# 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 Emissions Inventory Quality Assurance Project Plan

# 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

## 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)

## Table of Contents

FUTURE YEAR EMISSION INVENT ORY 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. (A SIP 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 Servi ces, Inc. (Mobile Source Task Leader)Project: ASIP Projection Inventories Section: 1 Revision No. 0 Revision Date: 3/17/06 Page 1 of 10



### 1.0 PROJECT MANAGEMENT

1.1 Problem Definition/Background

The Southeastern States Air Resource Manage rs (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 Ca rolina, South Carolina and Tennessee) plus Virginia, West Virginia, and Tribes. Through a memorandum of understanding, these parties are collaborating in the organization Visibility Improvement St ate and Tribal Associa tion of the Southeast (VISTAS) on the technical analysis and planni ng activities that support state implementation plans for regional haze. The part icipating agencies have conclude d that a collaborative regional process is also the most efficient approach fo r the states to develop the technical analyses supporting attainment demonstrations for the fi ne particulate matter (PM2.5) and eight-hour ozone standards. Along with the local air regul atory 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 si gnatory parties to the collaborative effort called the Association for Southeastern Integrat ed Planning (ASIP). SESARM will coordinate among participating agencies and oversee the pe rformance of the ASIP inventory and modeling tasks in parallel with the VISTAS regional haze project tasks. Emissions inventory efforts include the development of emissi ons 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 Virgin ia) has been designated as nonattainment for the PM 2.5 

### . In addition, South Carolina has one three-county area that was designated as unclassifiable. The PM 2.5 

### compliance date is April 2010 unless a state demonstr ates that more time is necessary in which case up to five additional years ma y be granted. State implementati on plans (SIPs) w ill be due in April 2008 and the modeling year for the PM 2.5 

### attainment demonstration will be 2009.

The States of Alabama, Georgia, Kentuck y, 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 demons trate attainment for the year 2008 and moderate areas will require 2009 as the modeling year

The objective of this pr oject is to compile future year emission inventories to support fine particulate matter and ozone modeling efforts in th e ASIP region for all s ource categories. This project has the following ove rall design specifications:

• 

### Pollutant Coverage - primary and precursor annual and seasonal emissions necessary to accurately model fine particulate ma tter and ozone, including primary PM2.5 andProject: ASIP Projection Inventories Section: 1 Revision No. 0 Revision Date: 3/17/06 Page 2 of 10



### PM10, ammonia (NH3), oxides of sulfur (S Ox), volatile organic compounds (VOCs), oxides of nitrogen (NOx) a nd carbon monoxide (CO)

• 

### Source Coverage – all source ca tegories except biogenic.

• 

### Geographic Areas – the ASIP states (A labama, 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 ch emical transport modeling of fine particulate matter and ozone in the southeastern U.S. and to evaluate potential control strategies for the National Ambient Air Qual ity Standards (NAAQS) for fine particulate and ozone. Two future year inventories will be prep ared 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 deve lopment of complete, accurate, and consistent emission inventories. The QAPP is consistent with the recommendations in the EPA quality assurance requirements 1 

### and the Emission Inventory Improvement Program’s QA guidance 2 

### . The QAPP includes tasks associated w ith 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 st akeholder review of the em ission inventory, and providing documentation of the maintenance (revisions, updates, corrections) of the inventory.

1.2 Project/Task Description

EPA 3 

### has specified that calendar year 2002 be used as the base year for emission inventories to support planning efforts under the 8-hour ozone, PM 2.5, and Regional Haze programs. ASIP has planned an iterative process to use and enhance the 2002 base i nventory prepared by MATEC for the Visibility Improvement State and Tribal Asso ciation of the Southeast (VISTAS) as part of regional haze planning, that inco rporates improved information as it becomes available. In addition, work on the PM2.5 and ozone NAAQS calls fo r 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 inve ntory 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 particulat e 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 Implementati on Plans (SIP) will be incorporated. The growth and control factors will be those developed fo r the VISTAS 2009 regional haze and PM2.5 inventory development effort augmented by updated informationProject: ASIP Projection Inventories Section: 1 Revision No. 0 Revision Date: 3/17/06 Page 3 of 10



### from other regional inventory development work and modifications based on State comments. Typical year emissions for elect ric 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 th e 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 VI STAS inventory to account for Base G changes to the base year 2002 inventory and an y 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 200 8 which are not currently available.

4. Prepare the 2008 ASIP projection inventor y using data deve loped in step 3.

5. Recommend methods for cont rol 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 re lated to projecting emissions, a report detailing the methods used to develo p the projections will be prepared.

1.3 Project Organization

Figure 1 and Table 1 identify the in dividuals and organizations partic ipating in the project. Their specific roles and res ponsibilities include:

• 

### Ms. Pat Brewer, Technical Coordinator, wi ll plan, conduct, and supervise technical and managerial aspects of the project. She will fa cilitate communications among State/local agencies, MACTEC, and the SESARM Executive Director.Project: ASIP Projection Inventories Section: 1 Revision No. 0 Revision Date: 3/17/06 Page 4 of 10

• 

### Mr. Greg Stella, Emission Inventory Technical Advisor, will work with the Technical Coordinator to define the emission inve ntory 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 projec t and will serve as a senior primary contact with ASIP on contract and project management issu es. Mr. Barnard will also direct aspects of the projection inventory development related to area and mobile sources. He will plan and conduct the technica l aspects of the development of the area and mobile source inventories, supervise daily activities, identify effective QC procedures and make recommenda tions on needed QC procedures.

• 

### Edward Sabo, MACTEC Point Source Task L eader, 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 recommendati ons 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 MO BILE and NONROAD models and for nonroad sources not covered by the NONROAD model. He will help identify effective QC procedures and make recommendati ons on needed QC procedures.

• 

### Douglas Toothman, MACTEC QA Coordinato r, will help ensure that adequate QA/QC procedures are incorpor ated into the inventory deve lopment process. He will work independent of the inventory developm ent Task Leaders to assist in the conduct of project QA/QC assessments.Project: ASIP Projection Inventories Section: 1 Revision No. 0 Revision Date: 3/17/06 Page 5 of 10

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 CHARTProject: ASIP Projection Inventories Section: 1 Revision No. 0 Revision Date: 3/17/06 Page 6 of 10



### 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 purpo se is to ensure that the final data will be sufficient for the intended use of the inventory. A well-develop ed 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 representati veness. The second column identifies the procedures that will be used to achieve each objec tive. The third column identifies Data Quality Indicators (DQI), which are qualitative and quan titative descriptors used in interpreting the degree of acceptability or utility of data.

1.5 Special Training/Certification

All staff performing data review and analysis ar e air quality professionals and have sufficient education/experience to perfor m emission estimation calculations and work with emission inventory databases. Most st aff have received specific emi ssion inventory tr aining 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 ini tiated 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 th e affected pages. It will be the responsibility of the initiating person to di stribute copies of the changed pages to all the persons on the Distribution List and to the appr opriate 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 descriptio n of the data received, the na me 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 Inve ntory (Version 2 Final)

• 

### State CERR submittals

• 

### State/local agency data submittals in NIF 3.0 format

• 

### Growth factors assembled by EPAProject: ASIP Projection Inventories Section: 1 Revision No. 0 Revision Date: 3/17/06 Page 7 of 10

• 

### 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 relate d to fugitive dust sources, primarily paved and unpaved roads, livesto ck activities and ag ricultural activity (tilling).

• 

### State agency submittals of information necessary to calculate fire emissions and geographically locate where th ese 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, co rrections in response to various QA/QC checks. These may be provided in a variet y of formats depending on the nature of the
response.

• 

### Department of Energy fuel efficiency data

• 

### EGAS growth factors

• 

### VMT dataProject: ASIP Projection Inventories Section: 1 Revision No. 0 Revision Date: 3/17/06 Page 8 of 10



### TABLE 1 DATA QUALITY OBJECTIVES, PR OCEDURES, AND INDICATORS

Data Quality Objective Pro cedures 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 in cluded 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).Project: ASIP Projection Inventories Section: 1 Revision No. 0 Revision Date: 3/17/06 Page 9 of 10



### 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 w ith the projection and base year inventories. Nearly all data being developed and/or comp iled 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 al so maintain a log of activities to document how the data described above were incorporated to cr eate 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 en sure 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 leas t 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. Do cumentation of each fi nding will include a description of the action or data reviewed that led to the qual ity 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 th e 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 pertinen t to the inventory;

• 

### A quality assurance summary report that describes the quality assurance efforts completed, summarizes the corrective actio ns taken, and provide s suggestions for further inventory improvement based on the re sults 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 ACQUISITIONProject: ASIP 2002 Inventory Section: 2 Revision No. 0 Revision Date: 3/17/2006 Page 2 of 4



### 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 a nd documentation point s in the inventory development process to verify data in tegrity, 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 solu tions, until the iterations are complete.

2.1 Projection Year Inventory Procedures

For the projection inventories, the following pro cedures 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 fo r 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; incorpor ate 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” a nd “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 inform ation will be obtained from VISTASsponsored IPM runs, supplemented with st ate-supplied adjustments as to where future controls will be installed.

c. Determine/obtain growth factors for non-E GAS sources (e.g., ag ricultural crops, fertilizers, etc.). Growth factors for th ese sources will be calculated from existing projection inventories prepared by EPA (e.g., EPA Ammonia Inve ntory). Growth factors will be calculated us ing 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 grow th factors, apply growth and control factors. b. For sources not using EGAS growth fact ors, apply non-EGAS growth factors.Project: ASIP 2002 Inventory Section: 2 Revision No. 0 Revision Date: 3/17/2006 Page 3 of 4



### 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 requir ing “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 St ates/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 require d 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 em issions for nonroad sources not in the NONROAD model. Growth factors will be based on existing estimates from EPA rulemaking projections (e.g., Heavy Du ty Diesel and othe r rules). Provide growth factors for review by States/Stakeholders.

f. Prepare non-NONROAD model emissi on estimates. Provide for States/Stakeholder review/comment.

6. Conduct QA/QC to identify e rrors and inconsistencies

a. Prepare ad-hoc reports to identify ga ps and logical inconsistencies. b. Ask States/local agencies to provide feedback on large scale inconsistencies and on missing sources.

c. Update database with Stat e/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 informa tion pertinent to the inventoryProject: ASIP 2002 Inventory Section: 2 Revision No. 0 Revision Date: 3/17/2006 Page 4 of 4



### b. Prepare a quality assurance summary report that describes the quality assurance efforts completed, summarizes the co rrective actions taken, and provides suggestions for further inventory improveme nt based on the resu lts 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 a nd prepare final report s and electronic files

3.0 ASSESSMENT AND OVERSIGHT

The subsections in this group ad dress the activities for assessi ng the effectiven ess 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 pres cribed. The assessment consists of external activities that include a planned system of revi ew and audit procedures by personnel not actively involved in the inventory development proce ss. 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 w ill conduct technical systems audits throughout the project. Audits are managerial tools used to evaluate how effectively the emission inventory team complies with predetermined specificati ons 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 a nd 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 prepar ation 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 we eks 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 Pr ogram Manager to keep him informed of the quality issues found and actions bein g 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.Project: ASIP 2002 Inventory Section: 3 Revision No. 0 Revision Date: 3/17/2006 Page 2 of 3



### 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. R ecord 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 th e 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 docu menting 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 th e inventory process are requests for missing data made?

___________________________________________________________________________

___________________________________________________________________________ How is the receipt of the missing data handled?

___________________________________________________________________________

___________________________________________________________________________ Is the procedure followed to ascertain mi ssing 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 unders tandable and explainable? ................................................. Y/N If any of the values are incorrect, explain how the emissions data were corrected.

___________________________________________________________________________ Figure 2 Audit Check FormProject: 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 incorpora tion into the database?

__________________________

_______________________________________________________________________ B. Was there evidence that the data were re viewed for accuracy and completeness prior to incorporation in the database? ............................................................................................ ... Y/N C. Were data logs maintained to descr ibe 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 th e 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 USABILITYProject: 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 discrepa ncies identified in the QA/QC process.

A final semi-quantitative discussion of accur acy will consist of pollutant summaries for individual facilities, industry t ypes, source categories, and statew ide 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 disc uss 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 individu al facilities (current year to prior year), industry types, processes, and statewide inventory will be calculat ed. Explanations of any large differences will be made.

4.4 Representativeness Assessment

A statement will be prepared describing where nati onal defaults have been used instead of local activity data.

5.0 REFERENCESProject: 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/memb ers/committee/criteria/EPA200211181.pdf

# Appendix D.2a

Point Source Emissions Inventory DocumentationAppendix D.2a - 1

### TABLE OF CONTENTS

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

2. 2002 POINT SOURCE I NVENTORY 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 Poin t Source Inventories .......................................................... 33

Appendix D.2a - 2

### 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 NO x 

### 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 SO 2 

### Emission Comparison ........................................................ 30
Table 41.7-2 EGU Point Source NO x 

### Emission Comparison ...................................................... 30
Table 4.1-3 EGU Point Sour ce VOC Emission Comparison ...................................................... 31 Table 4.1-4 EGU Point Source CO Emission Comparison ......................................................... 31
Table 4.1-5 EGU Point Source PM 10 

### -PRI Emission Comparison ............................................... 32
Table 4.1-6 EGU Point Source PM 2.5 

### -PRI Emission Comparison ............................................. 32
Table 4.1-7 EGU Point Source NH 3 

### Emission Comparison ........................................................ 33
Table 4.2-1 Non-EGU Point Source SO 2 

### Emission Comparison ................................................ 33
Table 4.2-2 Non-EGU Point Source NO x 

### Emission Comparison ................................................ 34
Table 4.2-3 Non-EGU Point S ource VOC Emission Comparison .............................................. 34
Table 4.2-4 Non-EGU Point S ource CO Emission Comparison ................................................. 35
Table 4.2-5 Non-EGU Point Source PM 10 

### -PRI Emission Comparison ....................................... 35
Table 4.2-6 Non-EGU Point Sour ce PM25-PRI Emission Comparison ..................................... 36
Table 4.2-7 Non-EGU Point Source NH 3 

### Emission Comparison ................................................ 36

Appendix D.2a - 3Appendix D.2a - 4 

## 1. INTRODUCTION

The regional haze modeling was performed by th e Southeast Regional Planning Organization, Visibility Improvement State and Tribal Association of the Sout heast (VISTAS) and is run by the ten Southeast states (Alabama, Florida, Ge orgia, Kentucky, Mississippi, North Carolina, South Carolina, Tennessee, Virginia and West Virginia) and the local programs and tribal agencies located within these states. The re gional haze modeling uses annual simulations and includes an intermediate year. VISTAS deve loped emission estimates for all pollutants of concern for regional haze.



## 2. 2002 POINT SOURCE INVE NTORY 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 (U SEPA) 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 primar y coarse particulate matter (PM 10 

### -PRI) and primary fine particulate matter (PM 2.5 

### -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 ne w information from S/L agencies based on their review of the 2002 inventory. The processe s used to perform the emission inventory development are described in the first portion of this section.

The second component was the development of a “t ypical” year inventory for electric generating units (EGUs). The VISTAS states determined th at 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 i nventory 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.2a - 5 

### 2.1 Development of 2002 Actual Point Source Inventory

VISTAS/ASIP contracted with MACTEC to deve lop the 2002 emission inventory. The South Carolina Department of Health and Environmental Control (SCDHEC) submitted the most updated statewide emission inventor y 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 ch ecking tool to ensure that the files were submitted in standard
NIF format and that there were no refere ntial integrity issues with the files.

The primary task in preparing th e 2002 base year point source i nventory 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. Th e 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 Regist er 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 em ission inventories. The rule applies to S/L agencies. This rule consolidates the emission inventory reporting requirements found in various parts of the Clean Ai r 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.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 th e CERR data was limited to major sources, while the VISTAS draft 2002 inventory containe d data for both major and minor sources;
verified with S/L contacts that minor s ources not included in the CERR point source inventory were included in th e CERR area source inventory.

• 

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

• 

### Checked for types of particulate matter (P M) 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 disc ussed 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 file s 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 (NH 3 

### ) 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 (SO 2 

### ) and nitrogen oxides (NOx) emissions for EGUs to the USEPA’s Clean Air Markets Division (CAM D) continuous emission monitoring (CEM) database to identify any outstanding disc repancies. A full discussion of the EGU emissions analysis is disc ussed 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 compar e emissions as reported in the 1999 NEI Version 2, the VISTAS draft 2002 i nventory, and the CERR submittals.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 an d updates as necessary to resolve any QA/QC
issues from these checks.

2.1.3 PM Augmentation
Particulate matter emissions can be reporte d in many different forms, as follows:

PM Category Description

PM-PRI Primary PM (include s filterable and condensable)

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

PM-FIL Primary PM, filterable portion only

PM 10 

### -PRI Primary PM 10 

### (includes filterable and condensable)

PM 10 

### -FIL Primary PM 10 

### , filterable portion only

PM 2.5 

### -PRI Primary PM 2.5 

### (includes filterable and condensable)

PM 2.5 

### -FIL Primary PM 2.5 

### , filterable portion only

South Carolina reports PM-PRI, PM 10 

### -PRI

, 

### and PM 2.5 

### –PRI in the CERR. From any one of these pollutants, the USEPA has developed augmen tation procedures to estimate PM10-PRI,
PM 10 

### -FIL, PM 2.5 

### -PRI, PM 2.5 

### -FIL, and PM-CON. If not included in a S/L inventory, PM 10 

### -PRI
and PM 2.5 

### -PRI were calculated by adding PM 10 

### -FIL and PM-CON or PM 2.5 

### -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 Polluta nts and Ammonia – Point Sources , January 31, 2004.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 PM 10 

### Emissions for Input to the PM Calculator

• 

### Step 2: Develop and Apply S ource-Specific Conversion Factors

• 

### Step 3: Prepare Factor s from PM Calculator

• 

### Step 4: Develop and Apply Algorithms to Es timate 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 co mplete description of the PM augmentation procedures.

Table 2.1.3-1compares the original PM emissi on 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 th at MACTEC took whatever State/Local provided in the way of PM and filled in gaps to add in PM-CON where emi ssions were missing in order to
calculate PM 10 

### -PRI and PM 2.5 

### -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 emissionsAppendix D.2a - 9 

### Table 2.1.3-1. Comparison of South Carolin a 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 pa rticulate 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 perfor med, MACTEC executed a series of checks to identify potential inconsistencies in the PM inventory. These checks included:

• PM-PRI less than PM 10 

### -PRI, PM 2.5 

### -PRI, PM 10 

### -FIL, PM 2.5 

### -FIL, or PM-CON;

• PM-FIL less than PM 10 

### -FIL or PM 2.5 

### -FIL;

• PM 10 

### -PRI less than PM 2.5 

### -PRI, PM 10 

### -FIL, PM 2.5 

### -FIL or PM-CON;

• PM 10 

### -FIL less than PM 2.5 

### -FIL;

• PM25-PRI less than PM 2.5 

### -FIL or PM-CON;

• The sum of PM 10 

### -FIL and PM-CON not equal to PM 10 

### -PRI; and

• The sum of PM 2.5 

### -FIL and PM-CON not equal to PM 2.5 

### -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 PM 10 

### -PRI and PM 2.5 

### -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.2a - 10 

### 2.1.4 EGU Analysis
MACTEC made a compar ison of the annual SO 2 

### and NO x 

### 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. Fa cilities report hourly CEM data to the USEPA for units that are subject to CEM reporting requirements of the NO x 

### SIP Call rule and Title IV of the CAA. The USEPA sums the hourly CEM emi ssions 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 we re 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 prep aring 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 Informati on Systems (ORIS) identification (ID) code identifies unique facilities a nd the unit ID identifies unique boi lers and internal combustion engines (i.e., turbines and reciprocating engine s). In the South Carolina point source emissions inventories, the state and county code (FIPS code) and State facili ty ID together identify unique facilities and the emission unit ID identifies unique boilers or internal co mbustion engines. In most cases, there is a one-to-one corresponden ce between the CAMD identifiers and the S/L identifiers. However, in some of the S/L inve ntories, 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 OR IS ID and unit ID in th e CEM inventory matched to the State and county FIPS, State facility ID, a nd 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 pr epare an Excel spreadsheet that compared the annual emissions from the hourly CAMD inventor y to the annual emissions reported in the S/L inventory. The facility-level compar ison of CEM to emission inventory NO x 

### and SO 2 

### emissions found that for most facilities, the annual emissi ons from the S/L inventory equaled the CAMD CEM emissions. Minor differences could be explai ned because the facility in the S/L inventory contained additional small or emergency units th at were not included in the CAMD database.

The final step in the EGU analysis was to compare the SO 2 

### and NO x 

### emissions for select Southern Company units in the VISTAS regi on. Southern Company is a super-regional company that owns EGUs in four VISTAS States (Alabama, Florida, Geor gia, and Mississippi)Appendix D.2a - 11 

### and participates in VISTAS as an industry stakeholder. S outhern 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 inconsiste ncies between the Southern Company data, the CAMD data, and the S/L CERR data.

The minor inconsistencies found included small di fferences in emission estimates (<2 percent difference), exclusion/inclusion of small gas-fired units in the di fferent databases, and grouping
of emission units in S/L CERR submittals wh ere CAMD listed each un it individually. MACTEC compared SO 2 

### and NO x 

### emissions on a unit-by-unit basi s 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 en sure that a full and complete inventory was developed for VISTAS. QA was an important co mponent 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 th at there were no missing sources.

2. State-level EGU and non-EGU comparisons (by pollutant) were developed between the 2002 base year inventory, the draf t 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 representative s for review and comment. Changes based on these comments were reviewed and appr oved 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.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

SO 2 

### 259,916 206,399 53,518

NO x 

### 130,394 88,241 42,153

VOC 38,927 470 38,458

CO 63,305 6,990 56,315

PM 10 

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

PM 2.5 

### -PRI 27,399 17,154 10,245

NH 3 

### 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, eco nomic, and outage factor s in 2002. The typical year inventory represents the five year (2000-20 04) 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 VIST AS contractor used th e ratio of the 2000-2004 average heat input and the 2002 actual heat i nput to normalize the 2002 actual emissions.
MACTEC obtained data from th e USEPA CAMD for utilities regulated by the Acid Rain program. Annual data for the period 2000 to 20 04 were obtained from th e CAMD web site. The parameters available were the SO2 and NOx emi ssion 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.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 di d operate. The annual actual emissions were multiplied by the normalization factor to dete rmine the typical emissions for 2002, as follows:

Typical Emissions = 2002 actual em issions x Normalization Factor

After applying the normalizatio n 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 prio r to 2002, then MACTEC us ed zero emissions for the typical year. If the unit had not been perm anently 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 emi ssions with the 2002 typical year data described above. MACTEC provided the raw data and resu lts of the typical year calculations in a spreadsheet for S/L agency to review and commen t. Any comments made were incorporated into the typical 2002 inventory.

Table 2.2-1 summarizes emissions by State and po llutant 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 NO x 

### Emissions Comparison for EGUs

NO x 

### 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 3.3Appendix D.2a - 14 

### VA 86,886 85,081 2.1

WV 230,977 222,437 3.7



## 3. 2018 POINT SOURCE EMISSION S 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 cont rol 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 update d 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 genera ted 2018 inventory with control s cenarios that account for post2002 emission reductions from promulgated and propos ed Federal, State, Lo cal, and site-specific control programs as of July 1, 2004. Sec tion 3.1 discusses the E GU projection inventory development, while Section 3.2 discusses the non-EGU projection inve ntory 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 pr ocess 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 r uns 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 account ed for in IPM were not double-counted in the non-EGU inventory.

• The QA/QC checks that were made to ensu re that the IPM results were properly incorporated into the VISTAS inventory.Appendix D.2a - 15 

### • The changes to the IPM results that S/L ag encies 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; thes e 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 ra tes, 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 Organizatio n (MRPO). These runs incorporated VISTAS specific unit and regulat ion modified parameters, a nd generate results for 2018 explicitly.

An additional consideration for each of the th ree options was the inclusion of emission projections developed by the S outhern Company specifically for their units. Southern Company used their energy budget forecast to project net generati on and heat input for every existing and future Southern Company EGU for the year 2018.

Each of these three options and the Southern Comp any 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.2a - 16 

### The Option 3 IPM modeling resulte d 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 re cently to support th eir rulemaking for the CAIR, VISTAS stakeholders felt that certain model inputs need ed 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 gene rally less than IPM be cause 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 know n laws and regulations, including those that come into force in the study horizon. The CAIR Ca se is the Base Case w ith 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 or ganizations (RPOs), conducted a nother 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 a nd stakeholder comments. Several conference calls were conducted in the sp ring/summer of 2005 to discu ss 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 generate d 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 f ile 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 us ed the fossil only results for CAIR analyses. For the 10 VISTAS states, no n-fossil fuels accounted fo r only 0.13 percent of the NOx emissions and 0.04 percent of th e SO2 emissions in the 2018 IPM runs

VISTAS asked S/L agencies to review the re sults of the summer 2006 set of IPM runs, which were incorporated into the VISTAS inventory. The SCDHEC primarily reviewed andAppendix D.2a - 17 

### commented on the IPM results wi th 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 Se ctor 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 e xpansion, electricity dispatch, and emission control strategies for meeting energy demand and environmental, transmission, dispatch, and reliability constraints.” The underlying databa se 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 exis ting and planned/committe d unit data in the USEPA modeling applications of IPM. NEEDS includes basi c geographic, operating, air emissions, and other data on these generating units . VISTAS States and stakeholders provided changes for:

• NO x 

### post-combustion cont rol on existing units

• SO 2 

### scrubbers on existing units

• SO 2 

### emission limitations

• PM controls on existing units

• Summer net dependable capacity

• Heat rate for existing units

• SO 2 

### and NO x 

### control plans based on State ru les or enforcement settlements

The years 2009 and 2018 were explicitly modele d in this set of runs. Again, the SCDHEC
decided to let the IPM m odel solve the 2018 emissions.

3.1.3 Post-Processing of IPM Parsed Files
The following summary of the VISTAS/MRP O IPM modeling is based on Pechan’s
documentation LADCO IPM Model Parsed File Post -Processing Methodology and FileAppendix D.2a - 18 

### Preparation , February 8, 2005. The essence of th e IPM model post-processing methodology is to take an initial IPM model outpu t 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 SO 2 

### and NO x 

### ), and separate NO x 

### control information; annual SO 2 

### and NO x 

### emissions and heat input; summer season (MaySeptember) NO x 

### and heat input; July day NO x 

### and heat input; coal he at 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. Th e 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 ge neric units are sited in a county and given appropriate ID s. This processing is descri bed in more detail below.

The data are prepared by transfor ming the generic aggregates into units similar to the existing units in terms of the available data. The generi c aggregates are split into smaller generic units based on their unit types and capacity, are provided a dummy ORIS unique plan t and boiler ID, and are given a county FIPS code based on an algorithm that si tes each generic by assigning a sister plant that is in a count y based on its attainment/nonattainme nt 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 usi ng 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 th e data were not provided in the 2002 NIF files. Stack parameters were attached , first using the USEPA-provided data files, se condly using a March 9, 2004 Pechan in-house stack parameter file based on previous Energy Information Adminitration form 767 (EIA-767) data, and la stly using an the USEPA June 2003 SCC-based default stack parameter file. Thes e data were only used when th e data were not provided in the
2002 NIF files.Appendix D.2a - 19 

### Additional data were required for estimating VOC, CO, filterable primary PM 10 

### and PM2.5, PM condensable, and NH 3 

### 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 de fault of 99.2 percent is used for coal units if necessary); fuel use was back calculated from th e 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 emi ssion factors. Note that this updated file is not the one used for estimating emissions for previous USEPA post-pr ocessed IPM files. Emissions for 28 temporal-pollutant combinations were estimated since there are seven pollutants (VOC, CO, primary PM 10 

### and PM 2.5 

### , NH 3 

### , SO 2 

### and NO x 

### ) and four temporal periods (annual, summer season, winter season, July day).

The next step was to match the IPM unit IDs wi th the identifiers in VISTAS 2002 inventory. A
crosswalk file was used to obt ain FIPS State and county, plant ID (within State and county), and point ID. If the FIPS State a nd county, plant ID and point ID are in the 2002 VISTAS NIF tables, then the process ID and stack ID are obtai ned from the NIF; otherwise, defaults, described above, were used.

Pechan provided the post-processe d files in NIF 3.0 format. Two sets of tables were developed:
“NIF files” for IPM units that have a crossw alk match and are in th e 2002 VISTAS inventory,
and “NoNIF files” for IPM units that are no t in the 2002 VISTAS inventory (which includes existing units with or without a crossw alk match as well as generic units).

3.1.4 Eliminating Double Counting of EGU Units
The following procedures were used to avoi d 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 bot h EGUs and non-EGU point sources, and EGU emissions are projected using the IPM, it was nece ssary 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 ot her 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). Th is was done on a unit-by-unit basis based on a cross-reference table that matches IPM emission unit identifiers (ORISPL plant code and BLRIDAppendix D.2a - 20 

### emission unit code) to VISTAS NIF emission un it 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 assi gned to the non-EGU inventory.

If an emission unit was contained in the NIF fi les created by Pechan from the IPM output, the corresponding unit was removed from the init ial 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 un its 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 Classificati on (SIC) code of “4911 Electri cal Services” or SCC of “1-
01-xxx-xx External Combustion Bo iler, 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 th e EGU component of the VISTAS revised
2009 and 2018 EGU inventories: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 IP M) 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 IP M 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 ag encies 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 IP M results primarily involved S/L agency addition
or subtraction of future emission controls base d 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 c ontrols assigned by IPM to reflect their best
estimates of emission controls. The VISTAS contractors used a scrubber contro l efficiency of 90 percent when adding or removing SO 2 

### scrubber controls, used a cont rol efficiency of 90 percent when adding or removing NO x 

### SCR controls at coal-fired pl ants, 80 percent when adding or removing NO x 

### SCR controls at gas-fired plants, a nd 35 percent when adding or removing NO x 

### 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 informati on 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 SO 2 

### scrubber in either 2009 or 2018, some st ates were able to provide revised stack parameters for some units based on design f eatures for the new control system. Other units projected to install scrubbers by 2009 or 2018 are not far enough al ong in the design process toAppendix D.2a - 22 

### have specific design details. For those un its, the VISTAS EGU SIWG made the following assumptions: 1) the scrubber is a we t scrubber; 2) keep the current stack height the same; 3) keep the current flow rate the same, and 4) change th e stack exit temperature to 169 °F (this is the virtual temperature derived from a wet temperatur e of 130 °F). VISTAS determined that exit temperature (wet) of 130 °F +/- 5 °F is represen tative 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; Up grade 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 SO 2 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 PM 10 and PM25 emissions in 2018 by 95 percent based on change in allowable emission rate

Unit 2: Install low NO x burners that meet 0.46 lb/mmBTU from 0.9 by May 1, 2004. Recalculated NO x 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 PM 10 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 PM 10 and PM25 emissions in 2018 by 94.44 percent based on change in allowable emission rateAppendix D.2a - 23 Unit 4: Upgrade ESP from 0.54 to 0.03 lb/mmBTU by Dec. 31, 2012. Reduced PM 10 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
NO x control technology by April 30, 2006 to meet 0.27 lb/mmBTU, down from 0.45 lb/mmBtu. Calculated NO x 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 th e data used in the USEPA’s CAIR analyses, supplement these data with available stakeholder input , and provide the resu lts 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 sour ces, 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 acco unted 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.2a - 24 • 

### Obtained, reviewed, and applied any State-sp ecific or sector-speci fic growth factors submitted by stakeholders;

• 

### Obtained and incorporated information regard ing sources that have shut down after 2002
and set the emissions to zero in the projection inventories;

• 

### Obtained, reviewed, and app lied 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 ne w information from S/L agencies based on
their review of the 2009 and 2018 inventories.

The following sections discu ss each of these steps.

3.2.1 Growth assumptions for non-EGU sources
The growth factor data used in developing th e emission inventory were consistent with the USEPA’s analyses for the CAIR rulemaking. Thes e growth factors are fu lly 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 Re gional 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 indus try sector, population, farm sector value added, and gasoline and oil expenditures) ar e 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, w ith Projections through 2025 for use in generating growth factors for non-E GU fuel combustion sources. These data include regional or national fuel-use forecast da ta that were mapped to specific SCCs for the non-EGU fuel use sectors (e.g., commercial coal, industrial natu ral gas). Growth factors for the residential natural gas com bustion category, for example, are based on residential natural gas consumption forecasts that are reported at the Census divisionAppendix D.2a - 25 • 

### Specific changes for sectors (e.g., plastics , synthetic rubber, carbon black, cement manufacturing, primary metals, fabricated me tals, motor vehicles and equipment) where
the REMI-based rates were unrealistic or hi ghly uncertain. Growth projections for these sectors were based on industry group foreca sts, Bureau of Labor Statistics (BLS) projections and Bureau of Ec onomic Analysis (BEA) histor ical growth from 1987-2002.

In addition to the growth data described above, VISTAS received two sets of growth projections
from stakeholders. The American Forest an d Paper Association (A F&PA) supplied growth projections for the pulp and pa per sector, which were applie d 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). Th e VISTAS contractor used the above AF&PA growth factors by SIC instead of the factors obt ained 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 Te xtile Mill Products, 23xx Apparel and Other Fabrics, and 25xx Furniture and Fixtures. Fo r 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 industr ies in North Carolina, the NC DAQ wanted to maintain the
emission levels at 2002 levels so the future emissi on 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 pr oduced. 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.2a - 26 

### VISTAS used the USEPA’s EGAS model and updated the corresponding AEO2006 projection tables to create growth factors by SCC. VI STAS applied the updated growth factors to 2002 actual emissions and replaced the 2009 and 2018 emi ssions 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 s cenario to address the CAA requirements. The control scenario accounts for pos t-2002 emission reductions from promulgated Federal, State, Local, and site-specific control pr ograms and are listed below.

• Atlanta / Northern Kentucky / Birmingham 1-hour ozone SIPs

• Industrial Boiler/Process Heater/RICE MACT

• NO x RACT in 1-hour NAA SIPs

• NO x SIP Call (Phase I)

• NO x 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 emissi on reductions associated with these emission control programs are discusse d in the following sections.

NO x 

### SIP Call (Phase I)

Phase I of the NO x 

### SIP call applies to certain large non-EG Us, including large industrial boilers and turbines, and cement kilns. States in the VISTAS region affected by the NO x 

### SIP call have developed rules for the control of NOx emissi ons 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 y ear 2007, VISTAS capped the emissions for NO x 

### SIP call
affected sources at 2007 levels and carried forward the capped levels for the 2009 and 2018 future year inventories.

NO x 

### SIP Call (Phase II)

The final Phase II NO x 

### SIP call rule was finalized on April 21, 2004. States had until April 21, 2005, to submit SIPs meeting the Phase II NO x 

### budget requirements. The Phase II ruleAppendix D.2a - 27 

### applies to large IC en gines, 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). Th e final rule reflects a control level of 82 percent for natural gas-fired IC engines and 90 pe rcent for diesel or dual fuel categories.

Industrial Boiler/Pr ocess Heater MACT
The USEPA anticipates reductions in PM and SO 2 

### as a result of the Industrial Boiler/Process
Heater MACT standard. The methods used to acc ount for these reductions are the same as those used for the CAIR analysis. Reductions were in cluded for existing units firing solid fuel (coal, wood, waste, biomass), which ha d a design capacity greater than 10 mmBtu/hr. The USEPA prepared a list of SCCs for solid fuel industr ial, commercial/ institutio nal boilers and process
heaters. The VISTAS contractor identified boile rs greater than 10 mmB tu/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 th e USEPA for assigning default capacities based on SCC code. The applied MACT cont rol efficiencies were 4 percent for SO 2 

### and 40 for percent for PM 10 

### and PM 2.5


### .

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 a nd associated emission reductions were designed from Federal Re gister notices and discussions w ith the USEPA’s Emission Standards
Division staff. VISTAS did not apply reductions for MACT st andards with an initial compliance date of 2001 or earlier, assuming that the effects of these controls are al ready accounted for in the 2002 inventories supplied by th e States. Emission reductions were applied only for MACT
standards with an in itial compliance date of 2002 or greater.

Combustion Turbine MACT
The projection inventory does not include the NO x 

### 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.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 projec tion inventory data set to ensure that all corrections provided by the S/L ag encies and stakeholders were co rrectly incorporated into the
S/L inventories and that there were no remaini ng QA issues that could be addressed during the duration of the project. After exporting the inve ntory 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 a ddressed were resolved.

Throughout the inventory development process, quality assurance steps were performed to ensure that no double counting of emissions occu rred, and to ensure that a full and complete inventory was developed for South Carolina. Qu ality assurance was an important component to the inventory development process and MACT EC 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 gr owth and emission reductions from CAA
controls.

2. State-level non-EGU comparisons (by polluta nt) were developed for the base year 2002 emissions, 2009/2018 projected emissi ons accounting only for growth, 2009/2018 projected emissions ac counting 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 Sout h Carolina point source contact prior to implementing the changes in the files.

4. Version numbering was used for all i nventory files developed. The version numbering process used a decimal system to track major and minor changes.Appendix D.2a - 29 

## 4. Emission Inventory Summaries

The following sections include the emissions invent ory summaries for the point source for all of
the states within the VIST AS region. The first sec tion 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 2 002 base year inventory and 2009 and 2018 projection inventories for the EGU source sect or. The 2009 and 2018 inventories include the adjustments to the IPM results specified by the S/L agencies in the previous section.Appendix D.2a - 30 

### Table 4.1-1 EGU Point Source SO 2 

### 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 NO x 

### 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 1,523,449 727,388 560,208Appendix 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 141,218 152,790 225,129Appendix D.2a - 32 

### Table 4.1-5 EGU Point Source PM 10 

### -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 PM 2.5 

### -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 81,884 120,896Appendix D.2a - 33 

### Table 4.1-7 EGU Point Source NH 3 

### 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 SO 2 

### 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 580,556 562,797 635,763Appendix D.2a - 34 

### Table 4.2-2 Non-EGU Point Source NO x 

### 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 Sou rce 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 448,970 432,387 516,131Appendix D.2a - 35 

### Table 4.2-4 Non-EGU Point Sou rce 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 PM 10 

### -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 212,714 217,066 256,668Appendix 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 NH 3 

### 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.2b

Area and Nonroad Mobile Source Emissions Inventory DocumentationAppendix D.2b - 1 

### (This Page Intentionally Left Blank)

### 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 EM ISSION ESTIMATION APPROACH ..................... 7

2. DISCUSSION OF AREA SOURCE CATEGORIES

................................................................ 8
2.1 2002 VISTAS-DEVELOPED AR EA 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 NONRO AD 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 EMI SSION 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 EM ISSION 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

### LIST OF TABLES

Table 2.1.5-1 2002 VISTAS Region Ac tual 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 Impact s Relative to VISTAS 2002 Base
Year Values ........................................................................................................................... 29

Appendix D.2b - 3

### 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

### (This Page Intentionally Left Blank)

Appendix D.2b - 5Appendix D.2b - 6 

## 1. OVERALL METHODOLOGY

1.1 SOURCE CATEGORY IDENTIFICATION

The regional haze modeling was done by the south east 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 si mulation, 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 Pr ogram (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 In ventories for Carbon Monoxide and Precursors of Ozone (Procedures document). Nonroad mobile source emis sions 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 Ai r 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, uni dentified points of air pollution emissions within a specified geographical area, emitting less than the mi nimum 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 ar e the smallest areas fo r 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 multiplyi ng an emission factor by some known indicator of collective activity for each source category with in the inventory area. An indicator is any

### 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 estima tion approaches is used to calculate the area source emissions: per capita emission factors, employment-related emissi on 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 Em ission 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 betw een 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 govern ments, tribal governments, and various federal agencies established to initiate and coordinate activities associated with the management of regional haze, visibility and othe r air quality issues in the Sout heastern United States. VISTAS is comprised of the following te n southeastern states: Alabama, Florida, Georgia, Kentucky, Mississippi, North Carolina, S outh 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 EMIS SION 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 ar e further divided into nonroad mobile, railroad locomotives, aircraft e ngines, and commercial marine vessels (CMV).
The estimation of emissions from mobile sour ces, 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

### engine emissions, the Federal Aviation Admi nistration (FAA) Emissions and Dispersion Modeling System (EDMS) model was used. Airc raft operations were inputted into the model and the model predicts the engine emissions ba sed on average landing and take-off practices for the aircraft type. For railroad locomotive em issions, emission factors were obtained from the
Mobile Source Procedures document and the ac tivity level was obtained from the various railroad companies.



## 2.0 DISCUSSION OF AR EA SOURCE CATEGORIES

2.1 2002 VISTAS-DEVELOPED AR EA SOURCE INVENTORY

Section 2.2 details the portion of the 2002 base year area source inventor y, which was developed for VISTAS by the VISTAS contractor, MACTEC, In c. 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, In c. 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 throug h 2.2.5. Ammonia emissions from livestock and
fertilizer sources are disc ussed in Section 2.2.1. PM 2.5 

### and PM 10 

### emissions from paved and unpaved roads are discussed in Section 2.2.2. Stag e II emissions are disc ussed 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 devel opment of the fires emissions inventory and distinguishes the difference between an actual versus typical invent ory 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 Emissi on 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) NH 3 

### model ( http://www.cmu.edu/ammonia/ ) to calculate NH 3 

### emission estimates for livestock and
fertilizers. Results from this model were us ed for all VISTAS States. The CMU model version 3.6 was used in large part because it had been ju st recently been updated to include the latest (2002) Census of Agriculture animal population statistics.

Appendix D.2b - 8

### For the ammonia inventory, VISTAS removed all wildlife and human perspiration emissions.
Thus all emissions from these two cate gories were deleted in the 2002 inventory.

2.1.2 Paved and Unpaved Road Emissions

VISTAS used the most recent PM emission esti mates developed by the USEPA as part of the NEI development effort (Roy Huntley, USEP A, email communicati on, 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 20 04 in NIF format from the USEPA FTP site.

PM 2.5 

### 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 PM 2.5 

### / PM 10 

### ratio in several fugitive dust. Based on data received from the USEPA, VISTAS decided to revise the PM 2.5 

### emissions from construction, paved roads and unpaved road sources. PM 2.5 

### emissions in the emission inve ntory, which was developed by the USEPA were multiplied by 0.67, 0.6, and 0.67 for cons truction, paved roads and unpaved roads, respectively, to produce the values found in 2002 inventory. No changes were made to PM 10 

### , only to PM 2.5 

### 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 th em 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 contai ners. This category typically accounts for permeation, di urnal, transport, spillage, and vapor displacement emissions. Spillage from refueling operations and vapor disp lacement emissions were not included to avoid double counting refueling in the non-road sector.

MACTEC found that the USEPA ha d prepared a national inventor y 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 Qu ality, USEPA, Report # EPA420-D-06-003, February 2006”.

Appendix D.2b - 9

### The 2002 county-level emission estimates were obt ained 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 USEP A’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 contai ners for refueling were acquired from the spillage file of the NONROAD model. Then th e 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 include s wildfire, prescribed burning, a nd land clearing fires. These fires can be intermittent in nature, but many of th ese 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 mana gement practice to estab lish favorable seedbeds, remove competing underbrush, accelerate nutrient cycli ng, 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 haza rds in forests and to remove
brush that can serve as a hos t for destructive insects.

The total wildfire acreage burned was obtained from the South Carolina Forestry Commission (SCFC) for each county in the State. Thes e 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 devel opment of the typical year inventory is described below.

State level ratios of acres over a lo nger-term record (three or more years) developed for each fire type relative to 2002. The 2002 acreage was then scaled up or down ba sed on these ratios to develop a typical year inventory. VISTAS Fire Special In terest Work Group based the ratio on county-level data for States that supplied long-ter m fire-by-fire acreage da ta rather than Statelevel ratios. Where States did not supply long-term fire-by-fire acreage data, MACTEC reverted

Appendix D.2b - 10

### 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 year s 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 2 002 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 calcu late emissions. Below are three exceptions to this method.

Exception 1: Use of State Ratios for Wildfires

Wildfires estimates were developed using State ra tios 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, exception ally 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 (U SFS) for wildfires and prescribed fires on USFS lands, represented perimeter acres rather than “bl ackened” acres. Therefore, for prescribed fires greater than 100 acres in size, the acreage was ad justed to be 80 percent of the initial reported value. For prescribed fires of 100 acres or less, the acreage valu es were maintained as reported. All reported acreage values for wildfires were ad justed to be 66 percen t 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

### 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 “norther n” or “southern” tier State. Development of “southern” and “nor thern” tier data was an attemp t 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, agricu ltural fires and land clearing fires in the VISTAS Region.

Appendix D.2b - 12

### Table 2.1.5-1 2002 VISTAS Region Actual and Typical Fire EmissionsNH3001,3337,616002,4517,425PM2.562,14630,38524,926145,17580,59829,89245,635141,636PM1062,14630,95828,923168,93880,59830,46553,070164,811SO2001,6119,839003,0729,590CO492,409164,273298,8351,678,216675,838161,667547,1741,635,776VOC33,79921,94616,80478,98846,38921,59528,49176,990NOx14,5689036,62836,56119,99590311,95535,650Actual Fire Emissions (TPY)Fire TypeLand ClearingAgriculturalBurningWildfiresPrescribedBurningTypical Fire Emissions (TPY)Land ClearingAgriculturalBurningWildfiresPrescribedBurning

Appendix D.2b - 13Appendix D.2b - 14 

## 3.0 2002 VISTAS-DEVELOPED NONROAD MOBILE SOURCE
INVENTORY

Development of emission estimates for nonroad m obile 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 thos e sources that can move but do not use the highway system. Examples include lawn mowers, agricultural equipment, construction equipment, aircraft engines, railroad locomotives, power boats, and commercial marine (s hips). All but the aircraft engine, railroad locomotive emissions and commerc ial marine activity were estimated using the
USEPA’s off-road mobile model NONROAD 2005c, 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 recrea tional and large spark-ignition engines rules that were publis hed 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 promulgate d nonroad mobile engine standards.

Nonroad mobile sources calcula ted through the NONROAD model ar e discussed in Section 6.1 Aircraft, railroad and commercia l marine emissions are discussed in Sections 3.2 through 3.4.

3.1 NONROAD Model Sources

The nonroad mobile source categor y 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 gene rates emissions directly a nd includes more than 80 different types of equipment. To facilitat e 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

### Additionally, the emissions are broken out by fi ve different engine t ypes. These include: 2-stroke and 4-stroke spark engi nes, diesel engines, liquid petroleum gas (LPG) and compressed natural gas (CNG) fueled engines.

3.2 Aircraft Engines

Aircraft engines, like other engines, emit pollu tants whenever the engines are in operation. However, the only emissions that are of concer n for this inventory are the portion of the operation that occurs below the mi xing layer. This is because th e emissions tend to disperse whenever the aircraft is above the mixing laye r and therefore has littl e 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 airpor t, 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 crui sing altitudes, above the mixing layer.

Aircraft can be categorized by use into four classifications: commercial, air taxis, general aviation and military. Commercial aircraft includ e 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 air ports 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. Emissi ons are calculated by obtaining data for the number of LT O cycles of the various aircraft at each airport in question, multiplying by the appropriate factors, and summi ng 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

### The starting point for developmen t of aircraft emissions estima tes 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 No rfolk Southern Corporation and CS X Corporation . Class II and Class III railroads are short lines, serving locali zed markets. These entities lease trackage from
Class I railroads.

Development of railroad emissions is de scribed 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 star ted with 1999 emission estimates developed for the USEPA’s 1999 NEI Version 2 as base year es timates 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 tw o steps as described below. For 1999 to 2001, State-level rail fuel consumption was obtai ned 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 fo r 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

### 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 co mmercial marine emission estimates is based on excerpts, with some editing, take n 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 inventor y 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 em issions 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 dist ribution of CMV emission estimates for the VISTAS region.

Ideally, CMV emission estimates would be develope d using local activity data that account for vessel type, engine type and m ode of operation (cruise, maneuvering, and hotelling). Creating this type of “bottom-up” emission inventory requir es a large amount of effort. Therefore, Pechan utilized port-specific emission estimates develope d for the 1999 NEI, distributed using a revised allocation methodology, which incorporates informa tion on the number of port facilities in each
county.

Appendix D.2b - 17

### The 2002 VISTAS commercial marine inventor y is based on the USEPA’s 1999 NEI Version 2.0, projected to 2002 using approp riate growth factors. The 1999 NEI estimated emissions for these categories accordi ng 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 deve loped by obtaining 2000 emission estimates for all pollutants except SO 2 

### 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 simila r method was used for diesel SO 2 

### emissions. National diesel usage was estimated assuming a sulfur content of 0.25 percent and the USEPA emission factors.

In apportioning, distillate and resi dual fuels are considered separa tely. National diesel emissions were disaggregated into port and underway emissi ons estimates based on the assumption that 75 percent of distillate fuel is c onsumed within the port, while the remaining fuel is consumed while underway, consistent with the USEPA guid ance. National residual emissions were disaggregated into port and underway emission s 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 a ssigned 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 th e port-level traffic by the total traffic. Emissions for each port were then assigned to a single county.

Underway emissions are assigned to counties ba sed on a county’s shippi ng lane traffic. The Bureau of Transportation Statistics’ (BTS) National Transportation Atlas Databases-1999 contains data on the thousand tons per mile trav eled for each shipping lane link in the United States (BTS-CD26). Where navi gable rivers form a county or State boundary, the shipping lane traffic is proportioned to individua l counties based on the length of shoreline that is shared. For

Appendix D.2b - 18

### example, if two counties share a navigable rive r, and both counties ha ve the same length of shoreline, the shipping traffic is split evenly be tween the two counties. Shipping lanes that are not within counties, for example in the o cean, 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. A ll shipping activity is su mmed at the county-level and compared with national shipping activity to determine what portion of activity can be
attributed to individual countie s. These proportions were used in disaggregating the national CMV emission estimates to the county level.

States that share borders with non-VISTAS Stat es 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 w ould allow more accurate modeling of emissions
in the VISTAS states.

For underway emissions, Pechan believes that the allocation procedure resu lts in a reasonable distribution of county-level emi ssions. However, the methodology to allocate port emissions
results in all the emissions bei ng assigned to a single county.

Port areas encompass multiple States and coun ties and in some cases, multiple waterways. Therefore, the emissions allocation process must in corporate all counties in the vicinity of the port where activity is occu rring. This is especially true fo r 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 Commer ce of the United States, Calenda r Year 1999, hereafter referred to as Waterborne Commerce, presents the car go 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 St ates by amount of cargo tonnage. As discussed previously, the 1999 NEI allocates all the port emissions to th ese 150 ports based on the cargo tonnage handled by the port. Pechan uses this allocation of emissi ons 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 Standa rd 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 - 19Winyah 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 deve lop a surrogate for the am ount of CMV activity in each county of the port area. Pechan assumed th at the activity of vessels in each county is related to the number of port f acilities operating in a given count y. Port fac ilities 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 por t 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 include s 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 facili ties in all counties that make up the port area. This ratio was
used to allocate emissions for ea ch port area to the county-level . The ratio for Morehead City
Harbor was 1.0 in Carteret count y 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/a p42/, factors given in the Procedures document, and factors given
in the documents of the Em ission Inventory Improvement Program website located at http://www.epa.gov/ttn/chief/eiip/ . Sources of error would pr imarily be associated with multiplier values, data entry errors, and the accuracy of formulas.

For the portion of the 2002 inventory deve loped 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 invent ory development process, quality assurance steps were performed to ensure that no double coun ting of emissions occurred, and to ensure that a full and complete inventory was developed fo r VISTAS. Quality assurance was an important

Appendix D.2b - 20

### component to the inventory development process and MACTEC performed the following QA steps on the area source compone nt 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 th ere 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 Fi res SIWG representatives for review and comment. Changes based on these comment s were implemented in the files.

5. Version numbering was used for all i nventory 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 equiva lent) as well as U.S. Forest Service and National Park Service personnel.

In addition, for the nonroad portion of the invent ory prepared by VISTAS, tier comparisons (by pollutant) were developed between the revised 2002 ba se year inventory and the initial base year inventory.

Appendix D.2b - 21

## 5.0 2009 AND 2018 AREA SOURCE EMISSION INVENTORY
DEVELOPMENT

This subsection describes the methodology us ed to develop the 2009 and 2018 area source inventories. Separate methods for projec ting emissions were us ed for non-agricultural (stationary area), agricultural area sources and forest fire area sources. The agricultural area
sources method are for ammonia emissions and im portant 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 Emissi on Inventories for
VISTAS and prepared by MACTEC, Inc.

5.1 Projection of Stationary Area Sources

VISTAS 2002 base year inventor y emissions were used as a starting point for calculating 2009 and 2018 emissions. MACTEC, Inc. first back calculated uncontrolled em issions from the 2002 base year inventory. Growth and control factors were then applied based on controls initially identified for the Clean Air Interstate Rule (CAI R) 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 av ailable from the CAIR growth factor files.

The 2009 and 2018 growth factors were obtained from the USEPA gr owth factors an d indirectly from 2010 and 2015 CAIR growth factors. Usi ng a 2001 base year, interpolation of 2010 and 2015 CAIR growth factors yielde d 2009 and 2018 growth factors. MACTEC used the TREND
function of Microsoft Excel for inte rpolation. 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 obt ained 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 fu el firing technologies.

Appendix D.2b - 22

### 5.2 Projection of Agricultural Area Sources

MACTEC used the version 3.1 2002 base year inventory data (w hich was based on the CMU ammonia model version 3.6). MAC TEC obtained State specific growt h, if available. Otherwise, growth factors were used, which were devel oped from the USEPA Interstate Air Quality Transport Rule (IAQTR)/Ammonia i nventory. Growth factors for several agricultural livestock categories were developed by the USEPA, as part of the NEI.

In addition, ammonia growth fact ors for a few categories (mainly feedlots) were assigned to be the same as growth factors for PM emissions fr om 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 consta nt 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 emi ssions were removed from the 2009 and 2018
inventories.

5.3 Projection of Forest Fires Area Sources

Several Federal agencies indicated that they ha d plans for increased prescribed fire burning in future years and that the “typical” fire inve ntory would likely not adequately capture those increases. Thus, MACTEC acquired the data nece ssary to provide 2009 sp ecific 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 estimat es of acres burned per day for each NWR. USFS provided fire-by-fire acreage estimates ba sed on mapping projected burning acreage to current 2002 modeling days. However, USFWS di d not submit data for VISTAS original base year preparation process, thus there was no known US FWS data in the 2002 actual or typical
inventories. MACTEC therefor e developed a method that coul d 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 wa s used in those States for the 2002 actual inventory.

Appendix D.2b - 23

### The method used by MACTEC to include the U SFS 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 “t ypical” acreage to determine fu ture acreage burned for 2009 and
2018.

MACTEC then allocated the increased acreage to current modeling days. The average daily acres burned data provided by US FWS 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 dail y acres burned could potentially be allocated to any one day.

For the USFS fire-by-fire acreage estimates, MA CTEC 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 th at 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 fo r blackened acres applie d to all USFS supplied acreage as the supplied values represented perimeter acres.

An additional problem with developing year-speci fic prescribed fire projections was how to adequately capture the temporal profile for those fires. In th e 2002 actual fire inventory, fires occur on same days as state/FL M records. In the 2002 “typical” year inventory, fire acreage increased or decreased from acreage on the same fi re 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 a nnual increase (if acreage on a day is 10 percent of annual, add 10 percent of projected incr ease to that same day).

Appendix D.2b - 24

## 6.0 2009 AND 2018 NONROAD SO URCE EMISSION INVENTORY
DEVELOPMENT

The subsections that follow describe the proj ection 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 commerci al 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 ga soline and diesel powered equipment.

3. Implement a limited number of local cont rol program changes (national control program changes are handled in ternally within the NONROAD model, so explicit input file changes are not required).

All equipment population growth an d fleet turnover impacts are ha ndled internally within the NONROAD model, so that explicit input file change s are not required.

The final NONROAD2005c that wa s 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 follo wing 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 fo r aircraft, locomotives, and CMV prepared as described earlier in this document, corres ponding emission projections for 2009 and 2018 were developed. The following description is la rgely taken from the MACTEC document titled

Appendix D.2b - 25

### Documentation of the Base G 2002 Base Year , 2009 and 2018 Emission In ventories for VISTAS

. Briefly, the methodology relies on growth and contro l 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 fr om 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 stra ight line interpolation between 1996 and 2010 (for 2009) and 2015 and 2020 (for 2018). This is done at the State-county-SCC-polluta nt level of detail.

(c) The USEPA growth and control data are matched against the 2002 VISTAS base year data using State-county-SCC-po llutant as the match key. Ideally, there would be a one-to-one match and the proce ss would end at this point. Unfortunately, actual match results were not always ideal, so additiona l 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 simila r, 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 criter ion is applied to records in th e 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 re ference, this is denoted as the “CAIR-Secondary” criterion. At the end of this process, a number of unmatched records continued to remain, so a thir d level matching criterion was required.

(e) In the third matching step, the most freque ntly used SCC in the USEPA CAIR files for each of the aircraft, locomotive, and commercia l 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, St ate-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. Th e default growth and control SCCs are 2275020000 (commercial airc raft) for the airc raft sector, 2280002000 (commercial marine diesel total) fo r the CMV sector, and 2285002000 (railroad

Appendix D.2b - 26

### equipment diesel total) for the locomotive sector. Matches made using this criterion are denoted as “CAIR-Tertiary” matches.

(f) According to USEPA documentation, the CA IR baseline emissions include the impacts of the (then proposed) Tier 4 (T4) nonroad mobile diesel rulemaking, which implements a low sulfur fuel requirement that affect s both future CMV and locomotive emissions. However, the impacts of this rule were origin ally intended to be excluded from the initial VISTAS 2018 forecast, which was to include on ly “on-the-books” controls. (The T4 rule was finalized subsequent to the developmen t of the preliminary 2018 inventory in March of 2004.) Given its final status, T4 impact s have now been moved into the “on the books” inventory for nonroad mobile source eq uipment. In addition, since there are no other proposed rules affecti ng 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 si ngle forecast inventory (for each evaluation year) was developed. Nevertheless, sin ce the algorithms developed to produce the VISTAS forecasts were developed when th ere was a distinction between the “on the books” and “on the way” inventories, the dis tinct algorithms used to produce the two inventories have been maintained even though th e conceptual distinctions have been lost. This approach was taken for two reasons. Fi rst, 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, prev ious USEPA inventories that include the proposed T4 impacts would not be accurate. Therefore, the procedural discussion
continues to reflect the distinctions be tween non-T4 and T4 emissions, as these distinctions continue to be intrinsically impor tant to the forecasting process. Therefore, a
second set of USEPA CAIR files that exclude d the Tier 4 diesel impacts was obtained and the same matching exercise described a bove 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 repl aced 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 ma tching exercise was augmented (rather than replaced) with an additional three criteria analogous to those described in steps (c) through (e), and these are denot ed 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 matche s made using the basic CAIR criteria (as described in steps (c) through (e) above). The CAIR matching crit eria were overridden for any record for which States provided local growth data.

Appendix D.2b - 27

### (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 CM V 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 th e 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 st andards 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 SO 2 

### 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: C ontrol of Emissions from Nonroad Diesel Engines , EPA420-R-04-007, May 2004). Acco rding to that document, the average diesel fuel sulf ur content for locomotives a nd CMV is expect ed 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 estimat es for base and T4 control scenarios to estimate emission reduction impacts.

Table 6.2-2 Estimated Emission Reduc tion Impacts based on T-4 Rule

2009 2018
CMV SO 2 = Non-T4 SO 2 × 0.1569 0.0241
Locomotive SO 2 = Non-T4 SO 2 × 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 c ontent 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 modes tly different sulfur contents (2599 ppmW for 2009 and 2336 ppmW for 2018). Correcting for these modest differences produces the

Appendix D.2b - 28

### 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 SO 2 = Non-T4 SO 2 × 0.1632 0.0225
Locomotive SO 2 = Non-T4 SO 2 × 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 Ma rch 2004, this process yielded reasonable results and exhi bited no particular systematic concerns. However, when the 2009 Base F inventory was developed, significant concerns related to SO 2 

### 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 SO 2 

### 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 occu r, the observed growth extremes generally
affect both SO 2 

### 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 c ontent will only affect a fraction of PM (i.e., sulfate), while directly reducing SO 2


### .

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 - 29Appendix D.2b - 30 

## 7.0 QUALITY ASSURANCE OF 2009 AND 2018 EMISSIONS
INVENTORY

For the 2009 and 2018 inventories develope d by VISTAS and MAC TEC, Inc., specific QA procedures were followed. These procedur es 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 invent ory 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 ar ea 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 th ere were no missing sources.

3. Tier comparisons (by pollutant) were developed between the 2002 base year inventory and the 2009 and 2018 projection in ventories. In a ddition, total VISTAS pollutant summaries were prepared to compare total emissions by pollutant between
earlier and later versi ons 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 i nventory 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.2c

On-Road Mobile Source Emissions Inventory Documentation

# 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 Mile s Traveled (VMT) Assumptions ..................... 7

3.0 Quality Assurance Measures ................................................................................... 7

## 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, Te nnessee, Virginia and West Virginia) and the local programs and tribal agencies located within thes e states. The regional haze modeling uses an annual simulation, therefore the emission estimates for all pollutants of concern for regional haze were de veloped for an annual modeling run.

On-road mobile sources are considered those vehicles that travel on the roadways. Emissions from motor vehicles occur throughou t 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 informa tion 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 sma ll 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 fi ne 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 USEP A document Emissions Inventory Guidance for Implementation of Ozone and Particul ate Matter National Ambient Air Quality Standards (NAAQS) and Regional Haze Regulati ons. The estimation of emissions from

### on-road mobile sources, like area sources, involves multiplying an activity level (e.g. vehicle miles traveled (VMT) by vehicle t ype 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 dioxi de (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 li nk location and subsequently summed to the grid cell level, the off-ne twork emissions estimates are spatially allocated based on a
combination of the FHWA Version 2.0 hi ghway networks and population. For the VISTAS 36/12 km modeling, no link based data was used. The MOBILE6 emissions factors are based on episode-spe cific temperatures predicte d by the meteorological model (MM5).

Further, the MOBILE6 emissions fact ors model accounts for the following:

• Hourly and daily minimum/maximum te mperatures, 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 emissi ons into separate source categories;

• VMT, fleet turnover, and changes in fu el composition and Reid vapor pressure (RVP).

2.2 Mobile6.2 Input Assumptions

2.2.1 Speed Assumptions Emissions from motor vehicles vary with th e 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 stop light. South Carolina does not ha ve state-specific speed data and therefore used default speed assump tions built into the MOBILE6 model.

### 2.2.2 Vehicle Age Distribution Emissions vary with the age of a vehicle. Ol der vehicles have typical ly been driven more miles and have experienced more deterioratio n 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 di fferent 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 us e data that accurately reflects the vehicles traveling on each of these different road types. South Carolin a does not have statespecific data and therefore used default vehi cle 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 Princi p al Arterial RM j C Rural Ma j or Collecto r 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 SM OKE are significantly influenced by temperature and to a lesser extent, humidit y. The most desirable approach from an accuracy standpoint is to model on-road mob ile 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 fo r all counties during May-September, as
indicated in EPA’s Guide on Federal and St ate Summer RVP Standards for Conventional Gasoline Only (EPA420-B-05-012 Novemb er 2005). For remaining months, RVPs follow the ASTM D4814 Standard Specifica tion for Automotive Spark-Ignition Engine Fuel. They are as follows:

### • 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 Mile s Traveled (VMT) Assumptions

In order to calculate emissions from on -road mobile sources, emission factors are developed as discussed thr oughout 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 emissi ons 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 belo w. 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 MO BILE6 and used the same assu mptions as the CERR. Since much of South Carolina’s on-road mobile inpu ts were default, we put more emphasis on reviewing the resulting emi ssions for reasonableness.

Upon completion of the modeling, VISTAS s upplied the emissions output summary files to the SCDHEC. The modeling was an iter ative process. To ensure the latest

### 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.3 Emissions Inventory SummariesTable 1. Typical 2002 BaseG2 Annual EmissionSummary for South Carolina Counties (tons)PointNon-roadArea (includes Fires)MobileCountyVOCNOxSO2PM-10PM-2.5NH3VOCNOxSO2PM-10PM-2.5NH3VOCNOxSO2PM-10PM-2.5NH3VOCNOxSO2PM-10PM-2.5NH3Abbeville137.054.6119.79.68.60.0235.0325.726.321.420.30.21642.2214.795.72980.9680.4278.5626.6739.632.919.714.524.3Aiken1156.25732.211009.21502.21087.410.31081.71339.894.087.183.11.14949.0769.9717.18112.81648.51404.54369.85006.7213.7122.087.9171.1Allendale112.325.147.628.616.80.0153.5388.228.719.818.60.1765.9184.146.42102.4823.4217.3304.7372.816.610.17.511.8Anderson1447.62995.310540.11192.5853.96.11721.11101.092.0107.8103.21.27457.0869.0648.911959.11989.4998.15107.06685.3276.0163.4119.8209.0Bamberg12.89.37.17.84.60.0166.8202.317.015.915.10.1819.3118.332.32340.4551.9231.0484.9602.926.616.212.119.0Barnwell221.273.268.216.815.20.0302.3472.632.322.421.10.21270.5256.5118.32521.6922.5178.8634.4714.831.919.014.023.8Beaufort38.9301.1203.548.832.80.05382.72203.9232.7391.2375.52.33702.9726.0148.95673.81542.979.92879.33297.0147.385.662.2114.7Berkeley2153.431443.966339.97284.45909.380.23951.03479.5429.6217.6202.21.25776.11274.6324.79292.53382.0282.83673.34363.3186.4107.778.0146.8Calhoun104.333.85.624.213.8860.0336.3214.418.217.716.80.1820.1119.018.83645.4769.8210.11002.01666.663.138.628.845.3Charleston2521.32632.33383.9639.0538.9173.86491.78694.41108.8642.6606.53.010432.51615.01086.38141.91833.8176.69046.38997.2399.8213.2147.7352.6Cherokee432.8534.6788.0232.7146.00.0327.7797.952.535.032.80.32447.9283.1113.14167.6704.0304.21944.12796.8110.366.549.281.2Chester705.62021.1415.0183.5158.60.0302.5542.238.929.628.00.22174.8229.0194.33782.8676.0451.91425.62068.283.750.837.760.8Chesterfield590.766.22.924.919.40.0397.2325.723.125.324.10.33338.0415.3155.75120.21437.91011.11336.31637.672.843.932.653.2Clarendon66.50.20.03.42.60.02304.9438.041.767.663.50.61934.2276.2125.25464.11365.61420.11511.52384.492.756.742.366.5Colleton313.84915.920760.71289.0974.30.0870.6659.950.941.138.50.32811.6665.9127.86346.62495.3274.32064.33114.6123.074.555.389.6Darlington1389.75888.912311.31257.7935.611.2397.3357.029.933.232.00.43117.1405.0412.28362.81546.1521.61981.42510.4107.063.947.179.6Dillon70.936.41.52.62.00.0157.3563.143.932.530.90.21907.5192.3101.14190.6936.21603.91383.42097.781.749.436.659.9Dorchester407.83864.93785.6739.6510.27.8581.0716.766.262.059.30.53328.6545.6136.84913.21164.8538.72646.73485.3140.782.159.8109.0Edgefield351.738.80.00.60.50.0195.2279.521.616.916.00.11147.5180.776.52504.1621.6304.9575.3714.731.719.314.422.7Fairfield102.1177.018.935.026.40.0648.5449.332.728.026.20.21463.2174.454.62806.9596.5464.91080.11690.966.140.530.147.4Florence2175.13122.74181.0791.5641.961.0917.21189.791.884.580.50.85994.9712.7370.59179.21819.21113.74142.25152.3216.7126.692.2167.5Georgetown2075.124121.440287.75781.44780.250.82154.51148.2134.190.484.80.82761.4564.1290.25625.41788.6192.11838.82138.695.256.041.072.6Greenville3048.7732.9747.0212.9146.70.03158.83241.9282.5292.8280.83.016198.51716.91894.616231.22927.7195.710133.710514.3460.4252.0177.3391.8Greenwood303.7322.854.0441.9294.50.0541.5873.360.145.342.80.52989.6357.2319.35675.4920.4261.51581.11822.181.347.734.962.3Hampton3153.3132.0168.6165.6111.40.0236.3431.832.223.221.80.11679.8327.782.23724.41378.9183.5746.51062.743.726.719.931.3Horry302.23418.510488.9631.4529.00.03058.72225.6247.9269.0258.02.16364.9978.6297.610395.02139.6916.77590.58266.0367.4207.6148.6299.2Jasper14.732.75.322.013.40.0639.6470.937.831.229.20.21501.0468.6112.13908.71802.197.11939.93263.3124.476.256.789.2PointNon-roadArea (includes Fires)MobileCountyVOC NOx SO2 PM-10 PM-2.5 NH3 VOC NOx SO2 PM-10PM-2.5NH3VOC NOx SO2 PM-10 PM-2.5 NH3 VOC NOx SO2 PM-10PM-2.5NH3Kershaw325.21019.33345.3267.9184.30.0745.3327.428.134.432.60.42492.9312.687.24954.8923.21580.51781.92508.6102.261.645.675.0Lancaster134.41246.92548.8263.1221.80.0493.9429.135.033.431.60.31804.5333.2152.45951.9944.01069.51712.42043.991.254.640.367.6Laurens288.3131.243.3126.586.50.0519.8766.455.341.639.10.32849.9398.1317.15996.31079.0746.92169.73006.8121.873.053.990.4Lee255.37.91.31.31.20.0138.8161.217.119.418.70.11295.8172.744.34204.5979.9617.5835.51297.451.131.323.336.6Lexington1718.74791.914373.41426.91300.40.03122.01749.1179.3201.1191.81.57494.01083.6392.810659.11874.81540.67067.38459.1356.6203.7146.6286.3McCormick88.40.00.078.354.30.0838.3240.320.824.022.40.2645.2105.821.61657.0442.8203.9321.8390.717.410.67.912.4Marion89.213.52.811.58.20.0265.7515.837.426.625.00.21876.6235.5127.23342.2692.7284.71130.91396.162.137.327.545.9Marlboro867.11400.4665.3469.0350.30.0247.5436.133.828.927.50.21575.4186.3125.54245.3857.9638.6838.9988.544.226.419.532.9Newberry1041.6182.04.3375.4301.10.0636.0580.345.438.936.70.32001.7259.2146.03925.7827.01584.51640.02430.396.158.042.970.5Oconee302.088.19.343.532.40.51674.0785.665.272.868.70.74240.7414.9123.56542.11150.21265.71787.02210.296.558.042.971.0Orangeburg1654.67864.76024.72510.31897.1155.1996.5852.567.567.764.40.65746.1605.4170.311173.32313.91498.33771.35277.3214.5129.295.5157.9Pickens154.1206.8432.491.063.60.01396.6853.872.381.276.80.75514.3625.9422.88610.41453.1104.82396.62666.8119.469.550.692.6Richland1886.211943.041234.45237.93768.8100.02678.23002.4286.3260.4248.92.410942.91564.81396.49049.22016.8211.69194.59877.4426.4230.9161.3369.2Saluda51.820.92.746.531.70.0340.8185.016.918.517.50.11291.3138.450.42480.2509.61371.6560.3701.531.219.014.222.3Spartanburg2615.14394.9204.6331.8270.60.01713.92666.2229.0191.6183.51.811597.71300.11302.016505.12680.6317.07622.09824.9403.9234.8170.5315.5Sumter1007.2269.3121.065.350.20.2744.8678.256.355.252.90.64590.3596.5257.17864.31617.2779.02649.73044.0131.575.454.5104.4Union134.5245.9554.476.822.80.2158.4523.634.819.518.10.11691.5244.9160.43291.0749.174.1705.0826.636.921.916.127.8Williamsburg92.880.115.070.362.25.2213.3572.346.534.232.50.23528.5568.1152.57859.22580.5675.31043.21346.059.836.627.342.8York2811.74046.78465.31905.51284.430.01381.51791.9171.5151.4145.21.45690.4815.7432.29680.81644.2596.74684.75500.3235.5135.197.5187.5

# Appendix D.4a Emissions Modeling Documentation

# 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

## 1.0 Background

The Sparse Matrix Operator Kernel Emissions (SMOKE) modeling system is an emissions modeling system that generates ho urly gridded speciated emission inputs of mobile, nonroad mobile, area, point, fi re 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 flexib le processing of emissions data. The processing is rapid because SMOKE utilizes a series of matrix calculations instead of less efficient algorithms used in previous system s. The processing is flexible because the processing steps of temporal projection, controls, chemical speciation, temporal allocation, and spatial allocation have b een separated into i ndependent operations wherever possible. The results from these st eps 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 flex ibility in producing alternate strategies. With the exception of biogenic and on-road m obile source emissions that are generated using the BEIS and MOBILE6 modules in SMOKE, pre-computed annual emissions were processed using the month, day, and hou r 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 s ource 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 app lication of these profiles. Additionally, EGU CEM-based temporal profiles and onroa d 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 provi ded default temporal profiles that are generally accepted as not accurately depicti ng temporal distribution of emissions from EGUs in the U.S. (see Attachment 2 in A ppendix D.5 by Stella et. al.). VISTAS EGU temporal profiles were developed using hourly CEM data as reporte d to USEPA’s Clean Air Market’s Division (CAMD) for the Acid Rain Program.

The work conducted in this process had th e 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 distri butions represent a significant improvement over the USEPA defaults.

On-road mobile modeling in SMOKE was done fo r selected weeks (sev en 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 in clusion 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 matte r concentrations (and thus regional haze) that were nearly indisti nguishable from the “everyday” mobile method. VISTAS determined that the difference in the mode led 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 in ventory 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 inventor ies, chemical mass balance modeling, and visibility modeling.

Depending on the purpose of a particular emi ssions inventory, the inventory may include TOG, NO X 

### , sulfur oxides (SO X 

### ), CO, total suspended particulate matter (TSP), particulate matter less than 10 micrometers in aerodynamic diameter (PM 10 

### ), or ammonia (NH 3 

### ). However, modeling inventories may requi re these emissions to be expressed in terms of other pollutants. A dditionally, for some models, NO X 

### emissions may need to be

### specified as NO and NO 2 

### . Also, PM may need to be se parated into various fractions, such as PM 10 

### and PM less than 2.5 micrometers in aerodynamic diameter (PM 2.5 

### ).

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 speciati on profile cross-reference files. The speciation profiles and source category cross-references us e in VISTAS modeling are based on USEPA’s CAIR/CAVR/CAMR modeli ng platform with files located on USEPA’s CAIR file transfer website ( ftp://www.airmodelingftp.com/ ). Minor modifications were made to reflect State sp ecific profiles or updated state of knowledge application of these profiles. One major ch ange made in the VISTAS modeling was the modification of coal combustion cross-refe rence from speciation profile “NCOAL” to profile “22001.”

1.3 Spatial Allocation

Because air quality modeling strives to re plicate the actual physical and chemical processes that occur in an i nventory domain, it is important that the physical location of emissions be determined as accurately as po ssible. In an ideal situation, the physical location of all emissions would be known ex actly. In reality, however, the spatial allocation of emissions in a modeling inventor y only approximates th e actual location of emissions.

Gridding surrogates are used to spatially allocate emission sources from a coarse geographic area to finer grid cells used fo r modeling. There can be hundreds of unique source categories in an emissions inventory, which is typically de veloped for counties, states, or other areas. The exact location of most major emission sources is known and their geographic coordinates ar e usually contained in the i nventory. These usually are referred to as major point sources and incl ude electric utilitie s and major industrial facilities. However, other emission sources are estimated for the entire c ounty or other area as an aggregate since the exact locati ons of each source are not included in the modeling inventory. Surrogates are human ac tivities 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 cro ss-references were or iginally 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.

### 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. Gene rate the grid polyg ons (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 c ounty’s portion of the surrogate in that cell.

2. Extract and convert each geodatabase tabl e 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 surr ogates where both are of the same type (i.e., Heavy and High Tech Industrial ar e both area) were summed prior to calculating the ratio. Combination surrogate s with unlike data (i.e., 3/4 Roadway Miles plus 1/4 Population ar e line and area data) were summed after calculating the ratios and then normalized. The su rrogate cross-refere nce 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 ex ample, 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 th e assigned surrogate, a different source of data (a different surrogate) for that part icular county. We incorporate secondary surrogates even if there is no emission s ource that requires it for that particular county. We denote this process as “gap-f illing.” 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 exampl e mentioned above, we chose total railroads as the secondary surrogate . The secondary or tertiary surrogate chosen would be the same across all counties and apply to all SCCs that use the particular primary surrogate. We pull in and substitute the secondary surrogate for counties where the primary surrogate is missing. Tertiary surrogates will then be assigned to those counties that are still without surrogates.

### For identified counties having no values for each surrogate, we assign the data based on the appropriate sec ondary 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 developmen t. 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 in to SMOKE-ready format and used in the spatial allocation process.

1.5 Treatment of Large Fire Plume Rise

Wildfire, agricultural, and prescribed burn em issions were handled separately from the standard area source input f iles. We used day specific or monthly estimates of fire emissions from VISTAS, which include bur n 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 st andard distribution prof iles. 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 pr ofile (meters above ground level)

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

For those fires as having the necessary data elem ents 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/doc uments/WRAP_2002_PhII_EI_Report_20050722 .pdf) has documented an approach for calcula ting 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 calc ulate representative hourly plume profiles. These profiles are then used by SMOKE 2.1 to distribute the vertical emissions for the fires. To successfu lly model fires as elevat ed point sources, the data included both the day or days on which th e fire occurs, and a spat ial identifier of the fire location. 

## 2.0 Quality Assurance

[This section is from the prelimi nary VISTAS modeling protocol... at http://pah.cert.ucr.edu/vistas/vistas2 /reports/vistasII_ Final_Protocol_05_10_2004.pdf ]

### 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 erro rs in the emissions inputs. Importing the model-ready emissions into PAVE and looking at both the spatial and temporal distribution of the emission prov ides insight into the quality and accuracy of the emissions inputs. So me 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 err oneously located in water cells.

• Spot-check the holiday emi ssions files to confirm that they are temporally allocated like Sundays.

• Producing pie charts emission summaries th at 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 stat e totals versus the sa me state totals with the speciation matrix applied was completed.

For checking the vertical allo cation of the emissions, reports by source, hour, and layer for randomly selected states in the domain were created. Th ese reports were created for a representative weekday in each of the ep isodes for each of these selected states.

The quantitative QA analyses ofte n 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 inve ntory for each source category

o (example provided in Table 1 below for Area sources)

• State and county totals after spatia l allocation for each source category

• State and county totals by day after tem poral allocation for each source category for representative days

### • State and county totals by model species af ter 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 indi cating which sources have been selected as elevated and plume-in-grid will be included.

• Totals by source category code (SCC) fr om 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 invent ory for area, mobile, and point sources

• Totals by SCC and spatial surrogates code for area and mobile sources

• Totals by speciation profile code fo r area, mobile, and point sources

• Totals by speciation profile code and S CC 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 c ode 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 c ode and SCC for area, mobile, and point

sources

• 

### PAVE plots of gridded inventory pollutants for all pollutants for area, mobile, and point sources

### Table 1. Example of a State to tals 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]

------------------------------------------------------------------------------------------------------------------------------------------------------------------ 07/18/200 2 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 07/18/200 2 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 07/18/200 2 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 07/18/200 2 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 07/18/200 2 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 07/18/200 2 9000 Connectic u 6.37E+00 1.05E+01 1.18E+02 1.99E+01 5.39E-01 3.81E+00 3.54E+00 2.69E-01 07/18/200 2 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 07/18/200 2 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 07/18/200 2 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 07/18/200 2 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 07/18/200 2 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 07/18/200 2 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 07/18/200 2 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 07/18/200 2 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 07/18/200 2 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 07/18/200 2 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 07/18/200 2 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 07/18/200 2 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 07/18/200 2 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 07/18/200 2 25000 Massachu s 9.95E+01 6.14E+01 2.80E+02 3.87E+01 3.46E+01 5.01E+01 4.36E+01 6.46E+00 07/18/200 2 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 07/18/200 2 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 07/18/200 2 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 07/18/200 2 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 07/18/200 2 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 07/18/200 2 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 07/18/200 2 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 07/18/200 2 33000 New Ham p 3.82E+01 2.61E+01 8.11E+01 7.21E+00 7.62E+00 2.30E+01 2.06E+01 2.40E+00 07/18/200 2 34000 New Jerse y 1.45E+01 2.24E+01 2.70E+02 5.55E+01 8.55E+00 1.94E+01 9.83E+00 9.72E+00 07/18/200 2 35000 New Mexi c 8.55E+01 7.68E+01 1.48E+02 1.37E+02 1.78E+01 1.52E+01 1.37E+01 1.55E+00 07/18/200 2 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 07/18/200 2 37000 North Car o 1.08E+03 1.12E+02 4.50E+02 5.32E+02 1.63E+01 1.97E+01 1.53E+01 4.51E+00 07/18/200 2 38000 North Dak o 1.80E+01 5.18E+01 1.28E+02 1.25E+02 1.43E+02 7.04E+00 3.74E+00 3.30E+00 07/18/200 2 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 07/18/200 2 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 07/18/200 2 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 07/18/200 2 42000 Pennsylva n 3.47E+02 5.72E+01 5.80E+02 3.18E+02 7.39E+01 7.30E+01 5.08E+01 2.22E+01 07/18/200 2 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 07/18/200 2 45000 South Car o 1.23E+02 5.25E+01 3.95E+02 9.36E+01 3.12E+01 2.27E+01 1.95E+01 3.20E+00 07/18/200 2 46000 South Dak o 1.73E+01 1.43E+01 8.98E+01 2.69E+02 5.49E+01 5.92E+00 4.44E+00 1.48E+00 07/18/200 2 47000 Tennesse e 8.91E+01 3.53E+01 4.42E+02 8.82E+01 7.95E+01 3.51E+01 2.80E+01 7.16E+00 07/18/200 2 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 07/18/200 2 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 07/18/200 2 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 07/18/200 2 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 07/18/200 2 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 07/18/200 2 54000 West Virgi n 3.84E+01 3.07E+01 1.57E+02 3.65E+01 3.15E+01 1.56E+01 1.36E+01 2.03E+00 07/18/200 2 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 07/18/200 2 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

### 2.1 Additional State QA Procedures

South Carolina reviewed the state summary da ta 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 ca rried forward in subsequent runs.

# Appendix D.4b Temporal Profile Development for Electric Generating UnitsAtmospheric Sciences Group



## Development of Temporal Profiles for Electric Generating Units (EGUs) In the VISTAS States:

Final Technical MemorandumAtmospheric 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 2006Atmospheric Sciences GroupA tmospheric 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 Le vel Monthly Profiles [Tabular]

Appendix B Actual 2002 State Le vel Monthly Profiles [Graphical]

Appendix C Actual 2002 State Level Mont hly-Day of Week Profiles [Tabular]

Appendix D Actual 2002 State Level M onthly-Diurnal Profiles [Tabular]

Appendix E Average 2000-2004 State Le vel Monthly Profiles [Tabular]

Appendix F Average 2000-2004 State Leve l Monthly Profiles [Graphical]

Appendix G Average 2000-2004 State Level Mo nthly-Day of Week Profiles [Tabular]

Appendix H Average 2000-2004 State Level Monthly-Diurnal Pr ofiles [Tabular]A tmospheric 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 Mark et’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 necessa ry to apply in the generation of SMOKE
PTHOUR formatted emissions. Additionall y, State-level monthly, day-of-week, and diurnal profiles were developed for app lication to non-CEM ma tched units in the VISTAS emissions inventory. These temporal distributions represent a significant improvement over the SMOKE defaults, a nd will be used for both actual 2002 and “typical” 2002 and 2018 modeling.

Two sets of monthly profile s were developed by Alpine:

1. Profiles based solely on act ual 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 configur ing 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 pr ofiles were developed an d are recommended to beA tmospheric Sciences Group



### used to represent consistent “typical” ope rating conditions at EGUs in the VISTAS domain for the base year and future year emission estimates.

Analyses conducted by Alpine Geophysics indi cate an added benefit to the modeling results with the application of CEM-based da y-of-week and diurnal profiles, in addition to the monthly profiles for each state. As pa rt of this analysis, specific day-of-week (Monday, Tuesday, Wednesday, etc.) and diurna l 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 un its 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 states 1 

### . The “Prepackaged Data” option allows the download of files containing emi ssions 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 (O RISPL), Unit ID, Date, Hour, SO 2 

### Emissions (lbs), CO 2 

### Emissions (tons), NO x 

### Emissions Rate (lb/mmBtu), NO x 

### Emissions (lbs), Heat Input (mmBtu), Oper ating 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.cf m?fuseaction=prepackaged.selectA tmospheric Sciences Group



## 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 th e facility is located.

Facility Name The name given by the owners and operators to a facility.

Facility ID (ORISPL) The unique six-digit facility id entification 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 meas ured in megawatts.

Steam Load (1000 lb/hr) Steam load is the output of the unit as m easured in 1000 lb/hr of steam.

SO 2 Mass (lbs) SO 2 released for the hour in pounds.

SO 2 Mass Measurement Flag Indicates whether the value for SO 2 mass was measured or derived due to missing data.

SO 2 Rate (lbs/mmBtu) SO 2 emissions rate in pounds per million British thermal units (lbs/mmBtu).A tmospheric Sciences Group

SO 2 Rate Measurement Flag Indicates whether the value for SO 2 rate was measured or derived due to missing data.

NO x Rate (lb/mmBtu) NOx emissions rate in pounds per million British thermal units (lbs/mmBtu).

NO x Rate Measurement Flag Indicates whether the value for NO x rate was measured or derived due to missing data.

NO x Mass (lbs) NO x released for the hour in pounds.

NO x Mass Measurement Flag Indicates whether the value for NO x mass was measured or derived due to missing data.

CO 2 Mass (lbs) CO 2 released for the hour in pounds.

CO 2 Mass Measurement Flag Indicates whether the value for CO 2 mass was measured or derived due to missing data.

CO 2 Rate (lbs/mmBtu) CO 2 emissions rate in pounds per million British thermal units (lbs/mmBtu).

CO 2 Rate Measurement Flag Indicates whether the value for CO 2 rate was measured or derived due to missing data.

Heat Input (mmBtu) Heat per hour as calculated by multipl ying the quantity of fuel by the fuel's heat content.



## 2.3. Quality Control / Quality Assurance

Each file was reviewed to de termine if NOx, SO2 and heat in put 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 indi cated no operation duri ng 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 vari able data to ensure that data corruption was not impacting the statistical analyses. A dditionally, 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.A tmospheric Sciences Group



### 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 cas es, certain months or quarters of the year
were blank for individual units or facilitie s 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-specifi c 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 leve l, 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 accounte d 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 VI STAS 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 hour ly temporal distribution profiles.

Under the direction of VISTAS, staff at MACTEC Engineering and Consulting, Inc. prepared comparisons of the VISTAS 2002 emi ssion inventory of EGU sources to that of CEM-based emissions, heat input, and operati ng 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 identifica tion was made for a majority of units who maintained the same numbering and nomenclatu re 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 st eps were followed in order to match the outstanding facilities and emissions as repor ted by VISTAS States in the 2002 emission inventory.A tmospheric Sciences Group



### MACTEC developed county-level reports of the remaining unma tched facilities and units from the VISTAS inventory and made compar isons 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 incr emental amount of emissions and units to be accounted for and assigned unit-specific pr ofiles for model pe rformance evaluation.

Finally, remaining VISTAS inventory and CEM sources were manually compared to each other in an effort to determine if reporti ng errors in State or county codes or facility
or unit identification codes accounted for th is reminder of unmatched sources. These
manual matches were confirmed or revise d with VISTAS State and stakeholder participation and input. With this step, a fe w 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 exha usted, the remaining unmatched VISTAS emission inventory of EGU sources was excluded from the unit-specific profile assignment steps and was allocated more generali zed 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 deve loped for modeling EGU emissions within the VISTAS domain. The first set are to be applied to individual units ab le to be matched toA tmospheric Sciences Group



### 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 av erage of the CEM data variable s (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 hou r 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 identif ied as non-operating hours. This allo wed for the best representation of actual 2002 conditions for the expected us e 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 leve l monthly, day-of-week, and diurnal profiles
for VISTAS States.

For the 2000-2004 averaging period, representa tion of typical operating conditions was desired, so in the averagi ng calculation only valid opera ting hour NOx, SO2, and heat input values were used. This prevented th e introduction of equipment shutdown because of power outages, control insta llation, or planned maintenan ce into the temporal profile
calculation.

4.1.1. Actual 2002 ProfilesA tmospheric Sciences Group



### Through the EPA’s Clean Air Market’s Data and Maps website, quarterly unit-level hourly emissions data by State and calenda r 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 (ORI SPL) code (assigned by the Department of Energy at the Energy Information Administra tion), unit identification code, date of record, hour of record, SO2, CO2, and NOx ma ss (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 we re 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. Ta ble 1 provides an example result of the ratio
calculation.

Equation (1) 

# ∑

= 1

31 , ,, 

### / Jan

Dec datehr datehrratio 

### hi hi 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 NO x Mass Heat Input SO2 NOx Heat InputA tmospheric Sciences Group

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 1097 9533.36 5375215.8 0 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 fi rst, State second) could be assigned as necessary. Table 2 shows example ratios calculated for each month by State.A tmospheric Sciences Group



### Table 3 reflects an example of the State-mont h-day of week ratio calculation and Table 4
shows a State-month-diurnal ratio calculati on 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 VIST AS base year emissions inventory.

Three parameter values (SO2 mass, NOx ma ss, 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 se t 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 0.1014A tmospheric Sciences Group

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 0.1521A tmospheric Sciences Group

GA Mar Total 89,015,786 27,868,311 69,184,812 1.0000 1.0000 1.0000A tmospheric Sciences Group



### 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 0.0399A tmospheric Sciences Group

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 1.0000A tmospheric Sciences Group



### Figure 1. 

### Monthly variation in 2002 of CEM re ported 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 DecRelative 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 variati on, shutdowns, or regular maintenance or installation of equipment. As an example, Fi gure 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 rela tively high during the summer months, and heat input peaks during the month of Ju ly. 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.A tmospheric Sciences Group



### Finally, when these data are reviewed at a unit level, the differences become incrementally more distinct due to the uni que nature of individual facilities, their operating schedules, pollution regulation, fuel characteristics, and applied technologies. For example, a facility that is complyi ng with summertime NOx regulation may have selective catalytic reduction (SCR) installe d on its boiler(s) which in practice may only be run during ozone season months. During th is period of time, heat input and SO2 emissions may remain consistent with Stat e or regional monthly profiles, but the NOx emissions may drop significantly re lative to the rest of the year.

Figure 3 represents an extreme unit-specific ca se for monthly differences from State or regional temporal allocation. The unit presen ted 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 cons istent levels during the entire period, but due to a planned maintenance outage was not in operation in la te 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 distributi on would introduce signif icant inaccuracy to air quality modeling in the im mediate or downwind area associ ated with this facility. While this may not be significan t at great distance downwind of the source or for annual concentration estimates, more locally, and espe cially over shorter time scales (daily or weekly), such simplifications would have a noticeable effect on air quality model predictions.A tmospheric Sciences Group



### Figure 2. 

### Monthly variation in 2002 of CEM re ported 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 DecRelative Distribution Heat Input NOx SO2



### Figure 3. 

### Monthly variation in 2002 of CEM re ported heat input, NOx mass, and SO2 mass for specific baseload coal-fired unit in Mississippi with planned outage in late January through mid April.A tmospheric Sciences Group

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 DecRelative Distribution Heat Input NOx SO2



### Thus, while improving the representativeness of unit-specific monthly temporal profiles is desirable, providing day a nd hour-specific values are clea rly better. For this reason, during the model performance evaluation pr ocess in the VISTAS Phase II modeling, hour-specific temporal ratios were devel oped 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 distribut ion of SO2 and NOx emissions and heat input from the Mississippi baseload unit from the a bove example. As can been seen in this figure, not only is the planned January th rough April outage repres ented correctly, there are significant peaks and valleys throughout the calendar year which could not be accurately represented with th e application of average monthly, day-of-week, or hourly
distribution factors. In realit y, only the actual operating charac teristics of this unit could capture the differences from hour to hour whic h are potentially quite important in termsA tmospheric Sciences Group



### of correctly modeling the imp act of the source on downwind oxidant and fine particulate
concentrations 2



### .

Figure 4. 

### Actual daily unit-specific 2002 SO2 (t ons), NOx, (tons), and heat input (MMBtu) distribution from CEM data.

0 10 20 30 40 50 60 70

01-Jan-02

0 1- F eb

- 02

0 1- Ma

r - 02 01-Apr-02 01-May- 0 2

0 1- J un

- 02 01-Jul-02

01 - Aug-02

01 -S

ep-

02 01-Oct-02

01 - N ov -02

01 - D ec -02 31-De c -02Emissions (Tons)

0 20,000 40,000 60,000 80,000 100,000 120,000 140,000Heat Input (MMBtu) SO2

NOx Heat Input

2 Stella, G.M., “Development of Hourly Inventor ies Utilizing CEM-Based Data,” presented at the International Emission Inventory Conference, Las Vegas, NV, April, 2005.A tmospheric Sciences Group



### 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 reco rd in the CEM data base, the arithmeticmean 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 av eraged results can be seen in Table 6.A tmospheric Sciences Group



### Table 6. 

### Arithmetic Mean of CEM-based Variab les 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 5121.75A tmospheric Sciences Group

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 mont hs 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 distribu tions 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-04A tmospheric Sciences Group

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 1 2635319.86



### Equation 2 reflects this calculation for h eat input for a summer hour. Ratios were calculated for NOx, SO2, and heat input valu es. These ratios were then applied to each unit’s seasonal (summer or winter) emission va lue for NOx, SO2, and all other pollutants, respectively.

Equation (2) 

# ∑

= 1

30 ,, ,,, 

### / May

Sep sumdatehr sumdatehrratio 

### hi hi hiA tmospheric Sciences Group



### where hi 

### = heat input (MMBtu)

The actual hour-of-day-of-month averages calc ulated from the CEM data were not used directly as emissions for that hour, but were used only in the calcula tion of the ratios to be applied to a pre-calculated seasonal (sum mer or winter) emission value. This allowed
for the retention of emission estimates calcula ted 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 distribu tion 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. Inst ead 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 aggreg ation required by the calculation (e.g., State-month. State-month- day-of-week, or State-month-hour). Table 8 shows example ratios calculated fo r each month by State. Table 9 reflects an example of the State-month-day of week ratio calculat ion and Table 10 shows a State-month-diurnal ratio calculation example. Each of these ra tios 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, NO x 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.A tmospheric Sciences Group



### 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 1.0000A tmospheric Sciences Group



### 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 1.0000A tmospheric Sciences Group



### 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 0.0373A tmospheric 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 1.0000A tmospheric 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 SM OKE’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 de velop a set of hourly emissions inputs to circumvent this limitation. These were generated in the EMS PTHOUR format as described in SMOKE input file documentation 3



### .

The CEM format for individual hour-specific da ta files as available in SMOKE was not utilized for VISTAS emissions processing as the emissions allowa ble by hour would have been limited to NOx, SO2, and CO2. If this file format and opti onal run configuration were exercised, the NOx, SO2, and CO2 em issions 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 monthl y, 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 emissi ons would be slightly different that the original annual inventory.

In VISTAS Phase II modeling, for those E GU sources where CEM data were utilized, NOx, SO2, and heat input-based hour-specifi c profiles were developed and applied to annual NOx, SO2, and all other emissions, resp ectively, 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

.A tmospheric Sciences Group



### the majority of remaining compounds are not as significantly impacted by controls or fuel content, yet the distribution of these emi ssions 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 main tained, but distribute d 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 poi nt sources, the NOx, SO2, and heat input
data were collected from the 2002 or 2000-2004 CEM datase ts, 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 dist ribution as the unit-specifi c factors, the State-based temporal distribution provided improved result s to the default profiles provided with the emissions model. Figure 10 represents the m onthly distribution comparisons of VISTAS State heat input to the default monthly di stribution from Version 2.0 of SMOKE for source category code (SCC) 10100201, repr esenting External Combustion Boilers; Electric Generation; Bituminous/Subbitumi nous Coal; Pulverized Coal: Wet Bottom (Bituminous Coal), a relatively common boiler type and fuel configuration in the VISTAS domain. This example is fo r 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 inappropria tely be distribute d during the episode of interest. InA tmospheric Sciences Group



### addition to the unique distribution provided by the unit-specific factors based on CEM data, aggregate State level dail y and diurnal temporal distri bution factors were developed and applied during this process. Figure 11 s hows the variance in di urnal distribution from Tennessee’s average CEM-based NOx emissions data for each of the twelve months of calendar year 2002 as would have been appl ied to units unmatched to CEM sources.A tmospheric Sciences Group



### 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 DecRelative 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.A tmospheric 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 hr24Relative Distribution

Jan Feb Mar Apr May Jun
Jul Aug Sep Oct Nov DecA tmospheric Sciences Group

# Appendix D.4c
White Paper on Mobile Source SMOKE Modeling

## 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 th e generation of onroad mobile sources. Motor vehicle emissions are influenced by meteorological variab ility and the processing requirements for daily motor vehicle emissions have been determined to be rate limiting under most modeling schedules. Rather than utili zing averaged meteorological data or pre-calculated motor vehicle emissions, the Visibility Improvement State and Tr ibal Association of th e Southeast (VISTAS) / Association for Southeastern Integrated Planni ng (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 consis tent 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 exclud e holidays that would requ ire 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 si mulating everyday for onroad mobile emissions to support 8-hr ozone modeling. We will present 12km ev aluations of everyday vs. representative week emissions and associated air quality for a number of domains and disc uss the benefits and limitations of the various methods relative to ozone, PM and regional haze prediction.

### INTRODUCTION

On December 17, 2004, EPA ma de fine particle (PM 2.5 

### ) nonattainment determina tions for at least one area in seven of the states participating in the Visibility Impr ovement State and Tribal Association of the Southeast (VISTAS) regional haze project. They are Alabama, Georgia, No rth Carolina, Kentucky, Tennessee, Virginia, and West Virginia. In additi on, South Carolina has one three-county area that was designated as unclassifiable in the same action. EP A’s Clean Air Interstate Rule (CAIR) modeling indicated that certain n onattainment areas may still be in nonatta inment after full implementation of CAIR. These areas include Jefferson County, Alabam a and Clayton and Fulton Counties in Georgia.

The PM 2.5 

### compliance date is April 2010 unless a state dem onstrates that more time is necessary in which case up to five additional years may be gran ted. 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 th at a significant requirement of PM 2.5 

### SIPs will be attainment demonstrations using, at least in part, modeling analys es to define effective em issions control strategies and confirm that attainment can be achieved after implementation of the strategies. 2009 is the modeling year for the PM 2.5 

### 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, Ge orgia, Kentucky, North Carolina, South Carolina, Te nnessee, 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 basi c 8-hour ozone nonattainment areas to model 2008 as the SIP modeling demonstration year while moderate nonattainment areas wi ll 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 (t he SESARM EPA Region 4 st ates 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 de velop information upon which to base the PM 2.5 

### and 8-hour ozone attainment demonstrations. The local air regulatory agencies for Jefferson County, AL, Jefferson County, KY, Mecklenburg County, NC, Forsyth C ounty, NC, Knox County, TN, and Shelby County, TN have also become signatory pa rties to this collaborative effo rt. SESARM will coordinate among participating agencies and oversee the performance of the inventory and modeling tasks in parallel with the VISTAS regional ha ze project tasks.

The name of this collaborative effort is the Asso ciation for Southeastern In tegrated Planning (ASIP). SESARM was awarded a grant from EPA on February 8, 2005 to conduct what wa s originally called the fine particle SIP development support project but is now known as ASIP.

These states need to submit their 8-hour ozone St ate Implementation Plans (SIPs) to EPA by June 2007; the PM 2.5 

### 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 defi nition of the SIP control pl ans is needed in early 2006. Consequently, the ASIP regional ozone an d PM modeling has an aggressive schedule.

### Figure 1. PM 2.5 

### nonattainment counties designed by EPA in December 2004.

Figure 2. 8-hour ozone nonattainment counties in the US designated by EPA in April 2004.

### By far the most computationally limiting step in emissions modeli ng is typically the generation of onroad mobile sources. Motor vehicle emissions ar e influenced by meteorologi cal variability and the processing requirements for daily mo tor vehicle emissions have been determined to be rate limiting under most modeling schedules. Rather than utilizing averaged meteor ological data or pre-calculated motor vehicle emissions, the VISTAS and ASIP m odeling 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 a nd consistent with modeling schedule 1 

### . This representative week was selected from mid-month, to tr y to best represent the average te mperature ranges for the month, and also adjusted to exclude holidays th at would require at ypical 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 partic ulate matter (PM) of size of 2.5 microns or less (PM 2.5 

### ):

3⁄4 MM5 2,3 

### : The Pennsylvania State University/Na tional Center for Atmospheric Research (PSU/NCAR) Mesoscale Meteorological M odel (MM5) is a no nhydrostatic, prognostic meteorological model routinely used for urba n- and regional-scale photochemical, fine particulate and regional haze regulatory modeling studies.

3⁄4 SMOKE 4 

### : The Sparse Matrix Operator Kernel Emissions (SMOKE) modeling system is an emissions modeling system that generates hourly gridded speciated emissi on inputs of mobile, nonroad, area, point, fire and biogenic emissi on sources for photochemical grid models.

3⁄4 CMAQ 5,6 

### : EPA’s Models-3/Community Multiscale Ai r Quality (CMAQ) modeling system is a ‘One-Atmosphere’ photochemical grid model cap able 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 si mulating everyday for onroad mobile emissions to support 8-hr ozone modeling. We will present 12km ev aluations of everyday vs. representative week emissions and associated air quality for a number of domains and disc uss 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 inventor y modeling, SMOKE was configured to generate point, area, nonroad, highway, and bioge nic 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. W ith 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 specifi c temporal profiles of the SMOKE model. Area, nonroad, and point sources were modeled as a bloc k 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 onroa d 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.

### 2) Modeling selected weeks (seven days) of each month and using these days as representative of the entire month. This selection criterion allows for th e representation of day-of -the-week variability in the onroad motor vehicles, and models a representa tion of the meteorological variability in each month. The modeled weeks were se lected 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 4 th 

### 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) kilome ters 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 la yer positions defined in the meteorology files to be provided by VISTAS meteorological contractor.

### 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, PNO 3, POA, PSO4, and PMC.

Meteorology data: Daily (25-hour). SMOKE requires the followi ng 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 poten tially elevated. No plume-in-grid sources will be modeled. Wildfire emissions will be handled as point sources.

Figure 3. 36-km national unified RPO doma in and VISTAS 12-km domain.

### DEVELOPMENT OF ONROAD MOTO R VEHICLE SOURCE EMISSIONS

The MOBILE6 module of SMOKE was used to devel op the onroad mobile sour ce emissions estimates for CO, NOX, PM, and VOC emissions. The MOBILE6 parameters, vehicle fleet descriptions, and VMT estimates are combined with gridded, episode-sp ecific temperature data to calculate the gridded, temporalized emission estimates. The MOBILE6 em issions 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 emi ssions 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 she ll file. The MOBILE shell contains the various options (e.g. type of inspection a nd maintenance program in effect, t ype of oxygenated fuel program in effect, alternative vehicle mix profile s, RVP of in-use fuel, operating mode) that direct the calculation of the MOBILE6 emissions factors. The shells used in these runs were ba sed on VISTAS/ASIP BaseF modeling inputs 7 

### . The options for all MOBILE6 parameters were held constant between the annual and representative week runs.

Daily results of these model runs fo r a winter (January 17) and summer (July 18) day are represented in Figures 4 through 6 below. These data provide a co mparison 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 fo r both ozone and PM that we have based our conclusions.

Each of the onroad mobile source emissions r uns conducted with the MOBILE6 module of SMOKE were performed on a dual Athlon MP 2600+ with 1.5 G RAM. With th is configurati on, 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 pr ocessing 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 confi guration would require 22,995 minutes (365 days x 63 minutes per run day) or about 383.2 5 hours (16 days) of CPU run time.

### 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.

### 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 representati ve week run were calculated by mapping each day of week (Mon, Tue, Wed, etc.) from the modeled m onth 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 Janua ry 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 th e representative week MOBILE6 module runs. In comparison, Table 3 presen ts these emissions for the month of July.

Table 1. Representative day mapping for January ep isode (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

### 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)

### 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)

### These aggregate emission summaries would lead one to believe that on an exte nded 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 oz one, there is enough of a temperature variability a nd therefore apparent ozone precursor em issions 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 ha s applied the CMAQ Version 4.5 O 3 

### /PM 2.5 

### photochemical grid modeling system. The VISTAS/ASIP modeling team implemented a comprehensive evaluation of the meteorological 8 

### , emissions and air quality models. The CM AQ 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 underestim ation bias for nitrate and reasonably good performance for elemental carbon (EC), albeit with lots of scatter and low correlation. However, organic carbon (OC) was unde restimated with the su mmer OC underestimation bias being quite severe. After an intense focuse d analysis of the OC underestimation issue, the VISTAS/ASIP modeling team identified processes im portant to the formation of secondary organic aerosols (SOA) that were not included in the CMAQ SOA module that may be im portant to OC in the Southeastern U.S. 9 

### Consequently, VISTAS/ASIP enhanced th e CMAQ SOA module by adding several new processes. This enhancement, called “SOAm ods”, was implemented in CMAQ Version 4.5 and exhibited much improved OC model perfor mance over the standard CMAQ SOA treatment 10 

### . A complete description of the modeling methods, configurations and perf ormance are described elsewhere 1,7



### .
CMAQ was applied using both of the mobile emissi ons modeling methods described above. Recall, all emissions and air quality model inputs and configurati ons were held constant, with the exception of the mobile source emissions. This will allow us to isolat e 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 “VIS TAS/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 (da ily 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 differe nces in predicted ozone and PM 2.5 

### concentrations due to the different mobile emissions modeling methodologies. Spatial plots were generated for each day simulated, including:

1) daily maximum 8-hour oz one difference plots; 2) maximum 1-hour ozone maxi mum difference plots; and, 3) daily PM 2.5 

### difference plots.

### RESULTS

Our examination of the two air quality simula tions began with the daily differences in PM 2.5 

### concentrations. Figures 7 and 8 represen t the percent difference in the daily PM 2.5 

### concentrations between the air quality simulations with representative week mobile emissions and the daily mobile emissions for one winter day (January 22 nd 

### ) and one summer day (July 9 th 

### ). No change is seen in either plot indicating daily PM 2.5 

### concentrations changed less than one pe rcent. Absolute differences in daily PM 2.5 

### concentrations are shown in Figures 9 and 10 for the same two days (January 22 nd 

### and July 9 th 

### ). Again, no change is seen in e ither plot indicating daily PM 2.5 

### concentrations cha nged less than 0.2 μg/m 3 

### . In fact, all of the fourteen days modeled (s even winter days and se ven summer days) show no differences as high as 0.2 μg/m 3



### .
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 presen t the percent difference in the daily maximum 8- hour ozone concentrations for one winter day (January 28 th 

### ) and one summer day (July 15 th 

### ). In most areas for the winter day, daily ma ximum 8-hour ozone concen trations changed less than one percent. In a few urban corridors, namely, near Chicago, IL, Atlanta, GA and Baltimor e, 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 inte rest because most, if not all high ozone events in the middle latitudes of the northern hemisphere o ccur during the summertime. Therefore the remainder of the ozone analysis will focus on summertim e differences. On the summer day, July 15 th 

### , presented in Figure 12, no changes are seen indicating daily maxi mum 8-hour ozone concentr ations changed less than one percent. In fa ct, all seven of the summer days modele d showed no changes as high as one percent.

Absolute differences in daily maximum 8-hour ozone concentrations are show n in Figure 13 for July 15 th 

### . 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 Fi gure14 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).

### Figure 7. Percent difference in 24-hour PM 2.5 

### concentrations for Januar y 22nd (Representative week mobile emissions versus daily mobile emissions).

Figure 8. Percent difference in 24-hour PM 2.5 

### concentrations for July 9 th 

### (Representative week mobile emissions versus daily mobile emissions).

### Figure 9. Absolute differences in 24-hour PM 2.5 

### concentrations for January (Representative week mobile emissions versus daily mobile emissions).

Figure 10. Absolute differences in 24-hour PM 2.5 

### concentrations for July 9 th 

### (Representative week mobile emissions versus daily mobile emissions).

### 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 15 th 

### (Representative week mobile emissions versus daily mobile emissions).

### Figure 13. Absolute differences in daily 8-hour ma ximum ozone concentrations for July 15 th 

### (Representative week mobile emissions versus daily mobile emissions).

### Figure 14. Absolute differences in 1-hour maximu m ozone concentrations for July 12 th



### .

Figure 15. Absolute differences in 1-hour maxi mum ozone concentrations for July 14 th



### .

### CONCLUSIONS

U.S. EPA attainment demonstration modeling guidance 11,12 

### notes that in some cases it may be useful to evaluate how the response of an air quality model to emissions ch anges varies as a function of alternative model inputs or model al gorithms. 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 predic ted effectiveness of a control strategy. If the model response doe s not differ greatly over a vari ety of alternative plausible configurations, this increases confidence in the model results.

The parameters for these sensitivity tests can in clude, but are not limited to: different chemical mechanisms, finer or coarser grid resolution, me teorological 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 th roughout 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 onr oad mobile emissions for each month of our episodes within our 12km modeling domain pr edicts ozone and particulate matte r 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 combin ation 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 PM 2.5 

### and 8-hour ozone modeling.

REFERENCES

1. “Draft Modeling Protocol For Association for Southeastern Integrated Planning (ASIP) – Emissions and Air Quality Modeling to Addre ss 8-Hour Ozone and PM2.5 Nonattainment in the Southeastern United States”; Prepared fo r 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 th e Fifth Generation Penn State/NCAR Mesoscale Model (MM5). NCAR Tech. Note, 1994. NCAR TN-398-STR, 138 pp.

4. University of North Caro lina at Chapel Hill, Sparse Matrix Operator Kernel Emissions (SMOKE) Modeling System

, http://cf.unc.edu/cep/em pd/products/smoke/index.cfm

.

5. Byun, D.W., and J.K.S. Ching. “Science Algor ithms of the EPA Models-3 Community Multiscale Air Quality (CMAQ) M odeling System”, EPA/600/R-99/030. 1999.

6. Pleim J. et al. “New Developments in CMAQ Model Physics”, Presented at the 4 th 

### Annual CMAS Models-3 User’s Conference, September 26-28, 2005, Friday Center Chapel Hill North Carolina.

### 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 3 th 

### AMS Joint Conference on the Applications of Air Pollution Meteor ology 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 Attainme nt of Air Quality Goals for PM 2.5 

### and Regional Haze

, Draft Report, U.S. Environmental Protecti on Agency, Research Triangle Park, NC. 2001.

ACKNOWLEDGEMENTS

VISTAS and ASIP are funded by gran ts 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 Stat e, local and Trib al air quality agencies, participating stakeholders , and associated contractors for th eir contribution and input in the emission inventory development and modeling. Add itionally, the authors woul d like to recognize the support of the North Carolina Department of E nvironment and Natural Re sources, Division of Air Quality and Dennis McNally, Alpine Geophysics for his time in preparing the CMAQ runs used in this analysis.

### KEYWORDS

ASIP MOBILE Modeling Onroad Mobile Ozone Particulate Matter Regional Haze Regional Planning Organization SMOKE VISTAS

# Appendix D.5 VISTAS Emission Inventory

Supporting DocumentationAppendix D.5 - 1Appendix 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

# 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

### [ This page intentionally left blank]

## 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 T ype 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 DE VELOPMENT USING MOBILE6.2 ..... 8 1. General MOBILE6.2 File Structure ..................................................................... 8 C. 2002 ONROAD EMISSION INVE NTORY CALCULATIONS ............................. 14 D. DATA PROVIDED BY STA TE 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

## TABLES

Table II-1. VMT Data Provide d by State/Local Agencies ............................................................ 3 Table II-2. VMT Growth Factors Used for Virginia ..................................................................... 4 Table II-3. Allocation of VMT from HPMS Vehi cle Categories to MOBILE6 Vehicle Types for 2001........................................................................................................................... ................ 5 Table II-4. Default VMT Seasonal and M onthly 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 In corporated 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 Port s and Ports of Adjoining States ............................................... 33 Table III-6. Definition of Port Areas Obtained from Waterborne Commerce ............................ 36 Table III-7a. Growth Factors for Ra ilroad Distillate Fuel Oil Use.............................................. 40 Table III-7b. 2002 National Rail Trans portation 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 Re fueling Emissions by State.................................................... 45 Table VI-1. 2002 VISTAS Onroad Emissions and VMT by State.............................................. 46 Table VI-2. 1999 NEI Version 2 Onro ad 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 Dr aft 2002 Nonroad Sector Annua l Emissions by State ..................... 48 Table VI-6. Summary of Dr aft 2002 NONROAD Model Emission Estimates by State............. 49

### Table VI-7. Summary of Dr aft 2002 Other Nonroad* Emission Estimates by State.................. 50



## FIGURES

Figure II-1. Header Section of MOBILE6.2 Input File Including VOC Exhaust and PM 10 

### Emission Factors............................................................................................................... .............. 9 Figure II-2. Header Section of MOBILE6.2 Input File Including VOC Evaporative and PM 2.5 

### Emission Factors............................................................................................................... .............. 9 Figure II-3. Run Data Secti on 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 fo r a Typical MOBILE6.2 Input File ........................................... 12 Figure III-1. VISTAS Re gion and Surrounding States, Underway NO x 

### Emissions ................... 30 Figure III-2. VISTAS Re gion and Surrounding States, Port NO x 

### Emissions ............................. 31 Figure III-3. VISTAS Region and Surrounding States, Revised Port Emissions of NO X 

### ............ 35

## 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 NH 3 

### ammonia NO x 

### oxides of nitrogen OTAQ Office of Transportation and Air Quality Pechan E.H. Pechan & Associates, Inc. PM 2.5 

### particulate matter with an aerodynamic diameter less than or equal to 2.5 micrometers PM 10 

### 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 SO 2 

### 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

### 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 emi ssions inventory is to support the modeling and assessment of speciated particulate matter with an aerodynamic diameter less than or equal to 2.5 micrometers (PM 2.5 

### ). 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 a nd 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 refueli ng 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.

### 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. Fina lly, 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 a nd content of the VMT provided by the VISTAS State and Local agencies varied significantly fr om agency to agency, this draft 2002 VISTAS inventory is based at a minimum on county/ro adway 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.

### 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 fr om 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 Volu me 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.

### 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, Feder al Highway Administration, “Traffic Volume Trends, December 2002”, (http:// www.fhwa.dot.gov/ohi m/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 System s (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

### 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 t ypes 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 fr om 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

### 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 da ta 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 leve l 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, a ny State or locally supplied temporal adjustment factors will be included in the final versi on of the 2002 VISTAS onroad emission inventory.7Table II-4. Default VMT Seasonal and Monthly Temporal Allocation FactorsRoadway Seasonal VMT FactorsVehicle Type Roadway Type Winter Spring Summer FallLDV,LDT,MC Rural0.2160 0.2390 0.2890 0.2560LDV,LDT,MC Urban0.2340 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 DecLDV,LDT,MC Rural 0.0744 0.0672 0.0805 0.07790.0805 0.0942 0.0974 0.09740.0844 0.0872 0.0844 0.0744LDV,LDT,MC Urban 0.0806 0.0728 0.0859 0.0832 0.08590.0864 0.0893 0.0893 0.0808 0.0835 0.0808 0.0806HDV All 0.0861 0.0778 0.08420.0815 0.0842 0.0815 0.0842 0.0842 0.0842 0.0852 0.0824 0.0861

### 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 possibl e to obtain emission factors for both PM 10 

### and PM 2.5 

### within a single MOBILE6.2 scenario. Therefore, two sets of MOBILE6.2 input files were created—one set to model VOC exhaust, NO x 

### , CO, SO 2 

### , PM 10 

### , and NH 3 

### emission factors and a second set to model VOC evaporative and PM 2.5 

### emission factors. Figure II-1 illustrates the header section of a sample VISTAS MOBILE6.2 input file used to generate the VOC exhaust, NO x 

### , CO, SO 2 

### , PM 10 

### , and NH 3 

### 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 PM 2.5 

### emission factors. The primary difference between these tw o 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 i ndicates 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 SO 2 

### and NH 3 

### 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.

### 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 term s 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 sour ce inventory rather than the onroad inventory. Chapter IV discusses the onroad refueling MOBI LE6 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 di stributions 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 MOBILE 6.2 Input File Including VOC Exhaust and PM 10 

### Emission Factors

Figure II-2. Header Section of MOBILE 6.2 Input File Including VOC Evaporative and PM 2.5 

### Emission Factors

### 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 Secti on of a MOBILE6.2 Input File

Figure II-4. Run Data Section of a MOBI LE6.2 Input File with Significant Local Inputs

### 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

### 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 comb ination 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 re ductions 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 fo r a Typical MOBI LE6.2 Input File

### 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 i nputs 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 f iles, 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 a nd 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.

### 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 modele d 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 provide d 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 VM T 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 factor s 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 fracti ons, 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 emi ssion 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.

### 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 Monthl y 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

### 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 statewi de 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 MOBILE 6 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 adj ustment factors by month and day of week for each road type (not used in the 01/04 inventory)

### 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 MO BILE6 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 TD M by county/road type/vehicle type

MOBILE6 Input Files Provided MOBILE6 input files repres enting 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 inform ation extracted from MOBILE5 input file

Other Provided Northern Kentucky VMT di stributions by facility type and by hour in MOBILE6 format

### 18 Table II-10. Summary of Onroad Data Provided by Mississippi

Data Element Data Supplied by Responsible Agency VMT Data Provided 2002 actual annual VM T 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

### 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 file s 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

### 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 i nput 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 regi stration data provided for nonattainment counties Fuel Information Verified counties in reformulated gasoline program I/M Program Information I/M and ATP inputs prov ided 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

### 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 de sired 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 pro cedure 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 reformulat ed gasoline. Thus, the Jefferson County VMT was divided into four subsets and four MOBILE6 i nput 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

### 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 oxyge nated 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 repl ace 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 Virg inia 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 “VISTA S NONROAD County Inputs.xls,” Pechan prepared seasonal option files for each of four seasons (winter, spring, summer, and autumn), and ran the

### 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-le vel, SCC-level annual emissions inventory for all pollutants except NH 3


### .

The NH 3 

### emissions for NONROAD model categories were developed using the following procedures. OTAQ recently reviewed the basis of NH 3 

### 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 performe d 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/gall on 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 incl uded 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 PM 10 

### 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, NO x , and CO

Florida 2001 Aircraft, Locomotive and Commercial Marine Vessel emissions for Palm Beach County VOC, NO x , CO, PM10, and SO x Tennessee 1999 Aircraft and Locomotive emissions for Davidson County VOC, NO x , CO, SO x , and primary PM 10 Virginia 1999 Statewide Inventory for Aircraft, Locomotive and Commercial Marine Vessels VOC, NO x , CO

### 24 Table III-3a. Replacement Procedur es 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 16.3325Table III-3b. Replacement Procedures for 1999 Aircraft, Locomotive, and Commercial Marine Vessel Emissionsfor Palm Beach County, FloridaSTATE_FIPSCOUNTY_FIPSSCC VersionNotesSTART_DATEEND_DATEVOC NOX CO SO2PM10-PRIPM25-PRI12 099 2275000000 V3 Apply a Growth Factor to 2001state-supplied aircraft emissions tobackcast to 1999Estimate PM2.5-PRI off PM10-PRI19990101 19991231 470.39 805.94 4,121.41 1.98 0.0012 099 2275001000 V2 Delete all records for this SCC19990101 19991231 0.44 0.05 9.03 0 0.19 0.1312 099 2275020000 V2 Delete all records for this SCC19990101 19991231 79.1 275.5 330.6 26.3412 099 2275050000 V2 Delete all records for this SCC19990101 19991231 13.93 2.37 437.43 0.36 8.62 5.9512 099 2275060000 V2 Delete all records for this SCC19990101 19991231 9.23 1.19 212.32 0.11 4.55 3.1412 099 2280000000 V3 Apply a Growth Factor to 2001state-supplied cmv emissions tobackcast to 1999Estimate PM2.5-PRI off PM10-PRI19990101 19991231 10.42 115.60 0.97 9.94 33.9112 099 2280002100 V2 Delete all records for this SCC19990101 19991231 25.5 815.4 107.51 36.95 34.3 31.5512 099 2280002200 V2 Delete all records for this SCC19990101 19991231 0.22 7.05 0.93 0.32 0.3 0.2712 099 2280003100 V2 Delete all records for this SCC19990101 19991231 6.8 217.5 28.63 115.6 9.48 8.7312 099 2280003200 V2 Delete all records for this SCC19990101 19991231 0.06 1.93 0.25 1.43 0.11 0.112 099 2285002000 V3 Apply a Growth Factor to 2001state-supplied locomotive emissions tobackcast to 1999Estimate PM2.5-PRI off PM10-PRI19990101 19991231 28.19 658.78 83.64 48.09 15.5012 099 2285002006 V2 Delete all records for this SCC19990101 19991231 6.11 164.1 16.17 10.26 4.07 3.6612 099 2285002008 V2 Delete all records for this SCC19990101 19991231 0.45 12.15 1.2 0.76 0.3 0.2712 099 2285002009 V2 Delete all records for this SCC19990101 19991231 6.78 182.2 17.95 11.39 4.52 4.0712 099 2285002010 V2 Delete all records for this SCC19990101 19991231 3.75 64.36 6.77 3 1.64 1.471Palm Beach County provided emission estimates corresponding to 2001; as such, 2001 emission estimates were backcast to 1999 using growth factors presented in this reportbefore incorporation.

### 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

### 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.

### 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 require s a large amount of effort. Therefore, Pechan utilized port-specific emission estimates develope d 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 fact ors. 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 SO 2 

### 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 obtai ning fuel usage data from OTAQ and applying fuel-based emission factors (EPA, 1989). A similar method was used for diesel SO 2 

### 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).

### 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 th e 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 fo rm 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 NO x 

### 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.30

Figure 1. VISTAS Region and Surrounding States VISTAS Region an d Surrounding States Underway NOx EmissionsNOx Emissions(Tons per Year)00 - 400400 - 800800 - 1,3001,300 - 2,4002,400 - 3,680E.H. Pechan & Associates, Inc.Date: December 11, 2003.Figure III-1. VISTAS Region and Surrounding States,Underway NOxEmissions31

Figure 2. VISTAS Region and Surrounding States VISTAS Region and Surrounding States Port NOx EmissionsNOx Emissions(Tons per Year)00 - 3,0003,000 - 7,0007,000 - 13,00013,000 - 26,00026,000 - 67,000E.H. Pechan & Associates, Inc.Date: December 11, 2003.Figure III-2. VISTAS Region and Surrounding States,Port NOxEmissions

### 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 th e 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 Stat es. 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).

### 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 Po rts 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

### 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 refl ects 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.35Figure III-3. VISTAS Region and Surrounding States,Revised Port Emissions of NOX

### 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 M anatee 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 Beac h inner channel and Turning Basins, thence a Barge canal through a lock in the perimeter dike and conti nuing to the Intracoastal Waterway, Jacksonville to Miami.

CHARLOTTE HARBOR, FL Gulf of Mexico to Municipal Terminal at Punta Go rda, 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 mile s; and “Palm Beach, FL side channel and basin.”

PORT MANATEE, FL 40 feet deep by 400 feet wide entrance channel and bas in. The entrance channel extends approximately 3 miles in length from the turning basin to its in tersection 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. Pr oject Depth: Approach channel, 34 feet; across Lands End, 32 feet; Watson Bayou, 10 feet.

### 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 Ha rbor, 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 Bart hrough St. Simon Sound, Brunswick River, and Turtle River to the upper end of the Allied Chemical Com pany’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 Ba ck 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 t he 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 Divers ion 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.

### 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 poi nt about 18 miles below the foot of Castle St. in Wilmington to a point about 2 miles above the Ra ilroad 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 waterf ront 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 m outh 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; t hence 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 branc h; 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.

### 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-rive r 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-Roches ter, 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 Outl et 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.

### 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 opera tions 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 em issions without a county match in ATADS, Stateaverage growth factors were calculated and app lied. 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 Administ ration Fuel Oil and Kerosene Sales 1999 & Fuel Oil and Kerosene Sales 2001 Table 23. Adjusted Sales for Transportati on 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 )

### 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.ei a.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.

### 42 Table III-8. Growth Factors for Commerc ial Marine Vessel Distillate and Residual Fuel Oil Use

Fuel Oil Sales (Thousand Gallons) Growth Factor (GF) FIPSST State

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 Kentucky 1 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 Tennessee 1 124 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 Administ ration, Fuel Oil and Kerosene Sales 1999 & Fuel Oil and Kerosene Sales 2002, Table 23. Adjusted Sales for Transportati on 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 ha ve 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

### 43 refueling for the LDGV, LDGT, and HDGV vehicle cat egories. 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% B oone, Campbell, Kenton Kentucky 1992 2 95% Jefferson Tennessee 1993 3 95% Davidson, Ruther ford, Sumner, Williamson, Wilson Virginia 1993 2 95% Counties: Arlingt on, 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 creat ed to simulate the onroad refueling emission factors. These input files were simplified becau se 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 ar e 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 MO BILE6 output tables to obtain VOC refueling emission factors for the 8 vehicle types incl uded 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

### 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 sour ce inventory and are not distinguished by vehicle type, all refueling emissions from onroad vehicl es 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 di splaces 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 gaso line 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.

### 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 onroa d 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 anot her 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 de viations 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 NH 3 

### ) 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.

### 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

### 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.

### 48 For the NONROAD model categories, SO 2 

### , PM 10, 

### PM 2.5 

### , and NH 3 

### decrease consistently across all States. SO 2 

### 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 NH 3 

### is due primarily to corrections made to compresses natural gas (CNG) engine NH 3 

### 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 NH 3 

### emissions are now reported as zero. CO and NO x 

### 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 PM 10 

### and PM 2.5 

### is due to the addition of commercial aircraft PM emissions. Commercial aircraft PM 10 

### and PM 2.5 

### 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/NO x 

### emission ratio to commercial aircraft NO x 

### emissions. States with a higher proportion of co mmercial aircraft show significant PM increases (e.g., FL, TN, VA). In addition, NO x 

### emissions decrease due to new State data for other nonroad from AL and VA.

Table VI-5. Summary of Draft 2002 Nonroa d 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

### 49 Table VI-6. Summary of Draft 2002 NONRO AD 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%

### 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_A NN 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_A NN 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_A NN 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, co mmercial marine and locomotive SCCs

### 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 downl oad 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 vehicl e 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

### 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

NH 3 

### 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 NH 3 

### 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 NH 3 

### 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 NH 3 

### emissions using the available NH 3 

### 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 Emi ssions Inventory,” prepared by Eastern Research Group, Morrisville, NC for the U.S. Environmental Protection Agency, Office of Air Quality Planning and Standards, Re search 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 Pr otection 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.

### 53 USACE, 2003: U.S. Waterways Data, Port and Wa terway 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.MACTEC, Inc. 

### Documentation of the Base G 2002 Base Year, 2009 and 2018, Emission Inventories for VISTAS

Prepared for:

Visibility Improvement State and Tr ibal Association of the Southeast (VISTAS)

Prepared by:

MACTEC, Inc.

______________________ ______________________

William R. Barnard Edward Sabo Sr. Principal Scientist Principal ScientistMACTEC, Inc. 2 

### Table of Contents

Introduction .................................................................................................................................................................1

1.0 2002 BASE YEAR INVENTORY DEVELOPMENT ....................................................................................... .4

1.1 P OINT S OURCES

............................................................................................................................... ............4
1.1.1 Development of 2002 Po int Source Inventory ..................................................................................... ..4

1.1.1.1 Data Sources ................................................................................................................... ............................. 5
1.1.1.2 Initial Data Evaluation ........................................................................................................ ......................... 6
1.1.1.3 PM Augmen tation................................................................................................................ ........................ 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 Upda tes and Co rrections...................................................................................... ........ 12
1.1.1.7 Summary of Base G 2002 Inve ntory............................................................................................... ........... 15
1.1.2 Development of Typical Year EGU in ventory...................................................................................... 18
1.2 A REA S OURCES

............................................................................................................................... ..........20
1.2.1 Development of a “typical ” year fire inventory ................................................................................. .22
1.2.2 Development of n on-fire inve ntory .............................................................................................. ........31
1.2.3 2002 Base G inventory updates .................................................................................................. .........34

1.2.3.1 Changes resulting from St ate review and comment ................................................................................ ... 34
1.2.4 Ammonia and paved road emissions............................................................................................... .....37
1.2.5 Global Changes Ma de for Base G................................................................................................. ......38
1.2.6 Quality Assura nce steps........................................................................................................ ...............39
1.3 M OBILE S OURCES

............................................................................................................................... ......40
1.3.1 Development of on-road mobile so urce input files and VMT estim ates...............................................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 NONRO AD model s ources........................................................................................... ... 45
1.3.2.2 Emissions from Commercial Marine Vesse ls, Locomotives, a nd Airpla nes.............................................. 61
Base G R evision s: ..............................................................................................................................................78

1.3.2.3 Emissions from NONROAD Model Sources in Illinois, Indian a, and Ohio .............................................. 85
1.3.3 Quality Assura nce steps........................................................................................................ ...............87

2.0 PROJECTION INVENTORY DEVELOPMENT........................................................................................... .88

2.1 P OINT S OURCES

............................................................................................................................... ..........88
2.1.1 EGU Emission Projec tions ....................................................................................................... ...........88

2.1.1.1 Chronology of the Development of EGU Projec tions............................................................................... . 88
2.1.1.2 VISTAS IPM runs for EGU sources................................................................................................ .......... 92
2.1.1.3 Post-Processing of IP M Parsed Files ............................................................................................ ............. 93
2.1.1.4 Eliminating Double C ounting 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 Inventor ies ............................................. 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 S hutdow ns............................................................................................................... ..................... 112
2.1.2.3 Control Programs applie d to non-E GU sour ces .................................................................................... ... 114MACTEC, Inc. 3 2.1.2.4 Quality Assurance steps........................................................................................................ ................... 117
2.1.2.5 Additional Base G Upda tes and Co rrections...................................................................................... ...... 117
2.1.2.6 Summary of Revised 2009/2018 non-E GU Point Source I nventorie s...................................................... 117
2.2 A REA S OURCES

............................................................................................................................... ........123
2.2.1 Stationary ar ea 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 be tween 2009/2018

.................................................................................................. ............. 127
2.2.2 Agricultural ar ea sources ...................................................................................................... ............128

2.2.2.1 Control assumptions for agri cultural area sources .............................................................................. ..... 128
2.2.2.2 Growth assumptions for agri cultural area sources ............................................................................... .... 128
2.2.3 Changes to Prescribed Fire for 2009/201 8 Base G...........................................................................133
2.2.4 Quality Assura nce steps........................................................................................................ .............138
2.3 M OBILE S OURCES

............................................................................................................................... ....138
2.3.1 Development of on-road m obile source i nput file s ............................................................................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 R evisions ............................................................................................................... ................150
2.3.4 Development of non-road emission estimat es....................................................................................1 50

2.3.4.1 NONROAD model source s.......................................................................................................... ............ 151
2.3.4.2 Non-NONROAD m odel s ources...................................................................................................... ........ 153
2.3.4.3 Emissions from NONROAD Model Sources in Illinois, Indian a, and Ohio ............................................ 188
2.3.4.4 Differences be tween 2009/2018

.................................................................................................. ............. 188
2.3.5 Quality Assura nce 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 EmissionsDocumentation of the Base G 2002 Base Year, 2009 and 2018, Emission Inventories for VISTAS

MACTEC, Inc. i 

### List of Tables

Table I-1. Inventory Version in Use by Y ear and Source Sector Through Base G - 2002 Table I-2. Inventory Version in Use by Y ear and Source Sector Through Base G - 2009 Table I-3. Inventory Version in Use by Y ear 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 SO 2 (tons/year) Table 1.1-5. Base G 2002 VISTAS Point Source Inventory for NO x (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 PM 10 -PRI (tons/year) Table 1.1-9. Base G 2002 VISTAS Point Source Inventory for PM 2.5 -PRI (tons/year) Table 1.1-10. Base G 2002 VISTAS Point Source Inventory for NH 3 (tons/year) Table 1.1-11. Comparison of SO 2 and NO x Emissions (tons/year) for EGUs from Base G Actual 2002 Inventory and Typical 2002 Inventory.
Table 1.2-1. Emissions from Fires in the VI STAS 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-NO x Ratios by Aircraft Type in Initial 2002 Base Year Inventory Table 1.3-8. Tucson, AZ PM-to-NO x 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 Em issions due to Aircraft PM Emission Rate Modifications
Table 1.3-12. CERR Aircraft NO x Records with No Corresponding PM Record.Documentation of the Base G 2002 Base Year, 2009 and 2018, Emission Inventories for VISTAS

MACTEC, Inc. ii 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 NO x > 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 NO x > 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 SO 2 Emission Comparison for 2002/2009/2018
Table 2.1-4. EGU Point Source NO x 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 PM 10 -PRI Emission Comparison for 2002/2009/2018
Table 2.1-8. EGU Point Source PM 2.5 -PRI Emission Comparison for 2002/2009/2018
Table 2.1-9. EGU Point Source NH 3 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 SO 2 Emission Comparison for 2002/2009/2018
Table 2.1-14. Non-EGU Point Source NO x 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 PM 10 -PRI Emission Comparison for 2002/2009/2018
Table 2.1-18. Non-EGU Point Source PM 2.5 -PRI Emission Comparison for 2002/2009/2018.
Table 2.1-19. Non-EGU Point Source NH 3 Emission Comparison for 2002/2009/2018 List of Tables (continued)Documentation of the Base G 2002 Base Year, 2009 and 2018, Emission Inventories for VISTAS

MACTEC, Inc. iii Table 2.2-1 2002 Base Year Emissions and Per centage Difference for Base F and Base G (based on actual emissions). Table 2.2-2 2019 Projection Year Emissions &P ercentage Difference for Base F & Base G Table 2.2-3 2018 Projection Year Emissions & Pe rcentage 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, Locomotiv e, 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. SO 2 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)Documentation of the Base G 2002 Base Year, 2009 and 2018, Emission Inventories for VISTAS

MACTEC, Inc. iv 

### List of Figures

Figure 1.2-1. CO Emissions from Agricultura l 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 Bu rning for the Original Base Year, 2002 Actual Base G and 2002 Typical Base G Inventories. Figure 1.2-4. CO Emissions from Wildfire Burn ing 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 NO x Emissions (Base F) Figure 2.3-5. Locomotive NO x Emissions (Base F) Figure 2.3-6. Total Aircraft, Locomotive, and CMV PM 10 Emissions (Base F) Figure 2.3-7. Locomotive PM 10 Emissions (Base F) Figure 2.3-8. Total Aircraft, Locomotive, and CMV PM 2.5 Emissions (Base F) Figure 2.3-9. Locomotive PM 2.5 Emissions (Base F) Figure 2.3-10. Total Aircraft, Locomotive, and CMV SO 2 Emissions (Base F) Figure 2.3-11. Locomotive SO 2 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 NO x Emissions (Base G) Figure 2.3-17. Locomotive NO x Emissions (Base G) Figure 2.3-18. Total Aircraft, Locomotive, and CMV PM 10 Emissions (Base G) Figure 2.3-19. Locomotive PM 10 Emissions (Base G) Figure 2.3-20. Total Aircraft, Locomotive, and CMV PM 2.5 Emissions (Base G) Figure 2.3-21. Locomotive PM 2.5 Emissions (Base G) Figure 2.3-22. Total Aircraft, Locomotive, and CMV SO 2 Emissions (Base G) Figure 2.3-23. Locomotive SO 2 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)Documentation of the Base G 2002 Base Year, 2009 and 2018, Emission Inventories for VISTAS

MACTEC, Inc. v 

### 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 technologyDocumentation of the Base G 2002 Base Year, 2009 and 2018, Emission Inventories for VISTAS

MACTEC, Inc. vi Acronyms and Abbreviations (continued) MACTEC MACTEC Engineering and Consulting, Inc.
MOBILE 6 MOBILE emissions estimation model version 6 MRPO Midwest Regional Planning Organization

NH 3 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)
NO x 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
PM 10 -FIL Particulate matter less than or equal to 10 microns in diameter that can be captured on a filter
PM 10 -PRI Particulate matter less than or equal to 10 microns in diameter that includes both the filterable and condensable components of particulate matter
PM 2.5 -FIL Particulate matter less than or equal to 2.5 microns in diameter that can be captured on a filter

PM 2.5 -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 CodeDocumentation of the Base G 2002 Base Year, 2009 and 2018, Emission Inventories for VISTAS

MACTEC, Inc. vii 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
SO 2 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 PartnershipDocumentation of the Base G 2002 Base Year, 2009 and 2018, Emission Inventories for VISTAS

MACTEC, Inc. 1 

### 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 hist ory 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 th e 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 jo intly 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 fi nal along with updated information on VMT, fuel programs, and other inputs to the MOBI LE6 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 charact eristics 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 us ing 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 th e 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 a nd 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 th e States ranged from 1999 to 2001 and was all projected to 2002 using appropriate gr owth surrogates from Economic GrowthDocumentation of the Base G 2002 Base Year, 2009 and 2018, Emission Inventories for VISTAS

MACTEC, Inc. 2 Analysis System (EGAS) version 4. Toxic Rel ease Inventory (TRI) data were used to augment the inventory for NH 3 . Continuous Emissions Monitor (CEM) data from the
U.S. EPA’s Clean Air Markets Division wa s 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) ammoni a 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/documen ts/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 Apr il of 2004 and the final one in October of 2004. The August 2003 and April 2004 inventor ies 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 Oct ober 2004. That 2002 base year inventory was solely prepared by MACTEC. The Oc tober 2004 inventory incorporated 2002 Consolidated Emissions Reporting Rule (CERR) data into the inventory along with some updated data from the VISTAS States. This i nventory 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 “p reliminary” 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 versi on of the 2002 base year inventory, States were asked to review and provide comment s on that inventory to MACTEC for update