Appendix M.1



Technical Support Document for VISTAS Emissions

and Air Quality Modeling to Support Regional Haze 

State Implementation Plans 

Tennessee Department of Environment and Conservation 

Division of Air Pollution Control 

9th Floor L&C Annex 

401 Church Street 

Nashville, TN 37243

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Appendix M.1 - 1

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 International Corporation Air Sciences

Preliminary Draft 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 

Bo Wang 

ENVIRON International Corporation 

101 Rowland Way, Suite 220 

Novato, CA 94945 

Gregory Stella 

Dennis McNalley 

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 

October 10, 2007 

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

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i 

TABLE OF CONTENTS 

Page 

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

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

1.2 VISTAS Modeling Approach .......................................................................................... 1-2 

 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................................................1.24 

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

 1.3.9 Meteorological Input Preparation .......................................................................1.27 

2. DEVELOPMENT OF EMISSIONS MODEL 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........................................................................................................... 2-28 

 2.3.3 Spatial Allocation................................................................................................2.28 

2.4 Development of Modeling Input Inventories/Run 

 Scripts/ASSISGNS Files................................................................................................ 2-30 

2.5 Products of the Emissions Inventory Development 

 Process for QA............................................................................................................... 2-30 

2.6 Uncertainties and Caveats.............................................................................................. 2-33 

 2.6.1 Updated Inventories........................................................................................... 2-34 

 2.6.2 Low Level Fires ................................................................................................. 2-34 

 2.6.3 MMS Offshore Shipping.................................................................................... 2-34 

 2.6.4 Incorrect Location Parameters ............................................................................2.34 

 2.6.5 SMOKE/IOAPI Coordinate Transformation Error.............................................2.35 

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

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

3.2 CMAQ Evaluation Methodology..................................................................................... 3-1 

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

 3.2.2 Particulate Matter and Component Species ......................................................... 3-2 

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

3.3 Model Performance Statistics and Goals ......................................................................... 3-4 

3.4 Regional Model Performance Evaluation........................................................................ 3-5 

 3.4.1 Sulfate (SO4) Model Performance....................................................................... 3-5 

 3.4.2 Nitrate (NO3) Modeling Performance................................................................. 3-9 

 3.4.3 Organic Mass Carbon (OMC) Performance ...................................................... 3-12 

 3.4.4 Elemental Carbon (EC) Performance ................................................................ 3-15 

 3.4.5 Other PM2.5 or Soil Performance ....................................................................... 3-18 

 3.4.6 Coarse Mass (CM) Performance........................................................................ 3-19 

 3.4.7 Fine Particulate (PM2.5) Performance ................................................................ 3-24 

3.5 Evaluation at VISTAS and Nearby Class I Areas ......................................................... 3-27 

 3.5.1 Example Spatial Distribution Model Performance............................................ 3-27 

 3.5.2 Sipsey (SIPS), Alabama Best and Worst 20 Percent 

 Days Performance.............................................................................................. 3-31 

 3.5.3 Chassahowitzka (CHAS), Florida Best and Worst 20 

 Percent Days Performance................................................................................. 3-31 

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

 Days Performance.............................................................................................. 3-31 

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

 Days Performance.............................................................................................. 3-38 

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

 Days Performance.............................................................................................. 3-38 

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

 Days Performance.............................................................................................. 3-38 

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

 Percent Days Performance]................................................................................ 3-38 

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

 Percent Days Performance................................................................................. 3-47 

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 

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iii 

 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.................................................................... 4-16 

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

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

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

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1.0 INTRODUCTION 

This is the first draft 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). 

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 are due December 17, 2007 and an important component of the SIP will 

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

air quality models are used to project visibility to 2018 to determine whether 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. 

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 

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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. 

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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 

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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. 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. Final 2018 emissions and air quality modeling for the Best and Final inventory 

will be provided in fall 2007 and is not available to be included in this report. 

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 

ambient air quality standards. The same model configuration and methods and the 2002 

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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 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 

air quality Modeling Protocol (Morris, Tesche and Tonnesen, 2004) provides details on the 

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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); 

• Asymmetric Convective Mixing (ACM) planetary boundary layer (PBL) model; 

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• 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 2.3 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 

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 

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F:\VISTAS Phase II\TSD\draft#2\Chap_1_Intro2.doc 1-10 

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. 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 Mass Carbon (OMC) 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 OMC 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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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. 

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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 Mass 

Carbon (OMC) IMPROVE and STN monitoring sites in the VISTAS, MRPO, MANEVU 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 OMC (i.e., OMC = 1.4 x OC). Whereas the 

standard CMAQ V4.4 underestimates OMC 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 OMC 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 OMC across the urban STN sites is likely due 

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

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

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

OMC 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 OMC 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. 

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

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

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

July 2002 IMPROVE OMC STN OMC 

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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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) 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 

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

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. 

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

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

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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 Pres(mb) Height(m Depth(m) Layer Sigma Pres(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 0 1.000 1000 0 0

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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 NAAQS. 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 years 

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: 

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• 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 1o

x1.25o 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. 

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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, 2007). 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. 

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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 2.3 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 

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along the lateral edges of the 36 km inter-RPO continental U.S. modeling domain; and (3) air 

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 mass carbon (OMC) 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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250 500 750 1000 1250 1500 1750 2000

-1500

-1250

-1000

-750

-500

-250

0

250

500

IMPROVE

CASTNet

SEARCH

STN

NADP

AQS

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 (2007). 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, 2007). 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 G2 modeling scenario: 

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• 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 

target, and States may establish a RPG that provides for greater, lesser, or equivalent visibility 

improvement as that described by the glidepath” (EPA, 2007b). 

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Figure 1-8 displays the VISTAS 2018 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 and original (old) IMPRVE equation. 

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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 VISTS 2018 visibility projections with those from CENRAP, MRPO 

and MANE-VU. 

• Analysis of alternative visibility projections: 

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. 

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2.0 DEVELOPMENT OF EMISSIONS MODEL 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. 

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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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). 

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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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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 Chapter 4). 

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 

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schedule. Selecting representative model days for some or all of the source categories reduces 

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 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 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 

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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 

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 

 

3 

ftp://ftp.epa.gov/EmisInventory/2002finalnei/documentation/point/augmentation_point/2002nei_qa_augmentation_r

eport0206.pdf 

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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 SCCmatched 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 

 

4 http://www.epa.gov/ttn/chief/emch/invent/index.html#dust 

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and humidity are calculated from the hourly temperatures in each grid cell in the state/county 

groups.

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: 

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• PTOP – Top of the fire plume profile (meters above ground level) 

• PBOT – Bottom of the fire plume profile (meters above ground level) 

• Lay1 – The percent of the emissions entrained in the first modeling layer 

For those fires as 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. 

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 

 

5 http://www.wrapair.org/forums/fejf/documents/WRAP_2002_PhII_EI_Report_20050722.pdf 

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speciation, temporal allocation, and spatial allocation have been separated into independent 

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: 

 

6 ftp://www.airmodelingftp.com/ 

7 http://www.epa.gov/ttn/chief/conference/ei15/session9/abraczinskas.pdf 

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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. 

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. As part of this analysis, specific day-of-week (Monday, Tuesday, 

Wednesday, etc.) and diurnal profiles were developed for each month and State to better 

represent operating conditions at units within each State. The 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 hour-of-day. These profiles are intended to be applied to units were 

CEM matches cannot 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 

 

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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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. 

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. 

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Table 2-2. CEM data file format.

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. 

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. 

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F:\VISTAS Phase II\TSD\draft#2\Chap2_Emissions2.doc 2-14 

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 

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. 

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F:\VISTAS Phase II\TSD\draft#2\Chap2_Emissions2.doc 2-15 

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

hi datehrratio hi datehr 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 

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. 

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F:\VISTAS Phase II\TSD\draft#2\Chap2_Emissions2.doc 2-16 

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#2\Chap2_Emissions2.doc 2-17 

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 

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F:\VISTAS Phase II\TSD\draft#2\Chap2_Emissions2.doc 2-18 

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 

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F:\VISTAS Phase II\TSD\draft#2\Chap2_Emissions2.doc 2-19 

in operation in late January through the middle of April in 2002. Given the unique operation of 

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. 

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F:\VISTAS Phase II\TSD\draft#2\Chap2_Emissions2.doc 2-20 

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 

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F:\VISTAS Phase II\TSD\draft#2\Chap2_Emissions2.doc 2-21 

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 

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F:\VISTAS Phase II\TSD\draft#2\Chap2_Emissions2.doc 2-22 

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 

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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

hi sumdatehrratio hi sumdatehr 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 

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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 

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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, 

 

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 

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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 

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. 

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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. 

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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 

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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 

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 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 different 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. 

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For each surrogate, we assign a secondary or tertiary surrogate where needed for gap filling. For 

the class 1 railroad surrogate example mentioned above, we chose total railroads as the 

secondary surrogate. The secondary or tertiary surrogate chosen would be the same across all 

counties and apply 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. Tertiary 

surrogates will then be 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/ASSISGNS 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. 

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• 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. 

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F:\VISTAS Phase II\TSD\draft#2\Chap2_Emissions2.doc 2-32 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 grid) ASSIGNS File: /assigns_pII_point/ASSIGNS.vistas.cmaq.cb4p25.rpoXX.phaseIIrev.ag 

Logical File Name Directory Name Actual File Name Comments 

PTINV $SMKDAT/inventory/2002typ/point ptinv_2002typ_date.ida date typically indicates date of file preparation PTHOUR $SMKDAT/inventory/2002typ/point pthour.typ.NNN.lst 

NNN indicates the month abbreviation. The list file points to "pthour_rev2002typ_NNN_12nov04.ems" 

PTPRO $SMKDAT/inventory/2002typ /point amptpro_typ_us_can_NNN_vistas_date.txt date typically indicates date of file preparation PTREF $SMKDAT/inventory/2002typ /point ptref_2002_us_can_vistas_date.txt date typically indicates date of file 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 

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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 Appendices of this document. 

2.6 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 question is felt to have minimal impact on regional haze or visibility 

results domain wide. 

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2.6.1 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. 

2.6.2 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. 

2.6.3 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. 

2.6.4 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), 

 

12 

http://www.gomr.mms.gov/homepg/regulate/environ/airquality/gulfwide_emission_inventory/20

00GulfwideEmissionInventory.html

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those that were incorrectly sited had emission values of very low (less than 80 tons per year) of 

any single pollutant. 

2.6.5 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 

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3.0 MODEL PERFORMANCE EVALUATION 

3.1 Introduction 

The discussion of model performance evaluation in this Chapter 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 results are 

compared with observational data from the Interagency Monitoring of PROtected Visual 

Environments (IMPROVE), Speciated Trends Network (STN), 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 primarily focuses 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 (i.e., visibility). The FRM 

networks only collects total PM2.5 mass and these monitors are almost exclusively located in 

urban areas so are not as relevant for judging how well the model is predicting the components 

of light extinction at the more rural Class I areas. 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. 

• 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). 

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3.2.1 Evaluation Approach 

The CMAQ model performance evaluation effort focused on the first two components, 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/. 

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: 

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• Sulfate (SO4) 

• Nitrate (NO3) 

• Ammonium (NH4) 

• Organic Matter Carbon (OMC) 

• 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 PM) 

• 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 regional evaluation of the CMAQ 

model 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. However, these sites are urban-oriented and not as useful for 

the regional evaluation of the CMAQ modeling system. The study team has performed a 

detailed evaluation of the CMAQ 2002 base case simulation using the AQS that is available on 

the modeling website: http://pah.cert.ucr.edu/vistas/vistas2/mpe2.shtml. 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. 

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 

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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. 

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 18.4.1 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. 

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. 

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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). 

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 the 

VISTAS 12 km grid versus just the VISTAS states for used in the monthly performance 

evaluation in the 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%. 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% (Figure 3-2). The exception is the SEARCH Hourly network with error values 

ranging from 50% to 70%. 

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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 not 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 

model performance goal for both bias and error. 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). 

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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. 

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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). 

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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 fractional error and NO3 (Figure 

3-5, bottom), but the goal is always achieved across the CASTNet and usually achieved across 

the IMPROVE monitoring networks. 

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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. 

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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). 

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3.4.3 Organic Mass Carbon (OMC) Performance 

The monthly fractional bias and error plot for the 12 km CMAQ base case simulation 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 OMC underprediction bias of -35% to -60%. Part of the underprediction bias may be due 

to measurement uncertainties (e.g., blank corrections) and part may be due to the model over 

diluting the urban OMC emissions through the coarse 36/12 km grid. The underprediction of 

urban OMC is a common problem in PM modeling and likely also points to uncertainties in the 

OMC and SOA precursor emission inventories. 

The Bugle Plots of monthly fractional bias and gross error (Figure 3-7) concurs that the OMC 

performance across the IMPROVE monitoring sites is fairly good achieving the model 

performance goals. But the OMC 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. The OMC performance across the SEARCH network is mixed usually 

achieving the model performance goal and always achieving the model performance criteria. 

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Figure 3-6. Monthly OMC fractional bias (top) and fractional gross error (bottom) statistical 

measures for IMPROVE, STN and SEARCH monitoring sites in the VISTAS region. 

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Figure 3-7. Bugle Plots of monthly fractional bias (top) and gross error (bottom) for OMC in the 

VISTAS region and comparison to model performance goals (blue) and criteria (yellow). 

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3.4.4 Elemental Carbon (EC) Performance 

The monthly bias and error performance statistics for EC across the VISTAS region are shown in 

Figure 3-8. Bias and error are lower in the first quarter of the year, and then the IMPROVE and 

SEARCH networks begin to exhibit an underprediction bias that peaks in the summer and gets 

lower again with the onset of winter. Surprisingly, given the large OMC underprediction, the 

STN network exhibits very low EC bias during the summer to fall which 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. 

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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. 

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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). 

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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 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. 

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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. 

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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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3.4.6 Coarse Mass (CM) Performance 

Coarse Mass (CM) measurements are 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 fractions 

(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 (by ~75% on average in the VISTAS region) to account for the fact that 

these emissions are deposited locally and are not transported out of the grid cell where they are 

emitted. However, such local 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. 

Figure 3-12 displays the monthly fraction 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. 

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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. 

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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). 

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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 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%) and the errors are also larger. 

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). 

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Figure 3-14. Monthly PM2.5 fractional bias (top) and fractional gross error (bottom) statistical 

measures in the VISTAS region. 

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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). 

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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% most impaired days, or “worst days”, and the 20% least impaired days, or “best days”, for 

use in 2018 visibility projections. Thus, the model performance of the visibility impairment 

pollutants for the best and worst 20 percent days at the Class I areas is very important for the 

VISTAS modeling. Table C-1 in Appendix C lists of the best 20 percent and worst 20 percent 

days for all of Class I areas within and nearby of 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 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 and 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, scatter plots of predicted and observed PM species concentrations 

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 pots 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 we first display a table like the one shown in 

Table 3-2 for July 10, 2002 that shows which Class I areas this day is a worst 20 percent or best 

20 percent day for. In the case of July 10, 2002, it is a worst 20 percent day for 13 Class I areas 

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 on the VISTAS region.. 

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 (Figure 3-16c) concentrations. In this case, the model is 

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capturing the very high sulfate concentrations across the Appalachian Mountains and interior 

part of the VISTAS region, as well as the much lower SO4 conditions in southern Georgia, 

Florida and the coastal regions of South Carolina and North Carolina (Figure 3-16a). The low 

NO3 conditions are reproduced well by the model and even the spatial distribution of OMC is 

reproduced well (Figure 3-16a). The observed low EC levels are replicated well by the model, 

but the spatial distribution of the observed Soil and CM is not reproduced well with the model 

underpredicting the CM in the eastern and overpredicting the Soil in the western portions of the 

VISTAS region (Figure 3-16b). Also shown in Figure 3-16b are compares of modeled and 

measured Sea Salt which the model grossly underestimates. Finally, Figure 3-16c shows that the 

model does an excellent job in reproducing the spatial distribution of the observed total PM2.5

concentrations on this day, which is not surprising since it is dominated by SO4. 

Figure 3-16a. Modeled Predicted And Observed Daily Average Sulfate (SO4) Component 

Concentrations (top left), Daily Average Nitrate (NO3) Component Concentrations (top right), 

Daily Average Ammonium (NH4) Component Concentrations, And Daily Average Organic 

Carbon (OC) Component Concentrations Spatial Plots For July 10, 2002. 

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Figure 3-16b. Modeled Predicted And Observed Daily Average Elemental Carbon (EC) 

Component Concentrations (top left), Daily Average Coarse Particle Mass (CM) Component 

Concentrations (top right), Daily Average Sea Salt Component Concentrations, And Daily 

Average Soil Component Concentrations Spatial Plots For July 10, 2002.

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Figure 3-16c. Modeled Predicted And Observed Daily Average Total Fine Particulate Matter 

(PM2.5) Concentration Spatial Plots For July 10, 2002. 

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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 OMC is the best performance 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 OC achieve the 

performance goal but NO3, SO4 and SO4 do not even achieve the performance criteria, with 

SO4 falling barely out of the criteria range. 

 

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 Figure 3-18. For the best 20 percent days EC, Soil and SO4 achieve the model 

performance goals and OC achieves the model performance criteria. However, NO3 and CM fall 

outside of the performance criteria. On the worst 20 percent days EC and CM achieve the 

performance goal and OC is right at the goal. SO4 achieves the performance criteria but not the 

performance goal and the Soil over prediction 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 3-20). 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 fall out of the range for bias, but within the criteria range for error. 

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Figure 3-17a. Bugle Plots of Fractional Bias and Fractional Gross Error for Sipsey, 

Alabama and the Best 20 Percent Days. 

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Figure 3-17b. Bugle Plots of Fractional Bias and Fractional Gross Error for Sipsey, 

Alabama and the Worst 20 Percent Days. 

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Figure 3-18a. Bugle Plots of Fractional Bias and Fractional Gross Error for CHAS and 

the Best 20 Percent Days. 

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Figure 3-18b. Bugle Plots of Fractional Bias and Fractional Gross Error for CHAS and 

the Worst 20 Percent Days. 

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Figure 3-19a. Bugle Plots of Fractional Bias and Fractional Gross Error for EVER and 

the Best 20 Percent Days. 

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Figure 3-19b. Bugle Plots of Fractional Bias and Fractional Gross Error for EVER and 

the Worst 20 Percent Days. 

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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 follows 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 OC right at the 

performance criteria. 

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Figure 3-20a. Bugle Plots of Fractional Bias and Fractional Gross Error for SAMA and 

the Best 20 Percent Days. 

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Figure 3-20b. Bugle Plots of Fractional Bias and Fractional Gross Error for SAMA and 

the Worst 20 Percent Days. 

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Figure 3-21a. Bugle Plots of Fractional Bias and Fractional Gross Error for COHU and 

the Best 20 Percent Days. 

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Figure 3-21b. Bugle Plots of Fractional Bias and Fractional Gross Error for COHU and 

the Worst 20 Percent Days. 

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Figure 3-22a. Bugle Plots of Fractional Bias and Fractional Gross Error for OKEF and 

the Best 20 Percent Days. 

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Figure 3-22b. Bugle Plots of Fractional Bias and Fractional Gross Error for OKEF and 

the Worst 20 Percent Days. 

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Figure 3-23a. Bugle Plots of Fractional Bias and Fractional Gross Error for MACA and 

the Best 20 Percent Days. 

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Figure 3-23b. Bugle Plots of Fractional Bias and Fractional Gross Error for MACA and 

the Worst 20 Percent Days. 

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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 

exceeding all the goals and 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 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 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. 

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 fallout outside of the criteria range, with SO4 just at the criteria and other species 

achieving the performance goals. 

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Figure 3-24a. Bugle Plots of Fractional Bias and Fractional Gross Error for LIGO and 

the Best 20 Percent Days. 

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Figure 3-24b. Bugle Plots of Fractional Bias and Fractional Gross Error for LIGO and 

the Worst 20 Percent Days. 

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Figure 3-25a. Bugle Plots of Fractional Bias and Fractional Gross Error for SHRO and 

the Best 20 Percent Days. 

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Figure 3-25b. Bugle Plots of Fractional Bias and Fractional Gross Error for SHRO and 

the Worst 20 Percent Days. 

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Figure 3-26a. Bugle Plots of Fractional Bias and Fractional Gross Error for SWAN and 

the Best 20 Percent Days. 

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Figure 3-26b. Bugle Plots of Fractional Bias and Fractional Gross Error for SWAN and 

the Worst 20 Percent Days. 

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Figure 3-27a. Bugle Plots of Fractional Bias and Fractional Gross Error for ROMA and 

the Best 20 Percent Days. 

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Figure 3-27b. Bugle Plots of Fractional Bias and Fractional Gross Error for ROMA and 

the Worst 20 Percent Days. 

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Figure 3-28a. Bugle Plots of Fractional Bias and Fractional Gross Error for GRSM and 

the Best 20 Percent Days. 

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Figure 3-28b. Bugle Plots of Fractional Bias and Fractional Gross Error for GRSM and 

the Worst 20 Percent Days. 

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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 OMC for the worst 20 percent days that just achieve the 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 and 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 with most species achieving the performance goal and 

almost all species achieving the performance criteria, with the exception of CM for the worst 20 

percent days (Figure 3-21). 

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 an underprediction bias (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. 

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Figure 3-29a. Bugle Plots of Fractional Bias and Fractional Gross Error for JARI and 

the Best 20 Percent Days. 

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Figure 3-29b. Bugle Plots of Fractional Bias and Fractional Gross Error for JARI and 

the Worst 20 Percent Days. 

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Figure 3-30a. Bugle Plots of Fractional Bias and Fractional Gross Error for SHEN and 

the Best 20 Percent Days. 

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Figure 3-20b. Bugle Plots of Fractional Bias and Fractional Gross Error for SHEN and 

the Worst 20 Percent Days. 

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Figure 3-31a. Bugle Plots of Fractional Bias and Fractional Gross Error for DOSO and 

the Best 20 Percent Days. 

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Figure 3-31b. Bugle Plots of Fractional Bias and Fractional Gross Error for DOSO and 

the Worst 20 Percent Days. 

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Figure 3-32a. Bugle Plots of Fractional Bias and Fractional Gross Error for BRET and 

the Best 20 Percent Days. 

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Figure 3-32b. Bugle Plots of Fractional Bias and Fractional Gross Error for BRET and 

the Worst 20 Percent Days. 

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Figure 3-33a. Bugle Plots of Fractional Bias and Fractional Gross Error for CACR and 

the Best 20 Percent Days. 

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Figure 3-33b. Bugle Plots of Fractional Bias and Fractional Gross Error for CACR and 

the Worst 20 Percent Days. 

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Figure 3-34a. Bugle Plots of Fractional Bias and Fractional Gross Error for UPBU and 

the Best 20 Percent Days. 

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Figure 3-34b. Bugle Plots of Fractional Bias and Fractional Gross Error for UPBU and 

the Worst 20 Percent Days. 

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3.5.19 Mingo (MING) and Hercules, 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, but performance for the remainder of the species 

achieves the performance goal (Figure 3-36). The overprediction of Soil is also a problem for 

the worst 20 percent days at these two sites. 

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

With the exception of CM and NO3 error for the worst days, all OM species achieve the 

performance criteria for the best and worst 30 percent days at BRIG. The SO4 performance is 

also a concern as it falls between the performance goal and criteria (Figure 3-37). 

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Figure 3-35a. Bugle Plots of Fractional Bias and Fractional Gross Error for MING and 

the Best 20 Percent Days. 

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Figure 3-35b. Bugle Plots of Fractional Bias and Fractional Gross Error for MING and 

the Worst 20 Percent Days. 

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Figure 3-36a. Bugle Plots of Fractional Bias and Fractional Gross Error for HEGL and 

the Best 20 Percent Days. 

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Figure 3-36b. Bugle Plots of Fractional Bias and Fractional Gross Error for HEGL and 

the Worst 20 Percent Days. 

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Figure 3-37a. Bugle Plots of Fractional Bias and Fractional Gross Error for BRIG and 

the Best 20 Percent Days. 

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Figure 3-37b. Bugle Plots of Fractional Bias and Fractional Gross Error for BGIG and 

the Worst 20 Percent Days. 

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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. 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. 

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). 

Pre-Hearing Draft

Regional Haze SIP for TN Class I Areas

Appendix M.1 - 150

January 18, 2008