2-3 represents CEM data from the State of Mississippi during calendar 

year 2002 and reveals that SO2 emissions increase throughout the year, NOx emissions stay 

relatively high during the summer months, and heat input peaks during the month of July. 

Although Figures 2-2 and 2-3 are roughly comparable in shape and monthly distribution, the 

relative distribution of these values is quite different. In Mississippi’s case, close to thirteen 

percent of the State’s CEM-based heat input occurs in July. This compares to the VISTAS 

average of just over ten percent of CEM-based heat input in July. 

Finally, when these data are reviewed at a unit level, the differences become incrementally more 

distinct due to the unique nature of individual facilities, their operating schedules, pollution 

regulation, fuel characteristics, and applied technologies. For example, a facility that is 

complying with summertime NOx regulation may have selective catalytic reduction (SCR) 

installed on its boiler(s) which in practice may only be run during ozone season months. During 

this period of time, heat input and SO2 emissions may remain consistent with State or regional 

monthly profiles, but the NOx emissions may drop significantly relative to the rest of the year. 

Figure 2-4 represents an extreme unit-specific case for monthly differences from State or 

regional temporal allocation. The unit presented is a Mississippi baseload coal-fired boiler which 

in 2002 emitted over 4,000 tons of NOx and over 11,000 tons of SO2. This unit would typically 

run at consistent levels during the entire period, but due to a planned maintenance outage was not 

in operation in late January through the middle of April in 2002. Given the unique operation of 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chap_2_Emissions4.doc 2-18 

this boiler during this year, the use of a regional or even State-level monthly temporal 

distribution would introduce significant inaccuracy to air quality modeling in the immediate or 

downwind area associated with this facility. While this may not be significant at great distance 

downwind of the source or for annual concentration estimates, more locally, and especially over 

shorter time scales (daily or weekly), such simplifications would have a noticeable effect on air 

quality model predictions. 

0.020

0.040

0.060

0.080

0.100

0.120

0.140

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Relative Distribution

Heat Input NOx SO2

Figure 2-3. Monthly variation in 2002 of CEM reported heat input, NOx mass, and SO2 mass 

for Mississippi. 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chap_2_Emissions4.doc 2-19 

0.000

0.020

0.040

0.060

0.080

0.100

0.120

0.140

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Relative Distribution

Heat Input NOx SO2

Figure 2-4. Monthly variation in 2002 of CEM reported heat input, NOx mass, and SO2 mass 

for specific baseload coal-fired unit in Mississippi with planned outage in late January through 

mid April. 

Thus, while improving the representativeness of unit-specific monthly temporal profiles is 

desirable, providing day and hour-specific values are clearly better. For this reason, during the 

model performance evaluation process in the VISTAS modeling, hour-specific temporal ratios 

were developed for every CEM reporting unit in the VISTAS domain. These ratios allowed for 

the hour-by-hour accounting of emissions released at each unit at each facility within the 

VISTAS domain that reported output under the CEM guidelines. 

Figure 2-5 represents the actual daily distribution of SO2 and NOx emissions and heat input 

from the Mississippi baseload unit from the above example. As can been seen in this figure, not 

only is the planned January through April outage represented correctly, there are significant 

peaks and valleys throughout the calendar year which could not be accurately represented with 

the application of average monthly, day-of-week, or hourly distribution factors. In reality, only 

the actual operating characteristics of this unit could capture the differences from hour to hour 

which are potentially quite important in terms of correctly modeling the impact of the source on 

downwind oxidant and fine particulate concentrations10. 

 

10 http://www.epa.gov/ttn/chief/conference/ei14/session11/stella.pdf 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chap_2_Emissions4.doc 2-20 

0

10

20

30

40

50

60

70

01-Jan-02

01-Feb-02

01-Mar-02

01-Apr-02

01-May-02

01-Jun-02

01-Jul-02

01-Aug-02

01-Sep-02

01-Oct-02

01-Nov-02

01-Dec-02

31-Dec-02

Emissions (Tons)

0

20,000

40,000

60,000

80,000

100,000

120,000

140,000

Heat Input (MMBtu)

SO2

NOx

Heat Input

Figure 2-5. Actual daily unit-specific 2002 SO2 (tons), NOx, (tons), and heat input (MMBtu) 

distribution from CEM data. 

Typical EGU Profiles

Hour of day of month specific temporal profiles were developed by calculating the arithmetic 

mean of each unit’s NOx, SO2, and heat input by specific hour of day per month (e.g., Hour 21 

of Wednesdays in July) from the data obtained from 2000 through 2004. In order to accomplish 

this calculation, each record of CEM data was first assigned a day of week. This assignment was 

based on the actual CEM’s date of record and day of week of that record. An example of this 

assignment is shown in Table 2-7. 

Table 2-7. Example Day-of-Week per Month Assignment. 

Once days of week were assigned to each record in the CEM data base, the arithmetic mean of 

each unit’s NOx, SO2, and heat input were calculated for the ORISPL-UNITID-MONTH-DAY 

OF WEEK-HOUR combination. Only records where the CEMs were operating for more than 

half the recorded hour (OPTIME > 0.5) were used in the averaging calculation. An example of 

the averaged results can be seen in Table 2-8. 

Date Day of Week 

08/01/02 Thu 

08/02/02 Fri 

08/03/02 Sat 

08/04/02 Sun 

08/05/02 Mon 

08/06/02 Tue 

08/07/02 Wed 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chap_2_Emissions4.doc 2-21 

Table 2-8. Arithmetic Mean of CEM-based Variables for Temporal Profile Calculation. 

These values were then applied to each unit and hour based on the 2002 calendar to match the 

meteorological data used in the emissions processing. An example of this application can be seen 

in Table 2-9. The date specific hourly averages were then summed to a unit summer (May – 

Sept) and winter months total and ratios were developed based on each daily hour’s average 

value divided by the average sum total depending on the season of the day. This permitted the 

appropriate allocation of summertime NOx (as forecasted by IPM) when summer control only 

was predicted. Using the annual average ratios instead of the seasonal distributions would 

produce summertime emissions different than what was output from the model. 

Calculated Average Values 

[2000 – 2004] 

State Facility ORISPL UnitID Month

Day of 

Week Hour

SO2 

Mass 

NOx 

Mass 

Heat 

Input 

WV Mount Storm Power Station 3954 3 1 Tue 0 406.0526 3384.074 5196.11 

WV Mount Storm Power Station 3954 3 1 Tue 1 389.6474 3287.845 5103.06 

WV Mount Storm Power Station 3954 3 1 Tue 2 395.2737 3342.848 5175.95 

... ... ... ... ... ... ... ... ... ... 

WV Mount Storm Power Station 3954 3 7 Mon 4 524.7864 2505.9391 4654.34 

WV Mount Storm Power Station 3954 3 7 Mon 5 690.5636 2602.9887 4795.64 

WV Mount Storm Power Station 3954 3 7 Mon 6 912.4227 2572.0275 4727.08 

WV Mount Storm Power Station 3954 3 7 Mon 7 1060.3 2664.8686 4914.25 

WV Mount Storm Power Station 3954 3 7 Mon 8 850.2364 2678.231 5029.58 

WV Mount Storm Power Station 3954 3 7 Mon 9 415.3455 2716.8 5042.55 

WV Mount Storm Power Station 3954 3 7 Mon 10 408.8591 2876.5008 5123.71 

WV Mount Storm Power Station 3954 3 7 Mon 11 371.9909 2776.0361 5147.85 

WV Mount Storm Power Station 3954 3 7 Mon 12 327.2045 2785.5325 5129.66 

WV Mount Storm Power Station 3954 3 7 Mon 13 316.0364 2826.901 5172.29 

WV Mount Storm Power Station 3954 3 7 Mon 14 317.1136 2816.1328 5146.07 

WV Mount Storm Power Station 3954 3 7 Mon 15 329.6455 2789.0962 5121.75 

WV Mount Storm Power Station 3954 3 7 Mon 16 332.7773 2818.5379 5147.05 

... ... ... ... ... ... ... ... ... ... 

WV Mount Storm Power Station 3954 3 12 Tue 21 806.7 3432.001 5375.49 

WV Mount Storm Power Station 3954 3 12 Tue 22 806.5778 3447.709 5377.68 

WV Mount Storm Power Station 3954 3 12 Tue 23 795.4667 3419.069 5359.43 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chap_2_Emissions4.doc 2-22 

Table 2-9. Application of Calculated Ratios to Day of Year by Unit. 

Equation 2-2 reflects this calculation for heat input for a summer hour. Ratios were calculated 

for NOx, SO2, and heat input values. These ratios were then applied to each unit’s seasonal 

(summer or winter) emission value for NOx, SO2, and all other pollutants, respectively. 

Equation (2-2) = ∑

1

30

, , , , , /

May

Sep

hiratio hr date sum hihr date sum hi

 where hi = heat input (MMBtu) 

The actual hour-of-day-of-month averages calculated from the CEM data were not used directly 

as emissions for that hour, but were used only in the calculation of the ratios to be applied to a 

pre-calculated seasonal (summer or winter) emission value. This allowed for the retention of 

emission estimates calculated using means other than CEM data, if a State or local agency found 

them to be more appropriate or if it were derived by other means (e.g., IPM) but an improved 

distribution of emissions using CEM-based ratios. 

As in the actual 2002 profiles calculations, these same calculations were additionally performed 

for each State so that a hierarchical application of ratios (unit first, State second) could be 

assigned as necessary. Instead of having variables at the unit level, however, State level values 

were used. These State value calculations were based on the sum of the unit-level variable 

averages to the level of aggregation required by the calculation (e.g., State-month. State-monthday-of-week, or State-month-hour). Table 2-10 shows example ratios calculated for each month 

by State. Table 2-11 reflects an example of the State-month-day of week ratio calculation and 

Table 2-12 shows a State-month-diurnal ratio calculation example. Each of these ratios were 

Calculated Average Values 

[2000 – 2004] Calculated Ratios 

ORISPL UnitID Date 

Day of 

Week Hour 

SO2 

Mass NOx Mass Heat Input SO2 NOx Heat Input 

3797 4 09/30/02 Mon 19 2591.95 381.78 1527.26 2.899E-04 2.396E-04 2.863E-04 

3797 4 09/30/02 Mon 20 2596.81 379.88 1525.60 2.904E-04 2.385E-04 2.860E-04 

3797 4 09/30/02 Mon 21 2569.03 370.50 1506.74 2.873E-04 2.326E-04 2.824E-04 

3797 4 09/30/02 Mon 22 2547.62 367.24 1498.66 2.849E-04 2.305E-04 2.809E-04 

3797 4 09/30/02 Mon 23 2483.88 360.66 1465.68 2.778E-04 2.264E-04 2.747E-04 

3797 4 10/01/02 Tue 0 1968.94 478.47 1170.76 1.587E-04 1.517E-04 1.604E-04 

3797 4 10/01/02 Tue 1 1942.47 480.28 1160.68 1.565E-04 1.522E-04 1.590E-04 

3797 4 10/01/02 Tue 2 1858.54 462.44 1122.29 1.498E-04 1.466E-04 1.537E-04 

3797 4 10/01/02 Tue 3 1988.43 486.07 1187.56 1.602E-04 1.541E-04 1.627E-04 

3797 4 10/01/02 Tue 4 2125.96 528.59 1263.00 1.713E-04 1.676E-04 1.730E-04 

3797 4 10/01/02 Tue 5 2255.22 562.18 1325.40 1.818E-04 1.782E-04 1.815E-04 

3797 4 10/01/02 Tue 6 2267.27 558.77 1337.81 1.827E-04 1.771E-04 1.832E-04 

3797 4 10/01/02 Tue 7 2313.00 579.94 1370.73 1.864E-04 1.838E-04 1.878E-04 

3797 4 Summer 

 

8941480.78 

 

1593123.80 

 

5334723.17 

3797 4 Winter 12408352.17 

 

3154758.40 

 

7300596.69 

3797 4 

Annual 

Sum 21349832.95 4747882.21 12635319.86 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chap_2_Emissions4.doc 2-23 

calculated for each State in the VISTAS domain and used in instances where CEM unit matches 

could not be made to the VISTAS base year emissions inventory. 

Again, three parameter values (SO2 mass, NOx mass, heat input) were calculated at each 

aggregation as NOx and SO2 emissions vary due to fuel blend, sulfur content, or seasonal 

control and are not necessarily representative of the other variables’ seasonal, daily, or even 

hourly variation. 

Table 2-10. Application of Calculated Ratios for Typical Operation by Example State and Month. 

Table 2-11. Application of Calculated Ratios for Typical Operation by Example State and Month 

and Day of Week. 

 

Calculated Average Values [2000 – 2004] Calculated Ratios

State Month SO2 Mass NOx Mass Heat Input SO2 NOx Heat Input 

FL Jan 116,011,253 62,989,958 198,751,048 0.0858 0.0875 0.0831 

FL Feb 93,958,786 50,831,818 164,949,702 0.0695 0.0706 0.0690 

FL Mar 111,505,553 60,285,972 196,705,697 0.0824 0.0838 0.0822 

FL Apr 107,015,438 59,071,792 195,338,204 0.0791 0.0821 0.0817 

FL May 118,589,361 61,811,604 207,803,996 0.0877 0.0859 0.0869 

FL Jun 116,068,987 59,801,640 202,716,214 0.0858 0.0831 0.0848 

FL Jul 123,868,749 62,500,169 212,478,437 0.0916 0.0868 0.0888 

FL Aug 125,384,940 64,572,843 214,637,218 0.0927 0.0897 0.0897 

FL Sep 113,080,789 59,913,723 206,712,956 0.0836 0.0832 0.0864 

FL Oct 109,960,828 61,551,310 206,924,170 0.0813 0.0855 0.0865 

FL Nov 101,781,383 55,861,718 186,984,665 0.0752 0.0776 0.0782 

FL Dec 115,588,740 60,566,444 197,592,133 0.0854 0.0841 0.0826 

FL Total 1,352,814,807 719,758,990 2,391,594,439 1.0000 1.0000 1.0000 

Calculated Average Values [2000 – 

2004] Calculated Ratios 

State Month 

Day of 

Week SO2 Mass NOx Mass Heat Input SO2 NOx Heat Input

GA Mar Sun 15,495,089 4,641,883 12,167,666 0.1455 0.1403 0.1319 

GA Mar Mon 14,334,901 4,513,847 13,362,335 0.1346 0.1365 0.1449 

GA Mar Tue 14,420,895 4,532,750 13,463,600 0.1354 0.1370 0.1460 

GA Mar Wed 14,170,345 4,489,531 13,057,182 0.1331 0.1357 0.1416 

GA Mar Thu 14,004,649 4,446,853 12,249,119 0.1315 0.1344 0.1328 

GA Mar Fri 17,177,952 5,357,920 14,842,639 0.1613 0.1620 0.1609 

GA Mar Sat 16,881,455 5,096,281 13,100,433 0.1585 0.1541 0.1420 

GA Mar Total 106,485,284 33,079,065 92,242,973 1.0000 1.0000 1.0000 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chap_2_Emissions4.doc 2-24 

Table 2-12. Application of Calculated Ratios for Typical Operation by Example State and Month 

and Hour of Day. 

Application of Factors

VISTAS chose to prepare its air quality modeling inventories with Version 2.1 of the Sparse 

Matrix Operating Kernel Emissions (SMOKE) model. For this reason, all emissions were 

required to be converted to SMOKE’s data formats. In particular, because hour specific temporal 

profiles for each day of a year are not accepted directly by the model, it was necessary to develop 

a set of hourly emissions inputs to circumvent this limitation. These were generated in the EMS 

PTHOUR format as described in SMOKE input file documentation11. 

The CEM format for individual hour-specific data files as available in SMOKE was not utilized 

for VISTAS emissions processing as the emissions allowable by hour would have been limited to 

NOx, SO2, and CO2. If this file format and optional run configuration were exercised, the NOx, 

SO2, and CO2 emissions processed by the model would have been accurate for CEM reported 

emissions, but the remaining pollutants coupled with each CEM unit would have received the 

 

11 University of North Carolina at Chapel Hill, Sparse Matrix Operator Kernel Emissions (SMOKE) Modeling 

System, http://cf.unc.edu/cep/empd/products/smoke/index.cfm. 

Calculated Average Values 

[2000 – 2004] Calculated Ratios 

State Month Hour SO2 Mass NOx Mass Heat Input SO2 NOx 

Heat 

Input 

SC Dec 0 1,790,705 674,127 1,908,284 0.0401 0.0395 0.0375 

SC Dec 1 1,760,847 659,279 1,877,399 0.0395 0.0386 0.0369 

SC Dec 2 1,766,498 660,719 1,890,957 0.0396 0.0387 0.0372 

SC Dec 3 1,766,455 664,407 1,922,535 0.0396 0.0389 0.0378 

SC Dec 4 1,788,023 677,882 2,005,041 0.0401 0.0397 0.0394 

SC Dec 5 1,839,136 708,796 2,193,779 0.0412 0.0415 0.0431 

SC Dec 6 1,903,431 736,781 2,379,535 0.0427 0.0432 0.0468 

SC Dec 7 1,957,422 760,608 2,504,498 0.0439 0.0446 0.0492 

SC Dec 8 1,958,923 768,669 2,515,860 0.0439 0.0450 0.0494 

SC Dec 9 1,974,624 767,392 2,419,052 0.0443 0.0450 0.0475 

SC Dec 10 1,944,825 751,207 2,264,252 0.0436 0.0440 0.0445 

SC Dec 11 1,888,552 723,857 2,140,166 0.0423 0.0424 0.0421 

SC Dec 12 1,833,408 694,261 2,022,036 0.0411 0.0407 0.0397 

SC Dec 13 1,781,162 673,316 1,936,841 0.0399 0.0395 0.0381 

SC Dec 14 1,755,403 663,791 1,911,001 0.0393 0.0389 0.0376 

SC Dec 15 1,743,443 660,042 1,897,088 0.0391 0.0387 0.0373 

SC Dec 16 1,775,717 669,264 1,937,000 0.0398 0.0392 0.0381 

SC Dec 17 1,877,548 713,920 2,099,275 0.0421 0.0418 0.0413 

SC Dec 18 1,948,165 753,627 2,255,923 0.0437 0.0442 0.0443 

SC Dec 19 1,940,185 753,123 2,258,417 0.0435 0.0441 0.0444 

SC Dec 20 1,941,859 750,942 2,221,568 0.0435 0.0440 0.0437 

SC Dec 21 1,930,605 743,880 2,182,689 0.0433 0.0436 0.0429 

SC Dec 22 1,909,077 732,480 2,140,416 0.0428 0.0429 0.0421 

SC Dec 23 1,841,457 702,464 2,003,560 0.0413 0.0412 0.0394 

SC Dec Daily 44,617,469 17,064,833 50,887,170 1.0000 1.0000 1.0000 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chap_2_Emissions4.doc 2-25 

monthly, daily, and diurnal temporal profiles associated with the source category codes from the 

unit. This could lead to potentially displaced emissions if a unit were operating at different times 

than the default profiles indicated. Additionally, in cases where States may not have reported 

annual emission estimates directly based on CEMs, these emissions would be slightly different 

that the original annual inventory. 

In VISTAS modeling, for those EGU sources where CEM data were utilized, NOx, SO2, and 

heat input-based hour-specific profiles were developed and applied to annual NOx, SO2, and all 

other emissions, respectively, for both the actual and typical 2002 modeling. Heat input was 

chosen as a surrogate for non-CEM reported pollutants as the majority of remaining compounds 

are not as significantly impacted by controls or fuel content, yet the distribution of these 

emissions would occur during the same times CEM reported pollutants were emitted. 

The application of hourly ratios to annual emissions ensured that the annual values provided by 

States under the CERR were maintained, but distributed using actual hourly to annual profiles. 

Additionally, for stakeholder sources providing hour-specific data approved by the State in 

which they operated, data were substituted for State provided emissions and CEM-based 

distributions. 

To temporally allocate the remaining EGU point sources, the NOx, SO2, and heat input data 

were collected from the 2002 or 2000-2004 CEM datasets, and used to develop State-level 

temporal distributions. These month-specific hour and day of week temporal profiles were used 

in conjunction with the emissions inventory to calculate hourly EGU emissions by unit. 

Although not as accurate a distribution as the unit-specific factors, the State-based temporal 

distribution provided improved results to the default profiles provided with the emissions model. 

Figure 2-6 represents the monthly distribution comparisons of VISTAS State heat input to the 

default monthly distribution from Version 2.0 of SMOKE for source category code (SCC) 

10100201, representing External Combustion Boilers; Electric Generation; 

Bituminous/Subbituminous Coal; Pulverized Coal: Wet Bottom (Bituminous Coal), a relatively 

common boiler type and fuel configuration in the VISTAS domain. This example is for the 

actual 2002 modeling exercise. 

Much like the distinction in month to month variation of the profiles, day of week and diurnal 

patterns based on CEM data vary from unit to unit. Again, if one were to assign the same day of 

week or diurnal profile to every unit in the inventory, emissions from these sources would 

inappropriately be distributed during the episode of interest. In addition to the unique distribution 

provided by the unit-specific factors based on CEM data, aggregate State level daily and diurnal 

temporal distribution factors were developed and applied during this process. Figure 2-7 shows 

the variance in diurnal distribution from Tennessee’s average CEM-based NOx emissions data 

for each of the twelve months of calendar year 2002 as would have been applied to units 

unmatched to CEM sources. 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chap_2_Emissions4.doc 2-26 

0.040

0.050

0.060

0.070

0.080

0.090

0.100

0.110

0.120

0.130

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Relative Distribution

AL FL GA KY MS NC SC TN VA WV SMOKE

Figure 2-6. Relative distribution of actual 2002 monthly VISTAS State CEM-based heat input. 

0.0370

0.0380

0.0390

0.0400

0.0410

0.0420

0.0430

0.0440

hr1 hr2 hr3 hr4 hr5 hr6 hr7 hr8 hr9 hr10 hr11 hr12 hr13 hr14 hr15 hr16 hr17 hr18 hr19 hr20 hr21 hr22 hr23 hr24

Relative Distribution

Jan Feb Mar Apr May Jun

Jul Aug Sep Oct Nov Dec

Figure 2-7. Relative distribution of diurnal actual 2002 CEM-based NOx emissions for 

Tennessee. 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chap_2_Emissions4.doc 2-27 

The work conducted in this process had the main objective of developing temporal profiles for 

VISTAS EGUs necessary to apply in the generation of SMOKE PTHOUR formatted emissions. 

Additionally, State-level monthly, day-of-week, and diurnal profiles were developed for 

application to non-CEM matched units in the VISTAS emissions inventory. These temporal 

distributions represent a significant improvement over the EPA defaults. 

2.3.2 Speciation 

Speciation is the process of disaggregating inventory pollutants into individual chemical species 

components or groups of species. The need for speciation is determined by the inventory 

purpose. Inventory applications that require detailed speciation include photochemical modeling, 

air toxics inventories, chemical mass balance modeling, and visibility modeling. 

Depending on the purpose of a particular emissions inventory, the inventory may include TOG, 

NOx, sulfur oxides (SOx), CO, total suspended particulate matter (TSP), particulate matter less 

than 10 micrometers in aerodynamic diameter (PM10), or ammonia (NH3). However, modeling 

inventories may require these emissions to be expressed in terms of other pollutants. 

Additionally, for some models, NOx emissions may need to be specified as NO and NO2. Also, 

PM may need to be separated into various fractions, such as PM10 and PM less than 2.5 

micrometers in aerodynamic diameter (PM2.5). 

SMOKE was configured to speciate the emissions estimates according to the requirements of the 

Carbon Bond Mechanism version four (CBM-IV, CB-IV or CB4). The SMOKE model 

reformats the emissions estimates for use in CMAQ modeling based on source category code 

(SCC) and speciation profile cross-reference files. The speciation profiles and source category 

cross-references use in VISTAS modeling are based on EPA’s CAIR/CAVR/CAMR modeling 

platform with files located on EPA’s CAIR file transfer website 

(ftp://www.airmodelingftp.com/). Minor modifications were made to reflect State specific 

profiles or updated state of knowledge application of these profiles. One major change made in 

the VISTAS modeling was the modification of coal combustion cross-reference from speciation 

profile “NCOAL” to profile “22001.” 

2.3.3 Spatial Allocation 

Because air quality modeling strives to replicate the actual physical and chemical processes that 

occur in an inventory domain, it is important that the physical location of emissions be 

determined as accurately as possible. In an ideal situation, the physical location of all emissions 

would be known exactly. In reality, however, the spatial allocation of emissions in a modeling 

inventory only approximates the actual location of emissions. 

Gridding surrogates are used to spatially allocate emission sources from a coarse geographic area 

to finer grid cells used for modeling. There can be hundreds of unique source categories in an 

emissions inventory, which is typically developed for counties, states, or other areas. The exact 

location of most major emission sources is known and their geographic coordinates are usually 

contained in the inventory. These usually are referred to as major point sources and include 

electric utilities and major industrial facilities. However, other emission sources are estimated for 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chap_2_Emissions4.doc 2-28 

the entire county or other area as an aggregate since the exact locations of each source are not 

included in the modeling inventory. Surrogates are human activities or land use information that 

are used to represent a more precise location of emission source category groups. A gridded 

surrogate ratio is the ratio of the amount of a surrogate in a modeling grid cell to the total amount 

of that surrogate in a county. Grid cell emissions are calculated by multiplying the cell’s gridded 

surrogate ratio by the county emissions. 

These surrogates and their associated SCC cross-references were originally developed by EPA 

(http://www.epa.gov/ttn/chief/emch/spatial/newsurrogate.html) and converted to the gridded 

domain definitions of the VISTAS model requirements. 

2.3.3.1 Development of Gridded Surrogate Files 

The general process for creating the SMOKE-ready gridded surrogate files from the ArcGis 

shape files is as follows: 

1. Overlay the grid on the surrogates. Generate the grid polygons (36/12/4km) with the 

specifications of the VISTAS domain and spatially overlay (intersect) the grid onto the 

surrogate area polygons or points. The resulting geodatabase contains, for each surrogate, the 

county FIPS code, the grid column and row number, and the amount (area, miles or count) of 

the county’s portion of the surrogate in that cell. 

2. Extract and convert each geodatabase table to a useful dataset. Each table contains the 

gridded area, miles or count in each county for a specific surrogate. The variables include 

FIPS code, column number, row number and area, miles or count. 

3. Calculate surrogate ratios. Surrogate ratios are calculated for each surrogate using a series 

of program files. The programs sum the surrogates for each county and calculate each ratio 

by dividing the county cell surrogate value by the total county surrogate value. Combination 

surrogates where both are of the same type (i.e., Heavy and High Tech Industrial are both 

area data) were summed prior to calculating the ratio. Combination surrogates with unlike 

data (i.e., 3/4 Roadway Miles plus 1/4 Population are line and area data) were summed after 

calculating the ratios and then normalized. The surrogate cross-reference code was also 

assigned here. 

4. Gap-fill surrogates for counties missing data. There will be many instances where 

inventory emissions exist for a particular county but there is no spatial data, for that county, 

for the surrogate assigned. For example, a county with class 1 locomotive emissions may not 

have data for the class 1 railroad surrogate. In this case we have selected to incorporate, 

within the assigned surrogate, a secondary source of data (a different surrogate) for that 

particular county. We incorporate secondary surrogates even if there is no emission source 

that requires it for that particular county. We denote this process as “gap-filling.” All 

surrogates resulting from the gap-filling process have ratios for all counties. 

For each primary spatial surrogate, we assign a secondary or tertiary spatial surrogate where 

needed for gap filling. For the class 1 railroad example mentioned above, we chose total 

railroads as the secondary spatial surrogate since emissions had been estimated for class 1 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chap_2_Emissions4.doc 2-29 

railroads but no class 1 spatial surrogate was available in that county from our coverage files. 

The secondary or tertiary spatial surrogate chosen is the same across all counties for a 

particular primary spatial surrogate and applies to all SCCs that use the particular primary 

surrogate. We pull in and substitute the secondary surrogate for counties where the primary 

surrogate is missing to allow for an otherwise omitted spatial distribution of a pre-calculated 

emission value. Tertiary surrogates are assigned to those counties that are still without 

surrogates. 

For identified counties having no values for each surrogate, we assign the data based on the 

appropriate secondary or tertiary surrogate to these counties. Checks to see that surrogate 

ratios for each county sum to approximately 1.00 were also performed in our surrogate 

development. Ratios will not always sum exactly to 1.00 due to rounding. However, SMOKE 

will normalize surrogates greater than 1.00. 

5. Create SMOKE-formatted spatial surrogate files. The resulting data from the previous 

steps is then reconfigured into SMOKE-ready format and used in the spatial allocation 

process. 

2.4 Development of Modeling Input Inventories/Run Scripts/ASSIGNS Files 

The VISTAS State emissions inventories modeled for the Base G2 runs were obtained from the 

emissions inventory contractor in NIF 3.0 format (exception of fires and onroad mobile inputs). 

These files were converted to SMOKE IDA format using procedures previously applied for the 

VISTAS Phase I and earlier Phase II modeling analysis and allocated to subcategory major 

source level (EGU, non-EGU point, area, nonroad, onroad, fires) for ease of processing and data 

management. Most of the 2002 and 2018 modeling files for non-VISTAS States were obtained 

directly from the other RPOs in SMOKE format (MANE-VU, CENRAP, WRAP) or converted 

from NIF 3.0 (MRPO) as necessary. 

Additionally, with the annual episode required for modeling, certain source categories were 

prepared in a monthly format and the SMOKE model was run for each month of the year. For 

this reason, individual run scripts and ASSIGNS files were prepared and executed during each 

SMOKE year/scenario run. An example of the configurations used can be seen in Table 2-13 

below. This particular example is for point source processing of the typical 2002 emissions 

episode. Comparable configurations were developed for stationary area, nonroad mobile, 

elevated fire, low-level fire, onroad mobile, and fugitive dust sources for 2002 typical and actual 

and 2009 and 2018 episodes. 

2.5 Products of the Emissions Inventory Development Process for QA 

In addition to the CMAQ-ready input files generated for each hour of the days modeled in the 

annual runs, a number of quality assurance (QA) files will be prepared and used to check for 

gross errors in the emissions inputs. Importing the model-ready emissions into PAVE and 

looking at both the spatial and temporal distribution of the emission provides insight into the 

quality and accuracy of the emissions inputs. 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chap_2_Emissions4.doc 2-30 

• Visualizing the model-ready emissions with the scale of the plots set to a very low value, 

we can determine whether there are areas omitted from the raw inventory or if emissions 

sources are erroneously located in water cells. 

• Spot-check the holiday emissions files to confirm that they are temporally allocated like 

Sundays. 

• Normalizing the emissions by population for each state will illustrate where the 

inventories may be deficient and provide a reality check of the inventories. 

• Spot check vertical allocation of point sources using PAVE. 

We used state inventory summaries prepared prior to the emissions processing to compare 

against SMOKE output report totals generated after each major step of the emissions generation 

process. 

To check the chemical speciation of the emissions to CB-IV terms and the vertical allocation of 

the emissions, we compared reports generated with SMOKE reports to target these specific areas 

of the processing. For speciation, we compared the inventory import state totals versus the same 

state totals with the speciation matrix applied. 

For checking the vertical allocation of the emissions, we created reports by source, hour, and 

layer for randomly selected states in the domain. We created these reports for a representative 

weekday in each of the episodes for each of these selected states. 
March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chap_2_Emissions4.doc Table 2-13. Example SMOKE Setup for 2002 Typical Point Source Processing. Run Script: /scripts/run_rev/run_pnt_XXk_allmet.typical.bat (XX replace with 12 or 36 for desired ASSIGNS File: /assigns_pII_point/ASSIGNS.vistas.cmaq.cb4p25.rpoXX.phaseIIrev.ag 

Logical File Name Directory Name Actual File Name Co

PTINV $SMKDAT/inventory/2002typ/point ptinv_2002typ_date.ida date typically 

preparation 

PTHOUR $SMKDAT/inventory/2002typ/point pthour.typ.NNN.lst 

NNN indicates The list "pthour_rev2002t

PTPRO $SMKDAT/inventory/2002typ /point amptpro_typ_us_can_NNN_vistas_date.txt date typically 

preparation 

PTREF $SMKDAT/inventory/2002typ /point ptref_2002_us_can_vistas_date.txt date typically 

preparation 

PELVCONFIG $SMKDAT/inventory/2002typ /point pelvconfig.top50.txt REPCONFIG PSTK $SMKDAT/ge_dat pstk.m3.txt INVTABLE $SMKDAT/inventory/2002typ /other invtable_onroad.cb4.120202.txt.ag GRIDDESC $SMKDAT/ge_dat GRIDDESC COSTCY $SMKDAT/ge_dat costcy.txt HOLIDAYS $SMKDAT/ge_dat holidays.txt SCCDESC $SMKDAT/ge_dat scc_desc.txt SRGDESC $SMKDAT/ge_dat srg_desc.us36.txt ORISDESC $SMKDAT/ge_dat oris_info.txt GSCNV $SMKDAT/ge_dat gscnv.txt GSREF $SMKDAT/ge_dat gsref.cmaq.cb4p25.txt GSPRO $SMKDAT/ge_dat gspro.cmaq.cb4p25.txt 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chap_2_Emissions4.doc 2-32 

The quantitative QA analyses often reveal significant deficiencies in the input data or the model 

setup. It was necessary to tailor these procedures to track down the source of each major 

problem. As such, we only outline the basic quantitative QA steps that we performed in an 

attempt to reveal the underlying problems with the inventories or processing. Following are some 

of the reports that were generated to review the processed emissions: 

• State and county totals from inventory for each source category 

• State and county totals after spatial allocation for each source category 

• State and county totals by day after temporal allocation for each source category for 

representative days 

• State and county totals by model species after chemical speciation for each source 

category 

• State and county model-ready totals (after spatial allocation, temporal allocation, and 

chemical speciation) for each source category and for all source categories combined 

• Totals by source category code (SCC) from the inventory for area, mobile, and point 

sources 

• Totals by state and SCC from the inventory for area, mobile, and point sources 

• Totals by county and SCC from the inventory for area, mobile, and point sources 

• Totals by SCC and spatial surrogates code for area and mobile sources 

• Totals by speciation profile code for area, mobile, and point sources 

• Totals by speciation profile code and SCC for area, mobile, and point sources 

• Totals by monthly temporal profile code for area, mobile, and point sources 

• Totals by monthly temporal profile code and SCC for area, mobile, and point sources 

• Totals by weekly temporal profile code for area, mobile, and point sources 

• Totals by weekly temporal profile code and SCC for area, mobile, and point sources 

• Totals by diurnal temporal profile code for area, mobile, and point sources 

• Totals by diurnal temporal profile code and SCC for area, mobile, and point sources 

• PAVE plots of gridded inventory pollutants for all pollutants for area, mobile, and point 

sources 

Examples of some of these reports are located in the Appendix A of this document. 

2.6 2009 and 2018 Base G4 Best and Final Inventories 

The current version of the VISTAS emissions inventory is referred to as the Best and Final (BaF) 

or Base G4 inventory. After the release of the 2009 and 2018 Base G2 inventory in July 2007, 

states specified additional changes to the point source inventory to reflect improved information 

on controls and shutdowns in the 2009 and 2018 future year point source emissions. No changes 

to the other source sectors (e.g., on-road mobile, non-road mobile, area fires, etc.) were made in 

going from the 2009 and 2018 Base G2 to the Base G4/BaF emission inventories. The 2018 

Base G4/BaF inventory was released in October 2007, and the 2009 Base G4/BaF inventory was 

released in December 2007. The development of the 2002 Base G2 and 2009 and 2018 Base 

G4/BaF inventories is given in MACTEC (2008). 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chap_2_Emissions4.doc 2-33 

Table 2-14 displays the point source SO2 and NOx emissions by VISTAS State and VISTAS 

region for the 2002 Base G2 and 2009 and 2018 Base G2 and Base G4 (BaF) emission scenarios. 

The largest changes in future-year EGU emissions between Base G2 and Base G4 occurred in 

Florida where SO2 EGU emissions were increased by 57% (2009) and 40% (2018) and NOx 

emissions were increased by 54% (2009) and 17% (2018). In the other VISTAS States there 

were mostly small reductions or no change in EGU SO2 emissions. EGU NOx emissions were 

also increased in Mississippi. 

With the exception of a 27% and 16% reduction in the 2018 Tennessee and Virginia, 

respectively, non-EGU SO2 emissions, the non-EGU SO2 and NOx emissions exhibited no or 

small (<±10%) changes between the future year Base G2 and Base G4 inventories. 

Across all VISTAS States, the total change in point source SO2 emissions between Base G2 and 

Base G4 was +1% and -1% for 2009 and 2018, respectively. There were larger changes in the 

future year Base G2 and Base G4 NOx point source inventories across the VISTAS States that 

were increased by 5% in 2009 and 1% in 2018. 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chap_2_Emissions4.doc 2-34 

Table 2-14a. Point source SO2 emissions (tons per year) by VISTAS State for the 2002 Base 

G2 and the 2009 and 2018 Base G2 and Base G4/BaF inventories and the differences between 

the 2009 and 2018 Base G2 and Base G4/BaF inventories. 

EGU Point Sources 

State 

2002 

Base G2 

2009 

Base G2 

2009 

Base G4 

2009 

G4-G2 

2009 

G4-G2 

2018 

Base G2 

2018 

Base G4 

2018 

G4-G2 

2018 

G4-G2 

AL 447,194 378,052 378,052 0 0% 135,851 135,851 0 0% 

FL 453,631 186,055 291,831 105,776 57% 138,340 194,028 55,688 40% 

GA 514,952 417,449 408,679 -8,770 -2% 79,430 68,515 -10,915 -14% 

KY 484,057 290,193 271,669 -18,524 -6% 226,062 222,102 -3,960 -2% 

MS 67,429 76,579 76,646 67 0% 15,146 15,213 67 0% 

NC 477,990 242,286 242,286 0 0% 114,771 120,165 5,394 5% 

SC 206,399 124,608 129,122 4,514 4% 93,274 95,377 2,103 2% 

TN 334,151 255,410 255,410 0 0% 112,672 112,672 0 0% 

VA 241,204 193,112 174,777 -18,335 -9% 114,255 98,988 -15,267 -13% 

WV 516,084 277,489 268,952 -8,537 -3% 105,935 106,199 264 0% 

Total 3,743,091 2,441,233 2,497,424 56,191 2% 1,135,736 1,169,110 33,374 3% 

Non-EGU Point Sources

State 

2002 

Base G2 

2009 

Base G2 

2009 

Base G4 

2009 

G4-G2 

2009 

G4-G2 

2018 

Base G2 

2018 

Base G4 

2018 

G4-G2 % 

AL 96,481 101,246 101,246 0 0% 113,224 103,303 -9,921 -9% 

FL 65,090 65,511 62,651 -2,860 -4% 75,047 71,810 -3,237 -4% 

GA 53,778 53,987 53,987 0 0% 59,349 59,349 0 0% 

KY 34,029 36,418 36,418 0 0% 40,682 40,682 0 0% 

MS 35,960 25,564 25,564 0 0% 26,678 25,674 -1,004 -4% 

NC 44,123 42,536 42,536 0 0% 46,314 46,314 0 0% 

SC 53,518 48,324 47,193 -1,131 -2% 53,577 52,410 -1,167 -2% 

TN 79,604 70,678 64,964 -5,714 -8% 77,247 56,682 -20,565 -27% 

VA 63,903 62,560 58,039 -4,521 -7% 68,909 57,790 -11,119 -16% 

WV 54,070 55,973 55,598 -375 -1% 62,193 61,702 -491 -1% 

Total 580,556 562,797 548,196 -14,601 -3% 623,220 575,716 -47,504 -8% 

Total Point Sources (EGU plus non-EGU)

State 

2002 

Base G2 

2009 

Base G2 

2009 

Base G4 

2009 

G4-G2 

2009 

G4-G2 

2018 

Base G2 

2018 

Base G4 

2018 

G4-G2 % 

AL 543,675 479,298 479,298 0 0% 249,075 239,154 -9,921 -4% 

FL 518,721 251,566 354,482 102,916 41% 213,387 265,838 52,451 25% 

GA 568,730 471,436 462,666 -8,770 -2% 138,779 127,864 -10,915 -8% 

KY 518,086 326,611 308,087 -18,524 -6% 266,744 262,784 -3,960 -1% 

MS 103,389 102,143 102,210 67 0% 41,824 40,887 -937 -2% 

NC 522,113 284,822 284,822 0 0% 161,085 166,479 5,394 3% 

SC 259,917 172,932 176,315 3,383 2% 146,851 147,787 936 1% 

TN 413,755 326,088 320,374 -5,714 -2% 189,919 169,354 -20,565 -11% 

VA 305,107 255,672 232,816 -22,856 -9% 183,164 156,778 -26,386 -14% 

WV 570,154 333,462 324,550 -8,912 -3% 168,128 167,901 -227 0% 

Total 4,323,647 3,004,030 3,045,620 41,590 1% 1,758,956 1,744,826 -14,130 -1% 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chap_2_Emissions4.doc 2-35 

Table 2-14b. Point source NOx emissions (tons per year) by VISTAS State for the 2002 Base 

G2 and the 2009 and 2018 Base G2 and Base G4/BaF inventories and the differences between 

the 2009 and 2018 Base G2 and Base G4/BaF inventories. 

EGU Point Sources

State 

2002 

Base G2 

2009 

Base G2 

2009 

Base G4 

2009 

G4-G2 

2009 

G4-G2 

2018 

Base G2 

2018 

Base G4 

2018 

G4-G2 State 

AL 161,038 82,305 82,305 0 0% 64,358 64,358 0 0%

FL 257,677 86,165 132,535 46,370 54% 74,640 87,645 13,005 17%

GA 147,517 98,497 98,497 0 0% 75,717 69,856 -5,861 -8%

KY 198,817 92,021 97,263 5,242 6% 64,378 64,378 0 0%

MS 43,135 36,011 47,276 11,265 31% 10,271 21,535 11,264 110%

NC 151,853 66,522 66,521 -1 0% 62,353 61,110 -1,243 -2%

SC 88,241 46,915 48,668 1,753 4% 51,456 51,751 295 1%

TN 157,307 66,405 66,405 0 0% 31,715 31,715 0 0%

VA 86,886 62,547 64,358 1,811 3% 66,074 64,344 -1,730 -3%

WV 230,977 86,328 85,476 -852 -1% 51,241 51,474 233 0%

Total 1,523,448 723,716 789,304 65,588 9% 552,203 568,166 15,963 3%

Non-EGU Point Sources

State 

2002 

Base G2 

2009 

Base G2 

2009 

Base G4 

2009 

G4-G2 

2009 

G4-G2 

2018 

Base G2 

2018 

Base G4 

2018 

G4-G2 State 

AL 83,310 69,409 69,409 0 0% 78,318 77,960 -358 0%

FL 45,156 46,020 47,125 1,105 2% 51,902 52,959 1,057 2%

GA 49,251 50,353 50,353 0 0% 55,824 55,824 0 0%

KY 38,392 37,758 37,758 0 0% 41,034 41,034 0 0%

MS 61,526 56,397 56,398 1 0% 61,533 61,252 -281 0%

NC 44,929 34,767 34,768 1 0% 37,801 37,802 1 0%

SC 42,153 40,019 39,368 -651 -2% 44,021 43,331 -690 -2%

TN 64,344 57,883 57,514 -369 -1% 63,453 62,519 -934 -1%

VA 60,415 51,046 51,001 -45 0% 55,945 55,734 -211 0%

WV 46,612 38,031 38,023 -8 0% 43,359 43,280 -79 0%

Total 536,088 481,683 481,717 34 0% 533,190 531,695 -1,495 0%

Total Point Sources (EGU plus non-EGU)

State 

2002 

Base G2 

2009 

Base G2 

2009 

Base G4 

2009 

G4-G2 

2009 

G4-G2 

2018 

Base G2 

2018 

Base G4 

2018 

G4-G2 State 

AL 244,348 151,714 151,714 0 0% 142,676 142,318 -358 0%

FL 302,833 132,185 179,660 47,475 36% 126,542 140,604 14,062 11%

GA 196,768 148,850 148,850 0 0% 131,541 125,680 -5,861 -4%

KY 237,209 129,779 135,021 5,242 4% 105,412 105,412 0 0%

MS 104,661 92,408 103,674 11,266 12% 71,804 82,787 10,983 15%

NC 196,782 101,289 101,289 0 0% 100,154 98,912 -1,242 -1%

SC 130,394 86,934 88,036 1,102 1% 95,477 95,082 -395 0%

TN 221,651 124,288 123,919 -369 0% 95,168 94,234 -934 -1%

VA 147,301 113,593 115,359 1,766 2% 122,019 120,078 -1,941 -2%

WV 277,589 124,359 123,499 -860 -1% 94,600 94,754 154 0%

Total 2,059,536 1,205,399 1,271,021 65,622 5% 1,085,393 1,099,861 14,468 1%
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chap_2_Emissions4.doc 2-36 

2.7 Uncertainties and Caveats 

Following the completion of VISTAS Base G2 modeling, a number of issues were identified 

with the input emission inventories and emission processing modeling system that are identified 

here. Total impact to the overall processed results has not been quantified for these issues 

although individually each issue is believed to have minimal impact on regional haze or visibility 

results domain wide. 

2.7.1 Updated Inventories 

As in the normal process among emissions and air quality analysis, non-VISTAS RPOs have 

continued to update and revise the base year and future year emission inventories used in the 

development of regional haze and visibility modeling simulations. The non-VISTAS RPO data 

obtained for this analysis were gathered during the November-December 2006 timeframe and it 

is recognized that each of the four sister RPOs to VISTAS has since modified either their 2002 

base year and/or their 2018 base case projection inventories. 

2.7.2 Low Level Fires 

During the Base G2 modeling, a series of low level fire emissions were inadvertently omitted 

from the processing stream. These fires include emissions for six counties in Kentucky, and one 

county each in Tennessee and West Virginia for prescribed burning. These fires are data for 

prescribed burning that were only received from the USFS during the initial file development for 

these counties. No State data was received for this category in these counties and thus the NEI 

values from EPA’s inventory were maintained for this fire type consistent with the replacement 

scheme developed when Base D inventories were prepared. During the update process of fire 

emissions from Base D to Base G2, this file was not remerged with the other updated fire types. 

2.7.3 MMS Offshore Shipping 

During the update of emission inventories from Base D to Base G, VISTAS obtained from MMS 

a set of updated 2000 emission files (GOADS12) which included off-shore marine vessel 

emissions in the Gulf of Mexico oil/gas development area and platform and platform support 

equipment that has always been included in past MMS inventories. When VISTAS overlaid 

recently obtained offshore emission estimates from shipping lanes, we double counted the offshore marine emissions in the area. 

 

12

http://www.gomr.mms.gov/homepg/regulate/environ/airquality/gulfwide_emission_inventory/2000GulfwideEmissio

nInventory.html
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chap_2_Emissions4.doc 2-37 

2.7.4 Incorrect Location Parameters 

During the quality assurance steps of the Base G2 emission inventories in preparation for 

VISTAS’ best and final modeling, a number of emission release points were identified to have 

stack location parameters that were of distances greater than 100 miles from the county centroid 

that the stack’s FIPS codes indicated it was situated. Although many of these release points were 

sited at valid locations (in counties with outer boundaries greater than 100 miles from centroid), 

those that were incorrectly sited had emission values of very low (less than 80 tons per year) of 

any single pollutant. 

2.7.5 SMOKE/IOAPI Coordinate Transformation Error 

During emissions processing of inventories for an unassociated project, the modeling team 

became aware of an issue13 with one of the IOAPI libraries necessary to convert input file 

coordinates into output file locations. This issue occurs with the precompiled version of SMOKE 

utilized by the modeling team in VISTAS emissions processing. During the processing step of 

point source emissions, the latitude/longitude coordinates of the input files are transformed into 

Lambert conformal projection, consistent with the CMAQ setup. The error identified in the 

IOAPI library shifted this location by several kilometers. Although this error was corrected in 

subsequent versions of SMOKE and the IOAPI libraries, no corrections have been made to Base 

G2 modeling or earlier simulations. 

 

13 http://bugz.unc.edu/show_bug.cgi?id=511 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-1 

3.0 MODEL PERFORMANCE EVALUATION 

3.1 Introduction 

The discussion of model performance evaluation is summarized in this Chapter with details 

provided in Appendices B, C and D. The model performance evaluation focuses on the 

evaluation of the 2002 Base G2 Actual base case modeling results from the Community 

Multiscale Air Quality (CMAQ) modeling system Version 4.51 with SOAmods enhancement. 

This evaluation focuses on monitoring sites within the VISTAS states and VISTAS 12 km grid. 

The CMAQ evaluation of the 2002 Base G2 Actual base case is the product of numerous 

evaluations of predecessor CMAQ base case simulations using interim versions of the base case 

modeling inputs and CMAQ model (Morris et al., 2004b,c; 2005; 2006). With the exception of 

Organic Carbon Mass (OCM), the basic model performance evaluation features have remained 

unchanged from the initial evaluations in Phase I and II of VISTAS (e.g., Morris et al., 

2004a,b,c). As noted previously, during the course of the VISTAS Phase II modeling VISTAS 

updated the secondary organic aerosol (SOA) module in CMAQ to include important SOA 

processes missing in the standard CMAQ model that significantly improved the OCM 

performance in the summer (Morris et al., 2006). 

The CMAQ results are compared with observational data from the Interagency Monitoring of 

PROtected Visual Environments (IMPROVE), Speciated Trends Network (STN)1

, Clean Air 

Status Trends Network (CASTNet), Federal Reference Method (FRM) PM2.5 mass, National 

Acid Deposition Program (NADP) and South East Aerosol Research and CHaracterization 

(SEARCH) study monitoring networks. The evaluation focuses primarily on the operational 

model evaluation of the air quality model’s performance with respect to individual components 

of fine particulate matter (PM2.5), as good model performance of the PM component species will 

dictate good model performance of total or reconstituted fine particulate matter from which 

visibility is based. The FRM networks only collects total PM2.5 mass so are not as relevant for 

judging how well the model is predicting the components of light extinction. However, model 

performance of the total fine particulate matter and light extinction will also be provided as a 

means to discuss the overall model performance. We have also performed a diagnostic 

evaluation analyzing the ability of the model to reproduce gaseous PM precursor (e.g., SO2 and 

NOx) and product (e.g., HNO3) species. 

This chapter presents a brief summary of the CMAQ Base G2 model performance evaluation 

with details provided in three Appendices: 

• Appendix B presents the regional model performance evaluation of the CMAQ modeling 

system across PM monitoring networks in the VISTAS states and VISTAS 12 km grid as 

well as the diagnostic evaluation for PM precursor and product species. 

 

• Appendix C contains a detailed evaluation of the CMAQ model performance at Class I 

areas within and nearby the VISTAS region with particular focus on the best and worst 

20 percent days during 2002 that are used in the 2018 visibility projections. 

 

1

 The STN network is now referred to as the Chemical Speciation Network (CSN) 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-2 

• Appendix D contains spatial maps that compare predicted and observed PM species 

concentrations for each day from 2002 that was either a best or worst 20 percent day at a 

Class I area within or nearby the VISTAS region. 

3.2 CMAQ Evaluation Methodology 

EPA’s integrated ozone, PM2.5 and regional haze modeling guidance calls for a comprehensive, 

multi-layered approach to model performance testing, consisting of the four major components: 

operational, diagnostic, mechanistic (or scientific) and probabilistic (EPA, 2007a). 

3.2.1 Evaluation Approach 

The CMAQ model performance evaluation effort focused on the first two components of the 

EPA’s recommended evaluation approach, namely: 

• Operational Evaluation: Tests the ability of the model to estimate PM concentrations 

(both fine and coarse) and the components at PM10 and PM2.5 including the quantities 

used to characterize visibility, that is, sulfate, nitrate, organic carbon, elemental carbon, 

other PM2.5, and coarse matter (PM2.5-10). This evaluation examines whether the 

measurements are properly represented by the model predictions but does not necessarily 

ensure that the model is getting “the right answer for the right reason”; and 

• Diagnostic Evaluation: Tests the ability of the model to predict visibility and extinction, 

PM chemical composition including PM precursors (e.g., SOx, NOx, and NH3) and 

associated oxidants (e.g., ozone and nitric acid); PM size distribution; temporal variation; 

spatial variation; mass fluxes; and components of light extinction (i.e., scattering and 

absorption). 

The diagnostic evaluation also includes the performance of diagnostic tests to better understand 

model performance and identify potential flaws in the modeling system that can be corrected. 

The diagnostic evaluation may also includes the use of “probing tools” to understand why the 

model obtains a given prediction; probing tools include Process Analysis (PA), decoupled direct 

method (DDM) and source apportionment (SA). 

In this final model performance evaluation for the VISTAS 2002 Actual Base G2 CMAQ 36/12 

km base case simulation, the operational evaluation has been given the greatest attention since 

this is the primarily thrust of EPA’s modeling guidance. However, we have also examined 

certain diagnostic features dealing with the model’s ability to simulate sub-regional and 

monthly/diurnal gas phase and aerosol concentration distributions. In the course of the VISTAS 

study numerous diagnostic sensitivity tests were performed to investigate and improve model 

performance and test the model assumptions that are available on the VISTAS modeling website: 

http://pah.cert.ucr.edu/vistas/vistas2/ and reported by Morris and co-workers (2004a,b,c; 2005; 

2006). 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-3 

3.2.2 Particulate Matter and Component Species 

Regional haze is calculated by estimating light scattering and absorption by components of 

PM2.5. Regional haze is measured by an extinction coefficient (bext), which represents the light 

attenuation resulting from scattering and absorption of light from ambient particulate matter plus 

scattering of light due to gas molecules in the air (i.e., Rayleigh scattering). 

Some scattering does occur from air molecules; however, the bulk of light extinction is caused 

by the presence of suspended particles, particularly fine particles (aerodynamic mass <2.5 μm). 

Fine particulate matter can be composed of varying amounts of several different species, 

including: 

• Sulfate (SO4) 

• Nitrate (NO3) 

• Ammonium (NH4) 

• Organic Carbon Matter (OCM) 

• Elemental Carbon (EC) 

• Coarse particulate matter or Coarse Mass (i.e., PM10 – PM2.5 or CM). 

• Soil (also known as crustal material, fine soil, major metal oxides, inorganic particulates, 

or other PM2.5) 

• Sea Salt (NaCl) 

3.2.3 Ambient Air Quality Data for VISTAS Model Evaluation 

A ground-level model evaluation database for 2002 was compiled by the modeling team using 

several routine and research-grade databases. The focus of the VISTAS evaluation of the 

CMAQ model was for use in simulating visibility in the southeastern is on the PM components 

that can cause visibility impairment. The primary monitoring networks available to evaluate this 

component of the CMAQ are: (a) Interagency Monitoring of Protected Visual Environments 

(IMPROVE); (b) Clean Air Status and Trends Network (CASTNET); (c) Southeastern Aerosol 

Research and Characterization (SEARCH); (d) EPA Federal Reference Method PM2.5 and PM10

Mass Networks (EPA-FRM); (e) EPA Speciation Trends Network (STN) of PM2.5 species; and 

(f) National Acid Deposition Network (NADP). These PM monitoring networks may also 

provide ozone and other gas phase precursors and product species, and visibility measurements 

at some sites. There are also gas-phase criteria pollutant measures from the EPA’s Air Quality 

System (AQS) that are available for use. The VISTAS 2002 36/12 km Actual base case 

simulation was also used by the Association for Southeastern Integrated Planning (ASIP) who 

are analyzing 8-hour ozone and PM2.5 attainment issues mainly in urban areas. ASIP performed 

a detailed focused evaluation of the CMAQ 36/12 km Base G2 Actual model performance for 

ozone and PM2.5 using mainly the AQS, STN and FRM urban monitoring networks. The ASIP 

evaluation of the CMAQ base case modeling for urban areas can be found at on the VISTAS 

modeling website (http://pah.cert.ucr.edu/vistas/vistas2/mpe2.shtml) and is not repeated here. 

Table 3-1 and Figure 3-1 summarizes the species collected and locations of the monitoring sites 

for the IMPROVE, STN, CASTNet, NADP and SEARCH monitoring networks use in the 

VISTAS model evaluation. 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-4 

For Organic Carbon Mass (OCM), the monitoring networks measure just Organic Carbon (OC) 

and don’t measure the other elements (e.g., oxygen) associated with OCM. Thus, the OC 

measurement needs to be converted to OCM in the model performance evaluation. OCM/OC 

ratios are generally believe to vary between 1.2 and 2.4 (Turpin and Lim, 2001), with lower 

OCM/OC ratios for fresh OCM emissions and higher OCM/OC ratios for aged OCM that has 

undergone photochemical processing. VISTAS used an OCM/OC ratio of 1.4 for all the 

networks. It should be noted that the STN OC measurements have artifacts and are less accurate 

than the IMPROVE OC measurements (Solomon et al., 2004;2008). The inaccuracies and 

artifacts of the STN OC measurements is one of the reasons that the SANDWICH method that 

uses the STN PM speciation measurements to speciate FRM PM2.5 mass doesn’t directly use the 

STN OC measurement but instead obtains OC through difference between the PM2.5 mass and 

the remainder of the PM2.5 species components (Frank, 2005). 

Table 3-1. Ambient monitoring data available in the VISTAS region during 2002. 

Monitoring 

Network Chemical Species Measured 

Sampling Frequency; 

Duration 

IMPROVE Speciated PM2.5 and PM10 1 in 3 days; 24 hr 

CASTNET Speciated PM2.5, Ozone Hourly, Weekly; 1 hr, Week 

SEARCH 

24-hr PM25 (FRM Mass, OC, BC, SO4, NO3, 

NH4, Elem.); 24-hr PM coarse (SO4, NO3, 

NH4, elements); Hourly PM2.5 (Mass, SO4, 

NO3, NH4, EC, TC); and Hourly gases (O3, 

NO, NO2, NOy, HNO3, SO2, CO) Daily, Hourly; 

NADP WSO4, WNO3, WNH4 Weekly 

EPA-FRM Only total fine mass (PM2.5) 1 in 3 days; 24 hr 

EPA-STN Speciated PM2.5 Varies; Varies 

AIRS/AQS CO, NO, NO2, NOx, O3 Hourly; Hourly 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-5 

250 500 750 1000 1250 1500 1750 2000

-1500

-1250

-1000

-750

-500

-250

0

250

500

IMPROVE

CASTNet

SEARCH

STN

NADP

AQS

Figure 3-1. IMPROVE, STN, CASTNet, NADP and SEARCH monitoring sites located in the VISTAS 

region and the VISTAS 12 km grid (locations of AQS sites not shown due to their large number). 

3.3 Model Performance Statistics and Goals 

To quantify model performance, several statistical measures were calculated and evaluated for 

all the IMPROVE, STN, CASTNet, SEARCH and NADP monitors within the VISTAS region or 

VISTAS 12 km domain and individually for each IMPROVE monitor associated with a VISTAS 

or nearby Class I areas. The statistical measures selected were based on the recommendations 

outlined in section 18.4 of the USEPA’s Guidance On The Use Of Models And Other Analyses 

for Demonstrating Attainment of Air Quality Goals for Ozone, PM2.5, and Regional Haze (EPA, 

2007a). 

In 2004, VISTAS established model performance goals and criteria for components of fine 

particle mass (Table 3-2) based on previous model performance for ozone and fine particles 

(e.g., Morris et al., 2004a,b,c). EPA modeling guidance for fine particulate matter at the time 

noted that PM models might not be able to achieve the same level of performance as ozone 

models. VISTAS’ evaluation considered several statistical performance measures and displays. 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-6 

Table 3-2. Model performance goals and criteria for components of fine particle mass. 

Fractional Bias Fractional Error Comment 

<15% <35% Goal for PM model performance based on ozone model 

performance, considered excellent performance 

<30% <50% Goal for PM model performance, considered good 

performance 

<60% <75% Criteria for PM model performance, considered average 

performance. Exceeding this level of performance 

indicates fundamental concerns with the modeling 

system and triggers diagnostic evaluation. 

The statistical measures were calculated for all of the component species of particulate matter 

responsible for light extinction, and for total PM2.5, PM10 and light extinction. For convenience, 

these statistical measures or metrics, along with a variety of additional statistical measures, are 

summarized in Table C-3 in Appendix C. Appendix C also contains summary tables of all of the 

performance statistics and each PM component across the VISTAS region and at each individual 

VISTAS and nearby Class I area. 

3.4 Regional Model Performance Evaluation 

Details on the regional model performance evaluation across the VISTAS region and the 

VISTAS 12 km grid are provided in Appendix B. Below we summarize model performance for 

each PM species using monthly fractional bias and fractional performance statistics and monthly 

time series plots and Bugle Plots. The reader is referred to Appendix B for more details and 

additional summary performance displays (e.g., scatter plots and quantile-quantile plots). 

3.4.1 Sulfate (SO4) Model Performance 

Figure 3-2 compares the monthly SO4 concentration fractional bias and error across the VISTAS 

region for the IMPROVE, STN, CASTNet, SEARCH Daily and SEARCH Hourly monitoring 

networks. Note that these monthly model performance statistics were calculated across monitors 

within the VISTAS 12 km grid as compared to just monitors in the VISTAS states that is used in 

the monthly performance evaluation scatter plots and statistics given in Figures B-2 through B-5 

in Appendix B. The underprediction bias in SO4 concentrations is clearly evident across most of 

the year and monitoring networks with the best (i.e., lowest) SO4 bias occurring in October. 

However, this underprediction bias is not severe usually not going lower than -20% and never 

going lower than -25% and achieves the goal for PM model performance (Table 3-2). The 

exception to this is the SEARCH Hourly network that exhibits a positive overprediction SO4 bas 

for several months. 

The SO4 monthly fractional error values are usually under 40% and, with one exception, always 

under 50% thereby achieving the PM performance goal (Figure 3-2). The exception is the 

SEARCH Hourly network with error values ranging from 50% to 70%. The SEARCH Hourly 

SO4 measurements are based on research grade monitoring equipment and likely have higher 

uncertainties and artifacts than the other monitoring networks. 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-7 

Figure 3-3 displays Bugle Plots of monthly SO4 fractional bias and error. Bugle Plots compare 

the monthly bias and error values with a performance goal and performance criteria and accounts 

for the concept that better performance is needed (i.e., more stringent model performance goals 

and criteria) for species with higher concentrations/extinctions and that performance goals and 

criteria can be relaxed as the species concentrations become low and are no longer an important 

component of the PM or extinction budget. The Bugle Plots in Figure 3-3 show that SO4 model 

performance tends to be good across all months and, with one exception, always achieves the 

PM model performance goal for both bias and error and frequently achieves the more stringent 

ozone performance goal. The one exception is for fractional error across the SEARCH Hourly 

network that is above the performance goal, but still achieves the performance criteria (Figure 3-

3, bottom). 

As shown in Figures B-2 through B-5, wet deposition of SO4 tends to be overstated, particularly 

in the summer. This is due in part to overstated convective precipitation from the MM5 

simulation (Olerud, 2003c,d). During the winter, when precipitation is dominated by large scale 

synoptic events, the SO4 wet deposition bias is near zero (e.g., Figure B-2). Thus, some of the 

SO4 underprediction bias is due to overstated wet scavenging that is likely due to overstated 

MM5 convective precipitation. The SO4 underprediction bias is also due to a general tendency 

of the model to understate SO4 at the coastal sites. For example, during July the CMAQ model 

tended to underestimate the observed SO4 at the Breton Island and Everglades IMPROVE 

monitors by approximately -100% and underestimated SO4 at the other coastal sites (e.g., 

SAMA, ROMA, SWAN) by -20% to -40% with the fractional bias at the inland sites closer to 

zero. For EVER and BRET, part of the SO4 underestimation bias is likely due to missing 

emissions from the Caribbean (e.g., Cuba) and for all coastal sites there could be some issues 

associated with the characterization of the 36/12 km grid cell containing the monitors as water 

when the monitor itself is clearly on land. In addition, the strength and penetration of the sea 

breeze is critical on some days to define whether the coastal monitor is affected more by dirtier 

air coming off of land or the cleaner air coming of the sea, which is not well simulated by MM5 

using a coarse grid resolution. 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-8 

VISTAS CMAQ 2002 Actual Base G2 12km

SO4

-40

-30

-20

-10

0

10

20

30

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Fractional Bias [%]

CASTNET IMPROVE STN SEARCH SEARCH_H

0

10

20

30

40

50

60

70

80

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Fractional Error [%]

Figure 3-2. Monthly SO4 fractional bias (top) and fractional gross error (bottom) statistical 

measures for CASTNet, IMPROVE, STN and SEARCH (daily and hourly) monitoring sites in the 

VISTAS 12 km grid. 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-9 

VISTAS CMAQ 2002 Actual Base 12km Bugle Plot

SO4

-200

-150

-100

-50

0

50

100

150

200

0 4 8 12 16 20

Average Concentration (ug/m3)

Fractional Bias (%)

(+) Goal (-) Goal (+) Criteria (-) Criteria blank

CASTNET IMPROVE STN SEARCH SEARCH_H

0

50

100

150

200

0 4 8 12 16 20

Average Concentration (ug/m3)

Fractional Error (%)

Figure 3-3. Bugle Plots of monthly fractional bias (top) and gross error (bottom) for SO4 in the 

VISTAS 12 km grid and comparison to model performance goals (blue) and criteria (yellow). 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-10 

3.4.2 Nitrate (NO3) Modeling Performance 

The monthly fractional bias time series plot for NO3 clearly shows a seasonal dependence of this 

performance measure with a severe summer underprediction bias of -100% to -150% and bias 

values generally within ±50% in the winter (Figure 3-4). The time series of fractional error 

values also exhibit a seasonal dependence with an 80% error in the winter peaking to a 160% 

error in the summer with a bell-like distribution. At first glance it appears that the summer 

underprediction bias would adversely affect the regional haze modeling as that is when a vast 

majority of the worst 20 percent days occur at Class I areas in the VISTAS region. However, as 

shown in the Bugle Plots in Figure 3-5, these large summer underprediction bias values occur 

when NO3 concentrations are extremely low (< 1.0 μg/m3) and in fact when the average 

observed NO3 concentration exceeds 1 μg/m3 the model mostly achieves the model performance 

goal and almost always achieves the model performance criteria for bias (Figure 3-5, top). The 

model performance goal and criteria is achieved less often for NO3 fractional error (Figure 3-5, 

bottom), but the goal is always achieved across the CASTNet and usually achieved across the 

IMPROVE monitoring networks. 

The summer NO3 underprediction bias is likely due in part to the overstated MM5 convective 

precipitation discussed previously, which is somewhat confirmed by a -20% fractional bias for 

wet NO3 deposition in July (Figure B-9). Like we saw for SO4, the NO3 performance at coastal 

sites tends to be worse with near zero NO3 predictions at many of the sites during the summer 

and many days with very low values in the winter. At the inland sites the NO3 winter model 

performance is better. For example, the NO3 fractional bias in January at the coastal VISTAS 

IMPROVE sites is approximately -80%, whereas at the inland IMPROVE sites in ranges from 

0% (36 km) to -20% (12 km). 

The modeled NO3 concentrations are highly sensitive to ammonia (NH3) emissions as whether 

oxidized NOx is gaseous nitric acid (HNO3) or particulate NO3 depends in part on the 

availability of NH3 or other basic compound to buffer the NO3. Although VISTAS spent 

considerable effort updating and improving the ammonia emissions, ammonia emissions are still 

highly uncertain. NH3 is emitted from a variety of sources quite unique from the other precursor 

emission sources (e.g., livestock, soil, etc.) and the sampling of these emission rates is extremely 

difficult, thus uncertainties in the ammonia emissions will likely persist. Another potential 

reason NO3 is underestimated by CMAQ that is particularly important for the coastal sites is the 

fact that the CMAQ model appears to greatly understate Sea Salt. As shown in Figure B-37 and 

the spatial maps in Appendix D (example given in lower left panel of Figure 3-16b), the CMAQ 

model is estimating near zero Sea Salt when the observed values are frequently a few tenths of a 

μg/m3

 or even up to one μg/m3. The sodium (Na) in the Sea Salt can also act as a buffering agent 

for HNO3 to form particulate NO3 and it appears that Sea Salt is understated in the CMAQ 

modeling. Note that this issue is somewhat complicated by the fact that sodium nitrate (NaNO3) 

would likely be a coarse particle so would not even appear in the IMPROVE speciated NO3 

measurement and instead be embedded in the IMPROVE CM measurement. 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-11 

VISTAS CMAQ 2002 Actual Base G2 12km

NO3

-200

-150

-100

-50

0

50

100

150

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Fractional Bias [%]

CASTNET IMPROVE STN SEARCH SEARCH_H

0

20

40

60

80

100

120

140

160

180

200

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Fractional Error [%]

Figure 3-4. Monthly NO3 fractional bias (top) and fractional gross error (bottom) statistical 

measures for CASTNet, IMPROVE, STN and SEARCH daily and hourly monitoring sites in the 

VISTAS region. 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-12 

VISTAS CMAQ 2002 Actual Base 12km Bugle Plot

NO3

-200

-150

-100

-50

0

50

100

150

200

012345678

Average Concentration (ug/m3)

Fractional Bias (%)

(+) Goal (-) Goal (+) Criteria (-) Criteria blank

CASTNET IMPROVE STN SEARCH SEARCH_H

0

50

100

150

200

012345678

Average Concentration (ug/m3)

Fractional Error (%)

Figure 3-5. Bugle Plots of monthly fractional bias (top) and gross error (bottom) for NO3 in the 

VISTAS region and comparison to model performance goals (blue) and criteria (yellow). 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-13 

3.4.3 Organic Carbon Mass (OCM) Performance 

The monthly fractional bias and error plot for the 12 km CMAQ base case simulation OCM 

performance given in Figure 3-6 displays fairly good performance across the IMPROVE 

monitors with bias usually within ±15% and errors between 40% and 50%. However, at the 

urban STN sites the model exhibits a large OCM underprediction bias of -60% to -90%. Part of 

the underprediction bias may be due to measurement uncertainties and artifacts and part may be 

due to the model over diluting the urban OCM emissions through the coarse 36/12 km grid. The 

underprediction of urban OCM is a common problem in PM modeling and likely also points to 

uncertainties in the OCM and SOA precursor emission inventories. 

One source of uncertainty in the OCM measurements is the fact that OCM is actually derived 

from OC measurements. The amount of additional elements (e.g., oxygen) attached to the OC to 

form OCM varies with the age and level of photochemical processing of the organic particles 

with OCM/OC ratios typically ranging from 1.2 to 2.4 for fresh to aged processed OCM. For the 

model performance evaluation we used a 1.4 OCM/OC ratio that is consistent with the original 

IMPROVE equation and based on measurements from Los Angeles. The new IMPROVE 

equation uses a higher 1.8 OCM/OC ratio reflecting the fact that OCM that reaches the mostly 

rural Class I areas will be aged and subject to photochemical processing. There really is no one 

right OCM/OC factor to use in all cases. However, in interpreting the model performance it is 

important to know which OCM/OC ratio was used and recognize that selection of another ratio 

could make a 30% or so difference in the OCM measurements. 

Unlike the IMPROVE OC measurements, the STN OC measurements are not blank corrected, 

which is believed to result in an approximate 0.5 μg/m3

 positive artifact in the STN OC 

measurement (which would be 0.5-0.9 positive artifact in OCM depending on which OCM/OC 

factor is used). As seen in the scatter plots in Figures B-12 through B-15 in Appendix B, the 

subtraction of 0.5-0.9 μg/m3 from the measured OCM value would greatly improve the OCM 

model performance across the STN network and bring the CMAQ OCM bias closer to zero. 

The Bugle Plots of monthly fractional bias and gross error (Figure 3-7) concurs that the OCM 

performance across the IMPROVE monitoring sites is fairly good achieving the model 

performance goals. But the OCM underprediction bias for the STN monitors is significant 

enough so that it always falls out of the model performance goals and is right at the model 

performance criteria. However, as noted above this is partly due to measurement artifacts. The 

OCM performance across the SEARCH network is mixed usually achieving the model 

performance goal and always achieving the model performance criteria. 

In the preliminary evaluation of the CMAQ model using early VISTAS databases, the OCM 

performance was a great cause of concern due to a large summer OCM underprediction bias 

(Morris et al.., 2004b,c). This resulted in VISTAS conducting a focused research study on the 

reasons for the summer OCM underprediction, the identification of processes missing in the 

CMAQ model and the development of the SOAmods enhancement to CMAQ (see Section 

1.3.3.3.1) that greatly improved the CMAQ OCM model performance (Morris et al., 2006). 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-14 

VISTAS CMAQ 2002 Actual Base G2 12km

OC

-100

-90

-80

-70

-60

-50

-40

-30

-20

-10

0

10

20

30

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Fractional Bias [%]

IMPROVE STN SEARCH

0

10

20

30

40

50

60

70

80

90

100

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Fractional Error [%]

Figure 3-6. Monthly OCM fractional bias (top) and fractional gross error (bottom) statistical 

measures for IMPROVE, STN and SEARCH monitoring sites in the VISTAS region. 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-15 

VISTAS CMAQ 2002 Actual Base 12km Bugle Plot

OC

-200

-150

-100

-50

0

50

100

150

200

0 4 8 12 16 20

Average Concentration (ug/m3)

Fractional Bias (%)

(+) Goal (-) Goal (+) Criteria (-) Criteria

IMPROVE STN SEARCH

0

50

100

150

200

0 4 8 12 16 20

Average Concentration (ug/m3)

Fractional Error (%)

Figure 3-7. Bugle Plots of monthly fractional bias (top) and gross error (bottom) for OCM in the 

VISTAS region and comparison to model performance goals (blue) and criteria (yellow). 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-16 

3.4.4 Elemental Carbon (EC) Performance 

The monthly bias and error performance statistics for EC across the VISTAS 12 km domain are 

shown in Figure 3-8. Bias and error are lower in the first and last quarters of the year with EC 

performance across the IMPROVE networks exhibiting a large underprediction bias (-40% to 

-80%) that peaks in June. The EC performance across the SEARCH network is somewhat 

similar to IMPROVE only the underprediction bias in the summer is not as great. The STN 

network has fairly low EC fraction bias during quarters 1, 3 and 4 (achieving the most stringent < 

±15% ozone goal), but has larger underprediction bias during quarter 2 (-20% to -40%). The 

STN network very low EC bias during most of the year suggests that the anthropogenic EC 

emissions inventory may be adequately characterized in urban areas. 

The Bugle Plot in Figure 3-9 shows that although the EC underprediction produces large 

fractional bias values, because the EC concentrations are low the EC performance achieves the 

model performance goal for all months and networks since it is in the flared horn section of the 

Bugle Plot. 

The scatter plots of EC performance in Figures B-17 through B-20 in Appendix B provide 

additional insight into the EC model performance. Across the urban STN sites the EC 

underprediction bias is much less using the CMAQ 12 km modeling results compared to the 36 

km modeling results. Thus, for at least the STN sites and probably the urban SEARCH sites as 

well, some of the EC underprediction bias is likely due to the coarse grid spacing used. Across 

the more rural IMPROVE sites the opposite is generally true with the underprediction bias 

greater with the 12 km results than 36 km results, although the differences are not as pronounced 

as across the STN monitoring network. One reason for the EC underprediction across the 

IMPROVE monitoring network is because the model fails to capture a few isolated high 

observed EC observations. For example, in January 2002 most of the observed and modeled EC 

values are less than 1 μg/m3 but there are two observed values that are at 2 and 3 μg/m3

 that are 

not captured by the model (Figure B-17). These high values could be due to fires or other 

biomass burning that is not captured by the model. In the summer, however, the model EC 

underprediction bias appears to be more systematic. But again both the modeled and observed 

values are mostly less than 0.5 μg/m3

, so EC is a minor component of the visibility and PM2.5

budget. 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-17 

VISTAS CMAQ 2002 Actual Base G2 12km

EC

-80

-60

-40

-20

0

20

40

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Fractional Bias [%]

IMPROVE STN SEARCH SEARCH_H

0

10

20

30

40

50

60

70

80

90

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Fractional Error [%]

Figure 3-8. Monthly EC fractional bias (top) and fractional gross error (bottom) statistical 

measures for IMPROVE, STN and SEARCH daily and hourly monitoring sites in the VISTAS 

region. 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-18 

VISTAS CMAQ 2002 Actual Base 12km Bugle Plot

EC

-200

-150

-100

-50

0

50

100

150

200

012345678

Average Concentration (ug/m3)

Fractional Bias (%)

(+) Goal (-) Goal (+) Criteria (-) Criteria

IMPROVE STN SEARCH SEARCH_H

0

50

100

150

200

012345678

Average Concentration (ug/m3)

Fractional Error (%)

Figure 3-9. Bugle Plots of monthly fractional bias (top) and gross error (bottom) for EC in the 

VISTAS region and comparison to model performance goals (blue) and criteria (yellow). 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-19 

3.4.5 Other PM2.5 or Soil Performance 

The seasonal dependence of the Soil model performance across the IMPROVE and STN 

monitors in the VISTAS region is clearly evident in the monthly bias time series plot in Figure 3-

10 that show a large (> 100%) overprediction bias in the winter and near zero (IMPROVE) or 

much lower (SEARCH) bias in the summer. As seen in the scatter plots in Figures B-22 through 

B-25 in Appendix B, the modeled Soil values tend to always be between 0 and 2 μg/m3 year 

round, however the observed values are much lower in the winter (0 to 0.5 μg/m3) and 

comparable to the modeled values in the summer. These results suggest that the poor Soil model 

performance in the winter, spring and fall is likely due to incorrect emission temporal adjustment 

factors. For example, the effect of wetted surfaces that suppresses fugitive dust emissions may 

not be properly characterized in the seasonal adjustments to the emissions inventory. 

The Bugle plots of bias and error (Figure 3-11) also display the seasonal dependence of the Soil 

model performance with the model achieving the model performance goals for Soil at the 

IMPROVE monitoring network for approximately half the year centered on the summer and not 

achieving both the model performance goals and criteria at the IMPROVE network for the other 

half centered on the winter. Performance for Soil across the SEARCH network is worse with the 

model not achieving either the performance goals or criteria for most months. 

The Soil model performance is confounded by the incommensurability between the modeled and 

measured Soil species. The IMPROVE observed Soil is built up off of measured elements (see 

Table 4-1). The modeled “Soil”, on the other hand, is fine particulate matter emissions that have 

not been explicitly identified and speciated as SO4, NO3, EC and OC in the PM speciation 

profiles used in the SMOKE emissions modeling. The emissions PM speciation profiles may 

have unidentified PM that is lumped in the other PM category that are not Soil. The measured 

Soil will typically be associated with fugitive dust emissions, so the separate tracking of fugitive 

dust may be an approach to separate the true Soil component from the other PM2.5 component. 

VISTAS investigated this issue with an inert (no chemistry) CMAQ sensitivity run that 

separately tracked just fugitive dust emissions and found improved Soil model performance with 

lower bias when just other PM2.5 concentrations due to just fugitive just emissions were mapped 

to the Soil species. 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-20 

VISTAS CMAQ 2002 Actual Base G2 12km

SOIL

-50

0

50

100

150

200

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Fractional Bias [%]

IMPROVE SEARCH

0

20

40

60

80

100

120

140

160

180

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Fractional Error [%]

Figure 3-10. Monthly Soil fractional bias (top) and fractional gross error (bottom) statistical 

measures for IMPROVE and SEARCH Daily monitoring sites in the VISTAS region. 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-21 

VISTAS CMAQ 2002 Actual Base 12km Bugle Plot

SOIL

-200

-150

-100

-50

0

50

100

150

200

012345678

Average Concentration (ug/m3)

Fractional Bias (%)

(+) Goal (-) Goal (+) Criteria (-) Criteria

Series6 IMPROVE SEARCH Series7

0

50

100

150

200

012345678

Average Concentration (ug/m3)

Fractional Error (%)

Figure 3-11. Bugle Plots of monthly fractional bias (top) and gross error (bottom) for Soil in the 

VISTAS region and comparison to model performance goals (blue) and criteria (yellow). 
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F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-22 

3.4.6 Coarse Mass (CM) Performance 

Observed Coarse Mass (CM) is defined as the difference between the PM10 mass and PM2.5 mass 

measurements. Since IMPROVE network only speciates the PM2.5 fraction of PM, then any 

coarse SO4 or NO3 would be in the CM measurement. In the model, on the other hand, CM is 

due solely to primary PM emissions that have been speciated to the coarse fraction (PM2.5-10). 

Thus, there are potential incompatibility issues with the measured and modeled CM 

concentrations. 

In addition, CM has a much shorter transport distance than fine PM. In the modeling this is 

accounted for though fugitive dust transport factors (FDTF) that adjust the fugitive dust 

emissions downward to account for the fact that these emissions are deposited locally and are not 

transported out of the grid cell where they are emitted. VISTAS used county-specific FDTF 

provided by EPA that ranged from 0.03 to 0.87 (i.e., reduced the fugitive dust emissions from 

97% to 13%). The level of the FDTF depends on land cover, areas with dense canopy vegetation 

have higher deposition rates (lower FDTF) than a desert area with no vegetation. Across the 

VISTAS domain, the FDTF typically ranged from 0.1 to 0.4 (i.e., 60% to 90% deposit out before 

being transported). 

Although use of the FDTF is appropriate for modeling transport of fugitive dust over long 

distances, the local fugitive dust emissions can impact an IMPROVE monitor. Thus, much of 

the measured CM at the IMPROVE monitors is likely local in nature and subgrid-scale to the 

model. In this case we would expect the model to underestimate the measured CM which is 

what is seen. 

Figure 3-12 displays the monthly fractional bias and gross error values for CM across the 

IMPROVE monitors in the VISTAS region. The underprediction bias ranges from -60% to 

-120% with errors of 80% to 140%. The Bugle Plot (Figure 3-13) shows that these performance 

statistics are either right at or exceeds the performance criteria indicating poor CM model 

performance. 

The poor CM performance is not as bad of a problem as indicated in Figures 3-12 and 3-13 since 

it has the lowest extinction efficiency and is usually not a major component of visibility 

impairment at VISTAS Class I areas. It also has no effect on the PM2.5 concentrations so would 

not affect the ASIP PM2.5 modeling and projections 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-23 

VISTAS CMAQ 2002 Actual Base G2 12km

CM

-140

-120

-100

-80

-60

-40

-20

0

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Fractional Bias [%]

IMPROVE

0

20

40

60

80

100

120

140

160

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Fractional Error [%]

Figure 3-12. Monthly coarse mass (CM) fractional bias (top) and fractional gross error (bottom) 

statistical measures for IMPROVE monitoring sites in the VISTAS region. 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-24 

VISTAS CMAQ 2002 Actual Base 12km Bugle Plot

CM

-200

-150

-100

-50

0

50

100

150

200

0 2 4 6 8 10

Average Concentration (ug/m3)

Fractional Bias (%)

(+) Goal (-) Goal (+) Criteria (-) Criteria

Series6 IMPROVE Series5 Series7

0

50

100

150

200

0 2 4 6 8 10

Average Concentration (ug/m3)

Fractional Error (%)

Figure 3-13. Bugle Plots of monthly fractional bias (top) and gross error (bottom) for coarse 

mass in the VISTAS region and comparison to model performance goals (blue) and criteria 

(yellow). 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-25 

3.4.7 Fine Particulate (PM2.5) Performance 

Figure 3-14 displays the monthly time series of fractional bias and error for PM2.5 mass 

concentrations for the IMPROVE, STN, SEARCH Daily, SEARCH Hourly and FRM networks 

in the VISTAS region. The model generally is exhibiting good performance for PM2.5

concentrations. For the first three and last three months of the year the model performs well for 

PM2.5 with low bias (<±20%) and error (<40%, except for SEARCH Hourly). During April 

through July the underprediction bias becomes larger and is in the -20% to -45% range and the 

errors are also larger. This is driven in part by the SO4 underprediction bias discussed 

previously as SO4 is a major component of PM2.5 in the summer and the VISTAS region. 

The Bugle Plots confirm the good performance for PM2.5 mass concentrations that achieves the 

model performance goals most of the time and always achieves the model performance criteria 

(Figure 3-15). 

The scatter plots in Figures B-32 through B-35 in Appendix B confirm that the performance for 

total PM2.5 mass is fairly good, albeit with a summer underestimation bias. 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-26 

VISTAS CMAQ 2002 Actual Base G2 12km

PM25

-50

-40

-30

-20

-10

0

10

20

30

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Fractional Bias [%]

IMPROVE STN SEARCH SEARCH_H FRM

0

10

20

30

40

50

60

70

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Fractional Error [%]

Figure 3-14. Monthly PM2.5 fractional bias (top) and fractional gross error (bottom) statistical 

measures in the VISTAS region. 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-27 

VISTAS CMAQ 2002 Actual Base 12km Bugle Plot

PM25

-200

-150

-100

-50

0

50

100

150

200

0 5 10 15 20 25 30 35 40

Average Concentration (ug/m3)

Fractional Bias (%)

(+) Goal (-) Goal (+) Criteria (-) Criteria blank

IMPROVE STN SEARCH SEARCH_H FRM

0

50

100

150

200

0 5 10 15 20 25 30 35 40

Average Concentration (ug/m3)

Fractional Error (%)

Figure 3-15. Bugle Plots of monthly fractional bias (top) and gross error (bottom) for PM2.5 in 

the VISTAS region and comparison to model performance goals (blue) and criteria (yellow). 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-28 

3.5 Evaluation at VISTAS and Nearby Class I Areas 

Projections of light extinction for the evaluation of regional haze are based on a subset of days 

for which there is monitored data. IMPROVE monitors collect 24-hour PM samples every third 

day. Observations of component species on these days are used to calculate light extinction, 

which is the basis on which these days are ranked. These rankings are then used to identify the 

20 percent most impaired days, or “worst 20 percent days”, and the 20 percent least impaired 

days, or “best 20 percent days”. EPA guidance recommends using the modeling results for the 

worst 20 percent and best 20 percent days from 2002 to make 2018 visibility projections (EPA, 

2007a). Thus, the model performance of the visibility impairment pollutants for the best and 

worst 20 percent days at the Class I areas is critically important for making reliable 2018 

visibility projections in the VISTAS modeling. Table C-1 in Appendix C lists the best 20 

percent and worst 20 percent days for all of Class I areas within and nearby the VISTAS region. 

Appendix C provides details on the CMAQ 2002 Actual Base G2 base case model performance 

evaluation at each Class I area within and near the VISTAS region for the worst and best 20 

percent days including model performance statistics, scatter plots, time series plots and other 

summary performance evaluation measures. Appendix D displays the spatial distribution of the 

predicted and observed PM species concentrations for each day from 2002 that is a best and 

worst 20 percent day for a VISTAS or nearby Class I areas (i.e., days in Table C-1 in Appendix 

C). Below in Section 3.5.1 we provide an example of the spatial distribution evaluation plots for 

one day (July 10, 2002) that are reproduced from Appendix D, the reader is referred to Appendix 

D for the spatial plots for the remainder of the best and worst 20 percent days during 2002. In 

sections 3.5.2 through 3.5.22 we summarize the CMAQ 12 km PM species model performance 

across the best and worst 20 percent days separately for each Class I area in or near the VISTAS 

region. This summary model performance uses Bugle Plots. Additional summaries of model 

performance for each Class I area and the best and worst 20 percent days are provided in 

Appendix C that includes summary tables of a complete set of model performance statistics and 

scatter plots of predicted and observed PM species concentrations for the best and worst 20 

percent days and time series of predicted and observed concentrations across all monitoring days 

in 2002. 

3.5.1 Example Spatial Distribution Model Performance 

Table 3-2 and Figure 3-16 reproduce the spatial model performance plots for one of the 

approximately 180 best or worst 20 percent days from Appendix D for July 10, 2002. For each 

of the worst or best 20 percent days in Appendix D, a table like the one shown in Table 3-2 for 

July 10, 2002 is presented that lists which Class I areas the day represents either a worst or best 

20 percent day. In the case of July 10, 2002, it is a worst 20 percent day for 13 Class I areas that 

are generally inland (exception for BRIG) and a best 20 percent day for 5 Class I area that tend 

to be in the southern (e.g., Florida) or coastal (e.g., ROMA) areas of the VISTAS region (Table 

3-2). 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-29 

Table 3-2. Class I areas with worst or best 20 percent days from 2002 occurring on July 10, 

2002. 

Date Julian Day Type Class I Areas Affected 

07/10/02 191 W20% LIGO, SHRO, GRSM, JARI, SIPS, CACR, DOSO, 

HEGL, COHU, MACA, UPBU, MING, BRIG 

07/10/02 191 B20% OKEF, BRET, CHAS, EVER, ROMA 

Figure 3-16 compares the spatial distributions of the predicted and observed PM species (Figures 

3-16a and 3-16b) and then total PM2.5 mass (Figure 3-16c) concentrations. In this case, the 

model is capturing the very high sulfate concentrations across the Appalachian Mountains and 

interior part of the VISTAS region well (Figure 3-16a, top left). It is also reproducing the much 

lower SO4 conditions in southern Georgia, Florida and the coastal regions of South Carolina and 

North Carolina. The low NO3 conditions are also reproduced well by the model and even the 

spatial distribution of OCM is reproduced well (Figure 3-16a). The spatial plot of the modeled 

and observed NH4 concentrations suggests that the model is underestimating the observed NH4 

(Figure 3-16a, bottom left). However, the IMPROVE monitoring network doesn’t measure NH4 

and the “observed” NH4 in Figure 3-16a is actually derived NH4 assuming that the SO4 and 

NO3 are completely neutralized by NH4. If SO4 is not completely neutralized by NH4 then the 

modeled NH4 should be less than the derived NH4 values which is the case. Previous VISTAS 

sensitivity analyses have found that modeled SO4 is not completely neutralized by NH4 all the 

time, especially in the summer. 

Both the modeled and observed EC levels are low and the model does a good job in reproducing 

the region’s lower less than 0.25 μg/m3

 on the coast versus areas with more elevated (0.5-1.5 

μg/m3) EC levels inland (Figure 3-16b, top left). The model does a good job in reproducing the 

Soil levels in the eastern part of the VISTAS domain, but fails to reproduce the higher values 

toward the western part of the VISTAS region and on into the CENRAP region. Although the 

model reproduces the CM levels over Missouri and adjacent regions, it is too clean in the 

VISTAS region where CM is underestimated. 

The Sea Salt performance looks poor with the model greatly underestimating the elevated 

observed values at the ROMA and EVER coastal sites. However, care must be taken in the 

interpretation of the Sea Salt performance. The new IMPROVE equation recommendation is to 

represent Sea Salt as 1.8 times the Chloride measurement and if the Chloride measurement is 

missing or below detection to substitute the Chlorine ion for the Chloride. However, Chloride 

was almost always missing so the Sea Salt evaluation is based on the Chlorine measurement. 

Another approach to obtain measured “Sea Salt” is to multiple the observed Sodium (Na) particle 

concentrations by 2.5. However, this was not recommended for the new IMPROVE equation 

since there are other sources of Na (e.g., fugitive dust). However, the Na measurement appears 

more stable than Chloride and perhaps future Sea Salt evaluations should use the Na 

measurement at coastal sites as well. Although the model Sea Salt is not used in the visibility 

projections (the new IMPROVE equation uses the measured Sea Salt and holds it constant 

between the Baseline and 2018), the Sea Salt underprediction may affect the NO3 performance 

as discussed previously. 

Figure 3-16c displays the model and IMPROVE measured spatial distribution of PM2.5 mass 

across the VISTAS 12 km modeling domain. The model does an excellent job in reproducing 

the spatial distribution of the observed total PM2.5 concentrations on this day, which is not 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-30 

surprising since it is dominated by SO4 for which the model performs well. The gradients in the 

observed PM2.5 concentrations between the higher values across the southern Appalachian 

Mountains to the lower values at the coast and to the south is reproduced well by the model 

along with the higher values in the CENRAP region. The elevated observed PM2.5

concentrations in the northern Appalachian Mountains and across to BRIG are underrepresented 

by the model. 

Appendix D contains similar spatial evaluations for all of the worst and best 20 percent days 

during 2002 at VISTAS and nearby Class I areas. The reader is referred to Appendix D for 

further analysis of the spatial model performance of the CMAQ 2002 Base G2 Actual base case 

simulation. 

Figure 3-16a. Predicted and observed spatial maps of daily average sulfate (SO4) (top left), 

nitrate (NO3) (top right), ammonium (NH4) (bottom left), and Organic Carbon Mass (OCM) 

concentrations (μg/m3

) for July 10, 2002. 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-31 

Figure 3-16b. Predicted and observed spatial maps of daily average elemental carbon (EC) 

(top left), coarse mass (CM) (top right), sea salt (NaCl) (bottom left), and Soil (bottom right) 

concentrations (μg/m3

) for July 10, 2002. 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-32 

Figure 3-16c. Predicted and observed spatial maps of daily average total fine particulate 

matter (PM2.5) concentration (μg/m3

) for July 10, 2002. 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-33 

3.5.2 Sipsey (SIPS), Alabama Best and Worst 20 Percent Days Performance 

Figure 3-17a and 3-17b display Bugle Plots of fractional bias and gross error by PM species for 

the SIPS Class I area and the, respectively, best 20 percent and worst 20 percent days in 2002. 

For the best 20 percent days, OCM is the best performing species followed by NO3 and EC 

which all achieve the model performance goal. SO4 and Soil achieve the performance criteria 

but not the performance goal, and CM performance is the worst not achieving the performance 

criteria for the best 20 percent days. 

On the worst 20 percent days, EC, Soil and OCM all achieve the performance goal, although 

because of the low concentrations of the EC and Soil they benefit from being in the flared 

portion of the Bugle Plot. NO3, SO4 and CM fail to achieve both the performance goal and 

criteria. Due to its high concentrations and high extinction efficiency SO4 is the most important 

species and it is encouraging that it is right at the performance criteria for the worst 20 percent 

days at Sipsey. 

Additional model performance statistics for Sipsey and the best and worst 20 percent days are 

provided in Tables C-4t and C-5t in Appendix C. Also in Appendix C are scatter plots of 

predicted and observed extinction by species for the best and worst 20 percent days at Sipsey 

(Figure C-3). Finally, time series of predicted and observed PM component species 

concentrations for all days during 2002 and Sipsey are provided in Figure C-7. 

3.5.3 Chassahowitzka (CHAS), Florida Best and Worst 20 Percent Days Performance 

The performance of the CMAQ model for the best and worst 20 percent days at the CHAS Class 

I area is shown in Figures 3-18 and C-8 through C-12. For the best 20 percent days, EC, Soil 

and SO4 achieve the model performance goal and OCM achieves the model performance 

criteria. However, NO3 and CM fall outside of the performance criteria. CM has the highest 

concentrations for the best 20 percent days at CHAS that is over twice as high as the other 

species and just barely fails to achieve the performance criteria. NO3 has the lowest 

concentrations of all the species. 

On the worst 20 percent days (Figure 3-18b), EC and CM achieve the performance goal and 

OCM is right at the goal. SO4 achieves the performance criteria but not the performance goal 

and the Soil overprediction bias and error makes it fall outside of the performance criteria range. 

3.5.4 Everglades (EVER), Florida Best and Worst 20 Percent Days Performance 

EC, Soil and OC achieves the performance goal for the best 20 percent days at EVER, SO4 is 

right at the criteria and CM and NO3 fall out of the criteria range (Figure 3-19a). In fact NO3 

bias and error are pegged at -200% and 200% which suggests that it is near zero most of the time 

for the best 20 percent of the days at EVER. For the worst 20 percent days at EVER, NO3 and 

CM are again the poorest performing species falling out of the performance criteria range with 

SO4 just falling out of the range for bias, but within the criteria range for error. Again NO3 bias 

and error are almost at -200% and 200% indicating near zero concentrations. As noted in 

Chapters 4 and 5, the CMAQ 12 km near zero NO3 concentrations at EVER does affect the 2018 

visibility projections. 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-34 

Figure 3-17a. Bugle Plots of Fractional Bias and Fractional Gross Error for Sipsey, 

Alabama and the Best 20 Percent Days. 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-35 

Figure 3-17b. Bugle Plots of Fractional Bias and Fractional Gross Error for Sipsey, 

Alabama and the Worst 20 Percent Days. 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-36 

Figure 3-18a. Bugle Plots of Fractional Bias and Fractional Gross Error for CHAS and 

the Best 20 Percent Days. 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-37 

Figure 3-18b. Bugle Plots of Fractional Bias and Fractional Gross Error for CHAS and 

the Worst 20 Percent Days. 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-38 

Figure 3-19a. Bugle Plots of Fractional Bias and Fractional Gross Error for EVER and 

the Best 20 Percent Days. 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-39 

Figure 3-19b. Bugle Plots of Fractional Bias and Fractional Gross Error for EVER and 

the Worst 20 Percent Days. 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-40 

3.5.5 St. Marks (SAMA), Florida Best and Worst 20 Percent Days Performance 

The SAMA model performance for the best/worst 20 percent days is similar to the other Class I 

areas with CM being the biggest performance problem followed by NO3 and the remainder of 

the species performing fairly well. NO3 has worse performance statistics than SO4 but much 

lower mass so is less of a concern. 

3.5.6 Cohutta (COHU), Georgia Best and Worst 20 Percent Days Performance 

Better performance is seen at the COHU Class I area for the best and worst 20 percent days 

(Figure 3-21). For the best 20 percent days all PM species achieve the model performance goal, 

except CM that achieves the performance criteria but not the goal. For the worst 20 percent days 

at COHU, EC, NO3, Soil and OC achieve the performance goal, SO4 achieves the performance 

criteria and CM achieves the criteria for bias but not for error. 

3.5.7 Okefenokee (OKEF), Georgia Best and Worst 20 Percent Days Performance 

Performance for the PM species and the best and worst 20 percent days mostly achieves the 

model performance goals and/or criteria for all species by CM (Figure 3-22). The SO4 

performance on the worst 20 percent days achieves the performance criteria but not the 

performance goal. 

3.5.8 Mammoth Cave (MACA), Kentucky Best and Worst 20 Percent Days Performance] 

MACA has a several species right at the performance goal or criteria level for the best 20 percent 

days, with Soil barely exceeding the criteria for bias and CM barely exceeding in for error 

(Figure 3-23). For the worst 20 percent days, EC is the only species that clearly achieves the 

performance goal with Soil and SO4 right at the performance goal and OCM right at the 

performance criteria. 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-41 

Figure 3-20a. Bugle Plots of Fractional Bias and Fractional Gross Error for SAMA and 

the Best 20 Percent Days. 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-42 

Figure 3-20b. Bugle Plots of Fractional Bias and Fractional Gross Error for SAMA and 

the Worst 20 Percent Days. 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-43 

Figure 3-21a. Bugle Plots of Fractional Bias and Fractional Gross Error for COHU and 

the Best 20 Percent Days. 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-44 

Figure 3-21b. Bugle Plots of Fractional Bias and Fractional Gross Error for COHU and 

the Worst 20 Percent Days. 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-45 

Figure 3-22a. Bugle Plots of Fractional Bias and Fractional Gross Error for OKEF and 

the Best 20 Percent Days. 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-46 

Figure 3-22b. Bugle Plots of Fractional Bias and Fractional Gross Error for OKEF and 

the Worst 20 Percent Days. 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-47 

Figure 3-23a. Bugle Plots of Fractional Bias and Fractional Gross Error for MACA and 

the Best 20 Percent Days. 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-48 

Figure 3-23b. Bugle Plots of Fractional Bias and Fractional Gross Error for MACA and 

the Worst 20 Percent Days. 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-49 

3.5.9 Linville Gorge (LIGO), North Carolina Best and Worst 20 Percent Days 

Performance 

Performance for the best 20 percent days at LIGO is fairly good with all species achieving the 

performance goal except CM that does not even achieve the performance criteria (Figure 3-24). 

Similar good performance is seen for the worst 20 percent days at LIGO with most species 

achieving the performance goal except SO4 that achieves the performance criteria and CM that 

does not meet either the performance goal or criteria. 

3.5.10 Shining Rock (SHRO), North Carolina Best and Worst 20 Percent Days 

Performance 

With the exception of CM, fairly good model performance for the best and worst 20 percent days 

is also seen at SHRO (Figure 3-25). On the best days all species except CM achieve the 

performance criteria and on the worst days there are similar results only SO4 just barely achieves 

the performance criteria. 

3.5.11 Swanquarter (SWAN), North Carolina Best and Worst 20 Percent Days 

Performance 

CM is the poorest performance species at SWAN, although NO3 performance also falls out of 

the criteria range for the worst 20 percent days (Figure 3-26). SO4 does exceed the performance 

goal range but achieves the performance criteria. 

3.5.12 Cape Romain (ROMA), South Carolina Best and Worst 20 Percent Days 

Performance 

For the best 20 percent days at ROMA, only EC and Soil are clearly achieving the performance 

goal with NO3, OC, SO4 and CM between the performance goal and criteria (Figure 3-27). On 

the worst 20 percent days, EC Soil and CM achieve the performance goal and OC and SO4 

achieve the performance criteria. But NO3 falls out of the performance criteria range, but has 

low concentrations. 

3.5.13 Great Smoky Mountains (GRSM), Tennessee-North Carolina Best and Worst 20 

Percent Days Performance 

Performance for the best 20 percent days at GRSM is fairly good with Soil and CM the only 

species exhibiting performance issues (Figure 3-28). For the worst 20 percent days, CM is the 

only species that falls outside of the criteria range, with SO4 just at the criteria and other species 

achieving the performance goal. 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-50 

Figure 3-24a. Bugle Plots of Fractional Bias and Fractional Gross Error for LIGO and 

the Best 20 Percent Days. 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-51 

Figure 3-24b. Bugle Plots of Fractional Bias and Fractional Gross Error for LIGO and 

the Worst 20 Percent Days. 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-52 

Figure 3-25a. Bugle Plots of Fractional Bias and Fractional Gross Error for SHRO and 

the Best 20 Percent Days. 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-53 

Figure 3-25b. Bugle Plots of Fractional Bias and Fractional Gross Error for SHRO and 

the Worst 20 Percent Days. 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-54 

Figure 3-26a. Bugle Plots of Fractional Bias and Fractional Gross Error for SWAN and 

the Best 20 Percent Days. 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-55 

Figure 3-26b. Bugle Plots of Fractional Bias and Fractional Gross Error for SWAN and 

the Worst 20 Percent Days. 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-56 

Figure 3-27a. Bugle Plots of Fractional Bias and Fractional Gross Error for ROMA and 

the Best 20 Percent Days. 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-57 

Figure 3-27b. Bugle Plots of Fractional Bias and Fractional Gross Error for ROMA and 

the Worst 20 Percent Days. 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-58 

Figure 3-28a. Bugle Plots of Fractional Bias and Fractional Gross Error for GRSM and 

the Best 20 Percent Days. 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-59 

Figure 3-28b. Bugle Plots of Fractional Bias and Fractional Gross Error for GRSM and 

the Worst 20 Percent Days. 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-60 

3.5.14 James River Face Wilderness Area (JARI), Virginia Best and Worst 20 Percent 

Days Performance 

As seen for the other Class I areas, CM is the only clear performance problem at JARI for the 

best and worst 20 percent days with all other species achieving the performance goals except 

SO4 for the best and SO4 and OCM for the worst 20 percent days that just achieve the 

performance criteria (Figure 3-29). 

3.5.15 Shenandoah (SHEN), Virginia Best and Worst 20 Percent Days Performance 

The NO3 performance at SHEN for the best 20 percent days is a cause for concern, with most 

other species achieving or nearly achieving the performance criteria (Figure 3-30). On the worst 

20 percent days CM is the only species that is clearly performing poorly with the other species 

except SO4 achieving the performance goal with SO4 falling between the performance goal and 

criteria. 

3.5.16 Dolly Sods (DOSO), West Virginia Best and Worst 20 Percent Days Performance 

Performance at DOSO is fairly good for the best and worst 20 percent days with most species 

achieving the performance goal and almost all species achieving the performance criteria (Figure 

3-31). The exception is CM for the worst 20 percent days that is underpredicted by over -100%. 

3.5.17 Breton Island (BRET), Louisiana Best and Worst 20 Percent Days Performance 

The performance at BRET is a cause for concern on the worst 20 percent days since the SO4, OC 

and CM all fail to achieve the performance criteria due to large underprediction biases (Figure 3-

32). Better performance is seen on the best 20 percent days. 

3.5.18 Caney Creek (CACR) and Upper Buffalo (UPBU), Arkansas Best and Worst 20 

Percent Days Performance 

Performance at CACR (Figure 3-33) and UPBU (Figure 3-34) is similar. For the best days only 

CM is a problem species. And on the worst days CM is the main problem species but SO4 is 

also underestimated, although it still achieves the performance criteria. 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-61 

Figure 3-29a. Bugle Plots of Fractional Bias and Fractional Gross Error for JARI and 

the Best 20 Percent Days. 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-62 

Figure 3-29b. Bugle Plots of Fractional Bias and Fractional Gross Error for JARI and 

the Worst 20 Percent Days. 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-63 

Figure 3-30a. Bugle Plots of Fractional Bias and Fractional Gross Error for SHEN and 

the Best 20 Percent Days. 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-64 

Figure 3-30b. Bugle Plots of Fractional Bias and Fractional Gross Error for SHEN and 

the Worst 20 Percent Days. 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-65 

Figure 3-31a. Bugle Plots of Fractional Bias and Fractional Gross Error for DOSO and 

the Best 20 Percent Days. 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-66 

Figure 3-31b. Bugle Plots of Fractional Bias and Fractional Gross Error for DOSO and 

the Worst 20 Percent Days. 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-67 

Figure 3-32a. Bugle Plots of Fractional Bias and Fractional Gross Error for BRET and 

the Best 20 Percent Days. 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-68 

Figure 3-32b. Bugle Plots of Fractional Bias and Fractional Gross Error for BRET and 

the Worst 20 Percent Days. 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-69 

Figure 3-33a. Bugle Plots of Fractional Bias and Fractional Gross Error for CACR and 

the Best 20 Percent Days. 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-70 

Figure 3-33b. Bugle Plots of Fractional Bias and Fractional Gross Error for CACR and 

the Worst 20 Percent Days. 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-71 

 

Figure 3-34a. Bugle Plots of Fractional Bias and Fractional Gross Error for UPBU and 

the Best 20 Percent Days. 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-72 

Figure 3-34b. Bugle Plots of Fractional Bias and Fractional Gross Error for UPBU and 

the Worst 20 Percent Days. 
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F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-73 

3.5.19 Mingo (MING) and Hercules Glade (HEGL), Missouri Best and Worst 20 Percent 

Days Performance 

At the two Missouri Class I areas for the best 20 percent days, the Soil performance exhibits a 

large overprediction bias that exceeds all goals and criteria and the CM performance is right at 

the goal or between the goal and criteria. Performance for the remainder of the species achieves 

the performance goal (Figures 3-25 and 3-36). The overprediction of Soil is also a problem for 

the worst 20 percent days at these two sites. Although SO4 performs fairly well at MING for the 

worst 20 percent days (Figure 3-35b), it is underestimated at HEGL resulting in a fractional bias 

that is right at the performance criteria (Figure 3-36b). 

3.5.20 Brigantine (BRIG), New Jersey Best and Worst 20 Percent Days Performance 

With the exception of CM for both the best and worst 20 percent days and NO3 error for the 

worst 20 percent days, all PM species achieve the performance criteria for the best and worst 20 

percent days at BRIG. The SO4 performance is also a concern as the underprediction is severe 

enough so that it falls between the performance goal and criteria (Figure 3-37). 
 March 2009 

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Figure 3-35a. Bugle Plots of Fractional Bias and Fractional Gross Error for MING and 

the Best 20 Percent Days. 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-75 

Figure 3-35b. Bugle Plots of Fractional Bias and Fractional Gross Error for MING and 

the Worst 20 Percent Days. 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-76 

 

Figure 3-36a. Bugle Plots of Fractional Bias and Fractional Gross Error for HEGL and 

the Best 20 Percent Days. 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-77 

Figure 3-36b. Bugle Plots of Fractional Bias and Fractional Gross Error for HEGL and 

the Worst 20 Percent Days. 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-78 

Figure 3-37a. Bugle Plots of Fractional Bias and Fractional Gross Error for BRIG and 

the Best 20 Percent Days. 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-79 

Figure 3-37b. Bugle Plots of Fractional Bias and Fractional Gross Error for BGIG and 

the Worst 20 Percent Days. 
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F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-80 

3.6 Conclusions of Model Performance Evaluation 

As noted previously, the CMAQ modeling results for the best and worst 20 percent days are used 

to project the observed PM concentrations from the 2000-2004 Baseline to 2018 which the 2018 

visibility projections are based on. Thus, model performance for these days is fairly critical. 

There are several common themes in the model performance of the CMAQ model for the best 

and worst 20 percent days at Class I areas within and near the VISTAS region: 

• Poor CM Performance: Coarse Matter is consistently underestimated by the model for 

the best and worst 20 percent days such that is rarely achieves the model performance 

goal or criteria. As noted previously, this is likely partially due to local fugitive dust 

impacts that are subgrid-scale to the model. CM has a low extinction efficiency and is a 

minor contributor to visibility impairment in the VISTAS region, so the CM performance 

issues are probably not a big issue for the worst 20 percent days. For the best 20 percent 

days CM is a larger component of the extinction budget. If in fact CM at the IMPROVE 

monitors is local fugitive dust impacts (e.g., wind blown dust), then it may be appropriate 

to hold the CM constant between the 2000-2004 Baseline and 2018. This issue is 

addressed in Chapter 5 in a sensitivity analysis that assumes CM alone and both CM and 

Soil are natural and are held constant in the 2018 visibility projections for the worst 20 

percent days. This sensitivity analysis found essentially no difference in the 2018 

visibility projections at VISTAS Class I areas when CM alone and both CM and Soil 

were assumed natural and held constant between 2000-2004 and 2018. This was because 

there is very little change in the CM emissions between the 2002 and 2018 emission 

scenarios so that the RRFs are near unity anyway. Thus, the CM performance issues are 

not a cause for concern in the VISTAS regional haze modeling. 

 

• Nitrate Underprediction for Worst 20 Percent Days: NO3 is frequently underpredicted at 

Class I areas for the worst 20 percent days. Even though the NO3 concentrations are low 

so that their performance statistics are in the flared horn of the Bugle Plots, they 

frequently still fail to achieve the performance goal or criteria. However, NO3 is usually 

a minor contributor to visibility impairment at VISTAS Class I areas for the worst 20 

percent days and most of the time for the best 20 percent days as well. An examination 

of the extinction budgets for the worst and best 20 percent days (see Stacked Bar Charts 

in Appendix E and F) reveals that there are occasionally high NO3 days. For the worst 

20 percent days most days are summer SO4 days where the observed and modeled NO3 

concentrations are extremely low, although occasionally there are non-summer NO3 days 

as well. In this case, the NO3 RRFs used in the 2018 projections will be dominated by 

the few NO3 days. Given the low contribution of NO3 to visibility impairment in the 

VISTAS region at this time (as SO2 is controlled NO3 may become a larger component 

of the extinction budget), then the NO3 performance is not a big cause for concern. The 

exception to this is the situation at EVER where the CMAQ 12 km modeled NO3 

concentrations are near zero for all of the worst 20 percent days. We suspect that model 

noise may be affecting the NO3 RRFs and the CMAQ 12 km 2018 visibility projections 

presented in Chapter 4. This is addressed with a 2018 visibility projection sensitivity 

analysis that holds the NO3 constant between the 2000-2004 Baseline and 2018 if the 

2002 modeled average NO3 concentrations across the worst 20 percent days is less than a 

tenth of a μg/m3 which is presented in Chapter 5. The NO3 sensitivity analysis did show 

that the CMAQ 12 km 2018 visibility projections are affected by the low NO3 

concentrations at EVER. 
 March 2009 

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• Sulfate Underprediction Bias for Worst 20 Percent Days: SO4 is consistently 

underpredicted across the best and worst 20 percent days and frequently exceeded the 

model performance goal and sometimes even the performance criteria. This is especially 

important for the worst 20 percent days when SO4 is highest and dominates the visibility 

impairment at most VISTAS Class I areas. It is also important because most of the 

visibility improvements between the 2000-2004 Baseline and 2018 are due to reductions 

in SO4. If the model is estimating much cleaner SO4 conditions in 2002 for the worst 20 

percent days than it should, the SO4 RRFs will be stiffer than they should be (i.e., show 

less reductions) because background SO4 is a higher fraction of the total SO4. An 

examination of the Stacked Bar Charts of predicted and observed visibility extinction for 

the worst 20 percent days in Appendix E reveals that most of the SO4 underprediction 

bias on the worst 20 percent days is driven by the model predicting low SO4 on a few of 

the high observed SO4 days, while predicting higher SO4 on others. As the modeled 

SO4 RRFs that are used to project the 2000-2004 Baseline to 2018 are based on the 

average of the SO4 concentrations across the worst/best 20 percent days, then the RRFs 

will in fact be dominated by the higher modeled SO4 days where the model is actually 

performing reasonably well. The inclusion of the low modeled SO4 days in the RRFs 

will make them slightly stiffer than they should be and this would add an element of 

conservatism in the analysis (i.e., 2018 visibility projections that show less visibility 

improvements then will really occur). This issue is also addressed in the Additional 

Analysis presented in Chapter 5 where the SO4 RRFs are based on only those worst 20 

percent days in which the modeled and observed SO4 concentrations are within a factor 

of 2 of each other. Again, the sensitivity analysis demonstrates that the low modeled 

SO4 concentrations on the worst 20 percent days did not significantly affect the 2018 

visibility projections. 
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F:\VISTAS Phase II\TSD\draft#4\Chapter_4_VisProj4.doc 4-1 

4.0 VISIBILITY PROJECTIONS 

This section presents the future-year visibility projections for Class I areas within and near the 

VISTAS region and their comparison with the 2018 Uniform Rate of Progress. Most of the work 

on the visibility projections for the VISTAS Class I areas was performed using the CMAQ 36/12 

km modeling results for the 2002 and 2012 Base G2 emission scenarios whose emissions were 

released in July 2007. Since then several VISTAS States have made revisions to emissions for 

some point sources based on improved information on controls and shutdowns to generate a 

2018 Base G4 Best and Final (BaF) emissions inventory that was released in October 2007. The 

change in total VISTAS region point source emissions between the 2018 Base G2 and 2018 Base 

G4/BaF was very small (~1%), as noted in Table 2-14 in Section 2.6. The 2018 visibility 

projections for the 2018 Base G4 Best and Final scenario are presented at the end of this Chapter. 

As noted in Chapter 1, the Regional Haze Rule (RHR) requires states with Class I areas to 

develop State Implementation Plans (SIPs) that include reasonable progress goals for improving 

visibility in each Class I area and emission reduction measures to meet those goals. For the 

initial SIPs due in December 2007, states are required to adopt reasonable progress goals for 

improving visibility from observed Baseline Conditions. The 2000-2004 five-year period is used 

to define Baseline Conditions and the first future progress period is 2018. A state is required to 

set reasonable progress goals for each Class I area in the state for two visibility metrics: 

• Provide for an improvement in visibility for the most impaired visibility days (i.e., the 

worst 20 percent days); and 

• Ensure no degradation in visibility for the least impaired visibility days (i.e., the best 20 

percent days). 

The reasonable progress goals are to provide for a rate of improvement sufficient to be on a 

course to attain “Natural Conditions” by 2064. States are to define controls to meet reasonable 

progress goals every 10 years, starting in 2018, which defines progress periods ending in 2018, 

2028, 2038, 2048, 2058 and finally 2064. Reasonable progress goals are defined by the states to 

include all reasonable control measures for each review period. States will determine whether 

they are meeting their goals by comparing visibility conditions from one five-year period to 

another (e.g., 2000-2004 to 2013-2017). As stated in 40 CFR 51.308 (d) (1), baseline visibility 

conditions, reasonable progress goals, and changes in visibility must be expressed in terms of 

deciview (dv) units. The haze index (HI) metric of visibility impairment, in deciviews, is 

derived from light extinction (bext) as follows: 

HI = 10 ln (bext/10), 

Where light extinction (bext) is expressed in terms of inverse megameters (Mm-1 = 10-6 m

-1). 

Light extinction (bext) is calculated using the observed fine particulate concentrations from the 

IMPROVE monitors using either the original or the new IMPROVE aerosol extinction equation. 

Both equations are discussed below. 
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4.1 Guidance for Visibility Projections 

EPA has published several guidance documents that relate to how modeling results should be 

used to project future-year visibility and how states should define reasonable progress goals: 

“Guidance on the Use of Models and Other Analyses for Demonstrating Attainment of 

Air Quality Goals for Ozone, PM2.5 and Regional Haze” (EPA, 2007a). 

“Guidance for Tracking Progress Under the Regional Haze Rule” (EPA, 2003a). 

“Guidance for Estimating Natural Visibility Conditions Under the Regional Haze Rule” 

(EPA, 2003b). 

“Guidance for Setting Reasonable Progress Goals Under the Regional Haze Program” 

(EPA, 2007b). 

The first EPA modeling guidance document listed above (EPA, 2007a) discusses the use of 

modeling results to project future-year visibility. The second EPA guidance document (EPA, 

2003a) focuses on monitored visibility, how to define the visibility Baseline Conditions and how 

to track visibility goals. The third EPA guidance document (EPA, 2003b) discusses procedures 

for defining Natural Conditions for a Class I area. Natural Conditions are the visibility goal for 

2064. Although states may propose alternative approaches for defining Natural Conditions, in 

this section we use the EPA default Natural Conditions at Class I areas updated to the new 

IMPROVE equation as available on the VIEWS website. The final EPA guidance document 

discusses how states should define their reasonable progress goals and their relationship to the 

2018 Uniform Rate of Progress. 

The EPA documents discussed above are followed for the visibility projections presented in this 

section with one notable exception. Some of the EPA documents are based on the original 

IMPROVE equation (e.g., EPA, 2003a,b). The VISTAS visibility projections are based on the 

new IMPROVE equation, although projections based on the original IMPROVE equation are 

also presented as an alternative approach in Chapter 5. 

4.2 Calculation of Visibility and 2018 Uniform Rate of Progress from IMPROVE 

Measurements 

EPA guidance recommends using the model in a relative sense to project future-year visibility 

conditions (EPA, 2007a). This projection is made using Relative Response Factors (RRFs) that 

are defined as the ratio of the future-year to the base-year modeling results. The RRFs are 

applied to the baseline visibility conditions to project future-year visibility. The major features 

of EPA’s recommended visibility projection approach are as follows (EPA, 2003a, b; 2007a): 

• Monitored data are used to define current visibility Baseline Conditions using 

IMPROVE monitoring data from the 2000-2004 five-year period. 

• Monitored concentrations of PM10 are divided into six major components (note that 

Sea Salt is also included in the new IMPROVE equation and held constant between 
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F:\VISTAS Phase II\TSD\draft#4\Chapter_4_VisProj4.doc 4-3 

the current and future years), the first five of which are assumed to be PM2.5 and the 

sixth is coarse mass (CM or PM2.5-10). 

3⁄4 SO4 (sulfate) that is assumed to be ammonium sulfate [(NH4)2SO4]; 

3⁄4 NO3 (particulate nitrate) that is assumed to be ammonium nitrate [NH4NO3]; 

3⁄4 OC (organic carbon) that is assumed to be total organic carbon mass (OCM) 

3⁄4 EC (elemental carbon); 

3⁄4 IP (other fine inorganic particulate or Soil); and 

3⁄4 CM (coarse mass). 

• Models are used in a relative sense to develop RRFs between baseline and future 

predicted concentrations of each component. 

• PM component-specific RRFs are multiplied by observed Baseline monitored values 

to estimate future-year PM component concentrations. 

• Estimates of future-year component concentrations are consolidated to provide an 

estimate of future-year air quality and visibility using either the original or new 

IMPROVE equation. 

• Future-year model projected visibility is compared with the 2018 Uniform Rate of 

Progress to assist in evaluating the visibility improvements. 

• It is assumed 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]. 

In order to facilitate tracking visibility progress, three important visibility concepts are required 

for each Class I area: 

Baseline Conditions: Baseline Conditions represent visibility for the 20 percent best (B20%) 

and 20 percent worst (W20%) visibility days for the initial five-year baseline period of the 

regional haze program. Baseline Conditions are calculated using IMPROVE monitor data 

collected during the 2000-2004 five-year period and are the starting point in 2004 for the 

Uniform Rate of Progress glidepath and 2018 visibility projections. The baseline data are 

available on the VIEWS website (http://vista.cira.colostate.edu/views/). 

Natural Conditions: Estimates of natural visibility conditions for the best 20 percent and 

worst 20 percent days at a Class I area (i.e., visibility conditions that would be experienced in 

the absence of human-caused impairment). EPA has defined a set of default Natural 

Conditions (EPA, 2003b) that have been updated for the new IMPROVE equation on the 

VIEWS website and are used in this section. 

2018 Uniform Rate of Progress: The 2018 Uniform Rate of Progress is defined by 

constructing a linear glidepath in deciviews starting with the observed 2000-2004 Baseline 

Conditions in 2004 and ending at Natural Conditions in 2064 for each Class I area. Where 

the linear glidepath passes through 2018 is the 2018 Uniform Rate of Progress in deciviews. 
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F:\VISTAS Phase II\TSD\draft#4\Chapter_4_VisProj4.doc 4-4 

4.2.1 Calculation of Visibility from IMPROVE PM Measurements 

Baseline Conditions for Class I areas are calculated using the procedures in EPA’s guidance 

documents (EPA, 2003a; 2007a) and fine and coarse particulate matter concentrations measured 

at IMPROVE monitors (Malm et al., 2000; Debell et al., 2006). Currently, each Class I area in 

the VISTAS region has an associated IMPROVE monitor. The IMPROVE monitors do not 

directly measure visibility, but instead measure speciated fine particulate (PM2.5) and total PM2.5

and PM10 mass concentrations from which visibility is obtained through the IMPROVE equation. 

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

measurements for six PM species: 

• Sulfate [(NH4)2SO4]; 

• Particulate Nitrate [(NH4NO3]; 

• Organic Carbon Matter or Organic Mass by Carbon [OCM]; 

• Elemental Carbon [EC] or Light Absorbing Carbon [LAC]; 

• Other fine particulate [Soil]; and 

• Coarse Matter or Coarse Mass [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 of light extinction. For example, in the IMPROVE equation sulfate and particulate 

nitrate are assumed to be completely neutralized by ammonium. In addition, only the fine mode 

(PM2.5) of PM is speciated by the IMPROVE monitor to obtain sulfate and nitrate measurements 

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

IMPROVE measurement). Concentrations for the above six components of light extinction in 

the IMPROVE equation are obtained from the IMPROVE measured species using the mappings 

shown in Table 4-1: 

Table 4-1. Definition of IMPROVE PM Components from Measured IMPROVE Species. 

IMPROVE Component IMPROVE Measured Species 

Sulfate 1.375 x (3 x S) 

Nitrate 1.29 x NO3 

-

OCM 1.4*OC (original IMPROVE) and 1.8*OC (new IMPROVE) 

LAC EC 

Soil 2.2*AL + 2.49*SI + 1.63*CA + 2.42*FE + 1.94*TI 

CM MT – MF 

Where: 

• S is elemental sulfur as determined from proton induced x-ray emissions (PIXE) analysis 

of the IMPROVE Module A1

. To estimate the mass of the sulfate ion (SO4

=

), S is 

multiplied by 3 to account the presence of oxygen. If S is missing then the sulfate (SO4) 

measured by ion chromatography analysis of the Module B is used to replace (3 x S). For 

 

1

 The IMPROVE sampler consists of four independent modules (A, B, C and D). Each module incorporates a 

separate inlet, filter pack and pump assembly and are controlled by a common timing mechanism. Module A 

measures fine PM mass and elements. Module B measures sulfate and nitrate ions. Module C measures EC and 

OC. Module D measures PM10 mass. (see http://vista.cira.colostate.edu/improve/ for more details). 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chapter_4_VisProj4.doc 4-5 

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 the 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 Carbon Mass (OCM) using the original IMPROVE equation and multiplied by 

1.8 for the new IMPROVE equation. This adjustment of the measured OC accounts for 

mass due to other elements in the OCM besides Carbon (e.g., oxygen). IMPROVE also 

uses the term Particulate Organic Matter (POM) to describe OCM. 

• 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. Since K and FE are products of the 

combustion of vegetation, they are both represented in the formula by 0.6 x Fe and K is 

not shown explicitly. 

• MT and MF are total PM10 and PM2.5 mass, respectively whose difference gives CM. 

4.2.1.1 Original and New IMPROVE Equations 

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

μg/m3

) to light extinction (in inverse megameters, Mm-1). Sulfate and nitrate are hygroscopic 

which means that they can absorb water from the atmosphere which changes their extinction 

efficiency. This is accounted for through relative humidity adjustment factors [f(RH)] that 

increase the particle’s extinction efficiency with increasing RH to account for the particles taking 

on water Note that some OCM may also have hygroscopic properties, but the IMPROVE 

equations assume OCM is non-hygroscopic. 

There are currently two IMPROVE equations that are used to convert PM concentrations to light 

extinction, the original (or old) and the new (or revised) IMPROVE equations. 

4.2.1.1.1 Original IMPROVE Equation 

The original IMPROVE equation that converts PM species concentrations to light extinction is 

given as follows (Malm et al., 2000): 

bSulfate = 3 x f(RH) x [Sulfate] 

bNitrate = 3 x f(RH) x [Nitrate] 

bEC = 10 x [EC] 

bOCM = 4 x [OCM] 

bSoil = 1 x [Soil] 

bCM = 0.6 x [CM] 

Monthly average f(RH) factors are used as recommended in EPA’s guidance (EPA, 2003a). 

These values are available in the final EPA guidance document (EPA, 2003a) and at: 

ftp://ftp.saic.com/raleigh/RegionalHaze_2002FRHcurve/fRH_analysis/. 
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F:\VISTAS Phase II\TSD\draft#4\Chapter_4_VisProj4.doc 4-6 

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

species listed above plus Rayleigh (blue sky) background (bRay) that is assumed to be 10 Mm-1. 

 bext = bRay + bSulfate + bNitrate + bEC +bOCM + bSoil + bCM

The total light extinction (bext) in Mm-1 is related to visual range (VR) in km using the following 

relationship: 

 VR = 3912 / bext, 

for bext in Mm-1. 

The Regional Haze Rule requires that visibility be expressed in terms of a haze index (HI) in 

units of deciviews (dv), which is calculated as follows: 

 HI = 10 ln(bext/10) 

4.2.1.1.2 New IMPROVE Equation 

The new IMPROVE equation is nonlinear in SO4, NO3 and OCM concentrations accounting for 

the different light scattering efficiency characteristics as a function of concentrations for these 

three species (DeBell et al., 2006). It is expressed as follows: 

bSulfate = 2.2 x fS(RH) x [Small Sulfate] + 4.8 fS(RH) x [Large Sulfate] 

bNitrate = 2.4 x fS(RH) x [Small Nitrate] + 5.1 fS(RH) x [Large Nitrate] 

bEC = 10 x [Elemental Carbon] 

bOCM = 2.8 x [Small Organic Mass] + 6.1 x [Large Organic Mass] 

bSoil = 1 x [Fine Soil] 

bCM = 0.6 x [Coarse Mass] 

bNaCl = 1.7 x fSS(RH) x [Sea Salt] 

bNO2 = 0.33 x [NO2 (ppb)] 

The total Sulfate, Nitrate and OCM are each split into two fractions, representing small and large 

size distributions of those components. As noted in Table 4-1, the OCM is 1.8 times the 

IMPROVE OC measurement in the new IMPROVE algorithm, compared to 1.4 times the 

IMPROVE OC measurement in the original IMPROVE equation. New terms have been added 

for Sea Salt (important for coastal and possibly other areas) and for light absorption by NO2 

(only used where NO2 observations are available). Consistent with IMPROVE guidance, 

VISTAS used Cl- mass from ion chromatography to define sea salt concentrations. As NO2 is 

rarely measured at IMPROVE monitoring sites and, except for conditions of plume blight has 

little effect on regional haze, this component of the new IMPROVE equations was not used. 

Site-specific Rayleigh scattering for each IMPROVE monitoring site is used in the new 

IMPROVE equation, as compared to a constant 10 Mm-1 value assumed in the original 

IMPROVE equation. 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chapter_4_VisProj4.doc 4-7 

The apportionment of the Small and Large components of Sulfate, Nitrate and Organic Mass is 

done as follows: 

[Large Sulfate] = [Total Sulfate] / 20 x [Total Sulfate], for [Total Sulfate] < 20 μg/m3

[Large Sulfate ] = [Total Sulfate], for [Total Sulfate] > 20 μg/m3

[Small Sulfate] = [Total Sulfate] – [Large Sulfate] 

The same equations are used to apportion Total Nitrate and Total OCM among their Large and 

Small components. 

The total extinction (bext) in the new IMPROVE equations is the sum of all the extinction 

components associated with each PM species. The new IMPROVE equation adds Sea Salt and 

NO2 as noted above. In addition, site-specific Rayleigh background is used with the new 

IMPROVE equation: 

bext = bRay + bSulfate + bNitrate + bEC +bOCM + bSoil + bCM + bNaCl + bNO2

The Haze Index (HI) and Visual Range (VR) are calculated from the total extinction from the 

new IMPROVE equation using the same formulas as given above for the original IMPROVE 

equation. 

4.2.1.1.3 Justification for Using the New IMPROVE Equation 

The new IMPROVE equation was developed using the latest scientific information on PM 

species extinction properties combined with fitting reconstructed light extinction based on 

IMPROVE measured PM and NO2 concentrations with actual co-located measured light 

extinction (e.g., nephelometer measurements). Figure 4-1 displays example comparisons of 24-

hour light extinction using the original and new IMPROVE equations compared against 24-hour 

nephelometer measurements of light extinction at the VISTAS Great Smoky Mountains Class I 

area IMPROVE monitor. The original IMPROVE equation has a bias toward understating light 

extinction at the high end and overstating it at the low end, whereas the new IMPROVE equation 

does a better job in estimating light extinction from measured PM at all extinction levels. 

Because the new IMPROVE equation is based on more recent science and fits the observed light 

extinction values better, the VISTAS states have elected to perform their primary visibility 

projections using the new IMPROVE equation. Further justification of the new IMPROVE 

equation is provided in other VISTAS documents by Dr. Ivar Tombach and Air Resource 

Specialists available on the VISTAS website (http://www.vistassesarm.org/documents/FinalDocs.asp). Results using the original IMPROVE equation are 

presented in Section 5 as an alternative approach. 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chapter_4_VisProj4.doc 4-8 

Figure 4-1. Comparisons of observed light extinction with reconstructed light extinction using the 

new (left) and original (right) IMPROVE equations at the Great Smoky Mountains National Park. 

4.2.2 Calculation of the Baseline Conditions 

The visibility Baseline Conditions for the worst 20 percent and best 20 percent days is calculated 

from the IMPROVE observations from the 2000-2004 period for each Class I area following 

EPA’s guidance (EPA, 2003a). The basic procedures for calculating the Baseline Conditions are 

as follows: 

1. Determine whether the observed IMPROVE data for each site and year satisfies EPA’s 

minimal data capture criteria (EPA, 2003a). If there are less than three years with valid 

data capture for the 2000-2004 Baseline, then the Baseline Conditions can not be 

calculated and data filling is needed. 

2. For each year in the 2000-2004 period with sufficient valid data, rank the visibility in 

terms of extinction or deciview using either the original or new IMPROVE equation and 

monthly average f(RH) factors (EPA, 2003a). 

3. For the worst 20 percent days, extract the 20% most impaired visibility days for each year 

(similarly for best 20 percent days extract 20% cleanest days). With a complete yearly 

data capture of IMPROVE 1:3 day sampling frequency this would result in 24 worst 20 

percent and 24 best 20 percent days in a year. 

4. For each worst 20 percent (or best 20 percent) day in each year, calculate 24-hour average 

visibility extinction using the IMPROVE measurements and either the original or new 

IMPROVE equation. Convert the daily extinction to daily deciview and then average 

across each year to get yearly average deciview extinction for the worst 20 percent (or best 

20 percent) days for each valid year from the 2000-2004 period. 

5. Average the annual average deciview worst 20 percent (or best 20 percent) days deciview 

across each valid year in the 2000-2004 period (minimum of 3 valid years required) to get 

the worst 20 percent (or best 20 percent) Baseline Conditions. 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chapter_4_VisProj4.doc 4-9 

4.2.3 Data Filling for Sites with Insufficient Valid Data to Calculate Baseline Conditions 

Several Class I areas within and near the VISTAS region did not contain sufficient IMPROVE 

observations during the five-year 2000-2004 Baseline to have three valid years of data from 

which Baseline Conditions could be constructed: Chassahowitzka (CHAS), Florida, Cohutta 

(COHU), Georgia, St. Marks (SAMA), Florida, Shining Rock (SHRO), North Carolina, and 

Swan Quarter (SWAN), North Carolina within the VISTAS region, plus Breton Island (BRET), 

Louisiana, and Mingo (MING), Missouri in the CENRAP region. For these Class I areas, Air 

Resource Specialists developed substituted data sets so that at least three years of valid data were 

available from which valid Baseline Conditions could be calculated using EPA’s minimal valid 

data requirement (EPA, 2003a). These substituted IMPROVE databases are also available on the 

VIEWS website. More information on the data filling procedures can be found at the VIEWS 

website: (http://vista.cira.colostate.edu/views/). 

4.2.4 Natural Conditions 

EPA has published default Natural Conditions for Annual Average and the worst 20 percent and 

best 20 percent days using the old IMPROVE equation (EPA, 2003b). These EPA default worst 

20 percent Natural Conditions were updated to the new IMPROVE equation and are available on 

the VIEWS website (http://vista.cira.colostate.edu/views/). The default Natural Conditions were 

used in constructing the linear glidepaths to generate the 2018 Uniform Rate of Progress. These 

EPA default Natural Conditions are used as the anchor point for the glidepaths in 2064 that are 

provided in Appendix E for the VISTAS and nearby Class I areas. 

4.2.5 2018 Uniform Rate of Progress 

The Uniform Rate of Progress glidepath is constructed by generating a linear glidepath in 

deciviews from the Baseline Conditions in 2004 to Natural Conditions in 2064. Where the linear 

glidepath crosses 2018 is the 2018 Uniform Rate of Progress. Figure 4-2 displays an example 

linear glidepath for the Great Smoky Mountains Class I area in Tennessee-North Carolina. 

There are five years of sufficient valid IMPROVE data during the 2000-2004 Baseline with 

annual average worst 20 percent values of 30.71, 30.60, 29.94, 30.51 and 29.62 dv (blue 

diamonds) resulting in worst 20 percent Baseline Conditions of 30.28 dv that is placed as the 

starting point in 2004 for the glidepath. The ending point for the glidepath is 11.05 dv which is 

the default Natural Conditions for the worst 20 percent days using the new IMPROVE equation. 

The linear glidepath crosses 2018 at 25.79 dv which becomes the 2018 Uniform Rate of 

Progress. 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chapter_4_VisProj4.doc 4-10 

Uniform Rate of Progress Glide Path

Great Smoky Mountains - 20% Worst Days

30.28 28.99

25.79

22.59

19.38

16.18

12.97

11.05

0

5

10

15

20

25

30

35

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

Figure 4-2. Uniform Rate of Progress glidepath for Great Smoky Mountains (GRSM), 

Tennessee-North Carolina Class I area that linearly connects the 30.28 dv Baseline Conditions 

in 2004 with the 11.05 dv Natural Conditions in 2064 resulting in a 2018 Uniform Rate of 

Progress of 25.79 dv. 

4.3 EPA Default Approach for Visibility Projections 

For VISTAS model application for a single year (2002), EPA’s regional haze modeling guidance 

recommends developing Class I area-specific and PM species-specific RRFs based on the 

average concentrations for the worst 20 percent days from 2002 (EPA, 2007a). Thus, this is the 

methodology used to project 2018 visibility estimates in this section. For example, if 

SO4(2002)i and SO4(2018)i are the model estimated sulfate concentrations for the 2002 worst 20 

percent days (i=1...N) at a given Class I area for the 2002 and 2018 emission scenarios then the 

RRF for sulfate and this Class I area is given by: 

 RRF(SO4)i = ∑SO4(2018)i / ∑SO4(2002)i

4.3.3 Mapping of Modeling Results to the IMPROVE Measurements 

As noted above, to project future-year visibility at Class I areas the modeling results are used in a 

relative sense to scale current observed visibility for the worst 20 percent and best 20 percent 

visibility days using RRFs that are the ratio of modeling results for the future-year to currentyear. This scaling is done separately for each of the six components of light extinction in the 

IMPROVE equations. The CMAQ modeled species do not exactly match up with the 

IMPROVE PM species, thus assumptions must be made to map the modeled species to the 

IMPROVE PM species for the purpose of projecting visibility improvements. For example, 

CMAQ explicitly simulates ammonium and sulfate may or may not be fully neutralized in the 

model by ammonium. In contrast the IMPROVE equations assume sulfate is fully neutralized by 

ammonium. For the CMAQ Version 4.5 SOAmods model, the mapping of modeled species to 

IMPROVE equation PM species is listed in Table 4-2. 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chapter_4_VisProj4.doc 4-11 

Table 4-2. Mapping of CMAQ V4.5 SOAmods modeled species concentrations to IMPROVE 

PM components. 

IMPROVE 

Component 

CMAQ V4.3 Species 

Sulfate ASO4J + ASO4I 

Nitrate ANO3J + ANO3I 

LAC AECJ + AECI 

OCM AORGAJ + AORGAI + AORGPAJ + AORGPAI + AORGBJ + AORGBI + 

ASOC1J + ASOC1I + ASOC2J + ASOC2I + ASOC3J + ASOC3I 

Soil A25J + A25I 

CM ACORS + ASOIL + ANAK + ACLK + ASO4K + ANO3K 

For the VISTAS visibility projections using the 2002 Typical and 2018 base case Base G2 

emission scenarios, the secondary organic aerosol (SOA) module in the CMAQ V4.5 released in 

September 2005 was enhanced (SOAmods) to include additional processes related to the 

generation of SOA from biogenic emissions. In particular, three new species have been added 

that represent SOA products from biogenic emission compounds that are not included in the 

standard version of CMAQ V4.5 (Morris et al., 2006): 

• ASOC1 – SOA from biogenic sources (e.g., terpenes and isoprene) that has become 

polymerized so is no longer volatile. 

• ASOC2 – SOA from biogenic sesquiterpene and higher reactivity and higher yield 

monoterpene emissions. 

• ASOC3 – SOA from biogenic isoprene emissions. 

Thus, the species mapping for Organic Carbon Mass (OCM) and the CMAQ V4.5 SOAmods 

version of the model used in VISTAS visibility projections given in Table 4-2 has the addition of 

the three new biogenic SOA species not in the standard version of CMAQ V5.4 (ASOC1J + 

ASOC1I + ASOC2J + ASOC2I + ASOC3J + ASOC3I). 

4.3.4 Using Modeling Results to Project Changes in Visibility 

Modeling results are used in a relative fashion to project future-year visibility using relative 

response factors (RRFs). RRFs are expressed as the ratio of the modeling results for the futureyear to the results of the base year (2018/2002) and are Class I area and PM species specific. 

RRFs are applied to the Baseline Condition observed PM species to project future-year PM 

levels from which visibility can be assessed using the IMPROVE equations listed above. The 

following six steps are used to project future-year visibility for the worst 20 percent and best 20 

percent visibility days (discussion is for worst 20 percent days but also applies to best 20 percent 

days): 

1. For each Class I area and each monitored day, daily visibility is ranked using IMPROVE 

observations and the IMPROVE equation (either original or new IMPROVE equation) 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chapter_4_VisProj4.doc 4-12 

for each year from the five-year baseline period (2000-2004) to identify the worst 20 

percent visibility days for each year (see Baseline Conditions discussion above). 

2. Use an air quality model to simulate a base year period (ideally the five-year Baseline 

period of 2000-2004, but for VISTAS just the 2002 annual period was simulated) and a 

future-year (e.g., 2018) and use the resulting information to develop Class I area-specific 

RRFs for each of the six components of light extinction in the IMPROVE equation (SO4, 

NO3, EC, OCM, Soil and CM). 

3. Multiply the RRF times the measured 24-hour PM concentration data for each day from 

the worst 20 percent days in each year from the five-year Baseline period to obtain 

projected future-year 24-hour PM concentrations for the worst 20 percent days and the 

five-year Baseline. 

4. Compute the future-year daily extinction using the IMPROVE equation and the projected 

PM concentrations for each of the worst 20 percent days in the five-year baseline from 

Step 3. 

5. For each of the worst 20 percent days within each year of the five-year baseline, convert 

the future-year daily extinction to deciview and average the daily deciview values within 

each of the five years separately to obtain five-years (or as many years with valid data in 

the 2000-2004 Baseline) of average deciview visibility for the worst 20 percent days. 

6. Average the five-years of average deciview visibility to obtain the future-year visibility 

Haze Index estimate that is the future-year estimated visibility. 

In calculating the RRFs, EPA draft guidance recommends selecting estimated 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 cell containing the 

monitor. The NX x NY array of cells is grid resolution dependent with EPA recommending that 

NX=NY=1 for 36 km grids, NX=NY=3 for 12 km grids and NX=NY=7 for 4 km grids (EPA, 

2007a). VISTAS performed modeling using both a 36 km and 12 km grids, so used the grid cell 

containing the monitor or the 9 grid cells centered in the monitoring site when constructing the 

RRFs using the, respectively, 36 km and 12 km CMAQ modeling results. 

 

4.4 EPA Default 2018 Visibility Projections at VISTAS and Nearby Class I areas and 

Comparisons to 2018 Uniform Rate of Progress 

Using the EPA default visibility projection procedure described in Section 4.3 and the VISTAS 

2002 Typical Base G2 and 2018 Base G2 36 km and 12 km CMAQ modeling results, 2018 

visibility projections were made for VISTAS and nearby Class I areas. Appendix E details the 

2018 visibility projections for the worst 20 percent days and each Class I area in and nearby the 

VISTAS region using the new IMPROVE equation. 2018 visibility projections for the best 20 

percent days are given in Appendix F. Results for the Sipsey (SIPS) Alabama Class I area and 

the worst 20 percent days area are discussed in Section 4.4.1 below. Displays for 2018 visibility 

projections for the worst 20 percent days and the other VISTAS Class I areas are provided in 

Appendix E and summarized in Section 4.4.2 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chapter_4_VisProj4.doc 4-13 

4.4.1 Example 2018 Base G2 Visibility Projections for Worst 20% Days Sipsey, Alabama 

The 2018 visibility projections for the Sipsey (SIPS), Alabama Class I area and the worst 20 

percent days are shown in Figure E-1 in Appendix E and reproduced in Figure 4-3 below. 

Similar worst 20 percent days 2018 visibility projections and glidepaths for all VISTAS and 

nearby Class I areas are provided in Appendix E. 

4.4.1.1 EPA Default 2018 Visibility Projections 

The 2018 Base G2 CMAQ 12 km visibility projection using the EPA default method (EPA, 

2007a) new IMPROVE equation and comparison with the 2018 Uniform Rate of Progress for the 

worst 20 percent days for the SIPS Class I area is shown in Figure 4-3a (and Figure E-1a). The 

2000-2004 Baseline Condition for SIPS is 29.03 dv and the 2018 Uniform Rate of Progress is 

24.80 dv so that a 4.23 dv reduction in visibility for the worst 20 percent days is needed to meet 

the 2018 Uniform Rate of Progress. The 2018 Base G2 12 km CMAQ projected visibility is 

23.69 dv so the modeling predicts more visibility improvement (5.34 dv reduction) than the 2018 

Uniform Rate of Progress (4.23 dv reduction). When looking at visibility projections across 

Class I areas it is useful to present the 2018 visibility projections as a percentage of the 2018 

Uniform Rate of Progress; where 100% is meeting the 2018 Uniform Rate of Progress, greater 

than 100% surpassing the visibility improvements needed for the 2018 Uniform Rate of Progress 

and less than 100% means that less visibility improvement is achieved than the 2018 Uniform 

Rate of Progress. For 2018 Base G2 12 km CMAQ modeling at SIPS, 126% of the visibility 

reduction needed to meet the 2018 Uniform Rate of Progress is projected. Note that meeting the 

2018 Uniform Rate of Progress is not a requirement of the RHR SIPs, rather it just serves as a 

benchmark to compare progress toward Natural Conditions in 2064 and is designed to help states 

in selecting their 2018 reasonable progress goals. As clearly stated in EPA guidance “The 

glidepath is not a presumptive target, and States may establish a reasonable progress goal that 

provides for greater, lesser, or equivalent improvement as that described by the glidepath”

(EPA, 2007b). 

Figure 4-3b displays “StackedBar Chart” plots of observed and model estimated extinction for 

each of the worst 20 percent days in 2002 and the 2002 Typical Base G2 CMAQ simulation and 

the average across the worst 20 percent days. This figure allows a comparison of how well the 

model is reproducing the observed extinction at SIPS for the worst 20 percent days in 2002 

(more details on model performance were presented in Chapter 3 and Appendices B, C and D 

that evaluated the 2002 Actual Base G2 CMAQ 36 and 12 km simulations). 2002 worst 20 

percent days at SIPS are dominated by SO4 days (yellow) with OCM being the next most 

important PM component. During the winter there are also several days with high observed 

and/or modeled NO3 as well (Julian Days 80, 296, 338 and 341). For most of the worst 20 

percent days at SIPS the model reproduces the observed extinction reasonably well, although it 

does tend to understate SO4 and total extinction on several days. The observed average 

extinction across the 2002 worst 20 percent days (excluding Rayleigh and Sea Salt) at SIPS is 

173 Mm-1, compared to a modeled value that is 27% (43 Mm-1) lower (127 Mm-1). This 

understatement is primarily due to the model understating the observed SO4 extinction 

(130 Mm-1) by 42 Mm-1. 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chapter_4_VisProj4.doc 4-14 

Figure 4-3c displays “Boxplots” of differences in modeled extinction for the 2002 worst 20 

percent days between the 2018 Base G2 and 2002 Typical Base G2 12 km CMAQ simulations. 

SO4 is always the largest component of the extinction that is reduced from 2002 to 2018 at SIPS 

for the worst 20 percent days. There are several days that exhibit very little extinction reduction 

going from 2002 to 2018 (Julian Days 80, 122, 260, 338, 341). These are either high NO3 days 

or days in which the observed SO4 extinction is greatly underestimated (see Figure 4-3c). On 

worst 20 percent days when the model grossly underestimates the observed extinction, the model 

is very stiff (i.e., shows little response to reductions in emissions, see Figure 4-3d). However, 

since the modeled RRFs used to project future-year visibility are based on the average model 

changes between 2018 and 2002 across all the 2002 worst 20 percent days, then the average 

RRFs are driven more by the higher modeled days and are less affected by the days with gross 

under predictions, as long as there are only a few days with such poor performance. 

The absolute modeling results are not used directly in the visibility projections, rather they are 

used to develop RRFs specific to each PM-species for each Class I area. That is, important 

attributes of Figures 4-3c and 4-3d are the relative changes in modeled PM species between the 

2002 and 2018 emission scenarios averaged across the worst 20 percent days that are represented 

by the last bar in each figure. These results are summarized for SIPS in Table 4-2 below. Table 

4-2 compares the average extinction across the 2002 worst 20 percent days at SIPS from the 

measured IMPROVE data (2000-2004), the modeled 2002 values and the modeled change in 

extinction between the 2018 and 2002 emissions scenarios. Although the results in Table 4-2 are 

not RRFs (RRFs are based on ratios of concentrations not extinction) they do show how the 

RRFs may magnify or deflate the importance of a modeled PM species when the RRFs are 

applied to the observed values in the projections. For example, the model estimates that CM is 

three times lower than observed and predicts a small increase (+10%) between 2002 to 2018 of 

less than 0.1 Mm-1. However, when this small 10% increase is applied to the higher observed 

CM extinction value it magnifies the modeled changes in CM in the 2018 visibility projections. 

Most of the changes in emissions from 2002 to 2018 are reductions in SO2 and NOx emissions, 

with very little changes in carbon, soil and coarse mass. Therefore, visibility improvements are 

primarily due to reductions in SO4 and NO3, with very little change in EC, OCM, Soil and CM. 

Table 4-2. Observed and Modeled Extinction by Species Averaged Across the Worst 20 

Percent Days in 2002 at SIPS using 2002/2018 Base G2 12 km CMAQ results. 

2002 

Average 

Observed 

W20% 

(Mm-1) 

2002 

Average 

Modeled 

W20% 

(Mm-1) 

2018 

Average 

Modeled 

W20% 

(Mm-1)

2018-2002 

Reduction 

(Mm-1) 

2018-2002 

Reduction 

(%) 

bSO4 130.0 88.1 40.0 -48.1 -54% 

bNO3 10.1 9.5 7.7 -1.7 -19% 

bOCM 23.3 25.6 21.6 -3.0 -12% 

bEC 5.9 2.6 1.8 -0.8 -31% 

bSOIL 1.0 1.4 1.7 +0.3 +18% 

bCM 3.1 0.6 0.7 +0.07 -10% 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chapter_4_VisProj4.doc 4-15 

Uniform Rate of Progress Glide Path

Sipsey - Worst 20% Days

29.03

27.52

24.80

21.78

18.76

15.74

12.72

10.90

26.43

23.69

0

5

10

15

20

25

30

35

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 4-3a. 2018 Visibility Projections and 2018 Uniform Rate of Progress Glidepaths in 

Deciview for Sipsey (SIPS), Alabama and Worst 20 Percent (W20%) days Using 2002/2018 

Base G2 CMAQ 12 km Modeling Results. 

Worst 20% Obs (left) vs 2002gt2a (right) at SIPS1

0

50

100

150

200

250

300

350

80 122 131 143 152 155 161 185 188 191 215 218 221 224 251 254 260 263 284 296 338 341 _ _ _ Avg

Julian Day in Worst 20% group

bEXT (1/Mm)

bCM

bSOIL

bEC

bOC

bNO3

bSO4

Figure 4-3b. Comparison of Observed (left) and 2002 Base G2 CMAQ 12 km Modeled (right) 

Daily Extinction for Sipsey (SIPS), Alabama and Worst 20 Percent (W20%) days in 2002. 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chapter_4_VisProj4.doc 4-16 

Bext Response (2018g2b - 2002gt2a) at SIPS1 on Worst 20% Days

-180

-160

-140

-120

-100

-80

-60

-40

-20

0

20

80 122 131 143 152 155 161 185 188 191 215 218 221 224 251 254 260 263 284 296 338 341 Avg

Julian Day

Delta Bext (1/Mm)

bCM

bSOIL

bEC

bOC

bNO3

bSO4

Figure 4-3c. Differences in Modeled 2002 and 2018 Base G2 12 km CMAQ Results (2018-

2002) Daily Extinction for Sipsey (SIPS), Alabama and Worst 20 Percent (W20%) Days in 2002. 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chapter_4_VisProj4.doc 4-17 

4.4.2 Summary 2018 Visibility Projections for Worst 20 Percent Days Across Class I 

Areas 

Figure 4-4 displays a “DotPlot” of 2018 visibility projections using the 2002 Typical and 2018 

Base G2 CMAQ 12 km and 36 km modeling results. DotPlots present the 2018 visibility 

projections as a percentage of achieving the 2018 Uniform Rate of Progress. For example, at 

SIPS the 2018 Base G2 modeling achieved 126% and 127% of the visibility reduction needed to 

meet the 2018 Uniform Rate of Progress using the 12 km and 36 km CMAQ modeling results, 

respectively. So the dots under SIPS are plotted at 126% and 127%, which are above the 100% 

line so the 2018 Base G2 base case modeling results achieve the 2018 Uniform Rate of Progress 

at SIPS. Class I areas’ with dots above 100% achieve more visibility improvements than the 

2018 Uniform Rate of Progress, whereas Class I areas’ with dots that are under 100% do not 

meet the 2018 Uniform Rate of Progress. 

CMAQ 2018g2b/2002gt2a Method 1 predictions for VISTAS+ sites

0%

20%

40%

60%

80%

100%

120%

140%

160%

180%

200%

COHU1DOSO1

GRSM1

JARI1

LIGO1CADI1

MACA1

SHEN1

SHRO1

SIPS1

CHAS1

EVER1OKEF1

ROMA1

SAMA1

SWAN1

BRET1

BRIG1

CACR1

HEGL1

MING1

UPBU1

Percent of target reduction achieved

12km NIA

36km NIA

VISTAS non-VISTAS

Figure 4-4. 2018 Base G2 CMAQ 36 km and 12 km Visibility Projections for VISTAS and 

Nearby Class I areas Using DotPlots that Express 2018 Visibility as a Percentage of Meeting 

the 2018 URP Goal On the Deciview Linear Glidepath. 

The Class I areas within the Appalachian Mountains all achieve the 2018 Uniform Rate of 

Progress, typically by 150% or more. Essentially most of the 20% worst days at these sites are 

dominated by sulfate and occur during the warmer months; reductions in SO2 under the 2018 

Base G2 emissions scenario results in large sulfate reductions. Mammoth Cave (MACA) and 

Sipsey (SIPS) in the interior of the eastern U.S. west of the Appalachian Mountains also achieve 

the 2018 Uniform Rate of Progress with visibility reductions of approximately 120% to 125% of 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chapter_4_VisProj4.doc 4-18 

the Uniform Rate of Progress. The smaller average improvements at MACA and SIPS are 

attributed to some 20% worst days occurring in the winter months with elevated NO3 levels (see 

Appendix E). These days show little change in SO4 and NO3 by 2018 so the average RRF are 

smaller than at those the Appalachian Mountains Class I areas. The Swanquarter (SWAN) 

coastal site in North Carolina achieves 140% of the 2018 Uniform Rate of Progress whereas the 

Cape Romain (ROMA) coastal site in South Carolina achieves 112% of the 2018 Uniform Rate 

of Progress. The Florida and Georgia Class I coastal areas are projected to be close to the 2018 

Uniform Rate of Progress. Chassahowitzka (CHAS) is estimating more visibility improvements 

than the 2018 Uniform Rate of Progress (113%-115%), with St. Marks (SAMA) right on the 

2018 Uniform Rate of Progress (100% and 106%). Okefenokee (OKEF) and Everglades 

(EVER) are estimate to achieve less visibility improvement than the 2018 Uniform Rate of 

Progress. 

For a several sites, GRSM, SHRO, and EVER, there are fairly large differences between the 12 

km and 36 km CMAQ 2018 visibility projections; these differences are discussed in more detail 

below. 

Figure 4-5 displays the model estimated absolute change in extinction (Mm-1) averaged across 

the 2002 worst 20 percent days at Class I areas in and near the VISTAS region. The largest 

modeled reductions are in SO4 extinction. There are some differences in the modeled extinction 

reductions between the 12 km grid modeling results (Figure 4-5a) and the 36 km grid modeling 

results (Figure 4-5b). In particular, the 12 km modeling results exhibit much higher NO3 

increases at several Class I areas including GRSM, SHRO and EVER. Figure 4-6 displays the 

percent change in the projected PM extinction by PM species for each VISTAS and nearby Class 

I area for the worst 20 percent days (i.e., the relative modeled change). These results are 

analogous to the RRFs used in the visibility projections. There are some significant differences 

in the relative changes in the 12 km (Figure 4-6a) and 36 km (Figure 4-6b) modeling results 

between 2002 and 2018 NO3 values for several of the Class I areas. In particular, the relative 

increases in NO3 in 2018 for GRSM, SHRO and EVER are much higher using the 12 km 

modeling results (> 100%) than the 36 km modeling results (< 100%). These higher relative 

NO3 increases using the 12 km modeling results explain why the 12 km 2018 visibility 

projections are not as optimistic as the 36 km modeling results (see Figure 4-4). The modeled 

NO3 concentrations for the 2002 base case at these three Class I areas are near zero (see Figures 

E-3c, E-9c and E-12c) for the 12 km modeling results and lower than the 2002 base case NO3 

for the 36 km results. Thus, although the model is estimating extremely small increases in NO3 

in the 2018 scenario at 12 km, because it is starting with near zero NO3 in the 2002 base case it 

estimates a relatively larger percentage change in NO3 than in the 36 km scenario. This larger 

percentage change for the 12 km results is then applied to higher observed NO3 concentrations 

from the 2000-2004 Baseline in the 2018 projections. When the modeled NO3 (or other species) 

concentrations approach zero, there is less confidence in the modeled relative changes in 

concentrations as model noise may affect the results. To examine this issue in more detail, in 

Chapter 5 we look at alternative projection techniques in which we only use the modeled derived 

RRFs for NO3 if the 2002 base case NO3 concentration is greater than 0.1 μg/m3

. 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chapter_4_VisProj4.doc 4-19 

 

Average change in extinction components from 2002 baseline to 2018 projected

at VISTAS sites using 2018g2b/2002gt2a RRFs

-120.0

-100.0

-80.0

-60.0

-40.0

-20.0

0.0

20.0

COHU1

CADI1

DOSO1

GRSM1

JARI1

LIGO1

MACA1

SHEN1

SHRO1

SIPS1

CHAS1

EVER1

OKEF1

ROMA1

SAMA1

SWAN1

IMPROVE site

[2018 project Bext - (2000-2004 baseline Bext)] (1/Mm)

bSO4

bNO3

bOC

bEC

bSOIL

bCM

Average change in extinction components from 2002 baseline to 2018 projected

at non-VISTAS sites using 2018g2b/2002gt2a RRFs

-70.0

-60.0

-50.0

-40.0

-30.0

-20.0

-10.0

0.0

10.0

BRET1 BRIG1 CACR1 HEGL1 MING1 UPBU1

IMPROVE site

[2018 project Bext - (2000-2004 baseline Bext)] (1/Mm)

bSO4

bNO3

bOC

bEC

bSOIL

bCM

Figure 4-5a. Absolute Model Estimated Changes in Extinction (Mm-1) by PM Species for Class 

I Areas in the VISTAS region (top) and Near VISTAS (bottom) using CMAQ 2002/2018 Base G2 

12 km modeling results. 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chapter_4_VisProj4.doc 4-20 

Average change in extinction components from 2002 baseline to 2018 projected

at VISTAS sites using 2018g2a/2002gt2a RRFs

-120.0

-100.0

-80.0

-60.0

-40.0

-20.0

0.0

20.0

COHU1

CADI1

DOSO1

GRSM1

JARI1

LIGO1

MACA1

SHEN1

SHRO1

SIPS1

CHAS1

EVER1

OKEF1

ROMA1

SAMA1

SWAN1

IMPROVE site

[2018 project Bext - (2000-2004 baseline Bext)] (1/Mm)

bSO4

bNO3

bOC

bEC

bSOIL

bCM

Average change in extinction components from 2002 baseline to 2018 projected

at non-VISTAS sites using 2018g2a/2002gt2a RRFs

-80.0

-70.0

-60.0

-50.0

-40.0

-30.0

-20.0

-10.0

0.0

10.0

BRET1 BRIG1 CACR1 HEGL1 MING1 UPBU1

IMPROVE site

[2018 project Bext - (2000-2004 baseline Bext)] (1/Mm)

bSO4

bNO3

bOC

bEC

bSOIL

bCM

Figure 4-5b. Absolute Model Estimated Changes in Extinction (Mm-1) by PM Species for Class 

I Areas in the VISTAS region (top) and Near VISTAS (bottom) using CMAQ 2002/2018 Base G2 

36 km modeling results.
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chapter_4_VisProj4.doc 4-21 

Percent change in extinction components from 2002 baseline to 2018 projected

at VISTAS sites using 2018g2b/2002gt2a RRFs

-100%

-50%

0%

50%

100%

150%

200%

COHU1

CADI1

DOSO1

GRSM1

JARI1

LIGO1

MACA1

SHEN1

SHRO1

SIPS1

CHAS1

EVER1

OKEF1

ROMA1

SAMA1

SWAN1

IMPROVE site

Percent change in bEXT

bSO4

bNO3

bOC

bEC

bSOIL

bCM

Percent change in extinction components from 2002 baseline to 2018 projected

at non-VISTAS sites using 2018g2b/2002gt2a RRFs

-80%

-60%

-40%

-20%

0%

20%

40%

60%

80%

BRET1 BRIG1 CACR1 HEGL1 MING1 UPBU1

IMPROVE site

Percent change in bEXT

bSO4

bNO3

bOC

bEC

bSOIL

bCM

Figure 4-6a. Percent Change In Modeled Extinction by PM Species Averaged Across the 2002 

Worst 20 Percent Days for Class I areas in the VISTAS region (top) and near VISTAS (bottom) 

using 2002/2018 Base G2 12 km CMAQ results. 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chapter_4_VisProj4.doc 4-22 

Percent change in extinction components from 2002 baseline to 2018 projected

at VISTAS sites using 2018g2a/2002gt2a RRFs

-80%

-60%

-40%

-20%

0%

20%

40%

60%

80%

COHU1

CADI1

DOSO1

GRSM1

JARI1

LIGO1

MACA1

SHEN1

SHRO1

SIPS1

CHAS1

EVER1

OKEF1

ROMA1

SAMA1

SWAN1

IMPROVE site

Percent change in bEXT

bSO4

bNO3

bOC

bEC

bSOIL

bCM

Percent change in extinction components from 2002 baseline to 2018 projected

at non-VISTAS sites using 2018g2a/2002gt2a RRFs

-80%

-60%

-40%

-20%

0%

20%

40%

60%

80%

BRET1 BRIG1 CACR1 HEGL1 MING1 UPBU1

IMPROVE site

Percent change in bEXT

bSO4

bNO3

bOC

bEC

bSOIL

bCM

Figure 4-6b. Percent Change In Modeled Extinction by PM Species Averaged Across the 2002 

Worst 20 Percent Days for Class I areas in the VISTAS region (top) and near VISTAS (bottom) 

using 2002/2018 Base G2 36 km CMAQ results.
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chapter_4_VisProj4.doc 4-23 

4.4.3 2018 Visibility Projections for the Best 20 Percent Days 

Figure 4-7a displays the 2018 visibility projections for the Best 20 percent days at Sipsey, similar 

plots for other Class I areas are given in Appendix F. Recall that the RHR goal for the Best 20 

percent days is a no worsening in visibility impairment from current conditions. Thus the 2018 

visibility projection (14.37 dv) is compared with the current observed visibility for the Best 20 

percent days from the Baseline (16.67 dv). Also shown in Figure 4-7a is the natural conditions 

for the Best 20 percent days that is plotted at 2064 (5.03 dv). Figure 4-7b compares the predicted 

and observed daily extinction for the best 20 percent days at Sipsey. In general the model 

predicts total extinction on the clean days fairly well, although the observed extinction is 

overstated on Julian Days 26 and 176. The differences in the modeled extinction between the 

2018 and 2002 Base G2 emission scenarios on the best 20 percent days at Sipsey are shown in 

Figure 4-7c. Visibility improvements on the best 20 percent days are also primarily due to SO4 

and NO3 reductions, although it is not completely dominated by SO4 as seen for the worst 20 

percent days and reductions in extinction due to EC are also evident. Although not as significant 

at Sipsey, at many of the Class I areas the reductions in SO4 is resulting in increases in NO3 that 

is likely due to “nitrate replacement” (see Appendix F). Nitrate replacement occurs when the 

reductions in SO4 results in the release of ammonium (NH4) molecules that were bonded to the 

SO4 that then react with gaseous HNO3 to form particulate NO3. As SO4 can bind up to two 

NH4 molecules [SO4(NH4)2-], than nitrate replacement could potentially form two particulate 

NO3 [NO3NH4] for every one SO4 molecule reduced, however this is unlikely in the real 

atmosphere due to aerosol thermodynamics and the fact that NO3 formation is not always 

completely ammonia limited. We also see occasions when SO4 increases when NO3 is reduced 

even thought SO2 emissions are reduced as well. One potential chemical reason for increased 

SO4 when NO3 is reduced is through aqueous-phase chemistry formation of SO4 which is pH 

sensitive. Reductions in NO3 will make water droplets less acidic that in turn can result in faster 

conversion of SO2 to SO4 via aqueous-phase chemistry. 

Figure 4-8 displays a DotPlot of 2018 visibility projections for the Best 20 percent days using the 

CMAQ 12 km and 36 km Base G2 2002/2018 modeling results. For the Best 20 percent days 

2018 projections, the DotPlots display the percent reductions from current visibility conditions 

for the 20 percent best days. With one exception, all of the 2018 visibility projections show 

improved visibility from current visibility conditions for the best 20 percent days. The exception 

is for Everglades that shows a worsening of visibility for the best 20 percent days when the 36 

km CMAQ modeling results are used, but improved visibility using the 12 km CMAQ modeling 

results. This issue was examined more closely using the side-by-side model/observed 

StackedBar charts and BoxPlots of extinction differences between modeled 2018 and 2002 

values for the best 20 percent days at EVER (Figure 4-9). Although the 36 km and 12 km 

modeling results at EVER look similar, the relative changes are quite different as shown by the 

percent changes in the average extinction across the 2002 worst 20 percent days between the 

CMAQ 2018 and 2002 simulation using the 36 and 12 km grids (Figure 4-10). Using the 12 km 

NO3 averaged across the worst 20 percent days is projected to increase approximately 30 percent 

from 2002 to 2018. However, when the 36 km grid is used the projected NO3 increase is 

approximately 550%. An examination of the results more closely reveals that CMAQ 36 km and 

12 km modeling both project that NO3 extinction increases by approximately 0.001 Mm-1 from 

2002 to 2018 averaged across the best 20 percent days. However, the CMAQ 36 km CMAQ 

results is starting with a ~0.001 Mm-1 NO3 extinction in 2002 compared to a ~0.01 Mm-1 NO3 

extinction using the 12 km grid so the 36 km modeling results estimates a much larger relative 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chapter_4_VisProj4.doc 4-24 

change in extinction. When the modeled NO3 concentrations are so low, the reactive changes 

are less reliable which is why one alternative projection approach, is not to use the modeled 

RRFs when NO3 concentrations are less than 0.1 μg/m3

. 

Reasonable Progress Assessment Base G2, new IMPROVE Algorithm

Sipsey - Best 20% Days

15.57 15.57 15.57

14.37

5.03

0

5

10

15

20

25

2000 2004 2008 2012 2016 2020 2024 2028 2032 2036 2040 2044 2048 2052 2056 2060 2064

Year

Haziness Index (Deciviews)

Baseline Natural Condition (20% Best Days) Observation Method 1 Prediction

Figure 4-7a. 2018 visibility projections for Best 20 percent days at Sipsey, Alabama using Base 

G2 12 km CMAQ results and comparisons with current (2000-2004) observed Best 20 percent 

days and natural conditions for the best days (plotted at 2064). 

Best 20% Obs (left) vs 2002gt2a (right) at SIPS1

0

20

40

60

80

100

120

11 14 26 32 59 77 89 113 116 167 176 179 212 230 257 302 308 347 356 359 362 _ _ _ _ Avg

Julian Day in Best 20% group

bEXT (1/Mm)

bCM

bSOIL

bEC

bOC

bNO3

bSO4

Figure 4-7b. Compared of predicted and observed daily extinction for the best 20 percent days 

at the Sipsey, Alabama Class I area and the CMAQ 2002 Typical Base G2 12 km simulation. 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chapter_4_VisProj4.doc 4-25 

Bext Response (2018g2b - 2002gt2a) at SIPS1 on Best 20% Days

-60

-50

-40

-30

-20

-10

0

10

20

11 14 26 32 59 77 89 113 116 167 176 179 212 230 257 302 308 347 356 359 362 Avg

Julian Day

Delta Bext (1/Mm)

bCM

bSOIL

bEC

bOC

bNO3

bSO4

Figure 4-7c. Modeled changes in daily extinction using CMAQ 12 km results between the 2018 

and 2002 emission scenarios for the best 20 percent days at the Sipsey, Alabama Class I area. 

Figure 4-8. 2018 visibility projections at VISTAS and nearby Class I areas for the Best 20 

percent days using the CMAQ 2002/2018 Base G2 12 km and 36 km modeling results. 

CMAQ 2018g2a/2002gt2a Visibility Projection for VISTAS+ sites on Best 20% 

days -50%

-40%

-30%

-20%

-10%

0%

10%

20%

30%

40%

50%

COHU1DOSO1GRSM1JARI1LIGO1CADI1MACA1SHEN1SHRO1SIPS1CHAS1EVER1OKEF1ROMA1SAMA1SWAN1BRET1BRIG1CACR1HEGL1MING1UPBU1

Percent of target reduction achieved

12km

36km

VISTAS non-VISTAS
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chapter_4_VisProj4.doc 4-26 

Best 20% Obs (left) vs 2002gt2a (right) at EVER1

0

5

10

15

20

25

30

23 29 32 71 80 104 134 137 140 164 176 191 206 212 242 245 248 251 254 284 287 296 299 302 _ Avg

Julian Day in Best 20% group

bEXT (1/Mm)

bCM

bSOIL

bEC

bOC

bNO3

bSO4

Best 20% Obs (left) vs 2002gt2a (right) at EVER1

0

5

10

15

20

25

23 29 32 71 80 104 134 137 140 164 176 191 206 212 242 245 248 251 254 284 287 296 299 302 _ Avg

Julian Day in Best 20% group

bEXT (1/Mm)

bCM

bSOIL

bEC

bOC

bNO3

bSO4

Bext Response (2018g2b - 2002gt2a) at EVER1 on Best 20% Days

-9

-8

-7

-6

-5

-4

-3

-2

-1

0

1

23 29 32 71 80 104 134 137 140 164 176 191 206 212 242 245 248 251 254 284 287 296 299 302 Avg

Julian Day

Delta Bext (1/Mm)

bCM

bSOIL

bEC

bOC

bNO3

bSO4

Bext Response (2018g2a - 2002gt2a) at EVER1 on Best 20% Days

-5.0

-4.5

-4.0

-3.5

-3.0

-2.5

-2.0

-1.5

-1.0

-0.5

0.0

0.5

23 29 32 71 80 104 134 137 140 164 176 191 206 212 242 245 248 251 254 284 287 296 299 302 Avg

Julian Day

Delta Bext (1/Mm)

bCM

bSOIL

bEC

bOC

bNO3

bSO4

Figure 4-9. Comparison of StackedBar predicted and observed extinction (top) and BoxPlots 

modeled 2018-2002 extinction difference plots for Everglades using the 12 km (left) and 36 km 

(CMAQ) modeling results. 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chapter_4_VisProj4.doc 4-27 

Percent change in extinction components from 2002 baseline to 2018 projected

at VISTAS sites using 2018g2b/2002gt2a RRFs on Best 20% days

-50%

-40%

-30%

-20%

-10%

0%

10%

20%

30%

40%

COHU1

CADI1

DOSO1

GRSM1

JARI1

LIGO1

MACA1

SHEN1

SHRO1

SIPS1

CHAS1

EVER1

OKEF1

ROMA1

SAMA1

SWAN1

IMPROVE site

Percent change in bEXT

bSO4

bNO3

bOC

bEC

bSOIL

bCM

Percent change in extinction components from 2002 baseline to 2018 projected

at VISTAS sites using 2018g2a/2002gt2a RRFs on Best 20% days

-100%

0%

100%

200%

300%

400%

500%

600%

COHU1

CADI1

DOSO1

GRSM1

JARI1

LIGO1

MACA1

SHEN1

SHRO1

SIPS1

CHAS1

EVER1

OKEF1

ROMA1

SAMA1

SWAN1

IMPROVE site

Percent change in bEXT

bSO4

bNO3

bOC

bEC

bSOIL

bCM

Figure 4-10. Percent Change In Modeled Extinction by PM Species Averaged Across the 2002 

Best 20 Percent Days for Class I areas in the VISTAS region using the CMAQ 12 km (top) and 

36 km (bottom) modeling results.
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chapter_4_VisProj4.doc 4-28 

4.5 2018 Visibility Projections using the 2018 Best and Final Emissions 

Figure 4-11 compares the 2018 visibility projections for the worst 20 percent days for the 2018 

Base G2 and 2018 Base G4 Best and Final emission scenarios using the DotPlot format that 

expresses them as a percentage of achieving the 2018 Uniform Rate of Progress (URP). With 

three notable exceptions, the 2018 visibility projections for the Base G4 Best and Final emissions 

scenarios shows more visibility improvements than the 2018 Base G2 emissions scenarios. The 

three exceptions are the Class I areas in Florida. Table 4-3 displays the numerical values of the 

200-2004 Baseline, 2018 URP and 2018 Base G2 and G4 visibility projections in deciview that 

shows increases in 2018 deciview at the three Florida Class I areas of Chassahowitzka, St. Marks 

and Everglades in the Best and Final scenario. 

Figure 4-12 displays the 2018 visibility projections for the Best 20 percent days and the 2018 

Base G2 and 2018 Base G4 Best and Final emissions scenarios. This figure demonstrates that 

both 2018 emission scenarios have no worsening in visibility for the Best 20 percent days. 

CMAQ 2018g4a vs 2018g2b Method1 Prediction at VISTAS+ sites on W20% Days

0%

20%

40%

60%

80%

100%

120%

140%

160%

180%

200%

220%

240%

COHU1DOSO1

GRSM1

JARI1

LIGO1

CADI1

MACA1

SHEN1

SHRO1

SIPS1

CHAS1

EVER1

OKEF1

ROMA1

SAMA1

SWAN1

BRET1

BRIG1

CACR1

HEGL1

MING1

UPBU1

Percent of target reduction achieved

2018g4a 12k

2018g2b 12k

VISTAS non-VISTAS

Figure 4-11. 2018 Base G4 Best and Final and Base G2 CMAQ 12 km visibility projections for 

VISTAS and nearby Class I areas and the Worst 20 percent days using DotPlots that express 

the 2018 visibility projections as a percentage of reaching the 2018 URP goal on the deciview 

linear glidepath to natural conditions in 2064.
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chapter_4_VisProj4.doc 4-29 

Table 4-3. 200-2004 Baseline, 2018 Uniform Rate of Progress (URP) and 2018 visibility 

projections using the 2018 Base G2 and 2018 Best and Final (Base G4) emissions and the 

differences between the 2018 Base G2 and 2018 URP and 2010 Base G4 Best and Final and 

2018 Base G2 (deciview). 

Class I 

Area 

2000-2004 

Baseline 

Uniform 

Rate of 

Progress 

2018 

Base G2 

2018 

Difference

G2 – URP 

2018 

Base G4 

Best & Final 

2018 

Difference 

G4 – G2 

CHAS 25.75 22.31 21.85 -0.46 22.27 +0.42 

COHU 30.25 25.71 22.84 -2.87 22.80 -0.03 

DOSO 29.04 24.69 21.64 -3.04 21.50 -0.14 

EVER 22.30 19.92 20.65 +0.73 20.76 +0.11 

GRSM 30.28 25.79 23.66 -2.13 23.50 -0.16 

JARI 29.12 24.91 22.59 -2.32 22.36 -0.24 

LIGO 28.77 24.67 21.98 -2.69 21.65 -0.33 

MACA 31.37 26.64 25.56 -1.07 25.40 -0.17 

OKEF 27.13 23.42 23.85 +0.43 23.82 -0.03 

ROMA 26.48 23.12 22.72 -0.40 22.65 -0.07 

SAMA 26.31 22.89 22.89 0.00 23.01 +0.12 

SHEN 29.31 25.12 22.11 -3.01 21.93 -0.18 

SHRO 28.66 24.65 22.11 -3.01 21.93 -0.18 

SIPS 29.03 24.80 23.69 -1.11 23,53 -0.16 

SWAN 24.71 21.64 20.37 -1.27 20.26 -0.12 

CMAQ 2018g4a vs 2018g2b Visibility Projection at VISTAS+ sites on Best 20% 

days -50%

-40%

-30%

-20%

-10%

0%

10%

20%

30%

40%

50%

COHU1

DOSO1

GRSM1

JARI1

LIGO1

CADI1

MACA1

SHEN1

SHRO1

SIPS1

CHAS1

EVER1

OKEF1

ROMA1

SAMA1

SWAN1

BRET1

BRIG1

CACR1

HEGL1

MING1

UPBU1

Percent of target reduction achieved

12km 2018g4a

12km 2018g2b

VISTAS non-VISTAS

Figure 4-12. 2018 Base G4 Best and Final and Base G2 CMAQ 12 km visibility projections for 

VISTAS and nearby Class I areas and the Best 20 percent days using DotPlots that express the 

2018 visibility projections as a percentage of reaching the 2018 regional haze goal of no 

worsening in visibility on the Best visibility days. 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chapter_5_AddAnal4.doc 5-1 

5.0 ADDITIONAL SUPPORTING ANALYSIS 

This Chapter presents additional supporting analysis to the modeled 2018 visibility projections at 

VISTAS and nearby Class I areas presented in Chapter 4. This supporting analysis may be used 

by the states in their RHR SIPs, along with their factor analysis, to assist in justifying their 2018 

Reasonable Progress Goals (RPGs) for the worst 20 percent days and best 20 percent days. 

5.1 Comparison of VISTAS 2018 Visibility Projections with Other Groups 

2018 visibility projections for VISTAS and nearby Class I area have also been performed by the 

other RPOs. Thus, it is useful to compare the VISTAS 2018 visibility projections with those 

from the other RPOs as a quality assurance (QA) check and to corroborate the VISTAS visibility 

projections. 

5.1.1 Comparison of CENRAP, VISTAS, MRPO and MANE-VU Visibility Projections 

The VISTAS 2018 Base G2 visibility projections were compared to the following other RPO 

visibility projections: 

• CENRAP 2018 visibility projections based on their CMAQ 36 km 2002 annual modeling 

results for their 2002 and 2018 Base G emission scenarios (ENVIRON and UCR, 2007). 

• MRPO 2018 visibility projections based on their CAMx 36 km 2002 annual modeling for 

Round 5.1 using Base M emissions that were presented at their October 10, 2007 meeting 

(www.lado.org). 

• MANE-VU 2018 visibility results based CMAQ results for the 2018 OTB/OTW scenario 

received October 29, 2007 (Kleiman, personal communication, 2007). 

Figure 5-1 displays a DotPlot comparison of the four RPO visibility projections at VISTAS and 

nearby Class I areas that express the 2018 visibility projections for the worst 20 percent days as a 

percentage of achieving the 2018 Uniform Rate of Progress. The MRPO and CENRAP have 

reported 2018 visibility projections for four and two of the VISTAS Class I areas, respectively. 

The CENRAP 2018 visibility projections at MACA and SIPS almost exactly match the VISTAS 

projections (CENRAP vs. VISTAS values of 122% vs. 123% at MACA and 130% vs. 126% at 

SIPS). The MRPO 2018 visibility projections, on the other hand, are less optimistic at the four 

common VISTAS Class I areas (DOSO, JARI, MACA and SHEN), although VISTAS and 

MRPO agree that more visibility improvements are projected to occur than necessary to meet the 

2018 Uniform Rate of Progress. The reasons why the MRPO visibility projections are less 

optimistic than VISTAS and CENRAP are unclear. VISTAS and CENRAP may have included 

more controls in their states than the MRPO assumed (e.g., CENRAP assumed BART controls in 

several of their non-CAIR states). 

For the four CENRAP Class I areas just west of the Mississippi River in Arkansas and Missouri 

that are the furthest right of the Class I areas in Figure 5-1 (CACR, UPBU, HEGL and MING), 

2018 visibility projections are available from the VISTAS, CENRAP and MRPO. At HEGL, 

Missouri, the VISTAS and CENRAP 2018 visibility projections are in close agreement with each 

other with both estimating that the 2018 Uniform Rate of Progress is just barely achieved. 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chapter_5_AddAnal4.doc 5-2 

However, the MRPO 2018 visibility projection estimate that HEGL will only obtain 71% of 

visibility improvements needed to achieve the 2018 Uniform Rate of Progress. At Ming, 

Missouri, again there is fairly close agreement between VISTAS (110%) and CENRAP (118%) 

2018 visibility projections with both agreeing that MING will achieve the 2018 Uniform Rate of 

Progress. The MRPO projection, however, is less optimistic estimating MING will obtain only 

91% of the visibility improvements needed to achieve the 2018 Uniform Rate of Progress. There 

is close agreement between the three RPO 2018 visibility projections at CACR and UPBU with 

them essentially having the exactly the same 2018 visibility projections at CACR (112%-115%) 

and very close ones at UPBU (108%-120%) with both Class I areas estimated to achieve the 

2018 Uniform Rate of Progress. 

For the Breton Island (BRET) Class I area, 2018 visibility projections are available from both 

VISTAS and CENRAP. VISTAS estimates that BRET will obtain 79% of the visibility 

improvements needed to achieve the 2018 Uniform Rate of Progress, whereas CENRAP is 

slightly more optimistic with a 94% value. One potential contributor to these differences at 

BRET is that emissions from off-shore marine vessel emissions in the oil and gas production 

areas of the Gulf of Mexico are double counted in the VISTAS Base G2 modeling. As these 

emissions were assumed to remain unchanged between 2002 and 2018, the double counting of 

their emissions will result in stiffer RRFs in the VISTAS modeling and consequently less 

visibility benefits in 2018 than the CENRAP modeling. The double counting occurred because 

off-shore marine vessels were present in both the MMS off-shore oil/gas development inventory 

for the Gulf of Mexico and the VISTAS off-shore marine vessel inventory for the Pacific and 

Atlanta Oceans and the Gulf of Mexico. 

2018 visibility projections at the BRIG Class I area in New Jersey were available from VISTAS, 

MRPO and MANE-VU. The VISTAS 2018 visibility projections for BRIG (111%) lie halfway 

between those from the MRPO (101%) and those by MANE-VU (120%). All three RPOs 

estimate that BRIG will achieve the 2018 Uniform Rate of Progress. 

The similarity between the VISTAS and the CENRAP and MANE-VU 2018 visibility 

projections provides more confidence that the VISTAS projections are correctly calculated and 

are likely reliable. The differences between the VISTAS and MRPO projections raise some 

concerns. 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chapter_5_AddAnal4.doc 5-3 

CMAQ Method 1 predictions with new IMPROVE algorithm at VISTAS+ sites Across 

RPOs

0%

20%

40%

60%

80%

100%

120%

140%

160%

180%

200%

COHU1DOSO1

GRSM1

JARI1

LIGO1

CADI1

MACA1

SIPS1

SHRO1

SHEN1CHAS1

EVER1

OKEF1

ROMA1

SAMA1

SWAN1

BRIG1

BRET1

CACR1

HEGL1

UPBU1

MING1

Percent of target reduction achieved

VISTAS 12k 2018g2b/2002gt2a

CENRAP 36k Base18g/Typ02g

WRAP 36k Base18b/Plan02b corrected

MRPO Round 5.1

MANU-VU 2018

Mountain Coastal non-VISTAS

Figure 5-1. DotPlot comparing the VISTAS, CENRAP, MRPO and MANE-VU 2018 visibility 

projections expressed as a percentage of achieving the 2018 Uniform Rate of Progress new 

IMPROVE equation). 

5.2 Alternative 2018 Visibility Projection Software 

The VISTAS 2018 visibility projections presented in Chapter 4 were made using software 

developed by the VISTAS modeling team. PM species concentrations in the 36 km and 12 km 

grid cells containing each of the Class I area IMPROVE monitoring sites were extracted using 

the UCR Analysis Tool. These modeling data were then ported into Excel spreadsheets that also 

include the filled Regional Haze Rule (RHR) IMPROVE database available from the VIEWS 

website along with the EPA default Natural Conditions that were either available from VIEWS 

for the new IMPROVE equation or from EPA guidance documents for the original IMPROVE 

equation (EPA, 2003b). Excel macros are then used to perform the visibility projections using 

the EPA default procedures described in Chapter 4 and the EPA modeling guidance document 

(EPA, 2007a) as well as the alternative projection procedures described in this Chapter. 

EPA has developing a Modeled Attainment Test Software (MATS) program that codifies the 8-

hour ozone, PM2.5 and visibility projection procedures given in EPA’s latest air quality modeling 

guidance (EPA, 2007a). The June 2007 release of the beta version of MATS is capable of 

performing 8-hour ozone and visibility projections; MATS is still under development for making 

PM2.5 projections. The June 2007 beta versions of MATS was applied to the VISTAS 2002 and 

2018 Base G2 12 km CMAQ results and the resultant 2018 visibility projections were compared 

with the VISTAS values using the EPA default projection approach (see Chapter 4) at VISTAS 

and nearby Class I areas. The projected 2018 visibility estimates using the VISTAS and EPA 

MATS software are shown in Table 5-1. The biggest differences in the two 2018 visibility 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chapter_5_AddAnal4.doc 5-4 

projection procedures are for the following five Class I areas: Breton Island (BRET), Louisiana; 

Cohutta (COHU), Georgia; Mingo (MING), Missouri; St. Marks (SAMA), Florida; and Shining 

Rock (SHRO), North Carolina. For these five Class I areas, the June 2007 release of MATS 

produces no 2018 visibility projections. This is because there is insufficient capture of valid 

IMPROVE PM measurements within the 2000-2004 five-year baseline to generate at least three 

years of annual visibility estimates for the worst 20 percent days, which is the minimum needed 

to develop the Baseline Conditions following EPA’s guidance (EPA, 2003a). For the VISTAS 

projections, data filling was used to fill out the IMPROVE measurements with sufficient data so 

that Baseline Conditions could be calculated at these five Class I areas. 

As noted above, of the 21 VISTAS and nearby Class I areas, 5 had insufficient data in the raw 

IMPROVE data set used in MATS to generate a valid Baseline so MATS could not make 2018 

visibility projections. Of the remaining 16 Class I areas, the VISTAS 2018 visibility projections 

exactly match those by MATS at 11 of the Class I areas and are within 0.01 dv at two others, 

which could be due to just round off error in the calculations. Thus, of the 16 Class I areas with 

sufficient valid data, the VISTAS Excel approach and EPA’s MATS tool agree on the visibility 

projections at 13 of the Class I areas (i.e., > 80% of the Class I areas). 

The differences in the 2018 visibility projections at the three remaining Class I areas (CHAS, 

GRSM and SWAN) are small, but larger than one would expect from just round off errors. An 

examination of these differences found small differences in the observed IMPROVE data used to 

define the 2000-2004 Baseline conditions (see columns 4 and 5 in Table 5-1). These small 

differences are due to slight changes in a few PM concentration measurements between the raw 

IMPROVE database included in MATS and the final RHR database distributed by VIEWS. 

Table 5-1. Comparison of VISTAS Excel based and EPA MATS 2018 visibility projections 

(deciview) at VISTAS and nearby Class I areas. 

MATS VISTAS MATS VISTAS 

Class I 

Area 

2018 

Projected (dv) 

2018 

Projected (dv) 

2000-04 

Baseline (dv) 

2000-04 

Baseline (dv) 

BRET NA 23.41 NA 26.00 

BRIG 24.62 24.62 29.01 29.01 

CACR 22.38 22.38 26.36 26.36 

CHAS 22.22 21.85 26.09 25.75 

COHU NA 22.84 NA 30.25 

DOSO 21.64 21.64 29.04 29.04 

EVER 20.66 20.65 22.30 22.30 

GRSM 23.55 23.66 30.28 30.28 

HEGL 23.09 23.09 26.75 26.75 

JARI 22.59 22.59 29.12 29.12 

LIGO 21.98 21.98 28.77 28.77 

MACA 25.56 25.56 31.37 31.37 

MING NA 23.85 NA 28.05 

OKEF 23.85 23.85 27.13 27.13 

ROMA 22.72 22.72 26.48 26.48 

SAMA NA 22.89 NA 26.31 

SHEN 22.10 22.11 29.31 29.31 

SHRO NA 22.11 NA 28.66 

SIPS 23.69 23.69 29.03 29.03 

SWAN 21.26 20.37 25.49 24.71 

NA = Not Available due to insufficient data in raw IMPROVE database used in MATS to 

generate worst 20 percent days for at least three years during 2000-2004 period to 

define Baseline visibility conditions.
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chapter_5_AddAnal4.doc 5-5 

5.3 Alternative Visibility Projection Procedures 

In this section we analyze several alternative visibility projection procedures to EPA’s default 

approach where RRFs are based on the ratio of the modeled average concentrations across the 

worst 20 percent days for the 2018 and 2002 emission scenarios discussed in Chapter 4 (EPA, 

2007a). Chapter 4 presented 2018 visibility projections at VISTAS and nearby Class I areas 

using the 2002 and 2018 Base G2 CMAQ 36 km and 12 km modeling results and the new 

IMPROVE equation. The results in Chapter 4 and Appendix E showed that a vast majority of 

the visibility improvements between the 2000-2004 Baseline and 2018 were due to reductions in 

sulfate (SO4). Differences were noted in the 36 km and 12 km 2018 visibility projections at 

Everglades that were traced to extremely low nitrate (NO3) predictions for the 2002 worst 20 

percent days in the CMAQ 12 km modeling results resulting in modeled RRFs that were likely 

affected by model noise. Thus, some of the alternative visibility projection investigate their 

sensitivity to SO4 model performance and model noise in the NO3 predictions. The alternative 

visibility projection approaches discussed below analyzed the following: 

• Use of the original IMPROVE equation; 

• Effects of assuming coarse mass (CM) and Soil are natural; 

• Effects of model noise and low NO3 modeled values; 

• Effects of SO4 model performance; and 

• Contributions of international transport. 

5.3.1 Use of Original IMPROVE Equation 

The first rounds of 2018 visibility projections performed by VISTAS used the original 

IMPROVE equation (Malm et al., 2000) to convert PM species concentrations to light extinction 

and deciview. During the course of the VISTAS modeling, a new IMPROVE equation was 

developed to convert PM concentrations to light extinction, which was subsequently adopted by 

VISTAS as the primary procedure for converting measured and modeled PM concentrations to 

light extinction. The justification for using the new IMPROVE equation over the original 

IMPROVE equation was presented previously in Section 4.2.1.1.3. 

Figure 5-2 displays 2018 visibility projections using the CMAQ 12 km and 36 km Base G2 

modeling and the original IMPROVE equation and compares them with the projections using the 

new IMPROVE equation from Chapter 4. Use of the original IMPROVE equations for the 2018 

visibility projections at the VISTAS mountain Class I areas results in projections that still 

achieve the 2018 Uniform Rate of Progress, but by less margin than using the new IMPROVE 

equation. The original IMPROVE equation estimates the VISTAS Mountain Class I areas will 

be from 6 to 28 percentage points less in achieving the 2018 Uniform Rate of Progress, but it is 

still achieved at these mountain sites. 

With the exception of the Everglades (EVER), there is good agreement between the visibility 

projections at VISTAS Coastal Class I areas using the new and original IMPROVE equations as 

they are within 2 to 20 percentage points of each other in achieving the 2018 Uniform Rate of 

Progress. At EVER, however, the new and original IMPROVE equation estimates that it would 

achieve, respectively, 69% and 41% of the 2018 Uniform Rate of Progress. 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chapter_5_AddAnal4.doc 5-6 

At the non-VISTAS nearby Class I areas, the original IMPROVE equation is slightly less 

optimistic than the new IMPROVE equation with values 2 to 17 percentage points lower than the 

new IMPROVE equation, with the largest difference at BRET and the others within ~10 

percentage points. 

In conclusion, the original IMPROVE equation estimates lower visibility improvements between 

the 2000-2004 Baseline and 2018 than the new IMPROVE equation, but the visibility projections 

are usually close to each other. For the VISTAS Class I areas, those areas that are estimated to 

achieve the 2018 Uniform Rate of Progress using the new IMPROVE equation are also estimated 

to achieve it with the old IMPROVE equation. And those areas that are estimated to not achieve 

the 2018 Uniform Rate of Progress do so using both versions of the IMPROVE equations. 

Finally, the SAMA area that is right at the 2018 Uniform Rate of Progress is in the too close to 

call range using both IMPROVE equations. 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chapter_5_AddAnal4.doc 5-7 

CMAQ 2018g2b/2002gt2a Method 1 predictions for VISTAS+ sites

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CMAQ Old IMPROVE Algorithm

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CMAQ 2018g2a/2002gt2a Method 1 predictions for VISTAS+ sites

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CMAQ New IMPROVE Algorithm

CMAQ Old IMPROVE Algorithm

Mountain Coastal non-VISTAS

Figure 5-1. DotPlot comparing the VISTAS 2018 visibility projections using the new and 

original IMPROVE equations based on the 12 km (top) and 36 km (bottom) CMAQ modeling 

results. DotPlots express the 2018 visibility projections as a percentage of achieving the 

visibility reduction needed to meet the 2018 Uniform Rate of Progress.
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chapter_5_AddAnal4.doc 5-8 

5.3.2 Treatment of Coarse Mass and Soil as Natural 

Much of the coarse mass (CM) and, to a lesser extent, Soil measured at the IMPROVE monitor 

is likely due to local wind blown dust that is natural in origin and not captured by the model. 

Consequently, even using the modeling results in a relative sense with the RRFs may not be 

appropriate for projecting CM and Soil. If CM and Soil are in fact primarily due to local impacts 

due to wind blown dust from natural lands, then it would be appropriate to assume they remain 

unchanged between the 2000-2004 Baseline and 2018. This is probably certainly appropriate for 

CM that is primarily due to fugitive dust and has a short transport distance that is likely subgridscale to the model. In fact, in the model evaluation discussed in Chapter 3 and Appendix C CM 

performance is characterized by a large underprediction bias that is likely due to local fugitive 

dust impacts at the IMPROVE monitors. For Soil this is less clear as fine particles can be 

transported over longer distances and are produced by anthropogenic sources, such as 

combustion and road dust, as well as natural sources. Thus we performed three CM and Soil 

sensitivity tests to investigate whether assuming that all or part of them were of natural origin 

would affect the 2018 visibility projections. The first CM/Soil sensitivity test, assumed that all 

CM was natural so would remain unchanged between the 2000-2004 Baseline to 2018 (i.e., set 

the RRF for CM equal to 1.0). The second sensitivity test assumed both CM and Soil were all 

natural so RRFs for both of them are set to unity. In the third CM/Soil sensitivity test, part of the 

Soil is assumed to be natural and part of it is assumed to be anthropogenic in origin. This was 

done by subtracting the modeled 2002 base case Soil from the observed values for the worst 20 

percent days and assume that the remainder (if any) was natural. This natural component of the 

Soil from the 2000-2004 baseline was held constant in 2018 and added to the 2018 modeled Soil 

values. 

The results of the CM and Soil visibility projection sensitivity analysis using the CMAQ 12 km 

and 36 km modeling results are shown in the DotPlot given in Figure 5-3. The CM and Soil 

visibility projection sensitivity analysis have little effect on the 2018 visibility projections at the 

VISTAS Class I areas. There are some differences at the non-VISTAS Class I areas in Arkansas 

and Missouri, but these differences are small. There are two reasons why the CM and Soil 

sensitivity tests have little effect on the 2018 visibility projections: (1) unlike some of the Class I 

areas in the desert southwestern portion of the U.S., CM and Soil generally make up a small 

component of visibility impairment at the VISTAS Class I areas for the worst 20 percent days; 

and (2) there is very little change in the CM and Soil emissions between 2002 and 2018 so the 

RRFs for these two species are near unity anyway. 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chapter_5_AddAnal4.doc 5-9 

CMAQ 2018g2b/2002gt2a Method 1 predictions for VISTAS+ sites

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wCalculated RRF

RRF_CM=1

RRF_CM&SOIL=1

RRF_CM&rest(SOIL)=1

Mountain Coastal non-VISTAS

CMAQ 2018g2a/2002gt2a Method 1 predictions for VISTAS+ sites

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Percent of target reduction achieved

wCalculated RRF

RRF_CM=1

RRF_CM&SOIL=1

RRF_CM&rest(SOIL)=1

Mountain Coastal non-VISTAS

Figure 5-3. Sensitivity of 2018 visibility projections to assumptions regarding whether all CM, 

all CM and all Soil and all CM and part of the Soil is natural using the CMAQ 12 km (top) and 

36 km (bottom) modeling results. 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chapter_5_AddAnal4.doc 5-10 

5.3.3 Accounting for Model Performance in Visibility Projections 

In Chapter 4 we noted significant differences in the 2018 visibility projections at the Everglades 

(EVER) Class I area using the 36 km and 12 km CMAQ modeling results that were traced back 

to the modeled NO3 RRFs (see Figure 4-4). The ISORROPIA aerosol equilibrium algorithm in 

CMAQ contains model noise that can be comparable to modeled concentrations at very low 

concentrations for species affected by the ISORROPIA aerosol equilibrium equations (e.g., SO4, 

NH4 and NO3). Although this is not a big issue with SO4 since its concentrations are typically 

high for most worst 20 percent days in the southeastern U.S., it may affect NO3 (note that 

modeled NH4 concentrations are not used in the visibility projections as the IMPROVE 

equations assume SO4 and NO3 are completely neutralized by NH4). This is because NO3 is 

highly volatile so on hot summer days, which a majority of the worst 20 percent days in the 

VISTAS region tend to be, its concentrations become low. Furthermore, as shown in Chapter 3 

and Appendices B, C and D, the model also tends to underestimate the observed NO3 

concentrations in the summer. In the case of EVER, this resulted in a CMAQ 12 km estimated 

NO3 concentration averaged across the worst 20 percent days that was extremely low resulting 

in very low extinction (~0.1 Mm-1). For the 2018 CMAQ 12 km simulation, the NO3 

concentrations for the worst 20 percent days were still very low but were over 100% higher than 

the 2002 values resulting in an NO3 RRF of over 2. The 2000-2004 Baseline NO3 extinction at 

EVER was not as low (~5 Mm-1) so that the modeled high RRF based on extremely small 

concentrations were applied to higher observed NO3 in the Baseline which had a significant 

effect on the visibility projections. Given the potential for model noise in the CMAQ model at 

such low NO3 concentrations, we developed an alternative projection approach to hold NO3 

concentrations constant from the 2000-2004 Baseline to 2018 (i.e., set NO3_RRF=1.0) when the 

modeled 2002 NO3 concentrations averaged across the worst 20 percent days is extremely low: 

NOxconc>0.1: Set the NO3 RRF to 1.0 if the average NO3 concentration across the 

2002 worst 20 percent days at a Class I area (i.e.,, the denominator of the RRF) is less 

than 0.1 μg/m3

. 

Also discussed in the model performance evaluation (Chapter 3 and Appendices) are occasions 

when the model greatly underestimates the observed extinction on some of the worst 20 percent 

days. One concern is that if the model estimates concentrations near background on a worst 20 

percent day when the observed concentrations are high, the model will be very stiff in response 

to emission reductions and provide less reductions in the RRF than there should be. Similarly, if 

the modeled concentrations are much higher than observed, the model may be more responsive 

to changes in emissions than it should be. This effect would be particularly important for SO4 as 

it is usually the highest component of light extinction on the worst 20 percent days at VISTAS 

Class I areas and is the component with the largest reductions between the 2000-2004 Baseline 

and 2018. Thus, a second alternative visibility projection approach was formulated that only 

includes those worst 20 percent days in 2002 in the SO4 RRF when the model SO4 prediction 

matches the observed SO4 concentration to within a factor of 2: 

SO4_MPE: Sulfate RRFs are based on modeled 2018 and 2002 SO4 concentrations for 

just those 2002 worst 20 percent days when the modeled and measured SO4 are within a 

factor of 2 of each other. 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chapter_5_AddAnal4.doc 5-11 

The final alternative 2018 visibility projection approached combined the minimum NO3 

concentration check with the SO4 MPE check (NO3conc>0.1 + SO4_MPE). 

Figure 5-4 shows DotPlot displays of the 2018 visibility projections using the 12 km and 36 km 

CMAQ results accounting for low NO3 concentrations and SO4 model performance in the RRFs. 

The NO3conc, SO4_MPE and combined alternative visibility projections approaches have little 

effect on the 2018 visibility projections at most VISTAS Class I areas. In particular, the 

visibility projections at the VISTAS inland mountain Class I areas show little change due to the 

NO3 concentration and SO4 model performance tests. The largest differences in the visibility 

projections due to the NO3/SO4 tests are at the EVER and BRET southern coastal Class I areas. 

In the case of EVER, the NO3conc test makes a difference in achieving sufficient visibility 

improvements to meet the 2018 Uniform Rate of Progress with the NO3conc>0.1 test to not 

achieving the 2018 Uniform Rate of Progress when the EPA default RRFs are used. At BRET, 

the NO3 and SO4 tests both result in more optimistic 2018 visibility projections such that their 

combined effects result in BRET achieving the 2018 Uniform Rate of Progress. The remainder 

of the Class I areas’ 2018 visibility projections exhibit little sensitivity to the NO3 concentration 

and SO4 model performance tests. 

Note that these results are not surprising given the model performance and visibility projection 

discussions in, respectively, Chapters 3 and 4 and their companion appendices. SO4 model 

performance was poorest at the EVER and BRET Class I areas that exhibited a large 

underprediction bias. The SO4_MPE sensitivity test eliminated days from the SO4 RRF with 

large SO4 underestimation bias leaving the higher SO4 days so that the model was more 

responsive and exhibited more visibility improvements in 2018. 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chapter_5_AddAnal4.doc 5-12 

CMAQ 2018g2b/2002gt2a Method 1 predictions for VISTAS+ sites

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NO3conc>0.1

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CMAQ 2018g2a/2002gt2a Method 1 predictions for VISTAS+ sites

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NO3conc>0.1

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SO4_MPE

Mountain Coastal non-VISTAS

Figure 5-4. Sensitivity of 2018 visibility projections to setting NO3 RRFs to 1.0 when NO3 

concentrations are extremely low and/or eliminating worst 20 percent days in the SO4 RRFs 

when modeled/observed SO4 concentrations differ by over a factor of 2. 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chapter_5_AddAnal4.doc 5-13 

5.3.4 Use of Classification and Regression Tree (CART) Weighted RRFs 

ICF International used the Classification and Regression Tree (CART) procedure to classify the 

worst and best 20 percent days from the 2000-2004 Baseline at each VISTAS and nearby Class I 

area into separate bins of days with similar conditions (Douglas, Hudischewskyj and Wei, 2007). 

Days from the 2000-2004 5-year baseline were classified into several bins based on the measured 

PM at and near each Class I area and meteorological conditions at nearby meteorological 

stations. Using the CART classifications for the worst 20 percent days from the 5-year 

Baseline, weighting factors were developed to be applied to days from the 2002 modeling period 

to produce a weighted mix of days from 2002 that match the mix of days within the 2000-2004 

Baseline for each Class I area. Rather then using just the modeling days from the worst 20 

percent days during 2002 in the RRFs, as is done with the EPA default projection approach, the 

CART weights can potentially weigh model estimates from all 365 days during the 2002 year to 

develop RRFs. However, in practice only about 40 days were assigned positive CART weights. 

Two types of CART weights were developed (see Douglas, Hudischewskyj and Wei, 2007 for 

more details): 

CARTrev3: Based on the total number of days and number of worst 20 percent days in 

each bin. 

CARTrev3_wwtonly: Based on the number of worst 20 percent days in each bin. 

The CART weights used in the alternative visibility projections were based on files received in 

an e-mail from Sharon Douglas of ICF International to Pat Brewer of VISTAS dated July 12, 

2007. 

Figure 5-5 displays the DotPlot of visibility projections using the EPA default approach for 

calculating RRFs (wCalculated RRF) used in Chapter 4 and using the two sets of CART weights 

for calculating the RRFs. The use of CART weights in the visibility projections has either 

essentially no change over the EPA default approach, or results in less visibility improvements 

and a lower percentage of meeting the 2018 Uniform Rate of Progress. We believe the reasons 

for this is because the RRFs using the CART weights are weighting days in 2002 for the RRFs 

that have lower SO4 concentrations (e.g., no longer a worst 20 percent day) and consequently the 

model is more stiff and less responsive to the SO2 reductions between 2002 and 2018. For the 

VISTAS Class I areas, if an area achieves the 2018 Uniform Rate of Progress using the EPA 

default RRF approach then it also achieves it using the CART weights approach, although the 

degree of overachievement is less at many of the Class I areas using the CART weights 

approach.
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chapter_5_AddAnal4.doc 5-14 

CMAQ 2018g2b/2002gt2a Method 1 predictions for VISTAS+ sites

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CMAQ 2018g2a/2002gt2a Method 1 predictions for VISTAS+ sites

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Mountain Coastal non-VISTAS

Figure 5-5. Comparison of 2018 visibility projections using RRFs based on CART weighted 

modeling results versus EPA default approach (wCalculated RRF). 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chapter_5_AddAnal4.doc 5-15 

5.4 Effects of International Transport on 2018 Visibility Projections 

VISTAS performed a focused analysis of the contributions of International Transport on 

visibility impairment at their Class I areas. These results were summarized in a document 

written by Dr. Ivar Tombach dated July 26, 2007 that is paraphrased below (Tombach, 2007). 

Anthropogenic and natural aerosols from emissions in Mexico, Canada, Central America, Asia, 

and Africa have been shown to affect air quality and regional haze in the continental U.S. States 

may consider the fraction of the ambient aerosol that is beyond the regulatory control of the U.S 

as they evaluate emission control strategies to achieve reasonable progress in reducing regional 

haze in Class I areas. To support the VISTAS states in evaluating the contribution from 

international emissions, the VISTAS RPO carried out analyses to estimate how much of the haze 

at Class I areas in the VISTAS region in 2002 and 2018 (as projected by the CMAQ model) 

results from international emissions. 

The VISTAS CMAQ 36 km domain includes all of the continental U.S. and portions of northern 

Mexico and southern Canada. Thus, the contributions of emissions these portions of Canada and 

Mexico are explicitly accounted for in the CMAQ modeling. The effects of emissions from 

more distant sources that are outside of the 36-km national domain were estimated using outputs 

from a global air chemistry model (GEOS-CHEM). The Big Bend Regional Aerosol and 

Visibility Observational (BRAVO) study had found that the performance of national-scale 

modeling was enhanced by using monthly averages of outputs from a global air pollution model, 

GOCART, to generate SO2 and sulfate boundary conditions for its 36-km national modeling grid 

(Schichtel, et al., 2005), instead of using the EPA default concentrations that up to the time of the 

BRAVO study were primarily used. VISTAS elected to use a similar approach as BRAVO, using 

the outputs of the GEOS-CHEM global air pollution model, which also contains a detailed 

representation of ozone-NOx-VOC-PM chemistry. 

Under EPRI sponsorship, Harvard University had already applied the GEOS-CHEM model, with 

2°x2.5° horizontal resolution (approximately 200 km x 200 km), to estimate natural conditions 

and transboundary influences on elemental and organic carbon aerosols in the United States in 

1998 (Park et al., 2003). After some emission inventory and model improvements, similar 

information was developed for sulfate and nitrate aerosols in 2001 (Park et al., 2004). 

Under contract to VISTAS (which was acting on behalf of all the RPOs), Harvard then simulated 

global air pollution for the year 2002 (the VISTAS modeling year) with 4°x5° horizontal 

resolution (approximately 400 km x 400 km) and 3-hour temporal resolution (Jacob, Park and 

Logan, 2005). The coarser grid scale was considered sufficient to provide boundary conditions 

outside of North America, largely over the oceans. Although the VISTAS simulation was 

generally similar to the 2001 EPRI simulation mentioned above, the VISTAS modeling 

incorporated the following substantive changes in addition to the change in grid scale: (1) use of 

U.S. anthropogenic emissions from the EPA NEI 1999 inventory, except for ammonia; (2) use of 

forest fire information specific to 2002; (3) inclusion of the secondary organic aerosol (SOA) 

formation mechanism from Chung and Seinfeld (2002); and (4) inclusion of prototype soil dust 

and sea salt simulations. An additional model improvement, which has since been implemented 

in the standard version of GEOS-CHEM, was the application of surface emissions and dry 

deposition to the entire mixed layer column diagnosed by GEOS (the Global Earth Observation 

System of NASA) rather than to just the surface layer of the model. Details of the emissions, 

meteorology, model configuration, and model performance evaluation for the VISTAS 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chapter_5_AddAnal4.doc 5-16 

simulations are described in the report to VISTAS (Jacob, Park and Logan, 2005) and were 

summarized previously in Section 1.3.7. 

For North America, the VISTAS GEOS-CHEM modeling used monthly-average emissions, so 

the model results do not reflect diurnal or weekday-weekend variability in emissions, and also 

diminish the impacts of episodic emissions, such as the July 2002 wildfire in Ontario, Canada. 

The Mexican emissions were from the inventory of the BRAVO Study, while Canadian 

emissions for the year 2000 were used. Emissions from Caribbean islands were not included, 

which could impact model estimates at the Everglades. 

The surface meteorological data inputs to GEOS-CHEM had 3-hour temporal resolution and the 

upper air meteorological inputs had 6-hour resolution, with a horizontal resolution of 1°x1° or 

better and 48 vertical layers. 

The output of the GEOS-CHEM modeling, as delivered to VISTAS, contained concentrations of 

all visibility-related components of particulate matter with 3-hour temporal resolution and 4°x5°

horizontal resolution over multiple layers of the atmosphere. 

Even though the coarse spatial resolution degraded the performance of the model over North 

America, relative to results from prior modeling for other years using finer grids, the 

concentrations simulated by GEOS-CHEM maintained the synoptic-scale structure and did not 

have a continental-scale bias (See Appendix G). At a finer scale, the concentrations simulated by 

GEOS-CHEM for the surface layer in 2002 were generally within a factor of two of regionalaverages (~500 km scale) of measurements of the various chemical components of particulate 

matter in the United States and, except for organic carbon, satisfactorily simulated the seasonal 

variability in the observations. 

In the meantime, Harvard University had also carried out new finer-grid simulations for 2001 for 

EPRI, using a 1°x1° grid over North America, nested in a coarser grid that covered the globe. 

The final version of these simulations included modifications to the modeling grid to improve 

delineation between domestic US emissions and those of foreign sources at the national 

boundaries, and revision (reduction) of the Canadian and Mexican sulfur emissions. These 

GEOS-CHEM 1°x1° grid scale simulations for 2001 and their results are described in the paper 

by Park et al. (2006). Over appropriate geographic and temporal scales, the results of these 

simulations for 2001 were similar to those of the 2002 VISTAS simulations, which gave 

confidence in the VISTAS GEOS-CHEM results. 

The concentration fields that resulted from the GEOS-CHEM simulations were not directly 

usable as boundary conditions for the 36-km grid scale CMAQ simulations. GEOS-CHEM and 

CMAQ use different horizontal and vertical grid scales and different map projections, and have 

different labels for chemical constituents. The GEOS-CHEM outputs were processed by Dr. 

Daewon Byun at the University of Houston to adjust for these discrepancies and to develop 

boundary conditions for the 36-km CMAQ modeling (Byun, 2004). Since the CMAQ modeling 

has temporal resolution of one hour, the boundary conditions were given for every hour. 

However, since the GEOS-CHEM outputs had 3-hr resolution, the hourly boundary conditions 

were the same for three consecutive hours. An example of the resulting boundary conditions for 

sulfate at the surface is shown in Figure 5-6. 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chapter_5_AddAnal4.doc 5-17 

Average Sulfate Boundary Conditions 

GEOS-CHEM for July 1, 2001 

Figure 5-6. Example GEOS-CHEM projections of sulfate boundary conditions for the national 

CMAQ modeling grid on July 1, 2001, as indicated by the colors along the boundary of the 

modeling domain. 

Using the boundary conditions provided by the GEOS-CHEM global simulations, VISTAS 

calculated the impacts of international anthropogenic emissions on haze at U.S. Class I areas in 

two different ways, which are summarized in Table 5-2: 

• First, as the difference between the results of two GEOS-CHEM simulations: (1) with 

2002 international emissions (natural and anthropogenic) but only natural U.S. emissions; 

and (2) with only natural emissions worldwide. Such differences could be calculated with 

the 4°x5° grid of the VISTAS simulations or the 1°x1° grid of the EPRI simulations. 

Since the chemical reactions in these GEOS-CHEM simulations do not reflect the 

contribution to the chemistry from anthropogenic emissions in the U.S., the result is an 

estimate of what the international contribution would be if the U.S. emissions were only 

from natural sources (the 2064 goal) while the emissions of the rest of the world 

remained at 2002 levels. (See the upper half of Table 5-2 for portrayal of this 

calculation.) 

• Second, as the difference between the results of two CMAQ 36-km grid simulations of 

the U.S., both using 2018 OTW Base F emissions, with boundary conditions based on 

GEOS-CHEM simulations: (1) with all global sources active; and (2) with U.S. 

anthropogenic emissions and all global natural emissions (Mexican and Canadian 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chapter_5_AddAnal4.doc 5-18 

anthropogenic emissions within the 36-km CMAQ domain were also removed). Since the 

GEOS-CHEM simulations are based on 2002 emissions, the result of this calculation is 

an estimate of what the international contribution would be in 2018 if international 

emissions remain at 2002 levels. (See the lower half of Table 5-2 for portrayal of this 

calculation.) 

Both cases underestimate the total international contribution because the GEOS-CHEM 

simulations assumed that all fires outside of the US were natural, that is that there are no 

international fire emissions due to anthropogenic burning. 

Table 5-2. Approaches for estimating international contributions using GEOS-CHEM results. 

Case Natural Sources Anthropogenic Sources 

GEOS-CHEM 

(1) International and US International 

(2) International and US -- 

Difference = (1) – (2) -- International 

CMAQ with GEOS-CHEM boundary conditions

(1) International and US International and US 

(2) International and US US 

Difference = (1) – (2) -- International 

The CENRAP RPO applied a third method to estimate the impacts of international emissions. 

They used the PSAT (PM Source Apportionment Technology) module of the CAMx regional air 

quality model to track the 2002 contributions of three non-domestic source categories (Canadian 

emissions in the CAMx modeling domain, Mexican emissions in the CAMx modeling domain, 

and boundary conditions) at all Class I areas (ENVIRON and UCR, 2007). The boundary 

conditions applied at the boundaries of the CAMx 36 km scale grid were based on the outputs of 

the GEOS-CHEM modeling described above. The international impacts in 2002 were then 

calculated as the sums of the PSAT-determined contributions of these three source categories. 

One major difference between this approach and the VISTAS approaches is that the PSAT 

international impacts included the contributions from fire emissions. This will result in larger OC 

and EC international impacts compared to the VISTAS approach. 

To illustrate the magnitudes of the international contributions to two of the principal components 

of the visibility-impairing aerosol, Figures 5-7 and 5-8 show the annual averages of estimates of 

international contributions to ammonium sulfate and organic carbonaceous mass (OCM) at Class 

I areas in the VISTAS region and in adjacent states. Results are presented for all four of the 

methods described above. Two sets of GEOS-CHEM results are shown: one for 2002 with the 

VISTAS 4°x5° grid and one for 2001 with the EPRI 1°x1° grid. 

 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chapter_5_AddAnal4.doc 5-19 

Figure 5-7. Estimates by four methods of impacts of international emissions on annual average 

ammonium sulfate [(NH4)2SO4] concentrations at Class I areas.

Transboundary Man-Made Impacts

0.00

0.05

0.10

0.15

0.20

0.25

0.30

ACAD

LYBR

BRIG

BOWA

ISLE

BIBE

HEGL

CACR

MING

UPBU

BRET

EVER

CHAS

SAMACOHU

OKEF

ROMA

SIPS

SHRO

GRSM

LIGO

MACASWAN

JARI

DOSO

SHEN

OCM Conc. (

_g/m3

)

GEOS_4x5 GEOS_1x1 PSAT CMAQ

Figure 5-8. Estimates by four methods of impacts of international emissions on annual average 

organic carbonaceous mass (OCM) or organic carbon mass (OCM) concentrations at Class I 

areas. 

For ammonium sulfate there is reasonable agreement (better than a factor of two or so 

everywhere except at Everglades) between all pairs of methods, while the results are more 

variable for OCM. The impacts estimated by the VISTAS 4°x5° GEOS-CHEM simulations tend 

to be smaller than those of the other methods. Not surprisingly, the biggest impacts from 

international emissions tend to occur at Class I areas that are near the Canadian and Mexican 

borders. 

Transboundary Man-Made Impacts

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

ACAD

LYBRBRIG

BOWA

ISLEBIBE

HEGL

CACRMINGUPBUBRETEVERCHASSAMACOHU

OKEF

ROMA

SIPS

SHROGRSM

LIGOMACASWAN

JARI

DOSO

SHEN

GEOS_4x5 GEOS_1x1 PSAT CMAQ

(NH4)2SO4 Conc. (_g/m3) 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chapter_5_AddAnal4.doc 5-20 

The projected contributions from international emissions on the 20 percent worst visibility days 

in 2002 are compared to annual average contributions in Figures 5-9 and 5-10. Results obtained 

using both the CAMx/PSAT and CMAQ models are shown. At the VISTAS sites, CMAQ 

projected slightly higher international contributions to sulfate on the 20 percent worst visibility 

days than the annual average contributions. Compared to the corresponding annual average 

international contribution, CAMx/PSAT generally showed lower contribution to sulfate on the 

20 percent worst days at the mountain sites and equal or slightly higher contributions at the 

coastal sites. For organic carbonaceous mass, there were no clear trends between 20 percent 

worst and annual average days in either the CAMx/PSAT or CMAQ results. 

Transboundary Man-Made Impacts

0.0

0.2

0.4

0.6

0.8

1.0

1.2

BRIG

HEGL

CACR

MING

UPBU

BRET

EVER

CHAS

SAMACOHU

OKEF

ROMA

SIPS

SHRO

GRSM

LIGO

MACASWAN

JARI

DOSO

SHEN

(NH4)2SO4 Conc. 

(_g/m3

)

PSAT_20 PSAT CMAQ_20 CMAQ

Figure 5-9. Estimates by the PSAT and CMAQ models of the impacts of international emissions 

on ammonium sulfate mass concentrations at Class I areas on the 20% worst visibility days and 

on the annual average in 2002. 

Transboundary Man-Made Impacts

0.00

0.05

0.10

0.15

0.20

0.25

0.30

BRIG

HEGL

CACR

MING

UPBU

BRET

EVER

CHAS

SAMACOHU

OKEF

ROMA

SIPS

SHRO

GRSM

LIGO

MACASWAN

JARI

DOSO

SHEN

OCM Conc. (

_g/m3

)

PSAT_20 PSAT CMAQ_20 CMAQ

Figure 5-10. Estimates by the PSAT and CMAQ models of the impacts of international 

emissions on organic carbonaceous mass concentrations at Class I areas on the 20% worst 

visibility days and on the annual average in 2002. 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chapter_5_AddAnal4.doc 5-21 

Figures 5-11 and 5-12 put the international impacts into the context of current measured 

concentrations. They compare the international impact estimates with current monitored 

concentrations on the 20 percent worst visibility days at two VISTAS Class I areas chosen as 

examples, Shenandoah (SHEN) and St. Marks (SAMA). At both locations, the international 

impacts on concentrations on such days are small, typically less than 5% of the measured 

concentrations. 

At the same two Class I areas, the magnitudes of international contributions relative to the 

uniform rate of progress (URP) goal for 2018 are shown in Figures 5-13 and 5-14, where all the 

haze index values were calculated using the new IMPROVE algorithm. In each plot, the open 

triangle at 2018 indicates the estimated haze index, in deciviews, as predicted by the CMAQ 

model using the Base G2 emission inventory. The open diamond indicates the haze index in 

2018 if international emissions are removed from the 2018 CMAQ simulation, so the difference 

between the open triangle and the open diamond represents the international contribution. 

SHEN1

20% Worst Days 

0

5

10

15

20

25

30

35

40

45

2/28 5/26 5/29 6/4 6/10 6/13 6/19 6/25 7/1 7/4 7/16 7/19 7/22 7/31 8/3 8/12 8/21 8/24 8/27 9/11 9/14 9/17 9/29 10/2

Mass (μg/m3

)

Amm SO4 Amm NO3 POM LAC Soil CM Sea Salt

Figure 5-11. For the 20 percent worst visibility days in 2002 at Shenandoah, CMAQ-simulated 

international contributions (right bars) and domestic contribution to measured PM 

concentrations (measured component minus international contribution) (left bars).
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chapter_5_AddAnal4.doc 5-22 

SAMA1

20% Worst Days (2002)

0

5

10

15

20

25

30

35

2/4 2/13 2/16 2/25 3/30 4/5 4/8 4/17 4/26 5/2 5/5 5/8 5/11 6/4 7/4 7/7 7/16 7/19 9/17 10/1710/20 10/23 12/7

 Mass (μg/m3) 

Amm SO4 Amm NO3 POM LAC Soil CM Sea Salt

Figure 5-12. For the 20 percent worst visibility days in 2002 at St. Marks, CMAQ-simulated 

international contributions (right bars) and domestic contribution to measured PM 

concentrations (measured component minus international contribution) (left bars). 

Uniform Rate of Progress Glide Path

Shenandoah - 20% Worst Days

29.31

28.11

25.12

22.13

19.14

16.15

13.16

11.37

22.11

20.47

0

5

10

15

20

25

30

35

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 Method 1 Prediction w/o Intl. Trans.

Figure 5-13. Illustration of the effect of removing the international emissions contribution to the 

CMAQ-modeled haze index on 20% worst days at Shenandoah in 2018. Open triangle = 

estimate with all emissions; open diamond = estimate with international emissions removed. 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chapter_5_AddAnal4.doc 5-23 

Uniform Rate of Progress Glide Path

Saint Marks - 20% Worst Days

26.31 25.33

22.89

20.45

18.01

15.57

13.13

11.67

22.89

21.82

0

5

10

15

20

25

30

35

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 Method 1 Prediction w/o Intl. Trans.

Figure 5-14. Illustration of the effect of removing the international emissions contribution to the 

CMAQ-modeled haze index on 20% worst days at St. Marks in 2018. Open triangle = estimate 

with all emissions; open diamond = estimate with international emissions removed. 

At Shenandoah, Figure 5-13 shows that the modeled 2018 haze index (22.11 dv) is already 

below the 2018 Uniform Rate of Progress (25.12 dv). Removing the international contribution 

would lower the 2018 haze index by another 1.64 dv. Although the international contribution is 

small today, it becomes increasingly important as U.S. emissions are reduced. (For these 

analyses, the international emissions are assumed to remain constant from the baseline period to 

2018). 

At Saint Marks, Figure 5-14 shows that the modeled 2018 haze index (22.89) is right at the 2018 

Uniform Rate of Progress (22.89). Removing the international contribution would reduce the 

2018 haze index by 1.07 dv, to a value that lies just below (indicating greater reduction than) the 

2018 Uniform Rate of Progress. 

Information of the type portrayed for 2018 in Figures 5-13 and 5-14 is summarized in Figure 5-

15 for all VISTAS Class I areas and Class I areas in adjacent states using the DotPlot format 

presented previously that represents the 2018 visibility projection as a percentage of achieving 

the 2018 Uniform Rate of Progress. Figure 5-15 shows that removing the international 

contribution is sufficient to raise the reductions ratios at all Class I areas in the figure above 

100%. This means that when the international contributions are eliminated, all Class I areas 

exceed the Uniform Rate of Progress in 2018. 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chapter_5_AddAnal4.doc 5-24 

CMAQ 2018g2b/2002gt2a Method 1 predictions for VISTAS+ sites

0%

20%

40%

60%

80%

100%

120%

140%

160%

180%

200%

220%

240%

COHU1DOSO1

GRSM1

JARI1

LIGO1CADI1

MACA1

SHEN1

SHRO1

SIPS1

CHAS1

EVER1OKEF1

ROMA1

SAMA1

SWAN1

BRET1

BRIG1

CACR1

HEGL1

MING1

UPBU1

Percent of target reduction achieved

CMAQ New IMPROVE Algorithm

CMAQ New IMPROVE Algorithm w/ Intl Trans.

Mountain Coastal non-VISTAS

Figure 5-15. Percent of Uniform Rate of Progress haze index reduction achieved in 2018 

according to the CMAQ modeling for the Base G2 inventory. Results are given for 

improvements calculated using the new IMPROVE algorithm. Solid symbol indicate results 

when all emissions are considered; open symbols indicate results when effects of international 

emissions are removed from the 2018 visibility projection. 

5.5 Visibility Trends 

Figure 5-16 displays trends in visibility impairment at the VISTAS Class I areas using the period 

of available record of measurements (through 2006) that are available on the WRAP TSS 

(http:/vista.colostate.edu/tss/) at the associated IMPROVE monitor and the new IMPROVE 

equation. Trends are included for the worst 20 percent days, the best 20 percent days and all 

IMPROVE sampled days during a year. The EPA guidance procedures were used to construct 

the worst and best 20 percent days that includes a minimum data capture requirement (EPA, 

2003a), whereas no such minimum data capture was applied when looking at the “annual 

average” of all IMPROVE sampled days trends. So care must be taken when analyzing trends 

for the all sampled IMPROVE days trends as there could be large missing periods with high or 

low extinction that are not being account for. The WRAP Technical Support System (TSS) 

website was used to calculate the visibility trends at the VISTAS Class I areas that includes 

IMPROVE data from start of recording through 2004 and includes no data filling (see: 

http://vista.cira.colostate.edu/TSS/Default.aspx) . 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chapter_5_AddAnal4.doc 5-25 

The IMPROVE monitor at the SIPS Class I area has been operating for 13 years, however only 

eight years of trends are available for the worst and best 20 percent days due to the valid data 

capture requirements of these regional haze metrics (EPA, 2003a). Although there is a lot of 

year-to-year variability in the visibility due to meteorology, fires and other factors, there does 

appear to be a downward trend in visibility impairment at SIPS for the worst 20 percent and all 

IMPROVE sampled days (Figure 5-16a). There also is a reduction in visibility impairment for 

the worst 20 percents for before and after the year 2000. 

The visibility trends at CHAS are given in Figure 5-16b. Again there is lots of year-to-year 

variability but there also appears to be a general downward trend in visibility impairment at 

CHAS for the worst 20 percent and all sampled days. 

The trends at EVER are confounded by poor valid data capture and what appears to be impacts 

due to biomass burning in 2003 that enhance the OMC and EC contributions for the worst 20 

percent days (Figure 5-16c). Also changes in IMPROVE measurement protocols may affect the 

interpretation of the visibility trends at EVER. In any event, there is no indication of any 

downward trend in visibility impairment at EVER. 

SAMA (Figure 5-16d) and COHU (Figure 5-16e) only have 2-3 years of sufficient valid data to 

construct trends for the worst and best 20 percent days which is insufficient to form any opinion 

on trends given the year-to-year variability in visibility. 

There is a fairly long term record of IMPROVE measurements at the OKEF Class I area (16 

years) with 15 years of sufficient data capture to construct the worst and best 20 percent days 

regional haze metric (Figure 5-16f). For the worst 20 percent days, there appears to be a slight 

trend in improved visibility impairment that is driven by sulfate, although the high sulfate 

extinction in 2005 counters that argument. For the best 20 percent days, there is so much yearto-year variability that no trend can be distinguished. There is also a slight trend towards 

improvements in visibility for the all sampled days metric. 

There appears to be a slight downwind trend at MACA (Figure 5-16g), although the high 

extinction in 2005 confounds the analysis. Visibility at LIGO for the four years of sufficient 

valid data (2001-2004) is fairly flat for the worst and all sampled days metrics (Figure 5-16h). 

SHRO has sufficient valid data capture for the older four years of 1995-1998 and four more 

recent years of 2003-2006 to develop best/worst haze metrics (Figure 5-16i). 2004 has the least 

impaired worst 20 percent days of record at SHRO which suggests a trend toward improvements 

in visibility. However, 2004 was also a very wet year with cleaner than normal pollution 

conditions and removing it from the SHRO trend plots would make the more recent visibility 

within the range of year-to-year variability in the past. 

SWAN has only three years with sufficient data capture so no discussions on trends can be made 

(Figure 5-16j). 

ROMA has a fairly rich trend history dating back to 1995 with the cleanest three years occurring 

recently in 2001-2003 (Figure 5-16k). Although 2004-2006 is dirtier, so the occurrence of the 

recent cleaner years may not suggest a downward trend in visibility impairment at ROMA. 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chapter_5_AddAnal4.doc 5-26 

GRSM has one of the longest periods of record with the IMPROVE monitor operating for 19 

years and collecting sufficient valid data to produce 17 years of best and worst 20 percent days 

regional haze metrics (Figure 5-16l). Again there is lots of year-to-year variability in the 

visibility impairment, but it is encouraging that over the 6 most recent years light extinction for 

the worst 20 percent days is approximately 210 Mm-1 or less for 5 of them a level achieved only 

once in the previous ten years. A trend toward improvements in visibility appears to occur in the 

worst 20 percent and all sampled days metrics. 

There is a lot of year-to-year variability in visibility at JARI, so no trend can be inferred (Figure 

5-16m). 

Like GRSM, SHEN has a 19 year history of IMPROVE measurements that include sufficient 

data capture to produce 16 years of best and worst 20 percent days regional haze metrics (Figure 

5-16n). There is a clear downward trend in visibility impairment for the worst 20 percent days 

with the values for the first five years of the 1990s ranging from 240 to 285 Mm-1, whereas the 

values for the first five years of the 2000s ranging from 160 to 210 Mm-1. There also appears to 

be a distinct trend in improved visibility in the all sampled days metric and, although confounded 

by a lot of year-to-year variability. Even the best 20 percent days metric exhibits a downward 

trend. 

The DOSO Class I area also has a long record of IMPROVE measurements (Figure 5-16o). 

There is a slight trend toward visibility improvements in the worst 20 percent and all sampled 

days metrics. Although this is driven in part by the high visibility years of 1994-1995 which if 

removed suggests a more flat trend in visibility. 

. 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chapter_5_AddAnal4.doc 5-27 

Figure 5-16a. Time series of observed IMPROVE reconstructed light extinction (New IMPROVE) 

at Sipsey (SIPS), Alabama for the average of the Worst 20 Percent days (top), Best 20 Percent 

days (middle) days and all IMPROVE sampling days during the period of record (bottom). 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chapter_5_AddAnal4.doc 5-28 

Figure 5-16b. Time series of observed IMPROVE reconstructed light extinction (New 

IMPROVE) at Chassahowitzka (CHAS), Florida for the average of the Worst 20 Percent days 

(top), Best 20 Percent days (middle) days and all IMPROVE sampling days during the period 

of record (bottom). 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chapter_5_AddAnal4.doc 5-29 

Figure 5-16c. Time series of observed IMPROVE reconstructed light extinction (New IMPROVE) 

at Everglades (EVER), Florida for the average of the Worst 20 Percent days (top), Best 20 Percent 

days (middle) days and all IMPROVE sampling days during the period of record (bottom). 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chapter_5_AddAnal4.doc 5-30 

Figure 5-16d. Time series of observed IMPROVE reconstructed light extinction (New 

IMPROVE) at St. Marks (SAMA), Florida for the average of the Worst 20 Percent days (top), 

Best 20 Percent days (middle) days and all IMPROVE sampling days during the period of 

record (bottom). 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chapter_5_AddAnal4.doc 5-31 

Figure 5-16e. Time series of observed IMPROVE reconstructed light extinction (New IMPROVE) 

at Cohutta (COHU), Georgia for the average of the Worst 20 Percent days (top), Best 20 Percent 

days (middle) days and all IMPROVE sampling days during the period of record. 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chapter_5_AddAnal4.doc 5-32 

Figure 5-16f. Time series of observed IMPROVE reconstructed light extinction (New IMPROVE) 

at Okefenokee (OKEF), Georgia for the average of the Worst 20 Percent days (top), Best 20 

Percent days (middle) days and all IMPROVE sampling days during the period of record. 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chapter_5_AddAnal4.doc 5-33 

Figure 5-16g. Time series of observed IMPROVE reconstructed light extinction (New IMPROVE) 

at Mammoth Cave (MACA), Kentucky for the average of the Worst 20 Percent days (top), Best 20 

Percent days (middle) days and all IMPROVE sampling days during the period of record (bottom). 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chapter_5_AddAnal4.doc 5-34 

Figure 5-16h. Time series of observed IMPROVE reconstructed light extinction (New IMPROVE) 

at Linville Gorge (LIGO), North Carolina the average of the Worst 20 Percent days (top), Best 20 

Percent days (middle) days and all IMPROVE sampling days during the period of record (bottom). 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chapter_5_AddAnal4.doc 5-35 

Figure 5-16i. Time series of observed IMPROVE reconstructed light extinction (New IMPROVE) 

at Shinning Rock (SHRO), North Carolina for the average of the Worst 20 Percent days (top), 

Best 20 Percent days (middle) days and all IMPROVE sampling days during the period of record 

(bottom). 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chapter_5_AddAnal4.doc 5-36 

Figure 5-16j. Time series of observed IMPROVE reconstructed light extinction (New IMPROVE) 

at Swanquarter (SWAN), North Carolina for the average of the Worst 20 Percent days (top), Best 

20 Percent days (middle) days and all IMPROVE sampling days during the period of record 

(bottom). 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chapter_5_AddAnal4.doc 5-37 

Figure 5-16k. Time series of observed IMPROVE reconstructed light extinction (New IMPROVE) 

at Cape Romain (ROMA), South Carolina for the average of the Worst 20 Percent days (top), 

Best 20 Percent days (middle) days and all IMPROVE sampling days during the period of record 

(bottom). 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chapter_5_AddAnal4.doc 5-38 

Figure 5-16l. Time series of observed IMPROVE reconstructed light extinction (New IMPROVE) 

at Great Smokey Mountains (GRSM), Tennessee-North Carolina for the average of the Worst 20 

Percent days (top), Best 20 Percent days (middle) days and all IMPROVE sampling days during 

the period of record (bottom). 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chapter_5_AddAnal4.doc 5-39 

Figure 5-16m. Time series of observed IMPROVE reconstructed light extinction (New IMPROVE) 

at James River Face (JARI), Virginia for the average of the Worst 20 Percent days (top), Best 20 

Percent days (middle) days and all IMPROVE sampling days during the period of record (bottom). 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chapter_5_AddAnal4.doc 5-40 

Figure 5-16n. Time series of observed IMPROVE reconstructed light extinction (New IMPROVE) 

at Shenandoah (SHEN), Virginia for the average of the Worst 20 Percent days (top), Best 20 

Percent days (middle) days and all IMPROVE sampling days during the period of record (bottom). 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chapter_5_AddAnal4.doc 5-41 

Figure 5-16o. Time series of observed IMPROVE reconstructed light extinction (New IMPROVE) 

at Dolly Sods (DOSO), West Virginia for the average of the Worst 20 Percent days (top), Best 20 

Percent days (middle) days and all IMPROVE sampling days during the period of record (bottom). 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chapter_5_AddAnal4.doc 5-42 

5.6 Geographic Areas of Influence of Visibility Impairment for Class I Areas 

VISTAS has determined that SO2 emission reductions from EGU and non-EGU point sources in 

the VISTAS and nearby states would be the most effective sources to control to improve 

visibility at the Class I areas in the VISTAS region and vicinity. One of the next steps was to 

identify the specific geographic areas that most likely influence visibility in each Class I area, 

and then to identify the major SO2 point sources located in those geographic areas. An SO2 

Area of Influence was defined for each Class I area to represent the geographic area containing 

sources that would likely have the greatest impact on visibility at that Class I area. All SO2 

point sources within these Areas of Influence were identified and ranked by their 2018 

emissions. The following sections contain a broad overview of the steps in the Area of Influence 

analyses and the final results. See Appendix H for a more detailed discussion of how these 

analyses were developed. The Area of Influence analyses were conducted by Joe Adlhoch and 

Cassie Archuleta of Air Resources Specialists, Gerry Mansell of ENVIRON, Greg Stella of 

Alpine Geophysics and Pat Brewer of VISTAS and the reader is referred to additional 

documentation for more details at the analyses (Adlhoch et al., 2007; Mansell, 2007). 

5.6.1 Back Trajectory Analyses 

The first step in the Area of Influence analyses was to generate meteorological back trajectories 

for IMPROVE monitoring sites in VISTAS region and neighboring Class I areas for the 2000-

2004 worst 20 percent days baseline period. Back trajectory analyses use interpolated measured 

or modeled meteorological fields to estimate the most likely central path of air masses that arrive 

at a receptor at a given time. The method essentially follows a parcel of air backward in hourly 

steps for a specified length of time. Trajectories were started at 100 meters and 500 meters 

above the surface and run backward from the site for 72-hours. Figure 5-17 is an example of a 

back trajectory analysis for Great Smoky Mountains National Park for the worst 20 percent days 

in 2002. 

Figure 5-17. Example back trajectories for worst 20 percent visibility days in 2002 for Great 

Smoky Mountains National Park. 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chapter_5_AddAnal4.doc 5-43 

Figure 5-18. Example residence time plot for 20 percent worst visibility days in 2000-2004 for 

Great Smoky Mountains National Park based on trajectories with 100m start height. 

5.6.2 Residence Time Plots 

The next step was to plot residence time for each Class I area using five years of back trajectories 

for the worst 20 percent visibility days in 2000-2004. Residence time is the frequency that winds 

pass over a specific geographic area on the path to a Class I area. Separate residence time plots 

were generated using back trajectories with 100m and 500m start heights. As illustrated in 

Figure 5-18, winds influencing Great Smoky Mountains on the worst 20 percent days come from 

all directions and there is no single predominant wind direction influencing the 20 percent worst 

visibility days. In contrast, at Swanquarter, the residence time plot indicates a clear north-south 

gradient of influence along the eastern coast of the United States (Figure 5-19). 

GRSM1

2000-04

20% Worst Monitored Days

100m Residence Times (%)

< 0.01

0.01 - 0.02

0.02 - 0.04

0.04 - 0.08

0.08 - 0.16

0.16 - 0.32

> 0.32
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chapter_5_AddAnal4.doc 5-44 

Figure 5-19. Example residence time plot for 20 percent worst visibility days in 2000-2004 for 

Swanquarter based on trajectories with 100m start height. 

5.6.3 SO2 Areas of Influence 

The next step was to develop sulfate extinction weighted residence time plots to define the 

geographic area with highest probability of influencing the receptor on the worst 20 percent days 

in 2000-2004 that were dominated by sulfate. Each back trajectory was weighted by sulfate 

extinction for that day. This allows the focus to be on the worst 20 percent days that are 

influenced by sulfate and places less importance on days influenced by, for example, organic 

carbon from fires. Sulfate-weighted back trajectories for the 20 percent worst days were 

combined for the 5 years of data. The resulting sulfate extinction-weighted residence time plots 

were used to define the geographic Area of Influence for sources of SO2 emissions. In Figure 5-

20 the area representing 10 percent or greater residence time is outlined in red and the area 

representing 5 percent or greater residence time is outlined in gray. The VISTAS states focused 

their analyses on the Area of Influence defined by 5 percent or greater sulfate extinctionweighted residence time. 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chapter_5_AddAnal4.doc 5-45 

Figure 5-20. Example SO2 Area of Influence plot for sulfate extinction weighted residence time 

for 20 percent worst visibility days in 2000-2004 for Great Smoky Mountains National Park 

based on trajectories with 100m start height. 

5.6.4 Emissions Sources within SO2 Areas of Influence 

Residence time plots were then combined with geographically-gridded emission data based for 

the 2002 baseline and 2018 emissions inventories. Plots were generated for the Areas of 

Influence defined by trajectories with 100m and 500 m start heights. As a way of incorporating 

the effects of transport, deposition, and chemical transformation of point source emissions along 

the path of the trajectories, these data were weighted by 1/d, where d was calculated as the 

distance, in kilometers, between the center of the grid cell in which a source is located and the 

center of the grid cell in which the IMPROVE monitor is located. The distance-weighted point 

source SO2 emissions are then combined with the gridded extinction-weighted back-trajectory 

residence times at a spatial resolution of 36 km. 

The final step was to combine the residence times and gridded emissions data in plots and data 

sets. The distance weighted (1/d) gridded point source SO2 emissions were multiplied by the 

total extinction-weighted back-trajectory residence times on a grid cell by grid cell basis. These 

results were then normalized by the domain-wide total and displayed as a percentage. The 

analysis was done using both the 2002 and 2018 emissions inventories. 

Figure 5-21 illustrates the 2002 and 2018 distance weighted gridded emissions multiplied by 

sulfate extinction weighted residence time for Great Smoky Mountains National Park. These 

maps help visualize where the emissions reductions will be occurring between 2002 and 2018. 

SO2 Area of Influence for Great Smoky Mountains

Green circles indicate 100-km and 200-km radii from Class I area.

Red line perimeter indicate Area of Influence with Residence Time > 10%

Orange line perimeter indicate Area of Influence with Residence Time > 5%.

SO2 Area of Influence for Great Smoky Mountains

Green circles indicate 100-km and 200-km radii from Class I area.

Red line perimeter indicate Area of Influence with Residence Time > 10%

Orange line perimeter indicate Area of Influence with Residence Time > 5%.
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chapter_5_AddAnal4.doc 5-46 

The change in SO2 emissions between 2002 and 2018 can be seen by comparing emissions 

source strengths in the two plots. Note the emissions from each source are normalized by the 

total emissions in the domain. Sources that reduce SO2 emissions by 2018 will show a lower 

contribution to emissions in the domain. On the 2018, map the grid cells with these sources will 

show a lighter color gradient than on the 2002 map. For example, SO2 reductions from EGU in 

western and central North Carolina under the North Carolina Clean Smokestacks Act can be seen 

by comparing the 2002 and 2018 maps. Because the total emissions in the domain are smaller in 

2018, a source that does not change emissions between 2002 and 2018 may actually appear to 

increase in importance in 2018 compared to 2002. 

Figure 5-21. Great Smoky Mountains 2002 (left) and 2018 (right) SO2 distance weighted 

emissions times SO4 extinction-weighted residence time plots. 

5.6.5 Specific Source Types in the Areas of Influence 

The next step in the analysis was to review the emissions inventories to determine the source 

categories, as well as specific sources, found to have the greatest impact on visibility in VISTAS 

and nearby Class I areas. Lists of SO2 point sources within the Areas of Influence for each Class 

I areas were developed using the VISTAS 2002 base year and 2018 future year emissions. For 

this purpose, the Area of Influence was defined as the counties with maximum sulfate extinction 

weighted residence time greater than five. For SO2 sources within each Area of Influence, the 

following attributes were defined for each individual unit: 

• State, county, and source (plant), and industry identification codes 

• SO2 emissions for 2002 and 2018 

• 2018 control efficiency 

• Distance to Class I areas (defined by distance to the monitor at the Class I area) 

2002 vs 2018 SO2 Emissions weighted by Residence Time

Great Smoky Mtn., TN 

2002 SO2 emissions 2018 SO2 emissions

Green circles indicate 100-km and 200-km radii from Class I area.

Red line perimeter indicate Area of Influence with Residence Time > 10%.

Orange line perimeter indicate Area of Influence with Residence Time > 5%.

Max Value = 46%

2002 vs 2018 SO2 Emissions weighted by Residence Time

Great Smoky Mtn., TN 

2002 SO2 emissions 2018 SO2 emissions

Green circles indicate 100-km and 200-km radii from Class I area.

Red line perimeter indicate Area of Influence with Residence Time > 10%.

Orange line perimeter indicate Area of Influence with Residence Time > 5%.

Max Value = 46%

2002 vs 2018 SO2 Emissions weighted by Residence Time

Great Smoky Mtn., TN 

2002 SO2 emissions 2018 SO2 emissions

Green circles indicate 100-km and 200-km radii from Class I area.

Red line perimeter indicate Area of Influence with Residence Time > 10%.

Orange line perimeter indicate Area of Influence with Residence Time > 5%.

Max Value = 46%
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chapter_5_AddAnal4.doc 5-47 

• Emissions divided by distance (Q/d), a metric that accounts for the dispersion of 

emissions over distance 

• Maximum sulfate extinction weighted residence time (RTmax) 

Our review was conducted in a top down fashion starting with an analysis of the major source 

categories in each SO2 Area of Influence to determine which major categories had the highest 

residual contribution to the area in 2018. It was also important to identify reductions that are 

projected to occur between 2002 and 2018 within each category or at specific units. This 

allowed VISTAS States to determine if certain source categories or units that had yet to be 

controlled under the future year base case had the potential for reduction. Once the highest 

source types were identified, subcategories within those source types were reviewed. The 

contributions from major source categories to the 2018 inventory for the SO2 Areas of Influence 

for the Great Smokey Mountains Class I area is listed in Table 5-3. In this table, the source 

categories are broken out by the USEPA’s Tier 1 report categories and are defined below: 

• Fuel Comb Elec Utility Emissions from all fuel combustions at utility boilers 

• Fuel Comb Industrial Emissions from all fuel combustions at industrial boilers 

• Fuel Comb Other Emissions from all fuel combustions from commercial/institutional and 

residential sources (i.e., fireplaces, natural gas stoves, oil heaters, etc.) 

• Chemical & Allied 

Product Mfg Emissions from chemical manufacturing processes 

• Metal Processing Emissions from metal processing operations 

• Petroleum & Related 

Industries Emissions from petroleum refineries & related industries 

• Other Industrial 

Processes All other industrial processing not previously mentioned 

• Solvent Utilization Emissions from solvent utilization such as degreasing operations, surface 

coating operations, etc. 

• Storage & Transport Emissions from storage and transport of petroleum, organic and inorganic 

products 

• Waste Disposal & 

Recycling 

Emissions from open burning, incineration, landfills, publicly owned 

treatment works, treatment storage and/or disposal facilities, wastewater 

treatment facilities 

• Highway Vehicles Emissions from on-road mobile sources 

• Off-highway Emissions from off-road mobile sources 

• Miscellaneous (Ag, Fires) Emissions from agricultural operations, wildland fires, and other emissions 

sources not previously mentioned 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chapter_5_AddAnal4.doc 5-48 

Table 5-3. 2018 Emissions Contributions from Major Source Categories in the Area of 

Influence for Great Smoky Mountains.

Table 5-3 indicate that for the Great Smoky Mountains Class I area, EGUs and industrial boilers 

are the two major sources categories contributing to 2018 SO2 emissions in the Areas of 

Influence, even after implementation of the CSA and CAIR. Together these two source 

categories contribute 84 percent of the 2018 SO2 emissions for the Area of Influence of the 

Great Smokey Mountains Class I area. Other fuel combustion and other industrial processes 

comprise another 8 percent of the 2018 SO2 emissions. 

These types of tables can also be used to evaluate the major source categories contributing to 

emissions of NOx, NH3, and PM emissions in 2018. For instance, highway vehicles and off 

road vehicles are major sources of NOx emissions, in addition to electric utilities and industrial 

boilers. The source category “miscellaneous” (which includes agricultural sources and fires) is 

the major contributor to NH3 and primary PM. 

The contributions to SO2 emissions in 2018 from the three highest source categories, electric 

utilities, industrial boilers, and other fuel combustion have been further broken out into 

subcategories. Table 5-4 indicates subcategories for the Areas of Influence for the Great 

Smokey Mountains Class I areas. Within electric utilities, all the SO2 emissions are attributable 

to coal fired power plants. Within industrial boilers, most emissions are attributable to coal fired 

boilers with lesser contributions from oil and gas boilers. Commercial and institutional coal and 

oil boilers have smaller contributions. 

Miscellaneous (Ag, Fires) 1% 1% 5% 0% 59% 26% 86%

Off-highway 11% 18% 35% 1% 2% 4% 0%

Highway Vehicles 18% 25% 48% 0% 2% 2% 11%

Waste Disposal & Recycling 4% 2% 3% 0% 5% 11% 0%

Storage & Transport 6% 0% 0% 0% 0% 1% 0%

Solvent Utilization 44% 1% 0% 0% 0% 1% 0%

Other Industrial Processes 6% 5% 1% 3% 8% 11% 0%

Petroleum & Related Industries 0% 0% 0% 1% 0% 0% 0%

Metals Processing 1% 1% 2% 3% 2% 5% 0%

Chemical & Allied Product Mfg 2% 0% 0% 2% 1% 1% 0%

Fuel Comb. Other 5% 6% 3% 6% 5% 12% 1%

Fuel Comb. Industrial 1% 18% 2% 30% 3% 5% 0%

Fuel Comb. Elec. Util. 0% 22% 1% 54% 11% 21% 1%

Tier VOC NOX CO SO2 PM-10 PM-2.5 NH3

Miscellaneous (Ag, Fires) 1% 1% 5% 0% 59% 26% 86%

Off-highway 11% 18% 35% 1% 2% 4% 0%

Highway Vehicles 18% 25% 48% 0% 2% 2% 11%

Waste Disposal & Recycling 4% 2% 3% 0% 5% 11% 0%

Storage & Transport 6% 0% 0% 0% 0% 1% 0%

Solvent Utilization 44% 1% 0% 0% 0% 1% 0%

Other Industrial Processes 6% 5% 1% 3% 8% 11% 0%

Petroleum & Related Industries 0% 0% 0% 1% 0% 0% 0%

Metals Processing 1% 1% 2% 3% 2% 5% 0%

Chemical & Allied Product Mfg 2% 0% 0% 2% 1% 1% 0%

Fuel Comb. Other 5% 6% 3% 6% 5% 12% 1%

Fuel Comb. Industrial 1% 18% 2% 30% 3% 5% 0%

Fuel Comb. Elec. Util. 0% 22% 1% 54% 11% 21% 1%

Tier VOC NOX CO SO2 PM-10 PM-2.5 NH3
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chapter_5_AddAnal4.doc 5-49 

Table 5-4. 2018 Emissions Contributions from Major Source Categories in the Area of 

Influence for Great Smoky Mountains Class I area. 

5.6.6 Evaluating the Four Statutory Factors for Specific SO2 Emissions Sources in Each 

Area of Influence 

The next step in the Area of Influence analyses was to identify emission reductions that have 

already occurred within each source category and at specific units. Unit level tables of emission 

comparisons from 2002 to 2018 were developed, allowing VISTAS States to review existing 

emission reductions. These tables assigned future year control technology from IPM forecasting 

and State modification for EGU and from control efficiency tables for non-EGU point sources. 

Once emission control profiles for specific units were defined, the next step is to determine what, 

if any, additional control measures would feasibly be available, and to assign costs to those 

control measures. For EGUs, the 2018 IPM file used by VISTAS was obtained and matched to 

the 2018 base case inventory of EGU sources. This step was conducted to ensure that 

incremental controls assigned to these source types did not duplicate existing base case 

assumptions. 

VISTAS used the USEPA’s AirControlNET database, modified for the VISTAS emission 

inventories, for the non-EGUs. The core of AirControlNET is a relational database system in 

which control technologies are linked to sources within the USEPA emissions inventories. The 

system contains a database of control measure applicability, efficiency, and cost information for 

reducing emissions. The control measure data file in AirControlNET includes not only the 

technology's control efficiency, and calculated emission reductions for that source, but also 

estimates the costs (annual and capital) for application of the control measure. 

S

Fuel Comb. Other-Residential Other 1%

Fuel Comb. Other-Residential Wood 0%

Fuel Comb. Other-Misc. (Except Residential) 0%

Fuel Comb. Other-Comm/Institutional Gas 0%

Fuel Comb. Other-Comm/Institutional Oil 3%

Fuel Comb. Other-Comm/Institutional Coal 2%

Fuel Comb. Industrial-Internal Combustion 0%

Fuel Comb. Industrial-Other 1%

Fuel Comb. Industrial-Gas 2%

Fuel Comb. Industrial-Oil 5%

Fuel Comb. Industrial-Coal 22%

Fuel Comb. Elec. Util.-Internal Combustion 0%

Fuel Comb. Elec. Util.-Other 0%

Fuel Comb. Elec. Util.-Gas 0%

Fuel Comb. Elec. Util.-Oil 0%

Fuel Comb. Elec. Util.-Coal 54%

Tier GRSM S

Fuel Comb. Other-Residential Other 1%

Fuel Comb. Other-Residential Wood 0%

Fuel Comb. Other-Misc. (Except Residential) 0%

Fuel Comb. Other-Comm/Institutional Gas 0%

Fuel Comb. Other-Comm/Institutional Oil 3%

Fuel Comb. Other-Comm/Institutional Coal 2%

Fuel Comb. Industrial-Internal Combustion 0%

Fuel Comb. Industrial-Other 1%

Fuel Comb. Industrial-Gas 2%

Fuel Comb. Industrial-Oil 5%

Fuel Comb. Industrial-Coal 22%

Fuel Comb. Elec. Util.-Internal Combustion 0%

Fuel Comb. Elec. Util.-Other 0%

Fuel Comb. Elec. Util.-Gas 0%

Fuel Comb. Elec. Util.-Oil 0%

Fuel Comb. Elec. Util.-Coal 54%

Tier GRSM
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chapter_5_AddAnal4.doc 5-50 

Using the modified inventories identified above, VISTAS ran every available SO2 control 

strategy in AirControlNET against the EGU and non-EGU point source inventories to develop a 

master list of incremental control strategies for each unit in the VISTAS 36 km domain. 

For the sources within the Area of Influences for each VISTAS and nearby Class I area, the 

master list of incremental control measures was sorted to determine the costs of incremental 

control measures. These data were combined in a master spreadsheet with the distance from the 

emission release point to the Class I area IMPROVE monitor (in km), the 2018 residual 

emissions and distance (Q/d) or squared distance (Q/d2

), and the normalized 2018 SO2 point 

source emissions times distance-weighted residence time (RTMax) values for the county in 

which the emission release point was located. Each VISTAS state evaluated these control 

measures and costs as part of their review of the statutory factors for reasonable further progress. 

The regional haze rule requires that states consider the following factors and demonstrate how 

these factors were taken into consideration in selecting the reasonable progress goal: 

• the costs of compliance 

• the time necessary for compliance 

• the energy and non-air quality environmental impacts of compliance, and 

• the remaining useful life of any potentially affected sources. 

5.6.6.1 Cost of Compliance 

 

As defined in Section 5.6.5, coal-fired electric utilities and coal-fired industrial boilers were the 

largest source categories contributing to SO2 in 2018 in the Areas of Influence defined for the 

Great Smoky Mountains Class I area. Industrial boilers using oil and commercial and 

institutional boilers using coal or oil had small contributions to SO2 in 2018. 

Sulfur dioxide emissions from utility, industrial, commercial, or institutional boilers can be 

controlled either by switching the fuel source to a lower sulfur fuel content or by installing postcombustion controls. Costs vary by fuel source and boiler type and require source specific 

analyses for accuracy. 

Bituminous coal is commonly burned in boilers in the eastern US. Switching to another 

bituminous coal with lower sulfur content or blending bituminous coals can reduce SO2 

emissions with least impact to boiler performance. While sub-bituminous coal has a lower sulfur 

content than bituminous coal, it also has a lower heat rate and so more coal has to be burned to 

generate the same energy output. For boilers that are designed for bituminous coal, only a small 

fraction of the fuel can be switched to low sulfur sub-bituminous coal without affecting boiler 

operations. Costs of boiler modifications to accommodate low sulfur sub-bituminous coal may 

make switching to sub-bituminous coal impractical. Contract initiation and termination costs, 

differential fuel prices and heat rates, transportation costs, and modification to boiler operations, 

fuel handling and waste handling systems will have to be considered specific to each source. 

Flue-gas desulfurization is the common post-combustion control for coal-fired boilers. Flue gas 

is passed through an absorbent for sulfur dioxide (generally limestone or lime) in either a wet 

scrubber, dry scrubber, or spray dryer. A calcium sulfate by-product is produced that may be 

further processed to produce gypsum as a commercial byproduct. Costs of flue-gas 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chapter_5_AddAnal4.doc 5-51 

desulfurization include initial construction costs and ongoing operational and maintenance costs 

for the absorber tower, sorbent handling, and waste product handling facilities. Costs per ton 

vary with boiler size, type, and facility siting considerations. 

For oil-fired boilers, lower sulfur oil may be an option. Costs need to consider differential fuel 

prices and heat rate, boiler modifications, fuel handling costs, and maintenance costs. 

Conceptually, post-combustion controls can be used for oil boilers, but there is little precedence 

for such installations. 

5.6.6.2 Time Necessary for Compliance 

For fuel switching, the time necessary to terminate existing fuel contracts and initiate new 

contracts needs to be considered. Generally two to three years may be required. Installation of 

post-combustion controls will require 3 or more years depending on market availability of labor 

and materials and utility system-wide priorities. Time necessary for compliance will need to be 

refined for specific sources. 

5.6.6.3 Energy and Non-Air Environmental Impacts 

Switching to lower sulfur fuel or installing post-combustion controls may reduce boiler heat rate 

and energy output. Scrubbers and spray dryers will require additional safeguards for fuel 

handling and waste handling systems to avoid additional non-air environmental impacts such as 

increased effluents in waste water discharges and storm water runoff. These factors will need to 

be considered specific to individual sources. Carbon dioxide is emitted as a by-product of flue 

gas desulfurization, therefore impacts of increased carbon emissions will need to be considered, 

particularly if carbon emissions are limited in the future under climate change mitigation 

strategies. 

5.6.6.4 Remaining Useful Life 

The useful remaining life is specific to the unit for which controls are considered. 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chapter_5_AddAnal4.doc 5-52 

5.7 Area of Influence Analyses Results for VISTAS and Nearby Class I Areas 

VISTAS grouped the Area of Influence (AOI) results by Class I area for discussion of the four 

factors to be determined in defining reasonable progress. Figure 5-22 displays the Class I area 

groupings used by VISTAS for the AOI and four factors analysis that can be found at: 

http://www.vistas-sesarm.org/documents/FinalDocs.asp

Figure 5-22. Class I area groupings for VISTAS AOI displays and four factor analysis. 

. 

. 

. 

. 

Hercules 

Glade, 

MO . 

. 

VISTAS Class I 
 March 2009 

F:\VISTAS Phase II\TSD\draft#4\Chapter_6_References4.doc 6-1 

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