Appendix D 

Emissions Preparation and Results 
Table of Contents 

Appendix D.1 Emissions Inventory Quality Assurance Project Plan 

Appendix D.2.a Point Sources 

Appendix D.2.b Area and Nonroad Sources 

Appendix D.2.c On-Road Mobile Sources 

Appendix D.3 South Carolina County level Summary Emissions for 2002 

Appendix D.4.a Emission Modeling Documentation 

Appendix D.4.b Temporal Profile Development for Electric Generating Units 

Appendix D.4.c White Paper on MOBILE SMOKE Modeling 

Appendix D.5 VISTAS Emission Inventory Supporting Documentation

Appendix D - 1
Appendix D.1 

Emissions Inventory 

Quality Assurance Project Plan 

Appendix D - 2
Appendix D.1 - 1

Appendix D - 3
FUTURE YEAR EMISSION INVENTORY 

DEVELOPMENT TO SUPPORT 

ATMOSPHERIC MODELING OF FINE 

PARTICULATE MATTER AND OZONE 

IN THE SOUTHEASTERN US 

DRAFT 

QUALITY ASSURANCE 

PROJECT PLAN 

Effective Date: March 2006

Appendix D.1 - 2

Appendix D - 4
ASSOCIATION FOR SOUTHEASTERN INTEGRATED 

PLANNING 

ASIP 

FUTURE YEAR EMISSION INVENTORY DEVELOPMENT TO 

SUPPORT ATMOSPHERIC MODELING OF FINE 

PARTICULATE MATTER AND OZONE IN THE 

SOUTHEASTERN US 

QUALITY ASSURANCE PROJECT PLAN 

Effective Date: March 2006 

APPROVED BY 

(ASIP Technical Director) (ASIP Emission Inventory Tech. Advisor) 

(MACTEC Program Manager (MACTEC Quality Assurance Coordinator) 

(MACTEC Task Manager) (MACTEC Task Manager) 

 

Appendix D.1 - 3

Appendix D - 5
Table of Contents 

FUTURE YEAR EMISSION INVENTORY DEVELOPMENT TO SUPPORT 

ATMOSPHERIC MODELING OF FINE PARTICULATE MATTER AND OZONE IN 

THE SOUTHEASTERN US 

QA PROJECT PLAN 

1.0 PROJECT MANAGEMENT ......................................................................................... 1

1.1 Problem Definition/Background................................................................................. 1

1.2 Project/Task Description ............................................................................................. 2

1.3 Project Organization.................................................................................................... 3

1.4 Quality Objectives and Criteria.................................................................................. 6

1.5 Special Training/Certification..................................................................................... 6

1.6 Documents and Records .............................................................................................. 6

2.0 DATA ACQUISITION ................................................................................................... 9

2.1 Point Source Procedures.............................................................................................. 2

2.2 Area Source Procedures .............................................................................................. 2

3.0 ASSESSMENT AND OVERSIGHT.............................................................................. 4

3.1 Assessments and Response Actions ............................................................................ 4

3.2 Reports to Management............................................................................................... 4

4.0 DATA VALIDATION AND USABILITY .................................................................... 3

4.1 Accuracy Assessment ................................................................................................... 2

4.2 Completeness Assessment............................................................................................ 2

4.3 Comparability Assessment .......................................................................................... 2

4.4 Representativeness Assessment................................................................................... 2

TABLES AND FIGURES 

 Page

 Figure 1. Project Organization Chart.................................................. 1-5 

 Figure 2. Audit Check Form ............................................................... 3-2 

 Table 1. Data Quality Objectives, Procedures, and Indicators ........... 1-8 

Distribution List: 

Pat Brewer, ASIP (Technical Director) 

Greg Stella, Alpine Geophysics, Inc. (ASIP Emission Inventory Technical Advisor) 

Douglas Toothman, MACTEC (Quality Assurance Coordinator) 

William Barnard, MACTEC (Program Manager, Area and Nonroad Source Task Leader) 

Edward Sabo, MACTEC (Point Source Task Leader) 

Dan Meszler, MESZLER Engineering Services, Inc. (Mobile Source Task Leader) 

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1.0 PROJECT MANAGEMENT 

1.1 Problem Definition/Background 

The Southeastern States Air Resource Managers (SESARM) has been designated by the United 

States Environmental Protection Agency (EPA) as the entity responsible for coordinating and 

implementing regional planning for the eight SESARM states (Alabama, Florida, Kentucky, 

Georgia, Mississippi, North Carolina, South Carolina and Tennessee) plus Virginia, West 

Virginia, and Tribes. Through a memorandum of understanding, these parties are collaborating 

in the organization Visibility Improvement State and Tribal Association of the Southeast 

(VISTAS) on the technical analysis and planning activities that support state implementation 

plans for regional haze. The participating agencies have concluded that a collaborative regional 

process is also the most efficient approach for the states to develop the technical analyses 

supporting attainment demonstrations for the fine particulate matter (PM2.5) and eight-hour 

ozone standards. Along with the local air regulatory agencies for Jefferson County, AL, 

Jefferson County, KY, Mecklenberg County, NC, Forsythe County, NC, Knox County, TN, and 

Shelby County, TN, these agencies have become signatory parties to the collaborative effort 

called the Association for Southeastern Integrated Planning (ASIP). SESARM will coordinate 

among participating agencies and oversee the performance of the ASIP inventory and modeling 

tasks in parallel with the VISTAS regional haze project tasks. Emissions inventory efforts 

include the development of emissions inventories and forecasts to be utilized in ASIP modeling 

efforts. 

At least one area in seven states (Alabama, Georgia, North Carolina, Kentucky, Tennessee, 

Virginia, and West Virginia) has been designated as nonattainment for the PM2.5 . In addition, 

South Carolina has one three-county area that was designated as unclassifiable. The PM2.5

compliance date is April 2010 unless a state demonstrates that more time is necessary in which 

case up to five additional years may be granted. State implementation plans (SIPs) will be due in 

April 2008 and the modeling year for the PM2.5 attainment demonstration will be 2009. 

The States of Alabama, Georgia, Kentucky, North Carolina, South Carolina, Tennessee, 

Virginia, and West Virginia have one or more nonattainment areas for the eight hour ozone 

standard. Basic nonattainment areas are required to attain the 8-hour ozone standard by June 15, 

2009, while moderate nonattainment areas are required to attain by June 15, 2010. This will 

require states with basic 8-hour ozone nonattainment areas to demonstrate attainment for the year 

2008 and moderate areas will require 2009 as the modeling year 

The objective of this project is to compile future year emission inventories to support fine 

particulate matter and ozone modeling efforts in the ASIP region for all source categories. This 

project has the following overall design specifications: 

• Pollutant Coverage - primary and precursor annual and seasonal emissions necessary 

to accurately model fine particulate matter and ozone, including primary PM2.5 and 

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PM10, ammonia (NH3), oxides of sulfur (SOx), volatile organic compounds (VOCs), 

oxides of nitrogen (NOx) and carbon monoxide (CO) 

• Source Coverage – all source categories except biogenic. 

• Geographic Areas – the ASIP states (Alabama, Florida, Georgia, Kentucky, 

Mississippi, North Carolina, South Carolina, Tennessee, Virginia, West Virginia) 

The inventories created under this contract will be used in creating future year modeling 

inventories (modeled under other ASIP work tasks) to support chemical transport modeling of 

fine particulate matter and ozone in the southeastern U.S. and to evaluate potential control 

strategies for the National Ambient Air Quality Standards (NAAQS) for fine particulate and 

ozone. Two future year inventories will be prepared along with evaluations of various controls 

for those inventories. In addition, updates of the 2002 base year inventory will be performed 

under this contract as necessary to develop the projection years. 

The purpose of this Quality Assurance Project Plan (QAPP) is to outline and guide the process 

for quality assuring the inventory development to ensure the development of complete, accurate, 

and consistent emission inventories. The QAPP is consistent with the recommendations in the 

EPA quality assurance requirements1

 and the Emission Inventory Improvement Program’s QA 

guidance2. The QAPP includes tasks associated with obtaining State data, merging and 

augmenting State submittals with available EPA databases, improving the activity data and 

emission factors for important source categories, obtaining and developing growth and control 

factors, obtaining State and stakeholder review of the emission inventory, and providing 

documentation of the maintenance (revisions, updates, corrections) of the inventory. 

1.2 Project/Task Description 

EPA3 has specified that calendar year 2002 be used as the base year for emission inventories to 

support planning efforts under the 8-hour ozone, PM2.5, and Regional Haze programs. ASIP has 

planned an iterative process to use and enhance the 2002 base inventory prepared by MATEC for 

the Visibility Improvement State and Tribal Association of the Southeast (VISTAS) as part of 

regional haze planning, that incorporates improved information as it becomes available. In 

addition, work on the PM2.5 and ozone NAAQS calls for continued measures of progress. As a 

consequence, emissions inventories for 2008 and 2009 will be required to assess such progress. 

• A revised 2009 Base G future year inventory based off of the 2009 projections 

developed previously for VISTAS (Base G due May 2006). This inventory will be 

developed using the final version of the 2002 VISTAS base year. The revised 2009 

inventory is designed to support modeling runs for fine particulate and ozone. It will 

be created using readily available growth and control information from the Clean Air 

Interstate Rule (CAIR), the Heavy Duty Diesel Rule (HDD Rule), the DOE’s Annual 

Energy Outlook 2006, and other EPA rules. In addition, control programs under 

these rules as well as State Implementation Plans (SIP) will be incorporated. The 

growth and control factors will be those developed for the VISTAS 2009 regional 

haze and PM2.5 inventory development effort augmented by updated information 

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from other regional inventory development work and modifications based on State 

comments. Typical year emissions for electric generating units (EGUs), wildfire and 

prescribed burning sources will be revised as necessary to incorporate new data. 

Control programs that are “on-the-books” and “on-the-way” will be incorporated into 

the estimates. Three control strategy inventories will also be developed for 2009. 

• A 2008 Inventory Base G (available Spring/Summer 2006). This inventory will be 

created using information developed for the 2009 inventory with revised growth and 

control factors to account for a 2008 projection rather than a 2009 projection. The 

inventory will still include “on-the-books” and “on-the-way” control programs as 

well as any SIP or other State specific controls. 

This QAPP focuses on the tasks associated with developing these inventories. 

Projection Inventory Activities. The effort includes the following area source activities: 

1. Assemble data needed to update the 2009 VISTAS inventory to account for Base G 

changes to the base year 2002 inventory and any changes to growth or control factors for 

2009 based on State/workgroup review. 

2. Prepare the 2009 inventory using data received as part of step 1. 

3. Assemble data needed to develop the 2008 ASIP inventory. This includes development 

of growth and control factors for 2008 which are not currently available. 

4. Prepare the 2008 ASIP projection inventory using data developed in step 3. 

5. Recommend methods for control strategies for 2009. 

6. Prepare 2009 control strategy inventories. 

7. Revise the 2002 “typical year” inventory for electric generating units (EGUs) with any 

updated data. 

8. Revise the “typical year” inventory for wild and prescribed fires with any updated data. 

Other Activities. In addition to the above tasks related to projecting emissions, a report 

detailing the methods used to develop the projections will be prepared. 

1.3 Project Organization 

Figure 1 and Table 1 identify the individuals and organizations participating in the project. Their 

specific roles and responsibilities include: 

• Ms. Pat Brewer, Technical Coordinator, will plan, conduct, and supervise technical 

and managerial aspects of the project. She will facilitate communications among 

State/local agencies, MACTEC, and the SESARM Executive Director. 

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• Mr. Greg Stella, Emission Inventory Technical Advisor, will work with the Technical 

Coordinator to define the emission inventory development activities needed to 

support PM2.5 and ozone modeling and planning activities. 

• State/local Agency Coordinators will compile and submit data to MACTEC, 

participate in QA/QC reviews, and help revise, update, and correct the inventory. 

• William Barnard, MACTEC Program Manager, will direct and monitor technical and 

financial performance throughout the project and will serve as a senior primary 

contact with ASIP on contract and project management issues. Mr. Barnard will also 

direct aspects of the projection inventory development related to area and mobile 

sources. He will plan and conduct the technical aspects of the development of the area 

and mobile source inventories, supervise daily activities, identify effective QC 

procedures and make recommendations on needed QC procedures. 

• Edward Sabo, MACTEC Point Source Task Leader, will plan and manage all point 

source activities. He will plan and conduct the technical aspects of the development 

of the point source inventory, supervise daily activities, identify effective QC 

procedures and make recommendations on needed QC procedures. 

• Dan Meszler, MACTEC Mobile Source Task Leader, will plan and manage all 

mobile source activities. He will plan and conduct the technical aspects of the 

development of input files for the MOBILE and NONROAD models and for nonroad 

sources not covered by the NONROAD model. He will help identify effective QC 

procedures and make recommendations on needed QC procedures. 

• Douglas Toothman, MACTEC QA Coordinator, will help ensure that adequate 

QA/QC procedures are incorporated into the inventory development process. He will 

work independent of the inventory development Task Leaders to assist in the conduct 

of project QA/QC assessments. 

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ASIP 

Technical Coordinator

Pat Brewer 

ASIP Emission Inventory 

Technical Advisor 

Greg Stella 

ASIP 

State/local Coordinators 

MACTEC 

QA Coordinator 

Doug Toothman 

MACTEC 

Program Manager 

William Barnard 

MACTEC 

Point Source Manager 

Edward Sabo 

MACTEC Technical Support 

Engineers, Scientists, Programmers, Graphic Artists, Editors 

MACTEC 

Area/Mobile Source Manager

William Barnard 

FIGURE 1 – PROJECT ORGANIZATION CHART 

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1.4 Quality Objectives and Criteria 

The goal of the inventory process is to provide the best possible inventory under given resource 

constraints. Data Quality Objectives (DQO) are statements about the level of acceptable 

uncertainty or error. Their purpose is to ensure that the final data will be sufficient for the 

intended use of the inventory. A well-developed and implemented quality assurance program 

fosters confidence in the inventory and any resulting regulatory program. It also gives the end 

user important information about the limitations of the emission estimates in order to avoid 

misuse of data. 

Table 1 summarizes the Data Quality Objectives for the ASIP inventories that will be compiled 

for this contract. The first column of Table 1 defines four data quality objectives: accuracy, 

completeness, comparability and representativeness. The second column identifies the 

procedures that will be used to achieve each objective. The third column identifies Data Quality 

Indicators (DQI), which are qualitative and quantitative descriptors used in interpreting the 

degree of acceptability or utility of data. 

1.5 Special Training/Certification 

All staff performing data review and analysis are air quality professionals and have sufficient 

education/experience to perform emission estimation calculations and work with emission 

inventory databases. Most staff have received specific emission inventory training through 

conferences, workshops, self-study programs, and on-the-job work experiences. There are no 

specifically mandated training requirements for work performed on this project. 

1.6 Documents and Records 

QAPP Control. Any changes to this QAPP will be initiated either by the Program Manager, the 

Task Leaders, or the QA coordinator. Each change will be given a revision number and date in 

the document control block in the upper corner of the affected pages. It will be the responsibility 

of the initiating person to distribute copies of the changed pages to all the persons on the 

Distribution List and to the appropriate project team members. 

Data Collection Records. Clear documentation of the data collected from the State/local 

agencies, EPA, and other agencies is integral to the quality analysis review. Records will be 

maintained containing a description of the data received, the name of the person and agency 

submitting the data, the date of the submission, and other relevant information about the data 

submission. The following types of data will be collected during this project: 

• EPA’s 1999 National Emission Inventory (Version 2 Final) 

• State CERR submittals 

• State/local agency data submittals in NIF 3.0 format 

• Growth factors assembled by EPA 

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• EPA’s Final Summary Emission Reports for 2002 with CEM information for electric 

utilities regulated by the Acid Rain Program 

• EPA’s Toxic Release Inventory for 1999/2000 with ammonia emissions data 

• Point source surveys for target facilities to obtain missing information 

• State/local agency submittals of updated activity data related to fugitive dust sources, 

primarily paved and unpaved roads, livestock activities and agricultural activity 

(tilling). 

• State agency submittals of information necessary to calculate fire emissions and 

geographically locate where these fires occurred in 2002. 

• State agency submittals of updated activity data for animal operations for use with the 

Carnegie Mellon University ammonia model. 

• State/local agency revisions, updates, corrections in response to various QA/QC 

checks. These may be provided in a variety of formats depending on the nature of the 

response. 

• Department of Energy fuel efficiency data 

• EGAS growth factors 

• VMT data 

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TABLE 1

DATA QUALITY OBJECTIVES, PROCEDURES, AND INDICATORS 

Data Quality Objective Procedures Example Indicators 

Accuracy - reduce uncertainty in 

emission estimates where possible, 

validate that data elements needed 

for modeling are within accepted 

parameters, and verify that emission 

estimates agree with accepted 

reference values. 

1. Identify weaknesses in existing 

inventories, identify new 

methods/data to reduce 

uncertainty, and obtain new 

activity/emission factor data where 

available. 

2. Use EPA’s NIF QA tool and adhoc reports to perform 

computerized checks of valid 

codes/data ranges and to identify 

outliers. 

3. Conduct senior technical review 

of pollutant totals by facility, 

source category, state, and region. 

4. Compare to other published data. 

1. Qualitative assessment of the 

inventory’s strengths and 

weaknesses. 

2. 100% of stack data and temporal 

factors in valid ranges for > 100 

tpy sources. 

3. 100% of sources have valid 

geographic coordinates. 

4. 100% correction of significant 

outliers. 

5. Agreement of ASIP emissions and 

EPA CEM data and EPA TRI data. 

6. Compare projection emissions to 

base year emissions to ensure that 

values are within expected ranges. 

Completeness – include all major 

point sources, include emission 

estimates for PM2.5 and ammonia, 

verify that all important areas source 

categories are included for all 

counties and all mobile source 

categories are accounted for. 

1. Compare ASIP utility data to EPA 

CEM data. 

2. Compare ASIP point source 

ammonia data to EPA TRI data. 

3. State/local agencies compare 

facility list to their Title V permit 

lists. 

4. Compare small point source 

emissions to area source 

emissions. 

5. Compare PM10 and PM2.5 

emissions. 

6. Plot area source spatial 

distributions by source category 

and county. 

1. 100% of all utilities accounted for 

in database. 

2. 100% of large ammonia sources 

in TRI accounted for in database. 

3. 100% of Title V sources 

accounted for in database 

4. Small point sources included as 

either small point sources or as 

area sources. 

5. PM2.5 and ammonia emissions 

included in inventory 

6. Area source emissions for 

important source categories for all 

counties in region. 

7. All mobile sources accounted for. 

8. Explanation of any missing data 

or sources. 

Comparability – verify that 

emission estimates are similar to 

other peer-reviewed inventories and 

that any major deviations are 

explained. 

1. Compare emission totals by 

source category, pollutant, 

geographic region, and year with 

previous emission inventories. 

1. Explanation for large 

discrepancies in emissions 

Representativeness – use emission 

estimation methods that reflect local 

conditions and the time period of 

interest. 

1. Identify where national defaults 

used instead of local activity data. 

2. Identify where emissions were 

grown for base year 2002 data 

were not available. 

3. Identify growth factors for 

projection years. 

1. Explanation for use of national 

defaults. 

2. Determination of representative 

values for “typical years” for some 

sources for projections (i.e., fires, 

utility emissions). 

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Data Handling Records. Another key element of the QA program is maintaining written 

documentation of calculations, assumptions, and all other activities associated with incorporating 

the State/local agency submittals and other data with the projection and base year inventories. 

Nearly all data being developed and/or compiled will use computerized databases or other 

electronic files. For many of these databases, we will use blank fields in the database tables to 

keep track of the source of the data. We will also maintain a log of activities to document how 

the data described above were incorporated to create the ASIP inventories. The log will include 

complete descriptions of the data sources used, the procedures used to incorporate the data, the 

approach used to determine the completeness, and any contacts made with data submitters to 

resolve questions. A file will be maintained to ensure that the data handling records are retained 

and easily located. 

QA/QC Records. We will perform a variety of quality control reviews of the inventory. For 

example, we will check stack parameters, source classification codes, and geographic 

coordinates for point sources that emit at least 100 tons of any pollutant per year. Reports 

containing the results of these checks will be transmitted to the State/local agencies for 

investigation and correction. Documentation of each finding will include a description of the 

action or data reviewed that led to the quality concern and will provide recommendations for 

corrective actions. 

Corrective Action Records. Records of corrective and follow-up actions identified during the 

quality review process will be maintained. Both the corrective action identified and the results of 

the actions taken in response will be documented for inclusion in the final report. If no 

corrective action can be made, we will document the implications on the overall quality of the 

inventory. 

Data Reporting Package. The final data reporting package will contain four elements: 

• An emission summary report that describes the emissions inventory by pollutant and 

source category, summarizes the methods and data used to compile the inventory, 

assesses the limitations and appropriate uses of the inventory data, and contains any 

other information pertinent to the inventory; 

• A quality assurance summary report that describes the quality assurance efforts 

completed, summarizes the corrective actions taken, and provides suggestions for 

further inventory improvement based on the results of the quality assurance process; 

• Electronic data files containing the ASIP inventories in NIF 3.0 format; and 

• Electronic and paper files containing all original data submittals and all backup 

documentation will be stored on file at MACTEC for a period of no less than three 

years.

2.0 DATA ACQUISITION 

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The projection year ASIP inventories will rely primarily on air emission information from 

existing databases. The data collection, handling, and management process is described below, 

along with the associated quality control procedures and methods. The QC system is designed 

to: 

• Provide routine and consistent checks and documentation points in the inventory 

development process to verify data integrity, correctness, and completeness; 

• Identify and reduce errors and omissions; 

• Maximize consistency within the inventory preparation and documentation process; 

• Facilitate internal and external inventory review processes. 

The data acquisition process should be viewed as an iterative process. As decisions are made, 

new questions will surface that require solutions, until the iterations are complete. 

2.1 Projection Year Inventory Procedures

For the projection inventories, the following procedures will be used to compile and quality 

assure the inventory: 

1. Use the final version (Base G) of the 2002 VISTAS Base/Typical Year inventory as a 

starting point. 

a. Back calculate uncontrolled emissions for 2002 Base/Typical Year inventory to 

use as starting point for sources that will be grown for the projection inventory. 

(unclear) 

2. Prepare/Obtain Growth and Control files 

a. Obtain growth factor files from EGAS for use with categories that will be grown 

with EGAS growth factors; incorporate Annual Energy Outlook 2006 information 

into EGAS to replace the AEO 2004 data currently embedded in EGAS. 

b. Obtain control factors for “on-the-book” and “on-the-way” controls as well as any 

controls for control strategy evaluations. Controls will be obtained from recent 

EPA rulemakings, proposed rules (e.g., Clean Air Interstate Rule [CAIR]), and 

State SIPs. For EGUs, control information will be obtained from VISTASsponsored IPM runs, supplemented with state-supplied adjustments as to where 

future controls will be installed. 

c. Determine/obtain growth factors for non-EGAS sources (e.g., agricultural crops, 

fertilizers, etc.). Growth factors for these sources will be calculated from existing 

projection inventories prepared by EPA (e.g., EPA Ammonia Inventory). Growth 

factors will be calculated using linear interpolation of projected emissions if the 

actual year is not available. 

3. Project sources using growth and control factors 

a. For sources to be grown using EGAS growth factors, apply growth and control 

factors. 

b. For sources not using EGAS growth factors, apply non-EGAS growth factors. 

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c. Identify and resolve any errors/discrepancies from the use of EGAS growth 

factors or other growth factor data 

d. Track comments/concerns received and corrective actions taken 

4. Determine emissions for sources requiring “typical” year emission updates. 

a. These sources include EGUs and fires 

b. For fires make any modifications needed including incorporating the long-term 

effects of prescribed burning programs. Update and revise the typical emissions 

based on changes submitted by State air and forestry personnel and to include 

future year projections of prescribed burning. 

c. Update the typical year emission data from EGU sources based on State 

comments and any revised CEM or IPM data. 

5. Develop mobile source emission inventories 

a. Prepare projected VMT for review by States/stakeholders for onroad mobile 

sources. 

b. Prepare SMOKE ready MOBILE input files for review by States/stakeholders. 

MOBILE input files will contain required control programs either “on-the-books” 

or “on-the-way”. (my understanding is this subtask is not MACTEC’s 

responsibility) 

c. Prepare NONROAD model input files for review by States/stakeholders. 

NONROAD input files will contain required control programs either “on-thebooks” or “on-the-way”. 

d. Run the NONROAD 2005 model, develop emission summaries and provide to 

States/Stakeholders for review/comment. 

e. Develop growth factors and projected emissions for nonroad sources not in the 

NONROAD model. Growth factors will be based on existing estimates from 

EPA rulemaking projections (e.g., Heavy Duty Diesel and other rules). Provide 

growth factors for review by States/Stakeholders. 

f. Prepare non-NONROAD model emission estimates. Provide for 

States/Stakeholder review/comment. 

6. Conduct QA/QC to identify errors and inconsistencies 

a. Prepare ad-hoc reports to identify gaps and logical inconsistencies. 

b. Ask States/local agencies to provide feedback on large scale inconsistencies and 

on missing sources. 

c. Update database with State/local supplied revisions. 

d. Track comments/concerns received and corrective actions taken 

7. Provide inventory for review by stakeholders 

a. Prepare an emission summary report that describes the emissions inventory by 

pollutant and source category, summarizes the methods and data used to compile 

the inventory, assesses the limitations and appropriate uses of the inventory data, 

and contains any other information pertinent to the inventory 

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b. Prepare a quality assurance summary report that describes the quality assurance 

efforts completed, summarizes the corrective actions taken, and provides 

suggestions for further inventory improvement based on the results of the quality 

assurance process 

c. Provide electronic data files containing the ASIP inventories in NIF 3.0 format 

d. Track comments/concerns received and corrective actions taken 

8. Incorporate feedback from stakeholders and prepare final reports and electronic files 

3.0 ASSESSMENT AND OVERSIGHT 

The subsections in this group address the activities for assessing the effectiveness of project 

implementation and associated QA and QC activities. The purpose of the assessment is to ensure 

that the QA Project Plan is implemented as prescribed. The assessment consists of external 

activities that include a planned system of review and audit procedures by personnel not actively 

involved in the inventory development process. The key concept of this component is 

independent objective review by a third party to access the effectiveness of the internal Quality 

Control program and the quality of the inventory, and to reduce or eliminate any inherent bias in 

the inventory process.

3.1 Assessments and Response Actions 

The MACTEC Quality Assurance Coordinator will conduct technical systems audits throughout 

the project. Audits are managerial tools used to evaluate how effectively the emission inventory 

team complies with predetermined specifications for developing an accurate and complete 

inventory. The MACTEC QAC will conduct audits at the initiation of each project to review 

the Work Plan and QAPP, at the 50% complete and 75% complete levels to review the technical 

aspects of each project and at the 95% completion level to review the data submittal package. 

This provides assessment of the project during the planning stage, the data collection stage, the 

emissions calculations stage, and the report preparation stage. An example audit checklist for 

point sources is presented in Figure 2. 

3.2 Reports to Management 

Audit reports will be distributed within two weeks of the conduct of each audit to the persons 

interviewed and the MACTEC Task Leaders. A summary of the types of quality concerns found 

will be periodically forwarded to the MACTEC Program Manager to keep him informed of the 

quality issues found and actions being taken to resolve them. Audit reports will be retained in a 

file and used to conduct subsequent audits and plan follow-up activities. When an audit team 

finds items that require immediate action, they will inform the MACTEC Program Manager of 

the necessary corrective actions. 

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AUDIT CHECKLIST

Auditor: ______________________________________ 

Date: _________________________________________ 

Data/Procedure Reviewed: _____________________________________________________________ 

___________________________________________________________________________________ 

Project Personnel Involved in Work: _____________________________________________________ 

____________________________________________________________________________________ 

Instructions: Select a facility or source category with high emissions and evaluate the quality of the data 

and adequacy of the data handling procedures. Record the findings and recommendations for corrective 

actions, if any, on this checklist and comment sheet. If recommendations for corrective actions are made, 

discuss them with the Project Manager immediately following the audit. Conduct follow-up activities to 

determine if the actions taken in response to the recommendations appropriately resolved the quality 

issues identified. 

I. DATA

A. Identify the source category evaluated: _____________________________________________ 

B. Describe the data included in the master file for the facility or source category. 

 _____________________________________________________________________________ 

 _____________________________________________________________________________ 

C. Are the data documented in a manner that will not have the potential to be misinterpreted? Y/N 

 Were the instructions for documenting the data followed? ................................................. Y/N 

D. Are there missing data fields? .................................................................................................... Y/N 

 What procedures are taken by the Task Leaders to ascertain missing? 

___________________________________________________________________________ 

 ___________________________________________________________________________ 

 At what point in the inventory process are requests for missing data made? 

___________________________________________________________________________ 

 ___________________________________________________________________________ 

 How is the receipt of the missing data handled? 

 ___________________________________________________________________________ 

 ___________________________________________________________________________ 

 Is the procedure followed to ascertain missing data efficient and adequate? ............................ Y/N 

E. How do emissions compare to other inventories? 

 1999 NEI Version 2 Final ________________________________________________________ 

 2000 TRI _____________________________________________________________________ 

 2002 ETS/CEM ________________________________________________________________ 

 Are differences in emissions understandable and explainable? ................................................. Y/N 

 If any of the values are incorrect, explain how the emissions data were corrected. 

___________________________________________________________________________ 

Figure 2 Audit Check Form

Appendix D.1 - 17

Appendix D - 19
Project: ASIP 2002 Inventory

Section: 3

Revision No. 0

Revision Date: 3/17/2006

Page 3 of 3

II. EMISSIONS DATABASE

A. Who provided the data for incorporation into the database? __________________________ 

 _______________________________________________________________________ 

B. Was there evidence that the data were reviewed for accuracy and completeness prior 

 to incorporation in the database? ............................................................................................... Y/N 

C. Were data logs maintained to describe how the data was incorporated? ................................... Y/N 

D. Ask the data incorporation personnel to explain the QC procedures followed to ensure 

 data quality. Do they agree with the procedures described in the QAPP? ................................ Y/N 

E. Does the computer system appear to be adequate for its intended use? (Ask the 

 data processing personnel about the problems they have experienced with the system.) .......... Y/N 

F. Is the data entry progressing as expected and are the procedures followed adequate 

 to ensure data quality? ............................................................................................................... Y/N 

III. RECOMMENDATIONS FOR CORRECTIVE ACTIONS

_____________________________________________________________________________________ 

_____________________________________________________________________________________ 

_____________________________________________________________________________________ 

_____________________________________________________________________________________ 

_____________________________________________________________________________________ 

IV. COMMENTS

_____________________________________________________________________________________ 

_____________________________________________________________________________________ 

_____________________________________________________________________________________ 

_____________________________________________________________________________________ 

_____________________________________________________________________________________ 

V. SIGNATURES 

______________________________ ____________________________ 

(QA Auditor) (QA Coordinator) 

______________________________ ____________________________ 

(Program Manager) (Task Manager) 

______________________________ ____________________________ 

(Project Participant) (Project Participant) 

Figure 2 Audit Check Form (Concluded) 

4.0 DATA VALIDATION AND USABILITY 

Appendix D.1 - 18

Appendix D - 20
Project: ASIP 2002 Inventory

Section: 4

Revision No. 0

Revision Date: 3/17/2006

Page 2 of 1

Section 4 addresses the QA activities that occur after the data collection phase of the project is 

completed. Implementation of these subsections determines whether or not the data conform to 

the specified criteria, thus satisfying the project objectives. 

4.1 Accuracy Assessment 

A qualitative discussion of accuracy will include an assessment of the extent to which the 

initially identified weaknesses in the inventory have been remedied through the use of improved 

activity data, emission factors, or other sources of information. Remaining weaknesses will be 

assessed. 

The accuracy assessment will include a summary of whether any data identified as outside of its 

valid range remained outside of the valid range in the final inventory. If any data remained 

outside of its valid range, an explanation will be given. The qualitative discussion will also 

include a summary of errors or discrepancies identified in the QA/QC process. 

A final semi-quantitative discussion of accuracy will consist of pollutant summaries for 

individual facilities, industry types, source categories, and statewide totals. The ASIP inventory 

will be compared to other peer-reviewed inventories, and where major discrepancies exist, we 

will provide an assessment of the reasons for the differences in emission estimates.

4.2 Completeness Assessment 

A statement will be prepared assessing whether all required facilities, source categories, 

pollutants, and data elements were included in the inventory. If any facilities or source 

categories were not included, an explanation of the omission will be provided. If any individual 

data elements were not provided, we will discuss the elements, frequency of omissions, and 

overall impact on the quality of the inventory.

4.3 Comparability Assessment 

Several summations of emissions data will be made to address comparability. Overall 

percentage differences for individual facilities (current year to prior year), industry types, 

processes, and statewide inventory will be calculated. Explanations of any large differences will 

be made.

4.4 Representativeness Assessment 

A statement will be prepared describing where national defaults have been used instead of local 

activity data. 

 

5.0 REFERENCES 

Appendix D.1 - 19

Appendix D - 21
Project: ASIP 2002 Inventory

Section: 5

Revision No. 0

Revision Date: 3/17/2006

Page 2 of 1

1. U.S. Environmental Protection Agency. March 2001. EPA Requirements for Quality 

Assurance Project Plans (EPA/240/B-01/003). http://www.epa.gov/quality/qs-docs/r5-

final.pdf

2. Emission Inventory Improvement Program (EIIP) Document Series - Volume VI Quality 

Assurance Procedures and DARS Software. EPA | TTN CHIEF | EIIP | Technical Reports | 

Volume VI Quality Assurance Procedures

3. U.S. Environmental Protection Agency. November 18, 2002. 2002 Base Year Emission 

Inventory SIP Planning: 8-hr Ozone, PM2.5 and Regional Haze Programs. 

http://www.4cleanair.org/members/committee/criteria/EPA200211181.pdf

Appendix D.1 - 20

Appendix D - 22
Appendix D.2a 

Point Source Emissions 

Inventory Documentation 

Appendix D - 23
Appendix D.2a - 1 

Appendix D - 24
TABLE OF CONTENTS 

1. INTRODUCTION ...................................................................................................................... 4 

2. 2002 POINT SOURCE INVENTORY DEVELOPMENT ........................................................ 4 

2.1 Development of 2002 Actual Point Source Inventory.................................................... 5 

2.1.1 Consolidated Emissions Reporting Rule .................................................................... 5 

2.1.2 Initial Data Evaluation ................................................................................................ 5 

2.1.3 PM Augmentation....................................................................................................... 7 

2.1.4 EGU Analysis........................................................................................................... 10 

2.1.5 Emission Inventory QA Review............................................................................... 11 

2.1.6 Summary of the 2002 Actual Inventory.................................................................... 12 

2.2 Development of Typical Year EGU Inventory............................................................. 12 

3. 2018 POINT SOURCE EMISSIONS INVENTORY DEVELOPMENT................................ 14 

3.1 EGU Emission Projections ........................................................................................... 14 

3.1.1 Chronology of the Development of EGU Projections.............................................. 15 

3.1.2 VISTAS/MRPO IPM runs for EGU sources............................................................ 17 

3.1.3 Post-Processing of IPM Parsed Files........................................................................ 17 

3.1.4 Eliminating Double Counting of EGU Units............................................................ 19 

3.1.5 Quality Assurance steps............................................................................................ 20 

3.1.6 S/L Adjustments to IPM Modeling Results.............................................................. 21 

3.2 Non-EGU Emission Projections................................................................................... 23 

3.2.1 Growth assumptions for non-EGU sources.............................................................. 24 

3.2.2 Control Programs applied to non-EGU sources........................................................ 26 

3.2.3 Quality Assurance steps............................................................................................ 28 

4. Emission Inventory Summaries................................................................................................ 29 

4.1 Summary of EGU Point Source Inventories................................................................. 29 

4.2 Summary of non-EGU Point Source Inventories.......................................................... 33 

Appendix D.2a - 2 

Appendix D - 25
LIST OF TABLES 

Table 2.1.3-1. Comparison of South Carolina PM Emissions: CERR versus the 2002 VISTAS 

Point Source Inventory ........................................................................................................... 9 

Table 2.1.6-1 2002 Actual Point Source Inventory for South Carolina....................................... 12 

Table 2.2-1 NOx Emissions Comparison for EGUs..................................................................... 13 

Table 3.1.6-1 SCDHEC Adjustments to IPM Results for the 2009 and 2018 EGU Inventories. 22 

Table 4.1-1 EGU Point Source SO2 Emission Comparison ........................................................ 30 

Table 41.7-2 EGU Point Source NOx Emission Comparison...................................................... 30 

Table 4.1-3 EGU Point Source VOC Emission Comparison ...................................................... 31 

Table 4.1-4 EGU Point Source CO Emission Comparison ......................................................... 31 

Table 4.1-5 EGU Point Source PM10-PRI Emission Comparison............................................... 32 

Table 4.1-6 EGU Point Source PM2.5 -PRI Emission Comparison ............................................. 32 

Table 4.1-7 EGU Point Source NH3 Emission Comparison........................................................ 33 

Table 4.2-1 Non-EGU Point Source SO2 Emission Comparison ................................................ 33 

Table 4.2-2 Non-EGU Point Source NOx Emission Comparison................................................ 34 

Table 4.2-3 Non-EGU Point Source VOC Emission Comparison .............................................. 34 

Table 4.2-4 Non-EGU Point Source CO Emission Comparison ................................................. 35 

Table 4.2-5 Non-EGU Point Source PM10-PRI Emission Comparison....................................... 35 

Table 4.2-6 Non-EGU Point Source PM25-PRI Emission Comparison ..................................... 36 

Table 4.2-7 Non-EGU Point Source NH3 Emission Comparison................................................ 36 

Appendix D.2a - 3 

Appendix D - 26
Appendix D.2a - 4 

1. INTRODUCTION 

The regional haze modeling was performed by the Southeast Regional Planning Organization, 

Visibility Improvement State and Tribal Association of the Southeast (VISTAS) and is run by 

the ten Southeast states (Alabama, Florida, Georgia, Kentucky, Mississippi, North Carolina, 

South Carolina, Tennessee, Virginia and West Virginia) and the local programs and tribal 

agencies located within these states. The regional haze modeling uses annual simulations and 

includes an intermediate year. VISTAS developed emission estimates for all pollutants of 

concern for regional haze. 

2. 2002 POINT SOURCE INVENTORY DEVELOPMENT 

This section details the development of the 2002 base year inventory for point sources. There 

were two major components to the development of the point source sector of the inventory. The 

first component was the incorporation of data submitted by State and Local (S/L) agencies to the 

United States Environmental Protection Agency (USEPA) as part of the Consolidated Emissions 

Reporting Rule (CERR) requirements. Work on incorporating the CERR data into the revised 

base year involved: 1) obtaining the data from the USEPA or the S/L agencies, 2) evaluating the 

emissions and pollutants reported in the CERR submittals, 3) augmenting CERR data with 

annual emission estimates for primary coarse particulate matter (PM10-PRI) and primary fine 

particulate matter (PM2.5-PRI); 4) evaluating the emissions from electric generating units, 

5) completing quality assurance reviews for each component of the point source inventory, and 

6) updating the database with corrections or new information from S/L agencies based on their 

review of the 2002 inventory. The processes used to perform the emission inventory 

development are described in the first portion of this section. 

The second component was the development of a “typical” year inventory for electric generating 

units (EGUs). The VISTAS states determined that a typical year EGU inventory was necessary 

to smooth out any anomalies in emissions from the EGU sector due to meteorology, economic, 

and outage factors in 2002. The typical year EGU inventory is intended to represent the five-year 

(2000-2004) period that will be used to determine the regional baseline and to determine the 

regional haze reasonable progress goals. The second part of this section discusses the 

development of the typical year EGU inventory. 

Appendix D - 27
Appendix D.2a - 5 

2.1 Development of 2002 Actual Point Source Inventory 

VISTAS/ASIP contracted with MACTEC to develop the 2002 emission inventory. The South 

Carolina Department of Health and Environmental Control (SCDHEC) submitted the most 

updated statewide emission inventory to the contractor. Once all of the files were obtained, 

MACTEC ran the files through the USEPA’s National Emissions Inventory (NEI) Input Format 

(NIF) Basic Format and Content checking tool to ensure that the files were submitted in standard 

NIF format and that there were no referential integrity issues with the files. 

The primary task in preparing the 2002 base year point source inventory was the incorporation of 

corrections and new information as submitted by the S/L agencies based on their review of the 

previous draft versions of the inventory. The following subsections document the data sources 

for the inventory, the checks made on the CERR submittals, the evaluation of EGU emissions, 

and other quality assurance/quality control (QA/QC) checks. The final subsection summarizes 

the 2002 point source inventory by sector (EGU and non-EGU). 

Throughout the development of the point source emissions inventory, the SCDHEC completed 

detailed reviews of the inventories prepared by the VISTAS contractor and provided comments 

and data corrections when needed. 

2.1.1 Consolidated Emissions Reporting Rule 

The CERR was published in the Federal Register on June 10, 2002 (67 Federal Register 39602). 

This brief summary is provided as a quick introduction to the CERR and covers the major items 

in the rule. 

The purpose of the CERR is to simplify reporting, offer options for data collection and exchange, 

and unify reporting dates for various categories of criteria pollutant emission inventories. The 

rule applies to S/L agencies. This rule consolidates the emission inventory reporting 

requirements found in various parts of the Clean Air Act (CAA). States are required to prepare a 

comprehensive statewide inventory every three years. The first inventory was for the year 2002 

and was due June 1, 2004. This CERR inventory was used for the VISTAS 2002 base year.

2.1.2 Initial Data Evaluation 

MACTEC conducted an initial review of the 2002 point source CERR data. The following 

evaluations were completed to identify potential data quality issues associated with the 

CERR data: 

Appendix D - 28
Appendix D.2a - 6 

• Compared the number of sites in the CERR submittal to the number of sites in the 

VISTAS draft 2002 inventory; the number of sites in the CERR submittal was less than 

in the VISTAS draft 2002 inventory, since the CERR data was limited to major sources, 

while the VISTAS draft 2002 inventory contained data for both major and minor sources; 

verified with S/L contacts that minor sources not included in the CERR point source 

inventory were included in the CERR area source inventory. 

• Checked for correct pollutant codes and corrected to make them NIF-compliant. 

• Checked for types of particulate matter (PM) codes reported (i.e., PM-FIL, PM-CON, 

PM-PRI, PM10-PRI, PM10-FIL, PM25-PRI, PM25-FIL); corrected codes with obvious 

errors (i.e., changed PMPRI to PM-PRI). The PM augmentation process for filling in 

missing PM pollutants is discussed later in Section 2.1.3. 

• Converted all emission values that were not in tons to tons to allow for preparation of 

emission summaries using consistent units. 

• Checked start and end dates in the NIF files to confirm consistency with the 2002 base 

year. 

• Compared annual and daily emissions when daily emissions were reported; in some 

cases, the daily value was non-zero (but very small) but the annual value was zero. This 

was generally the result of rounding in an S/L agency’s submittal. 

• Compared ammonia (NH3) emissions as reported in the CERR submittals and the 2002 

Toxics Release Inventory; worked with S/L agencies to resolve any outstanding 

discrepancies. 

• Compared sulfur dioxide (SO2) and nitrogen oxides (NOx) emissions for EGUs to the 

USEPA’s Clean Air Markets Division (CAMD) continuous emission monitoring (CEM) 

database to identify any outstanding discrepancies. A full discussion of the EGU 

emissions analysis is discussed later in Section 2.1.4. 

• Prepared State-level emission summaries by pollutant for both the EGU and non-EGU 

sectors to allow S/L agencies to compare emissions as reported in the 1999 NEI 

Version 2, the VISTAS draft 2002 inventory, and the CERR submittals. 

Appendix D - 29
Appendix D.2a - 7 

• Prepared facility-level emission summaries by pollutant to allow S/L agencies to review 

facility level emissions for reasonableness and accuracy. 

MACTEC communicated the results of these analyses through email/telephone exchanges 

with the S/L point source contacts as well as through Excel summary spreadsheets. The 

VISTAS S/L agencies submitted corrections and updates as necessary to resolve any QA/QC 

issues from these checks. 

2.1.3 PM Augmentation 

Particulate matter emissions can be reported in many different forms, as follows: 

PM Category Description 

PM-PRI Primary PM (includes filterable and condensable) 

PM-CON Primary PM, condensable portion only (all less than 1 micron) 

PM-FIL Primary PM, filterable portion only 

PM10-PRI Primary PM10 (includes filterable and condensable) 

PM10-FIL Primary PM10, filterable portion only 

PM2.5-PRI Primary PM2.5 (includes filterable and condensable) 

PM2.5-FIL Primary PM2.5, filterable portion only 

South Carolina reports PM-PRI, PM10-PRI, and PM2.5–PRI in the CERR. From any one of these 

pollutants, the USEPA has developed augmentation procedures to estimate PM10-PRI, 

PM10-FIL, PM2.5-PRI, PM2.5-FIL, and PM-CON. If not included in a S/L inventory, PM10-PRI 

and PM2.5-PRI were calculated by adding PM10-FIL and PM-CON or PM2.5 -FIL and PM-CON, 

respectively. 

The procedures for augmenting point source PM emissions are documented in detail in the 

USEPA’s document Documentation for the Final 1999 National Emissions Inventory {Version 

3} for Criteria Air Pollutants and Ammonia – Point Sources, January 31, 2004. 

Appendix D - 30
Appendix D.2a - 8 

(http://www.epa.gov/ttn/chief/net/1999inventory.html). Briefly, the PM data augmentation 

procedure includes the following five steps: 

• Step 1: Prepare S/L/Tribal PM and PM10 Emissions for Input to the PM Calculator 

• Step 2: Develop and Apply Source-Specific Conversion Factors 

• Step 3: Prepare Factors from PM Calculator 

• Step 4: Develop and Apply Algorithms to Estimate Emissions from S/L/Tribal Inventory 

Data 

• Step 5: Review Results and Update the NEI with Emission Estimates and Control 

Information. 

Please refer to the USEPA documentation for a complete description of the PM augmentation 

procedures. 

Table 2.1.3-1compares the original PM emission estimates from the South Carolina CERR 

submittals and the revised 2002 VISTAS emissions estimates calculated using the above 

methodology. This table is intended to show that MACTEC took whatever State/Local provided 

in the way of PM and filled in gaps to add in PM-CON where emissions were missing in order to 

calculate PM10-PRI and PM2.5 -PRI for all processes to get a complete set of particulate data. 

MACTEC did not compare any other pollutants besides PM, since for other pollutants CERR 

emissions equal VISTAS emissions

Appendix D - 31
Appendix D.2a - 9 

Table 2.1.3-1. Comparison of South Carolina PM Emissions: CERR versus the 2002 

VISTAS Point Source Inventory 

Database PM-PRI PM-FIL PM-CON PM10-PRI PM10-FIL PM2.5-PRI PM2.5-FIL 

CERR 0 43837 0 0 32656 0 21852 

VISTAS 43844 38633 5210 34799 29588 26418 21,207 

Note 1: CERR refers to data as submitted by S/L agencies; VISTAS refers to data calculated by MACTEC using 

the PM augmentation methodologies described in this document. 

Note 2: The emission values in the VISTAS emission rows above differ slightly from the final values in the 

inventory. This is due to several corrections and updates to the 2002 inventory submitted by S/L agencies after the 

PM augmentation was performed as discussed in Section 2.1.3. 

Note 3: SCDHEC’s initial CERR submittal reported particulate matter emissions as PM-FIL, PM10-FIL, and 

PM2.5-FIL pollutant codes. MACTEC used these pollutant codes during the initial PM augementation routine. In 

August 2005, SCDHEC indicated that the data reported using the PM-FIL, PM10-FIl, and PM2.5-FIL pollutant 

codes should have been reported using PM-PRI, PM10-PRI, and PM2.5-PRI pollutant codes. MACTEC performed 

a subsequent PM augmentation routine in April 2006 using the corrected pollutant codes. These changes were 

reflected in the BASE G Emission Inventory.

After the PM augmentation process was performed, MACTEC executed a series of checks to 

identify potential inconsistencies in the PM inventory. These checks included: 

• PM-PRI less than PM10-PRI, PM2.5 -PRI, PM10-FIL, PM2.5 -FIL, or PM-CON; 

• PM-FIL less than PM10-FIL or PM2.5 -FIL; 

• PM10-PRI less than PM2.5 -PRI, PM10-FIL, PM2.5 -FIL or PM-CON; 

• PM10-FIL less than PM2.5 -FIL; 

• PM25-PRI less than PM2.5 -FIL or PM-CON; 

• The sum of PM10-FIL and PM-CON not equal to PM10-PRI; and 

• The sum of PM2.5 -FIL and PM-CON not equal to PM2.5 -PRI. 

MACTEC asked S/L agencies to review this information and provide corrections where the 

inconsistencies were significant. 

Note that for the inventory, only the PM10-PRI and PM2.5-PRI emission estimates were retained 

since they are the only two PM species that are included in the air quality modeling. Other PM 

species were removed from the inventory to facilitate emissions modeling. 

Appendix D - 32
Appendix D.2a - 10 

2.1.4 EGU Analysis 

MACTEC made a comparison of the annual SO2 and NOx emissions for EGUs as reported in the 

S/L agencies CERR submittals and the data from the USEPA’s CAMD CEM database to identify 

any outstanding discrepancies. Facilities report hourly CEM data to the USEPA for units that are 

subject to CEM reporting requirements of the NOx SIP Call rule and Title IV of the CAA. The 

USEPA sums the hourly CEM emissions to the annual level, and MACTEC compared these 

annual CEM emissions to those in the S/L inventories. The 2002 CEM inventory containing 

NOx and SO2 emissions and heat input data were downloaded from the USEPA CAMD web site 

(www.epa.gov/airmarkets). The data were provided by quarter and emission unit. 

The first step in the EGU analysis involved preparing a crosswalk file to match facilities and 

units in the CAMD inventory to facilities and units in the S/L inventories. In the CAMD 

inventory, the Office of Regulatory Information Systems (ORIS) identification (ID) code 

identifies unique facilities and the unit ID identifies unique boilers and internal combustion 

engines (i.e., turbines and reciprocating engines). In the South Carolina point source emissions 

inventories, the state and county code (FIPS code) and State facility ID together identify unique 

facilities and the emission unit ID identifies unique boilers or internal combustion engines. In 

most cases, there is a one-to-one correspondence between the CAMD identifiers and the S/L 

identifiers. However, in some of the S/L inventories, the emissions for multiple emission units 

are summed and reported under one emission unit ID. MACTEC created an Excel spreadsheet 

that contained an initial crosswalk with the ORIS ID and unit ID in the CEM inventory matched 

to the State and county FIPS, State facility ID, and emission unit ID in the emissions inventories. 

The initial crosswalk contained both the annual emissions summed from the CAMD database, as 

well as, the S/L emission estimate. The matching at the facility level was nearly complete. In 

some cases, however, S/L agencies or stakeholders’ assistance was needed to match some of the 

CEM units to emission units in the S/L inventories. 

The second step in the EGU analysis was to prepare an Excel spreadsheet that compared the 

annual emissions from the hourly CAMD inventory to the annual emissions reported in the S/L 

inventory. The facility-level comparison of CEM to emission inventory NOx and SO2 emissions 

found that for most facilities, the annual emissions from the S/L inventory equaled the CAMD 

CEM emissions. Minor differences could be explained because the facility in the S/L inventory 

contained additional small or emergency units that were not included in the CAMD database. 

The final step in the EGU analysis was to compare the SO2 and NOx emissions for select 

Southern Company units in the VISTAS region. Southern Company is a super-regional 

company that owns EGUs in four VISTAS States (Alabama, Florida, Georgia, and Mississippi) 

Appendix D - 33
Appendix D.2a - 11 

and participates in VISTAS as an industry stakeholder. Southern Company independently 

provided emission estimates for 2002 as part of the development of the preliminary VISTAS 

2002 inventory. Emission estimates were reviewed by the States and incorporated into the States 

CERR submittal. There were no major inconsistencies between the Southern Company data, the 

CAMD data, and the S/L CERR data. 

The minor inconsistencies found included small differences in emission estimates (<2 percent 

difference), exclusion/inclusion of small gas-fired units in the different databases, and grouping 

of emission units in S/L CERR submittals where CAMD listed each unit individually. MACTEC 

compared SO2 and NOx emissions on a unit-by-unit basis and did not find any major 

inconsistencies. 

2.1.5 Emission Inventory QA Review 

Throughout the inventory development process, QA steps were performed to ensure that no 

double counting of emissions occurred, and to ensure that a full and complete inventory was 

developed for VISTAS. QA was an important component to the inventory development process 

and MACTEC performed the following QA steps on the point source component of the VISTAS 

revised 2002 base year inventory: 

1. Facility level emission summaries were prepared and evaluated to ensure that 

emissions were consistent and that there were no missing sources. 

2. State-level EGU and non-EGU comparisons (by pollutant) were developed between 

the 2002 base year inventory, the draft VISTAS 2002 inventory, and the 1999 NEI 

Version 2 inventory. 

3. Data product summaries and raw NIF 3.0 data files were provided to the VISTAS 

Emission Inventory Technical Advisor and to the Point Source, EGU, and non-EGU 

Special Interest Work Group representatives for review and comment. Changes based 

on these comments were reviewed and approved by the S/L point source contact prior 

to implementing the changes in the files. 

Version numbering was used for all inventory files developed. The version numbering process 

used a decimal system to track major and minor changes. 

Appendix D - 34
Appendix D.2a - 12 

2.1.6 Summary of the 2002 Actual Inventory 

Tables 2.1.6-1 summarize the final 2002 actual base year inventory for South Carolina. All 

values are in tons per year. The EGU emissions include the emissions from all processes with a 

Source Classification Code (SCC) of either 1-01-xxx-xx (External Combustion Boilers – Electric 

Generation) or 2-01-xxx-xx (Internal Combustion Engines – Electric Generation). Emissions for 

all other SCCs are included in the non-EGU column. 

Table 2.1.6-1 2002 Actual Point Source Inventory for South Carolina 

Pollutant All Point 

Sources EGUs Non-EGUs 

SO2 259,916 206,399 53,518 

NOx 130,394 88,241 42,153 

VOC 38,927 470 38,458 

CO 63,305 6,990 56,315 

PM10-PRI 35,542 21,400 14,142 

PM2.5-PRI 27,399 17,154 10,245 

NH3 1,553 142 1,441 

VOC = Volatile Organic Compounds 

CO = Carbon Monoxide 

2.2 Development of Typical Year EGU Inventory 

VISTAS developed a typical year 2002 emission inventory for EGUs to avoid anomalies in 

emissions due to variability in meteorology, economic, and outage factors in 2002. The typical 

year inventory represents the five year (2000-2004) period, which are the years used to calculate 

the regional haze baseline. 

Data from the USEPA’s CAMD were used to develop normalization factors for producing a 

2002 typical year inventory for EGUs. The VISTAS contractor used the ratio of the 2000-2004 

average heat input and the 2002 actual heat input to normalize the 2002 actual emissions. 

MACTEC obtained data from the USEPA CAMD for utilities regulated by the Acid Rain 

program. Annual data for the period 2000 to 2004 were obtained from the CAMD web site. The 

parameters available were the SO2 and NOx emission rates, heat input, and operating hours. 

MACTEC used the actual 2002 heat input and the average heat input for the 5-year period from 

2000-2004 as the normalization factor, as follows: 

Appendix D - 35
Appendix D.2a - 13 

Normalization Factor: 2000-2004 average heat input 

 2002 actual heat input

If the unit did not operate for all five years, then the 2000-2004 average heat input was calculated 

for the one or two years in which the unit did operate. The annual actual emissions were 

multiplied by the normalization factor to determine the typical emissions for 2002, as follows: 

Typical Emissions = 2002 actual emissions x Normalization Factor 

After applying the normalization factor, some adjustments were needed for special 

circumstances. For example, a unit may not have operated in 2002 and thus have zero emissions. 

If the unit had been permanently retired prior to 2002, then MACTEC used zero emissions for 

the typical year. If the unit had not been permanently retired and would normally operate in a 

typical year, then MACTEC used the 2001 (or 2000) heat input and emission rate to calculate the 

typical year emissions. 

The final step was to replace the 2002 actual emissions with the 2002 typical year data described 

above. MACTEC provided the raw data and results of the typical year calculations in a 

spreadsheet for S/L agency to review and comment. Any comments made were incorporated into 

the typical 2002 inventory. 

Table 2.2-1 summarizes emissions by State and pollutant for the actual 2002 EGU inventory and 

the typical year EGU inventory. For the entire VISTAS region, actual 2002 NOx emissions were 

about 0.1 percent lower than the typical year emissions. 

Table 2.2-1 NOx Emissions Comparison for EGUs 

NOx Emissions (tons/year) 

State 

Actual 2002 Typical 2002 Percentage 

Difference 

AL 161,038 154,704 3.9 

FL 257,677 282,507 -9.6 

GA 147,517 148,126 -0.4 

KY 198,817 201,928 -1.6 

MS 43,135 40,433 6.3 

NC 151,854 148,812 2.0 

SC 88,241 88,528 -0.3 

TN 157,307 152,137 3.3 

Appendix D - 36
Appendix D.2a - 14 

VA 86,886 85,081 2.1 

WV 230,977 222,437 3.7 

3. 2018 POINT SOURCE EMISSIONS INVENTORY DEVELOPMENT 

Different approaches were used for different sectors of the point source inventory. For the 

EGUs, VISTAS relied primarily on the Integrated Planning Model® (IPM) to project future 

generation, as well as, to calculate the impact of future emission control programs. The IPM 

results were adjusted based on S/L agency knowledge of planned emission controls at specific 

EGUs. For non-EGUs, VISTAS used recently updated growth and control data consistent with 

the data used in the USEPA’s Clean Air Interstate Rule (CAIR) analyses, and supplemented 

these data with available S/L agency input and updated fuel use forecast data for the United 

States Department of Energy. 

For both sectors, VISTAS generated 2018 inventory with control scenarios that account for post2002 emission reductions from promulgated and proposed Federal, State, Local, and site-specific 

control programs as of July 1, 2004. Section 3.1 discusses the EGU projection inventory 

development, while Section 3.2 discusses the non-EGU projection inventory development. 

3.1 EGU Emission Projections 

The following subsections discuss the aspects of the development of the EGU projections. 

• A chronology of the EGU development process used by MACTEC and discuss key 

decisions in selecting the final methods for performing the emissions projections. 

• The development of the final set of IPM runs that are included in the VISTAS 2018 

inventory. 

• The process of transforming the IPM parsed files into NIF format. 

• The process for ensuring that units accounted for in IPM were not double-counted in 

the non-EGU inventory. 

• The QA/QC checks that were made to ensure that the IPM results were properly 

incorporated into the VISTAS inventory. 

Appendix D - 37
Appendix D.2a - 15 

• The changes to the IPM results that S/L agencies requested to be included in the 

VISTAS inventory based on new information that was not accounted for in the IPM 

runs. 

• Summary of 2002 and 2018 EGU emissions by state 

3.1.1 Chronology of the Development of EGU Projections 

Initially, VISTAS considered three options for developing the 2009 and 2018 projection 

inventories for EGUs: 

• Option 1 – Use the results of IPM modeling conducted in support of the proposed CAIR 

base and control case analyses as the starting point and refine the projections with readily 

available inputs from stakeholders; these IPM runs were conducted for 2010 and 2015, 

which VISTAS would use to represent projected emissions in 2009 and 2018 

respectively. 

• Option 2 – Use the VISTAS 2002 typical year as the starting point, apply growth factors 

from the Energy Information Administration, and refine future emission rates with 

stakeholder input regarding utilization rates, capacity, retirements, and new unit 

information. 

• Option 3 – Use the results of a new round of IPM modeling sponsored by VISTAS and 

the Midwest Regional Planning Organization (MRPO). These runs incorporated 

VISTAS specific unit and regulation modified parameters, and generate results for 2018 

explicitly. 

An additional consideration for each of the three options was the inclusion of emission 

projections developed by the Southern Company specifically for their units. Southern Company 

used their energy budget forecast to project net generation and heat input for every existing and 

future Southern Company EGU for the year 2018. 

Each of these three options and the Southern Company projections were discussed in a series of 

conference calls with the VISTAS EGU Special Interest Work Group (SIWG) during the fall of 

2004. During a conference call on December 6, 2004, the VISTAS EGU SIWG approved the 

use of the latest VISTAS/MRPO sponsored IPM runs (Option 3) to represent 2018 EGU 

forecasts of emissions the future year cases. 

Appendix D - 38
Appendix D.2a - 16 

The Option 3 IPM modeling resulted from a joint agreement by VISTAS and MRPO to work 

together to develop future year utility emissions based on IPM modeling. The decision to use 

IPM modeling was based in part on a study of utility forecast methods by E.H. Pechan and 

Associates, Inc. (Pechan) for MRPO, which recommended IPM as a viable methodology. 

Although the USEPA used IPM recently to support their rulemaking for the CAIR, VISTAS 

stakeholders felt that certain model inputs needed to be improved. Thus, VISTAS and MRPO 

decided to hire contractors to conduct new IPM modeling and to post-process the IPM results. 

For 2018, Southern Company projections were generally less than IPM because of assumptions 

made by Southern Company on which units would be economical to control and incorrect data in 

the NEEDS database which feeds IPM. 

In August 2004, VISTAS contracted with ICF to run IPM to provide utility forecasts for 2018 

under two future scenarios – Base Case and CAIR Case. The Base Case represents the current 

operation of the power system under currently known laws and regulations, including those that 

come into force in the study horizon. The CAIR Case is the Base Case with the proposed CAIR 

rule superimposed. The run results were parsed at the unit level for 2018. The IPM output files 

were delivered by ICF in November, and the post-processed data files were delivered by Pechan 

in December 2004. 

On March 10, 2005, the USEPA issued the final CAIR. VISTAS and MRPO, in conjunction 

with other regional planning organizations (RPOs), conducted another round of IPM modeling, 

which reflected changes to control assumptions based on the final CAIR as well as additional 

changes to model inputs based on S/L agency and stakeholder comments. Several conference 

calls were conducted in the spring/summer of 2005 to discuss and provide comments on IPM 

assumptions related to six main topics: power system operation, generating resources, emission 

control technologies, set-up parameters and rule, financial assumptions, and fuel assumptions. 

For the summer 2006 set of IPM runs, ICF generated two different parsed files. One file 

includes all fuel burning units (fossil, biomass, landfill gas), as well as, non-fuel burning units 

(hydro, wind, etc.). The second file contains just the fossil-fuel burning units (e.g., emissions 

from biomass and landfill gas are omitted). The RPOs decided to use the fossil-only file for 

modeling to be consistent with the USEPA, since the USEPA used the fossil only results for 

CAIR analyses. For the 10 VISTAS states, non-fossil fuels accounted for only 0.13 percent of 

the NOx emissions and 0.04 percent of the SO2 emissions in the 2018 IPM runs 

VISTAS asked S/L agencies to review the results of the summer 2006 set of IPM runs, which 

were incorporated into the VISTAS inventory. The SCDHEC primarily reviewed and 

Appendix D - 39
Appendix D.2a - 17 

commented on the IPM results with respect to IPM decisions on NOx post-combustion controls 

and SO2 scrubbers. The SCDHEC decided to let the IPM solve the 2018 emissions. 

3.1.2 VISTAS/MRPO IPM runs for EGU sources 

The following summary of the VISTAS/MRPO IPM® modeling is based on ICF’s 

documentation Future Year Electricity Generating Sector Emission Inventory Development 

Using the IPM® in Support of Fine Particulate Mass and Visibility Modeling in the VISTAS and 

Midwest RPO Regions, April 2005. The ICF documentation is to be used as an extension to the 

USEPA's proposed CAIR modeling runs documented in Documentation Supplement for EPA 

Modeling Applications (V.2.1.6) Using the IPM, EPA 430/R-03-007, July 2003. 

IPM provides “forecasts of least-cost capacity expansion, electricity dispatch, and emission 

control strategies for meeting energy demand and environmental, transmission, dispatch, and 

reliability constraints.” The underlying database in this modeling is the USEPA’s National 

Electric Energy Data System (NEEDS) released with the CAIR Notice of Data Availability 

(NODA). The NEEDS database contains the existing and planned/committed unit data in the 

USEPA modeling applications of IPM. NEEDS includes basic geographic, operating, air 

emissions, and other data on these generating units. VISTAS States and stakeholders provided 

changes for: 

• NOx post-combustion control on existing units 

• SO2 scrubbers on existing units 

• SO2 emission limitations 

• PM controls on existing units 

• Summer net dependable capacity 

• Heat rate for existing units 

• SO2 and NOx control plans based on State rules or enforcement settlements 

The years 2009 and 2018 were explicitly modeled in this set of runs. Again, the SCDHEC 

decided to let the IPM model solve the 2018 emissions. 

3.1.3 Post-Processing of IPM Parsed Files 

The following summary of the VISTAS/MRPO IPM modeling is based on Pechan’s 

documentation LADCO IPM Model Parsed File Post-Processing Methodology and File 

Appendix D - 40
Appendix D.2a - 18 

Preparation, February 8, 2005. The essence of the IPM model post-processing methodology is 

to take an initial IPM model output file and transform it into air quality model input files. ICF 

via VISTAS/MRPO provided an initial spreadsheet file containing unit-level records of both 

(1) “existing” units and (2) committed or new generic aggregates. 

All records have unit and fuel type data; existing, retrofit (for SO2 and NOx), and separate NOx

control information; annual SO2 and NOx emissions and heat input; summer season (MaySeptember) NOx and heat input; July day NOx and heat input; coal heat input by coal type; 

nameplate capacity (mega-watts), and State FIPS code. Existing units also have county FIPS 

code, a unique plant identifier (ORISPL) and unit ID (also called boiler ID) (BLRID); generic 

units do not have these data. The processing includes estimating various types of emissions and 

adding in control efficiencies, stack parameters, latitude-longitude coordinates, and State 

identifiers (plant ID, point ID, stack ID, process ID). Additionally, the generic units are sited in 

a county and given appropriate IDs. This processing is described in more detail below. 

The data are prepared by transforming the generic aggregates into units similar to the existing 

units in terms of the available data. The generic aggregates are split into smaller generic units 

based on their unit types and capacity, are provided a dummy ORIS unique plant and boiler ID, 

and are given a county FIPS code based on an algorithm that sites each generic by assigning a 

sister plant that is in a county based on its attainment/nonattainment status. Within a State, 

plants (in county then ORIS plant code order) in attainment counties are used first as sister sites 

to generic units, followed by plants in PM2.5 nonattainment counties, followed by plants in 

8-hour ozone nonattainment counties. Note that no LADCO or VISTAS States provided 

blackout counties that would not be considered when siting generics, so this process is identical 

to the one used for the USEPA IPM post-processing. 

SCCs were assigned for all units; unit/fuel/firing/bottom type data were used for existing units’ 

assignments, while only unit and fuel type were used for generic units’ assignments. Latitudelongitude coordinates were assigned, first using the USEPA-provided data files, secondly using 

the September 17, 2004 Pechan in-house latitude-longitude file, and lastly using county 

centroids. These data were only used when the data were not provided in the 2002 NIF files. 

Stack parameters were attached, first using the USEPA-provided data files, secondly using a 

March 9, 2004 Pechan in-house stack parameter file based on previous Energy Information 

Adminitration form 767 (EIA-767) data, and lastly using an the USEPA June 2003 SCC-based 

default stack parameter file. These data were only used when the data were not provided in the 

2002 NIF files. 

Appendix D - 41
Appendix D.2a - 19 

Additional data were required for estimating VOC, CO, filterable primary PM10 and PM2.5, PM 

condensable, and NH3 emissions for all units. Thus, ash and sulfur contents were assigned by 

first using 2002 EIA-767 values for existing units or SCC-based defaults; filterable PM10 and 

PM2.5 efficiencies were obtained from the 2002 EGU NEI that were based on 2002 EIA-767 

control data and the PM Calculator program (a default of 99.2 percent is used for coal units if 

necessary); fuel use was back calculated from the given heat input and a default SCC-based heat 

content; and emission factors were obtained from an USEPA-approved October 7, 2004 Pechan 

emission factor file based on the USEPA’s AP-42 emission factors. Note that this updated file is 

not the one used for estimating emissions for previous USEPA post-processed IPM files. 

Emissions for 28 temporal-pollutant combinations were estimated since there are seven 

pollutants (VOC, CO, primary PM10 and PM2.5, NH3, SO2 and NOx) and four temporal periods 

(annual, summer season, winter season, July day). 

The next step was to match the IPM unit IDs with the identifiers in VISTAS 2002 inventory. A 

crosswalk file was used to obtain FIPS State and county, plant ID (within State and county), and 

point ID. If the FIPS State and county, plant ID and point ID are in the 2002 VISTAS NIF 

tables, then the process ID and stack ID are obtained from the NIF; otherwise, defaults, described 

above, were used. 

Pechan provided the post-processed files in NIF 3.0 format. Two sets of tables were developed: 

“NIF files” for IPM units that have a crosswalk match and are in the 2002 VISTAS inventory, 

and “NoNIF files” for IPM units that are not in the 2002 VISTAS inventory (which includes 

existing units with or without a crosswalk match as well as generic units). 

3.1.4 Eliminating Double Counting of EGU Units 

The following procedures were used to avoid double counting of EGU emissions in the 2018 

point source inventory. The 2002 VISTAS point source emission inventory contains both EGUs 

and non-EGUs. Since this file contains both EGUs and non-EGU point sources, and EGU 

emissions are projected using the IPM, it was necessary to split the 2002 point source file into 

two components. The first component contains those emission units accounted for in the IPM 

forecasts. The second component contains all other point sources not accounted for in IPM. 

As described in the previous section, Pechan developed 2018 NIF files for EGUs from the IPM 

parsed files. All IPM matched units were initially removed from the 2018 point source inventory 

to create the non-EGU inventory (which was projected to 2018 using the non-EGU growth and 

control factors described in Section 3.2.1). This was done on a unit-by-unit basis based on a 

cross-reference table that matches IPM emission unit identifiers (ORISPL plant code and BLRID 

Appendix D - 42
Appendix D.2a - 20 

emission unit code) to VISTAS NIF emission unit identifiers (FIPSST state code, FIPSCNTY 

county code, State Plant ID, State Point ID). When there was a match between the IPM 

ORISPL/BLRID and the VISTAS emission unit ID, the unit was assigned to the EGU inventory; 

all other emission units were assigned to the non-EGU inventory. 

If an emission unit was contained in the NIF files created by Pechan from the IPM output, the 

corresponding unit was removed from the initial 2018 point source inventory. The NIF 2018 

EGU files from the IPM parsed files were then merged with the non-EGU 2018 files to create the 

2018 point source files. 

Next, MACTEC prepared several ad-hoc QA/QC queries to verify that there was no doublecounting of emissions in the EGU and non-EGU inventories: 

• MACTEC reviewed the IPM parsed files to identify EGUs accounted for in IPM. 

MACTEC compared this list of emission units to the non-EGU inventory derived from 

the VISTAS cross-reference table to verify that units accounted for in IPM were not 

double-counted in the non-EGU inventory. As a result of this comparison, MACTEC 

made a few adjustments in the cross-reference table to add emission units for four plants 

to ensure these units accounted for in IPM were moved to the EGU inventory. 

• MACTEC reviewed the non-EGU inventory to identify remaining emission units with a 

Standard Industrial Classification (SIC) code of “4911 Electrical Services” or SCC of “1-

01-xxx-xx External Combustion Boiler, Electric Generation”. 

• MACTEC compared the list of sources meeting these selection criteria to the IPM parsed 

file to ensure that these units were not double-counted. 

MACTEC asked S/L agencies to review the 2018 point source inventory to verify whether there 

was any double counting of EGU emissions. 

3.1.5 Quality Assurance steps 

Quality assurance was an important component to the inventory development process and 

MACTEC performed the following QA steps on the EGU component of the VISTAS revised 

2009 and 2018 EGU inventories: 

Appendix D - 43
Appendix D.2a - 21 

1. Provided parsed files (i.e., Excel spreadsheets that provide unit-level results derived from 

the model plant projections obtained by the IPM) to the VISTAS EGU SIWG for review 

and comment. 

2. Provided facility level emission summaries for 2018 for both the base case and CAIR 

case to the VISTAS EGU SIWG to ensure that emissions were consistent and that there 

were no missing sources. 

3. Compared State-level emissions from the IPM parsed files with the post-processed NIF 

files to verify that the post-processed NIF files were consistent with the IPM parsed file 

results. 

VISTAS requested S/L review of these files – the changes specified by South Carolina as a result 

of this review are documented in the following subsection. 

3.1.6 S/L Adjustments to IPM Modeling Results 

After the S/L agency review of the final set of IPM runs, S/L agencies specified a number of 

changes to the IPM results to better reflect current information on when and where future 

controls would occur. These changes to the IPM results primarily involved S/L agency addition 

or subtraction of future emission controls based on the best available data from state rules, 

enforcement agreements, compliance plans, permits, and discussions/commitments from 

individual companies. 

Some S/L agencies specified changes to the controls assigned by IPM to reflect their best 

estimates of emission controls. The VISTAS contractors used a scrubber control efficiency of 90 

percent when adding or removing SO2 scrubber controls, used a control efficiency of 90 percent 

when adding or removing NOx SCR controls at coal-fired plants, 80 percent when adding or 

removing NOx SCR controls at gas-fired plants, and 35 percent when adding or removing NOx

SNCR controls. The specific changes from the SCDHEC to the IPM results are also summarized 

in Table 3.1.6-1. 

S/L agencies provided information and/or comment on changes in stack parameters from the 

2002 inventory for the 2009 and 2018 inventories. Changes to stack parameters were also made 

in cases where new controls are scheduled to be installed. In cases where an emission unit 

projected to have a SO2 scrubber in either 2009 or 2018, some states were able to provide revised 

stack parameters for some units based on design features for the new control system. Other units 

projected to install scrubbers by 2009 or 2018 are not far enough along in the design process to 

Appendix D - 44
Appendix D.2a - 22 

have specific design details. For those units, the VISTAS EGU SIWG made the following 

assumptions: 1) the scrubber is a wet scrubber; 2) keep the current stack height the same; 3) keep 

the current flow rate the same, and 4) change the stack exit temperature to 169 °F (this is the 

virtual temperature derived from a wet temperature of 130 °F). VISTAS determined that exit 

temperature (wet) of 130 °F +/- 5 °F is representative of different size units and wet scrubber 

technology. 

Table 3.1.6-1 SCDHEC Adjustments to IPM Results for the 2009 and 2018 EGU 

Inventories 

Plant Name and ID Unit Nature of Update/Correction 

Cross 

ORISID=130 

1, 2 

Unit 1: upgrade scrubber from 82 percent to 95 percent 

removal efficiency by June 30, 2006. Recalculate emissions 

based on upgrade in control efficiency. 

Unit 2: upgrade scrubber from 70 percent to 87 percent 

removal efficiency by June 30, 2006. Recalculate emissions 

based on upgrade in control efficiency. 

Winyah 

ORISID=6249 

1 – 4 

Unit 1: Install scrubber that meets 95 percent removal 

efficiency by Dec. 31, 2008; Upgrade ESP from 0.38 to 0.03 

lb/mmBTU by Dec. 31, 2008 

Unit 2: Replace scrubber with one that meets 95 percent 

removal efficiency from 45 percent by Dec. 31, 2008; 

Upgrade ESP from 0.10 to 0.03 lb/mmBTU by Dec. 31, 

2008 

Unit 3: Upgrade scrubber from 70 percent to 90 percent 

removal efficiency by Dec. 31, 2012; Upgrade ESP from 

0.10 to 0.03 lb/mmBTU by Dec. 31, 2012 

Unit 4: Upgrade scrubber from 70 percent to 90 percent 

removal efficiency by Dec. 31, 2007; Upgrade ESP from 

0.10 to 0.03 lb/mmBTU by Dec. 31, 2007 

Recalculated SO2 and PM emissions based on upgrade in 

control efficiencies. 

Dolphus Grainger 

ORISID=3317 

1, 2 

Unit 1: Upgrade ESP from 0.60 to 0.03 lb/mmBTU by Dec. 

31, 2012. Reduced PM10 and PM25 emissions in 2018 by 95 

percent based on change in allowable emission rate 

Unit 2: Install low NOx burners that meet 0.46 lb/mmBTU 

from 0.9 by May 1, 2004. Recalculated NOx emissions using 

0.46/lbs/mmBtu and IPM heat input 

Unit 2: Upgrade ESP from 0.60 to 0.03 lb/mmBTU by Dec. 

31, 2012. Reduced PM10 and PM25 emissions in 2018 by 95 

percent based on change in allowable emission rate 

Jeffries 

ORISID=3319 3, 4 

Unit 3: Upgrade ESP from 0.54 to 0.03 lb/mmBTU by Dec. 

31, 2012. Reduced PM10 and PM25 emissions in 2018 by 

94.44 percent based on change in allowable emission rate 

Appendix D - 45
Appendix D.2a - 23 

Unit 4: Upgrade ESP from 0.54 to 0.03 lb/mmBTU by Dec. 

31, 2012. Reduced PM10 and PM25 emissions in 2018 by 

94.44 percent based on change in allowable emission rate 

W S Lee 

ORISID=3264 

1, 2 

IPM does not indicate that these units are installing SOFA 

NOx control technology by April 30, 2006 to meet 0.27 

lb/mmBTU, down from 0.45 lb/mmBtu. Calculated NOx

emissions using IPM heat input and 0.27 lbs/mmBtu 

Generic Unit 

ORISID=900545 

All 

All predictions for generic units appear reasonable with the 

exception of Plant ID ORIS900545 Point ID GSC45 which 

was modeled in Georgetown County. It will be very difficult 

to add new generation this close to the Cape Romain Class I 

area. Santee Cooper has no plans for future generation in 

Georgetown County, but does have plans for new future 

generation in Florence County. This unit was moved to 

coordinates specified in Florence County. 

3.2 Non-EGU Emission Projections 

The general approach for assembling future year data was to use recently updated growth and 

control data consistent with the data used in the USEPA’s CAIR analyses, supplement these data 

with available stakeholder input, and provide the results for stakeholder review to ensure 

credibility. The VISTAS contractor used the 2002 VISTAS base year inventory, based on the 

2002 CERR submittals as the starting point for the non-EGU projection inventory. The 2002 

VISTAS/ASIP point source emission inventory contains both EGUs and non-EGUs. Since this 

file contains both EGUs and non-EGU point sources, and EGU emissions are projected using the 

IPM, it was necessary to split the 2002 point source file into two components. The first 

component contains those emission units accounted for in the IPM forecasts. The second 

component contains all other point sources not accounted for in IPM and constitutes the nonEGU emissions inventory. 

MACTEC performed the following activities to apply growth and control factors to the 2002 

non-EGU emissions inventory to generate the 2009 and 2018 projection inventories: 

• Obtained, reviewed, and applied the most current growth factors developed by the 

USEPA, based on forecasts from an updated Regional Economic Models, Inc. (REMI) 

model (version 5.5) and the latest Annual Energy Outlook published by the Department 

of Energy (DOE); 

Appendix D - 46
Appendix D.2a - 24 

• Obtained, reviewed, and applied any State-specific or sector-specific growth factors 

submitted by stakeholders; 

• Obtained and incorporated information regarding sources that have shut down after 2002 

and set the emissions to zero in the projection inventories; 

• Obtained, reviewed, and applied control assumptions; 

• Provided data files in NIF3.0 format and emission summaries in EXCEL format for 

review and comment; and 

• Updated the database with corrections or new information from S/L agencies based on 

their review of the 2009 and 2018 inventories. 

The following sections discuss each of these steps. 

3.2.1 Growth assumptions for non-EGU sources 

The growth factor data used in developing the emission inventory were consistent with the 

USEPA’s analyses for the CAIR rulemaking. These growth factors are fully documented in the 

USEPA’s reports entitled Development of Growth Factors for Future Year Modeling Inventories

(dated April 30, 2004) and CAIR Emission Inventory Overview (dated July 23, 2004). Three 

sources of data were used in developing the growth factors for the 2009 and 2018 emissions 

inventories: 

• State-specific growth rates from the Regional Economic Model, Inc. (REMI) Policy 

Insight® model, version 5.5 (being used in the development of the EGAS Version 5.0). 

The REMI socioeconomic data (output by industry sector, population, farm sector value 

added, and gasoline and oil expenditures) are available by 4-digit SIC code at the 

State level. 

• Energy consumption data from the Department of Energy, Energy Information 

Administration’s (EIA) Annual Energy Outlook 2004, with Projections through 2025 for 

use in generating growth factors for non-EGU fuel combustion sources. These data 

include regional or national fuel-use forecast data that were mapped to specific SCCs for 

the non-EGU fuel use sectors (e.g., commercial coal, industrial natural gas). Growth 

factors for the residential natural gas combustion category, for example, are based on 

residential natural gas consumption forecasts that are reported at the Census division 

Appendix D - 47
Appendix D.2a - 25 

• Specific changes for sectors (e.g., plastics, synthetic rubber, carbon black, cement 

manufacturing, primary metals, fabricated metals, motor vehicles and equipment) where 

the REMI-based rates were unrealistic or highly uncertain. Growth projections for these 

sectors were based on industry group forecasts, Bureau of Labor Statistics (BLS) 

projections and Bureau of Economic Analysis (BEA) historical growth from 1987-2002. 

In addition to the growth data described above, VISTAS received two sets of growth projections 

from stakeholders. The American Forest and Paper Association (AF&PA) supplied growth 

projections for the pulp and paper sector, which were applied to SIC 26xx Paper and Allied 

Products, for growth from 2002 to 2009 and 2002 to 2018. The AF&PA projection factors 

(1.067 and 1.169, respectively) are for the United States industry and apply to all States equally. 

The number come from the 15-year forecast for world pulp and recovered paper prepared by 

Resource Information Systems Inc. (RISI). The VISTAS contractor used the above AF&PA 

growth factors by SIC instead of the factors obtained from the USEPA’s CAIR analysis for the 

2009 and 2018 emission inventories. 

The NCDAQ considered recent projections for three key sectors in North Carolina where 

declining production was anticipated – SIC 22xx Textile Mill Products, 23xx Apparel and Other 

Fabrics, and 25xx Furniture and Fixtures. For 2009 and 2018 inventories, the NCDAQ decided 

to use a growth factor of 1.0 for these SIC codes. Although the NCDAQ has data that shows a 

steady decline in these industries in North Carolina, the NCDAQ wanted to maintain the 

emission levels at 2002 levels so the future emission reduction credits were available in the event 

that they are needed for nonattainment areas. 

For the 2009 and 2018 inventories, the VISTAS/ASIP contractor made one additional change to 

the growth factors. The Annual Energy Outlook (AEO) 2004 data was replaced with the more 

recent AEO2006 forecasts (released in February 2006) to reflect changes in the energy market 

and to improve the emissions growth factors produced. The VISTAS contractor obtained the 

corresponding AEO2006 projection tables from DOE. VISTAS developed tables comparing the 

growth factors based on AEO2004 and AEO2006 and these comparison tables were reviewed by 

the S/L agencies. Based on this review, the VISTAS states decided to use the AEO2006 growth 

factors for fuel burning SCCs. 

Appendix D - 48
Appendix D.2a - 26 

VISTAS used the USEPA’s EGAS model and updated the corresponding AEO2006 projection 

tables to create growth factors by SCC. VISTAS applied the updated growth factors to 2002 

actual emissions and replaced the 2009 and 2018 emissions in NIF EM tables for the affected 

SCCs. 

3.2.2 Control Programs applied to non-EGU sources 

The VISTAS’s contractor developed a control scenario to address the CAA requirements. The 

control scenario accounts for post-2002 emission reductions from promulgated Federal, State, 

Local, and site-specific control programs and are listed below. 

• Atlanta / Northern Kentucky / Birmingham 1-hour ozone SIPs 

• Industrial Boiler/Process Heater/RICE MACT 

• NOx RACT in 1-hour NAA SIPs 

• NOx SIP Call (Phase I) 

• NOx SIP Call (Phase II) 

• RFP 3 percent Plans where in place for one hour plans 

• VOC 2-, 4-, 7-, and 10-year MACT Standards 

• Combustion Turbine MACT 

The methodologies used to account for the emission reductions associated with these emission 

control programs are discussed in the following sections. 

NOx SIP Call (Phase I)

Phase I of the NOx SIP call applies to certain large non-EGUs, including large industrial boilers 

and turbines, and cement kilns. States in the VISTAS region affected by the NOx SIP call have 

developed rules for the control of NOx emissions that have been approved by the USEPA. 

VISTAS reviewed the available State rules and guidance documents to determine the affected 

sources and ozone season allowances. VISTAS also obtained and reviewed information in the 

USEPA’s CAMD NOx Allowance Tracking System – Allowances Held Report. Since these 

controls are to be in effect by the year 2007, VISTAS capped the emissions for NOx SIP call 

affected sources at 2007 levels and carried forward the capped levels for the 2009 and 2018 

future year inventories. 

NOx SIP Call (Phase II)

The final Phase II NOx SIP call rule was finalized on April 21, 2004. States had until 

April 21, 2005, to submit SIPs meeting the Phase II NOx budget requirements. The Phase II rule 

Appendix D - 49
Appendix D.2a - 27 

applies to large IC engines, which are primarily used in pipeline transmission service at 

compressor stations. VISTAS identified affected units using the same methodology as was used 

by the USEPA in the proposed Phase II rule (i.e., a large IC engine is one that emitted, on 

average, more than 1 ton per day during 2002). The final rule reflects a control level of 82 

percent for natural gas-fired IC engines and 90 percent for diesel or dual fuel categories. 

Industrial Boiler/Process Heater MACT

The USEPA anticipates reductions in PM and SO2 as a result of the Industrial Boiler/Process 

Heater MACT standard. The methods used to account for these reductions are the same as those 

used for the CAIR analysis. Reductions were included for existing units firing solid fuel (coal, 

wood, waste, biomass), which had a design capacity greater than 10 mmBtu/hr. The USEPA 

prepared a list of SCCs for solid fuel industrial, commercial/ institutional boilers and process 

heaters. The VISTAS contractor identified boilers greater than 10 mmBtu/hr using either the 

boiler capacity from the VISTAS 2002 inventory, or if the boiler capacity was missing, a default 

capacity based on a methodology developed by the USEPA for assigning default capacities 

based on SCC code. The applied MACT control efficiencies were 4 percent for SO2 and 40 for 

percent for PM10 and PM2.5. 

2, 4, 7, and 10-year MACT Standards

Maximum achievable control technology (MACT) requirements were also applied, as 

documented in the USEPA’s report entitled Control Packet Development and Data Sources, 

dated July 14, 2004. The point source MACTs and associated emission reductions were 

designed from Federal Register notices and discussions with the USEPA’s Emission Standards 

Division staff. VISTAS did not apply reductions for MACT standards with an initial compliance 

date of 2001 or earlier, assuming that the effects of these controls are already accounted for in 

the 2002 inventories supplied by the States. Emission reductions were applied only for MACT 

standards with an initial compliance date of 2002 or greater. 

Combustion Turbine MACT

The projection inventory does not include the NOx co-benefit effects of the MACT regulations 

for Gas Turbines or stationary Reciprocating Internal Combustion Engines, which the USEPA 

estimates to be small compared to the overall inventory. 

Appendix D - 50
Appendix D.2a - 28 

Clean Air Interstate Rule

CAIR does not require or assume additional emission reductions from non-EGU boilers and 

turbines. 

3.2.3 Quality Assurance steps 

Final QA checks were run on the revised projection inventory data set to ensure that all 

corrections provided by the S/L agencies and stakeholders were correctly incorporated into the 

S/L inventories and that there were no remaining QA issues that could be addressed during the 

duration of the project. After exporting the inventory to ASCII text files in NIF 3.0, the USEPA 

QA program was run on the ASCII files and the QA output was reviewed to verify that all QA 

issues that could be addressed were resolved. 

Throughout the inventory development process, quality assurance steps were performed to 

ensure that no double counting of emissions occurred, and to ensure that a full and complete 

inventory was developed for South Carolina. Quality assurance was an important component to 

the inventory development process and MACTEC performed the following QA steps on the 

point source component of the VISTAS inventory: 

1. Facility level emission summaries were prepared and evaluated to ensure that 

emissions were consistent and reasonable. The summaries included base year 2002 

emissions, 2009/2018 projected emissions accounting only for growth, 2009/2018 

projected emissions accounting for both growth and emission reductions from CAA 

controls. 

2. State-level non-EGU comparisons (by pollutant) were developed for the base year 

2002 emissions, 2009/2018 projected emissions accounting only for growth, 

2009/2018 projected emissions accounting for both growth and emission reductions 

from CAA controls. 

3. Data product summaries and raw NIF 3.0 data files were provided to the VISTAS 

Emission Inventory Technical Advisor and to the Point Source, EGU, and non-EGU 

Special Interest Work Group representatives for review and comment. Changes 

based on these comments were reviewed and approved by South Carolina point 

source contact prior to implementing the changes in the files. 

4. Version numbering was used for all inventory files developed. The version 

numbering process used a decimal system to track major and minor changes. 

Appendix D - 51
Appendix D.2a - 29 

4. Emission Inventory Summaries 

The following sections include the emissions inventory summaries for the point source for all of 

the states within the VISTAS region. The first section provides the 2002, 2009 and 2018 

emissions for the EGU sources and the second section provides the emissions for the non-EGU 

sources. 

4.1 Summary of EGU Point Source Inventories 

Tables 4.1-1 through 4.1-7 summarize the 2002 base year inventory and 2009 and 2018 

projection inventories for the EGU source sector. The 2009 and 2018 inventories include the 

adjustments to the IPM results specified by the S/L agencies in the previous section. 

Appendix D - 52
Appendix D.2a - 30 

Table 4.1-1 EGU Point Source SO2 Emission Comparison 

State 2002 VISTAS 

2009 IPM Based with 

S/L Adjustments 

2018 IPM Based with 

S/L Adjustments 

AL 447,828 378,052 305,262 

FL 453,631 186,055 132,177 

GA 514,952 417,449 230,856 

KY 484,057 290,193 226,062 

MS 67,429 76,579 15,146 

NC 477,990 242,286 108,492 

SC 206,399 124,608 93,274 

TN 334,151 255,410 112,672 

VA 241,204 225,653 140,233 

WV 516,084 277,489 115,324 

Total 3,743,725 2,473,774 1,479,498 

Table 4.1-2 EGU Point Source NOx Emission Comparison 

State 2002 VISTAS 

2009 IPM Based with 

S/L Adjustments 

2018 IPM Based with 

S/L Adjustments 

AL 161,038 82,305 64,358 

FL 257,677 86,165 73,125 

GA 147,517 98,497 75,717 

KY 198,817 92,021 64,378 

MS 43,135 36,011 10,271 

NC 151,854 66,522 62,353 

SC 88,241 46,915 51,456 

TN 157,307 66,405 31,715 

VA 86,886 66,219 75,594 

WV 230,977 86,328 51,241 

Total 1,523,449 727,388 560,208 

Appendix D - 53
Appendix D.2a - 31 

Table 4.1-3 EGU Point Source VOC Emission Comparison 

State 2002 VISTAS 

2009 IPM Based with 

S/L Adjustments 

2018IPM Based with 

S/L Adjustments 

AL 2,295 2,473 2,952 

FL 2,524 1,910 2,376 

GA 1,244 2,314 2,841 

KY 1,487 1,369 1,426 

MS 648 404 1,114 

NC 988 954 1,345 

SC 470 660 906 

TN 926 932 976 

VA 754 778 996 

WV 1,180 1,361 1,387 

Total 12,516 13,155 16,319 

Table 4.1-4 EGU Point Source CO Emission Comparison 

State 2002 VISTAS 

2009 IPM Based with 

S/L Adjustments 

2018 IPM Based with 

S/L Adjustments 

AL 11,279 14,986 24,342 

FL 57,113 35,928 53,772 

GA 9,712 23,721 44,476 

KY 12,619 15,812 17,144 

MS 5,303 5,051 15,282 

NC 13,885 14,942 20,223 

SC 6,990 11,135 14,786 

TN 7,084 7,213 7,723 

VA 6,892 12,509 15,420 

WV 10,341 11,493 11,961 

Total 141,218 152,790 225,129 

Appendix D - 54
Appendix D.2a - 32 

Table 4.1-5 EGU Point Source PM10-PRI Emission Comparison 

State 2002 VISTAS 

2009 IPM Based with 

S/L Adjustments 

2018 IPM Based with 

S/L Adjustments 

AL 7,646 6,969 7,822 

FL 21,387 9,007 9,953 

GA 11,224 17,891 20,909 

KY 4,701 6,463 6,694 

MS 1,633 4,957 7,187 

NC 22,754 22,152 37,376 

SC 21,400 19,395 28,826 

TN 14,640 15,608 15,941 

VA 3,960 5,508 13,775 

WV 4,573 5,657 6,349 

Total 113,918 113,607 154,832 

Table 4.1-6 EGU Point Source PM2.5 -PRI Emission Comparison 

State 2002 VISTAS 

2009 IPM Based with 

S/L Adjustments 

2018 IPM Based with 

S/L Adjustments 

AL 4,113 3,921 4,768 

FL 15,643 5,910 6,843 

GA 4,939 10,907 13,983 

KY 2,802 4,279 4,434 

MS 1,138 4,777 7,033 

NC 16,498 15,949 29,792 

SC 17,154 16,042 25,032 

TN 12,166 13,092 13,387 

VA 2,606 4,067 11,976 

WV 2,210 2,940 3,648 

Total 79,269 81,884 120,896 

Appendix D - 55
Appendix D.2a - 33 

Table 4.1-7 EGU Point Source NH3 Emission Comparison 

State 2002 VISTAS 

2009 IPM Based with 

S/L Adjustments 

2018 IPM Based with 

S/L Adjustments 

AL 317 359 1,072 

FL 234 1,631 2,976 

GA 83 686 1,677 

KY 326 400 476 

MS 190 333 827 

NC 54 445 663 

SC 142 343 617 

TN 204 227 241 

VA 127 694 622 

WV 121 330 180 

Total 1,798 5,448 9,351 

 Note: Emission summaries above are based on SCC’s 1-01-xxx-xx and 2-01-xxx-xx 

4.2 Summary of non-EGU Point Source Inventories 

Tables 4.2-1 through 4.2-7 summarize the 2002 base year inventory and the 2009 and 2018 

projection inventory for the non-EGU point source sector. 

Table 4.2-1 Non-EGU Point Source SO2 Emission Comparison 

State 2002 2009 2018 

AL 96,481 101,246 113,224 

FL 65,090 65,511 75,047 

GA 53,778 53,987 59,349 

KY 34,029 36,418 40,682 

MS 35,960 25,564 39,221 

NC 44,123 42,536 46,314 

SC 53,518 48,324 53,577 

TN 79,604 70,678 77,247 

VA 63,903 62,560 68,909 

WV 54,070 55,973 62,193 

Total 580,556 562,797 635,763 

Appendix D - 56
Appendix D.2a - 34 

Table 4.2-2 Non-EGU Point Source NOx Emission Comparison 

State 2002 2009 2018 

AL 83,310 69,409 78,318 

FL 45,156 46,020 51,902 

GA 49,251 50,353 55,824 

KY 38,392 37,758 41,034 

MS 61,526 56,397 61,533 

NC 44,928 34,767 37,801 

SC 42,153 40,019 44,021 

TN 64,344 57,883 63,453 

VA 60,415 51,046 55,945 

WV 46,612 38,031 43,359 

Total 536,087 481,683 533,190 

Table 4.2-3 Non-EGU Point Source VOC Emission Comparison 

State 2002 2009 2018 

AL 47,037 46,644 54,291 

FL 38,471 36,880 42,811 

GA 33,709 34,116 40,282 

KY 44,834 47,785 55,861 

MS 43,204 37,747 45,338 

NC 61,182 61,925 70,875 

SC 38,458 35,665 43,656 

TN 84,328 74,089 93,266 

VA 43,152 43,726 53,186 

WV 14,595 13,810 16,565 

Total 448,970 432,387 516,131 

Appendix D - 57
Appendix D.2a - 35 

Table 4.2-4 Non-EGU Point Source CO Emission Comparison 

State 2002 2009 2018 

AL 174,271 180,369 201,794 

FL 81,933 87,037 96,819 

GA 130,850 147,427 167,904 

KY 109,936 122,024 139,437 

MS 54,568 57,748 66,858 

NC 50,576 53,744 62,197 

SC 56,315 60,473 68,988 

TN 115,264 119,665 140,942 

VA 63,796 68,346 76,998 

WV 89,879 100,045 119,332 

 Total 927,388 996,878 1,141,269 

Table 4.2-5 Non-EGU Point Source PM10-PRI Emission Comparison 

State 2002 2009 2018 

AL 25,240 25,421 29,924 

FL 35,857 39,872 46,456 

GA 21,610 23,103 27,273 

KY 16,626 17,174 20,153 

MS 19,472 19,245 22,859 

NC 13,838 13,910 15,737 

SC 14,142 13,370 15,139 

TN 35,174 34,833 42,280 

VA 13,252 13,048 15,112 

WV 17,503 17,090 21,735 

Total 212,714 217,066 256,668 

Appendix D - 58
Appendix D.2a - 36 

Table 4.2-6 Non-EGU Point Source PM25-PRI Emission Comparison 

State 2002 2009 2018 

AL 19,178 19,230 22,598 

FL 30,504 33,946 39,430 

GA 17,462 18,982 22,416 

KY 11,372 11,686 13,739 

MS 9,906 9,199 10,739 

NC 10,500 10,458 11,825 

SC 10,245 9,390 11,086 

TN 27,807 27,577 33,532 

VA 10,165 9,988 11,594 

WV 13,313 12,769 16,516 

Total 160,452 163,225 193,475 

Table 4.2-7 Non-EGU Point Source NH3 Emission Comparison 

State 2002 2009 2018 

AL 1,883 2,132 2,464 

FL 1,423 1,544 1,829 

GA 3,613 3,963 4,797 

KY 674 760 901 

MS 1,169 668 764 

NC 1,180 1,285 1,466 

SC 1,411 1,578 1,779 

TN 1,613 1,841 2,214 

VA 3,104 3,049 3,604 

WV 332 341 413 

Total 16,402 17,161 20,231 

Appendix D - 59
Appendix D.2b 

Area and Nonroad Mobile Source 

Emissions Inventory Documentation 

Appendix D - 60
Appendix D.2b - 1 

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Appendix D - 61
TABLE OF CONTENTS

1. OVERALL METHODOLOGY.................................................................................................. 6 

1.1 SOURCE CATEGORY IDENTIFICATION...................................................................... 6 

1.2 AREA SOURCE EMISSION ESTIMATION APPROACH .............................................. 6 

1.3 NONROAD MOBILE SOURCE EMISSION ESTIMATION APPROACH..................... 7 

2. DISCUSSION OF AREA SOURCE CATEGORIES ................................................................ 8

2.1 2002 VISTAS-DEVELOPED AREA SOURCE INVENTORY ....................................... 8 

2.1.1 Ammonia from Agricultural Sources............................................................................... 8 

2.1.2 Paved and Unpaved Road Emissions............................................................................... 9 

2.1.3 Stage II emissions............................................................................................................. 9 

2.1.4 Portable Fuel Containers.................................................................................................. 9 

2.1.5 Forest Fires..................................................................................................................... 10 

3. 2002 VISTAS-DEVELOPED NONROAD MOBILE SOURCE INVENTORY .................... 14 

3.1 NONROAD Model Sources .............................................................................................. 14 

3.2 Aircraft Engines................................................................................................................. 15 

3.3 Railroad Locomotives........................................................................................................ 16 

3.4 Commercial Marine Vessel (CMV)................................................................................... 17 

4. QUALITY ASSURANCE OF 2002 EMISSIONS INVENTORY .......................................... 20 

5. 2009 AND 2018 AREA SOURCE EMISSION INVENTORY DEVELOPMENT ................ 22 

5.1 Projection of Stationary Area Sources................................................................................ 22 

5.2 Projection of Agricultural Area Sources............................................................................ 23 

5.3 Projection of Forest Fires Area Sources............................................................................ 23 

6. 2009 AND 2018 NONROAD SOURCE EMISSION INVENTORY DEVELOPMENT ....... 25 

6.1 Projection of NONROAD Model Sources.......................................................................... 25 

6.2 Projection of Non-NONROAD Model Sources ................................................................. 25 

7. QUALITY ASSURANCE OF 2009 AND 2018 EMISSIONS INVENTORY........................ 30 

Appendix D.2b - 2 

Appendix D - 62
LIST OF TABLES 

Table 2.1.5-1 2002 VISTAS Region Actual and Typical Fire Emissions................................... 13

Table 3.3-1 2002 National Rail Transportation Energy Use by Fuel Type (Trillion BTU)........ 16

Table 6.2-2 Estimated Emission Reduction Impacts based on T-4 Rule ................................. 28 

Table 6.2-3 Estimated Emission Reduction Impacts Relative to VISTAS 2002 Base 

Year Values........................................................................................................................... 29 

Appendix D.2b - 3 

Appendix D - 63
List of Acronyms 

Acronym Definition 

ATADS Air Traffic Activity Data System 

BTS Bureau of Transportation Statistics 

BTU British Thermal Units 

CAIR Clean Air Interstate Rule 

CARB California Air Resource Board 

CMV Commercial Marine Vessel 

CNG Compressed Natural Gas 

EDMS Emissions and Dispersion Modeling System 

EIA Energy Information Administration 

EIIP Emissions Inventory Improvement Program 

E-GAS 5.0 Economic Growth Analysis System version 5.0 

FAA Federal Aviation Administration 

GF Growth Factor 

IAQTR Interstate Air Quality Transport Rule 

LPG Liquid Petroleum Gas 

LTO Landing and Takeoff 

MSW Municipal Solid Waste 

NAAQS National Ambient Air Quality Standards 

NAICS North American Industry Classification System 

NEI National Emission Inventory 

NG Natural Gas 

NOx Nitrogen Oxides 

OTAQ Office of Transportation and Air Quality 

PFC Portable Fuel Container 

PM Particulate Matter 

QA Quality Assurance 

RDU Raleigh/Durham International Airport 

RIA Regulatory Impact Analysis 

SCC Source Classification Code 

SCDHEC South Carolina Department of Health and Environmental Control 

SCFC South Carolina Forestry Commission 

SI Spark-Ignition 

SIC Standard Industrial Classification 

SIP State Implementation Plan 

SIWG Special Interest Workgroup 

T4 Tier 4 

USACE U.S. Army Corps of Engineers 

USEPA U.S. Environmental Protection Agency 

USFA U.S. Fire Administration 

VISTAS Visibility Improvement State & Tribal Association of the Southeast 

VMT Vehicle Miles Traveled 

VOC Volatile Organic Compounds 

Appendix D.2b - 4 

Appendix D - 64
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Appendix D.2b - 5 

Appendix D - 65
Appendix D.2b - 6 

1. OVERALL METHODOLOGY 

1.1 SOURCE CATEGORY IDENTIFICATION 

The regional haze modeling was done by the southeast Regional Planning Organization, Visibility 

Improvement State and Tribal Association of the Southeast (VISTAS). VISTAS is run by the ten 

Southeast states (Alabama, Florida, Georgia, Kentucky, Mississippi, North Carolina, South Carolina, 

Tennessee, Virginia, and West Virginia) and the local programs and tribal agencies located within these 

states. The regional haze modeling uses an annual simulation, therefore the emission estimates for all 

pollutants of concern for regional haze were developed for an annual modeling run. 

The area source categories were identified from U. S. Environmental Protection Agency’s (USEPA’s) 

guidance document EPA-450/4-91-016, Procedures for the Preparation of Emission Inventories of 

Carbon Monoxide and Precursors of Ozone, Vol. 1, from this point on this document will be referred to 

as the Procedures document; the Emissions Inventory Improvement Program (EIIP) Technical Reports, 

Volume 3, Area Sources as of December 2002 (the most current version at the time of the inventory 

development), from this point on this document will be referred to as EIIP Tech Report; and a report 

entitled, Documentation of the Base G 2002 Base Year, 2009 and 2018, Emission Inventories for VISTAS 

and written by the VISTAS contractor company, MACTEC, Inc. 

Nonroad mobile sources were identified from the USEPA’s guidance document EPA-450/4-91-016, 

Procedures for the Preparation of Emissions Inventories for Carbon Monoxide and Precursors of Ozone

(Procedures document). Nonroad mobile source emissions are estimated by the methodologies suggested 

in the USEPA document, EPA-454/R-05-001, Emissions Inventory Guidance for Implementation of 

Ozone and Particulate Matter National Ambient Air Quality Standards (NAAQS) and Regional Haze 

Regulations; EPA-450/4-81-026d (Revised) Procedures for Emission Inventory Preparation, Volume IV; 

Mobile Sources (Mobile Source Procedures); and from the USEPA’s nonroad mobile model 

NONROAD2005c released March 21, 2006. 

1.2 AREA SOURCE EMISSION ESTIMATION APPROACH 

Area sources represent a collection of many small, unidentified points of air pollution emissions within a 

specified geographical area, emitting less than the minimum level prescribed for point sources. Because 

these sources are too small and/or too numerous to be surveyed and characterized individually, all area 

source activities are collectively estimated. The county is the geographic area for which emissions from 

area sources are compiled, primarily because counties are the smallest areas for which data used for 

estimating emissions is readily available. Emissions are calculated on an annual basis in tons per year. 

Area source emissions are estimated by multiplying an emission factor by some known indicator 

of collective activity for each source category within the inventory area. An indicator is any 

Appendix D - 66
parameter associated with the activity level of a source that can be correlated with the air 

pollutant emissions from that source, such as production, number of employees, or population. 

In general, one of the following emissions estimation approaches is used to calculate the area 

source emissions: per capita emission factors, employment-related emission factors, commodity 

consumption-related emission factors, and level of activity based emission factors. The emission 

factors used were obtained from the EIIP Tech Reports, the Procedures document or the 

USEPA’s AP-42 Compilation of Air Pollutant Emission Factors, Fifth Edition, referred to as 

AP-42. 

There are several methods for estimating the activity level for a specific area source category. These are: 

treating area sources as point sources, surveying local activity levels, apportioning national or statewide 

activity totals to local inventory areas, using population or employment data. 

For certain categories, there can be overlap between the point source emissions and the area source 

emissions calculated with emission factors. The 2002 point source emissions in these categories were 

identified so that they could be subtracted where appropriate. 

South Carolina used the CERR 2002 inventory as the basis for the VISTAS Area Source Inventory. 

Subsection 2.1 addresses the area source emissions inventories that were developed by VISTAS. 

VISTAS is a collaborative effort of state governments, tribal governments, and various federal 

agencies established to initiate and coordinate activities associated with the management of 

regional haze, visibility and other air quality issues in the Southeastern United States. VISTAS 

is comprised of the following ten southeastern states: Alabama, Florida, Georgia, Kentucky, 

Mississippi, North Carolina, South Carolina, Tennessee, Virginia, and West Virginia. 

MACTEC, Inc., a contractor company to VISTAS, developed the VISTAS portion of the area 

source emissions inventory. 

1.3 NONROAD MOBILE SOURCE EMISSION ESTIMATION APPROACH 

Non-highway mobile sources, sometimes referred to as off-road mobile, are those sources that 

can move but do not use the highway system. Off-road mobile sources are further divided into 

nonroad mobile, railroad locomotives, aircraft engines, and commercial marine vessels (CMV). 

The estimation of emissions from mobile sources, like area sources, involves multiplying an 

activity level by an emission factor. 

The majority of the off-road mobile emissions were estimated by using the USEPA’s off-road 

mobile model NONROAD2005c. Direct emissions are generated with this model. For aircraft 

Appendix D.2b - 7 

Appendix D - 67
engine emissions, the Federal Aviation Administration (FAA) Emissions and Dispersion 

Modeling System (EDMS) model was used. Aircraft operations were inputted into the model 

and the model predicts the engine emissions based on average landing and take-off practices for 

the aircraft type. For railroad locomotive emissions, emission factors were obtained from the 

Mobile Source Procedures document and the activity level was obtained from the various 

railroad companies. 

2.0 DISCUSSION OF AREA SOURCE CATEGORIES 

2.1 2002 VISTAS-DEVELOPED AREA SOURCE INVENTORY 

Section 2.2 details the portion of the 2002 base year area source inventory, which was developed 

for VISTAS by the VISTAS contractor, MACTEC, Inc. This information was obtained from the 

report entitled Documentation of the Base G 2002 Base Year, 2009, and 2018, Emission 

Inventories for VISTAS prepared for VISTAS by MACTEC, Inc. This report is included in 

Appendix D.5. 

Several major components of the area source sector of the inventory, which were developed by 

VISTAS, are discussed in Sections 2.2.1 through 2.2.5. Ammonia emissions from livestock and 

fertilizer sources are discussed in Section 2.2.1. PM2.5 and PM10 emissions from paved and 

unpaved roads are discussed in Section 2.2.2. Stage II emissions are discussed in Section 2.2.3 

and were removed from the area source inventory and included in the mobile sector of the 

inventory. Also, emissions from portable fuel containers were added and are discussed in 

Section 2.2.4. Section 2.2.5 describes the development of the fires emissions inventory and 

distinguishes the difference between an actual versus typical inventory with regards to fires. 

The following Sections are based on excerpts, with some editing, taken from a document 

entitled, Documentation of the Base G 2002 Base Year, 2009 and 2018 Emission Inventories for 

VISTAS and prepared by MACTEC, Inc. 

2.1.1 Ammonia from Agricultural Sources 

VISTAS used version 3.6 of the Carnegie Mellon University (CMU) NH3 model 

(http://www.cmu.edu/ammonia/) to calculate NH3 emission estimates for livestock and 

fertilizers. Results from this model were used for all VISTAS States. The CMU model version 

3.6 was used in large part because it had been just recently been updated to include the latest 

(2002) Census of Agriculture animal population statistics. 

Appendix D.2b - 8 

Appendix D - 68
For the ammonia inventory, VISTAS removed all wildlife and human perspiration emissions. 

Thus all emissions from these two categories were deleted in the 2002 inventory. 

2.1.2 Paved and Unpaved Road Emissions 

VISTAS used the most recent PM emission estimates developed by the USEPA as part of the 

NEI development effort (Roy Huntley, USEPA, email communication, 8/30/2004). The USEPA 

had developed and used an improved methodology for estimating paved road emissions for 2002. 

MACTEC obtained those emissions in March 2004 in NIF format from the USEPA FTP site. 

PM2.5 emissions from several fugitive dust sources were also updated for 2002 inventory. The 

Western Regional Air Partnership (WRAP) and the USEPA had been investigating 

overestimation of the PM2.5 / PM10 ratio in several fugitive dust. Based on data received from the 

USEPA, VISTAS decided to revise the PM2.5 emissions from construction, paved roads and 

unpaved road sources. PM2.5 emissions in the emission inventory, which was developed by the 

USEPA were multiplied by 0.67, 0.6, and 0.67 for construction, paved roads and unpaved roads, 

respectively, to produce the values found in 2002 inventory. No changes were made to PM10, 

only to PM2.5 emissions. 

2.1.3 Stage II emissions 

For the 2002 inventory, the VISTAS States all agreed to remove the Stage II refueling emissions 

from the area source inventory and include them in the non-road and on-road sectors. 

2.1.4 Portable Fuel Containers 

Portable fuel containers (PFCs), Source Classification Code (SCC) 2501060300, covers 

emissions from residential and commercial sector portable gasoline containers. This category 

typically accounts for permeation, diurnal, transport, spillage, and vapor displacement emissions. 

Spillage from refueling operations and vapor displacement emissions were not included to avoid 

double counting refueling in the non-road sector. 

MACTEC found that the USEPA had prepared a national inventory of emissions by State for 

portable fuel containers. Data on emissions from this source prepared by the USEPA were 

presented in the report, Estimating Emissions Associated with Portable Fuel Containers (PFCs), 

Draft Report, Office of Transportation and Air Quality, USEPA, Report # EPA420-D-06-003, 

February 2006”. 

Appendix D.2b - 9 

Appendix D - 69
The 2002 county-level emission estimates were obtained through an allocation method based on 

fuel usage. Initially, 2005 emission estimates, except those from vapor displacement and 

spillage from refueling operations, were obtained from the USEPA’s report and assumed to be 

equal to 2002 values. Permeation, diurnal, and transport emission estimates were summed and 

allocated to the county-level, based on the fuel usage information obtained from the 

NONROAD2005 model. The SCCs that use containers for refueling were acquired from the 

spillage file of the NONROAD model. Then the fuel usages by county from the NONROAD 

2005 runs prepared for VISTAS were summed for those SCCs by county. The county level fuel 

use was then divided by the State total fuel use for the same SCCs to determine the fraction of 

total State fuel usage and that fraction was used to allocate the State-level emissions to the 

county. 

2.1.5 Forest Fires 

The fires source category includes wildfire, prescribed burning, and land clearing fires. These 

fires can be intermittent in nature, but many of these can produce large quantities of air pollutant 

emissions. Wildfires in certain rural areas can produce large, short-term organic emissions. 

Prescribed burning is used a as a forest management practice to establish favorable seedbeds, 

remove competing underbrush, accelerate nutrient cycling, control tree pests and contribute other 

ecological benefits. Agricultural burning covers agricultural burning practices used to clear 

and/or prepare land for planting. In land clearing fires, waste from logging operations is often 

burned under controlled conditions to reduce the potential fire hazards in forests and to remove 

brush that can serve as a host for destructive insects. 

The total wildfire acreage burned was obtained from the South Carolina Forestry Commission 

(SCFC) for each county in the State. These numbers however are replaced with the 2002 

“typical” year for the purpose of modeling. Fire emissions are not easily grown or projected. 

Thus, the replacement was done so that the fires represented in the area source inventory are 

considered typical and do not reflect an abnormally low or high year as far as fires. The typical 

year forest fire inventories were developed by MACTEC, Inc. with input from state and federal 

forest resource staff. The typical year covered wildfire, prescribed burning, agricultural fires and 

land clearing fires. The development of the typical year inventory is described below. 

State level ratios of acres over a longer-term record (three or more years) developed for each fire 

type relative to 2002. The 2002 acreage was then scaled up or down based on these ratios to 

develop a typical year inventory. VISTAS Fire Special Interest Work Group based the ratio on 

county-level data for States that supplied long-term fire-by-fire acreage data rather than Statelevel ratios. Where States did not supply long-term fire-by-fire acreage data, MACTEC reverted 

Appendix D.2b - 10 

Appendix D - 70
to using State-level ratios. With one broad exception (wildfires) this method was implemented 

for all fires. MACTEC solicited long term fire-by-fire acreage data by fire type from each 

VISTAS State. A minimum of three or more years of data were used to develop the ratios. 

Those data were then used to develop a ratio for each county based on the number of acres 

burned in each county for each fire type relative to 2002. 

If VISTAS had long term county prescribed fire data from a State, a county acreage ratio, 

described below, was developed. 

acreageRx levelcounty actual 2002

acresRx levelcounty average termLong Ratio =

This ratio was then multiplied times the actual 2002 acreage to get a typical value (basically the 

long term average county level acres). Wherever possible this calculation was performed on a 

fire-by-fire basis. The acreage calculated using the ratio was then used with the fuel loading and 

emission factor values to calculate emissions. Below are three exceptions to this method. 

Exception 1: Use of State Ratios for Wildfires

Wildfires estimates were developed using State ratios rather than county ratios because some 

counties were showing unrealistic ratios, which were created by very short term data records or 

missing data. In addition, exceptionally large and small fires were removed from the database. 

VISTAS also removed all fires less than 0.1 acres from the dataset. 

Exception 2: Correction for Blackened Acres on Forest Service Lands

Acres, submitted by the U.S. Forest Service (USFS) for wildfires and prescribed fires on USFS 

lands, represented perimeter acres rather than “blackened” acres. Therefore, for prescribed fires 

greater than 100 acres in size, the acreage was adjusted to be 80 percent of the initial reported 

value. For prescribed fires of 100 acres or less, the acreage values were maintained as reported. 

All reported acreage values for wildfires were adjusted to be 66 percent of their values, as 

initially reported. 

Exception 3: Missing/Non-reported data

When VISTAS did not receive data from a VISTAS State for a particular fire type, a composite 

average for the entire VISTAS region was used to determine the typical value for that type fire. 

This technique was applied to all fire types when data was missing. 

Appendix D.2b - 11 

Appendix D - 71
For wildfires and prescribed burning, ratios were also developed for “northern” and “southern” 

tier States within the VISTAS region and those ratios were applied to each State with missing 

data depending upon whether they were considered a “northern” or “southern” tier State. 

Development of “southern” and “northern” tier data was an attempt to account for a change from 

a predominantly pine/evergreen ecosystem (southern) to a pine/deciduous ecosystem (northern). 

Table 2.2.5-1 below presents a comparison in tons per year of the 2002 actual fire emissions and 

the 2002 typical fire emissions for wildfires, prescribed burning, agricultural fires and land 

clearing fires in the VISTAS Region. 

Appendix D.2b - 12 

Appendix D - 72
Table 2.1.5-1 2002 VISTAS Region Actual and Typical Fire Emissions NH3 0 0 1,333 7,616 0 0 

2,451 7,425 

PM2.5

62,146 30,385 24,926 

145,175 

80,598 29,892 45,635 

141,636 

PM10

62,146 30,958 28,923 

168,938 

80,598 30,465 53,070 

164,811 

SO2

0 0 

1,611 9,839 

0 0 

3,072 9,590 

CO 

492,409 164,273 298,835 

1,678,216 

675,838 161,667 547,174 

1,635,776 

VOC 

33,799 21,946 16,804 78,988 46,389 21,595 28,491 76,990 

NOx 

14,568 

903 

6,628 

36,561 19,995 

903 

11,955 35,650 

Actual Fire Emissions (TPY)

Fire Type 

Land Clearing 

Agricultural 

Burning 

Wildfires 

Prescribed 

Burning 

Typical Fire Emissions (TPY)

Land Clearing 

Agricultural 

Burning 

Wildfires 

Prescribed 

Burning 

Appendix D.2b - 13 

Appendix D - 73
Appendix D.2b - 14 

3.0 2002 VISTAS-DEVELOPED NONROAD MOBILE SOURCE 

INVENTORY 

Development of emission estimates for nonroad mobile sources is documented in the MACTEC, 

Inc. document titled Documentation of the Base G 2002 Base Year, 2009 and 2018, Emission 

Inventories for VISTAS. 

Nonroad mobile sources are those sources that can move but do not use the highway system. 

Examples include lawn mowers, agricultural equipment, construction equipment, aircraft 

engines, railroad locomotives, powerboats, and commercial marine (ships). All but the aircraft 

engine, railroad locomotive emissions and commercial marine activity were estimated using the 

USEPA’s off-road mobile model NONROAD2005c, which was released March 21, 2006. 

Direct emissions are generated with this model. This version incorporates all the USEPA final 

nonroad mobile engine emission standards, including the recreational and large spark-ignition 

engines rules that were published in the Federal Register in November 2002. Although this 

model is considered to be a final model, an updated version is planned that may incorporate 

revised inputs for the small spark-ignition (SI) (<19 kW) and recreational marine SI categories in 

conjunction with additional promulgated nonroad mobile engine standards. 

Nonroad mobile sources calculated through the NONROAD model are discussed in Section 6.1 

Aircraft, railroad and commercial marine emissions are discussed in Sections 3.2 through 3.4. 

3.1 NONROAD Model Sources 

The nonroad mobile source category includes a diverse collection of equipment such as lawn 

mowers, chain saws, tractors, all terrain vehicles, fork lifts and construction equipment. The 

USEPA NONROAD2005c model generates emissions directly and includes more than 80 

different types of equipment. To facilitate analysis and reporting, the USEPA grouped the 

equipment types into ten equipment categories. These include: 

Agricultural equipment Lawn and garden equipment 

Airport ground support equipment Logging equipment 

Commercial equipment Railroad maintenance equipment 

Construction equipment Recreational marine equipment 

Industrial equipment Recreational equipment 

Appendix D - 74
Additionally, the emissions are broken out by five different engine types. These include: 

2-stroke and 4-stroke spark engines, diesel engines, liquid petroleum gas (LPG) and compressed 

natural gas (CNG) fueled engines. 

3.2 Aircraft Engines 

Aircraft engines, like other engines, emit pollutants whenever the engines are in operation. 

However, the only emissions that are of concern for this inventory are the portion of the 

operation that occurs below the mixing layer. This is because the emissions tend to disperse 

whenever the aircraft is above the mixing layer and therefore has little or no effect on ground 

level ozone. 

The aircraft operations of interest are termed the landing and takeoff (LTO) cycle. The cycle 

begins when the aircraft approaches the airport, descending below the mixing layer, lands and 

taxis to the gate. It continues as the aircraft idles at the gate and then taxis back out to the 

runway for the subsequent takeoff and climb out as it heads back to cruising altitudes, above the 

mixing layer. 

Aircraft can be categorized by use into four classifications: commercial, air taxis, general 

aviation and military. Commercial aircraft include those used for scheduled service transporting 

passengers and/or freight. Air taxis, or commuter aircraft, also fly scheduled service carrying 

passengers and/or freight but usually are smaller aircraft and operate on a more limited basis than 

commercial carriers. General aviation include all other non-military aircraft used for recreational 

flying, personal transportation, and various other activities. Military aircraft cover a wide range 

of sizes, uses and operating missions. The military aircraft are treated as a separate classification 

since the LTO operations reported at the airports group all military aircraft together. 

Emission factors are available for the many aircraft and engine combinations that exist. Factors 

for each aircraft exist for four operating modes in the LTO cycle. Emissions are calculated by 

obtaining data for the number of LTO cycles of the various aircraft at each airport in question, 

multiplying by the appropriate factors, and summing the results for the year under consideration. 

Development of the 2002 aircraft emissions are described in the MACTEC document titled 

Documentation of the Base G 2002 Base Year, 2009 and 2018 Emission Inventories for VISTAS . 

This document refers back to a document titled “Development of the VISTAS Draft 2002 Mobile 

Source Emission Inventory (February 2004 Version)” prepared by E.H. Pechan & Associates, 

Inc. Both of these documents are included in Appendix D.5. 

Appendix D.2b - 15 

Appendix D - 75
The starting point for development of aircraft emissions estimates is the 1999 National Emission 

Inventory (1999 NEI) prepared by the USEPA. These emissions were grown to appropriate 

values for 2002 and 2009 using growth factors developed by the USEPA for the CAIR. Along 

the way there was input by the various States to arrive at more accurate emission estimates. 

3.3 Railroad Locomotives 

Railroads are categorized by size (Class I, Class 2) and passenger service. Class I railroads are 

long haul operations, such as Norfolk Southern Corporation and CSX Corporation . Class II and 

Class III railroads are short lines, serving localized markets. These entities lease trackage from 

Class I railroads. 

Development of railroad emissions is described in the MACTEC document titled Documentation 

of the Base G 2002 Base Year, 2009 and 2018 Emission Inventories for VISTAS. The 

VISTAS/ASIP railroad emission estimates started with 1999 emission estimates developed for 

the USEPA’s 1999 NEI Version 2 as base year estimates for the VISTAS region. Additional 

information is provided in “Development of the VISTAS Draft 2002 Mobile Source Emission 

Inventory (February 2004 Version)” prepared by E.H. Pechan & Associates, Inc. 

Projected emissions for 2002 were developed in two steps as described below. For 1999 to 2001, 

State-level rail fuel consumption was obtained from the Department of Energy, Energy 

Information Administration’s (EIA’s) Fuel Oil and Kerosene Sales. For 2001 to 2002, VISTAS 

applied national growth factors developed from fuel consumption projections in EIA’s Annual 

Energy Outlook. A growth factor of 1.4 was used for locomotives and applied to 1999 emissions 

to first develop 2001 emissions. Table 3.3-1 lists the growth factors used to generate 2002 

emissions. 

Table 3.3-1 2002 National Rail Transportation Energy Use by Fuel Type (Trillion BTU) 

2001 2002 Growth Factor (GF)

Intercity Rail 

(Electric) 

10.17 10.40 1.0226

Intercity Rail (Diesel) 16.60 16.88 1.0169

Transit Rail (Electric) 46.36 47.40 1.0224

Intercity/Transit Rail Average (SCC 2285002008) 1.0206

Commuter Rail 

(Electric) 16.13 16.49 1.0223 

Commuter Rail 

(Diesel) 26.31 26.76 1.0171 

Appendix D.2b - 16 

Appendix D - 76
Commuter Rail Average (SCC 2285002009) 1.0197

Freight Rail 

(Distillate) 

(SCCs 2285002000, 

2285002005, 

2285002006, 

2285002007, 

2285002010)

512.81 492.32 0.9600

Source: Department of Energy, Energy Information Administration, Annual Energy Outlook 2003: Table 34. 

Transportation Sector Energy Use by Fuel Type Within a Mode 

3.4 Commercial Marine Vessel (CMV) 

The following description of development of commercial marine emission estimates is based on 

excerpts, with some editing, taken from the MACTEC document titled Documentation of the 

Base G 2002 Base Year, 2009 and 2018 Emission Inventories for VISTAS and a document titled 

“Development of the VISTAS Draft 2002 Mobile Source Emission Inventory (February 2004 

Version)” prepared by E.H. Pechan & Associates, Inc 

An initial 2002 base year emissions inventory for commercial marine vessels (CMV) was 

prepared for VISTAS in early 2004. The methods and data used to develop the inventory are 

presented in a February 9, 2004 report “Development of the VISTAS Draft 2002 Mobile Source 

Emission Inventory (February 2004 Version)” prepared by E.H. Pechan & Associates, Inc. 

Revisions to the initial 2002 emissions inventory (prepared by Pechan) were implemented to 

ensure that the latest State and local data were incorporated. For CMV, South Carolina provided 

no revised data. 

For 2002 commercial marine vessels (CMVs), Pechan used 1999 emission estimates developed 

for the USEPA’s 1999 NEI Version 2 as base year estimates for the VISTAS region. Pechan 

then improved the spatial distribution of CMV emission estimates for the VISTAS region. 

Ideally, CMV emission estimates would be developed using local activity data that account for 

vessel type, engine type and mode of operation (cruise, maneuvering, and hotelling). Creating 

this type of “bottom-up” emission inventory requires a large amount of effort. Therefore, Pechan 

utilized port-specific emission estimates developed for the 1999 NEI, distributed using a revised 

allocation methodology, which incorporates information on the number of port facilities in each 

county. 

Appendix D.2b - 17 

Appendix D - 77
The 2002 VISTAS commercial marine inventory is based on the USEPA’s 1999 NEI Version 

2.0, projected to 2002 using appropriate growth factors. The 1999 NEI estimated emissions for 

these categories according to the following SCCs: 

SCC Descriptor 1 Descriptor 3 Descriptor 6 Descriptor 8 

2280002100 Mobile Sources Marine Vessels, Commercial Diesel Port emissions 

2280002200 Mobile Sources Marine Vessels, Commercial Diesel Underway emissions 

2280003100 Mobile Sources Marine Vessels, Commercial Residual Port emissions 

2280003200 Mobile Sources Marine Vessels, Commercial Residual Underway emissions 

For the 1999 NEI, commercial marine diesel emissions were developed by obtaining 2000 

emission estimates for all pollutants except SO2 from the USEPA’s Office of Transportation and 

Air Quality (OTAQ) marine diesel regulatory background documentation (Draft Regulatory 

Impact Analysis - Control of Emissions from Compression-Ignition Marine Engines). To 

estimate emissions for 1999, 2000 estimates were backcast using growth factors obtained from 

the draft Regulatory Impact Analysis (RIA) cited above. Steam-powered residual CMV 

emission estimates were developed by obtaining fuel usage data from OTAQ and applying fuelbased emission factors. A similar method was used for diesel SO2 emissions. National diesel 

usage was estimated assuming a sulfur content of 0.25 percent and the USEPA emission factors. 

In apportioning, distillate and residual fuels are considered separately. National diesel emissions 

were disaggregated into port and underway emissions estimates based on the assumption that 75 

percent of distillate fuel is consumed within the port, while the remaining fuel is consumed while 

underway, consistent with the USEPA guidance. National residual emissions were 

disaggregated into port and underway emissions estimates based on the assumption that 25 

percent of residual fuel is consumed within the port, while the remaining fuel is consumed while 

underway. 

To allocate to counties, port emissions were assigned to the 150 largest U.S. ports based on 

activity obtained from the U.S. Army Corps of Engineers (USACE). The percentage of total 

traffic for each port was calculated by dividing the port-level traffic by the total traffic. 

Emissions for each port were then assigned to a single county. 

Underway emissions are assigned to counties based on a county’s shipping lane traffic. The 

Bureau of Transportation Statistics’ (BTS) National Transportation Atlas Databases-1999 

contains data on the thousand tons per mile traveled for each shipping lane link in the United 

States (BTS-CD26). Where navigable rivers form a county or State boundary, the shipping lane 

traffic is proportioned to individual counties based on the length of shoreline that is shared. For 

Appendix D.2b - 18 

Appendix D - 78
example, if two counties share a navigable river, and both counties have the same length of 

shoreline, the shipping traffic is split evenly between the two counties. Shipping lanes that are 

not within counties, for example in the ocean, are associated to States based on BTS 

assignments. These waterway weights are then evenly distributed among the counties within 

these States that have navigable waterways. All shipping activity is summed at the county-level 

and compared with national shipping activity to determine what portion of activity can be 

attributed to individual counties. These proportions were used in disaggregating the national 

CMV emission estimates to the county level. 

States that share borders with non-VISTAS States along the Mississippi and Ohio Rivers have 

expressed concern about the representativeness of port emission estimates at a county-level. 

Revising the county-level emissions estimates would allow more accurate modeling of emissions 

in the VISTAS states. 

For underway emissions, Pechan believes that the allocation procedure results in a reasonable 

distribution of county-level emissions. However, the methodology to allocate port emissions 

results in all the emissions being assigned to a single county. 

Port areas encompass multiple States and counties and in some cases, multiple waterways. 

Therefore, the emissions allocation process must incorporate all counties in the vicinity of the 

port where activity is occurring. This is especially true for inland rivers where activity takes 

place on both riverbanks and for ten river miles or more outside the port city. The revised 

methodology allocates port emissions based on a surrogate for port-related activity in each 

county, rather than using a single county to define the port. 

The report, Waterborne Commerce of the United States, Calendar Year 1999, hereafter referred 

to as Waterborne Commerce, presents the cargo tonnage and number of vessel trips in major 

waterways of the United States. The report defines port areas, which USACE uses to develop 

the Top 150 Ports in the United States by amount of cargo tonnage. As discussed previously, the 

1999 NEI allocates all the port emissions to these 150 ports based on the cargo tonnage handled 

by the port. Pechan uses this allocation of emissions to each port area as the starting point of its 

revised allocation process. Port Charleston (Charleston Harbor, SC) and the Port of Georgetown 

(Georgetown Harbor, SC – Winyah Bay) are the two main ports in South Carolina.

The next step was to develop a list of counties that make up the port area. Port area definitions were 

obtained from Waterborne Commerce. The port area definition for Port Charleston was ”Ocean to Goose 

Creek via Cooper River and Town Creek; to the Standard Wharf on Ashley River; to the Mount Pleasant 

Memorial Highway Bridge on Shem Creek; to the Airco Alloys Wharf on Shipyard River; Wando River 

to Cainhoy.” The port area definition for the Port of Georgetown was “Atlantic Ocean Entrance to 

Appendix D.2b - 19 

Appendix D - 79
Winyah Bay, SC, to and including turning basin in Sampit River at the City of Georgetown, SC.”. Using 

the port definitions by river mile, Pechan established which counties are included in each port area. Port 

Charleston is included in the counties of Charleston and Berkeley. The Port of Georgetown is included in 

Georgetown county. 

The next step in allocating emissions is to develop a surrogate for the amount of CMV activity in 

each county of the port area. Pechan assumed that the activity of vessels in each county is 

related to the number of port facilities operating in a given county. Port facilities include 

terminals, piers, wharves, and docks that are involved in all types of commercial activity and 

support services. Pechan obtained the number of port facilities in each county from USACE 

reports, The Port Series Reports. The USACE periodically surveys the commercial marine 

industry to obtain information on port facilities and publishes it in The Port Series Reports. The 

reports give the name, location, operations, and describe the physical and inter-modal 

characteristics of the facilities. The data includes the location of the facility by river mile, State, 

and county. 

For each port area, Pechan calculated the ratio between the number of port facilities in each 

county to the total number of facilities in all counties that make up the port area. This ratio was 

used to allocate emissions for each port area to the county-level. The ratio for Morehead City 

Harbor was 1.0 in Carteret county and the ratios for the Port of Wilmington were 0.8974 in New 

Hanover county and 0.1026 in Brunswick county. Pechan was directed to perform the 

reallocation for all VISTAS ports. 

 

4.0 QUALITY ASSURANCE OF 2002 EMISSIONS INVENTORY 

Many emission estimation methods are based on AP-42 factors located on the USEPA website at 

http://www.epa.gov/ttn/chief/ap42/, factors given in the Procedures document, and factors given 

in the documents of the Emission Inventory Improvement Program website located at 

http://www.epa.gov/ttn/chief/eiip/. Sources of error would primarily be associated with 

multiplier values, data entry errors, and the accuracy of formulas. 

For the portion of the 2002 inventory developed by VISTAS and MACTEC, specific QA 

procedures were followed. These procedures are outlined in the document titled Documentation 

of the Base G 2002 Base Year, 2009, and 2018, Emission Inventories for VISTAS prepared for 

VISTAS by MACTEC, Inc. Throughout the inventory development process, quality assurance 

steps were performed to ensure that no double counting of emissions occurred, and to ensure that 

a full and complete inventory was developed for VISTAS. Quality assurance was an important 

Appendix D.2b - 20 

Appendix D - 80
component to the inventory development process and MACTEC performed the following QA 

steps on the area source component of the 2002 inventory: 

1. All CERR and NIF format State supplied data submittals were run through the 

USEPA’s Format and Content checking software. 

2. SCC level emission summaries were prepared and evaluated to ensure that emissions 

were consistent and that there were no missing sources. 

3. Fields were either added or used within each NIF data table to track the sources of 

data for each emission record. 

4. Data product summaries were provided to both the VISTAS Emission Inventory 

Technical Advisor and to Area Source and Fires SIWG representatives for review and 

comment. Changes based on these comments were implemented in the files. 

5. Version numbering was used for all inventory files developed. The version 

numbering process used a decimal system to track major and minor changes. For 

example, a major change would result in a version going from 1.0 to 2.0. A minor 

change would cause a version number to go from 1.0 to 1.1. Minor changes resulting 

from largely editorial changes would result in a change from 1.00 to 1.01. 

6. All final NIF files were checked using the USEPA Format and Content checking 

software and summary information by State and pollutant were prepared comparing 

the previous versions of the inventory to the latest. 

For the fires inventory, data related to fuel loading and fuel consumption was reviewed and 

approved by the VISTAS Fire SIWG to ensure that values used for each type of fire and each 

individual fire were appropriate. Members of the VISTAS Fire SIWG included representatives 

from most State Divisions of Forestry (or equivalent) as well as U.S. Forest Service and National 

Park Service personnel. 

In addition, for the nonroad portion of the inventory prepared by VISTAS, tier comparisons (by 

pollutant) were developed between the revised 2002 base year inventory and the initial base year 

inventory. 

Appendix D.2b - 21 

Appendix D - 81
5.0 2009 AND 2018 AREA SOURCE EMISSION INVENTORY 

DEVELOPMENT 

This subsection describes the methodology used to develop the 2009 and 2018 area source 

inventories. Separate methods for projecting emissions were used for non-agricultural 

(stationary area), agricultural area sources and forest fire area sources. The agricultural area 

sources method are for ammonia emissions and important for regional haze and fine particulate 

matter. 

The following Sections are based on excerpts, with some editing, taken from a document 

entitled, Documentation of the Base G 2002 Base Year, 2009 and 2018 Emission Inventories for 

VISTAS and prepared by MACTEC, Inc. 

5.1 Projection of Stationary Area Sources 

VISTAS 2002 base year inventory emissions were used as a starting point for calculating 2009 

and 2018 emissions. MACTEC, Inc. first back calculated uncontrolled emissions from the 2002 

base year inventory. Growth and control factors were then applied based on controls initially 

identified for the Clean Air Interstate Rule (CAIR) and growth factors identified for the CAIR 

projections. In some cases, Economic Growth Analysis System version 5.0 (EGAS 5.0) growth 

factors were used if no growth factor was available from the CAIR growth factor files. 

The 2009 and 2018 growth factors were obtained from the USEPA growth factors and indirectly 

from 2010 and 2015 CAIR growth factors. Using a 2001 base year, interpolation of 2010 and 

2015 CAIR growth factors yielded 2009 and 2018 growth factors. MACTEC used the TREND 

function of Microsoft Excel for interpolation. Interpolated growth factors were calculated at the 

State and SCC level. 

In a few cases, additional growth factors had to be added for sources that had not initially been 

included in a draft 2002 inventory. These growth factors were obtained from EGAS 5.0. Finally 

updates to growth factors from EGAS 5.0 were made for fuel fired emission sources. The 

updated growth factors reflected the most recent data from the Department of Energy’s Annual 

Energy Outlook (AEO). These data were used to reflect changes in energy efficiency resulting 

from new or updated fuel firing technologies. 

Appendix D.2b - 22 

Appendix D - 82
5.2 Projection of Agricultural Area Sources 

MACTEC used the version 3.1 2002 base year inventory data (which was based on the CMU 

ammonia model version 3.6). MACTEC obtained State specific growth, if available. Otherwise, 

growth factors were used, which were developed from the USEPA Interstate Air Quality 

Transport Rule (IAQTR)/Ammonia inventory. Growth factors for several agricultural livestock 

categories were developed by the USEPA, as part of the NEI. 

In addition, ammonia growth factors for a few categories (mainly feedlots) were assigned to be 

the same as growth factors for PM emissions from the NEI projections. This assignment was 

made because the CMU model showed emissions from these categories but the NEI projections 

did not show ammonia emissions but did show PM emissions. No growth factors were found for 

horse and pony emissions. These emissions were held constant at 2002 levels. 

No controls were identified by South Carolina; thus, all projected emissions for agricultural area 

sources represent simple growth with no controls. 

Wild animal and human perspiration emissions were removed from the 2009 and 2018 

inventories. 

5.3 Projection of Forest Fires Area Sources 

Several Federal agencies indicated that they had plans for increased prescribed fire burning in 

future years and that the “typical” fire inventory would likely not adequately capture those 

increases. Thus, MACTEC acquired the data necessary to provide 2009 specific projections for 

the prescribed fire component of the fire inventory. 

The U.S. Fish and Wildlife Service (USFWS) submitted annual acreage data by National 

Wildlife Refuge (NWR) and by county with estimates of acres burned per day for each NWR. 

USFS provided fire-by-fire acreage estimates based on mapping projected burning acreage to 

current 2002 modeling days. However, USFWS did not submit data for VISTAS original base 

year preparation process, thus there was no known USFWS data in the 2002 actual or typical 

inventories. MACTEC therefore developed a method that could use the county-level data 

submitted by USFWS. 

Several VISTAS States run a prescribed fire-permitting program. To avoid double counting, 

only State data and not USFWS or USFS data was used in those States for the 2002 actual 

inventory. 

Appendix D.2b - 23 

Appendix D - 83
The method used by MACTEC to include the USFS data applied a county level data approach 

for USFS data where a State had a prescribed fire permitting program and a fire-by-fire 

replacement for USFS data in States without permit programs. MACTEC used a county level 

approach for all of the USFWS data. The approach used for each data set is discussed below. 

For USFWS data, 2002 annual county acres burned was subtracted from the USFWS projected 

acreage. A 0.8 factor was applied to the difference to account for blackened acres instead of the 

total perimeter acres that were reported. The revised total additional USFWS acreage was then 

added to the total county “typical” acreage to determine future acreage burned for 2009 and 

2018. 

MACTEC then allocated the increased acreage to current modeling days. The average daily 

acres burned data provided by USFWS per NWR/county was used to allocate the acreage to the 

correct number of days required to burn all of the acres. Guidance supplied by USFWS 

indicated that up to three times the average daily acres burned could potentially be allocated to 

any one day. 

For the USFS fire-by-fire acreage estimates, MACTEC summed the USFS data at a county-level 

for States that had permit programs, then added the sum to the typical acreage and allocated the 

acres to current modeling days. For States that do not have a State prescribed fire permit 

program, MACTEC simply replaced the current fire-by-fire records in the database with fire-byfire records from the USFS and recalculated emissions based on fuel model and fuel loading. 

VISTAS also applied the same 0.8 correction for blackened acres applied to all USFS supplied 

acreage as the supplied values represented perimeter acres. 

An additional problem with developing year-specific prescribed fire projections was how to 

adequately capture the temporal profile for those fires. In the 2002 actual fire inventory, fires 

occur on same days as state/FLM records. In the 2002 “typical” year inventory, fire acreage 

increased or decreased from acreage on the same fire days as were in the 2002 actual inventory, 

since the acres were simply increased for each day based on a multiplier used to convert from 

actual to typical. 

When prescribed fires acreage was added to a future year, MACTEC added acreage to individual 

fire days proportional to the annual increase (if acreage on a day is 10 percent of annual, add 10 

percent of projected increase to that same day). 

Appendix D.2b - 24 

Appendix D - 84
6.0 2009 AND 2018 NONROAD SOURCE EMISSION INVENTORY 

DEVELOPMENT 

The subsections that follow describe the projection process used to develop 2009 and 2018 

nonroad projection estimates for sources found in the NONROAD model and those sources 

estimated outside of the model (locomotives, airplanes and commercial marine vessels). 

6.1 Projection of NONROAD Model Sources 

NONROAD model input files were prepared based on the 2002 base year inventory input files 

with appropriate updates for the projection years. Other specific updates for the projection years 

for NONROAD model sources consist of: 

1. Revise the emission inventory year in the model (as well as various output file naming 

commands) to be reflective of the projection year. 

2. Revise the fuel sulfur content for gasoline and diesel powered equipment. 

3. Implement a limited number of local control program changes (national control 

program changes are handled internally within the NONROAD model, so explicit input 

file changes are not required). 

All equipment population growth and fleet turnover impacts are handled internally within the 

NONROAD model, so that explicit input file changes are not required. 

The final NONROAD2005c that was used for inventory development is capable of handling 

separate diesel fuel sulfur inputs for land-based and marine-based nonroad mobile source 

equipment in a single model execution. The following diesel fuel sulfur values were used: 

Diesel S (ppm) 2002 2009 2018 

Land-Based 2500 348 11 

Marine-Based 2638 408 56 

6.2 Projection of Non-NONROAD Model Sources 

Using the 2002 base year emissions inventory for aircraft, locomotives, and CMV prepared as 

described earlier in this document, corresponding emission projections for 2009 and 2018 were 

developed. The following description is largely taken from the MACTEC document titled 

Appendix D.2b - 25 

Appendix D - 85
Documentation of the Base G 2002 Base Year, 2009 and 2018 Emission Inventories for VISTAS. 

Briefly, the methodology relies on growth and control factors developed from inventories used in 

support of recent USEPA rulemakings, and consists of the following steps: 

(a) Begin with the 2002 base year emission estimates for aircraft, locomotive, and CMV. 

(b) Detailed inventory data (both before and after controls) for these same emission sources 

for 1996, 2010, 2015, and 2020 were obtained from the USEPA's CAIR Technical 

Support Document. Using these data, combined growth and control factors for the period 

2002-2009 and 2002-2018 were estimated using straight line interpolation between 1996 

and 2010 (for 2009) and 2015 and 2020 (for 2018). This is done at the 

State-county-SCC-pollutant level of detail. 

(c) The USEPA growth and control data are matched against the 2002 VISTAS base year 

data using State-county-SCC-pollutant as the match key. Ideally, there would be a 

one-to-one match and the process would end at this point. Unfortunately, actual match 

results were not always ideal, so additional matching criteria were required. For 

subsequent reference, this initial (highest resolution) matching criterion is denoted as the 

“CAIR-Primary” criterion. 

(d) A second matching criterion is applied that utilizes a similar, but higher-level SCC (lower 

resolution) matching approach. For example, SCC 2275020000 (commercial aircraft) in 

the 2002 base year inventory data would be matched with SCC 2275000000 (all aircraft) 

in the CAIR data. This criterion is applied to records in the 2002 base year emissions file 

that are not matched using the “CAIR-Primary” criterion, and is also performed at the 

State-county-SCC-pollutant level of detail. For subsequent reference, this is denoted as 

the “CAIR-Secondary” criterion. At the end of this process, a number of unmatched 

records continued to remain, so a third level matching criterion was required. 

(e) In the third matching step, the most frequently used SCC in the USEPA CAIR files for 

each of the aircraft, locomotive, and commercial marine sectors is averaged at the State 

level to produce a “default” State and pollutant-specific growth and control factor for the 

sector. The resulting factor is used as a “default” growth factor for all unmatched 

county-SCC-pollutant level data in each State. In effect, State-specific growth data are 

applied to county level data for which an explicit match between the VISTAS 2002 base 

year data and the USEPA CAIR data could not be developed. The default growth and 

control SCCs are 2275020000 (commercial aircraft) for the aircraft sector, 2280002000 

(commercial marine diesel total) for the CMV sector, and 2285002000 (railroad 

Appendix D.2b - 26 

Appendix D - 86
equipment diesel total) for the locomotive sector. Matches made using this criterion are 

denoted as “CAIR-Tertiary” matches. 

(f) According to USEPA documentation, the CAIR baseline emissions include the impacts 

of the (then proposed) Tier 4 (T4) nonroad mobile diesel rulemaking, which implements 

a low sulfur fuel requirement that affects both future CMV and locomotive emissions. 

However, the impacts of this rule were originally intended to be excluded from the initial 

VISTAS 2018 forecast, which was to include only “on-the-books” controls. (The T4 rule 

was finalized subsequent to the development of the preliminary 2018 inventory in March 

of 2004.) Given its final status, T4 impacts have now been moved into the “on the 

books” inventory for nonroad mobile source equipment. In addition, since there are no 

other proposed rules affecting the nonroad mobile source sector between 2002 and 2018, 

there is no difference between the 2018 “on the books” and 2018 “on the way” 

inventories for the sector; so that only a single forecast inventory (for each evaluation 

year) was developed. Nevertheless, since the algorithms developed to produce the 

VISTAS forecasts were developed when there was a distinction between the “on the 

books” and “on the way” inventories, the distinct algorithms used to produce the two 

inventories have been maintained even though the conceptual distinctions have been lost. 

This approach was taken for two reasons. First, it allowed the previously developed 

algorithms to be utilized without change. Second, it allowed for separate treatment of the 

T4 emissions impact, which was important as those impacts have changed between the 

proposed and final T4 rules. Thus, previous USEPA inventories that include the 

proposed T4 impacts would not be accurate. Therefore, the procedural discussion 

continues to reflect the distinctions between non-T4 and T4 emissions, as these 

distinctions continue to be intrinsically important to the forecasting process. Therefore, a 

second set of USEPA CAIR files that excluded the Tier 4 diesel impacts was obtained 

and the same matching exercise described above in steps (b) through (e) was performed 

using these “No T4” files. It is important to note that the matching exercise described in 

steps (b) through (e) cannot simply be replaced because the “No T4” files obtained from 

the USEPA include only those SCCs specifically affected by the T4 rule (i.e., diesel 

CMV and locomotives). So in effect, the matching exercise was augmented (rather than 

replaced) with an additional three criteria analogous to those described in steps (c) 

through (e), and these are denoted as the “No T4-Primary,” “No T4-Secondary,” and “No 

T4-Tertiary” criteria. Because they exclude the impacts of the proposed T4 rule, matches 

using the “No T4” criteria supersede matches made using the basic CAIR criteria (as 

described in steps (c) through (e) above). The CAIR matching criteria were overridden 

for any record for which States provided local growth data. 

Appendix D.2b - 27 

Appendix D - 87
 (h) Using this approach, each State-county-SCC-pollutant was assigned a combined growth 

and control factor using the USEPA CAIR forecast or locally provided data. The 22,838 

data records for aircraft, locomotives, and CMV in the 2002 base year emissions file were 

assigned growth factors in accordance with the following breakdown: 

48 records matched State-provided growth factors, 

 4,179 records matched using the CAIR-Primary criterion, 

240 records matched using the CAIR-Secondary criterion, 

 7,463 records matched using the CAIR-Tertiary criterion, 

720 records matched using the No T4-Primary criterion, 

 3,858 records matched using the No T4-Secondary criterion, and 

 6,330 records matched using the No T4-Tertiary criterion. 

(i) Finally, the impacts of the T4 rule as adopted were applied to the grown “non T4” 

emission estimates. The actual T4 emission standards do not affect aircraft, locomotive, 

or CMV directly, but associated diesel fuel sulfur requirements do affect locomotives and 

CMV. Lower fuel sulfur content affects both SO2 and PM emissions. Expected fuel 

sulfur contents were obtained for 2009 and 2018 from the USEPA technical support 

document for the final T4 rule (Final Regulatory Analysis: Control of Emissions from 

Nonroad Diesel Engines, EPA420-R-04-007, May 2004). According to that document, 

the average diesel fuel sulfur content for locomotives and CMV is expected to be 408 

parts per million by weight (ppmW) in 2009 and 56 ppmW in 2018. These compare to 

expected non-T4 fuel sulfur levels of 2599 ppmW in 2009 and 2336 ppmW in 2018. 

Table 6.2-2 uses calculated emissions estimates for base and T4 control scenarios to 

estimate emission reduction impacts. 

Table 6.2-2 Estimated Emission Reduction Impacts based on T-4 Rule 

2009 2018

CMV SO2 = Non-T4 SO2 × 0.1569 0.0241

Locomotive SO2 = Non-T4 SO2 × 0.1569 0.0241

CMV PM = Non-T4 PM × 0.8962 0.8762

Locomotive PM = Non-T4 PM × 0.8117 0.7734

However, since the diesel fuel sulfur content assumed for the 2002 VISTAS base year 

inventory, upon which the 2009 and 2018 inventories were based, is 2500 ppmW, a small 

adjustment to the emission reduction multiplier calculated from the T4 rule is appropriate 

since they are measured relative to modestly different sulfur contents (2599 ppmW for 

2009 and 2336 ppmW for 2018). Correcting for these modest differences produces the 

Appendix D.2b - 28 

Appendix D - 88
emission reduction impact estimates relative to forecasts based on the VISTAS 2002 

inventory shown in Table 6.2-3. 

Table 6.2-3 Estimated Emission Reduction Impacts Relative to VISTAS 2002 Base 

Year Values 

2009 2018

CMV SO2 = Non-T4 SO2 × 0.1632 0.0225

Locomotive SO2 = Non-T4 SO2 × 0.1632 0.0225

CMV PM = Non-T4 PM × 0.9004 0.8685

Locomotive PM = Non-T4 PM × 0.8187 0.7610

These factors were applied directly to the non-T4 emission forecasts to produce the final 

VISTAS 2009 and 2018 emissions inventories for aircraft, locomotive, and CMV. 

During the development of the preliminary 2018 VISTAS inventory in March 2004, this process 

yielded reasonable results and exhibited no particular systematic concerns. However, when the 

2009 Base F inventory was developed, significant concerns related to SO2 and PM were 

encountered. Essentially, what was revealed by the Base F 2009 forecast was a series of 

apparent inconsistencies in the CAIR 2010 and 2015 emission inventories (as compared to the 

1996 and 2020 CAIR inventories) that were masked during the construction of the “longer-term” 

2018 inventory. 

For the most part, the issue seems to be centered on SO2 and PM records, which are those 

records primarily affected by the T4 rule. But, as noted above, there does not seem to be any 

pattern of consistency that would indicate that either inclusion or exclusion of T4 rule impacts is 

the underlying cause. Moreover, where they occur, the observed growth extremes generally 

affect both SO2 and PM equally, while one would expect PM effects to be buffered if the T4 rule 

was the underlying cause, since changes in diesel fuel sulfur content will only affect a fraction of 

PM (i.e., sulfate), while directly reducing SO2. 

While forecast inventories for aircraft, locomotives, and CMV were developed for 2009 and 

2018 using both growth methods, it was ultimately decided to utilize the 1996-2020 growth basis 

since it provided more reasonable growth rates for 2009. 

Appendix D.2b - 29 

Appendix D - 89
Appendix D.2b - 30 

7.0 QUALITY ASSURANCE OF 2009 AND 2018 EMISSIONS 

INVENTORY 

For the 2009 and 2018 inventories developed by VISTAS and MACTEC, Inc., specific QA 

procedures were followed. These procedures are outlined in the document titled Documentation 

of the Base G 2002 Base Year, 2009, and 2018, Emission Inventories for VISTAS prepared for 

VISTAS by MACTEC, Inc. Throughout the inventory development process, quality assurance 

steps were performed to ensure that no double counting of emissions occurred, to ensure that a 

full and complete inventory was developed for VISTAS, and to make sure that projection 

calculations were working correctly. Quality assurance was an important component to the 

inventory development process and MACTEC performed the following QA steps on the 

stationary and agricultural area source, and the nonroad mobile source components of the 2009 

and 2018 emission inventories: 

1. All final files were run through EPA’s Format and Content checking software. 

2. SCC level emission summaries were prepared and evaluated to ensure that emissions 

were consistent and that there were no missing sources. 

3. Tier comparisons (by pollutant) were developed between the 2002 base year 

inventory and the 2009 and 2018 projection inventories. In addition, total VISTAS 

pollutant summaries were prepared to compare total emissions by pollutant between 

earlier and later versions of the inventory. 

4. Data product summaries were provided to both the VISTAS Emission Inventory 

Technical Advisor and to the SIWG representatives for review and comment. 

Changes based on these comments were implemented in the files. 

5. Version numbering was used for all inventory files developed. The version 

numbering process used a decimal system to track major and minor changes. For 

example, a major change would result in a version going from 1.0 to 2.0. A minor 

change would cause a version number to go from 1.0 to 1.1. Minor changes resulting 

from largely editorial changes would result in a change from 1.00 to 1.01. 

Appendix D - 90
Appendix D.2c 

On-Road Mobile Source 

Emissions Inventory Documentation 

Appendix D - 91
Appendix D.2.c - 1

Appendix D - 92
Table of Contents 

1.0 Introduction and Scope ................................................................................................. 4 

2.0 Overall Methodology.................................................................................................... 4 

2.1 Emissions Estimate Approach ............................................................................ 4 

2.2 Mobile6.2 Input Assumptions............................................................................. 5 

2.2.1 Speed Assumptions............................................................................................ 5 

2.2.2 Vehicle Age Distribution ............................................................................ 6 

2.2.3 Vehicle Mix Assumptions.................................................................................. 6 

2.2.4 Temperature, Relative Humidity, and Barometric Pressure Assumptions........ 6 

2.2.5 Reid Vapor Pressure .......................................................................................... 6 

2.3 On-road Mobile Vehicle Miles Traveled (VMT) Assumptions ..................... 7 

3.0 Quality Assurance Measures................................................................................... 7 

Appendix D.2.c - 2

Appendix D - 93
1.0 Introduction and Scope 

The regional haze modeling was done by the Southeast Regional Planning Organization, 

Visibility Improvement State and Tribal Association of the Southeast (VISTAS). 

VISTAS is run by the ten Southeast states (Alabama, Florida, Georgia, Kentucky, 

Mississippi, North Carolina, South Carolina, Tennessee, Virginia and West Virginia) and 

the local programs and tribal agencies located within these states. The regional haze 

modeling uses an annual simulation, therefore the emission estimates for all pollutants of 

concern for regional haze were developed for an annual modeling run. 

 On-road mobile sources are considered those vehicles that travel on the roadways. 

Emissions from motor vehicles occur throughout the day while the vehicle is in motion, 

at idle, parked, and during refueling. All of these emissions processes need to be 

estimated in order to properly reflect the total emissions from this source category. 

The US Environmental Protection Agency (USEPA) developed the MOBILE model to 

estimate emission factors based on information on the way vehicles are driven in a 

particular area. The newest version of the MOBILE model, MOBILE6.2, was used. In 

2004, MOBILE6.2 was incorporated into SMOKEv2.1 which was used in the VISTAS 

regional haze modeling. Key inputs for MOBILE6.2 include information on the age of 

vehicles on the roads, the average speed of those vehicles, what types of roads those 

vehicles are traveling on, any control technologies in place in an area to reduce emissions 

for motor vehicles (e.g., emissions inspection programs) and ambient temperature. 

On-road mobile source emissions are very small contributors to visibility degradation in 

Class I areas in the VISTAS region. Despite playing a small role in visibility 

impairment, on-road mobile emissions were generated following the same guidelines and 

methods as those applied to support ozone and fine particulate attainment demonstrations. 

2.0 Overall Methodology 

2.1 Emissions Estimate Approach 

On-road mobile sources were estimated using the U. S. Environmental Protection Agency 

(USEPA) highway mobile model MOBILE6.2. Mobile source emissions are estimated 

by the methodologies suggested in the USEPA document Emissions Inventory Guidance 

for Implementation of Ozone and Particulate Matter National Ambient Air Quality 

Standards (NAAQS) and Regional Haze Regulations. The estimation of emissions from 

Appendix D.2.c - 3

Appendix D - 94
on-road mobile sources, like area sources, involves multiplying an activity level (e.g. 

vehicle miles traveled (VMT) by vehicle type and road type) by an emission factor 

(grams/mile for a given vehicle type and road type). 

The MOBILE6 module of SMOKE was used to develop the on-road mobile source 

emissions estimates for carbon monoxide (CO), nitrogen oxides (NOx), ammonia (NH3), 

fine particulate matter (PM2.5), sulfur dioxide (SO2), and volatile organic compound 

(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 

(MM5). 

Further, the MOBILE6 emissions factors model accounts for the following: 

• Hourly and daily minimum/maximum temperatures, relative humidity and 

barometric pressure; 

• 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). 

2.2 Mobile6.2 Input Assumptions 

2.2.1 Speed Assumptions 

Emissions from motor vehicles vary with the manner in which the vehicle is operated. 

Vehicles traveling at 65 miles per hour (mph) emit a very different mix of pollutants than 

the car that is idling at a stoplight. South Carolina does not have state-specific speed data 

and therefore used default speed assumptions built into the MOBILE6 model. 

Appendix D.2.c - 4

Appendix D - 95
2.2.2 Vehicle Age Distribution 

Emissions vary with the age of a vehicle. Older vehicles have typically been driven more 

miles and have experienced more deterioration in the emission control systems. Also, 

older vehicles do not meet the newer, more stringent emissions standards. South 

Carolina does not have state specific data and therefore used default vehicle age 

distributions built into the MOBILE6 model. 

2.2.3 Vehicle Mix Assumptions 

The vehicle mix refers the percentage of different vehicle types on each of the twelve 

(12) FHWA road types. These road types are listed below in Table D.2c-1. It is critical 

for estimating mobile emissions in an area to use data that accurately reflects the vehicles 

traveling on each of these different road types. South Carolina does not have statespecific data and therefore used default vehicle mix data built into the MOBILE6 model. 

Table D.2c-1: Functional Road Classes in South Carolina 

RI Rural Interstate UI Urban Interstate 

RPA Rural Other Principle Arterial UF Urban Freeway & Expressway 

RMA Rural Minor Arterial UPA Urban Other Principal Arterial 

RMjC Rural Major Collector UMiA Urban Minor Arterial 

RMiC Rural Minor Collector UC Urban Collector 

RL Rural Local UL Urban Local 

2.2.4 Temperature, Relative Humidity, and Barometric Pressure Assumptions 

MOBILE6.2 emission factors used by SMOKE are significantly influenced by 

temperature and to a lesser extent, humidity. The most desirable approach from an 

accuracy standpoint is to model on-road mobile emissions using gridded, temporalized 

data from the meteorological model. The VISTAS on-road mobile inventories were 

developed using this approach. 

2.2.5 Reid Vapor Pressure 

South Carolina has an RVP of 9.0 psi for all counties during May-September, as 

indicated in EPA’s Guide on Federal and State Summer RVP Standards for Conventional 

Gasoline Only (EPA420-B-05-012 November 2005). For remaining months, RVPs 

follow the ASTM D4814 Standard Specification for Automotive Spark-Ignition Engine 

Fuel. They are as follows: 

Appendix D.2.c - 5

Appendix D - 96
• October RVP = 13.5 psi statewide 

• November RVP = 13.5 psi statewide 

• December RVP = 13.5 psi statewide 

• January RVP = 13.5 psi statewide 

• February RVP = 13.5 psi statewide 

• March RVP = 13.5 psi statewide 

• April RVP = 13.5 psi statewide 

2.3 On-road Mobile Vehicle Miles Traveled (VMT) Assumptions 

In order to calculate emissions from on-road mobile sources, emission factors are 

developed as discussed throughout this document. The emission factors are then 

multiplied by an activity level, which is VMT for on-road mobile sources. 

For all counties in South Carolina, the 2002 VMT was derived from the 2002 Highway 

Performance Maintenance System (HPMS) data provided by the SCDOT. The South 

Carolina 2018 VMT was projected by the SCDOT. 

3.0 Quality Assurance Measures 

The Quality Assurance (QA) of the data is one of the most important steps in performing 

an air quality modeling study. Because emissions inventory development is tedious, time 

consuming and involves complex manipulation of many different types of large data sets, 

errors are frequently made and, if rigorous QA measures are not in place, these errors 

may remain undetected. For the on-road mobile source category, QA can be broken into 

two components: (1) input files/data, and (2) SMOKE outputs/ summaries. On-road 

mobile input data QA is summarized below. SMOKE output QA is summarized in 

Appendix D.4a of this document for all source categories. 

South Carolina on-road mobile input data, such as speeds and vehicle mix, came from the 

default values built into MOBILE6 and used the same assumptions as the CERR. Since 

much of South Carolina’s on-road mobile inputs were default, we put more emphasis on 

reviewing the resulting emissions for reasonableness. 

Upon completion of the modeling, VISTAS supplied the emissions output summary files 

to the SCDHEC. The modeling was an iterative process. To ensure the latest 

Appendix D.2.c - 6

Appendix D - 97
information was incorporated into the current modeling effort, the latest versions of the 

county emissions were compared to previous draft versions of the county emissions. 

Appendix D.2.c - 7

Appendix D - 98
Appendix D.3 

Emissions Inventory Summaries 

Appendix D - 99
Appendix D.3 - 1

Appendix D - 100
Table 1. Typical 2002 BaseG2 Annual Emission Summary for South Carolina Counties (t

 Point Non-road Area (includes Fires) 

County VOC NOx SO2 PM-10 PM-2.5 NH3 VOC NOx SO2 PM-10 PM-2.5 NH3 VOC NOx SO2 PM-10 PM-2.5 NH3 VOAbbeville 137.0 54.6 119.7 9.6 8.6 0.0 235.0 325.7 26.3 21.4 20.3 0.2 1642.2 214.7 95.7 2980.9 680.4 278.5 626Aiken 1156.2 5732.2 11009.2 1502.2 1087.4 10.3 1081.7 1339.8 94.0 87.1 83.1 1.1 4949.0 769.9 717.1 8112.8 1648.5 1404.5 4369Allendale 112.3 25.1 47.6 28.6 16.8 0.0 153.5 388.2 28.7 19.8 18.6 0.1 765.9 184.1 46.4 2102.4 823.4 217.3 304Anderson 1447.6 2995.3 10540.1 1192.5 853.9 6.1 1721.1 1101.0 92.0 107.8 103.2 1.2 7457.0 869.0 648.9 11959.1 1989.4 998.1 5107Bamberg 12.8 9.3 7.1 7.8 4.6 0.0 166.8 202.3 17.0 15.9 15.1 0.1 819.3 118.3 32.3 2340.4 551.9 231.0 484Barnwell 221.2 73.2 68.2 16.8 15.2 0.0 302.3 472.6 32.3 22.4 21.1 0.2 1270.5 256.5 118.3 2521.6 922.5 178.8 634Beaufort 38.9 301.1 203.5 48.8 32.8 0.0 5382.7 2203.9 232.7 391.2 375.5 2.3 3702.9 726.0 148.9 5673.8 1542.9 79.9 2879Berkeley 2153.4 31443.9 66339.9 7284.4 5909.3 80.2 3951.0 3479.5 429.6 217.6 202.2 1.2 5776.1 1274.6 324.7 9292.5 3382.0 282.8 3673Calhoun 104.3 33.8 5.6 24.2 13.8 860.0 336.3 214.4 18.2 17.7 16.8 0.1 820.1 119.0 18.8 3645.4 769.8 210.1 1002Charleston 2521.3 2632.3 3383.9 639.0 538.9 173.8 6491.7 8694.4 1108.8 642.6 606.5 3.0 10432.5 1615.0 1086.3 8141.9 1833.8 176.6 9046Cherokee 432.8 534.6 788.0 232.7 146.0 0.0 327.7 797.9 52.5 35.0 32.8 0.3 2447.9 283.1 113.1 4167.6 704.0 304.2 1944Chester 705.6 2021.1 415.0 183.5 158.6 0.0 302.5 542.2 38.9 29.6 28.0 0.2 2174.8 229.0 194.3 3782.8 676.0 451.9 1425Chesterfield 590.7 66.2 2.9 24.9 19.4 0.0 397.2 325.7 23.1 25.3 24.1 0.3 3338.0 415.3 155.7 5120.2 1437.9 1011.1 1336Clarendon 66.5 0.2 0.0 3.4 2.6 0.0 2304.9 438.0 41.7 67.6 63.5 0.6 1934.2 276.2 125.2 5464.1 1365.6 1420.1 1511Colleton 313.8 4915.9 20760.7 1289.0 974.3 0.0 870.6 659.9 50.9 41.1 38.5 0.3 2811.6 665.9 127.8 6346.6 2495.3 274.3 2064Darlington 1389.7 5888.9 12311.3 1257.7 935.6 11.2 397.3 357.0 29.9 33.2 32.0 0.4 3117.1 405.0 412.2 8362.8 1546.1 521.6 198Dillon 70.9 36.4 1.5 2.6 2.0 0.0 157.3 563.1 43.9 32.5 30.9 0.2 1907.5 192.3 101.1 4190.6 936.2 1603.9 1383Dorchester 407.8 3864.9 3785.6 739.6 510.2 7.8 581.0 716.7 66.2 62.0 59.3 0.5 3328.6 545.6 136.8 4913.2 1164.8 538.7 2646Edgefield 351.7 38.8 0.0 0.6 0.5 0.0 195.2 279.5 21.6 16.9 16.0 0.1 1147.5 180.7 76.5 2504.1 621.6 304.9 575Fairfield 102.1 177.0 18.9 35.0 26.4 0.0 648.5 449.3 32.7 28.0 26.2 0.2 1463.2 174.4 54.6 2806.9 596.5 464.9 1080Florence 2175.1 3122.7 4181.0 791.5 641.9 61.0 917.2 1189.7 91.8 84.5 80.5 0.8 5994.9 712.7 370.5 9179.2 1819.2 1113.7 4142Georgetown 2075.1 24121.4 40287.7 5781.4 4780.2 50.8 2154.5 1148.2 134.1 90.4 84.8 0.8 2761.4 564.1 290.2 5625.4 1788.6 192.1 1838Greenville 3048.7 732.9 747.0 212.9 146.7 0.0 3158.8 3241.9 282.5 292.8 280.8 3.0 16198.5 1716.9 1894.6 16231.2 2927.7 195.7 1013Greenwood 303.7 322.8 54.0 441.9 294.5 0.0 541.5 873.3 60.1 45.3 42.8 0.5 2989.6 357.2 319.3 5675.4 920.4 261.5 1581Hampton 3153.3 132.0 168.6 165.6 111.4 0.0 236.3 431.8 32.2 23.2 21.8 0.1 1679.8 327.7 82.2 3724.4 1378.9 183.5 746Horry 302.2 3418.5 10488.9 631.4 529.0 0.0 3058.7 2225.6 247.9 269.0 258.0 2.1 6364.9 978.6 297.6 10395.0 2139.6 916.7 7590Jasper 14.7 32.7 5.3 22.0 13.4 0.0 639.6 470.9 37.8 31.2 29.2 0.2 1501.0 468.6 112.1 3908.7 1802.1 97.1 1939
 Point Non-road Area (includes Fires) 

County VOC NOx SO2 PM-10 PM-2.5 NH3 VOC NOx SO2 PM-10 PM-2.5 NH3 VOC NOx SO2 PM-10 PM-2.5 NH3 VOKershaw 325.2 1019.3 3345.3 267.9 184.3 0.0 745.3 327.4 28.1 34.4 32.6 0.4 2492.9 312.6 87.2 4954.8 923.2 1580.5 178Lancaster 134.4 1246.9 2548.8 263.1 221.8 0.0 493.9 429.1 35.0 33.4 31.6 0.3 1804.5 333.2 152.4 5951.9 944.0 1069.5 171Laurens 288.3 131.2 43.3 126.5 86.5 0.0 519.8 766.4 55.3 41.6 39.1 0.3 2849.9 398.1 317.1 5996.3 1079.0 746.9 216Lee 255.3 7.9 1.3 1.3 1.2 0.0 138.8 161.2 17.1 19.4 18.7 0.1 1295.8 172.7 44.3 4204.5 979.9 617.5 83Lexington 1718.7 4791.9 14373.4 1426.9 1300.4 0.0 3122.0 1749.1 179.3 201.1 191.8 1.5 7494.0 1083.6 392.8 10659.1 1874.8 1540.6 706McCormick 88.4 0.0 0.0 78.3 54.3 0.0 838.3 240.3 20.8 24.0 22.4 0.2 645.2 105.8 21.6 1657.0 442.8 203.9 321.8 Marion 89.2 13.5 2.8 11.5 8.2 0.0 265.7 515.8 37.4 26.6 25.0 0.2 1876.6 235.5 127.2 3342.2 692.7 284.7 113Marlboro 867.1 1400.4 665.3 469.0 350.3 0.0 247.5 436.1 33.8 28.9 27.5 0.2 1575.4 186.3 125.5 4245.3 857.9 638.6 83Newberry 1041.6 182.0 4.3 375.4 301.1 0.0 636.0 580.3 45.4 38.9 36.7 0.3 2001.7 259.2 146.0 3925.7 827.0 1584.5 164Oconee 302.0 88.1 9.3 43.5 32.4 0.5 1674.0 785.6 65.2 72.8 68.7 0.7 4240.7 414.9 123.5 6542.1 1150.2 1265.7 1787Orangeburg 1654.6 7864.7 6024.7 2510.3 1897.1 155.1 996.5 852.5 67.5 67.7 64.4 0.6 5746.1 605.4 170.3 11173.3 2313.9 1498.3 377Pickens 154.1 206.8 432.4 91.0 63.6 0.0 1396.6 853.8 72.3 81.2 76.8 0.7 5514.3 625.9 422.8 8610.4 1453.1 104.8 2396Richland 1886.2 11943.0 41234.4 5237.9 3768.8 100.0 2678.2 3002.4 286.3 260.4 248.9 2.4 10942.9 1564.8 1396.4 9049.2 2016.8 211.6 919Saluda 51.8 20.9 2.7 46.5 31.7 0.0 340.8 185.0 16.9 18.5 17.5 0.1 1291.3 138.4 50.4 2480.2 509.6 1371.6 56Spartanburg 2615.1 4394.9 204.6 331.8 270.6 0.0 1713.9 2666.2 229.0 191.6 183.5 1.8 11597.7 1300.1 1302.0 16505.1 2680.6 317.0 7622Sumter 1007.2 269.3 121.0 65.3 50.2 0.2 744.8 678.2 56.3 55.2 52.9 0.6 4590.3 596.5 257.1 7864.3 1617.2 779.0 264Union 134.5 245.9 554.4 76.8 22.8 0.2 158.4 523.6 34.8 19.5 18.1 0.1 1691.5 244.9 160.4 3291.0 749.1 74.1 70Williamsburg 92.8 80.1 15.0 70.3 62.2 5.2 213.3 572.3 46.5 34.2 32.5 0.2 3528.5 568.1 152.5 7859.2 2580.5 675.3 104York 2811.7 4046.7 8465.3 1905.5 1284.4 30.0 1381.5 1791.9 171.5 151.4 145.2 1.4 5690.4 815.7 432.2 9680.8 1644.2 596.7 468
Appendix D.4a

Emissions Modeling Documentation 

Appendix D - 103
Appendix D.4.a -1

Appendix D - 104
Table of Contents 

1.0 Background............................................................................................................. 4 

1.1 Temporal Allocation ........................................................................................... 4 

1.2 Speciation............................................................................................................ 5 

1.3 Spatial Allocation................................................................................................ 6 

1.4 Development of Gridded Surrogate Files........................................................... 7 

1.5 Treatment of Large Fire Plume Rise................................................................... 8 

2.0 Quality Assurance................................................................................................... 8 

2.1 Additional State QA Procedures....................................................................... 12 

Appendix D.4.a -2

Appendix D - 105
1.0 Background 

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 the fastest emissions processing tool currently 

available to the air quality modeling community. The sparse matrix approach utilized 

throughout SMOKE permits both rapid and flexible processing of emissions data. The 

processing is rapid because SMOKE utilizes a series of matrix calculations instead of less 

efficient algorithms used in previous systems. The processing is flexible because the 

processing steps of temporal projection, controls, chemical speciation, temporal 

allocation, and spatial allocation have been separated into independent operations 

wherever possible. The results from these steps are merged together at a final stage of 

processing. Each of these emissions processing steps are detailed below. 

1.1 Temporal Allocation 

VISTAS 2002, 2009 and 2018 annual emissions modeling was 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. Point and biogenic sources were modeled for each day of the annual period while 

area and nonroad sources were modeled as a block of Thursday, Friday, Saturday, 

Sunday, Monday, one per month (total of 60 days modeled). 

VISTAS based its temporal profiles and source category cross-reference files on the 

USEPA CAIR/CAMR/CAVR modeling platform with files located on USEPA’s CAIR 

file transfer website (ftp://www.airmodelingftp.com/). Modifications were made to reflect 

State specific profiles or updated state of knowledge application of these profiles. 

Additionally, EGU CEM-based temporal profiles and onroad emissions modeling were 

prepared in manners deviating from USEPA’s original CAIR platform. 

New temporal profiles used by VISTAS during modeling of the 2002, 2009 and 2018 

EGU emissions took the place of USEPA provided default temporal profiles that are 

generally accepted as not accurately depicting temporal distribution of emissions from 

EGUs in the U.S. (see Attachment 2 in Appendix D.5 by Stella et. al.). VISTAS EGU 

temporal profiles were developed using hourly CEM data as reported to USEPA’s Clean 

Air Market’s Division (CAMD) for the Acid Rain Program. 

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 

Appendix D.4.a -3

Appendix D - 106
profiles were developed for application to non-CEM matched units in the VISTAS 

emissions inventory. These temporal distributions represent a significant improvement 

over the USEPA defaults. 

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. The 

modeled weeks were selected from mid-month, avoiding inclusion of major holidays. 

Holidays were modeled as the Sunday of the representative week, while the day after a 

holiday was modeled as a Monday. VISTAS executed sensitivity tests to examine this 

“representative week” methodology versus an everyday on-road mobile modeling 

method. VISTAS determined that the use of representative week on-road 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. For more information on this study see 

Appendix D.4c by Abraczinskas et. al. 

On-road mobile emissions are represented by the following weeks per month:

 January 15-21 

 February 12-18 

 March 12-18 

 April16-22 

 May 14-20 

 June 11-17 

 July 16-22 

August 13-19 

September 17-23 

October 15-21 

November 12-18 

December 17-23 

1.2 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 

Appendix D.4.a -4

Appendix D - 107
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 USEPA’s CAIR/CAVR/CAMR modeling platform with files located on 

USEPA’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.”

1.3 Spatial Allocation 

Because air quality modeling strives to replicate the actual physical and chemical 

processes that occur in an inventory domain, it is important that the physical location of 

emissions be determined as accurately as possible. In an ideal situation, the physical 

location of all emissions would be known exactly. In reality, however, the spatial 

allocation of emissions in a modeling inventory only approximates the actual location of 

emissions. 

Gridding surrogates are used to spatially allocate emission sources from a coarse 

geographic area to finer grid cells used for modeling. There can be hundreds of unique 

source categories in an emissions inventory, which is typically developed for counties, 

states, or other areas. The exact location of most major emission sources is known and 

their geographic coordinates are usually contained in the inventory. These usually are 

referred to as major point sources and include electric utilities and major industrial 

facilities. However, other emission sources are estimated for 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 

USEPA (http://www.epa.gov/ttn/chief/emch/spatial/newsurrogate.html) and converted to 

the gridded domain definitions of the VISTAS model requirements.

Appendix D.4.a -5

Appendix D - 108
1.4 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 (36/12km) 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 the 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) 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. 

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. 

Appendix D.4.a -6

Appendix D - 109
For identified counties having no values for each surrogate, we assign the data 

based on the appropriate secondary or tertiary surrogate to these counties. A 

check to see that surrogate ratios for each county sum to approximately 1.00 is 

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. 

1.5 Treatment of Large Fire Plume Rise 

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 to calculate 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 v2.1 can model fire plume rise when provided with the following variables: 

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

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

• 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 Report (see 

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

.pdf) has documented an approach for calculating these plume descriptors. In this 

method, 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 2.1 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. 

2.0 Quality Assurance 

[This section is from the preliminary VISTAS modeling protocol... at 

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

Appendix D.4.a -7

Appendix D - 110
The Quality Assurance (QA) is one of the most important steps in performing an air 

quality modeling study. Because emissions inventory development is tedious, time 

consuming and involves complex manipulation of many different types of large data sets, 

errors are frequently made and, if rigorous QA measures are not in place, these errors 

may remain undetected. 

A number of QA files were 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. Some of the additional steps for checking the emissions are 

summarized in the bulleted list below. 

• 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. 

• Producing pie charts emission summaries that highlight the contribution of each 

emissions source component (e.g. nonroad mobile). 

• 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. 

State inventory summaries prepared prior to the emissions processing were compared 

against SMOKE output report totals generated after each major step of the emissions 

generation process. 

For speciation, a comparison of the inventory state totals versus the same state totals with 

the speciation matrix applied was completed. 

For checking the vertical allocation of the emissions, reports by source, hour, and layer 

for randomly selected states in the domain were created. These reports were created for a 

representative weekday in each of the episodes for each of these selected states. 

The quantitative QA analyses often reveal significant deficiencies in the input data or the 

model setup. It may become necessary to tailor these procedures to track down the 

source of each major problem. As such, we will only outline the basic quantitative QA 

steps that were 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 

o (example provided in Table 1 below for Area sources) 

• 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 

Appendix D.4.a -8

Appendix D - 111
• 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 

• If elevated source selection is chosen by user, the report indicating which sources 

have been selected as elevated and plume-in-grid will be included. 

• 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

Appendix D.4.a -9

Appendix D - 112
Table 1. Example of a State totals report for Area Sources 

Stationary area

Processed as Area sources

Base inventory year 2018

No gridding matrix applied

No speciation matrix applied

Temporal factors applied for episode from

 Thursday July 18, 2002 at 000000 to

 Thursday July 18, 2002 at 230000

Annual total data basis in report

Date Region State CO NOX VOC NH3 SO2 PM10 PM2_5 PMC

 [tons/day] [tons/day] [tons/day] [tons/day] [tons/day] [tons/day] [tons/day] [tons/day]

------------------------------------------------------------------------------------------------------------------------------------------------------------------

 07/18/2002 1000 Alabama 5.21E+01 2.19E+01 3.88E+02 2.16E+02 9.34E+01 3.66E+01 2.52E+01 1.14E+01

 07/18/2002 4000 Arizona 4.34E+02 1.94E+02 3.55E+02 1.22E+02 1.01E+01 5.17E+01 4.84E+01 3.24E+00

 07/18/2002 5000 Arkansas 4.48E+02 6.06E+01 2.14E+02 4.51E+02 7.90E+01 7.08E+01 6.25E+01 8.30E+00

 07/18/2002 6000 California 8.67E+02 3.73E+02 1.48E+03 9.55E+02 2.65E+01 2.71E+02 2.18E+02 5.26E+01

 07/18/2002 8000 Colorado 9.01E+00 1.01E+01 1.83E+02 2.16E+02 2.80E+00 2.39E+00 2.30E+00 9.67E-02

 07/18/2002 9000 Connecticu 6.37E+00 1.05E+01 1.18E+02 1.99E+01 5.39E-01 3.81E+00 3.54E+00 2.69E-01

 07/18/2002 10000 Delaware 6.56E+00 4.74E+00 2.36E+01 6.12E+01 1.48E+00 7.90E+00 2.75E+00 5.15E+00

 07/18/2002 11000 District of C 1.74E+00 1.99E+00 1.33E+01 2.68E-02 2.32E+00 9.36E-01 8.92E-01 4.37E-02

 07/18/2002 12000 Florida 1.14E+02 8.36E+01 1.35E+03 1.08E+02 1.36E+02 3.07E+01 2.56E+01 5.03E+00

 07/18/2002 13000 Georgia 1.63E+02 8.88E+01 8.71E+02 3.11E+02 1.68E+02 6.60E+01 5.05E+01 1.55E+01

 07/18/2002 16000 Idaho 1.06E+02 1.32E+02 7.85E+02 2.00E+02 2.93E+00 9.50E+01 6.70E+01 2.81E+01

 07/18/2002 17000 Illinois 8.25E+01 6.82E+01 5.73E+02 2.14E+02 1.50E+01 2.69E+01 2.40E+01 4.38E+00

 07/18/2002 18000 Indiana 1.27E+02 6.27E+01 5.45E+02 2.69E+02 1.58E+02 1.56E+02 8.07E+01 7.51E+01

 07/18/2002 19000 Iowa 4.81E+01 6.41E+00 3.07E+02 7.24E+02 2.23E+00 1.86E+01 1.65E+01 2.19E+00

 07/18/2002 20000 Kansas 2.26E+03 1.09E+02 3.71E+02 2.32E+02 3.86E+01 2.86E+02 2.19E+02 6.72E+01

 07/18/2002 21000 Kentucky 8.95E+01 1.21E+02 2.70E+02 1.24E+02 1.02E+02 3.00E+01 2.55E+01 4.50E+00

 07/18/2002 22000 Louisiana 1.66E+03 3.31E+02 3.21E+02 2.24E+02 2.51E+02 1.93E+02 1.65E+02 2.81E+01

 07/18/2002 23000 Maine 2.74E+01 7.23E+00 1.21E+02 3.06E+01 1.53E+01 1.19E+01 1.09E+01 1.04E+00

 07/18/2002 24000 Maryland 2.49E+02 2.66E+01 2.06E+02 9.56E+01 2.21E+01 3.43E+01 3.31E+01 1.14E+00

 07/18/2002 25000 Massachus 9.95E+01 6.14E+01 2.80E+02 3.87E+01 3.46E+01 5.01E+01 4.36E+01 6.46E+00

 07/18/2002 26000 Michigan 4.64E+01 5.89E+01 5.22E+02 1.39E+02 1.06E+02 1.98E+01 8.99E+00 1.08E+01

 07/18/2002 27000 Minnesota 1.80E+02 1.41E+02 4.52E+02 3.66E+02 3.55E+01 4.87E+01 4.19E+01 6.78E+00

 07/18/2002 28000 Mississippi 4.91E+00 3.25E+00 3.41E+02 1.76E+02 7.73E-01 5.19E+00 1.50E+00 3.69E+00

 07/18/2002 29000 Missouri 2.32E+02 7.43E+01 4.40E+02 3.90E+02 1.24E+02 6.70E+01 5.39E+01 1.31E+01

 07/18/2002 30000 Montana 4.60E+01 3.25E+01 1.02E+02 9.16E+01 2.63E+00 8.46E+00 7.97E+00 4.88E-01

 07/18/2002 31000 Nebraska 1.38E+02 4.25E+01 2.07E+02 3.43E+02 2.68E+01 2.20E+01 1.80E+01 4.06E+00

 07/18/2002 32000 Nevada 1.44E+02 1.95E+01 1.35E+02 2.41E+01 8.17E+00 1.77E+01 1.71E+01 6.02E-01

 07/18/2002 33000 New Hamp 3.82E+01 2.61E+01 8.11E+01 7.21E+00 7.62E+00 2.30E+01 2.06E+01 2.40E+00

 07/18/2002 34000 New Jersey 1.45E+01 2.24E+01 2.70E+02 5.55E+01 8.55E+00 1.94E+01 9.83E+00 9.72E+00

 07/18/2002 35000 New Mexic 8.55E+01 7.68E+01 1.48E+02 1.37E+02 1.78E+01 1.52E+01 1.37E+01 1.55E+00

 07/18/2002 36000 New York 7.14E+01 1.15E+02 7.02E+02 2.64E+02 2.28E+02 9.92E+01 4.78E+01 5.14E+01

 07/18/2002 37000 North Caro 1.08E+03 1.12E+02 4.50E+02 5.32E+02 1.63E+01 1.97E+01 1.53E+01 4.51E+00

 07/18/2002 38000 North Dako 1.80E+01 5.18E+01 1.28E+02 1.25E+02 1.43E+02 7.04E+00 3.74E+00 3.30E+00

 07/18/2002 39000 Ohio 4.02E+01 5.19E+01 6.51E+02 2.81E+02 3.48E+01 5.26E+00 4.70E+00 5.61E-01

 07/18/2002 40000 Oklahoma 1.00E+03 3.38E+02 5.98E+02 3.30E+02 3.23E+01 1.48E+02 1.18E+02 2.97E+01

 07/18/2002 41000 Oregon 1.77E+02 5.42E+01 5.19E+02 9.80E+01 5.96E+01 7.30E+01 6.81E+01 4.93E+00

 07/18/2002 42000 Pennsylvan 3.47E+02 5.72E+01 5.80E+02 3.18E+02 7.39E+01 7.30E+01 5.08E+01 2.22E+01

 07/18/2002 44000 Rhode Isla 1.11E+01 5.57E+00 1.29E+02 2.85E+00 5.43E+00 2.17E+00 2.00E+00 1.65E-01

 07/18/2002 45000 South Caro 1.23E+02 5.25E+01 3.95E+02 9.36E+01 3.12E+01 2.27E+01 1.95E+01 3.20E+00

 07/18/2002 46000 South Dako 1.73E+01 1.43E+01 8.98E+01 2.69E+02 5.49E+01 5.92E+00 4.44E+00 1.48E+00

 07/18/2002 47000 Tennessee 8.91E+01 3.53E+01 4.42E+02 8.82E+01 7.95E+01 3.51E+01 2.80E+01 7.16E+00

 07/18/2002 48000 Texas 1.71E+03 2.24E+02 1.18E+03 8.96E+02 1.87E+01 1.96E+02 1.65E+02 3.32E+01

 07/18/2002 49000 Utah 1.99E+02 5.54E+01 1.62E+02 9.43E+01 2.39E+01 2.68E+01 2.54E+01 1.41E+00

 07/18/2002 50000 Vermont 3.95E+01 4.55E+00 3.79E+01 4.14E+01 1.05E+01 6.09E+00 5.48E+00 6.09E-01

 07/18/2002 51000 Virginia 1.98E+02 1.26E+02 3.34E+02 1.40E+02 2.87E+01 5.71E+01 2.31E+01 3.40E+01

 07/18/2002 53000 Washingto 3.64E+02 4.41E+01 3.26E+02 1.21E+02 4.90E+00 6.00E+01 5.81E+01 1.92E+00

 07/18/2002 54000 West Virgin 3.84E+01 3.07E+01 1.57E+02 3.65E+01 3.15E+01 1.56E+01 1.36E+01 2.03E+00

 07/18/2002 55000 Wisconsin 8.57E+01 3.00E+01 4.35E+02 3.56E+02 9.08E+00 1.62E+01 1.49E+01 1.30E+00

 07/18/2002 56000 Wyoming 5.24E+01 2.14E+02 4.57E+01 6.93E+01 3.83E+01 7.36E+00 6.09E+00 1.27E+00

Appendix D.4.a -10

Appendix D - 113
2.1 Additional State QA Procedures 

South Carolina reviewed the state summary data to ensure the totals were reasonable. 

The following QA checks were performed both together and separate for EGU and nonEGU point sources: 

• Data product summaries and raw NIF 3.0 data files were examined. 

• Errors detected in earlier model runs were rechecked with each successive model 

run to assure their correction was carried forward in subsequent runs. 

Appendix D.4.a -11

Appendix D - 114
 

 

Appendix D.4b 

Temporal Profile Development for 

Electric Generating Units 

Appendix D - 115
Atmospheric Sciences Group 

Development of Temporal Profiles for Electric Generating Units 

(EGUs) In the VISTAS States: 

Final Technical Memorandum 

Appendix D.4.b -1

Appendix D - 116
Atmospheric Sciences Group 

Prepared for: 

Pat Brewer 

VISTAS Technical Coordinator 

2090 US Highway 70 

Swannanoa, NC 28778 

Prepared by: 

Gregory Stella 

Alpine Geophysics, LLC 

387 Pollard Mine Road 

Burnsville, NC 28714 

27 February 2006

Appendix D.4.b -2

Appendix D - 117
Atmospheric Sciences Group 

Appendix D.4.b -3

Appendix D - 118
Atmospheric Sciences Group 

 

TABLE OF CONTENTS

1.0 Introduction.....................................................................................................................................................5

2.0 Data Obtained .................................................................................................................................................6

2.1. Source of Information ................................................................................................................................6

2.2. File Contents ..............................................................................................................................................7

2.3. Quality Control / Quality Assurance ..........................................................................................................8

3.0 Inventory Matching.........................................................................................................................................9

4.0 Assumptions and Calculations......................................................................................................................10

4.1. Profile Calculations..................................................................................................................................10

4.1.1. Actual 2002 Profiles............................................................................................................................11

4.1.2. Typical EGU Profiles ..........................................................................................................................24

5.0 Application of Factors...................................................................................................................................33

Appendix A Actual 2002 State Level Monthly Profiles [Tabular] 

Appendix B Actual 2002 State Level Monthly Profiles [Graphical] 

Appendix C Actual 2002 State Level Monthly-Day of Week Profiles [Tabular] 

Appendix D Actual 2002 State Level Monthly-Diurnal Profiles [Tabular] 

Appendix E Average 2000-2004 State Level Monthly Profiles [Tabular] 

Appendix F Average 2000-2004 State Level Monthly Profiles [Graphical] 

Appendix G Average 2000-2004 State Level Monthly-Day of Week Profiles [Tabular] 

Appendix H Average 2000-2004 State Level Monthly-Diurnal Profiles [Tabular] 

Appendix D.4.b -4

Appendix D - 119
Atmospheric Sciences Group 

 

1.0 Introduction 

The objective of this analysis was to develop temporal profiles for EGUs in the VISTAS 

states (AL, GA, FL, KY, MS, NC, SC, TN, VA, and WV). These temporal profiles will 

be used by the VISTAS Emissions and Air Quality Modeling Team during modeling of 

the VISTAS 2002 and 2018 EGU emissions, in place of SMOKE default temporal 

profiles that are accepted to not accurately depict temporal distribution of emissions from 

EGUs in the U.S. VISTAS EGU temporal profiles were developed using hourly CEM 

data as reported to EPA’s Clean Air Market’s Division (CAMD) for the Acid Rain 

Program. 

This technical memorandum describes the work conducted by Alpine Geophysics 

(Alpine) in order to assist VISTAS in this task with 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 SMOKE defaults, and will be used for both actual 2002 and 

“typical” 2002 and 2018 modeling. 

Two sets of monthly profiles were developed by Alpine: 

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 

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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 Alpine Geophysics 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. 

2.0 Data Obtained 

2.1. Source of Information 

Five years (2000 through 2004) of hourly CEM information from EPA=s CAMD website 

were obtained for each unit in the VISTAS states1

. 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 unitlevel hourly emissions data by state and year. Using these data, we reformatted the files 

and quality assured for applicability to this analysis. 

1

 http://cfpub.epa.gov/gdm/index.cfm?fuseaction=prepackaged.select

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2.2. File Contents 

The reformatted files were prepared as identified in Table 1. 

Table 1. CEM data file format. 

Column Description 

State State in which the facility is located. 

Facility Name The name given by the owners and operators to a facility. 

Facility ID (ORISPL) The unique six-digit facility identification number, also called an ORISPL, assigned by the 

Energy Information Administration, a component of the Department of Energy. 

Unit ID 

Each unit at a facility has a unique identification number. It is alphanumeric and may be from 

one to six characters in length. For utility units and other units that generate energy for sale, the 

unit ID used for Part 75 reporting is the same unit ID that appears in the National Allowance 

Database (NADB) (for Acid Rain Program units) or in the State's allowance allocation list. 

Day Day on which a unit was operating. 

Hour Hour on which a unit was operating. 

Operating Hours Percent of hour in which a unit was operating. 

Gross Load (MW) Gross load is the output of the unit as measured in megawatts. 

Steam Load (1000 lb/hr) Steam load is the output of the unit as measured in 1000 lb/hr of steam. 

SO2 Mass (lbs) SO2 released for the hour in pounds. 

SO2 Mass Measurement Flag Indicates whether the value for SO2 mass was measured or derived due to missing data. 

SO2 Rate (lbs/mmBtu) SO2 emissions rate in pounds per million British thermal units (lbs/mmBtu). 

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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. 

2.3. Quality Control / Quality Assurance 

Each file was reviewed to determine if NOx, SO2 and heat input values were represented 

for each hour of every day for each unit in the obtained data. Zero values were considered 

to be valid if operating time identifiers indicated no operation during that hour (e.g., data 

value of zero but operating hours greater than zero). 

Using the measurement flags and field values in the reformatted files, numerous spot 

checks were made of anomalous or missing variable data to ensure that data corruption 

was not impacting the statistical analyses. Additionally, each year’s hourly total of NOx, 

SO2, and heat input (per state) were summed and compared to EPA annual summaries of 

the same data elements. 

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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. 

3.0 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. 

Under the direction of VISTAS, staff at MACTEC Engineering and Consulting, Inc. 

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. 

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MACTEC developed county-level reports of the remaining unmatched facilities and units 

from the VISTAS inventory and made comparisons of annual emissions of SO2 and NOx 

to the CEM-based SO2 and NOx for sources also identified within the same county. This 

step of the matching process allowed an incremental amount of emissions and units to be 

accounted for and assigned unit-specific profiles for model performance evaluation. 

Finally, remaining VISTAS inventory and CEM sources were manually compared to 

each other in an effort to determine if reporting errors in State or county codes or facility 

or unit identification codes accounted for this reminder of unmatched sources. These 

manual matches were confirmed or revised with VISTAS State and stakeholder 

participation and input. With this step, a few sources were identified to have facility 

identification changes or misreported county codes preventing automated matching from 

occurring and corrected for the final application of factors. 

Once all methods of comparison were exhausted, the remaining unmatched VISTAS 

emission inventory of EGU sources was excluded from the unit-specific profile 

assignment steps and was allocated more generalized facility or State temporal profiles as 

described in the following section. 

This inventory comparison process allowed for the match of over 650 of the 861 CEM 

identified units (76%) to the VISTAS EGU emission inventory for 2002. More 

importantly, however, was the match of 99.95 percent of the SO2 emissions and over 

99.4 percent of the NOx emissions from these sources in the VISTAS domain. 

4.0 Assumptions and Calculations 

4.1. 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 

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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. 

4.1.1. Actual 2002 Profiles 

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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 1 

provides this calculation for heat input. Table 1 provides an example result of the ratio 

calculation. 

Equation (1) = ∑

1

31

,, , /

Jan

Dec

hi datehrratio hi datehr hi

 where hi = heat input (MMBtu) 

Table 1. Application of Calculated Ratios for Actual 2002 by Unit 

Actual Reported Values [2002] Calculated Ratios 

ORISPL UnitID Date Hour SO2 Mass NOx Mass Heat Input SO2 NOx Heat Input 

Appendix D.4.b -12

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 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 

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 shows example ratios calculated for each month by State. 

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Table 3 reflects an example of the State-month-day of week ratio calculation and Table 4 

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 1, when viewed on a VISTAS-domain total, the 

monthly variation in relative distribution of SO2, NOx, and heat input differs enough to 

justify calculating each parameter value set of temporal profiles with CEM data. 

Table 2. Application of Calculated Ratios for Actual 2002 by Example State and Month 

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 

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 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 

Table 3. Application of Calculated Ratios for Actual 2002 by Example State and Month 

and Day of Week 

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

Appendix D.4.b -15

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GA Mar Total 89,015,786 27,868,311 69,184,812 1.0000 1.0000 1.0000

 

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Table 4. Application of Calculated Ratios for Actual 2002 by Example State and Month 

and Hour of Day 

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 

Appendix D.4.b -17

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 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 

Appendix D.4.b -18

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Figure 1. Monthly variation in 2002 of CEM reported heat input, NOx mass, and SO2 

mass for VISTAS domain. 

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

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 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 1 and 2 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. 

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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 3 represents an extreme unit-specific case for monthly differences from State or 

regional temporal allocation. The unit presented is a Mississippi baseload coal-fired 

boiler which in 2002 emitted over 4,000 tons of NOx and over 11,000 tons of SO2. This 

unit would typically run at consistent levels during the entire period, but due to a planned 

maintenance outage was not in operation in late January through the middle of April in 

2002. Given the unique operation of 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. 

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Figure 2. Monthly variation in 2002 of CEM reported heat input, NOx mass, and SO2 

mass for Mississippi. 

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 3. 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. 

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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

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 Phase II 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 4 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 

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of correctly modeling the impact of the source on downwind oxidant and fine particulate 

concentrations2

. 

Figure 4. Actual daily unit-specific 2002 SO2 (tons), NOx, (tons), and heat input 

(MMBtu) distribution from CEM data. 

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

 

2

 Stella, G.M., “Development of Hourly Inventories Utilizing CEM-Based Data,” presented at the 

International Emission Inventory Conference, Las Vegas, NV, April, 2005. 

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4.1.2. 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 5. 

Table 5. Example Day-of-Week per Month Assignment 

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 

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-UNITIDMONTH-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 6. 

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Table 6. Arithmetic Mean of CEM-based Variables for Temporal Profile Calculation 

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 

Appendix D.4.b -25

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 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 

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 7. 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. 

Table 7. Application of Calculated Ratios to Day of Year by Unit 

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 

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 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 

Equation 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) = ∑

1

30

,,, ,, /

May

Sep

hi sumdatehrratio hi sumdatehr hi

Appendix D.4.b -27

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 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-month-day-of-week, or State-month-hour). Table 8 

shows example ratios calculated for each month by State. Table 9 reflects an example of 

the State-month-day of week ratio calculation and Table 10 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. 

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. 

Appendix D.4.b -28

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Table 8. Application of Calculated Ratios for Typical Operation by Example State and 

Month 

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 

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Table 9. 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 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 10. Application of Calculated Ratios for Typical Operation by Example State and 

Month and Hour of Day 

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 

Appendix D.4.b -31

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Atmospheric Sciences Group 

 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 

Appendix D.4.b -32

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Atmospheric Sciences Group 

 

 

5.0 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 documentation3

. 

The CEM format for individual hour-specific data files as available in SMOKE was not 

utilized for VISTAS emissions processing as the emissions allowable by hour would have 

been limited to NOx, SO2, and CO2. If this file format and optional run configuration 

were exercised, the NOx, SO2, and CO2 emissions processed by the model would have 

been accurate for CEM reported emissions, but the remaining pollutants coupled with 

each CEM unit would have received the 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 Phase II 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 

3

 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. 

Appendix D.4.b -33

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Atmospheric Sciences Group 

 

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 10 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 

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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 11 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. 

Appendix D.4.b -35

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Figure 10. Relative distribution of actual 2002 monthly VISTAS State CEM-based heat 

input. 

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 11. Relative distribution of diurnal actual 2002 CEM-based NOx emissions for 

Tennessee. 

Appendix D.4.b -36

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Atmospheric Sciences Group 

 

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

Appendix D.4.b -37

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Appendix D.4.b -38

Appendix D - 153
Appendix D.4c 

White Paper on Mobile Source 

SMOKE Modeling 

Appendix D - 154
Processing Mobile Emissions in SMOKE: Is it worth simulating everyday onroad 

mobile emissions to support 8-hr ozone modeling? 

 

Mike Abraczinskas 

North Carolina Department of Environment and Natural Resources 

Division of Air Quality 

1641 Mail Service Center 

Raleigh, NC 27699-1641 

(919) 715-3743 

Michael.Abraczinskas@ncmail.net 

Cynthia Loomis 

Alpine Geophysics, LLC 

7341 Poppy Way 

Arvada, CO 80007 

(303) 421-2211 

cfl@alpinegeophysics.com 

Gregory Stella 

Alpine Geophysics, LLC 

387 Pollard Mine Road 

Burnsville, NC 28714 

(828) 675-9045 

gms@alpinegeophysics.com 

ABSTRACT 

The most computationally limiting step in emissions modeling is typically the generation of onroad 

mobile sources. Motor vehicle emissions are influenced by meteorological variability and the processing 

requirements for daily motor vehicle emissions have been determined to be rate limiting under most 

modeling schedules. Rather than utilizing averaged meteorological data or pre-calculated motor vehicle 

emissions, the Visibility Improvement State and Tribal Association of the Southeast (VISTAS) / 

Association for Southeastern Integrated Planning (ASIP) modeling team developed an emissions 

processing approach that models a representative week for each month of the year in order to make the 

SMOKE processing time more manageable and consistent with VISTAS/ASIP modeling schedules. This 

representative 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 purpose of this paper is to describe processing options for onroad mobile source emissions using the 

MOBILE module of the SMOKE emissions processor and to determine, based on air quality predictions 

and time and resource expenditure, benefits of simulating everyday for onroad mobile emissions to 

support 8-hr ozone modeling. We will present 12km evaluations of everyday vs. representative week 

emissions and associated air quality for a number of domains and discuss the benefits and limitations of 

the various methods relative to ozone, PM and regional haze prediction. 

Appendix D.4.c - 1

Appendix D - 155
INTRODUCTION 

On December 17, 2004, EPA made fine particle (PM2.5) nonattainment determinations for at least one 

area in seven of the states participating in the Visibility Improvement State and Tribal Association of the 

Southeast (VISTAS) regional haze project. They are Alabama, Georgia, North Carolina, Kentucky, 

Tennessee, Virginia, and West Virginia. In addition, South Carolina has one three-county area that was 

designated as unclassifiable in the same action. EPA’s Clean Air Interstate Rule (CAIR) modeling 

indicated that certain nonattainment areas may still be in nonattainment after full implementation of 

CAIR. These areas include Jefferson County, Alabama and Clayton and Fulton Counties in Georgia. 

The PM2.5 compliance date is April 2010 unless a state demonstrates that more time is necessary in 

which case up to five additional years may be granted. The nonattainment designations triggered the 

requirement for development of state implementation plans (SIPs) that will be due in April 2008. The 

draft guidance from EPA indicates that a significant requirement of PM2.5 SIPs will be attainment 

demonstrations using, at least in part, modeling analyses to define effective emissions control strategies 

and confirm that attainment can be achieved after implementation of the strategies. 2009 is the modeling 

year for the PM2.5 attainment demonstration and also is an interim analysis year for the VISTAS regional 

haze demonstration. 

In April of 2004, EPA determined areas that were not meeting the 8-hour ozone standard. States having 

one or more 8-hour ozone nonattainment areas in the Southeast are Alabama, Georgia, Kentucky, North 

Carolina, South Carolina, Tennessee, Virginia, and West Virginia. EPA will require attainment of the 8-

hour ozone standard in basic nonattainment areas by June 15, 2009 and in moderate nonattainment areas 

by June 15, 2010. This will require states with basic 8-hour ozone nonattainment areas to model 2008 as 

the SIP modeling demonstration year while moderate nonattainment areas will require 2009 as the 

modeling year. Given that North Carolina and Virginia have two year SIP approval processes, there is an 

immediate need to complete an analysis of ozone attainment using air quality modeling. 

The states participating in the VISTAS project (the SESARM EPA Region 4 states plus Virginia and 

West Virginia from Region 3) have concluded that a collaborative process will be the most efficient 

approach for the collective states to develop information upon which to base the PM2.5 and 8-hour ozone 

attainment demonstrations. The local air regulatory agencies for Jefferson County, AL, Jefferson 

County, KY, Mecklenburg County, NC, Forsyth County, NC, Knox County, TN, and Shelby County, 

TN have also become signatory parties to this collaborative effort. SESARM will coordinate among 

participating agencies and oversee the performance of the inventory and modeling tasks in parallel with 

the VISTAS regional haze project tasks. 

The name of this collaborative effort is the Association for Southeastern Integrated Planning (ASIP). 

SESARM was awarded a grant from EPA on February 8, 2005 to conduct what was originally called the 

fine particle SIP development support project but is now known as ASIP. 

These states need to submit their 8-hour ozone State Implementation Plans (SIPs) to EPA by June 2007; 

the PM2.5 SIPs are due by April 2008. Some of the states involved in the ASIP ozone/PM modeling have 

two-year legislative review processes. Thus, the definition of the SIP control plans is needed in early 

2006. Consequently, the ASIP regional ozone and PM modeling has an aggressive schedule. 

Appendix D.4.c - 2

Appendix D - 156
Figure 1. PM2.5 nonattainment counties designed by EPA in December 2004. 

Figure 2. 8-hour ozone nonattainment counties in the US designated by EPA in April 2004. 

Appendix D.4.c - 3

Appendix D - 157
By far the most computationally limiting step in emissions modeling is typically the generation of 

onroad mobile sources. Motor vehicle emissions are influenced by meteorological variability and the 

processing requirements for daily motor vehicle emissions have been determined to be rate limiting 

under most modeling schedules. Rather than utilizing averaged meteorological data or pre-calculated 

motor vehicle emissions, the VISTAS and ASIP modeling team developed an emissions processing 

approach that models a representative week for each month of the year in order to make the SMOKE 

processing time more manageable and consistent with modeling schedule1

. This representative 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. 

Based on the findings in the VISTAS Phase I and II modeling activities, ASIP selected the following 

models for use in modeling 8-hour ozone and particulate matter (PM) of size of 2.5 microns or less 

(PM2.5): 

3⁄4 MM52,3: The Pennsylvania State University/National Center for Atmospheric Research 

(PSU/NCAR) Mesoscale Meteorological Model (MM5) is a nonhydrostatic, prognostic 

meteorological model routinely used for urban- and regional-scale photochemical, fine 

particulate and regional haze regulatory modeling studies. 

 

3⁄4 SMOKE4: The Sparse Matrix Operator Kernel Emissions (SMOKE) modeling system is an 

emissions modeling system that generates hourly gridded speciated emission inputs of mobile, 

nonroad, area, point, fire and biogenic emission sources for photochemical grid models. 

3⁄4 CMAQ5,6: 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. 

The purpose of this paper is to describe processing options for onroad mobile source emissions using the 

MOBILE module of the SMOKE emissions processor and to determine, based on air quality predictions 

and time and resource expenditure, benefits of simulating everyday for onroad mobile emissions to 

support 8-hr ozone modeling. We will present 12km evaluations of everyday vs. representative week 

emissions and associated air quality for a number of domains and discuss the benefits and limitations of 

the various methods relative to ozone and regional haze prediction. 

MOBILE6 / SMOKE PREPARATION 

For the VISTAS/ASIP 2009 annual emissions inventory modeling, 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. With the exception of biogenic and highway mobile source emissions 

that are generated using the BEIS and MOBILE6 modules in SMOKE, pre-computed annual emissions 

will be processed using the month, day, and hour specific temporal profiles of the SMOKE model. Area, 

nonroad, and point sources were modeled as a block of Thursday, Friday, Saturday, Sunday, Monday 

one per month (total of 60 days modeled). Biogenics were modeled for each day of the episode. 

For this investigation, the onroad mobile source emissions were produced using two approaches: 

1) Modeling every day of the annual episode, using the MM5 meteorology files for each model day. 

When full annual runs were executed, holidays were modeled as Sundays. 

Appendix D.4.c - 4

Appendix D - 158
2) Modeling selected weeks (seven days) of each month and using these days as representative of the 

entire month. This selection criterion allows for the representation of day-of-the-week variability in 

the onroad motor vehicles, and models a representation of the meteorological variability in each 

month. The modeled weeks were selected from mid-month, avoiding inclusion of major holidays. 

The parameters for the SMOKE runs are as follows: 

Episodes: 

2002 Initial Base Year, and 

2009 Future year, using 2009 inventory and modeled using the same meteorology and 

episode days as 2002. 

Episode represented by the following weeks per month:

 January 15-21 

 February 12-18 

 March 12-18 

 April16-22 

 May 14-20 

 June 11-17 

 July 16-22 

August 13-19 

September 17-23 

October 15-21 

November 12-18 

December 17-23 

Days modeled as holidays for annual run:

 New Year’s Day - January 1 

 Good Friday – March 29 

 Memorial Day – May 27 

 July 4th 

 Labor Day – September 2 

 Thanksgiving Day – November 28, 29 

 Christmas Eve – December 24 

 Christmas Day – December 25 

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 will be 19 layers, with specific layer positions defined in the 

meteorology files to be provided by VISTAS meteorological contractor. 

Appendix D.4.c - 5

Appendix D - 159
CMAQ model species: 

The CMAQ configuration will be for CB-IV with PM. The model species will be: 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. 

Elevated sources: 

All sources will be treated by SMOKE as potentially elevated. No plume-in-grid sources 

will be modeled. Wildfire emissions will be handled as point sources. 

Figure 3. 36-km national unified RPO domain and VISTAS 12-km domain. 

Appendix D.4.c - 6

Appendix D - 160
DEVELOPMENT OF ONROAD MOTOR VEHICLE SOURCE EMISSIONS 

The MOBILE6 module of SMOKE was used to develop the onroad mobile source emissions estimates 

for CO, NOX, PM, and VOC emissions. The MOBILE6 parameters, vehicle fleet descriptions, and 

VMT estimates are combined with gridded, episode-specific temperature data to calculate the gridded, 

temporalized emission estimates. 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. The shells used in these runs were based on VISTAS/ASIP BaseF 

modeling inputs7

. The options for all MOBILE6 parameters were held constant between the annual and 

representative week runs. 

Daily results of these model runs for a winter (January 17) and summer (July 18) day are represented in 

Figures 4 through 6 below. These data provide a comparison of the magnitude difference between ozone 

and particulate matter precursor species for each of these seasonally different episodes. As can be seen 

in these figures, the variable inputs (temperature, VMT, seasonal fuels) associated with each month’s 

run have an impact on the overall emissions generated for the onroad mobile source category. It is 

through modeling these differences with CMAQ for both ozone and PM that we have based our 

conclusions. 

Each of the onroad mobile source emissions runs conducted with the MOBILE6 module of SMOKE 

were performed on a dual Athlon MP 2600+ with 1.5 G RAM. With this configuration, the modeling 

team experienced run times of approximately sixty-three (63) minutes per run day on the 12km domain. 

Using this estimate, the representative week processing would require a total of 5,292 minutes (12 

months x 7 days x 63 minutes per run day) or about 88.2 hours (3.5 days) of CPU runtime to generate 

the files necessary to simulate the annual episode. In comparison, actually running each day’s onroad 

mobile source emissions using the same configuration would require 22,995 minutes (365 days x 63 

minutes per run day) or about 383.25 hours (16 days) of CPU run time. 

Appendix D.4.c - 7

Appendix D - 161
Figure 4. Daily VOC emissions as generated with the MOBILE6 module of SMOKE for a winter 

(January 17) and summer (July 18) episode day. 

Figure 5. Daily NOx emissions as generated with the MOBILE6 module of SMOKE for a winter 

(January 17) and summer (July 18) episode day. 

Appendix D.4.c - 8

Appendix D - 162
Figure 6. Daily PM-fine emissions as generated with the MOBILE6 module of SMOKE for a winter 

(January 17) and summer (July 18) episode day. 

EMISSIONS SUMMARY 

The reconstructed emissions based on the representative week run were calculated by mapping each day 

of week (Mon, Tue, Wed, etc.) from the modeled month to the same day of week generated in the 

representative week run. In the case of holidays, these days were mapped to representative week 

Sundays. An example of this mapping for the January episode is presented in Table 1. Note that 

although the emissions were generated for calendar year 2009, the meteorology is based on 2002. Table 

2 presents a comparison of January emissions as generated using the everyday MOBILE6 module run 

for each VISTAS/ASIP State and these emissions as reconstructed from the representative week 

MOBILE6 module runs. In comparison, Table 3 presents these emissions for the month of July. 

Table 1. Representative day mapping for January episode (Highlighted representative week). 

Modeled Representative Modeled Representative Modeled Representative 

Date Day Date Day Date Day 

1/1/2002* 1/20/2002 1/11/2002 1/18/2002 1/22/2002 1/15/2002 

1/2/2002 1/16/2002 1/12/2002 1/19/2002 1/23/2002 1/16/2002 

1/3/2002 1/17/2002 1/13/2002 1/20/2002 1/24/2002 1/17/2002 

1/4/2002 1/18/2002 1/14/2002 1/21/2002 1/25/2002 1/18/2002 

1/5/2002 1/19/2002 1/15/2002 1/15/2002 1/26/2002 1/19/2002 

1/6/2002 1/20/2002 1/16/2002 1/16/2002 1/27/2002 1/20/2002 

1/7/2002 1/21/2002 1/17/2002 1/17/2002 1/28/2002 1/21/2002 

1/8/2002 1/15/2002 1/18/2002 1/18/2002 1/29/2002 1/15/2002 

1/9/2002 1/16/2002 1/19/2002 1/19/2002 1/30/2002 1/16/2002 

1/10/2002 1/17/2002 1/20/2002 1/20/2002 1/31/2002 1/17/2002 

1/21/2002 1/21/2002 

* Modeled holiday 

Appendix D.4.c - 9

Appendix D - 163
Table 2. January 2009 onroad mobile emissions comparison. 

State VOC NOx CO SO2 PM-10 PM-2.5 NH3

Alabama 6,567 8,774 105,011 51 266 174 500

Florida 28,354 26,686 336,541 171 834 529 1,736

Georgia 15,558 17,935 224,920 100 509 328 999

Kentucky 5,321 8,618 102,603 47 250 165 453

Mississippi 3,928 5,999 61,323 31 191 130 312

North Carolina 13,590 18,406 231,897 104 489 311 988

South Carolina 5,372 7,934 92,169 44 240 159 429

Tennessee 8,729 12,954 142,906 67 356 238 609

Virginia 7,377 11,708 156,617 72 311 190 716

West Virginia 2,025 3,177 41,742 18 91 59 168

96,821 122,190 1,495,728 705 3,536 2,283 6,910

State VOC NOx CO SO2 PM-10 PM-2.5 NH3

Alabama 6,394 8,765 102,800 51 266 174 500

Florida 28,852 26,476 333,248 171 833 529 1,736

Georgia 15,337 17,867 218,990 100 509 328 999

Kentucky 5,023 8,679 104,247 47 250 165 453

Mississippi 3,710 6,012 60,454 31 191 130 312

North Carolina 12,605 18,383 225,563 104 489 311 988

South Carolina 5,226 7,911 89,001 44 240 159 430

Tennessee 8,011 13,000 141,962 67 356 238 609

Virginia 7,005 11,735 155,321 72 311 190 715

West Virginia 1,941 3,194 42,096 18 91 59 168

94,104 122,021 1,473,682 705 3,536 2,283 6,909

State VOC NOx CO SO2 PM-10 PM-2.5 NH3

Alabama -2.6% -0.1% -2.1% 0.0% 0.0% 0.0% 0.0%

Florida 1.8% -0.8% -1.0% 0.0% 0.0% 0.0% 0.0%

Georgia -1.4% -0.4% -2.6% 0.0% 0.0% 0.0% 0.0%

Kentucky -5.6% 0.7% 1.6% 0.0% 0.0% 0.0% 0.0%

Mississippi -5.5% 0.2% -1.4% 0.0% 0.0% 0.0% 0.0%

North Carolina -7.2% -0.1% -2.7% 0.0% 0.0% 0.0% 0.0%

South Carolina -2.7% -0.3% -3.4% 0.0% 0.0% 0.0% 0.0%

Tennessee -8.2% 0.4% -0.7% 0.0% 0.0% 0.0% 0.0%

Virginia -5.0% 0.2% -0.8% 0.0% 0.0% 0.0% 0.0%

West Virginia -4.1% 0.6% 0.8% 0.0% 0.0% 0.0% 0.0%

-2.8% -0.1% -1.5% 0.0% 0.0% 0.0% 0.0%

January 2009 Emissions (Difference as Percent)

January 2009 Emissions (Everyday Calculation)

January 2009 Emissions (Representative Day Calculation)

Appendix D.4.c - 10

Appendix D - 164
Table 3. July 2009 onroad mobile emissions comparison. 

State VOC NOx CO SO2 PM-10 PM-2.5 NH3

Alabama 5,968 8,654 61,362 58 278 175 584

Florida 21,715 27,067 208,947 190 864 531 1,971

Georgia 15,833 17,965 133,828 114 533 332 1,162

Kentucky 5,289 8,196 56,333 53 262 166 537

Mississippi 3,934 6,013 38,674 36 200 130 376

North Carolina 12,975 17,340 130,042 120 512 311 1,171

South Carolina 5,316 7,859 57,163 51 251 160 512

Tennessee 8,797 12,446 81,289 75 368 237 712

Virginia 7,064 11,221 87,946 82 331 195 832

West Virginia 2,038 3,006 23,429 21 96 61 205

88,930 119,768 879,013 800 3,695 2,299 8,063

State VOC NOx CO SO2 PM-10 PM-2.5 NH3

Alabama 6,017 8,682 61,581 58 278 175 585

Florida 22,006 27,217 210,901 190 864 531 1,971

Georgia 16,252 18,091 135,119 114 533 332 1,163

Kentucky 5,274 8,196 56,184 53 262 167 537

Mississippi 3,960 6,023 38,911 36 200 130 376

North Carolina 13,160 17,394 130,728 120 512 311 1,171

South Carolina 5,449 7,903 57,867 51 251 160 512

Tennessee 8,798 12,454 81,930 75 368 237 712

Virginia 7,104 11,248 87,523 82 331 195 832

West Virginia 2,047 3,010 23,419 21 96 61 205

90,068 120,218 884,162 800 3,695 2,299 8,063

State VOC NOx CO SO2 PM-10 PM-2.5 NH3

Alabama 0.8% 0.3% 0.4% 0.0% 0.0% 0.0% 0.0%

Florida 1.3% 0.6% 0.9% 0.0% 0.0% 0.0% 0.0%

Georgia 2.6% 0.7% 1.0% 0.0% 0.0% 0.0% 0.0%

Kentucky -0.3% 0.0% -0.3% 0.0% 0.0% 0.0% 0.0%

Mississippi 0.7% 0.2% 0.6% 0.0% 0.0% 0.0% 0.0%

North Carolina 1.4% 0.3% 0.5% 0.0% 0.0% 0.0% 0.0%

South Carolina 2.5% 0.6% 1.2% 0.0% 0.0% 0.0% 0.0%

Tennessee 0.0% 0.1% 0.8% 0.0% 0.0% 0.0% 0.0%

Virginia 0.6% 0.2% -0.5% 0.0% 0.0% 0.0% 0.0%

West Virginia 0.5% 0.1% 0.0% 0.0% 0.0% 0.0% 0.0%

1.3% 0.4% 0.6% 0.0% 0.0% 0.0% 0.0%

July 2009 Emissions (Difference as Percent)

July 2009 Emissions (Everyday Calculation)

July 2009 Emissions (Representative Day Calculation)

Appendix D.4.c - 11

Appendix D - 165
These aggregate emission summaries would lead one to believe that on an extended episode scale (like 

those required for PM or regional haze modeling), the use of representative week onroad mobile source 

emissions would be appropriate. However, in modeling either 1-hr or 8-hr ozone, there is enough of a 

temperature variability and therefore apparent ozone precursor emissions delta on an hour-to-hour basis 

that this same assumption could not be made without accompanying air quality simulations. 

AIR QUALITY MODELING 

The VISTAS/ASIP modeling team has applied the CMAQ Version 4.5 O3/PM2.5 photochemical grid 

modeling system. The VISTAS/ASIP modeling team implemented a comprehensive evaluation of the 

meteorological8

, emissions and air quality models. The CMAQ model performance evaluation indicated 

an underestimation of 8-hour ozone maximums during the summer. The model demonstrated reasonably 

good performance for sulfate, winter overestimation bias and summer underestimation bias for nitrate 

and reasonably good performance for elemental carbon (EC), albeit with lots of scatter and low 

correlation. However, organic carbon (OC) was underestimated with the summer OC underestimation 

bias being quite severe. After an intense focused analysis of the OC underestimation issue, the 

VISTAS/ASIP modeling team identified processes important to the formation of secondary organic 

aerosols (SOA) that were not included in the CMAQ SOA module that may be important to OC in the 

Southeastern U.S.9

 Consequently, VISTAS/ASIP enhanced the CMAQ SOA module by adding several 

new processes. This enhancement, called “SOAmods”, was implemented in CMAQ Version 4.5 and 

exhibited much improved OC model performance over the standard CMAQ SOA treatment10. A 

complete description of the modeling methods, configurations and performance are described 

elsewhere1,7. 

CMAQ was applied using both of the mobile emissions modeling methods described above. Recall, all 

emissions and air quality model inputs and configurations were held constant, with the exception of the 

mobile source emissions. This will allow us to isolate the air quality impacts of using the representative 

week mobile emissions versus the “actual” daily modeled mobile emissions. While the VISTAS/ASIP 

modeling is conducted on both 36-km National RPO and 12-km “VISTAS/ASIP” modeling domains as 

shown in Figure 3, this study focuses on evaluations of the 12-km air quality modeling results only. 

Using each of the mobile emissions databases (daily and the representative week) generated for the 

January and July study periods, we performed future-year air quality simulations for 2009 using CMAQ. 

We then post-processed the air quality model results to qualitatively evaluate the magnitude, location, 

and spatial extent of the differences in predicted ozone and PM2.5 concentrations due to the different 

mobile emissions modeling methodologies. Spatial plots were generated for each day simulated, 

including: 

1) daily maximum 8-hour ozone difference plots; 

2) maximum 1-hour ozone maximum difference plots; and, 

3) daily PM2.5 difference plots. 

Appendix D.4.c - 12

Appendix D - 166
RESULTS 

Our examination of the two air quality simulations began with the daily differences in PM2.5 

concentrations. Figures 7 and 8 represent the percent difference in the daily PM2.5 concentrations 

between the air quality simulations with representative week mobile emissions and the daily mobile 

emissions for one winter day (January 22nd) and one summer day (July 9th). No change is seen in either 

plot indicating daily PM2.5 concentrations changed less than one percent. Absolute differences in daily 

PM2.5 concentrations are shown in Figures 9 and 10 for the same two days (January 22nd and July 9th). 

Again, no change is seen in either plot indicating daily PM2.5 concentrations changed less than 0.2 

μg/m3

. In fact, all of the fourteen days modeled (seven winter days and seven summer days) show no 

differences as high as 0.2 μg/m3. 

We next examined the results of the two air quality simulations with the daily differences in maximum 

8-hour ozone concentrations. Figures 11 and 12 present the percent difference in the daily maximum 8-

hour ozone concentrations for one winter day (January 28th) and one summer day (July 15th). In most 

areas for the winter day, daily maximum 8-hour ozone concentrations changed less than one percent. In 

a few urban corridors, namely, near Chicago, IL, Atlanta, GA and Baltimore, MD, changes of one 

percent are noted. Near Philadelphia, PA, changes of up to two percent are noted. However, this was the 

only day of the seven wintertime days simulated that showed a daily maximum 8-hour ozone difference 

as high as one percent anywhere in the modeling domain. It should also be noted that predicting 

wintertime ozone concentrations is not usually an interest because most, if not all high ozone events in 

the middle latitudes of the northern hemisphere occur during the summertime. Therefore the remainder 

of the ozone analysis will focus on summertime differences. On the summer day, July 15th, presented in 

Figure 12, no changes are seen indicating daily maximum 8-hour ozone concentrations changed less 

than one percent. In fact, all seven of the summer days modeled showed no changes as high as one 

percent. 

Absolute differences in daily maximum 8-hour ozone concentrations are shown in Figure 13 for July 

15th. Again, no change as high as 0.5 ppb (0.0005 ppm) was noted on this day or any of the seven 

summer days modeled. In addition to the 8-hour ozone metrics discussed above, differences in 1-hour 

ozone maximums were examined. As shown in Figure14 and 15, only two days during the seven day 

summertime period simulated showed differences in 1-hour ozone maximums as high as 0.5 ppb (0.0005 

ppm). 

Appendix D.4.c - 13

Appendix D - 167
Figure 7. Percent difference in 24-hour PM2.5 concentrations for January 22nd (Representative week 

mobile emissions versus daily mobile emissions). 

Figure 8. Percent difference in 24-hour PM2.5 concentrations for July 9th (Representative week mobile 

emissions versus daily mobile emissions). 

Appendix D.4.c - 14

Appendix D - 168
Figure 9. Absolute differences in 24-hour PM2.5 concentrations for January (Representative week 

mobile emissions versus daily mobile emissions). 

Figure 10. Absolute differences in 24-hour PM2.5 concentrations for July 9th (Representative week 

mobile emissions versus daily mobile emissions). 

Appendix D.4.c - 15

Appendix D - 169
Figure 11. Percent differences in daily 8-hour maximum ozone concentrations for January 28th 

(Representative week mobile emissions versus daily mobile emissions). 

Figure 12. Percent differences in daily 8-hour maximum ozone concentrations for July 15th

(Representative week mobile emissions versus daily mobile emissions). 

Appendix D.4.c - 16

Appendix D - 170
Figure 13. Absolute differences in daily 8-hour maximum ozone concentrations for July 15th

(Representative week mobile emissions versus daily mobile emissions). 

Appendix D.4.c - 17

Appendix D - 171
Figure 14. Absolute differences in 1-hour maximum ozone concentrations for July 12th. 

Figure 15. Absolute differences in 1-hour maximum ozone concentrations for July 14th. 

Appendix D.4.c - 18

Appendix D - 172
CONCLUSIONS 

U.S. EPA attainment demonstration modeling guidance11,12 notes that in some cases it may be useful to 

evaluate how the response of an air quality model to emissions changes varies as a function of 

alternative model inputs or model algorithms. These types of tests can be used to assess the robustness 

of a base case or control strategy modeling evaluation. As an example, EPA remarks that States/Tribes 

could consider the effects of assumed boundary conditions on predicted effectiveness of a control 

strategy. If the model response does not differ greatly over a variety of alternative plausible 

configurations, this increases confidence in the model results. 

The parameters for these sensitivity tests can include, but are not limited to: different chemical 

mechanisms, finer or coarser grid resolution, meteorological inputs from alternative, credible 

meteorological model(s), different initial/boundary conditions, and multiple sets of reasonable emission 

projections. Sensitivity tests can and should be applied throughout the modeling process, not just when 

model performance is being evaluated. 

The modeling team’s research in using reasonable alternate sets of onroad emission projections has 

determined that the use of representative week onroad mobile emissions for each month of our episodes 

within our 12km modeling domain predicts ozone and particulate matter concentration differences from 

annual, everyday onroad mobile modeling which could be considered insignificant from an air quality 

modeling standpoint. The small differences in the air quality results in combination with the length of 

time necessary to conduct daily onroad mobile runs using the MOBILE6 module of SMOKE has 

resulted in the project team’s recommendation that representative week onroad mobile emissions 

methodology be carried forward in the VISTAS regional haze modeling and the ASIP PM2.5 and 8-hour 

ozone modeling. 

REFERENCES 

1. “Draft Modeling Protocol For Association for Southeastern Integrated Planning (ASIP) – 

Emissions and Air Quality Modeling to Address 8-Hour Ozone and PM2.5 Nonattainment in the 

Southeastern United States”; Prepared for ASIP by ENVIRON, Alpine Geophysics and 

University of California, Riverside, January 31, 2006. 

2. Dudhia, J. "A Non-hydrostatic Version of the Penn State/NCAR Mesoscale Model: Validation 

Tests and Simulation of an Atlantic Cyclone and Cold Front", Mon. Wea. Rev., 1993, Vol. 121. 

pp. 1493-1513. 

3. Grell, G. A., J. Dudhia, and D. R. Stauffer. "A Description of the Fifth Generation Penn 

State/NCAR Mesoscale Model (MM5). NCAR Tech. Note, 1994. NCAR TN-398-STR, 138 pp. 

4. 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. 

5. Byun, D.W., and J.K.S. Ching. “Science Algorithms of the EPA Models-3 Community 

Multiscale Air Quality (CMAQ) Modeling System”, EPA/600/R-99/030. 1999. 

6. Pleim J. et al. “New Developments in CMAQ Model Physics”, Presented at the 4th Annual 

CMAS Models-3 User’s Conference, September 26-28, 2005, Friday Center Chapel Hill North 

Carolina. 

Appendix D.4.c - 19

Appendix D - 173
7. VISTAS Phase II Modeling website, http://pah.cert.ucr.edu/vistas/vistas2/ . 

8. Abraczinskas, M.A., Olerud, D.T., Sims, A.P. “Characterizing Annual Meteorological Modeling 

Performance for Visibility Improvement Strategy Modeling in the Southeastern U.S.”, Presented 

at the 3th AMS Joint Conference on the Applications of Air Pollution Meteorology with the Air 

and Waste Management Association, Vancouver, BC, 2004. 

9. Morris, R.E., McNally, D.E., Tesche, T.W., Tonnesen, G., Boylan, J.W., Brewer, P. 

“Preliminary Evaluation of the Community Multiscale Air Quality Model for 2002 over the 

Southeastern United States”. 2005. J. Air & Waste Manag. Assoc. 55:1694-1708 

10. Morris, R.E.; Koo, B.; Yarwood, G.; McNally, D.E.; Tesche, T.W.; Tonnesen, G.S.; Boylan, J.; 

Brewer, P. “Model Sensitivity Evaluation for Organic Carbon Using Two Multi-Pollutant Air 

Quality Models that Simulate Regional Haze in the Southeastern United States”, Atmos. 

Environ. Submitted for publication. 

11. Guidance on the Use of Models and Other Analyses in Attainment Demonstrations for the 8-hour 

Ozone NAAQS, U.S. Environmental Protection Agency Office of Air Quality Planning and 

Standards Emissions, Monitoring, and Analysis Division Air Quality Modeling Group Research 

Triangle Park, North Carolina, 2005; EPA-454/R-05-002. 

12. Guidance for Demonstrating Attainment of Air Quality Goals for PM2.5 and Regional Haze, 

Draft Report, U.S. Environmental Protection Agency, Research Triangle Park, NC. 2001. 

ACKNOWLEDGEMENTS 

VISTAS and ASIP are funded by grants to the Southeastern States Air Resource Managers, Inc. from 

the United States Environmental Protection Agency and the VISTAS/ASIP state and local agencies. The 

authors of this paper would like to acknowledge VISTAS and ASIP State, local and Tribal air quality 

agencies, participating stakeholders, and associated contractors for their contribution and input in the 

emission inventory development and modeling. Additionally, the authors would like to recognize the 

support of the North Carolina Department of Environment and Natural Resources, Division of Air 

Quality and Dennis McNally, Alpine Geophysics for his time in preparing the CMAQ runs used in this 

analysis. 

Appendix D.4.c - 20

Appendix D - 174
KEYWORDS 

ASIP 

MOBILE 

Modeling 

Onroad Mobile 

Ozone 

Particulate Matter 

Regional Haze 

Regional Planning Organization 

SMOKE 

VISTAS 

Appendix D.4.c - 21

Appendix D - 175
Appendix D.5 

VISTAS Emission Inventory 

Supporting Documentation 

Appendix D - 176
Appendix D.5 - 1 

Appendix D - 177
Appendix D.5 - 2 

TABLE OF CONTENTS

Development of the VISTAS Draft 2002 Mobile Source Emission Inventory 

(February 2004 version), Pechan .....................................................................................................3 

Documentation of the Base G 2002 Base Year, 2009 and 2018 Emissions Inventory for 

VISTAS, MACTEC, Inc................................................................................................................62 

Memorandum to VISTAS State Point Contacts and VISTAS EGU Special Interest Workgroup, 

from Gregory Stella, June 13, 2005.............................................................................................318 

Appendix D - 178
PECHAN

5528-B Hempstead Way 

Springfield, VA 22151 

703-813-6700 telephone 

703-813-6729 facsimile 

3622 Lyckan Parkway 

Suite 2002 

Durham, NC 27707 

919-493-3144 telephone 

919-493-3182 facsimile 

P.O. Box 1345 

El Dorado, CA 95623 

530-295-2995 telephone 

530-295-2999 facsimile 

Development of the 

VISTAS Draft 2002 

Mobile Source 

Emission Inventory 

(February 2004 

Version) 

Prepared for: 

VISTAS 

59 Woodfin Place 

Asheville, NC 28801 

Prepared by: 

Maureen A. Mullen 

Kirstin B. Thesing 

E.H. Pechan & Associates, Inc. 

5528-B Hempstead Way 

Springfield, VA 22151 

February 9, 2004 

Pechan Rpt. No. 04.01.002/9440.000 

Appendix D.5 - 3

Appendix D - 179
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Appendix D.5 - 4

Appendix D - 180
CONTENTS

Page

TABLES........................................................................................................................................ iv 

FIGURES....................................................................................................................................... v 

ACRONYMS AND ABBREVIATIONS.................................................................................... vi 

I. INTRODUCTION/BACKGROUND ................................................................................ 1

II. ONROAD METHODS AND DATA.................................................................................. 2

A. 2002 VMT DEVELOPMENT..................................................................................... 2 

1. Conversion to Annual VMT................................................................................. 2 

2. Projection to 2002................................................................................................. 3 

3. Splitting VMT by Road Type............................................................................... 4 

a. Allocation of VMT to Vehicle Type using Default VMT Mix Data............. 4 

b. Allocation of VMT to Vehicle Type using State-Provided VMT Mix Data . 6 

c. Allocation of VMT by Month........................................................................ 6 

B. 2002 ONROAD EMISSION FACTOR DEVELOPMENT USING MOBILE6.2 ..... 8 

1. General MOBILE6.2 File Structure ..................................................................... 8 

C. 2002 ONROAD EMISSION INVENTORY CALCULATIONS ............................. 14 

D. DATA PROVIDED BY STATE AND LOCAL AGENCIES .................................. 14 

1. Temperature........................................................................................................ 21 

2. I/M and ATP Programs ...................................................................................... 21 

3. RVP and Fuel Programs ..................................................................................... 21 

III. NONROAD METHODS AND DATA............................................................................. 22

A. NONROAD MODEL CATEGORIES ...................................................................... 22 

B. AIRCRAFT, COMMERCIAL MARINE VESSELS AND LOCOMOTIVES ........ 23 

2. CMV Improvements........................................................................................... 28 

a. Current Allocation Method.......................................................................... 28 

b. Revised Port Allocation Method.................................................................. 29 

3. Projection Methods............................................................................................. 40 

IV. ONROAD REFUELING METHODS............................................................................. 42

A. CONTROLS .............................................................................................................. 42 

B. METHODS................................................................................................................ 43 

V. NONROAD REFUELING METHODS.......................................................................... 44

VI. SUMMARY OF RESULTS.............................................................................................. 45

A. ONROAD RESULTS................................................................................................ 45 

B. NONROAD RESULTS............................................................................................. 47 

VII. OBSERVATIONS AND RECOMMENDATIONS FOR IMPROVEMENT.............. 51

A. ONROAD SECTOR IMPROVEMENTS ................................................................. 51 

B. NONROAD SECTOR IMPROVEMENTS .............................................................. 52 

VIII. REFERENCES .................................................................................................................. 52

Appendix D.5 - 5

Appendix D - 181
TABLES 

Table II-1. VMT Data Provided by State/Local Agencies ............................................................ 3 

Table II-2. VMT Growth Factors Used for Virginia ..................................................................... 4 

Table II-3. Allocation of VMT from HPMS Vehicle Categories to MOBILE6 Vehicle Types for 

2001........................................................................................................................................... 5 

Table II-4. Default VMT Seasonal and Monthly Temporal Allocation Factors............................ 7 

Table II-5. Default Speeds Modeled by Road Type and Vehicle Type....................................... 13 

Table II-6. Summary of Onroad Data Provided by Alabama ...................................................... 15 

Table II-7. Summary of Onroad Data Provided by Florida......................................................... 15 

Table II-8. Summary of Onroad Data Provided by Georgia........................................................ 16 

Table II-9. Summary of Onroad Data Provided by Kentucky..................................................... 17 

Table II-10. Summary of Onroad Data Provided by Mississippi ................................................ 18 

Table II-11. Summary of Onroad Data Provided by North Carolina .......................................... 18 

Table II-12. Summary of Onroad Data Provided by South Carolina .......................................... 19 

Table II-13. Summary of Onroad Data Provided by Tennessee.................................................. 19 

Table II-14. Summary of Onroad Data Provided by Virginia ..................................................... 20 

Table II-15. Summary of Onroad Data Provided by West Virginia............................................ 20 

Table III-1. Summary of Comments by S/L/T Agencies on NONROAD Model Input Values 

Distributed in July 2003.......................................................................................................... 22 

Table III-2. Summary of S/L/T Agency Data Incorporated into the Draft VISTAS 2002 Other 

Nonroad Inventory.................................................................................................................. 23 

Table III-3a. Replacement Procedures for 1999 Locomotive Emissions for Pickens and 

Tuscaloosa County, Alabama ................................................................................................. 24 

Table III-3b. Replacement Procedures for 1999 Aircraft, Locomotive, and Commercial Marine 

Vessel Emissions for Palm Beach County, Florida ................................................................ 25 

Table III-3c. Replacement Procedures for 1999 Aircraft and Locomotive Emissions for 

Davidson County, Tennessee.................................................................................................. 26 

Table III-3d. Replacement Procedures for 1999 Aircraft, Locomotive, and Commercial Marine 

Vessel Emissions for Sample Counties in Virginia ................................................................ 27 

Table III-4. Port Areas Located in VISTAS and Adjoining States.............................................. 32 

Table III-5. List of VISTAS Ports and Ports of Adjoining States ............................................... 33 

Table III-6. Definition of Port Areas Obtained from Waterborne Commerce ............................ 36 

Table III-7a. Growth Factors for Railroad Distillate Fuel Oil Use.............................................. 40 

Table III-7b. 2002 National Rail Transportation Energy Use by Fuel Type............................... 41 

Table III-8. Growth Factors for Commercial Marine Vessel Distillate and Residual Fuel 

 Oil Use .................................................................................................................................... 42 

Table IV-1. Onroad Stage II Control Programs........................................................................... 43 

Table V-1. 2002 Draft Stage II Refueling Emissions by State.................................................... 45 

Table VI-1. 2002 VISTAS Onroad Emissions and VMT by State.............................................. 46 

Table VI-2. 1999 NEI Version 2 Onroad Emissions and VMT by State .................................... 46 

Table VI-3. Change in Onroad Emissions and VMT from 1999 NEI Version 2 to VISTAS 2002 

Inventory................................................................................................................................. 47 

Table VI-4. 2002 VISTAS Annual Onroad Refueling Emissions............................................... 47 

Table VI-5. Summary of Draft 2002 Nonroad Sector Annual Emissions by State ..................... 48 

Table VI-6. Summary of Draft 2002 NONROAD Model Emission Estimates by State............. 49 

Appendix D.5 - 6

Appendix D - 182
Table VI-7. Summary of Draft 2002 Other Nonroad* Emission Estimates by State.................. 50 

FIGURES 

Figure II-1. Header Section of MOBILE6.2 Input File Including VOC Exhaust and PM10

Emission Factors............................................................................................................................. 9 

Figure II-2. Header Section of MOBILE6.2 Input File Including VOC Evaporative and PM2.5

Emission Factors............................................................................................................................. 9 

Figure II-3. Run Data Section of a MOBILE6.2 Input File......................................................... 10 

Figure II-4. Run Data Section of a MOBILE6.2 Input File with Significant Local Inputs......... 10 

Figure II-5. Sample Scenario for a Typical MOBILE6.2 Input File ........................................... 12 

Figure III-1. VISTAS Region and Surrounding States, Underway NOx Emissions................... 30 

Figure III-2. VISTAS Region and Surrounding States, Port NOx Emissions............................. 31 

Figure III-3. VISTAS Region and Surrounding States, Revised Port Emissions of NOX............ 35 

Appendix D.5 - 7

Appendix D - 183
ACRONYMS AND ABBREVIATIONS 

ATADS Air Traffic Activity Data System 

ATP anti-tampering program 

BTS Bureau of Transportation Statistics 

BTU British thermal unit 

CMV commercial marine vessels 

CNG compressed natural gas 

CO carbon monoxide 

DOT Department of Transportation 

EIA Energy Information Administration 

EPA U.S. Environmental Protection Agency 

FHWA Federal Highway Administration 

FIPS Federal Information Processing Standards 

GF growth factor 

HDDV heavy-duty diesel vehicle 

HDGV heavy-duty gasoline vehicle 

HPMS Highway Performance Monitoring System 

I/M inspection and maintenance 

LDDT light-duty diesel truck 

LDDV light-duty diesel vehicle 

LDGT light-duty gasoline truck 

LDGV light-duty gasoline vehicle 

LPG liquified petroleum gas 

LTO landing and takeoff 

MC motorcycle 

mg milligram 

NAPAP National Acid Precipitation Assessment Program 

NEI National Emission Inventory 

NH3 ammonia 

NOx oxides of nitrogen 

OTAQ Office of Transportation and Air Quality 

Pechan E.H. Pechan & Associates, Inc. 

PM2.5 particulate matter with an aerodynamic diameter less than or equal to 2.5 micrometers 

PM10 particulate matter with an aerodynamic diameter less than or equal to 10 micrometers 

ppmv parts per million volume 

RFG reformulated gasoline 

RVP Reid vapor pressure 

SCC source classification code 

S/L/T State/Local/Tribal 

SO2 sulfur dioxide 

USACE U.S. Army Corps of Engineers 

VISTAS Visibility Improvement-State and Tribal Association of the Southeast 

VMT vehicle miles traveled 

VOC volatile organic compound

Appendix D.5 - 8

Appendix D - 184
1 

I. INTRODUCTION/BACKGROUND 

The Visibility Improvement – State and Tribal Association of the Southeast (VISTAS) has 

contracted with E.H. Pechan & Associates, Inc. (Pechan) to prepare a 2002 mobile source 

emissions inventory. The purpose of this emissions inventory is to support the modeling and 

assessment of speciated particulate matter with an aerodynamic diameter less than or equal to 2.5 

micrometers (PM2.5). Through this contract, Pechan first prepared an inventory review 

document. This document summarized several regional and national emission inventory efforts 

and identified strengths and weaknesses associated with the use of these inventories in regional 

haze modeling. This document also summarized data submittals by State and local air agencies 

within the VISTAS region that could be used in the VISTAS 2002 mobile source emissions 

inventory. 

Since that time, the State and local air agencies have updated their submittals for the mobile 

source sectors, including both onroad vehicles and nonroad engines. In July of 2003, Pechan 

delivered sets of inputs to the NONROAD model option files and MOBILE6.2 input files and 

vehicle miles traveled (VMT) data for each State and local agency to review. For the onroad 

sector, the MOBILE6.2 input files and VMT data represented Pechan’s processing of the State 

and local inputs in a consistent manner for use in calculating the 2002 onroad emissions 

inventory. The MOBILE6.2 input files and VMT data included as much of the local data 

supplied by the State and local agencies as possible, with missing information filled in with 

appropriate default data. The data delivered by Pechan for the State and local agencies to review 

related to the nonroad sector was primarily in the form of temperature and fuel data that would 

be used as inputs to the NONROAD model. It should be noted that the nonroad sector inputs 

were completed first and did not include some of the later temperature and fuel updates that did 

get incorporated in the onroad data. 

The State and local agencies were given a brief period to review, comment upon, and make 

updated submittals to the onroad and nonroad inputs that were delivered in July 2003. After 

receiving these comments and updated data, Pechan updated the appropriate MOBILE6.2 input 

files, VMT data, and nonroad inputs with the revised State and local data. Pechan then 

calculated 2002 onroad and nonroad emissions from these inputs. Pechan presented the 

preliminary results of these emission inventories at a VISTAS meeting on August 28, 2003. 

These draft August 2003 emission estimates, including inputs and methodology, were 

documented in a draft report circulated to VISTAS in October 2003. This October 2003 report 

also included documentation of draft 2002 refueling emissions from onroad and nonroad sources. 

The VISTAS States were asked to review this document, as well as the supporting files provided 

by Pechan, and provide comments or revisions by December 2003. Onroad and nonroad 2002 

emissions for the VISTAS States have since been calculated based on the updates provided by 

the States. This report documents the inputs and methodologies used in the February 2004 

version of the VISTAS 2002 onroad and nonroad mobile source emission inventories. 

Appendix D.5 - 9

Appendix D - 185
2 

II. ONROAD METHODS AND DATA 

A. 2002 VMT DEVELOPMENT 

Table II-1 summarizes the type of VMT data submitted by each agency. Depending upon the 

data submitted by the individual State or agency, up to three different procedures were 

performed on the data. First, VMT data that were not provided at the annual level were 

converted from daily VMT to annual VMT. Second, VMT provided for years other than 2002 

were grown from the base year provided. Finally, the VMT were allocated by vehicle type, if 

not already at that level of detail. The section discusses each of these procedures in more detail. 

It should be noted that although the format and content of the VMT provided by the VISTAS 

State and Local agencies varied significantly from agency to agency, this draft 2002 VISTAS 

inventory is based at a minimum on county/roadway type specific VMT, as provided by the 

individual agencies. This is a significant improvement over the spatial allocation methods used 

in the U.S. Environmental Protection Agency’s (EPA’s) National Emission Inventory (NEI) for 

onroad vehicles. 

1. Conversion to Annual VMT 

For use in the emission calculations, Pechan’s ultimate goal with the VMT data was to develop 

an annual 2002 VMT database by county, roadway type, and vehicle type. As indicated in Table 

II-1, the VMT data were submitted using three different time periods: annual, average annual 

day, and summer day. No temporal adjustments were applied to VMT data submitted as annual 

VMT. VMT data submitted as average annual day VMT were multiplied by 365 to convert from 

an average day to the annual time period. The Jefferson County, Kentucky VMT were submitted 

as summer day VMT. All annual VMT values were converted to units of millions of miles per 

year. Therefore, any VMT values submitted as miles were divided by a factor of 1,000,000 and 

VMT values submitted in units of 1,000 miles were divided by a factor of 1,000. 

The Jefferson County, Kentucky VMT submittal included a single factor for converting the 

summer day VMT to average annual day VMT. Thus, the Jefferson County summer day VMT 

data were first multiplied by a factor of 0.97752 (the temporal conversion factor provided by 

Jefferson County) to obtain average annual day VMT. The VMT data were then multiplied by 

365 to obtain the annual VMT. 

Appendix D.5 - 10

Appendix D - 186
3 

Table II-1. VMT Data Provided by State/Local Agencies 

State/Area 

Time 

Period 

2002 Actual 

VMT by 

County/Road 

Type/Vehicle 

Type 

2002 Actual 

VMT by 

County/Road 

Type 

2002 

Projected 

VMT by 

County/Road 

Type 

2002 VMT 

from TDM by 

County/Road 

Type/Vehicle 

Type 

1999 Actual 

VMT by 

County/Road 

Type/Vehicle 

Type 

Alabama AAD X 

Florida AAD X 

Georgia AAD X 

Kentucky AAD X 

Jefferson County, 

KY SD X 

Mississippi ANN X 

North Carolina AAD X 

South Carolina ANN X 

Tennessee AAD X 

Virginia ANN X 

West Virginia ANN X X 

Time Period Codes: AAD=Average Annual Day, SD=Summer Day, ANN=Annual 

2. Projection to 2002 

As indicated in Table II-1, the Virginia VMT submittal was for a base year of 1999 rather than 

2002. Thus, these VMT data needed to be projected to 2002 before calculating emissions. For 

Virginia, growth factors were developed by roadway type for the period from 1999 to 2001 

based on historical VMT data by roadway type from Table VM-2 “Functional System Travel” in 

DOT’s Highway Statistics series (DOT, 1999 and 2001). The growth factors, presented in Table 

II-2, were calculated by dividing Virginia’s 2001 VMT for each of the 12 roadway types from 

Highway Statistics 2001 by the corresponding 1999 VMT from Highway Statistics 1999. For the 

period from 2001 to 2002, the growth factors were developed using data obtained from the U.S. 

Department of Transportation’s Traffic Volume Trends report (DOT, 2002). This monthly 

publication provides a comparison of preliminary 2002 VMT estimates with comparable 2001 

VMT. For several roadway types, these data are provided only at a national level. However, for 

the combined rural interstates and arterials, these data are presented by State. The resultant data, 

used to project the 2001 Virginia VMT to 2002, are shown in Table II-2. The 2001 to 2002 

growth factors represent the 2002 VMT divided by the 2001 VMT, based on the data Virginia 

for the rural interstates and arterials and on the national data for the remaining roadway types. 

Once the growth factors were developed, the Virginia 1999 VMT data were first multiplied by 

the appropriate 1999 to 2001 growth factor and then by the appropriate 2001 to 2002 growth 

factor. 

Appendix D.5 - 11

Appendix D - 187
4 

Table II-2. VMT Growth Factors Used for Virginia 

Roadway Type 

Roadway 

Type 

Portion of 

SCC 

Virginia 1999 

to 2001 VMT 

Growth Factor

Virginia 2001 

to 2002 VMT 

Growth Factor

Rural Interstate 110 1.043 1.035 

Rural Other Principal Arterial 130 1.050 1.035 

Rural Major Arterial 150 1.130 1.035 

Rural Major Collector 170 0.982 1.011 

Rural Minor Collector 190 1.032 1.011 

Rural Local 210 0.923 1.011 

Urban Interstate 230 1.050 1.024 

Urban Other Freeway & Expressway 250 0.984 1.011 

Urban Other Principal Arterial 270 1.061 1.011 

Urban Minor Arterial 290 0.991 1.011 

Urban Collector 310 0.925 1.013 

Urban Local 330 0.690 1.013 

Sources: U.S. Department of Transportation, Federal Highway Administration, “Traffic Volume Trends, 

December 2002”, (http://www.fhwa.dot.gov/ohim/tvtw/tvtpage.htm); Highway Statistics 1999, and Highway 

Statistics 2001 (http://www.fhwa.dot.gov/policy/ohpi/hss/hsspubs.htm)

3. Splitting VMT by Road Type 

The final step in developing a consistent 2002 VMT data base was to allocate VMT from the 

county and roadway type level of detail to the county/roadway type/vehicle type level of detail. 

As shown in Table II-1, the Jefferson County, Kentucky; Mississippi; Virginia; and West 

Virginia VMT data supplied for these jurisdictions already included the vehicle type level of 

detail, so this final adjustment was not needed for these areas. For the remaining areas, some 

provided VMT mix by vehicle type fractions while others provided no information on the 

allocation of VMT by vehicle. In this latter case, default VMT fraction data from EPA’s 

MOBILE6 model were used. 

The States for which MOBILE6 default VMT mix data were used are: Alabama, Florida, 

Georgia, Kentucky (excluding Boone County, Campbell County, Kenton County, and Jefferson 

County), and South Carolina. It should be noted that Georgia initially provided VMT fractions 

based on Georgia's HPMS classification count data, but after review of ten years of these data 

determined that they are not reflecting the trend towards increasing travel by light trucks. 

Georgia therefore decided it was more conservative to assume MOBILE6 default VMT fractions. 

a. Allocation of VMT to Vehicle Type using Default VMT Mix Data 

To calculate 2002 VMT at the county/roadway type/vehicle type level using national default 

data, the VMT totals by county and roadway type need to be allocated among the 28 MOBILE6 

vehicle types. This was done based on the distribution of the 2001 rural and urban VMT among 

the six Highway Performance Monitoring Systems (HPMS) vehicle types found in Table VM-1 

(“Annual Vehicle Distance Traveled in Miles and Related Data - 1999 - by Highway Category 

and Vehicle Type”) of the Federal Highway Administration’s (FHWA’s) Highway Statistics 

Appendix D.5 - 12

Appendix D - 188
5 

2001 (http://www.fhwa.dot.gov/ohim/ hs01/index.htm) and a mapping of these HPMS vehicle 

categories to the 28 MOBILE6 vehicle types. This mapping of the MOBILE6 vehicle types to 

the HPMS vehicle types was developed by EPA’s Office of Transportation and Air Quality 

(OTAQ) and is used in the development of the NEI. The data first needed to be expanded to the 

28 vehicle type level of detail to obtain the proper cross reference between the HPMS and 

MOBILE6 vehicle types since the eight vehicle types used in the final VISTAS VMT data base 

cannot be directly mapped to the HPMS vehicle categories. First, the VMT totals for each of the 

six HPMS vehicle categories were calculated as a fraction of the total VMT. This calculation 

was performed separately for the rural VMT and the urban VMT. The resulting 2001 VMT 

fractions for rural VMT and urban VMT are shown in Table II-3. Note that 2002 VMT are not 

yet available at this level of detail. Using the default MOBILE6 VMT fractions for 2001 (since 

the HPMS data represents 2001), taken from a MOBILE6 output file for 2001, the MOBILE6 

VMT fractions were renormalized among all MOBILE6 vehicle types mapped to a given HPMS 

vehicle category. This renormalization is shown in the final column of Table II-3. 

Table II-3. Allocation of VMT from HPMS Vehicle Categories to 

MOBILE6 Vehicle Types for 2001 

HPMS Vehicle Category 

HPMS 2001 

Rural VMT 

Fractions 

HPMS 2001 

Urban VMT 

Fractions 

MOBILE6 

Vehicle 

Category 

MOBILE6 2001 

VMT Fractions by 

HPMS Category 

Passenger Cars 0.5454 0.6065 LDGV 0.9980 

 LDDV 0.0020 

Motorcycles 0.0039 0.0031 MC 1.0000 

Other 2-Axle 4-Tire Vehicles 0.3368 0.3375 LDGT1 0.1565 

 LDGT2 0.5211 

LDGT3 0.1585 

LDGT4 0.0729 

LDDT12 0.0005 

LDDT34 0.0032 

HDGV2B 0.0658 

HDDV2B 0.0216 

Single-Unit 2-Axle 6-Tire or 

More Trucks 

0.0332 0.0212 HDGV3 0.0376 

 HDGV4 0.0206 

HDGV5 0.0436 

HDGV6 0.0934 

HDGV7 0.0437 

HDDV3 0.1023 

HDDV4 0.0867 

HDDV5 0.0380 

HDDV6 0.2138 

HDDV7 0.3205 

Combination Trucks 0.0770 0.0300 HDGV8A 0.0001 

 HDGV8B 0.0000 

HDDV8A 0.2191 

HDDV8B 0.7808 

Buses 0.0037 0.0017 HDGB 0.1920 

 HDDBT 0.3258 

HDDBS 0.4822 

Total 1.0000 1.0000 

Appendix D.5 - 13

Appendix D - 189
6 

To calculate VMT by vehicle type, each VMT value representing a given county and road type 

was multiplied by the product of the HPMS VMT fraction (selected depending upon whether the 

road type represent VMT on rural or urban roads) and the corresponding MOBILE6 VMT 

fraction by HPMS category. This process resulted in 28 VMT values at the county/roadway 

type/vehicle type level of detail for each county/roadway type VMT value in the original VMT 

file. 

As an example, Table II-3 shows that the HPMS Passenger Car vehicle category accounts for 

54.54 percent of the total VMT on rural road types and that the MOBILE6 LDGV category 

accounts for 99.8 percent of the VMT in the HPMS Passenger Car category. Therefore, a VMT 

value representing rural interstates would be multiplied by 0.5454 times 0.9980 (0.5443), to 

obtain the VMT total on rural interstates from LDGVs. Once all county/roadway type VMT 

values were expanded to the corresponding set of values of VMT at the county/roadway type/28 

MOBILE6 vehicle type level of detail, the VMT data base was then totaled at the eight vehicle 

type level of detail (LDGV, LDGT1, LDGT2, HDGV, LDDV, LDDT, HDDV, MC). 

b. Allocation of VMT to Vehicle Type using State-Provided VMT Mix Data 

Both North Carolina and Tennessee provided VMT mix data at the eight vehicle type level of 

detail. The Tennessee data was provided for ten different county groupings, with a VMT mix 

provided for six aggregated roadway type categories. North Carolina provided statewide VMT 

mix fractions for each of the 12 roadway types. Since the VMT mix data for these two States 

were already at the eight vehicle type level, the procedure for allocating VMT by vehicle type 

was simpler than the procedure described above using the default data. Each county/roadway 

type VMT value was matched to the corresponding VMT mix for that county and roadway type 

and then separately multiplied by each of the eight VMT mix fractions to create eight VMT 

values by county/roadway type/vehicle type that would sum to the original VMT value at the 

county/roadway type level of detail. 

c. Allocation of VMT by Month 

The resulting annual county-level, vehicle, and roadway type-specific VMT data were 

temporally allocated to months during the emission calculations. National Acid Precipitation 

Assessment Program (NAPAP) temporal allocation factors were used to apportion the VMT to 

the four seasons. Monthly VMT data were obtained using a ratio between the number of days in 

a month and the number of days in the corresponding season. These temporal factors are shown 

in Table II-4. Several States provided some level of information on temporal adjustment factors 

for their VMT. These data were not used in this draft version of the 2002 VISTAS emission 

inventory due to time constraints. However, any State or locally supplied temporal adjustment 

factors will be included in the final version of the 2002 VISTAS onroad emission inventory.

Appendix D.5 - 14

Appendix D - 190
7 

Table II-4. Default VMT Seasonal and Monthly Temporal Allocation Factors 

Roadway Seasonal VMT Factors Vehicle Type Roadway Type Winter Spring Summer Fall LDV,LDT,MC Rural 0.2160 0.2390 0.2890 0.2560LDV,LDT,MC Urban 0.2340 0.2550 0.2650 0.2450HDV All 0.2500 0.2500 0.2500 0.2500Monthly VMT FactorsVehicle Type Roadway Type Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec LDV,LDT,MC Rural 0.0744 0.0672 0.0805 0.0779 0.0805 0.0942 0.0974 0.0974 0.0844 0.0872 0.0844 0.0744LDV,LDT,MC Urban 0.0806 0.0728 0.0859 0.0832 0.0859 0.0864 0.0893 0.0893 0.0808 0.0835 0.0808 0.0806HDV All 0.0861 0.0778 0.0842 0.0815 0.0842 0.0815 0.0842 0.0842 0.0842 0.0852 0.0824 0.0861

Appendix D.5 - 15

Appendix D - 191
8 

B. 2002 ONROAD EMISSION FACTOR DEVELOPMENT USING 

MOBILE6.2 

The onroad emission factors used in the calculation of the VISTAS 2002 onroad emission 

inventory were generated using EPA’s MOBILE6.2 emission factor model. In the development 

of the MOBILE6.2 input files, Pechan attempted to include as much of the relevant data supplied 

by the State and local agencies as possible, while at the same time, maintaining a generally 

similar overall structure to the MOBILE6.2 input files, such that the output emission factors 

could easily be matched to the appropriate VMT values. This section first discusses the overall 

general structure of the MOBILE6.2 input files. This is followed by details explaining how this 

general structure was adapted to include the State and local agency data and summaries of the 

types of data provided by each agency. 

1. General MOBILE6.2 File Structure 

Each MOBILE6.2 input file is divided into three sections: the header section, the run data 

section, and the scenario section. Information contained in the header section is primarily related 

to defining the output format and content desired by the user. For the processing of the VISTAS 

emission calculations, the database output format, aggregated to the daily level, was the desired 

output format. In addition, for proper modeling of the VOC emissions, it was desired to 

calculate the exhaust VOC emissions separately from the evaporative VOC emissions. However, 

within the constraints of MOBILE6.2 in the daily aggregated database output format, it is not 

possible to obtain evaporative and exhaust VOC emission factors broken out separately within 

each scenario. It is also not possible to obtain emission factors for both PM10 and PM2.5 within a 

single MOBILE6.2 scenario. Therefore, two sets of MOBILE6.2 input files were created—one 

set to model VOC exhaust, NOx, CO, SO2, PM10, and NH3 emission factors and a second set to 

model VOC evaporative and PM2.5 emission factors. Figure II-1 illustrates the header section of 

a sample VISTAS MOBILE6.2 input file used to generate the VOC exhaust, NOx, CO, SO2, 

PM10, and NH3 emission factors. Similarly, Figure II-2 illustrates the header section of a sample 

VISTAS MOBILE6.2 input file used to generate the VOC evaporative and PM2.5 emission 

factors. The primary difference between these two header sections is in the selection of the 

emission types included, using the DATABASE EMISSIONS command and in the selection of 

the pollutants to be included in the output. In Figure II-1, having the first two flags set to “2” 

following the DATABASE EMISSIONS command indicates that the startup and running 

exhaust emission factor components will be included in the output emission factor table. In 

Figure II-2, the last six flags of the DATABASE EMISSIONS command line are set to “2” to 

obtain the evaporative emission factor components in the emission factor output file. In Figure 

II-2, the pollutants SO2 and NH3 are eliminated from the PARTICULATES command line, as the 

emission factors for these pollutants will be reported in the output file resulting from the file 

shown in Figure II-1. 

Appendix D.5 - 16

Appendix D - 192
9 

MOBILE6 INPUT FILE : 

> HEADER 01 0012002 - EXHAUST - PM 10.0 

 

REPORT FILE : Vistas02/Output02/V0100110.TXT REPLACE 

DATABASE OUTPUT : 

WITH FIELDNAMES : 

DAILY OUTPUT : 

DATABASE EMISSIONS : 2211 1111 

PARTICULATES : SO4 OCARBON ECARBON GASPM LEAD SO2 NH3 BRAKE TIRE 

AGGREGATED OUTPUT : 

EMISSIONS TABLE : Vistas02/TB1_02/V0100110.TB1 REPLACE 

 

MOBILE6 INPUT FILE : 

> HEADER 01 0012002 - EVAPORATIVE - PM 2.50 

 

REPORT FILE : Vistas02/Output02/V0100125.TXT REPLACE 

DATABASE OUTPUT : 

WITH FIELDNAMES : 

DAILY OUTPUT : 

DATABASE EMISSIONS : 1122 2222 

POLLUTANTS : HC 

PARTICULATES : ECARBON SO4 OCARBON GASPM LEAD BRAKE TIRE 

AGGREGATED OUTPUT : 

EMISSIONS TABLE : Vistas02/TB1_02/V0100125.TB1 REPLACE 

The next section of the MOBILE6 input files is the run data section. This section includes data 

that applies to all scenarios in the input file. Figure II-3 shows an example of this section for a 

county using default data. The only commands included in this example tell MOBILE6 that the 

HC emission factors should be expressed in terms of VOC and that refueling emission factors 

should be excluded from the output. It should be noted that refueling emissions were calculated 

using a separate set of input files, but were excluded from the onroad input files here since 

refueling emissions are included in the area source inventory rather than the onroad inventory. 

Chapter IV discusses the onroad refueling MOBILE6 input files and emission calculations. 

Comments in Figure II-3 indicate that this input file is using default registration distributions and 

diesel sales fractions. For any input files that represent counties for which registration 

distribution, diesel sales fractions, or trip length distributions have been provided or that have an 

inspection and maintenance (I/M) program, anti-tampering program (ATP), or low emission 

vehicle program in place in 2002, additional inputs are required in the run data section of the 

MOBILE6.2 input file. Figure II-4 shows an example of an input file including all of these data. 

Some of these data inputs are included directly in the MOBILE6.2 input file, while other data are 

contained in external text files that are named by the commands in the run data section. For 

questions regarding the specifics of any of the MOBILE6 input commands listed, the MOBILE6 

User’s Guide should be consulted. 

Figure II-1. Header Section of MOBILE6.2 Input File Including VOC Exhaust and 

PM10 Emission Factors 

Figure II-2. Header Section of MOBILE6.2 Input File Including VOC Evaporative 

and PM2.5 Emission Factors 

Appendix D.5 - 17

Appendix D - 193
10 

RUN DATA : 

> 

 

EXPRESS HC AS VOC : 

NO REFUELING : 

* MOBILE6 Default Registration Distributions Applied 

* MOBILE6 Default Diesel Sales Fractions Applied 

RUN DATA : 

> 

 

EXPRESS HC AS VOC : 

NO REFUELING : 

REG DIST : Vistas02\ExtFiles\R02_ARLI.RDT 

* Diesel Sales Fractions Source File - 

E:\TrendsM6_New\Vistas02\ExtFiles\D02_ARLI.DSF 

DIESEL FRACTIONS : 

0.0012 0.0023 0.0026 0.0027 0.0029 0.0015 0.0008 0.0011 0.0001 0.0006 

0.0013 0.0015 0.0006 0.0014 0.0006 0.0099 0.0087 0.0446 0.0685 0.0857 

0.1922 0.1481 0.1132 0.0959 0.0126 

0.0056 0.0221 0.0167 0.0235 0.0126 0.0119 0.0206 0.0136 0.0155 0.0127 

0.0246 0.0206 0.0222 0.0184 0.0227 0.0115 0.0310 0.0568 0.0508 0.1211 

0.1077 0.2126 0.0711 0.0286 0.0176 

0.0056 0.0221 0.0167 0.0235 0.0126 0.0119 0.0206 0.0136 0.0155 0.0127 

0.0246 0.0206 0.0222 0.0184 0.0227 0.0115 0.0310 0.0568 0.0508 0.1211 

0.1077 0.2126 0.0711 0.0286 0.0176 

0.0126 0.0126 0.0126 0.0126 0.0126 0.0126 0.0126 0.0115 0.0111 0.0145 

0.0115 0.0129 0.0096 0.0083 0.0072 0.0082 0.0124 0.0135 0.0169 0.0209 

0.0256 0.0013 0.0006 0.0011 0.0001 

0.0126 0.0126 0.0126 0.0126 0.0126 0.0126 0.0126 0.0115 0.0111 0.0145 

0.0115 0.0129 0.0096 0.0083 0.0072 0.0082 0.0124 0.0135 0.0169 0.0209 

0.0256 0.0013 0.0006 0.0011 0.0001 

0.1998 0.1998 0.1998 0.1998 0.1998 0.1998 0.1998 0.2578 0.2515 0.3263 

0.2784 0.2963 0.2384 0.2058 0.1756 0.1958 0.2726 0.2743 0.3004 0.2918 

0.2859 0.0138 0.0000 0.0000 0.0000 

0.6774 0.6774 0.6774 0.6774 0.6774 0.6774 0.6774 0.7715 0.7910 0.8105 

0.8068 0.8280 0.8477 0.7940 0.7488 0.7789 0.7842 0.6145 0.5139 0.5032 

0.4277 0.0079 0.0000 0.0000 0.0001 

0.8606 0.8606 0.8606 0.8606 0.8606 0.8606 0.8606 0.8473 0.8048 0.8331 

0.7901 0.7316 0.7275 0.7158 0.5647 0.3178 0.2207 0.1968 0.1570 0.0738 

0.0341 0.0414 0.0003 0.0000 0.0000 

0.4647 0.4647 0.4647 0.4647 0.4647 0.4647 0.4647 0.4384 0.3670 0.4125 

0.3462 0.2771 0.2730 0.2616 0.1543 0.0615 0.0383 0.0333 0.0255 0.0111 

0.0049 0.0060 0.0000 0.0000 0.0000 

0.6300 0.6300 0.6300 0.6300 0.6300 0.6300 0.6300 0.6078 0.5246 0.5767 

Figure II-3. Run Data Section of a MOBILE6.2 Input File

Figure II-4. Run Data Section of a MOBILE6.2 Input File with Significant Local 

Inputs 

Appendix D.5 - 18

Appendix D - 194
11 

0.5289 0.5788 0.5617 0.4537 0.4216 0.4734 0.4705 0.4525 0.4310 0.3569 

0.3690 0.4413 0.3094 0.1679 0.1390 

0.8563 0.8563 0.8563 0.8563 0.8563 0.8563 0.8563 0.8443 0.7943 0.8266 

0.7972 0.8279 0.8177 0.7440 0.7184 0.7588 0.7567 0.7431 0.7261 0.6602 

0.6717 0.7344 0.6107 0.4140 0.3610 

0.9992 0.9992 0.9992 0.9992 0.9992 0.9992 0.9992 0.9989 0.9987 0.9989 

0.9977 0.9984 0.9982 0.9979 0.9969 0.9978 0.9980 0.9979 0.9976 0.9969 

0.9978 0.9982 0.9974 0.9965 0.9964 

1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 

1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 

1.0000 1.0000 1.0000 1.0000 1.0000 

0.9585 0.9585 0.9585 0.9585 0.9585 0.9585 0.9585 0.8857 0.8525 0.8795 

0.9900 0.9105 0.8760 0.7710 0.7502 0.7345 0.6733 0.5155 0.3845 0.3238 

0.3260 0.2639 0.0594 0.0460 0.0291 

 

> ANTI-TAMP PROG : E:\TrendsM6_New\Vistas02\ExtFiles\VA_ATP2002.ATP 

ANTI-TAMP PROG : 

89 68 50 22222 21111111 1 12 098. 22112222 

> Exhaust I/M - IDLE test program #1 

I/M PROGRAM : 1 1983 2050 2 TRC 2500/IDLE 

I/M MODEL YEARS : 1 1968 1980 

I/M VEHICLES : 1 22222 21111111 1 

I/M STRINGENCY : 1 35.0 

I/M COMPLIANCE : 1 98.0 

I/M WAIVER RATES : 1 2.0 2.0 

 

> Exhaust I/M - ASM final program #2 

I/M PROGRAM : 2 1983 2050 2 TRC ASM 2525/5015 PHASE-IN 

I/M MODEL YEARS : 2 1981 2050 

I/M VEHICLES : 2 22222 11111111 1 

I/M STRINGENCY : 2 35.0 

I/M COMPLIANCE : 2 98.0 

I/M WAIVER RATES : 2 2.0 2.0 

I/M EFFECTIVENESS : 0.94 0.94 0.94 

 

> Exhaust I/M - IDLE test program #1 

I/M PROGRAM : 3 1983 2050 2 TRC 2500/IDLE 

I/M MODEL YEARS : 3 1981 2050 

I/M VEHICLES : 3 11111 21111111 1 

I/M STRINGENCY : 3 35.0 

I/M COMPLIANCE : 3 98.0 

I/M WAIVER RATES : 3 2.0 2.0 

 

> Evap I/M - Gas Cap test program #3 

I/M PROGRAM : 4 1998 2050 2 TRC GC 

I/M MODEL YEARS : 4 1973 2050 

I/M VEHICLES : 4 22222 21111111 1 

I/M COMPLIANCE : 4 98.0 

I/M WAIVER RATES : 4 2.0 2.0 

 

94+ LDG IMP : Vistas02\ExtFiles\NLEVNE.D 

> WeekDay Trip Length Distribution 

WE DA TRI LEN DI : Vistas02\ExtFiles\WeekTLD2.wdt 

Appendix D.5 - 19

Appendix D - 195
12 

The third and final section of the MOBILE6.2 input files contains the scenario data. For this 

VISTAS inventory, each speed and road type combination or speed distribution were modeled in 

twelve consecutive scenarios representing the temperature and fuel properties applicable in each 

month. Thus, if a State agency supplied an average speed/road type combination for each of the 

12 HPMS road categories, the corresponding MOBILE6.2 input file would have 144 scenarios. 

The first scenario would represent January temperature and fuel conditions at the speed and 

MOBILE6 roadway type for the first speed/roadway type provided (typically rural interstates). 

This would be followed by the February scenario modeled for the same speed and roadway type, 

and so on through the twelfth scenario representing December conditions for the same speed and 

roadway type combination. 

Figure II-5 illustrates a sample scenario from one of the VISTAS MOBILE6.2 input files. This 

is the first scenario in the file—therefore, it represents January temperature and fuel conditions. 

The month of a given scenario in the VISTAS MOBILE6.2 input files can be determined by the 

last two digits of the SCENARIO RECORD command line. In this case, the last two digits are 

“01” indicating January. It should be noted that the only options for the EVALUATION 

MONTH command are “1” indicating January or “7” indicating July. For the VISTAS input 

files, the EVALUATION MONTH was set to “1” for all months from January through June and 

to 7 for months from July through December. When this flag is set to “1”, it indicates that 

MOBILE6 will use a January registration distribution. When the flag is set to “7”, MOBILE6 

ages the registration by a half year, applying a half year of fleet turnover to the distribution. The 

EVALUATION MONTH setting can also affect the reductions from reformulated gas programs. 

However, by including the SEASON command, as shown in Figure II-5, the EVALUATION 

MONTH flag setting will not affect reformulated gasoline reductions. With the SEASON flag 

set to “2”, winter reformulated gasoline rules will be applied in areas with a reformulated gas 

program modeled (using the FUEL PROGRAM command). Summer reformulated gas rules and 

reductions will be applied when the SEASON flag is set to “1” if reformulated gas has been 

modeled. In all of the VISTAS input files, the SEASON flag was included for all areas, whether 

or not a reformulated gasoline program was modeled. This flag has no effect when the FUEL 

PROGRAM command is not used. The SEASON flag was set to “1” for the months of May 

through September and to “2” for the remaining months. 

SCENARIO RECORD : 010010215.0_M01 

>FV FILE: SCENARIO: 1 

CALENDAR YEAR : 2002 

EVALUATION MONTH : 1 

MIN/MAX TEMPERATURE: 38.0 60.0 

ALTITUDE : 1 

PARTICULATE EF : PMGZML.CSV PMGDR1.CSV PMGDR2.CSV PMDZML.CSV PMDDR1.CSV 

PMDDR2.CSV 

SEASON : 2 

AVERAGE SPEED : 15.0 Arterial 

FUEL RVP : 12.5 

PARTICLE SIZE : 10.0 

DIESEL SULFUR : 500.0 

Figure II-5. Sample Scenario for a Typical MOBILE6.2 Input File

Appendix D.5 - 20

Appendix D - 196
13 

Local speed data were provided by the agencies in Georgia, Kentucky, North Carolina, 

Tennessee, and Virginia. A set of 12 monthly scenarios was developed for each speed input for 

these States, with one exception. The Northern Kentucky (Boone County, Campbell County, 

and Kenton County) and Jefferson County, Kentucky inputs were speed distribution files, rather 

than average speeds by individual roadway types (one for Northern Kentucky and one for 

Jefferson County, Kentucky). In this case, only 12 scenarios were modeled in total in the 

Jefferson County and Northern Kentucky input files, with the Jefferson County or Northern 

Kentucky speed distribution referenced in each scenario, respectively. No speed information 

was provided for Alabama, Florida, Mississippi, South Carolina, or West Virginia. The average 

speeds modeled in these files were the default speeds used in the NEI. These speeds are shown 

in Table II-5 and vary by both roadway type and vehicle category. It should be noted that 

several agencies provided speed information for ramps. Since the VMT data file is organized by 

SCC and no SCC currently exists for ramp VMT, the ramp speed information could not be used 

directly. In some cases, the fraction of VMT occurring on ramps was provided. In these cases, 

this information was combined with the freeway speeds, following the guidance in the 

MOBILE6 user’s guide to determine the overall freeway speed including the ramp speed, at 34.6 

mph (the assumed value for ramp speeds in MOBILE6), and the fraction of VMT occurring on 

the ramps. 

Table II-5. Default Speeds Modeled by Road Type and Vehicle Type 

(mph) 

Speed (mph) and MOBILE6 Road Type 

HPMS Road Type 

Light Duty 

Vehicles 

Light Duty 

Trucks 

Heavy Duty 

Trucks 

Rural Interstate 60 Freeway 55 Freeway 40 Freeway 

Rural Principal Arterial 45 Arterial 45 Arterial 35 Arterial 

Rural Minor Arterial 40 Arterial 40 Arterial 30 Arterial 

Rural Major Collector 35 Arterial 35 Arterial 25 Arterial 

Rural Minor Collector 30 Arterial 30 Arterial 25 Arterial 

Rural Local 30 Arterial 30 Arterial 25 Arterial 

Urban Interstate 45 Freeway 45 Freeway 35 Freeway 

Urban Other Freeway and Expressway 45 Freeway 45 Freeway 35 Freeway 

Urban Principal Arterial 20 Arterial 20 Arterial 15 Arterial 

Urban Minor Arterial 20 Arterial 20 Arterial 15 Arterial 

Urban Collector 20 Arterial 20 Arterial 15 Arterial 

Urban Local Local Local Local 

Another optional input included in the scenario section of the MOBILE6 input files is the VMT 

mix by 16 MOBILE6 vehicle categories. These vehicle categories are based on the 28 

MOBILE6 vehicle categories, but with gasoline and diesel vehicles of the same weight class 

combined together. When no information was provided on VMT mix, the MOBILE6 defaults 

were used. Local VMT mix information provided by Tennessee, Virginia, and Jefferson County, 

Kentucky were included in the MOBILE6.2 input files. In some cases, the same VMT mix was 

applied to all scenarios. In other cases, the VMT mixes were specific to roadway type, so the 

VMT mix would vary according to the roadway type being represented in the scenario. 

Appendix D.5 - 21

Appendix D - 197
14 

C. 2002 ONROAD EMISSION INVENTORY CALCULATIONS 

Once the MOBILE6.2 input files were set up and run through the MOBILE6.2 model, onroad 

emissions were calculated by multiplying the monthly VMT for a given county, roadway type, 

and vehicle type by the emission factor modeled for the same month, county, vehicle type and 

roadway type. Because the MOBILE6.2 input files were set up to create output files in the form 

of database tables, the output is provided by each of the 28 MOBILE6 vehicle types. Thus, the 

emission factors first were aggregated to the eight vehicle categories included in the VMT files. 

This was done using the VMT Fraction data provided in each of the MOBILE6 output files. For 

each of the MOBILE6 vehicle types included in one of the eight vehicle types needed, the VMT 

fractions were renormalized within that category. These eight vehicle categories are sometimes 

referred to as the MOBILE5 vehicle categories. For example, the LDGT1 and LDGT2 

MOBILE6 vehicle categories are both included in the MOBILE5 LDGT1 category. In this case, 

the MOBILE6 LDGT1 VMT fraction was divided by the sum of the MOBILE6 LDGT1 and 

LDGT2 VMT fractions. The same was done with the MOBILE6 LDGT2 VMT fraction, so that 

the renormalized MOBILE6 LDGT1 and LDGT2 VMT fractions should now sum to 1. Next, 

these normalized VMT fractions were multiplied by the corresponding MOBILE6 emission 

factor and all of these weighted emission factors for a given scenario, within a MOBILE5 vehicle 

category were summed to obtain the weighted emission factors at the MOBILE5 vehicle 

category level. The VMT fractions included in the MOBILE6 output files are affected by the 

registration distribution, diesel sales fractions, and VMT mixes supplied in the MOBILE6.2 

input files. Areas that used the MOBILE6 defaults for each of these inputs should all have the 

same VMT fractions, although even in these cases, there are two sets of VMT fractions—one for 

the months from January through June and another for the months July through December. This 

occurs due to the aging of the registration distribution caused by the use of the EVALUATION 

MONTH flag, as discussed above. These emission factors, now at the MOBILE5 vehicle 

category level, were multiplied by the corresponding VMT values to obtain monthly emissions 

by county, roadway type, and vehicle category. 

D. DATA PROVIDED BY STATE AND LOCAL AGENCIES 

The sections above describe some of the data that was supplied by the VISTAS State and local 

agencies for use in the development of the 2002 onroad emission inventory. Tables II-6 through 

II-15 summarize the data supplied by each agency in a consistent fashion. These tables primarily 

list the data that were actually used in this analysis. This section provides additional information 

on the data supplied by these agencies as well discussing why some of the data supplied could 

not be used. 

Appendix D.5 - 22

Appendix D - 198
15 

Table II-6. Summary of Onroad Data Provided by Alabama 

 

Data Element Data Supplied by Responsible Agency 

VMT Data 2002 actual daily VMT by county/road type 

MOBILE6 Input Files 

MOBILE5 Input Files 

VMT Mix Information 

Counties by Temperature Region 

Monthly Temperatures Monthly 2002 temperatures by county 

RVP Data March-September RVP values 

Speed Data 

Registration Data 

Fuel Information 

I/M Program Information N/A 

Other 

Table II-7. Summary of Onroad Data Provided by Florida 

 

Data Element Data Supplied by Responsible Agency 

VMT Data 2002 actual daily VMT by county/road type 

MOBILE6 Input Files 

MOBILE5 Input Files 

VMT Mix Information 

Counties by Temperature Region Supplied counties in each of 3 temperature regions 

Monthly Temperatures 

RVP Data Summer RVP values provided 

Speed Data 

Registration Data 

Fuel Information 

I/M Program Information N/A 

Other 

Appendix D.5 - 23

Appendix D - 199
16 

Table II-8. Summary of Onroad Data Provided by Georgia 

 

Data Element Data Supplied by Responsible Agency 

VMT Data 2002 actual average annual daily VMT by county and 

functional classification prepared by Georgia DOT 

MOBILE6 Input Files Provided MOBILE6 sample input files 

MOBILE5 Input Files 

VMT Mix Information 

Counties by Temperature Region 

Monthly Temperatures 

RVP Data Provided summer RVP values 

Speed Data Provided 2002 statewide speeds by road type (speeds 

based on VMT-weighted average speeds, from a 2002 

loaded highway network for the 13-county Atlanta area) 

Registration Data Provided one MOBILE6 registration distribution for 13-

county Atlanta area and one MOBILE6 registration 

distribution for rest-of-state 

Fuel Information Provided information on Georgia gasoline program, 

applied to 25 counties 

I/M Program Information Provided I/M inputs for 13-county Atlanta area in 

MOBILE6 format 

Other Provided VMT temporal adjustment factors by month and 

day of week for each road type (not used in the 01/04 

inventory) 

Appendix D.5 - 24

Appendix D - 200
17 

Table II-9. Summary of Onroad Data Provided by Kentucky

 

Data Element Data Supplied by Responsible Agency 

VMT Data 2002 actual daily VMT by county/road type 

MOBILE6 Input Files Provided sample MOBILE6 input files for several counties 

MOBILE5 Input Files 

VMT Mix Information 

Counties by Temperature 

Region Provided temperature stations to be used for several counties 

Monthly Temperatures 

RVP Data Provided summer RVP for several counties 

Speed Data Provided average speed by road type for several county groupings 

Registration Data 

Fuel Information Verified counties in reformulated gasoline program 

I/M Program Information I/M program information provided 

Other 

Jefferson County, Kentucky 

Data Element Data Supplied by Responsible Agency 

VMT Data 2002 summer day VMT from TDM by county/road type/vehicle type 

MOBILE6 Input Files 

Provided MOBILE6 input files representing the four different vehicle 

control combinations found in Jefferson County 

MOBILE5 Input Files 

VMT Mix Information Provided Jefferson County VMT mix in MOBILE6 format 

Counties by Temperature 

Region 

Monthly Temperatures Provided 2002 actual monthly temperature data for Louisville area 

RVP Data Provided summer and winter RVP values 

Speed Data Provided speed distribution file for Jefferson County 

Registration Data 

Provided registration distribution for Jefferson County in MOBILE6 

format 

Fuel Information Reformulated gasoline modeled 

I/M Program Information I/M program information provided 

Other Provided absolute humidity data 

Boone County, Campbell County, and Kenton County, Kentucky 

Data Element Data Supplied by Responsible Agency 

VMT Data 2002 actual daily VMT by county/road type 

MOBILE6 Input Files 

MOBILE5 Input Files Provided MOBILE5 input file for Northern Kentucky counties 

VMT Mix Information 

Counties by Temperature 

Region 

Monthly Temperatures 

RVP Data Provided summer and winter RVP values 

Speed Data Provided speed distribution file for Northern Kentucky 

Registration Data 

Provided registration distribution for Northern Kentucky in MOBILE6 

format—LDGVs and LDGT1s only 

Fuel Information Reformulated gasoline modeled 

I/M Program Information I/M program information extracted from MOBILE5 input file 

Other 

Provided Northern Kentucky VMT distributions by facility type and 

by hour in MOBILE6 format 

Appendix D.5 - 25

Appendix D - 201
18 

Table II-10. Summary of Onroad Data Provided by Mississippi 

 

Data Element Data Supplied by Responsible Agency 

VMT Data Provided 2002 actual annual VMT by county/road type/vehicle type 

MOBILE6 Input Files 

MOBILE5 Input Files 

VMT Mix Information 

Counties by Temperature 

Region 

Monthly Temperatures 

RVP Data Provided statewide RVP by season 

Speed Data 

Registration Data 

Fuel Information 

I/M Program Information N/A 

Other 

Table II-11. Summary of Onroad Data Provided by North Carolina 

 

Data Element Data Supplied by Responsible Agency 

VMT Data 2002 actual daily VMT by county/road type 

MOBILE6 Input Files 

MOBILE5 Input Files 

VMT Mix Information 

Counties by Temperature Region Indicated counties within each of several 

temperature regions in state 

Monthly Temperatures 

RVP Data 

Speed Data Provided average speed data by road type for 

several groups of counties and rest-of-state 

Registration Data Provided registration data for several groups of 

counties and rest-of-state based on 2001 data 

Fuel Information 

I/M Program Information Provided written description of I/M program 

Other 

Appendix D.5 - 26

Appendix D - 202
19 

Table II-12. Summary of Onroad Data Provided by South Carolina 

 

Data Element Data Supplied by Responsible Agency 

VMT Data 2002 actual annual VMT by county/road type 

MOBILE6 Input Files 

MOBILE5 Input Files 

VMT Mix Information 

Counties by Temperature Region Supplied counties in each of 7 temperature regions 

Monthly Temperatures 

RVP Data 

Speed Data 

Registration Data 

Fuel Information 

I/M Program Information N/A 

Other 

Table II-13. Summary of Onroad Data Provided by Tennessee 

 

Data Element Data Supplied by Responsible Agency 

VMT Data 2002 actual daily VMT by county/road type 

MOBILE6 Input Files 

Provided MOBILE6 input files for groups of counties 

covering state 

MOBILE5 Input Files 

VMT Mix Information Provided VMT mix fractions by road type 

Counties by Temperature Region 

Monthly Temperatures 

RVP Data Provided summer RVP information 

Speed Data 

Provided average speed data by road type for 

groups of counties 

Registration Data Provided registration data for most counties 

Fuel Information 

I/M Program Information Provided in MOBILE6 input files 

Other 

Appendix D.5 - 27

Appendix D - 203
20 

Table II-14. Summary of Onroad Data Provided by Virginia 

 

Data Element Data Supplied by Responsible Agency 

VMT Data 1999 actual annual VMT by county/road type/vehicle type 

MOBILE6 Input Files Provided MOBILE6 input files for representative counties 

MOBILE5 Input Files 

VMT Mix Information 

Counties by 

Temperature Region 

Provided listing of counties within each of several temperature 

regions 

Monthly Temperatures 

RVP Data Provided summer RVP data 

Speed Data Speed data provided for each VMT record 

Registration Data 2002 county-level registration data provided for nonattainment 

counties 

Fuel Information Verified counties in reformulated gasoline program 

I/M Program Information I/M and ATP inputs provided in MOBILE6 formats; verified counties 

that implement I/M 

Other LEV progam modeled statewide; provided diesel sales fractions 

Table II-15. Summary of Onroad Data Provided by West Virginia 

 

Data Element Data Supplied by Responsible Agency 

VMT Data 2002 actual annual VMT by county/road 

type/vehicle type 

MOBILE6 Input Files Supplied several sample MOBILE6 input 

files 

MOBILE5 Input Files 

VMT Mix Information VMT data included vehicle type splits 

Counties by Temperature Region Supplied counties in each of 4 

temperature regions 

Monthly Temperatures 

RVP Data Supplied summer RVP value statewide 

Speed Data Supplied speed data in MOBILE6 input 

files--speed data determined to be 

inappropriate for this analysis 

Registration Data 

Fuel Information 

I/M Program Information N/A 

Other 

Appendix D.5 - 28

Appendix D - 204
21 

1. Temperature 

The default average daily maximum and minimum temperature data for each month used in this 

analysis was obtained from the National Climatic Data Center. This temperature data was actual 

2002 data. It should be noted that a number of agencies provided information on ozone season 

or summer temperatures. This information could not be used in this analysis, as the ozone 

season temperature data are based on several years of temperature data and do not represent the 

average daily minimum and maximum monthly temperatures that were needed for this analysis. 

Information was provided by Alabama, Kentucky, North Carolina, South Carolina, Virginia, and 

West Virginia related to monthly temperature. In some cases, this data divided the counties 

within the State into several temperature regions and listing a city that should be used for 

obtaining the temperature data. In these cases, a temperature station from the National Climatic 

Data Center database was selected from the desired city, and this corresponding temperature set 

was applied to the counties listed by the States. Several of the States provided their own full set 

of 2002 temperature data either Statewide or by county. These data were included in the 

analysis, replacing the default temperature data for those States. 

2. I/M and ATP Programs 

Several agencies provided I/M and ATP inputs in the form of MOBILE5 input files. Pechan 

converted these inputs to MOBILE6 inputs, following the guidance in the MOBILE6 user’s 

guide. Agencies that provided the data in MOBILE5 format should review the MOBILE6 I/M 

and ATP inputs carefully to make sure that the conversions fully capture the actual programs as 

they were implemented in 2002. In addition, from information provided by North Carolina, 

Tennessee, and Jefferson County, Kentucky, the I/M and ATP programs should only be applied 

to a portion of the VMT in the corresponding counties. For the North Carolina and Tennessee 

I/M counties, duplicate MOBILE6.2 input files were created that eliminate the I/M and ATP 

programs. The VMT from these counties was divided according to the fraction of the VMT 

subject to I/M and the fraction of the VMT not subject to I/M. These fractions were provided by 

the corresponding agencies in North Carolina and Tennessee. The VMT data for each I/M 

county was then divided according to these VMT fractions to obtain one set of VMT for the 

portion of vehicles subject to I/M and another set for those not subject to I/M. The emission 

factors from the I/M files were multiplied by the portion of the VMT subject to I/M while the 

emission factors from the files without the I/M were multiplied by the remaining portion of the 

VMT. In Jefferson County, Kentucky, a similar procedure was followed. However, in this case, 

the county also has a significant portion of VMT from vehicles registered in Indiana that are not 

subject to I/M or that do not have reformulated gasoline. Thus, the Jefferson County VMT was 

divided into four subsets and four MOBILE6 input files were developed representing the four 

groups of vehicle types traveling in the county. 

3. RVP and Fuel Programs 

Default RVP by county and month were obtained from the data used in the 2002 NEI. The NEI 

fuel data are based on year 2000 fuel survey data for January and July, with data for intermediate 

months calculated by interpolation. RVP data for July were applied from May through 

September, the months when Phase II RVP regulations are in effect. For States that supplied 

Appendix D.5 - 29

Appendix D - 205
22 

July, summer, or ozone season RVP values, these values were also applied from May through 

September. If winter RVP values were supplied, these values were applied directly in each of 

the remaining months. As mentioned above, reformulated gasoline programs were modeled 

where appropriate. Georgia provided additional fuel inputs to capture the RVP and sulfur 

content values of its low sulfur gasoline program. 

 

III. NONROAD METHODS AND DATA 

A. NONROAD MODEL CATEGORIES 

Pechan used EPA’s draft NONROAD2002a model to generate 2002 annual emissions for the 

majority of nonroad engines. To improve the accuracy of these model runs, we asked State/ 

Local/Tribal (S/L/T) contacts to provide seasonal or monthly gasoline Reid Vapor Pressure 

(RVP) and temperature; appropriate data on reformulated gasoline (RFG), oxygenated fuel and 

Stage II programs, and diesel fuel sulfur levels. In addition, to improve the activity data inputs, 

we asked whether S/L/T agencies had collected information on equipment populations or activity 

(e.g., hours of use or load factors) to use in place of default populations in the NONROAD 

model. No S/L/T agencies provided activity data to replace the model defaults. 

Seasonal average RVP and average, maximum and minimum temperature values were calculated 

based on the county-level, monthly RVP and temperature data set prepared for onroad mobile 

sources. Information on RFG programs and oxygenated fuels programs obtained for the onroad 

mobile sector was also used. In July 2003, Pechan distributed the input values (RVP, percent 

O2, temperature, and Stage II control efficiency) to be used for the draft NONROAD model 

2002 inventory for review and comment by the VISTAS S/L/T agencies. Pechan obtained 

comments from the S/L/T agencies listed in Table III-1. 

Table III-1. Summary of Comments by S/L/T Agencies on NONROAD Model Input 

Values Distributed in July 2003 

State Comment 

Alabama Provided region specific data to replace the statewide default values for RVP and 

ambient temperature 

Georgia Changed oxygen weight percent to zero for all counties 

Kentucky No Stage II programs in Bullitt and Oldham Counties 

Tennessee Revised RVP value for Davidson County 

Mississippi Revised statewide RVP by season 

Virginia No Stage II program in Charles City County 

Additional comments on the August 2003 NONROAD model temperature and RVP inputs were 

incorporated for consistency with data submitted for the onroad mobile modeling (e.g., North 

Carolina). In addition, the State of West Virginia provided revised geographic allocation files 

for certain nonroad categories to improve upon the NONROAD model’s default county 

allocation. 

Using the inputs shown in the file “VISTAS NONROAD County Inputs.xls,” Pechan prepared 

seasonal option files for each of four seasons (winter, spring, summer, and autumn), and ran the 

Appendix D.5 - 30

Appendix D - 206
23 

NONROAD model at the county level. Model default values were used for all other inputs, with 

the exception of diesel fuel sulfur. A value of 2,500 parts per million volume (ppmv) was used 

instead of the default 2,318 ppm, since the default represented a national average including 

California’s lower diesel fuel sulfur level. Pechan summed the seasonal results, and then 

processed the model output to develop a county-level, SCC-level annual emissions inventory for 

all pollutants except NH3. 

The NH3 emissions for NONROAD model categories were developed using the following 

procedures. OTAQ recently reviewed the basis of NH3 data summarized in a report entitled, 

“A Study of the Potential Impact of Some Unregulated Motor Vehicle Emissions” (Harvey, 

1983). In conducting this review, OTAQ performed an analysis of the available light-duty 

noncatalyst engine data to develop defensible gasoline nonroad emission factors on a mg/gallon 

basis (Harvey, 2003). For both gasoline noncatalyst and diesel engines, fuel based emission 

factors were developed from emission factors expressed on a gram/mile basis by accounting for 

the reported fuel economy of each tested engine. For gasoline non-catalyst engines, this resulted 

in a value of 115.8 mg/gallon, which is applied to county-level fuel consumption estimates for 

2-stroke gasoline, 4-stroke gasoline and liquified petroleum gas (LPG) equipment. From the 

diesel engine test data, a value of 83.3 mg/gallon was derived, which is applied to diesel fuel 

consumption estimates. County-level fuel consumption for these engines, expressed in gallons, 

is an output from EPA’s NONROAD model. 

B. AIRCRAFT, COMMERCIAL MARINE VESSELS AND 

LOCOMOTIVES 

For 2002 aircraft, commercial marine vessels (CMVs), and locomotives, Pechan used 1999 

emission estimates developed for EPA’s 1999 NEI Version 2 as base year estimates for the 

VISTAS region. These categories are not included in the NONROAD model, and are hereafter 

referred to as “other nonroad.” Pechan then incorporated revised S/L/T estimates summarized in 

Table III-2, using the replacement procedures summarized in Tables III-3a through III-3d. 

Pechan tracked changes by labeling the default 1999 NEI records as Version 2 (V2) and the 

revised S/L/T records as Version 3 (V3). In cases where PM2.5 estimates were not provided, 

they were developed using the following category-specific fractions applied to the available 

PM10 emission estimates: 1) Aircraft: 0.69; 2) Locomotive: 0.90; and 3) CMV: 0.92 (EPA, 

2002). Commercial marine adjustments are described in detail in the following section. 

Table III-2. Summary of S/L/T Agency Data Incorporated into the Draft VISTAS 

2002 Other Nonroad Inventory

State Description of Inventory Pollutants 

Alabama 1999 Locomotive emissions for Pickens and Tuscaloosa 

counties 

VOC, NOx, and CO 

Florida 2001 Aircraft, Locomotive and Commercial Marine Vessel 

emissions for Palm Beach County 

VOC, NOx, CO, PM10, 

and SOx

Tennessee 1999 Aircraft and Locomotive emissions for Davidson County VOC, NOx, CO, SOx, and 

primary PM10

Virginia 1999 Statewide Inventory for Aircraft, Locomotive and 

Commercial Marine Vessels 

VOC, NOx, CO 

Appendix D.5 - 31

Appendix D - 207
24 

Table III-3a. Replacement Procedures for 1999 Locomotive Emissions for 

Pickens and Tuscaloosa County, Alabama 

 

STATE_ 

FIPS 

COUNTY_ 

FIPS SCC Version Notes 

START_ 

DATE 

END_ 

DATE VOC NOX CO 

01 107 2285002005 V3 7.73 179.7 22.81

01 107 2285002005 V2 

Replace VOC, NOx, and CO 

emissions 19990101 19991231 1962.9 45643 5794.5

01 107 2285002010 V3 5.39 53.48 9.47

01 107 2285002010 V2 

Replace VOC, NOx, and CO 

emissions 19990101 19991231 5.39 53.48 9.48

01 125 2285002005 V3 16.31 379.15 48.13

01 125 2285002005 V2 

Replace VOC, NOx, and CO 

emissions 19990101 19991231 3384.9 78711.4 9992.6

01 125 2285002010 V3 9.29 92.15 16.33

01 125 2285002010 V2 

Replace VOC, NOx, and CO 

emissions 19990101 19991231 9.29 92.15 16.33

Appendix D.5 - 32

Appendix D - 208
25 

Table III-3b. Replacement Procedures for 1999 Aircraft, Locomotive, and Commercial Marine Vessel Emissions 

for Palm Beach County, Florida 

STATE_ 

FIPS 

COUNTY_ 

FIPS SCC Version Notes 

START_ 

DATE 

END_ 

DATE VOC NOX CO SO2 

PM10- 

PRI 

PM25- 

PRI 

12 099 2275000000 V3 Apply a Growth Factor to 2001 

state-supplied aircraft emissions to backcast to 1999 Estimate PM2.5-PRI off PM10-PRI 

19990101 19991231 470.39 805.94 4,121.41 1.98 0.00 

12 099 2275001000 V2 Delete all records for this SCC 19990101 19991231 0.44 0.05 9.03 0 0.19 0.1312 099 2275020000 V2 Delete all records for this SCC 19990101 19991231 79.1 275.5 330.6 26.3412 099 2275050000 V2 Delete all records for this SCC 19990101 19991231 13.93 2.37 437.43 0.36 8.62 5.9512 099 2275060000 V2 Delete all records for this SCC 19990101 19991231 9.23 1.19 212.32 0.11 4.55 3.1412 099 2280000000 V3 Apply a Growth Factor to 2001 

state-supplied cmv emissions to backcast to 1999 Estimate PM2.5-PRI off PM10-PRI 

19990101 19991231 10.42 115.60 0.97 9.94 33.91

12 099 2280002100 V2 Delete all records for this SCC 19990101 19991231 25.5 815.4 107.51 36.95 34.3 31.5512 099 2280002200 V2 Delete all records for this SCC 19990101 19991231 0.22 7.05 0.93 0.32 0.3 0.2712 099 2280003100 V2 Delete all records for this SCC 19990101 19991231 6.8 217.5 28.63 115.6 9.48 8.7312 099 2280003200 V2 Delete all records for this SCC 19990101 19991231 0.06 1.93 0.25 1.43 0.11 0.112 099 2285002000 V3 Apply a Growth Factor to 2001 

state-supplied locomotive emissions to backcast to 1999 Estimate PM2.5-PRI off PM10-PRI 

19990101 19991231 28.19 658.78 83.64 48.09 15.50

12 099 2285002006 V2 Delete all records for this SCC 19990101 19991231 6.11 164.1 16.17 10.26 4.07 3.6612 099 2285002008 V2 Delete all records for this SCC 19990101 19991231 0.45 12.15 1.2 0.76 0.3 0.2712 099 2285002009 V2 Delete all records for this SCC 19990101 19991231 6.78 182.2 17.95 11.39 4.52 4.0712 099 2285002010 V2 Delete all records for this SCC 19990101 19991231 3.75 64.36 6.77 3 1.64 1.47

1 Palm Beach County provided emission estimates corresponding to 2001; as such, 2001 emission estimates were backcast to 1999 using growth factors presented in this report 

before incorporation.

Appendix D.5 - 33

Appendix D - 209
26 

Table III-3c. Replacement Procedures for 1999 Aircraft and Locomotive Emissions for 

Davidson County, Tennessee 

 

STATE_ 

FIPS 

COUNTY_ 

FIPS SCC Version Notes 

START_ 

DATE 

END_ 

DATE VOC NOX CO SO2

PM10-

PRI 

PM25-

PRI 

47 037 2275000000 V3 

Estimate PM2.5-PRI off 

PM10-PRI 19990101 19991231 232.125 634.35 1766 32.13 39.25 

47 037 2275001000 V2 

Delete all records for this 

SCC 19990101 19991231 1.7 0.2 35 0.02 0.75 0.52

47 037 2275020000 V2 

Delete all records for this 

SCC 19990101 19991231 187.45 649.92 782.93 62.34

47 037 2275050000 V2 

Delete all records for this 

SCC 19990101 19991231 4.72 0.8 148.3 0.12 2.92 2.02

47 037 2275060000 V2 

Delete all records for this 

SCC 19990101 19991231 15.22 1.97 349.97 0.19 7.51 5.18

47 037 2285002000 V3 

Estimate PM2.5-PRI off 

PM10-PRI 19990101 19991231 20.803 363.117 50.701 26.36 8.893 

47 037 2285002006 V2 

Delete all records for this 

SCC 19990101 19991231 31.91 857.26 84.46 53.6 21.27 19.15

47 037 2285002010 V2 

Delete all records for this 

SCC 19990101 19991231 19.6 336.23 35.39 15.68 8.54 7.69

Appendix D.5 - 34

Appendix D - 210
27 

Table III-3d. Replacement Procedures for 1999 Aircraft, Locomotive, and 

Commercial Marine Vessel Emissions for Sample Counties in Virginia 

STATE_ 

FIPS 

COUNTY_ 

FIPS SCC Version Notes 

START_

DATE 

END_ 

DATE VOC NOX CO SO2

PM10-

PRI 

PM25-

PRI 

51 001 2275001000 V3 19990101 19991231 3.47 0.78 3.74

51 001 2275001000 V2 Replace VOC, NOx, and CO emissions 

Keep SO2, PM10-PRI, and PM2.5-PRI 

emissions 

19990101 19991231 0.31 0.04 6.38 0 0.14 0.09

51 013 2275020000 V3 19990101 19991231 145.821 992.23 1634.2

51 013 2275020000 V2 Replace VOC, NOx, and CO emissions 

Keep SO2 emissions 

19990101 19991231 271.17 940.36 1132.7 90.2

51 001 2275050000 V3 19990101 19991231 1.25 0.21 39.34

51 001 2275050000 V2 Replace VOC, NOx, and CO emissions 

Keep SO2, PM10-PRI, and PM2.5-PRI 

emissions 

19990101 19991231 0.25 0.04 7.81 0.01 0.15 0.11

51 001 2275060000 V3 19990101 19991231 0.05 0.01 1.26

51 001 2275060000 V2 Replace VOC, NOx, and CO emissions 

Keep SO2, PM10-PRI, and PM2.5-PRI 

emissions 

19990101 19991231 1.47 0.19 33.8 0.02 0.72 0.5

51 670 2280002000 V3 Add SCC to the Inventory 19990101 19991231 3.3 18.16 6.94

51 670 2280002100 V2 Sum up SO2, PM10-PRI, and PM2.5-PRI 

emissions for SCCs 2280002100 and 

2280002200 and add to SCC 280002000. 

After that, delete all records for SCC 

2280002100 and 2280002200 

19990101 19991231 10.12 323.52 42.66 14.7 13.61 12.52

51 670 2280002200 V2 Sum up SO2, PM10-PRI, and PM2.5-PRI 

emissions for SCCs 2280002100 and 

2280002200 and add to SCC 

2280002000. After that, delete all records 

for SCC 2280002100 and 2280002200 

19990101 19991231 0.17 5.39 0.71 0.24 0.23 0.21

51 670 2280003000 V3 Add SCC to the Inventory 19990101 19991231 0.14 1.64 0

51 670 2280003100 V2 Sum up SO2, PM10-PRI, and PM2.5-PRI 

emissions for SCCs 2280003100 and 

2280003200 and add to SCC 

2280003000. After that, delete all records 

for SCC 2280003100 and 2280003200 

19990101 19991231 2.7 86.31 11.36 45.9 3.76 3.46

51 670 2280003200 V2 Sum up SO2, PM10, and PM2.5 

Emissions for SCCs 2280003100 and 

2280003200 and add to SCC 

2280003000. After that, delete all records 

for SCC 2280003100 and 2280003200 

19990101 19991231 0.05 1.48 0.19 1.09 0.08 0.08

51 199 2283002000 V3 19990101 19991231 8.46 53.47 15.51

51 199 2283002000 V2 Replace VOC, NOx, and CO emissions 19990101 19991231 7.43 47.26 13.63

51 740 2285002005 V3 Add SCC to the Inventory 19990101 19991231 3.76 100.99 9.95

51 740 2285002006 V2 Sum up SO2, PM10-PRI, and PM2.5-PRI 

emissions for SCCs 2285002006 and 

2285002007 and add to SCC 285002005. 

After that, delete all records for SCC 

2285002006 and 2285002007.1

19990101 19991231 0.7 18.77 1.85 1.17 0.47 0.42

51 740 2285002007 V2 Sum up SO2, PM10-PRI, and PM2.5-PRI 

emissions for SCCs 2285002006 and 

2285002007 and add to SCC 285002005. 

After that, delete all records for SCC 

2285002006 and 2285002007.1

19990101 19991231 0.08 2.26 0.22 0.14 0.06 0.05

51 036 2285002010 V3 19990101 19991231 0.59 10.13 1.06

51 036 2285002010 V2 Replace VOC, NOx, and CO emissions 

Keep SO2, PM10-PRI, and PM2.5-PRI 

emissions 

19990101 19991231 1.99 34.15 3.59 1.59 0.87 0.78

1 Other counties may also have emissions for SCCs 2285002008 and 2285002009. In these cases, sum up SO2, PM10-PRI, and PM2.5-PRI emissions 

for SCCs 2285002006, 2285002007, 2285002008, and 2285002009 and add to SCC 2285002005. After that, delete all records for SCC 2285002006, 

2285002007, 2285002008, and 2285002009. 

Appendix D.5 - 35

Appendix D - 211
28 

2. CMV Improvements 

This section describes procedures for improving the spatial distribution of CMV emission 

estimates for the VISTAS region. States that share borders with non-VISTAS States along the 

Mississippi and Ohio Rivers have expressed concern about the representativeness of port 

emission estimates at a county-level. Revising the county-level emissions estimates would allow 

more accurate modeling of emissions in the VISTAS States. 

Ideally, CMV emission estimates would be developed using local activity data that account for 

vessel type, engine type and mode of operation (cruise, maneuvering, and hotelling). Creating 

this type of “bottom-up” emission inventory requires a large amount of effort. Therefore, Pechan 

utilized port-specific emission estimates developed for the 1999 NEI, distributed using a revised 

allocation methodology, which incorporates information on the number of port facilities in each 

county. 

a. Current Allocation Method 

The current 2002 VISTAS commercial marine inventory is based on EPA’s 1999 NEI Version 

2.0, projected to 2002 using appropriate growth factors. State-supplied data were incorporated 

by EPA or by Pechan for some VISTAS States for this category, including Alabama, Virginia, 

West Virginia, and Palm Beach County, Florida. 

The 1999 NEI estimated emissions for these categories according to the following SCCs: 

SCC Descriptor 1 Descriptor 3 Descriptor 6 Descriptor 8

2280002100 Mobile Sources Marine Vessels, Commercial Diesel Port emissions 

2280002200 Mobile Sources Marine Vessels, Commercial Diesel Underway emissions 

2280003100 Mobile Sources Marine Vessels, Commercial Residual Port emissions 

2280003200 Mobile Sources Marine Vessels, Commercial Residual Underway emissions 

For the 1999 NEI, commercial marine diesel emissions were developed by obtaining 2000 

emission estimates for all pollutants except SO2 from OTAQ’s marine diesel regulatory 

background documentation (Draft Regulatory Impact Analysis - Control of Emissions from 

Compression-Ignition Marine Engines). To estimate emissions for 1999, 2000 estimates were 

backcast using growth factors obtained from the draft RIA cited above. Steam-powered residual 

CMV emission estimates were developed by obtaining fuel usage data from OTAQ and applying 

fuel-based emission factors (EPA, 1989). A similar method was used for diesel SO2 emissions. 

National diesel usage was estimated assuming a sulfur content of 0.25 percent and EPA emission 

factors (EPA, 2002). 

National diesel emissions were disaggregated into port and underway emissions estimates based 

on the assumption that 75 percent of distillate fuel is consumed within the port, while the 

remaining fuel is consumed while underway, consistent with EPA guidance. National residual 

emissions were disaggregated into port and underway emissions estimates based on the 

assumption that 25 percent of residual fuel is consumed within the port, while the remaining fuel 

is consumed while underway (EPA, 1989). 

Appendix D.5 - 36

Appendix D - 212
29 

To allocate to counties, port emissions were assigned to the 150 largest U.S. ports based on 

activity obtained from the U.S. Army Corps of Engineers (USACE). The percentage of total 

traffic for each port was calculated by dividing the port-level traffic by the total traffic. 

Emissions for each port were then assigned to a single county. 

Underway emissions are assigned to counties based on a county=s shipping lane traffic. The 

Bureau of Transportation Statistics’ (BTS=) National Transportation Atlas Databases-1999 

contains data on the thousand tons per mile traveled for each shipping lane link in the United 

States (BTS-CD26). Where navigable rivers form a county or State boundary, the shipping lane 

traffic is proportioned to individual counties based on the length of shoreline that is shared. For 

example, if two counties share a navigable river, and both counties have the same length of 

shoreline, the shipping traffic is split evenly between the two counties. Shipping lanes that are 

not within counties, for example in the ocean, are associated to States based on BTS 

assignments. These waterway weights are then evenly distributed among the counties within 

these States that have navigable waterways. All shipping activity is summed at the county-level 

and compared with national shipping activity to determine what portion of activity can be 

attributed to individual counties. These proportions were used in disaggregating the national 

CMV emission estimates to the county level. 

b. Revised Port Allocation Method 

Figures III-1 and III-2 present emission maps for CMV port and underway NOx emissions 

created from the 1999 NEI Version 2.0 data. For underway emissions, Pechan believes that the 

allocation procedure results in a reasonable distribution of county-level emissions. However, the 

methodology to allocate port emissions results in all the emissions being assigned to a single 

county. For example, Cabell County in West Virginia is assigned all emissions for Huntington 

Port, but no emissions are allocated to Lawrence County in Ohio, the county on the opposite 

river bank. 

Port areas encompass multiple States and counties and in some cases, multiple waterways. 

Therefore, the emissions allocation process must incorporate all counties in the vicinity of the 

port where activity is occurring. This is especially true for inland rivers where activity takes 

place on both riverbanks and for 10 river miles or more outside the port city. The revised 

methodology allocates port emissions based on a surrogate for port-related activity in each 

county, rather than using a single county to define the port. 

Appendix D.5 - 37

Appendix D - 213
30 

Figure 1.

d Surrounding States VISTAS Region and Surrounding States VISTAS Region an

Underway NOx Emissions NOx Emissions(Tons per Year) 00 - 400 400 - 800 800 - 1,300

1,300 - 2,4002,400 - 3,680

E.H. Pechan & Associates, Inc.

Figure III-1. VISTAS Re

Date: December 11, 2003.

gion and Surrounding States, Underway NOx Emissions

Appendix D.5 - 38

Appendix D - 214
31 

Figure 2.

VISTAS Region and Surrounding States VISTAS Region and Surrounding States

Port NOx Emissions NOx Emissions(Tons per Year) 00 - 3,000 3,000 - 7,000 7,000 - 13,000 13,000 - 26,000

26,000 - 67,000

E.H. Pechan & Associates, Inc.

Date: December 11, 2003.

Figure III-2. VISTAS Region and Surrounding States, Port NOx Emissions

Appendix D.5 - 39

Appendix D - 215
32 

The report, Waterborne Commerce of the United States, Calendar Year 1999 (USACE, 2000), 

hereafter referred to as Waterborne Commerce, presents the cargo tonnage and number of vessel 

trips in major waterways of the United States. The report defines port areas, which USACE uses 

to develop the Top 150 Ports in the United States by amount of cargo tonnage. As discussed in 

the previous section, the 1999 NEI allocates all the port emissions to these 150 ports based on the 

cargo tonnage handled by the port. 

Pechan uses this allocation of emissions to each port area as the starting point of its revised 

allocation process. Table III-4 presents the ports that are located in VISTAS and adjoining 

States, which are part of the Top 150 Ports. 

Table III-4. Port Areas Located in VISTAS and Adjoining States 

Port State Port State 

Mobile AL Pascagoula MS 

Guntersville AL Vicksburg MS 

Helena AR Biloxi MS 

Port Everglades FL Greenville MS 

Jacksonville FL Gulfport MS 

Miami FL Wilmington NC 

Port Canaveral FL Morehead City NC 

Palm Beach FL Cincinnati OH 

Panama City FL Pittsburgh PA 

Pensacola FL Charleston SC 

Tampa FL Georgetown SC 

Port Manatee FL Memphis TN 

Weedon Island FL Nashville TN 

Savannah GA Chattanooga TN 

Brunswick GA Norfolk Harbor VA 

Mount Vernon IN Newport News VA 

Louisville KY Hopewell VA 

New Orleans LA Huntington WV 

Baton Rouge LA 

The next step was to develop a list of counties that make up the port area. Port area definitions 

were obtained from Waterborne Commerce. Table III-6 presents the port definitions for the 

VISTAS States and adjoining States. Using the port definitions by river mile, Pechan established 

which counties are included in each port area. In many cases, these port areas encompass 

multiple counties. For example, Pittsburgh is defined in Waterborne Commerce as: 

Ohio River from Pittsburgh, PA to mile 40 (Pennsylvania/Ohio State Line); 

Allegheny River from Pittsburgh, PA to mile 72 (to head of project); 

Monongahela River from Pittsburgh, PA to mile 91 (to head of project). 

Appendix D.5 - 40

Appendix D - 216
33 

Therefore, the Port of Pittsburgh includes the following counties in Pennsylvania; Allegheny, 

Westmoreland, Armstrong, Washington, Fayette, Greene, Beaver. This process was repeated for 

all the port areas listed in Table III-4. 

The next step in allocating emissions is to develop a surrogate for the amount of CMV activity in 

each county of the port area. Pechan assumed that the activity of vessels in each county is 

related to the number of port facilities operating in a given county. Port facilities include 

terminals, piers, wharves, and docks that are involved in all types of commercial activity and 

support services. Pechan obtained the number of port facilities in each county from The Port 

Series Reports (USACE, 2003). The USACE periodically surveys the commercial marine 

industry to obtain information on port facilities and publishes it in The Port Series Reports. The 

reports give the name, location, operations, and describe the physical and inter-modal 

characteristics of the facilities. The data includes the location of the facility by river mile, State, 

and county. 

For each port area, Pechan calculated the ratio between the number of port facilities in each 

county to the total number of facilities in all counties that make up the port area. This ratio was 

used to allocate emissions for each port area to the county-level. Table III-5 presents the 

allocation ratios for each county in the port areas. Some port areas were still encompassed by 

one county using the definition of the port from Waterborne Commerce. However, a number of 

port areas include multiple counties. Note that New Orleans and Pittsburgh do not include any 

counties in VISTAS States. 

Table III-5. List of VISTAS Ports and Ports of Adjoining States 

Port State County Ratio Port State County Ratio Port State County Ratio 

Port Everglades FL Broward 1.0 AR Phillips 0.7778 TN Hamilton 0.7692 

Jacksonville FL Duval 1.0 

Helena 

MS Coahoma 0.2222 Chattanooga TN Marion 0.2308 

Miami FL Miami-Dade 1.0 FL Charlotte 0.7500 VA Norfolk City 0.5568 

Port Canaveral FL Brevard 1.0 

Charlotte 

FL Lee 0.2500 VA Chesapeake 

City 0.3068 

Palm Beach FL Palm Beach 1.0 IN Vanderburgh 0.3182 

Norfolk 

VA Portsmouth 0.1364 

Panama City FL Bay 1.0 IN Posey 0.4773 VA Newport News 0.6500 

Pensacola FL Escambia 1.0 

Mount 

Vernon 

KY Henderson 0.2045 

Newport 

News VA Hampton 0.3500 

Tampa FL Hillborough 1.0 KY Jefferson 0.6596 VA Hopewell 0.5000 

Port Manatee FL Manatee 1.0 

Louisville 

IN Clark 0.3404 Hopewell VA Charles City 0.5000 

Weedon Island FL Pinellas 1.0 LA St. Bernard 0.0858 PA Allegheny 0.5206 

Savannah GA Chatham 1.0 LA Plaquemines 0.1231 PA Westmoreland 0.0412 

Brunswick GA Glynn 1.0 LA Orleans 0.3284 PA Armstrong 0.0309 

Pascagoula MS Jackson 1.0 LA Jefferson 0.4366 PA Washington 0.1340 

Vicksburg MS Warren 1.0 LA St. Tammany 0.0224 PA Fayette 0.0412 

Biloxi MS Harrison 1.0 

New Orleans 

LA Tangipahoa 0.0037 PA Greene 0.0567 

Greenville MS Washington 1.0 NC New Hanover 0.8974 

Pittsburgh 

PA Beaver 0.1753 

Gulfport MS Harrison 1.0 Wilmington NC Brunswick 0.1026 KY Greenup 0.0795 

Morehead City NC Carteret 1.0 OH Hamilton 0.7931 KY Boyd 0.1023 

Georgetown SC Georgetown 1.0 KY Kenton 0.0862 OH Gallia 0.1136 

Nashville TN Davidson 1.0 

Cincinnati 

KY Boone 0.1207 OH Lawrence 0.2273 

Mobile AL Mobile 1.0 SC Charleston 0.7097 OH Scioto 0.1364 

Guntersville AL Marshall 1.0 Charleston SC Berkeley 0.2903 WV Wayne 0.1136 

TN Shelby 0.9123 WV Cabell 0.0795 Memphis AR Crittenden 0.0877 

Huntington 

WV Mason 0.1477 

Appendix D.5 - 41

Appendix D - 217
34 

Pechan was directed to perform the reallocation for all VISTAS ports. Figure III-3 presents the 

reallocation of port emissions in all States except Alabama. Alabama’s CMV data were 

provided to EPA and already incorporated into the 1999 NEI Version 2, and Pechan did not have 

access to the default 1999 NEI estimates for this State and category. Since State data take 

precedence, the inventory prepared by Pechan reflects the incorporation of State data for those 

areas that developed independent CMV emission estimates, including Virginia and Palm Beach 

County, Florida. In addition, West Virginia provided their own county fractions to allocate 

emissions for the Port of Huntington, using District-level data from the Army Corps of Engineers 

on tonnage of freight shipped and received. West Virginia also requested that residual-fueled 

CMV activity/emissions be zeroed out for their State. States providing their own data are 

encouraged to review the allocations Pechan developed for their port areas, and to provide 

further comment or direction as needed. 

Appendix D.5 - 42

Appendix D - 218
35 

Figure III-3. VISTAS Region and Surrounding States, Revised Port Emissions of NOX

Appendix D.5 - 43

Appendix D - 219
36 

Table III-6. Definition of Port Areas Obtained from Waterborne Commerce 

(USACE, 2000) 

VISTAS PORTS 

MOBILE, AL 

Entrance. bay and river channels, and channels into Chickasaw and Three Mile Creeks; Branch 

Channels; Theodore Ship Channel. 

GUNTERSVILLE, AL 

Both banks of the Tennessee River at mile 358 to mile 363. 

JACKSONVILLE HARBOR, FL 

Atlantic Ocean to the Florida East Coast Railway Bridge at Jacksonville, 26.8 miles. 

TAMPA, FL 

Gulf of Mexico to and including the channels of upper Tampa Harbor, 49.8 miles; Channel to Port Tampa 

and thence to Courtney Campbell Parkway, 17.5 miles; Natural channel leading from Port Tampa 

Channel toward St. Petersburg, 1.8 miles; Alafia River Channel, 3.6 miles; Hillsborough River to City 

Waterworks Dam, 10 miles; Channels in “Little Manatee River, Fl; Port Manatee, Fl Harbor.” 

MIAMI HARBOR, FL 

Atlantic Ocean to inner end of turning basin at Miami, 6 miles; Meloy Channel and thence natural 

channels along the easterly side of Biscayne Bay to Bakers Haulover Inlet, FL, about 11 miles; channel 

from turning basin to mouth of Miami River, 1.1 miles; existing Florida East Coast Railway Channel, 

Fishermans Channel from mouth of Miami River to Government Cut, 3.8 miles; and the channels reported 

under “Miami River, FL.” 

EVERGLADES HARBOR, COLLIER COUNTY, FL - No definition given 

CANAVERAL HARBOR, FL 

Entrance Channel (Atlantic Ocean) to Barrier Beach inner channel and Turning Basins, thence a Barge 

canal through a lock in the perimeter dike and continuing to the Intracoastal Waterway, Jacksonville to 

Miami. 

CHARLOTTE HARBOR, FL 

Gulf of Mexico to Municipal Terminal at Punta Gorda, about 29.5 miles; waterfront on Gasparilla Island 

from Port Boca Grande to Boca Grande, 4.5 miles; and Myakka River to El Jobean, 4 miles. 

PALM BEACH HARBOR, FL 

Atlantic Ocean to Port of Palm Beach Terminals, 1.7 miles; Lake Worth from Riviera Bridge to Southern 

Boulevard Bridge at West Palm Beach, 7.5 miles; and “Palm Beach, FL side channel and basin.” 

PORT MANATEE, FL 

40 feet deep by 400 feet wide entrance channel and basin. The entrance channel extends approximately 

3 miles in length from the turning basin to its intersection with Tampa Harbor main channel. Controlling 

Depth: 40 feet in entrance channel and turning basin. 

PANAMA CITY HARBOR, FL 

Entrance channel, inside bay and Watson Bayou. Project Depth: Approach channel, 34 feet; across 

Lands End, 32 feet; Watson Bayou, 10 feet. 

Appendix D.5 - 44

Appendix D - 220
37 

Table III-6. Definition of Port Areas Obtained from Waterborne Commerce 

(USACE, 2000) 

PENSACOLA HARBOR, FL 

Entrance channel and entire harbor, including Bayou Chico. 

Project Depth: entrance, 35 feet; Inner Harbor, 33 feet; Bayou Chico, 15 and 14 feet. 

WEEDON ISLAND, FL – no definition 

BRUNSWICK HARBOR, GA 

From 32-foot contour in the ocean across the Barthrough St. Simon Sound, Brunswick River, and Turtle 

River to the upper end of the Allied Chemical Company’s Wharf, formerly Atlantic Refining Company 

Wharf, 20.4 miles; from Brunswick River through East River, to the upper end of the project in Academy 

Creek, 2.7 miles; from St. Simon Sound through Back River to Mill Creek, the upper end of Back River 

improvement, 2.9 miles; from Back River through Terry Creek to the Glynn Canning Company’s Wharf, 

1.8 miles; a total distance of 27.8 miles. 

SAVANNAH HARBOR, GA 

From the 40-foot contour in the ocean to the Continental Can Company Plant, 32.15 miles. 

LOUISVILLE, KY 

Both banks of the Ohio River from mile 606 to mile 616 

Controlling Depth: 9 feet. Project Depth: 9 feet at low water stages. 

BILOXI HARBOR, MS 

Mississippi Sound, Biloxi Bay, Back Bay, and land cut to Gulfport Lake. 

Project Depth: East entrance channel, Mississippi Sound to Gulfport Lake, 12 feet: West entrance 

channel, Mississippi Sound to Biloxi Harbor, 10 feet; Ott Bayou, 12 feet. 

GREENVILLE, MS 

From Mississippi River mile 537 AHP left descending bank in an easterly direction, an entrance channel, 

8,000 feet long and 250 feet wide transitioning into the harbor and port area 10,000 feet long and 500 feet 

wide, then transitioning into Lake Ferguson, a channel 5,700 feet long and 250 feet wide. 

GULFPORT HARBOR, MS 

Mississippi Sound Channel, Ship Island Pass Channel, and Small Craft Harbor about 4,300 feet long 

west of the anchorage basin. 

Project Depth: Mississippi Sound, 30 feet; Ship Island Pass, 32 feet; Small Craft Harbor, 8 feet. 

PASCAGOULA HARBOR, MS 

Lower 4 miles of Dog River and lower 6.8 miles of Pascagoula River, Mississippi Sound, Bayou Casotte, 

and Horn Island Pass Channels. 

VICKSBURG, MS 

From Mississippi River mile 437 AHP on left descending bank in a northerly direction, a channel 14,500 

feet long by 150 feet wide in the Yazoo Diversion Canal, thence a dredged entrance channel 4,800 feet 

long and 150 feet wide, transitioning into a 300-foot wide dredged slack water harbor and turning basin 

10,700 feet long. 

MOREHEAD CITY HARBOR, NC 

Morehead City Harbor, NC. 

Appendix D.5 - 45

Appendix D - 221
38 

Table III-6. Definition of Port Areas Obtained from Waterborne Commerce 

(USACE, 2000) 

PORT OF WILMINGTON, NC 

(see also Wilmington Harbor NC for waterway data) 

Both banks of the Cape Fear River extending from a point about 18 miles below the foot of Castle St. in 

Wilmington to a point about 2 miles above the Railroad Bridge at Navassa, and both banks of Northeast 

(Cape Fear) River from its mouth to a point about 1.67 miles above the Hilton Railroad Bridge. 

CHARLESTON HARBOR, SC 

(Including Ashley River, Cooper River, Shem Creek And Shipyard River, SC) 

Ocean to Goose Creek via Cooper River and Town Creek; to the Standard Wharf on Ashley River; to the 

Mount Pleasant Memorial Highway Bridge on Shem Creek; to the Airco Alloys Wharf on Shipyard River; 

Wando River to Cainhoy. 

GEORGETOWN HARBOR, SC (Winyah Bay) 

Atlantic Ocean Entrance to Winyah Bay, SC, to and including turning basin in Sampit River at the City of 

Georgetown, SC. 

MEMPHIS, TN 

Section Inlcuded: From mile 715.5 to mile 741.0 on Lower Mississippi River and includes Memphis 

Harbor (McKellar Lake) and Wolf River Harbor, 

Tennessee. Controlling Depth: 9 feet. Project Depth: 9 feet at low water stages. 

PORT OF NASHVILLE, TN 

(included in traffic of Cumberland River, TN and KY) 

Both banks of Cumberland River, mile 182 to mile 194 

Controlling Depth: 9 feet. Project Depth: 9 feet at low water stages. 

CHATTANOOGA, TN 

Section Included: Both banks of the Tennessee River at mile 454 to 471. 

Controlling Depth: 9 feet. Project Depth: 9 feet at low water stages. 

PORT OF RICHMOND, VA 

(Included in James River, VA Consolidated Report) 

PORT OF NEWPORT NEWS, VA (Including Newport News Creek, VA) 

Lower east shore of James River from mouth to 1.8 miles, and portion of north shore of Hampton Roads 

covering approximately 15,000 linear feet of waterfront at Newport News; and Newport News Creek. 

PORT OF HOPEWELL, VA (Included In James River VA Consolidated Report) 

South side of James River, from City Point, at mouth of Appomattox River, 2 miles downstream to the 

mouth of Baileys Creek. 

Controlling Depth: 25 feet at mean low water. Project Depth: 35 feet, maintained to 25 feet. 

NORFOLK HARBOR, VA 

From 55-foot contour in Hampton Roads to Norfolk & Western (formerly Virginia) Railway Bridge Crossing 

Southern Branch of Elizabeth River, 14.78 miles; thence upstream in Southern Branch, 4.61 miles. In 

Eastern Branch, 2.54 miles upstream from the mouth of that branch; in Western Branch, 1.78 miles 

upstream from the mouth of that branch; and 0.73 miles in Scotts Creek. 

HUNTINGTON, WV 

Both banks of the Ohio River from mile 303 to mile 317 

Controlling Depth: 9 feet. Project Depth: 9 feet at low water stages. 

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39 

Table III-6. Definition of Port Areas Obtained from Waterborne Commerce 

(USACE, 2000) 

NON-VISTAS PORTS 

HELENA, AR 

Mile 659 through mile 663 on the Lower Mississippi River. 

The project provides for maintenance of an off-river harbor with dimensions of 9 feet deep and 450 feet 

wide for a length of 3,200 feet. 

MOUNT VERNON, IN 

Section Included: Right Bank of Ohio River from mile 151 to mile 154. 

Controlling Depth: 9 feet. Project Depth: 9 feet at low water stages. 

CINCINNATI, OH 

Both banks of the Ohio River from mile 465 to mile 491. 

Controlling Depth: 9 feet. Project Depth: 9 feet at low water stages. 

PORT OF PITTSBURGH, PA 

Ohio River from Pittsburgh, PA to mile 40 (Pennsylvania/Ohio State Line); Allegheny River from 

Pittsburgh, PA to mile 72(to head of project); Monongahela River from Pittsburgh, PA to mile 91(to head 

of project). Includes Aliquippa-Rochester, Pittsburgh, Clairton-Elizabeth. 

Controlling Depth: 9 feet. Project Depth: 9 feet. 

PORT OF PLAQUEMINES, LA 

Both banks of Mississippi River from mile 0 A.H.P. through mile 81.2 A.H.P 

Controlling and Project Depths: 45 feet. 

PORT OF BATON ROUGE, LA 

Both banks of Mississippi River from mile 168.5 A.H.P. through mile 253 A.H.P; including the Baton 

Rouge Barge Canal from a point on the east bank of the Mississippi River at mile 234.5 A.H.P., for a 

distance of 5 miles. 

PORT OF NEW ORLEANS, LA 

Both banks of the Mississippi River from mile 81.2 A.H.P. through mile 114.9 A.H.P.; Innerharbor 

Navigation Canal, 5.5 miles; Mississippi River-Gulf Outlet from its junction with the Innerharbor Navigation 

Canal to Bayou Bienvenue, 7 miles; and Harvey Canal, 5.5 miles. 

PORT OF SOUTH LOUISIANA (LA) 

Both banks of Mississippi River from mile 114.9 A.H.P. through mile 168.5 A.H.P. 

Controlling and Project Depths: 45 feet. 

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40 

3. Projection Methods 

Pechan then projected the revised 1999 inventory to 2002 using surrogate growth indicators. For 

the aircraft category, 1999 and 2002 approach operations by airport and aircraft type were 

compiled from the Federal Aviation Administration’s Air Traffic Activity Data System 

(ATADS). The airport-level landing and takeoffs (LTOs) were assigned to counties and summed 

for the county. For counties with aircraft emissions without a county match in ATADS, Stateaverage growth factors were calculated and applied. The county-level growth factors are not 

presented in this report, but could be provided to VISTAS S/L/Ts if requested. 

For locomotives, projected emissions were developed in two steps as described below. For 1999 

to 2001, State-level vessel bunkering and rail fuel consumption was obtained from the Energy 

Information Administration’s (EIA’s) Fuel Oil and Kerosene Sales. For 2001 to 2002, Pechan 

applied national growth factors developed from fuel consumption projections in EIA’s Annual 

Energy Outlook. Table III-7a lists the growth factors for locomotives that were applied to the 

1999 emissions to first develop 2001 emissions. Table III-7b lists the growth factors used to 

generate 2002 emissions. Locomotive emissions were not revised from the August 2003 draft 

VISTAS 2002 inventory. 

Table III-7a. Growth Factors for Railroad Distillate Fuel Oil Use 

Rail Distillate Fuel Oil Sales 

(Thousand Gallons) 

FIPSST State 

1999 2001 

Growth Factor 

(GF) 

01 Alabama 42,137 55,777 1.3 

12 Florida 127,269 107,084 0.8 

13 Georgia 73,494 70,538 1.0 

21 Kentucky 98,941 99,812 1.0 

28 Mississippi 14,267 24,812 1.7 

37 North Carolina 53,900 77,762 1.4 

45 South Carolina 13,051 15,936 1.2 

47 Tennessee 44,083 91,363 2.1 

51 Virginia 32,202 61,154 1.9 

54 West Virginia 9,160 8,787 1.0 

Source: Department of Energy, Energy Information Administration Fuel Oil and Kerosene Sales 1999 & Fuel Oil and Kerosene 

Sales 2001 Table 23. Adjusted Sales for Transportation Use: Distillate Fuel Oil and Residual Fuel Oil 

(http://tonto.eia.doe.gov/FTPROOT/pertroleum/053599.pdf), (http://tonto.eia.doe.gov/FTPROOT/pertroleum/053501.pdf) 

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41 

Table III-7b. 2002 National Rail Transportation Energy Use by Fuel Type 

(Trillion BTU) 

2001 2002 Growth Factor (GF) 

Intercity Rail (Electric) 10.17 10.40 1.0226 

Intercity Rail (Diesel) 16.60 16.88 1.0169 

Transit Rail (Electric) 46.36 47.40 1.0224 

INTERCITY/TRANSIT RAIL AVERAGE (SCC 2285002008) 1.0206 

Commuter Rail (Electric) 16.13 16.49 1.0223 

Commuter Rail (Diesel) 26.31 26.76 1.0171 

COMMUTER RAIL AVERAGE (SCC 2285002009) 1.0197 

Freight Rail (Distillate) 

(SCCs 2285002000, 2285002005, 2285002006, 

2285002007, 2285002010) 

512.81 492.32 0.9600 

Source: Department of Energy, Energy Information Administration, Annual Energy Outlook 2003: Table 34. Transportation Sector

Energy Use by Fuel Type Within a Mode (http://www.eia.doe.gov/oiaf/aeo/supplement/sup_tran.pdf) 

Since the CMV emissions were revised for the 1999 base year, these emissions were projected 

using 2002 Fuel Oil and Kerosene Sales data, which became available in November 2003. Table 

III-8 lists the growth factors for CMVs that were applied to 1999 emissions to generate 2002 

emissions. The same regional growth factor that accounts for an average regional growth rate 

was applied to CMV emissions for all VISTAS States. Because the State-level data represents 

sales and not use, and CMV activity spans State borders, a regional growth factor was deemed 

more appropriate. Pechan could make a similar adjustment for the locomotive growth factors, 

which are also based on fuel sales for 1999 to 2001, if requested by VISTAS. 

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42 

Table III-8. Growth Factors for Commercial Marine Vessel Distillate and Residual 

Fuel Oil Use 

Fuel Oil Sales 

(Thousand Gallons) 

FIPSST State Growth Factor (GF) 

1999 2002 

DISTILLATE 

01 Alabama 67,455 73,400 1.1 

12 Florida 139,809 143,577 1.0 

13 Georgia 17,697 22,327 1.3 

21 Kentucky 81,811 56,169 0.7 

28 Mississippi 12,749 68,668 5.4 

37 North Carolina 11,279 10,057 0.9 

45 South Carolina 12,732 19,782 1.6 

47 Tennessee 43,867 112,364 2.6 

51 Virginia 29,444 28,235 1.0 

54 West Virginia 54,560 46,981 0.9 

Regional Distillate GF 471,403 581,560 1.2 

RESIDUAL 

01 Alabama 46,093 93,487 2.0 

12 Florida 404,228 460,600 1.1 

13 Georgia 40,117 79,191 2.0 

21 Kentucky1 69 1.2 

28 Mississippi 48,644 54,031 1.1 

37 North Carolina 6,989 35,210 5.0 

45 South Carolina 20,056 22,758 1.1 

47 Tennessee1 124 1.2 

51 Virginia 60,090 36,445 0.6 

54 West Virginia 1.2 

Regional Residual GF 626,217 781,915 1.2 1

 For Kentucky, Tennessee and West Virginia, Pechan summed the 1999 and 2002 CMV residual fuel oil use to develop a total 

VISTAS State growth factor, which was then applied to the three States. 

Source: Department of Energy, Energy Information Administration, Fuel Oil and Kerosene Sales 1999 & Fuel Oil and Kerosene 

Sales 2002, Table 23. Adjusted Sales for Transportation Use: Distillate Fuel Oil and Residual Fuel Oil.

IV. ONROAD REFUELING METHODS

Emissions were separately calculated from onroad refueling, also known as Stage II emissions. 

Since refueling is a category of evaporative rather than exhaust emissions, VOC is the only 

criteria pollutant of concern for this category. This chapter discusses the controls modeled for 

this emission category and the methods used to calculate these emissions. Refueling emissions 

for onroad sources were updated in February 2004 to account for the VMT updates provided by 

several States. 

A. CONTROLS 

Based on default information from the NEI as well as some information provided by VISTAS 

agencies, portions of five of the VISTAS States have onroad Stage II refueling controls in place. 

These States, along with the specific counties with onroad Stage II controls, are listed in 

Table IV-1. This table also shows information about the Stage II control program in each State 

including the year a Stage II program began, the number of years that the program was phased-in 

over, and the control efficiency of the program in reducing VOC emissions from Stage II 

Appendix D.5 - 50

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43 

refueling for the LDGV, LDGT, and HDGV vehicle categories. These are the inputs required for 

modeling a Stage II control program using MOBILE6. States with Stage II programs should 

review this information and provide any corrections for the next round of emissions modeling. 

Table IV-1. Onroad Stage II Control Programs 

 

State 

Start 

Year 

Phase-In 

Years 

Control 

Efficiency Counties 

Florida 1993 2 95% Broward, Miami-Dade, Palm Beach 

Georgia 1992 3 81% Cherokee, Clayton, Cobb, Coweta, DeKalb, Douglas, 

Fayette, Forsyth, Fulton, Gwinnett, Henry, Paulding, 

Rockdale 

Kentucky 1999 2 86% Boone, Campbell, Kenton 

Kentucky 1992 2 95% Jefferson 

Tennessee 1993 3 95% Davidson, Rutherford, Sumner, Williamson, Wilson 

Virginia 1993 2 95% Counties: Arlington, Chesterfield, Fairfax, Hanover, 

Henrico, Loudoun, Prince William, Stafford 

 Independent Cities: Alexandria, Colonial Heights, 

Fairfax, Falls Church, Hopewell, Manassas, 

Manassas Park, Richmond 

B. METHODS 

A simplified set of MOBILE6.2 input files was created to simulate the onroad refueling emission 

factors. These input files were simplified because several of the inputs used for calculating the 

onroad exhaust and evaporative emission factors do not affect the refueling emission factors. 

For example, the refueling emission factors are unaffected by vehicle speed or I/M program. 

Thus, for each group of counties in a State with the same fuel parameters, temperature 

parameters, fleet characteristics (registration distribution, diesel sales fractions), and Stage II 

control program parameters, a MOBILE6.2 input file was created to model the onroad refueling 

emission factors. As mentioned above, speed does not affect the refueling emission factors, so 

each input file contained only 12 scenarios—one for each month of the year. Within each 

scenario, the temperature and fuel parameters were varied, using the same temperature and fuel 

data modeled in the onroad exhaust and evaporative MOBILE6.2 input files. Other fleet 

characteristics, such as registration distributions and diesel sales fractions, were included in the 

input files where applicable. The inputs shown in Table IV-1 were included for the input files 

representing counties with Stage II control programs. The header section of the MOBILE6.2 

input files was set up so that only refueling emission factors would be included in the tabular 

output file. 

After the MOBILE6.2 input files were generated, they were run through the MOBILE6.2 model 

to obtain refueling VOC emission factors in the database table format. These emission factors 

are produced for the 28 MOBILE6 vehicle types. The emission factors were then weighted using 

the VMT fraction information included in the MOBILE6 output tables to obtain VOC refueling 

emission factors for the 8 vehicle types included in the VISTAS VMT database. The VMT 

fraction information contained in the MOBILE6 input files is based on the default MOBILE6 

registration distributions, diesel sales fractions, and VMT fractions, or, when this information is 

Appendix D.5 - 51

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44 

provided in the input files, based on area-specific fleet parameters. A database of emission 

factors by month, county, and 8 vehicle types was then prepared. In calculating monthly onroad 

refueling emissions, the VISTAS annual VMT data were temporally allocated by month in the 

same manner as described in Chapter II for the onroad exhaust and evaporative emission 

calculations. These VMT were then multiplied by the corresponding monthly emission factor (in 

terms of grams per mile) to obtain refueling emissions from onroad vehicles. The monthly 

emissions for each county were then summed to obtain annual refueling emissions. Also, since 

refueling emissions are included in the area source inventory and are not distinguished by vehicle 

type, all refueling emissions from onroad vehicles were summed for each county in the VISTAS 

region. Summaries of the refueling emissions from onroad vehicles are presented in Chapter VI. 

V. NONROAD REFUELING METHODS 

The NONROAD model accounts for refueling emissions from nonroad equipment under two 

separate components, vapor displacement and spillage. Vapor displacement emissions result 

when new liquid fuel being added to a fuel tank displaces fuel vapors already present in the tank. 

Spillage emissions result when fuel is spilled during the refueling process. 

Nonroad equipment may be fueled from a gasoline pump or a portable container. Refueling 

nonroad equipment from a portable container results in different emissions for both spillage and 

vapor displacement compared to refueling from a gasoline pump. In addition, the use of portable 

containers also results in extra refueling events. Both spillage and displacement emissions will 

also occur when the container is filled from a gasoline pump. However, due to lack of data, the 

NONROAD2002 model does not attempt to quantify this set of refueling emissions. As such, 

the NONROAD model refueling emissions associated with nonroad equipment being filled 

directly at the gasoline pumps will be used to represent the nonroad Stage II emission 

component. Stage II control factors listed in Table IV-1 were input in the county-specific 

NONROAD model option files. Once the model runs were performed, Pechan extracted the 

refueling and spillage emissions corresponding only to those engines (typically the larger 

horsepower engines) within each SCC assumed to be refueled at the pump. The list of SCC and 

horsepower ranges associated with pump versus container refueling is specified in the model 

since different emission rates are assumed for these two types of refueling. 

Table V-1 presents draft annual Stage II VOC emission estimates by State. These emissions 

were combined with the onroad vehicle Stage II estimates described in Section IV of this report. 

Appendix D.5 - 52

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45 

Table V-1. 2002 Draft Stage II Refueling Emissions by State

FIPSST NAME VOC Emissions, tpy 

01 Alabama 167.25 

12 Florida 842.60 

13 Georgia 209.01 

21 Kentucky 112.65 

28 Mississippi 147.18 

37 North Carolina 298.49 

45 Tennessee 197.81 

47 South Carolina 155.33 

51 Virginia 174.70 

54 West Virginia 39.33 

VI. SUMMARY OF RESULTS 

This chapter presents the emission results from the February 2004 draft version of the 2002 

mobile source emissions inventory for the VISTAS region. These emissions result from the data 

and procedures described in the preceding chapters of this report. 

A. ONROAD RESULTS 

Table VI-1 summarizes the latest 2002 VISTAS onroad emissions inventory by State. This table 

also summarizes the total VMT for each State. Tables VI-2 and VI-3 are provided here for the 

purpose of comparing this inventory with another existing onroad inventory. The emissions 

shown in Table VI-2 are taken from Version 2 of EPA’s 1999 NEI. Table VI-3 then shows the 

percentage change from the 1999 NEI to the 2002 draft VISTAS inventory. If the two 

inventories had been developed using comparable data, one would generally expect to see 

reductions in the onroad emissions from 1999 to 2002 due to fleet turnover resulting in the 

replacement of older, dirtier vehicles with vehicles meeting more stringent emission standards. 

However, this reduction in per-vehicle emissions also needs to overcome increases in VMT for 

the overall emissions to decrease. All of the VISTAS States show increases in VMT from 1999 

to 2002, except North Carolina. This decrease in VMT needs to be further investigated by the 

State agency. States that were modeled with significant State or locally supplied inputs in the 

VISTAS modeling, such as Virginia and Georgia, would be expected to have more significant 

differences from the NEI data than States with no State-supplied information other than VMT. 

Some of the State inputs that cause significant deviations from the NEI estimates are registration 

distributions, VMT mixes by vehicle type, and speeds by road type. In addition, some of the 

pollutants are more affected by these inputs, while others (such as NH3) are minimally affected 

by these inputs. The 2002 VISTAS onroad emissions will continue to undergo review. Any 

comments or questions on these emissions by the State or local agencies will be investigated as 

part of this review. 

Appendix D.5 - 53

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46 

Table VI-1. 2002 VISTAS Onroad Emissions and VMT by State 

(February 2004 Version) 

 

 2002 Annual Emissions (tons per year) 2002 Annual VMT

State VOC NOx CO SO2 PM10 PM2.5 NH3 (million miles)

Alabama 99,650 154,908 1,275,969 6,515 4,344 3,231 5,619 55,723

Florida 457,309 463,419 4,678,471 19,739 12,666 9,232 18,240 178,681

Georgia 215,035 311,125 2,601,785 11,487 8,038 5,942 10,612 106,785

Kentucky 79,110 164,231 1,196,211 5,718 4,083 3,048 5,103 51,020

Mississippi 68,508 107,047 845,990 4,354 3,152 2,399 3,603 36,278

North Carolina 147,977 278,265 2,116,829 9,953 6,374 4,741 7,868 80,166

South Carolina 92,491 136,569 1,192,894 5,647 3,825 2,867 4,719 47,074

Tennessee 126,959 255,090 1,785,136 8,115 5,445 4,059 6,855 68,316

Virginia 115,044 182,513 1,858,629 6,110 4,413 3,032 7,937 76,566

West Virginia 34,197 57,941 512,592 2,361 1,550 1,155 1,947 19,544

VISTAS Total 1,436,279 2,111,108 18,064,506 79,999 53,890 39,705 72,504 720,153

Table VI-2. 1999 NEI Version 2 Onroad Emissions and VMT by State 

 

 1999 Annual Emissions (tons per year) 1999 Annual VMT

State VOC NOx CO SO2 PM10 PM2.5 NH3 (million miles)

Alabama 121,201 163,024 1,412,343 6,280 4,712 3,599 5,249 52,914

Florida 328,412 424,969 3,379,563 16,581 12,259 9,318 14,162 141,903

Georgia 207,562 313,568 2,526,592 12,028 9,263 7,139 9,787 98,859

Kentucky 97,286 162,160 1,225,414 6,006 4,772 3,715 4,703 47,816

Mississippi 74,579 126,344 830,477 4,478 3,908 3,106 3,406 34,955

North Carolina 187,346 285,380 2,252,671 10,829 8,462 6,552 8,663 87,759

South Carolina 98,010 153,346 1,207,336 5,616 4,515 3,527 4,330 44,146

Tennessee 138,629 211,133 1,697,778 7,876 6,108 4,716 6,392 64,570

Virginia 150,528 238,515 1,861,417 8,972 6,892 5,307 7,320 73,904

West Virginia 40,060 68,580 539,578 2,471 2,023 1,589 1,859 19,033

VISTAS Total 1,443,613 2,147,019 16,933,170 81,137 62,913 48,567 65,871 665,859

Appendix D.5 - 54

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47 

Table VI-3. Change in Onroad Emissions and VMT from 1999 NEI Version 2 to 

VISTAS 2002 Inventory (February 2004 Version) 

 

 Change from 1999 NEI V2 to 2002 VISTAS Draft Inventory 

State VOC NOx CO SO2 PM10 PM2.5 NH3 VMT

Alabama -18% -5% -10% 4% -8% -10% 7% 5%

Florida 39% 9% 38% 19% 3% -1% 29% 26%

Georgia 4% -1% 3% -4% -13% -17% 8% 8%

Kentucky -19% 1% -2% -5% -14% -18% 9% 7%

Mississippi -8% -15% 2% -3% -19% -23% 6% 4%

North Carolina -21% -2% -6% -8% -25% -28% -9% -9%

South Carolina -6% -11% -1% 1% -15% -19% 9% 7%

Tennessee -8% 21% 5% 3% -11% -14% 7% 6%

Virginia -24% -23% 0% -32% -36% -43% 8% 4%

West Virginia -15% -16% -5% -4% -23% -27% 5% 3%

VISTAS Total -1% -2% 7% -1% -14% -18% 10% 8%

Table VI-4 presents the latest 2002 VISTAS onroad refueling emission estimates by State. 

These refueling emissions are NOT included in the emissions shown in Tables VI-1 through VI3. 

Table VI-4. 2002 VISTAS Annual Onroad Refueling Emissions 

State 

2002 Annual Onroad VOC Refueling Emissions 

(tons per year) 

Alabama 8,408 

Florida 28,367 

Georgia 12,329 

Kentucky 6,885 

Mississippi 6,057 

North Carolina 15,320 

South Carolina 8,926 

Tennessee 9,901 

Virginia 8,657 

West Virginia 3,383 

VISTAS Total 108,233 

B. NONROAD RESULTS 

Table VI-5 provides a summary of draft 2002 nonroad sector annual emissions by State, 

including Stage II refueling emission estimates. Table VI-6 provides a summary of the draft 

2002 NONROAD model emission estimates by State, and compares the values to 2001 

NONROAD model NEI Version 2 estimates by showing the percent difference. A similar 

comparison is shown in Table VI-7 for other nonroad emission estimates compared to the 1999 

NEI Version 2. 

Appendix D.5 - 55

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48 

For the NONROAD model categories, SO2, PM10, PM2.5, and NH3 decrease consistently across 

all States. SO2 emissions decrease due in part to a lower diesel fuel sulfur content input for the 

NONROAD model runs, which also contributes to decreases in particulate emissions. The 

decrease in NH3 is due primarily to corrections made to compresses natural gas (CNG) engine 

NH3 emissions, which involved zeroing out the estimates. The 1999 NEI erroneously applied 

emission factors on a grams per gallon basis to CNG fuel consumption. Although reported as 

uncompressed gallons in the NONROAD model, the CNG fuel consumption estimates represent 

a gaseous, not liquid, volume. Based on OTAQ’s recommendations, CNG NH3 emissions are 

now reported as zero. CO and NOx show little change for all States, and changes in VOC vary 

by State and are dependent on the contribution of specific equipment categories (detail not 

shown). 

For other nonroad categories, the increase in PM10 and PM2.5 is due to the addition of 

commercial aircraft PM emissions. Commercial aircraft PM10 and PM2.5 emissions were zero in 

the 1999 NEI; hence, the large percent increase. To gap fill this portion of the inventory, Pechan 

calculated and applied an average air taxi PM/NOx emission ratio to commercial aircraft NOx

emissions. States with a higher proportion of commercial aircraft show significant PM increases 

(e.g., FL, TN, VA). In addition, NOx emissions decrease due to new State data for other nonroad 

from AL and VA. 

Table VI-5. Summary of Draft 2002 Nonroad Sector Annual Emissions by State, 

tons per year 

FIPSST STATE VOC NOX CO PM10-PRI PM25-PRI SO2 NH3 

01 Alabama 46,788 64,367 373,634 5,504 4,895 7,529 32

12 Florida 211,006 153,396 1,765,539 61,426 45,849 17,453 109

13 Georgia 66,712 87,053 712,159 10,411 8,666 7,914 55

21 Kentucky 35,537 100,989 294,929 8,538 7,249 13,771 28

28 Mississippi 33,443 90,190 217,407 5,795 5,194 11,537 23

37 North Carolina 75,020 81,264 742,822 12,814 10,379 7,281 62

45 South Carolina 43,231 46,518 375,469 4,115 3,678 4,465 29

47 Tennessee 52,333 118,690 461,976 14,727 11,692 12,478 41

51 Virginia 61,655 69,668 614,958 21,580 16,497 11,068 44

54 West Virginia 15,497 36,613 120,029 2,293 2,034 2,388 10

Appendix D.5 - 56

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49 

Table VI-6. Summary of Draft 2002 NONROAD Model Emission Estimates by State 

2002 DRAFT VISTAS NONROAD Model Inventory, tpy

FIPSST STATE VOC_ANN NOX_ANN CO_ANN PM10_ANN PM25_ANN SO2_ANN NH3_ANN

01 Alabama 44,501.18 28,635.48 365,161.12 3,306.84 3,044.48 2,729.32 31.92

12 Florida 205,489.66 86,654.40 1,730,125.77 12,890.06 11,862.13 9,113.26 109.02

13 Georgia 65,054.02 51,452.93 705,292.75 5,493.33 5,057.34 5,025.11 54.97

21 Kentucky 32,836.91 28,253.72 283,488.53 3,152.29 2,901.82 2,777.69 28.00

28 Mississippi 31,097.14 23,549.89 207,824.23 2,761.65 2,542.05 2,375.53 23.37

37 North Carolina 73,610.93 58,667.62 734,496.85 6,095.96 5,613.11 5,442.35 62.06

45 South Carolina 41,652.41 26,212.76 366,737.16 3,028.92 2,788.66 2,461.79 29.29

47 Tennessee 48,626.66 39,833.95 446,461.43 4,240.53 3,904.21 3,810.11 41.22

51 Virginia 56,973.85 40,914.48 594,020.13 4,739.47 4,362.61 4,103.01 44.22

54 West Virginia 14,498.68 9,502.33 115,652.49 1,038.29 955.70 980.17 10.31

2001 NONROAD Model NEI Version 2, tpy

FIPSST STATE VOC_ANN NOX_ANN CO_ANN PM10_ANN PM25_ANN SOX_ANN NH3_ANN

01 Alabama 43,602.83 28,786.95 360,439.36 3,422.60 3,150.91 3,110.79 581.69

12 Florida 188,868.96 86,835.32 1,713,539.62 13,243.04 12,186.78 10,456.05 1,305.25

13 Georgia 63,927.85 51,521.66 698,868.77 5,678.55 5,227.63 5,749.47 989.31

21 Kentucky 31,662.34 28,350.32 279,283.79 3,274.35 3,014.06 3,127.88 463.74

28 Mississippi 29,037.96 23,671.70 205,664.64 2,877.28 2,648.40 2,668.55 359.21

37 North Carolina 69,671.36 58,742.13 724,908.46 6,300.02 5,800.72 6,196.92 1,223.82

45 South Carolina 39,310.79 26,304.57 363,112.01 3,130.17 2,881.75 2,817.02 507.81

47 Tennessee 47,193.97 39,916.38 440,915.76 4,395.90 4,047.06 4,337.42 749.51

51 Virginia 55,459.80 41,082.63 585,850.58 4,887.90 4,499.09 4,677.52 627.60

54 West Virginia 13,912.53 9,568.82 113,766.38 1,076.32 990.67 1,113.21 179.75

Percent Difference

FIPSST STATE VOC_ANN NOX_ANN CO_ANN PM10_ANN PM25_ANN SOX_ANN NH3_ANN

01 Alabama 2.06% -0.53% 1.31% -3.38% -3.38% -12.26% -94.51%

12 Florida 8.80% -0.21% 0.97% -2.67% -2.66% -12.84% -91.65%

13 Georgia 1.76% -0.13% 0.92% -3.26% -3.26% -12.60% -94.44%

21 Kentucky 3.71% -0.34% 1.51% -3.73% -3.72% -11.20% -93.96%

28 Mississippi 7.09% -0.51% 1.05% -4.02% -4.02% -10.98% -93.50%

37 North Carolina 5.65% -0.13% 1.32% -3.24% -3.23% -12.18% -94.93%

45 South Carolina 5.96% -0.35% 1.00% -3.23% -3.23% -12.61% -94.23%

47 Tennessee 3.04% -0.21% 1.26% -3.53% -3.53% -12.16% -94.50%

51 Virginia 2.73% -0.41% 1.39% -3.04% -3.03% -12.28% -92.95%

54 West Virginia 4.21% -0.69% 1.66% -3.53% -3.53% -11.95% -94.26%

Appendix D.5 - 57

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50 

Table VI-7. Summary of Draft 2002 Other Nonroad* Emission Estimates by State 

2002 DRAFT VISTAS Other Nonroad Inventory, tpy

FIPSST STATE VOC_ANN NOX_ANN CO_ANN PM10_ANN PM25_ANN SO2_ANN 

01 Alabama 2,286.81 35,731.80 8,473.33 2,196.87 1,850.82 4,799.75 

12 Florida 5,516.71 66,741.52 35,413.13 48,536.33 33,987.28 8,340.05 

13 Georgia 1,657.99 35,599.76 6,865.94 4,917.40 3,609.14 2,889.06 

21 Kentucky 2,699.92 72,735.57 11,440.23 5,385.61 4,346.83 10,992.91 

28 Mississippi 2,345.96 66,640.48 9,582.89 3,033.69 2,652.14 9,161.66 

37 North Carolina 1,409.01 22,596.53 8,325.56 6,718.49 4,766.12 1,838.68 

45 South Carolina 1,578.34 20,304.80 8,732.26 1,086.01 889.24 2,002.78 

47 Tennessee 3,706.17 78,855.60 15,514.17 10,486.01 7,787.92 8,667.84 

51 Virginia 4,681.39 28,753.43 20,938.22 16,840.30 12,134.84 6,965.04 

54 West Virginia 998.41 27,110.49 4,376.64 1,254.86 1,077.93 1,408.05 

1999 Other Nonroad NEI Version 2, tpy

FIPSST STATE VOC_ANN NOX_ANN CO_ANN PM10_ANN PM25_ANN SO2_ANN 

01 Alabama 7,309.83 152,338.93 25,075.50 1,315.93 1,176.15 3,854.54 

12 Florida 3,945.18 56,197.72 25,350.10 2,110.74 1,881.95 6,878.28 

13 Georgia 2,594.07 39,245.14 12,198.09 1,072.08 953.43 3,070.41 

21 Kentucky 2,676.93 62,930.31 12,388.06 2,370.31 2,153.93 8,965.67 

28 Mississippi 1,755.99 48,927.22 8,072.51 1,917.16 1,747.89 7,051.91 

37 North Carolina 1,447.95 17,999.44 8,739.21 540.09 470.85 1,508.40 

45 South Carolina 2,470.03 18,034.10 13,291.47 561.99 503.60 1,858.19 

47 Tennessee 2,426.97 51,133.47 11,127.02 1,786.06 1,616.72 6,266.91 

51 Virginia 2,682.78 51,592.64 13,083.30 1,632.38 1,462.82 4,769.97 

54 West Virginia 1,133.03 30,991.75 4,858.71 1,151.55 1,048.38 4,097.15 

Percent Difference

FIPSST STATE VOC_ANN NOX_ANN CO_ANN PM10_ANN PM25_ANN SO2_ANN 

01 Alabama -69% -77% -66% 67% 57% 25% 

12 Florida 40% 19% 40% 2199% 1706% 21% 

13 Georgia -36% -9% -44% 359% 279% -6% 

21 Kentucky 1% 16% -8% 127% 102% 23% 

28 Mississippi 34% 36% 19% 58% 52% 30% 

37 North Carolina -3% 26% -5% 1144% 912% 22% 

45 South Carolina -36% 13% -34% 93% 77% 8% 

47 Tennessee 53% 54% 39% 487% 382% 38% 

51 Virginia 74% -44% 60% 932% 730% 46% 

54 West Virginia -12% -13% -10% 9% 3% -66% 

*Includes emissions from aircraft, commercial marine and locomotive SCCs 

Appendix D.5 - 58

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51 

VII. OBSERVATIONS AND RECOMMENDATIONS FOR 

IMPROVEMENT 

This chapter lists several areas where the onroad and nonroad emission inventories could be 

improved. Some of these improvements require a long lead-time for the States and would not 

likely be available for the final 2002 VISTAS modeling, but could improve future State and 

regional inventory efforts. 

A. ONROAD SECTOR IMPROVEMENTS 

In the onroad sector, significant improvements have been made to the inventory due to the State 

and local agencies providing 2002 VMT data by county and roadway type. For this February 

2004 version of the VISTAS onroad inventory, only the Virginia VMT were projected by 

Pechan. It is anticipated that this States will be able to provide 2002 VMT data for use in the 

next revision of the inventory. 

Local registration distribution data were provided by fewer than half of the VISTAS States. In 

many cases, registration data can be obtained from State Departments of Motor Vehicles. States 

that do not already do so should request a download of the data summarizing registrations by 

model year and vehicle class from their appropriate motor vehicle agency. Although it is 

probably too late in many cases to obtain 2002 data, 2003 registration data could be used with 

some adjustments in developing the 2002 emission inventories. Registration data will become 

even more important as VISTAS prepares to project a 2018 onroad emission inventory, since the 

2018 projections will be affected by the number of vehicles that are subject to the Tier 2 

emission standards and the new heavy duty vehicle standards. The registration distributions 

directly determine the proportion of vehicles subject to these new emission standards. 

A relatively small amount of data was obtained regarding the distribution of VMT by season or 

month. Many State Departments of Transportation collect data that could be used to better 

distribute VMT by season or month. States should check to see what is available. These 

distributions will affect the episodic modeling that will be conducted by VISTAS. Pechan is 

currently performing a VMT scoping study for VISTAS to determine what data are available for 

better allocating VMT and emissions by month, day, and hour. These temporal improvements 

are expected to be incorporated into the next update of the VISTAS onroad emission inventory. 

Due to the direct relationship between the VMT mix by vehicle type and the overall emissions, 

States should investigate potential sources of information for this data to replace the default data 

used here in most States. 

EPA is currently in the process of preparing guidance on estimating emissions from heavy duty 

vehicles during long-term idling (sometimes referred to as hotelling). While these emissions are 

theoretically included in the MOBILE6 HDDV emission factors, they are not currently 

accounted for in the appropriate locations. For example, these emissions would typically occur 

at rest stops, trucking centers, and warehouse and distribution centers. With the current 

modeling, these emissions are spread over all counties, based on the VMT traveled by HDDVs in 

each county. If significant sources of truck idling emissions occur in or near Class I areas, the 

Appendix D.5 - 59

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52 

current modeling may be underestimating the effect of these emissions. If States are able to 

obtain data on the locations and utilization of truck rest stops, some of this emissions effect could 

be more appropriately accounted for in future versions of VISTAS modeling. 

B. NONROAD SECTOR IMPROVEMENTS 

NH3 emissions for aircraft, commercial marine and locomotives are still reported as zero. As a 

result of recent communications with OTAQ, Pechan would suggest applying the updated 

nonroad diesel NH3 emission factors used for the NONROAD model categories to activity data 

for commercial marine vessels and locomotives. To develop ammonia from commercial marine 

vessels and locomotives, Pechan would need to obtain or compile the county-level fuel 

consumption estimates used as the basis for 1999 emissions for these categories to use as the 

activity data for calculating updated NH3 emissions. The presence of State or local data in the 

1999 NEI does not allow for this to be determined easily by backing out the reported emission 

factors, and in some cases (e.g., diesel commercial marine), actual emissions (instead of activity) 

were obtained at a national level and allocated to counties (EPA, 2002). Alternatively, Pechan 

could use county level fuel consumption estimates developed for these categories for 2000 or 

2001. These activity data were used by Pechan to estimate dioxin/furan emission estimates for 

the 2000 and 2001 NEI. Pechan could normalize the 2000 or 2001 county distribution to 

national level fuel consumption estimates for 1999. Due to the characteristics of aircraft jet and 

piston engines, Pechan does not recommend estimating aircraft NH3 emissions using the 

available NH3 emission factors. 

VIII. REFERENCES 

EPA, 1989: “Procedures for Emission Inventory Preparation, Volume IV: Mobile Sources.” 

U.S. Environmental Protection Agency, Office of Mobile Sources and Office of Air Quality 

Planning and Standards, EPA 450/4-81-026d (Revised). July 1989. 

EPA, 2002. “Documentation for Aircraft, Commercial Marine Vessel, Locomotive, and Other 

Nonroad Components of the National Emissions Inventory,” prepared by Eastern 

Research Group, Morrisville, NC for the U.S. Environmental Protection Agency, Office 

of Air Quality Planning and Standards, Research Triangle Park, NC. September 30, 

2002. 

Harvey, 1983. Craig Harvey, et al. “A Study of the Potential Impact of Some Unregulated 

Motor Vehicle Emissions.,” SAE Paper 830987. June 1983. 

Harvey, 2003. C. Harvey, U.S. Environmental Protection Agency, Office of Transportation and 

Air Quality, e-mail entitled “NH3 Emission Updates,” sent to K. Thesing, E.H. Pechan & 

Associates, Inc. June 30, 2003. 

USACE, 2000: Waterborne Commerce of the United States, Calendar Year 1999, IWR-WCUS99-2 U.S. Army Corps of Engineers. 2000. 

Appendix D.5 - 60

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53 

USACE, 2003: U.S. Waterways Data, Port and Waterway Facilities, [Data file]. Accessed from 

U.S. Army Corps of Engineers, http://www.iwr.usace.army.mil/ndc/data/datapwd.htm on 

December 12, 2003. 

Appendix D.5 - 61

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MACTEC, Inc. 

Documentation of the Base G 2002 Base Year, 2009 and 2018, 

Emission Inventories for VISTAS 

Prepared for: 

Visibility Improvement State and Tribal Association of the Southeast 

(VISTAS) 

Prepared by: 

MACTEC, Inc. 

______________________ ______________________ 

William R. Barnard Edward Sabo 

Sr. Principal Scientist Principal Scientist

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Table of Contents 

Introduction .................................................................................................................................................................1 

1.0 2002 BASE YEAR INVENTORY DEVELOPMENT ........................................................................................4

1.1 POINT SOURCES...........................................................................................................................................4

1.1.1 Development of 2002 Point Source Inventory .......................................................................................4

1.1.1.1 Data Sources ................................................................................................................................................ 5

1.1.1.2 Initial Data Evaluation ................................................................................................................................. 6

1.1.1.3 PM Augmentation........................................................................................................................................ 7

1.1.1.4 EGU Analysis ............................................................................................................................................ 10

1.1.1.5 QA Review of Base F Inventory................................................................................................................ 11

1.1.1.6 Additional Base G Updates and Corrections.............................................................................................. 12

1.1.1.7 Summary of Base G 2002 Inventory.......................................................................................................... 15

1.1.2 Development of Typical Year EGU inventory......................................................................................18

1.2 AREA SOURCES .........................................................................................................................................20

1.2.1 Development of a “typical” year fire inventory ..................................................................................22

1.2.2 Development of non-fire inventory ......................................................................................................31

1.2.3 2002 Base G inventory updates ...........................................................................................................34

1.2.3.1 Changes resulting from State review and comment ................................................................................... 34

1.2.4 Ammonia and paved road emissions....................................................................................................37

1.2.5 Global Changes Made for Base G.......................................................................................................38

1.2.6 Quality Assurance steps.......................................................................................................................39

1.3 MOBILE SOURCES .....................................................................................................................................40

1.3.1 Development of on-road mobile source input files and VMT estimates...............................................41

1.3.1.1 Emissions from on-road mobile sources .................................................................................................... 42

1.3.2 Development of non-road emission estimates......................................................................................45

1.3.2.1 Emissions from NONROAD model sources.............................................................................................. 45

1.3.2.2 Emissions from Commercial Marine Vessels, Locomotives, and Airplanes.............................................. 61

Base G Revisions: ..............................................................................................................................................78

1.3.2.3 Emissions from NONROAD Model Sources in Illinois, Indiana, and Ohio .............................................. 85

1.3.3 Quality Assurance steps.......................................................................................................................87

2.0 PROJECTION INVENTORY DEVELOPMENT............................................................................................88

2.1 POINT SOURCES.........................................................................................................................................88

2.1.1 EGU Emission Projections ..................................................................................................................88

2.1.1.1 Chronology of the Development of EGU Projections................................................................................ 88

2.1.1.2 VISTAS IPM runs for EGU sources.......................................................................................................... 92

2.1.1.3 Post-Processing of IPM Parsed Files ......................................................................................................... 93

2.1.1.4 Eliminating Double Counting of EGU Units ............................................................................................. 95

2.1.1.5 Quality Assurance steps............................................................................................................................. 96

2.1.1.6 S/L Adjustments to IPM Modeling Results for Base G Projections........................................................... 97

2.1.1.7 Summary of Base F and Base G 2009/2018 EGU Point Source Inventories ............................................. 98

2.1.2 Non-EGU Emission Projections ........................................................................................................110

2.1.2.1 Growth assumptions for non-EGU sources.............................................................................................. 110

2.1.2.2 Source Shutdowns.................................................................................................................................... 112

2.1.2.3 Control Programs applied to non-EGU sources ....................................................................................... 114

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2.1.2.4 Quality Assurance steps........................................................................................................................... 117

2.1.2.5 Additional Base G Updates and Corrections............................................................................................ 117

2.1.2.6 Summary of Revised 2009/2018 non-EGU Point Source Inventories...................................................... 117

2.2 AREA SOURCES .......................................................................................................................................123

2.2.1 Stationary area sources .....................................................................................................................123

2.2.1.1 Stationary area source controls ................................................................................................................ 125

2.2.1.2 Stationary area source growth.................................................................................................................. 127

2.2.1.3 Differences between 2009/2018............................................................................................................... 127

2.2.2 Agricultural area sources ..................................................................................................................128

2.2.2.1 Control assumptions for agricultural area sources ................................................................................... 128

2.2.2.2 Growth assumptions for agricultural area sources ................................................................................... 128

2.2.3 Changes to Prescribed Fire for 2009/2018 Base G...........................................................................133

2.2.4 Quality Assurance steps.....................................................................................................................138

2.3 MOBILE SOURCES ...................................................................................................................................138

2.3.1 Development of on-road mobile source input files ............................................................................141

2.3.1.1 Preparation of revised 2018 input data files............................................................................................. 141

2.3.1.2 Preparation of initial 2009 input data files ............................................................................................... 144

2.3.2 VMT Data ..........................................................................................................................................145

2.3.3 Base G Revisions ...............................................................................................................................150

2.3.4 Development of non-road emission estimates....................................................................................150

2.3.4.1 NONROAD model sources...................................................................................................................... 151

2.3.4.2 Non-NONROAD model sources.............................................................................................................. 153

2.3.4.3 Emissions from NONROAD Model Sources in Illinois, Indiana, and Ohio ............................................ 188

2.3.4.4 Differences between 2009/2018............................................................................................................... 188

2.3.5 Quality Assurance steps.....................................................................................................................189

Appendix A: State Emission Totals by Pollutant and Sector

Appendix B: State VMT totals

Appendix C: State Tier 1 Emission Totals

Appendix D: VISTAS Tier 1 Emission Totals 

Appendix E: Aircraft PM Excerpt from 2001 Tucson Report 

Appendix F: Comparison of Base F and Base G On-Road Mobile Emissions

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List of Tables 

Table I-1. Inventory Version in Use by Year and Source Sector Through Base G - 2002 

Table I-2. Inventory Version in Use by Year and Source Sector Through Base G - 2009 

Table I-3. Inventory Version in Use by Year and Source Sector Through Base G - 2018 

Table 1.1-1. State Data Submittals Used for the Base F 2002 Point Source Inventory 

Table 1.1-2. Comparison of Particulate Matter Emissions from the S/L Data Submittals and 

the Base G 2002 VISTAS Point Source Inventory 

Table 1.1-3. Summary of Updates and Corrections to the Base F 2002 Inventory Incorporated 

into the 2002 Base G Inventory 

Table 1.1-4. Base G 2002 VISTAS Point Source Inventory for SO2 (tons/year) 

Table 1.1-5. Base G 2002 VISTAS Point Source Inventory for NOx (tons/year) 

Table 1.1-6. Base G 2002 VISTAS Point Source Inventory for VOC (tons/year) 

Table 1.1-7. Base G 2002 VISTAS Point Source Inventory for CO (tons/year) 

Table 1.1-8. Base G 2002 VISTAS Point Source Inventory for PM10-PRI (tons/year) 

Table 1.1-9. Base G 2002 VISTAS Point Source Inventory for PM2.5 -PRI (tons/year) 

Table 1.1-10. Base G 2002 VISTAS Point Source Inventory for NH3 (tons/year) 

Table 1.1-11. Comparison of SO2 and NOx Emissions (tons/year) for EGUs from Base G 

Actual 2002 Inventory and Typical 2002 Inventory. 

Table 1.2-1. Emissions from Fires in the VISTAS Region – Comparison between Original 

Base Year 2002 (VISTAS 3.1), 2002 Actual and Typical Year Base G 

Emissions. 

Table 1.2-2. Summary of State Data Submittals for the 2002 VISTAS Area Source 

Base F Inventory 

Table 1.2-3. Data Source Codes and Data Sources for VISTAS 2002 Base F Area Source 

Emissions Inventory. 

Table 1.3-1. Representative day mapping for January episode 

Table 1.3-2. Summary of Base F NONROAD Modeling Revisions

Table 1.3-3. Base F NONROAD Input File Sequence and Structural Revisions 

Table 1.3-4. Summary of Base G NONROAD Modeling Revisions

Table 1.3-5. Spring 2006 NONROAD Input File Sequence and Structural Revisions 

Table 1.3-6. Initial 2002 Base Year Aircraft, Locomotive, and Non-Recreational Marine 

Emissions as Reported in February 2004 Pechan Report (annual tons) 

Table 1.3-7. PM-to-NOx Ratios by Aircraft Type in Initial 2002 Base Year Inventory 

Table 1.3-8. Tucson, AZ PM-to-NOx Ratios by Aircraft Type. 

Table 1.3-9. Non-Corresponding Aircraft Emissions Records 

Table 1.3-10. Initial 2002 Base Year Aircraft, Locomotive, and Non-Recreational Marine 

Emissions with Modified Aircraft PM Emission Rates (annual tons) 

Table 1.3-11. Change in Initial 2002 Base Year Emissions due to Aircraft PM Emission Rate 

Modifications 

Table 1.3-12. CERR Aircraft NOx Records with No Corresponding PM Record. 

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List of Tables (continued) 

Table 1.3-13. Calculated Emission Ratios for VA. 

Table 1.3-14. Base F 2002 Base Year Aircraft, Locomotive, and Non-Recreational Marine 

Emissions (tons/year) 

Table 1.3-15. Change in 2002 Emissions, Base F Inventory Relative to Initial Inventory 

Table 1.3-16. Base F Comparison of Aircraft Emissions (Airports with Aircraft NOx > 200 

tons per year)

Table 1.3-17. Base G VA Aircraft Records Updates 

Table 1.3-18. Calculated Base G Emission Ratios for VA 

Table 1.3-19. Base G-Revised 2002 Base Year Aircraft, Locomotive, and Non-Recreational 

Marine Emissions (tons/year) 

Table 1.3-20. Change in 2002 Emissions, Base G Inventory 

Relative to Base F Inventory 

Table 1.3-21. Base G Comparison of Aircraft Emissions (Airports with Aircraft NOx > 200 

tons per year)

Table 1.3-22. Non-Default Files Used for MRPO Modeling 

Table 2.1-1. Adjustments to IPM Control Determinations Specified by S/L Agencies for the 

Base G 2009/2018 EGU Inventories. 

Table 2.1-2. Other Adjustments to IPM Results Specified by S/L Agencies for the Base G 

2009/2018 EGU Inventories. 

Table 2.1-3. EGU Point Source SO2 Emission Comparison for 2002/2009/2018 

Table 2.1-4. EGU Point Source NOx Emission Comparison for 2002/2009/2018 

Table 2.1-5. EGU Point Source VOC Emission Comparison for 2002/2009/2018 

Table 2.1-6. EGU Point Source CO Emission Comparison for 2002/2009/2018 

Table 2.1-7. EGU Point Source PM10-PRI Emission Comparison for 2002/2009/2018 

Table 2.1-8. EGU Point Source PM2.5 -PRI Emission Comparison for 2002/2009/2018 

Table 2.1-9. EGU Point Source NH3 Emission Comparison for 2002/2009/2018 

Table 2.1-10. Summary of Source Shutdowns Incorporated in Base G Inventory 

Table 2.1-11. Non-EGU Point Source Control Programs Included in 2009/2018 

Projection Inventories. 

Table 2.1-12. Summary of Updates and Corrections to the Base F 2009/2018 Inventories 

Incorporated into the Base G 2009/2018 Inventories 

Table 2.1-13. Non-EGU Point Source SO2 Emission Comparison for 2002/2009/2018 

Table 2.1-14. Non-EGU Point Source NOx Emission Comparison for 2002/2009/2018 

Table 2.1-15. Non-EGU Point Source VOC Emission Comparison for 2002/2009/2018 

Table 2.1-16. Non-EGU Point Source CO Emission Comparison for 2002/2009/2018 

Table 2.1-17. Non-EGU Point Source PM10-PRI Emission Comparison for 2002/2009/2018 

Table 2.1-18. Non-EGU Point Source PM2.5-PRI Emission Comparison for 2002/2009/2018. 

Table 2.1-19. Non-EGU Point Source NH3 Emission Comparison for 2002/2009/2018 

List of Tables (continued) 

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Table 2.2-1 2002 Base Year Emissions and Percentage Difference for Base F and Base G 

 (based on actual emissions). 

Table 2.2-2 2019 Projection Year Emissions &Percentage Difference for Base F & Base G 

Table 2.2-3 2018 Projection Year Emissions & Percentage Difference for Base F & Base G 

Table 2.2-4 Percentage Difference Between Base F and Base G Fire Emissions by State 

Table 2.3-1. 2002 versus 2018 VMT (million miles per year) 

Table 2.3-2. VMT and HDD Rule Estimates for North Carolina (million miles per year) 

Table 2.3-3. VMT and HDD Rule Estimates for South Carolina (million miles per year) 

Table 2.3-4. VMT and HDD Rule Estimates for Tennessee (million miles per year) 

Table 2.3-5. VMT and HDD Rule Estimates for Virginia (million miles per year) 

Table 2.3-6. Pre-Base F 2002 Aircraft, Locomotive, and Non-Recreational Marine Emissions 

(annual tons, as of the fall of 2004) 

Table 2.3-7. Locally Generated Growth Factors for North Carolina 

Table 2.3-8. Estimated Emission Reduction Impacts based on T-4 Rule 

Table 2.3-9. Estimated Emission Reduction Impacts Relative to VISTAS 2002 Base 

Year Values 

Table 2.3-10. Diesel CMV Adjustment Ratios for Palm Beach County, FL 

Table 2.3-11. Overall Adjustment Factors for Palm Beach County, FL 

Table 2.3-12. SO2 Emissions for Diesel Rail in Autauga County, AL from the CAIR 

Projections 

Table 2.3-13 Growth Options based on CAIR Data 

Table 2.3-14. Base F 2009 Aircraft, Locomotive, and Non-Recreational Marine Emissions 

(annual tons) -- Based on Growth Using 1996 and 2020 EPA Inventories 

Table 2.3-15. Base F 2018 Aircraft, Locomotive, and Non-Recreational Marine Emissions 

(annual tons) -- Based on Growth Using 1996 and 2020 EPA Inventories 

Table 2.3-16. Change in Emissions between 2009 and 2002 Base F Inventories (Based on 

Growth Using 1996 and 2020 EPA Inventories) 

Table 2.3-17. Change in Emissions between 2018 and 2002 Base F Inventories (Based on 

Growth Using 1996 and 2020 EPA Inventories) 

Table 2.3-18. Base G 2002 Aircraft, Locomotive, and Non-Recreational Marine Emissions 

(annual tons) 

Table 2.3-19. Locally Generated Growth Factors for Kentucky 

Table 2.3-20. Base G 2009 Aircraft, Locomotive, and Non-Recreational Marine Emissions 

(annual tons) -- Based on Growth Using 1996 and 2020 EPA Inventories 

Table 2.3-21. Base G 2018 Aircraft, Locomotive, and Non-Recreational Marine Emissions 

(annual tons) -- Based on Growth Using 1996 and 2020 EPA Inventories 

Table 2.3-22. Change in Emissions between 2009 Base G and 2002 Base F Inventories (Based 

on Growth Using 1996 and 2020 EPA Inventories) 

Table 2.3-23. Change in Emissions between 2018 Base G and 2002 Base F Inventories (Based 

on Growth Using 1996 and 2020 EPA Inventories) 

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List of Figures 

Figure 1.2-1. CO Emissions from Agricultural Burning for the Original Base Year, 2002 

Actual Base G, and 2002 Typical Base G Inventories.

Figure 1.2-2. CO Emissions from Land Clearing Burning for the Original Base Year, 2002 

Actual Base G and 2002 Typical Base G Inventories. 

Figure 1.2-3. CO Emissions from Prescribed Burning for the Original Base Year, 2002 Actual 

Base G and 2002 Typical Base G Inventories. 

Figure 1.2-4. CO Emissions from Wildfire Burning for the Original Base Year, 2002 Actual 

Base G and 2002 Typical Base G Inventories. 

Figure 2.2-1. Prescribed Fire Projection for Okeefenokee NWR for 2009 

Figure 2.3-1. Impacts of the Apparent CAIR Inventory Discrepancy 

Figure 2.3-2. Total Aircraft, Locomotive, and CMV CO Emissions (Base F) 

Figure 2.3-3. Locomotive CO Emissions (Base F) 

Figure 2.3-4. Total Aircraft, Locomotive, and CMV NOx Emissions (Base F) 

Figure 2.3-5. Locomotive NOx Emissions (Base F) 

Figure 2.3-6. Total Aircraft, Locomotive, and CMV PM10 Emissions (Base F) 

Figure 2.3-7. Locomotive PM10 Emissions (Base F) 

Figure 2.3-8. Total Aircraft, Locomotive, and CMV PM2.5 Emissions (Base F) 

Figure 2.3-9. Locomotive PM2.5 Emissions (Base F) 

Figure 2.3-10. Total Aircraft, Locomotive, and CMV SO2 Emissions (Base F) 

Figure 2.3-11. Locomotive SO2 Emissions (Base F) 

Figure 2.3-12. Total Aircraft, Locomotive, and CMV VOC Emissions (Base F) 

Figure 2.3-13. Locomotive VOC Emissions (Base F) 

Figure 2.3-14. Total Aircraft, Locomotive, and CMV CO Emissions (Base G) 

Figure 2.3-15. Locomotive CO Emissions (Base G) 

Figure 2.3-16. Total Aircraft, Locomotive, and CMV NOx Emissions (Base G) 

Figure 2.3-17. Locomotive NOx Emissions (Base G) 

Figure 2.3-18. Total Aircraft, Locomotive, and CMV PM10 Emissions (Base G) 

Figure 2.3-19. Locomotive PM10 Emissions (Base G) 

Figure 2.3-20. Total Aircraft, Locomotive, and CMV PM2.5 Emissions (Base G) 

Figure 2.3-21. Locomotive PM2.5 Emissions (Base G) 

Figure 2.3-22. Total Aircraft, Locomotive, and CMV SO2 Emissions (Base G) 

Figure 2.3-23. Locomotive SO2 Emissions (Base G) 

Figure 2.3-24. Total Aircraft, Locomotive, and CMV VOC Emissions (Base G) 

Figure 2.3-25. Locomotive VOC Emissions (Base G) 

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Acronyms and Abbreviations 

AEO Annual Energy Outlook 

AF&PA American Forest and Paper Association 

APCD Air Pollution Control District 

ATP Anti-Tampering Program 

BLRID Boiler Identification (Boiler ID) 

CAA Clean Air Act 

CAIR Clean Air Interstate Rule 

CEM Continuous Emissions Monitoring 

CAMD Clean Air Markets Division 

CERR Consolidated Emissions Reporting Rule 

CMU Carnegie Mellon University 

CMV commercial marine vessels 

CE Control Efficiency 

CO carbon monoxide 

DENR North Carolina Department of Environment and Natural Resources 

DHEC South Carolina Department of Health and Environmental Control 

EDMS Emissions Data Management Systems 

ESD Emissions Standards Division 

EPA Environmental Protection Agency 

EGU Electric Generating Unit 

ICF ICF International, Inc. 

FIP Federal Implementation Plan 

FLM Federal Land Manager 

FTP File transfer protocol 

FR Federal Register 

FS Forest Service 

HDD Heavy Duty Diesel 

HDD RULE Heavy Duty Diesel Rule 

ICF ICF International, Inc. 

ID Identification 

I/M Inspection and Maintenance 

IPM® Integrated Planning Model®

IAQTR Interstate Air Quality Transport Rule 

LTO Landing and take off 

MACT Maximum achievable control technology 

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Acronyms and Abbreviations (continued) 

MACTEC MACTEC Engineering and Consulting, Inc. 

MOBILE 6 MOBILE emissions estimation model version 6 

MRPO Midwest Regional Planning Organization 

NH3 Ammonia 

NEI National Emission Inventory 

NIF National Emission Inventory Format 

NLEV National Low Emission Vehicle regulation 

NMIM National Mobile Inventory Model 

NONROAD no acronym (model name) 

NOx Oxides of nitrogen 

NWR National Wildlife Refuge 

OTB On the books 

OTW On the way 

ORIS Office of Regulatory Information Systems 

OTAQ Office of Transportation and Air Quality 

OTC Ozone Transport Commission 

PFC Portable fuel containers 

PM Particulate matter 

PM10-FIL Particulate matter less than or equal to 10 microns in diameter that can be 

captured on a filter 

PM10-PRI Particulate matter less than or equal to 10 microns in diameter that includes 

both the filterable and condensable components of particulate matter 

PM2.5-FIL Particulate matter less than or equal to 2.5 microns in diameter that can be 

captured on a filter 

PM2.5-PRI Particulate matter less than or equal to 2.5 microns in diameter that includes 

both the filterable and condensable components of particulate matter 

PM-CON Particulate matter created by the condensation of hot materials to form 

particulates, usually less than 2.5 microns in diameter 

ppmW parts per million by weight 

PRI Primary 

QA/QC Quality Assurance/Quality Control 

QAPP Quality Assurance Project Plan 

REMI Regional Economic Models, Inc. 

RFG Reformulated gasoline 

RVP Reid Vapor Pressure 

SCC Source Classification Code 

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Acronyms and Abbreviations (continued) 

SCR Selective Catalytic Reduction 

SIP State Implementation Plan 

SIWG Special Interest Workgroup 

S/L/T State/Local/Tribal 

SMOKE Sparse Matrix Operator Kernel Emissions Modeling System 

S/L State and Local 

SO2 Oxides of Sulfur 

T4 Tier 4 

VISTAS Visibility Improvement State and Tribal Association of the Southeast 

VMT Vehicle Miles Traveled 

VOC Volatile organic compounds 

WRAP Western Regional Air Partnership 

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Documentation of the Base G 2002 Base Year, 2009 and 2018, 

Emission Inventories for VISTAS 

Introduction

History of VISTAS Base and Projection Year Emission Inventory Development 

This section is provided to supply the history behind the development of the base and 

projection year inventories provided to VISTAS. Through the various iterations, the 

inventories that have been developed have typically had version numbers provided by the 

contractors who developed the inventories and to a certain extent these were also based 

on their purpose. Different components of the 2002 base year inventories have been 

supplied by E.H. Pechan and Associates, Inc. (Pechan), MACTEC Engineering and 

Consulting, Inc. (MACTEC), and by Alpine Geophysics, Inc. 

The initial 2002 base year inventory was jointly developed by Pechan and MACTEC. 

Pechan developed the on-road and non-road mobile source components of the inventory 

while MACTEC developed the point and area source component of the inventory. This 

version of the inventory included updates to on-road mobile that incorporated 

information from the 1999 NEI Version 2 final along with updated information on VMT, 

fuel programs, and other inputs to the MOBILE6 model to produce a draft version of the 

2002 inventory. For non-road sources, a similar approach was used. Updated State 

information on temperatures and fuel characteristics were obtained from VISTAS States 

and used with the NONROAD 2002 model to calculate 2002 emissions for NONROAD 

model sources. These estimates were coupled with data for commercial marine vessels, 

locomotives and airplanes projected to 2002 using appropriate growth surrogates. A draft 

version of these inventories was prepared in late 2003, with a final version in early 2004. 

An overview of the development of the on-road component can be found at: 

http://www.vistas-sesarm.org/documents/Pechan_drafton-roadinventory_082803.ppt

while an overview of the non-road component can be found at: 

http://www.vistas-sesarm.org/documents/Pechan_Non-roadInventory_082803.ppt. 

Similarly, draft versions of the 2002 point and area source base year inventories were 

prepared by MACTEC in the same timeframe (late 2003 for the draft, final in early 

2004). The point source component was based on data submitted by the VISTAS States 

or on the 1999 NEI. The data submitted by the States ranged from 1999 to 2001 and was 

all projected to 2002 using appropriate growth surrogates from Economic Growth 

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Analysis System (EGAS) version 4. Toxic Release Inventory (TRI) data were used to 

augment the inventory for NH3. Continuous Emissions Monitor (CEM) data from the 

U.S. EPA’s Clean Air Markets Division was used to supply emissions for electric 

generating utilities (EGUs). Particulate matter emissions were augmented (when missing) 

by using emission factor ratios. Details on all these calculations are discussed in Section 

1.1.1.3 of this document. 

The area source component of the 2002 draft base year emissions was prepared similarly 

to the point sources, using State submittals and the 1999 NEI Version 2 final as the basis 

for projecting emissions to 2002 using EGAS growth factors. For ammonia area sources 

the Carnegie Mellon University (CMU) ammonia model was used to calculate emissions. 

Finally, data on acreage burned on a fire by fire basis was solicited from State forestry 

agencies in order to calculate fire emissions on a fire by fire basis. Virtually all VISTAS 

State forestry agencies provided data for these calculations at least for wild and 

prescribed fires. An overview of the point and area source development methods can be 

found at: 

http://www.vistas-sesarm.org/documents/MACTEC_draftpointareainventory_82803.ppt. 

Three interim versions of the 2002 base year inventory were developed. The first was 

delivered in August of 2003, the second in April of 2004 and the final one in October of 

2004. The August 2003 and April 2004 inventories were prepared by MACTEC and 

Pechan. A draft version of the revised 2002 base year inventory was released in June of 

2004, with a final version released in October 2004. That 2002 base year inventory was 

solely prepared by MACTEC. The October 2004 inventory incorporated 2002 

Consolidated Emissions Reporting Rule (CERR) data into the inventory along with some 

updated data from the VISTAS States. This inventory is typically referred to as version 

3.1 of the VISTAS inventory 

Closely following the version 3.1 2002 base year inventory, a “preliminary” 2018 

projection inventory was developed. This “preliminary” 2018 inventory was developed in 

late 2004 (Oct/Nov) and was designed solely for use in modeling sensitivity runs to 

provide a quick and dirty assessment of what “on the books” and “on the way” controls 

could be expected to provide in terms of improvements to visibility and regional haze 

impairment. A brief overview of the history of the three versions of the 2002 base year 

and the 2018 preliminary inventory use can be found at: http://www.vistassesarm.org/documents/STAD1204/2002and2018Emissions14Dec2004.ppt. 

Following preparation of the final 3.1 version of the 2002 base year inventory, States 

were asked to review and provide comments on that inventory to MACTEC for update 

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