hi

) ,'.-F (~"--.

:['~- I\j ED

2010 ~.PR -7 PM 12: 46

TEXAS COMMISSION ON ENVIRONMENTAL QuAlflIn'filVISION

Bryan W. Shaw, Ph.D., Chairman

Buddy Garcia, Commissioner

Carlos Rubinstein, Commissioner

Mark R. Vickery, P.G., Executive Director

Protecting Texas by Reducing andPreventing Pollution

April 1,2010

Docket No. 2009-0801-SIP

Project No. 2009-018-SIP-NR

y",·~:·

-,...r;·, .

::: ~-~!'

Alfredo "AI" Armendariz, Ph.D., Regional Administrator

U.S. Environmental Protection Agency - Region 6

1445 Ross Avenue

Dallas, Texas 75202-2733

w -

S~2 c:' ~~ :"J~ r-~·

Dear Dr. Armendariz:

On March 10,2010, the Texas Commission on Environinental Quality (Commission) adopted a revision to the

state implementation plan (SIP) for demonstration of reasonable further progress for the Houston-GalvestonBrazoria (HGB) 1997 eight-hour ozone nonattainment area.

The Commission adopted the HGB Reasonable Further Progress (RFP) SIP revision, demonstrating that the

RFP emissions reduction requirement will be met for the six-year period (2003-2008) beginning Janumy 1 ofthe

year following the year used for the baseline emissions inventory (2002) and all remaining three-year periods

after the first six-year period (2009-2011, 2012-2014, and 2015-2017) out to the area's attainment date (2019).

This SIP revision also sets the nitrogen oxides (NOx) and volatile organic compounds (VOC) motor vehicle

emissions budgets (MVEB) for transportation conformity purposes for the milestone years 2008, 2011, 2014,

2017, and 2018.

Enclosed are the proposed revision to the SIP, a public hearing certification, a complete record of the public

hearings, and the accompanying order. I look forward to your expeditious approval ofthis SIP revision.

Sincerely,

~~;dAtv- Bryan W. Shaw, Ph.D.

Chairman

BWS/ts

Enclosures

cc: The Honorable Rick Perry, Governor ofTexas

Mr. Toby Baker, Office ofBudget, Planning and Policy, Office ofthe Governor

.Mr. MarkR. Vickery, P.G., Executive Director, Texas Commission on Environmental Quality

P.O. Box 13087 Austin, Texas 78711-3087 512-239-1000 Internet address:www.tceq.state.tx.us

'.' " 'd
Texas Commission on Environmental Quality

Public Hearings

October 28 and 29. 2009

Concerning Revisions to the

HGB Eight-Hour Ozone Reasonable Further Progress SIP

Project No. 2009-018-SIP-NR
REVISION TO THE STATE IMPLEMENTATION PLAN FOR 

THE CONTROL OF OZONE AIR POLLUTION 

HOUSTON-GALVESTON-BRAZORIA 1997 EIGHT-HOUR 

OZONE STANDARD NONATTAINMENT AREA 

TCEQ 

TEXAS COMMISSION ON ENVIRONMENTAL QUALITY 

P.O. BOX 13087 

AUSTIN, TEXAS 78711-3087 

Houston-Galveston-Brazoria Reasonable Further Progress State 

Implementation Plan Revision for the1997 Eight-Hour Ozone Standard 

PROJECT NO. 2009-018-SIP-NR 

Adopted 

March 10, 2010 
HGB Reasonable Further Progress SIP Revision for the 1997 Eight-Hour Ozone Standard 

ix

LIST OF APPENDICES 

Appendix Appendix Name

Appendix 1 HGB Reasonable Further Progress Demonstration Calculations Spreadsheet 

Appendix 2 Development of Reasonable Further Progress Point Source Inventories 

Appendix 3 Development of Reasonable Further Progress Area Source Inventories 

Appendix 4 Development of Reasonable Further Progress Non-Road Mobile Source 

Emissions Inventories for Source Categories in the EPA’s NONROAD 2005 

Model 

Appendix 5 Development of Reasonable Further Progress Non-Road Mobile Source 

Emissions Inventories for Airports 

Appendix 6 Development of Reasonable Further Progress Non-Road Mobile Source 

Emissions Inventories for Locomotives 

Appendix 7 Development of Reasonable Further Progress Non-Road Mobile Source 

Emissions Inventories for Commercial Marine Vessels 

Appendix 8 Development of Reasonable Further Progress Non-Road Mobile Source 

Emissions Inventories for Drilling Rigs 

Appendix 9 Houston-Galveston-Brazoria 1997 Eight-Hour Ozone Nonattainment Area 

Reasonable Further Progress On-Road Mobile Source Emissions Inventories 
APPENDIX 6 

DEVELOPMENT OF REASONABLE FURTHER PROGRESS NON-ROAD MOBILE 

SOURCE EMISSIONS INVENTORIES FOR LOCOMOTIVES 
 on 

ht 

e Brazoria, Chambers, Fort Bend, Galveston, Harris, Liberty, 

ontgomery, and Waller. 

Locomotive Emissions Inventory Methodology 

This technical support document describes the methodologies used by the Texas Commission

Environmental Quality (TCEQ) to develop RFP emissions inventories for locomotives. The

inventories developed include county level, ozone season day, controlled and uncontrolled 

emissions estimates for milestone years 2002, 2008, 2011, 2014, 2017, and 2018 for the eig

counties in the Houston-Galveston-Brazoria (HGB) ozone nonattainment area. The eight 

counties in the HGB area ar

M

The 2002 base year controlled estimated emission inventories were obtained from the 2005 

TexAER database. Emission inventories that are contained in TexAER were determined from 

data extrapolated from the final report for the Tier-Specific Locomotive Engine Update, August 

31, 2007, conducted for the TCEQ by Eastern Research Group, Inc. The study was developed to 

ascertain locomotive emissions activity in the Dallas-Fort Worth area, the HGB area, and

of Texas. The 2005 inventory was projected back to 2002 and forward to the other RFP 

milestone years using locomotive activity growth factors. The growth rate was develope

reports from railway companies 

 the state 

d using 

and regulatory controls data. The report is available as 

ttachment A to this appendix. 

nd II 

 engines were established. The entire document is available as Attachment B to this 

appendix. 

A

Uncontrolled estimated emissions were developed and extrapolated using emissions trends data 

from the Emission Factors for Locomotives document. In this 1997 EPA document, Tier I a

emission standards for manufactured and remanufactured diesel-powered locomotives and 

locomotive
Attachment A 

Tier-Specific Locomotive Engine Update 
ERG No. 0227.00.002.001 

TIER-SPECIFIC LOCOMOTIVE ENGINE UPDATE 

FINAL REPORT 

TCEQ Contract 582-7-84003 

Work Order No. 04 

Prepared for: 

Ms. Karla Hardison 

Texas Commission on Environmental Quality 

Air Quality Division 

Building E, Room 338 

P.O. Box 13087 

Austin, Texas 78711-3087 

Prepared by: 

Eastern Research Group, Inc. 

1600 Perimeter Park Drive 

Suite 200 

Morrisville, North Carolina 27560 

August 31, 2007
i 

TABLE OF CONTENTS 

Section Page

LIST OF TABLES......................................................................................................................... iii 

LIST OF FIGURES ....................................................................................................................... iii 

LIST OF ACRONYMS ................................................................................................................. iv 

EXECUTIVE SUMMARY .............................................................................................................v 

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

1.1 Objectives ............................................................................................................ 1-2 

1.2 Scope.................................................................................................................... 1-2 

1.3 Organization of this Report.................................................................................. 1-3 

2. METHODOLOGY .......................................................................................................... 2-1 

2.1 How the Model Calculates Emissions................................................................. 2-1 

2.1.1 Activity Data............................................................................................ 2-1 

2.1.2 Emission Factors...................................................................................... 2-3 

2.1.3 Annual Emissions.................................................................................... 2-6 

2.1.4 Average Ozone Season Dry Emissions.................................................... 2-7 

2.2 Structure of the Model ......................................................................................... 2-8 

2.2.1 Structure of Line Haul Data..................................................................... 2-8 

2.2.2 Structure of Switch Data........................................................................ 2-11 

2.3 Sample Calculations........................................................................................... 2-11 

2.3.1 Line Haul Example: What are the NOx Emissions for Segment X?...... 2-13 

2.3.2 Switch Example: What are the NOx Emissions for Yard X?................. 2-14 

2.4 Texas Railroad Emissions Inventory Model...................................................... 2-16 

2.4.1 Model Background................................................................................. 2-17 

2.4.2 User Options .......................................................................................... 2-17 

2.4.3 Navigating the Model ............................................................................ 2-17 

3. DATA COLLECTION .................................................................................................... 3-1 
ii 

TABLE OF CONTENTS (Cont.) 

Section Page

4. RESULTS AND ANALYSIS.......................................................................................... 4-1 

4.1 State Locomotive Emission Summary................................................................. 4-1 

4.2 Dallas-Fort Worth and Houston-Galveston-Brazoria Emission Summary.......... 4-2 

4.3 Line Haul Emission Summary............................................................................. 4-2 

4.4 Yard Locomotives Emission Summary ............................................................... 4-4 

4.5 Emission Projections............................................................................................ 4-5 

4.6 TERP Related Emission Reductions.................................................................... 4-7 

5. COMPARISON TO EXTERNAL STUDIES ................................................................. 5-1 

6. FUTURE ENHANCEMENTS ........................................................................................ 6-1 

7. CONCLUSIONS AND RECOMMENDATIONS .......................................................... 7-1 

8. REFERENCES............................................................... ... ...................8-1 

Appendix A – Emission Estimates for Non-Dallas/Ft. Worth and Houston-Galveston-Brazoria Counties 

Appendix B – User’s Manual for the Texas Railroad Emission Inventory Model (TREIM) 
iii 

LIST OF TABLES 

Table Page

E-1 Total 2006 Locomotive Contributions for the State of Texas ........................................... vi 

E-2 Projected Total Locomotive NOx Emission...................................................................... vii 

E-3 Projected TERP NOx Emission Reductions..................................................................... viii 

2-1 Summary of Tier Engine Counts for Union Pacific and Burlington Northern 

Santa Fe for the Dallas/Fort Worth and Houston/Galveston Areas................................. 2-2 

2-2 Specific Train Type Activities......................................................................................... 2-3 

2-3 Locomotive Emission Factors (grams/gal) by Tier ......................................................... 2-4 

2-4 U.S. EPA Fleet Average Emission Factor for all Locomotives (grams/gal) ................... 2-5 

2-5 Locomotive HAP Speciation Profiles.............................................................................. 2-5 

2-6 Yard Duty Cycle Data (U.S. EPA 1998) ......................................................................... 2-7 

2-7 General and Specific Line Haul Data Forms Contain the Same Fields......................... 2-18 

4-1 Total 2006 Total (Line and Yard) Locomotive Contributions......................................... 4-1 

4-2 2006 Total Locomotive Emissions for the State of Texas (tons per year)....................... 4-2 

4-3 2006 Line Haul Emission Estimates for Dallas-Fort Worth and HoustonGalveston-Brazoria Counties (tons per year)................................................................... 4-2 

4-4 2006 Switch Engine Emission Estimates for Dallas-Fort Worth and HoustonGalveston-Brazoria Counties (tons per year)................................................................... 4-4 

4-5 Projected Total Locomotive Contributions for the State of Texas.................................. 4-5 

4-6 Projected HAP Emissions................................................................................................ 4-6 

4-7 Projected TERP NOx Emission Reductions..................................................................... 4-8 

5-1 Locomotive Emissions in the Houston Nonattainment Region by 

Scenario (tons per day) .................................................................................................... 5-2 

5-2 Locomotive Emissions in the Dallas Nonattainment Region by Scenario 

(tons per day) ................................................................................................................... 5-2 

5-3 CENRAP Texas Locomotive Study (tons per year) ........................................................ 5-3 

LIST OF FIGURES 

Figures Page

2-1 Line Haul Components of TREIM Railroad.................................................................... 2-9 

2-2 Switch Locomotive Components of TREIM................................................................. 2-12 

2-3 Model Calculates Annual Emissions for all Texas Counties Based on Pre-population 

Activity Data.................................................................................................................. 2-16 

2-4 Each Record Represents Data for a Railway Segment .................................................. 2-18 

2-5 Line Haul Fleet Composition Form............................................................................... 2-20 

2-6 Example GIS Screen for General Line Haul Data ......................................................... 2-21 

4-1 Texas Railway Map with Additional High Traffic Counties Highlighted....................... 4-4 
iv 

Unit Conversions 

· 453.59 grams/pound 

· 1 ton/2,000 pounds 

· 1 gram/0.0022 pounds 

· 1 mile/1.6093 kilometers 

· 1 horsepower/0.7460 kilowatts 

List of Acronyms 

ATSF Atchison Topeka and Sante Fe (ATSF) 

EMD Elective Motive Diesel 

BNSF Burlington Northern Santa Fe 

BTS Bureau of Transportation Statistics 

CO carbon monoxide 

DFW Dallas -Fort Worth area 

ERG Eastern Research Group 

GAL gallon 

GIS geographic information system 

GTM gross ton mileage 

HAP Hazardous Air Pollutants 

HGB Houston-Galveston-Brazoria area 

KCS Kansas City Southern 

NAAQS National ambient air quality standards 

NOx oxides of nitrogen 

PEI Periodic Emissions Inventories 

PM particulate matter 

SCC Source Classification Codes 

SIP State Implementation Plan 

TCEQ Texas Commission for Environmental Quality 

TERP Texas Emission Reduction Plan 

TPD tons per day 

TPY tons per year 

TREIM Texas Railroad Emission Inventory Model 

UP Union Pacific 

VOC volatile organic compounds 
v 

EXECUTIVE SUMMARY 

The Texas Commission on Environmental Quality (TCEQ) must include estimates of air 

emissions from railway locomotives in its State Implementation Plans (SIPs). The accuracy of 

these emission estimates is important in determining whether the state is meeting the national 

ambient air quality standards (NAAQS) for ozone, oxides of nitrogen (NOx), carbon monoxide 

(CO), volatile organic compounds (VOC), and particulate matter (PM). Accurate emission 

estimates are especially important in the Dallas-Fort Worth (DFW) and Houston-GalvestonBrazoria areas (HGB), which are ozone nonattainment areas. 

To accurately estimate emissions, Eastern Research Group, Inc. (ERG) worked with 

TCEQ to develop a computer model that applies the latest U.S. EPA emission factors to detailed 

local activity and control data. Details concerning how the emissions are estimated are provided 

in this report. The intended users of the model are TCEQ staff evaluating emission inventories 

and/or potential control scenarios. Note the current version of the model is not intended for 

public release at this time. 

The earlier U.S. EPA methodology for estimating emissions from railway locomotives 

relied on U.S. EPA emission factors (i.e., pounds of pollutant per gallon of fuel) and railway 

system-wide fuel usage. To improve locomotive emission estimates for Texas, ERG gathered 

specific fuel usage data derived from railway segment level gross ton mileage activity (line haul 

locomotives) and hours of operation (yard locomotives) provided directly by the Class I railroad 

companies operating in Texas. The latest version of the model updates the locomotive Tier level 

profiles, reflecting the emission controls required by Federal regulations and Texas emission 

reduction programs. The latest version of the model also projects emissions to 2020 based on 

growth factors developed in a recent TCEQ project, and engine fleet data provided by the 

railway companies. 

Readers should note that locomotives are diverse emission sources that serve a wide 

geographic area. Because the current model reflects average fleet profiles and operating 

conditions, actual emissions at any given location or time may be significantly higher or lower 

than emissions estimated by the model. 

The activity and control data used in the model were provided by the two main railroads 

operating in Texas (UP 2007 and BNSF 2007). Union Pacific (UP) and Burlington Northern 

Santa Fe (BNSF) consider the data they provided for this project to be CONFIDENTIAL 

BUSINESS INFORMATION. Accordingly, this report should be handled as confidential 

business information, although the information provided in this Executive Summary does 

not identify specific railway companies or their facilities and may be released publicly. 

One of the great challenges for this project concerns the data provided by the railway 

companies. Much of the data were inconsistent with the U.S. Department of Transportation’s 

Bureau of Transportation Statistics (BTS) Geographic Information System (GIS) shape files 

(DOT/BTS 2002). Furthermore, the railroad company data were often inconsistent with the data 

they provided for the previous version of the model. ERG worked with the railway companies to 

correct many of the inconsistencies, but not all issues could be addressed, resulting in a 
vi 

considerable amount of variance between the emission estimates associated with the current 

railway data and previous submittals. 

The railway companies provided limited information regarding projected activity levels 

and future engine Tier level distributions. Accordingly, future emission estimates in the model 

are based on projection data developed in a separate project for the TCEQ (TCEQ August 31, 

2007). The projected emission estimates should be considered reasonable. but may underestimate 

anticipated activity levels, based on published projected national growth rates. Still these 

projection rates are at the railway segment level, and represent the best detailed data available at 

this time. 

ERG incorporated the submitted railway data into the Texas Railroad Emission Inventory 

Model (TREIM), which estimates and spatially allocates locomotive emissions for all Texas 

counties. The model applies as much railroad-specific data as possible to improve the accuracy 

of emissions estimates. The model also uses a methodology that allows emissions to be 

calculated uniformly across the state. 

The model is fully populated such that users need not develop additional input files to run 

the model. The user can modify the existing input files or assumptions to evaluate different 

control strategies. The model can output emission estimates by individual railway segments and 

yard, or it can aggregate emissions to the county level. 

In Texas in 2006, locomotives are estimated to emit approximately 8,843 tons per year 

(29.1 tons per day) of CO, 3,612 tons per year (11.9 tons per day) of VOC, 64,623 tons per year 

(212.5 tons per day) of NOx and 2,192 tons per year (7.2 tons per day) of PM. These values are 

slightly higher than the 2003 data summarized in the previous version of the TREIM—the 

increase is reasonable given the increased traffic from West Coast ports and Mexico between 

2003 and 2006. 

Collectively, the DFW and HGB counties account for approximately 33 percent of total 

Texas locomotive emissions, as noted in Table E-1. This value is a rough approximation because 

only the DFW and HGB areas were updated in this version—non-DFW and HGB area estimates 

are based on unadjusted 2003 activity levels. 

In 2006, the HGB area was responsible for approximately 50 percent more locomotive 

emissions than the DFW area, due to freight movements associated with marine vessel activities. 

Table E-1. Total 2006 Locomotive Contributions for the State of Texas 

Tons per Year 

Area CO VOC NOx PM 

Dallas-Fort Worth 1,118 528 9,604 268 

Houston-GalvestonBrazoria 1,701 818 14,678 407 

All Other Counties* 6,024 2,265 40,340 1,517 

TOTAL 8,843 3,612 64,623 2,192 
vii 

Table E-1. Total 2006 Locomotive Contributions for the State of Texas (Cont.) 

Tons per Average Ozone Day 

Area CO VOC NOx PM 

Dallas-Fort Worth 3.68 1.74 31.57 0.88 

Houston-GalvestonBrazoria 5.59 2.69 48.26 1.34 

All Other Counties* 19.80 7.45 132.62 4.99 

TOTAL 29.07 11.87 212.46 7.21 

Percentage Distribution 

Dallas-Fort Worth 12.64 14.62 14.86 12.21 

Houston-GalvestonBrazoria 19.23 22.66 22.71 18.58 

All Other Counties* 68.13 62.72 62.42 69.21 

Total 100 100 100 100 

* Other counties were not updated in the latest version so these values represent 2003 emission levels. Note 

that these emissions are for line haul locomotives, only, while the DRW and HGB emissions include both line 

haul and yard locomotives. 

Table E-2 shows the projected NOx emission levels based on the limited data provided by 

the railway companies, available growth factors, knowledge of the locomotive emission standard 

schedule, and data from the Texas Emissions Reduction Plan (TERP) program (TCEQ August 

31, 2007). The relatively lower emissions for 2005 are primarily due to inconsistent data 

provided by the railway companies in previous submittals. 

The projections suggest that current locomotive standards and the TERP program offset 

emissions associated with railway growth up to 2020. However, further work is needed to refine 

future year projections to account for infrastructure constraints that could limit traffic flow in the 

DFW and HGB areas. Furthermore, the projections do not take into consideration infrastructure 

improvements, particularly for intermodal traffic, that are being made in adjacent states that may 

divert some of the traffic that would otherwise pass through the DFW area. 

Table E-2. Projected Total Locomotive Emission 

Tons per year 

Area Pollutant 2000 2005 2010 2015 2020 

CO 1,143 792 1,142 1,179 1,290

VOC 541 348 465 465 510

NOx 8,029 6,625 7,528 7,545 8,222 Dallas-Fort Worth 

PM 279 193 229 230 248

CO 1,452 1,222 1,664 1,725 1,810

VOC 685 558 683 690 724

NOx 10,433 10,301 11,070 11,230 11,770 Houston-Galveston-Brazoria

PM 356 298 336 343 358
viii 

Table E-2. Projected Total Locomotive Emission (Cont.) 

Tons per year 

Area Pollutant 2000 2005 2010 2015 2020 

CO 3.76 2.60 3.75 3.88 4.24

VOC 1.78 1.14 1.53 1.53 1.68

NOx 26.40 21.78 24.81 24.81 27.03 Dallas-Fort Worth 

PM 0.92 0.63 0.75 0.76 0.82

CO 4.77 4.02 5.47 5.67 5.95

VOC 2.25 1.83 2.25 2.27 2.38

NOx 34.30 33.87 36.39 36.92 38.70 Houston-Galveston-Brazoria

PM 1.17 0.98 1.10 1.13 1.18

For this project the railway companies provided data on current and near term projected 

use of low emitting locomotive engines such as the “greengoat” hybrids and genset locomotives, 

both of which comply with stringent nonroad diesel engines standards. Much of this new 

equipment is funded by the TERP program. Table E-3 summarizes data from the model and data 

provided by TCEQ that quantify the anticipated NOx emission reductions from locomotives 

purchased through the TERP program. 

Table E-3. Projected TERP NOx Emission Reductions 

2006 2007 2008 2009 2010 

TERP Emission Reductions (tons per year) 

Dallas-Fort Worth 250 1,281 1,317 1,642 1,642

Houston-Galveston-Brazoria 176 1,457 1,491 1,979 1,979

Total 2,432 4,745 4,816 5,631 5,632

Estimated Emissions without TERP (tons per year) 

Dallas-Fort Worth 9,854 9,823 9,748 9,812 9,244

Houston-Galveston-Brazoria 14,854 13,704 13,606 13,744 13,049

TERP Emission Reductions as a Percentage of Total Emissions 

Dallas-Fort Worth 2.5 13.0 13.5 16.7 17.8

Houston-Galveston-Brazoria 1.2 10.6 11.0 14.4 15.2

TERP emission reductions have the biggest impact on emissions in 2007 when a large 

number of new TERP sponsored engines were put into service. The impact of these TERP 

engines continues to be significant into the future as higher Tier locomotives are naturally added 

to the railroad fleet through normal attrition, reducing the overall railroad emissions. 

The modification made to this version of the model focused on the nonattainment 

counties in the DFW and HGB areas. Tier profiles and projection data were not developed for 

the remaining counties in Texas. Future updates to the model should include the non-DFW and 

non-HGB counties. It should also be noted that in this update, specific train type data were not 

provided by the railway companies; therefore, these data elements were retained from the earlier 

version of the inventory and only represent 2003 activity. 
ix 

Initially the TREIM was developed as a repository for railway data and to estimate 

emissions. The model includes additional functions that allow for a limited number of data 

queries. To make data queries easier and further expand the types of data queries that the model 

can support, it is recommended that the database structure be revaluated and streamlined. 

As note earlier, special studies that focus on improving the projections data will be 

needed to provide more accurate estimates of future year emissions. This will be challenging 

because railway companies continue to treat such data as confidential information. 
1-1 

1.0 INTRODUCTION 

The TCEQ must include estimates of air emissions from railway locomotives in its SIPs. 

The accuracy of these emission estimates is important in determining whether the state is 

meeting NAAQS for ozone, NOx, CO, VOC, and PM. Accurate emission estimates are especially 

important in the Dallas-Fort Worth (DFW) and Houston-Galveston-Brazoria (HGB) areas, which 

are ozone nonattainment areas. The U.S. EPA methodology for estimating emissions from 

railway locomotives relies on U.S. EPA emission factors that are fuel based (i.e., pounds of fuel 

per gallon of fuel). This fuel based approach does not accurately account for the spatial 

distribution of fuel combusted outside of the counties where the fuel was sold. Use of GIS in the 

current railway model developed for the TCEQ allows for a more accurate estimate regarding the 

location where fuel is actually used. 

The latest version of the Texas Railroad Emission Inventory Model (TREIM) updates the 

locomotive Tier profiles, reflecting emission controls required by Federal regulations and Texas 

emission reduction programs. The Tier level of a locomotive indicates the level of control 

incorporated into the engine design to meet federal standards relative to the year that the 

locomotive was constructed. Non-Tier locomotives are those that were manufacture prior to 

1973, Tier 0 were manufactured between 1973 and 2001, Tier 1 were manufacture between 2001 

and 2004, and Tier 2 were manufactured after 2004. 

To incorporate the spatial component, ERG gathered specific fuel usage data derived 

from segment level gross ton mileage activity (for line haul locomotives) and hours of operation 

(for yard locomotives). The data were provided by the two main railway companies operating in 

Texas. ERG incorporated the data into the TREIM, which estimates and spatially allocates 

locomotive emissions for all Texas counties. The model applies as much railroad-specific data as 

possible to improve the accuracy of emissions estimates. The model also uses a methodology 

that allows emissions to be calculated uniformly across the state. 

The model is divided into the following three major components: general line haul data; 

specific line haul data; and yard data. The model was developed to easily incorporate additional 

data provided by the railway companies for line haul and yard engine operations. The model can 

also be easily modified to account for past and future changes in activity and the introduction of 

new fuels and control technologies. The model was also designed to use either general or specific 

data (based on train type) provided by the railway companies. 

Because railway companies compile activity data differently to meet their different 

business needs, ERG developed the data structure for the railroad model to be sufficiently 

flexible to include both aggregated and disaggregated data. To accommodate this need for 

flexibility, the model was developed as an Access database. By using a relational database 

system such as Access, data tables can easily be updated to incorporate new activity data 

elements and emission factors. Details concerning the structure and operation of the model are 

discussed in Section 2.0 of this report. 

ERG developed a User’s Manual to support the use of the updated model. The User’s 

Manual explains the steps necessary to run the model to estimate emissions (see Appendix B). 
1-2 

1.1 Objectives 

To meet the objective of improving locomotive emission estimates for Texas, and 

specifically for the DFW and HGB areas, ERG completed the following tasks: 

· Developed a model to estimate and spatially allocate locomotive emissions;

· Compiled a detailed activity dataset from the major railway companies;

· Gathered detailed data in GIS format to develop and spatially allocate accurate 

locomotives emission estimates in Texas; 

· Updated the model to include more accurate Tier engine profiles for line haul and 

yard operations; 

· Incorporated future year projection profiles into the model; and 

· Developed a User’s Manual to support the use of the updated TREIM. 

The TREIM has been populated with data for all Texas counties with active rail lines. 

However, only the data for the DFW and HGB areas data were provided directly by the railway 

companies. Data for all other counties were based on Bureau of Transportation Statistics (BTS) 

GIS shape files. 

1.2 Scope 

The model covers the following parameters: 

· Pollutants: CO, VOC, NOx, PM

· Source types: line haul locomotives and yard locomotives

· Base year: 2006

· Temporal resolution: annual 1999-2020

· Spatial resolution: state, county, rail line segment, and yard. 

This project focused on improving emission estimates of CO, VOC, NOx, and PM from 

railway locomotives by updating the railway activity data, improving the Tier profiles and 

modifying the model structure to allow for future year emission projections. Railway 

locomotives are divided into two activity types: line haul and yard. A line haul (or road) 

locomotive hauls freight over railway lines from one destination to another, while a yard (or 

switch) locomotive moves freight within a rail yard. The base year for the model is 2006, but 

users can project emissions for any year from 1999 to 2020. 

The model can provide emission estimates at the following spatial resolutions: state, 

county, rail line segment, and yard. Although the model includes estimates for all counties in 

Texas, the best quality data are associated with the two metropolitan areas of DFW and HGB 

areas that have the most railway traffic. The model is populated with specific railway activity 

data elements for the four core counties of the DFW area and eight HGB counties. It is 

recognized that the DFW nonattainment area includes counties in addition to those discussed in 

this report. The counties discussed here are limited to only those counties for which the Class I 

railway companies provided activity data. 
1-3 

As noted above, data for other counties are also incorporated into the model (See 

Appendix A) to provide a comprehensive assessment of locomotive emissions for the state of 

Texas. 

1.3 Organization of this Report 

This report is organized as follows: 

· Section 2.0 describes how the model calculates emissions; 

· Section 3.0 describes which data were collected, the sources of data, and how those 

data were collected; 

· Section 4.0 presents results and analysis of the estimated railway locomotive 

emissions; 

· Section 5.0 compares the model’s emission estimates with other railway emission 

estimates to ensure that the model’s results are reasonable; 

· Section 6.0 includes a discussion of future improvements that could be made to the 

model and/or input data; 

· Section 7.0 describes the conclusions and recommendations that resulted from this 

project; 

· Section 8.0 lists all references used in this study; 

· Appendix A includes emission estimates for non-DFW and HGB counties; and 

· Appendix B contains the TREIM User’s Manual. 
2-1 

2.0 METHOLOGY 

2.1 How the Model Calculates Emissions 

The model multiplies activity data by an emission factor to yield annual emission 

estimates of VOC, CO, NOx, and PM, as shown in Equation 1 below. Activity data were 

provided by the railway companies for the DFW and HGB areas. Activity data for the remaining 

Texas counties are from the BTS. Emission factors were obtained from “U.S. EPA Emission 

Factors for Locomotives” (U.S. EPA 1997). 

Equation 1. Activity data × Emission factor = Annual emissions 

Example Calculation:

Given: In 2006, 460,000 gallons of fuel were combusted along a specific railway 

segment; the recommended EPA emission factor is 10.1 grams of VOC per gallon of fuel. To 

estimate the VOC emissions the following calculation was used: 

Annual emissions = Activity data × Emission factor 

Annual emissions = 460,000 gal of fuel × 10.1 g of VOC / gal × 1.1026 E-6 tons / g 

Annual emissions = 5.12 tons of VOC 

2.1.1 Activity Data 

The model is populated with activity data provided by the railroad companies. Users can 

modify some of the data such as fuel consumption rates, hours of operation, or engine Tier 

profiles to evaluate specific scenarios such as increased traffic or introduction of engines with 

lower emissions. Section 2.4.2 and the Users Manual provide details concerning which data 

fields can be changes to evaluate emission scenarios. The updated engine tier data were provided 

by the railroad companies. These data were also compared to the TERP engine data to ensure 

that they were consistent with data in the TERP projects database. Table 2-1 provides a summary 

of tier engine counts provided for both Union Pacific and Burlington Northern Santa Fe for the 

Dallas/Fort Worth and Houston / Galveston areas for the years 2006 to 2009. 

The model divides locomotives into two types: line haul and switch yard. A line haul 

locomotive hauls freight over railroad lines from one destination to another, while a yard (or 

switch) locomotive moves freight within a rail yard. Line haul data is further broken into 

General data and Specific data. Activity data in the General data set are based on a generic train 

type that operates on a specific segment, while the Specific line haul data are based on a specific

train type that operates on a specific segment, as reported by the railways. The specific train 

types presented by BNSF and incorporated into the model are listed in Table 2-2. Note that 

specific line haul data were not provided by all railway companies or for all inventory years, but 

the model was designed to accommodate detailed data submittals in the future. Specific line haul 

data were provided for only the HGB and DFW areas. 
2-2 

Table 2-1. Summary of Tier Engine Counts for Union Pacific and Burlington Northern Santa Fe for the Dallas/Fort Worth 

and Houston/Galveston Areas 

2006 2007 2008 2009 

 

Nonregulated 

/ Tier 0 

Tier 

1 

Tier 

2 Hybrid 

Nonregulated 

/ Tier O 

Tier 

1 

Tier 

2 Hybrid 

Nonregulated 

/ Tier O 

Tier 

1 

Tier 

2 Hybrid 

Nonregulated 

/ Tier O 

Tier 

1 

Tier 

2 Hybrid 

Switch Engine Estimate 

Union Pacific 

Dallas/ 

Fort Worth 79 0 1 5 30 0 48 10 29 0 50 10 29 0 51 10 

Houston/ 

Galveston 126 0 0 5 76 0 52 5 76 0 52 5 76 0 53 5 

Burlington Northern Santa Fe 

Dallas/ 

Fort Worth 35 0 4 0 35 0 0 4 35 0 0 4 15 0 20 4 

Houston/ 

Galveston 50 0 0 0 23 0 10 7 23 0 10 7 0 0 40 7 

National Line Haul Engine Estimate 

Union 

Pacific 4,668 1,186 521 0 4,614 1,186 821 0 4,544 1,186 1,021 0 4,532 1,186 1,221 0 

Burlington 

Northern 

Santa Fe 2,168 625 682 0 2,065 625 882 0 1,955 625 1,082 0 1,845 625 1,302 0 
2-3 

Table 2-2. Specific Train Type Activities 

Locomotive

Code Locomotive Activity Description 

A Amtrak 

B Baretable Intermodal 

C Loaded Unit Coal 

D Lite Engine 

E Empty Unit Coal 

F Foreign Railroad Trains NSF 

G Loaded Unit Grain 

H Manifest (High Priority) 

I Deadhead Crew Moves 

J Not Used 

K Helper Service 

L Local Service 

M Manifest (Normal Priority) 

N Hours of Service Relief Crews 

O Officer Specials 

P Intermodal Premium 

Q Intermodal Guarantee 

R Road Switcher Service 

S Intermodal D-Stack 

T Transfer Service (Interchange Received/Delivered) 

U Unit (Not Grain or Coal) 

V Vehicle (Auto) 

W Work Train 

X Empty Unit Grain 

Y Yard Engines 

Z Priority Ups Intermodal 

The model was also modified to account for activity from 1999 to 2020. Data used to 

back cast and forecast activity were derived from growth factors included in the projections 

study developed for TCEQ entitled Development of Commercial Marine Vessel, Locomotive, and 

Air Traffic Activity Growth Factors: Draft Final Report, August 31, 2007 (TCEQ August 31 

2007). These projections use 2006 as a base year and project forward to 2020 and back cast to 

1999 assuming a linear growth pattern at the railway segment and yard level. 

2.1.2 Emission Factors

Emission factors were obtained from “U.S. EPA Emission Factors for Locomotives” 

(U.S. EPA 1997) as noted in Table 2-3. The model assigns these emission factors based on the 

classification of the locomotive as provided by the railway companies. 
2-4 

Table 2-3. Locomotive Emission Factors (grams/gal) by Tier 

Locomotive Pollutants 

type VOC CO NOx PM 

Non Tier (Pre-1973) 

Line Haul 10 26.6 270 6.7 

Yard 21 38.1 362 9.2 

Tier 0: Controlled emission rates for locomotives manufactured in 1973-2001 

Line Haul 10 26.6 178 6.7 

Yard 21 38.1 262 9.2 

Tier 1: Controlled emission rates for locomotives manufactured in 2002-2004 

Line Haul 9.8 26.6 139 6.7 

Yard 21 38.1 202 9.2 

Tier 2: Controlled emission rates for locomotives manufactured after 2004 

Line Haul 5.4 5.0 103 3.6 

Yard 11 7.3 152 4.3 

Hybrid Engines 

Yard 0.046 0.008 0.206 0.005 

Source: U.S. EPA Emission Factors for Locomotives – Table 9: Fleet Average Emission Factors for all 

Locomotives BPR 420-F-97-051, 1997. 

Note: Locomotives manufactured before 1973 are considered to be uncontrolled. 

Factors for hybrid engines were obtained from the railway companies and the manufacturers. 

The emission factors used to estimate projected emissions varied by railroad company 

because their submittal content and format were significantly different. Union Pacific provided 

an estimate of the Tier profile of their fleet from 2002 to 2009 (UP 2007). For the purpose of this 

project, uncontrolled emission factors were applied to Union Pacific activity data for 1999 to 

2001. The Tier profile was used as provided to weight the emission factors to more accurately 

represent the UP equipment fleet. Weighted emission factors were developed for each year using 

the data provided. The weighted emission factors developed for 2009 were applied to activity 

data 2009 to 2020. This will provide a higher than actual emission estimate because it does not 

account for the new locomotive Tier 3 and Tier 4 emission standards that have yet to be 

developed. 

BNSF did not provide projected Tier level data; therefore, for these data emission factors 

were applied to each year’s activity data. The U.S. EPA’s projected emission factors take into 

account fleet turnover and compliance with current emission standards. These factors are 

presented in Table 2-4. As with UP, this approach will provide slightly higher emission estimates 

for the future because the railway companies have been replacing older engines at a rate higher 

than that assumed in the U.S. EPA data. The U.S. EPA data also does not account for the new 

locomotive Tier 3 and Tier 4 emission standards that have yet to be promulgated. 
2-5 

Table 2-4. U.S. EPA Fleet Average Emission Factor 

for all Locomotives (grams/gal) 

Year VOC CO NOx PM 

1999 10.7 27.4 276.7 6.8

2000 10.7 27.4 273.8 6.8

2001 10.7 27.4 265.0 6.8

2002 10.7 27.4 248.8 6.8

2003 10.7 27.4 233.3 6.8

2004 10.7 27.4 218.1 6.8

2005 10.4 27.4 199.8 6.6

2006 10.1 27.4 185.6 6.4

2007 9.8 27.4 177.0 6.2

2008 9.6 27.4 172.5 6.0

2009 9.4 27.4 168.3 5.9

2010 9.1 27.4 163.0 5.7

2011 9.1 27.4 161.1 5.7

2012 8.9 27.4 158.5 5.6

2013 8.8 27.4 155.9 5.5

2014 8.7 27.4 153.4 5.4

2015 8.5 27.4 151.0 5.3

2016 8.4 27.4 148.5 5.2

2017 8.3 27.4 146.5 5.1

2018 8.2 27.4 144.4 5.1

2019 8.1 27.4 142.4 5.0

2020 7.9 27.4 140.3 4.9

The VOC emissions were speciated outside the model into associated organic hazardous 

air pollutants (HAP) by using speciation profiles derived from the EPA’s 2005 National 

Emission Inventory Documentation for Locomotives (EPA 2005). These speciation profiles are 

provided in Table 2-5. 

Table. 2-5 Locomotive HAP Speciation Profiles 

Pollutant Yard Line 

1,3-Butadiene 0.0015 0.0032

2,2,4 Trimethylpentane 0.0022 0.0022

Acetaldehyde 0.0089 0.0188

Acrolein 0.0015 0.0031

Benzene 0.0012 0.0026

Eythlybenzene 0.0020 0.0020

Formaldehyde 0.0206 0.0433

n-Hexane 0.0055 0.0055

Propionaldehyde 0.0061 0.0061

Styrene 0.0021 0.0021

Toluene 0.0032 0.0032

Xylene 0.0048 0.0048
2-6 

2.1.3 Annual Emissions

Line Haul Emissions. The TREIM model calculates annual emissions for line haul 

operations by multiplying activity data in gallons of fuel used, by an emission factor in grams of 

pollutant per gallon of fuel. Fuel usage is estimated by the railway companies by applying gross 

ton mileage (GTM) data for individual railway segments to a fuel consumption factor in gallons 

of fuel per gross ton mileage. BNSF used segment specific fuel consumption factors (BNSF 

2007) while UP used U.S. EPA fuel consumption factors (UP 2007) for this calculation. After 

converting grams to pounds, the model reports annual emissions for VOC, CO, NOx, and PM in 

pounds of pollutant per year, which are easily converted to tons, as shown in Equation 2. 

Equation 2. Annual line emissions = gallons of fuel used × grams of pollutant/gallon of 

fuel × tons /gram = tons of pollutant 

Example Calculation:

Given: In 2006, 460,000 gallons of fuel were combusted along a specific railway 

segment; the recommended EPA emission factor is 10.1 grams of VOC per gallon of fuel. To 

estimate the VOC emissions the following calculation was used: 

Annual emissions = Activity data × Emission factor 

Annual emissions = 460,000 gal of fuel × 10.1 g of VOC / gal × 1.1026 E-6 tons / g 

Annual emissions = 5.12 tons of VOC 

Yard Emissions. The TREIM model calculates annual emissions for yard operations by 

converting hours of operation into fuel based activity that are applied to the appropriate U.S. 

EPA emission factor in grams of pollutant per gallon of fuel used. The model calculates 

emissions for VOC, CO, NOx, and PM, similar to the approach discussed for line haul 

locomotives. To accurately convert hours of operation to fuel usage for yard locomotives, engine 

notch (or throttle) data are needed to approximate the amount of time an engine spends at a 

specific notch setting, each of which is associated with a specific fuel usage rate (U.S. EPA 

1998). For this project the percent time at each notch setting was obtained from the U.S. EPA’s 

Locomotive Emission Standards Regulatory Support Document (U.S. EPA 1998). The Atchison 

Topeka and Santa Fe (ATSF) and Elective Motive Diesel (EMD) duty cycle data included in the 

U.S. EPA support document were averaged to obtain appropriate values for this study, as shown 

in Table 2-6. The ATSF and EMD time in notch data were used because the U.S. EPA duty cycle 

data (also included in the support document) did not include idle time. The percent time spent in 

each notch setting for yard locomotives while the engine is running are also provided in 

Table 2-5. 
2-7 

Table 2-6. Yard Duty Cycle Data (U.S. EPA 1998) 

Time in Notch Setting Throttle

Notch ATSF EMD Average

Average 

Fuel 

Rate 

(lb/hr) 

Idle 77.00% 77.00% 77.00% 29

Notch 1 10.00% 7.00% 8.50% 48

Notch 2 5.00% 7.00% 6.00% 116

Notch 3 4.00% 4.00% 4.00% 196.5

Notch 4 2.00% 2.00% 2.00% 290

Notch 5 1.00% 1.00% 1.00% 387

Notch 6 1.00% 0.50% 0.75% 493

Notch 7 0.00% 0.50% 0.25% 619.5

Notch 8 0.00% 1.00% 0.50% 742

Fuel usage was calculated by notch setting using Equation 3. An example calculation is also 

provided. 

Equation 3. FUi = HR × NSi × FRi / DN 

Where: 

FU = Fuel usage (gallons); 

I = Notch setting in the duty cycle; 

HR = Annual operating hours; 

NS = Percent of time at the notch setting; 

FR = Fuel rate (lbs/hr); and 

DN = Density of fuel (7 lb/gal) 

Example Calculation:

Given: Four yard locomotives operate a total of 17,520 hours per year. These engines idle 

77 percent of the time. During the idling mode they consume 29 pounds of diesel fuel per hour. 

FUidle = 17,520 hours/year × 77% × 29 lbs/hour / 7 lbs/gallon 

= 5,888.8 gallon/year 

2.1.4 Average Ozone Season Day Emissions 

Corresponding activity for average ozone season day was calculated by scaling the 

annual daily average values by the seasonal adjustment factor (30/25), to account for railroad 

company estimates that 30 percent of activity occurs during summer months (UP 2007). 
2-8 

2.2 Structure of the Model 

The model was designed to use either general or specific activity data provided by the 

railway companies. Thus, the model is divided into the following three major components: 

general line haul data; specific line haul data; and yard data.

The TREIM has been populated with activity data for all Texas counties with active rail 

lines. Data for only the DFW and HGB areas were provided directly by the railways. All other 

counties were developed from the BTS GIS shape files. General data are from BTS and UP.

Specific data are from BNSF. 

The railroad model was developed to easily incorporate data provided by the railway 

companies for line haul and yard engine operations. Projecting future activity and associated 

emissions were not components of the original scope of work for this project, but the model was 

modified to account for past and future changes in activity based on segment and yard growth 

factors developed recently for TCEQ under a separate project (TCEQ August 31, 2007). These 

projections use 2006 as a base year and project forward to 2020. Where the railway companies 

did not provide their own data, the TCEQ projection data was used assuming linear growth. The 

data developed for the projections were used directly as provided—no modifications were 

needed because the team developing the projections data, provided growth factors for each rail 

line segments and yard locations included in the TREIM. 

2.2.1 Structure of Line Haul Data 

The original model structure considers line haul and yard locomotives as two separate 

components. For line haul operations, the following input data tables were developed: 

· Activity tables; 

· Emission factor table; 

· Fleet composition; and 

· Source Classification Codes (SCC) linkage (Line haul or Yard). 

The model generates the following output data tables: 

· Emissions table, and 

· GIS attribute table. 

Figure 2-1 shows the line haul data tables and their linkages. The model is populated with 

activity data for all counties. Data for the DFW and HGB areas were provided directly by the 

railway companies. Emissions were estimated based on the GTM rating of each railway segment 

included in the BTS shape files. The GTM data obtained from the BTS provides a range of 

activity values for each railway segment. For the model, the mid-point of the range was used and 

applied to the gallon of fuel/GTM factors developed from the data provided by the railway 

companies. These fuel usage estimates were applied to the U.S. EPA emission factors to estimate 

emissions for each segment. The model then generates emission estimates by county. 
2-9 

Fleet Composition Table

Emission Factor Table • Tier 0

• Tier 1

• Tier 2

• Year Data Represents

• Data Identification Code

• Adjusted Emission Factors

• Emission Estimate

Emissions Table

SCC Matching Table

Figure 2-1. Line Haul Components of TREIM Railroad

Per Segment

Activity Table

General Data Specific Data

• Railroad Company • Railroad Company

• Year Data Represents • Year Data Represents

C Fuel Usage Data • Data Identification Code

• Data Identification Code • Segment Identification Code

• Segment Identification Code • Railroad Activity Type Code

• GTM • Fuel Usage Rate

• GTM by Activity Type and Segment

GIS Attribute Table

• Data Identification Code • Railroad Activity Code

• Segment Identification Code • SCC Code

• Railroad Company • GTM by Activity Code

• Year the Data Represent • Fuel Usage Rate by Activity Code

• Fleet Composition • Total Fuel Usage by Activity Code

• Weighted Emission Factor • Emission Estimate

Fleet Composition Table

Emission Factor Table • Tier 0

• Tier 1

• Tier 2

• Year Data Represents

• Data Identification Code

• Adjusted Emission Factors

• Emission Estimate

Emissions Table

SCC Matching Table

Figure 2-1. Line Haul Components of TREIM Railroad

Per Segment

Activity Table

General Data Specific Data

• Railroad Company • Railroad Company

• Year Data Represents • Year Data Represents

C Fuel Usage Data • Data Identification Code

• Data Identification Code • Segment Identification Code

• Segment Identification Code • Railroad Activity Type Code

• GTM • Fuel Usage Rate

• GTM by Activity Type and Segment

Activity Table

General Data Specific Data

• Railroad Company • Railroad Company

• Year Data Represents • Year Data Represents

C Fuel Usage Data • Data Identification Code

• Data Identification Code • Segment Identification Code

• Segment Identification Code • Railroad Activity Type Code

• GTM • Fuel Usage Rate

• GTM by Activity Type and Segment

GIS Attribute Table

• Data Identification Code • Railroad Activity Code

• Segment Identification Code • SCC Code

• Railroad Company • GTM by Activity Code

• Year the Data Represent • Fuel Usage Rate by Activity Code

• Fleet Composition • Total Fuel Usage by Activity Code

• Weighted Emission Factor • Emission Estimate

GIS Attribute Table

• Data Identification Code • Railroad Activity Code

• Segment Identification Code • SCC Code

• Railroad Company • GTM by Activity Code

• Year the Data Represent • Fuel Usage Rate by Activity Code

• Fleet Composition • Total Fuel Usage by Activity Code

• Weighted Emission Factor • Emission Estimate
2-10 

Railway companies provided segment-level activity data for counties in the DFW and 

HGB areas. Because these data are more accurate, they were used in place of the BTS activity 

data for these areas. The data provided for DFW and HGB by the railway companies accounted 

for 29,985,017,751 (30.0 billion) GTM and replaced the 26,648,781,806 (26.6 billion) GTM 

estimated from the BTS data for these counties. The replacement of the BTS estimate with the 

more accurate railroad company data suggests that the BTS data underestimated actual railway 

traffic by approximately 10 percent. This is an important observation because BTS railway 

traffic data were used to estimate emissions from non-DFW and non-HGB counties; thus, 

emissions for these counties may also be underestimated. The BTS data provide a reasonable 

estimate for railway traffic, but more accurate data could be provided by the railway companies 

for these other counties in the future. 

Two different activity tables were developed for the model to allow for the inclusion of 

general and specific train type data. The general line haul section of the model was developed to 

incorporate data that reflect total activity per railway segment, regardless of railway traffic type. 

This generic railway type traffic is associated with the SCC for line haul locomotives, 

2285002006. The specific line haul data section of the model has been developed to manage data 

by the railway activity type listed in Table 2-2. 

In each component, the model gives the user the opportunity to add or edit activity data 

by segment. The data identification code serves as the Primary Key. The Primary Key is a unique 

identifier that links the different data elements in the different data tables together to calculate 

emissions. 

For the general activity table, the data fields include the following: 

· Segment identification code (Primary Key); 

· County name; 

· County FIPs; 

· Traffic density; 

· Miles of track; 

· Railroad company; 

· Calendar year of activity; 

· Fuel usage rate (gal/1,000 GTM); 

· Fuel usage (gal); 

· Train type; 

· Train count; and, 

· GTM per segment. 

For the general line haul data, the train type is set to “Generic” and the train type code is 

set at AA. For the specific line haul data, the railway companies provided additional train type 

data and segment specific fuel usage. The following additional data elements were incorporated 

into the model: 

· Unique identification code (Primary key); 

· Railroad company; 

· Calendar year of activity; 

· Segment identification code; 
2-11 

· Railway activity type code; 

· Fuel usage rate (gal/1000 GTM) by railway activity type and segment; and, 

· GTM by activity and segment. 

2.2.2 Structure of Switch Data 

For yard locomotives, many of the procedures discussed for line haul locomotive are 

applicable. As with line haul operations, the following input data tables were developed: 

· Activity tables; 

· Emission factor table; 

· Fleet composition; and, 

· SCC linkage. 

The model generates the following tables: 

· Emissions table, and 

· GIS attribute table. 

The data tables and their linkages are shown in Figure 2-2. 

Unlike the line haul component that allows for the inclusion of general or detailed railway data, 

only one activity table is required for yard locomotives. 

2.3 Sample Calculations 

The model multiplies activity data by an emission factor to yield annual emission 

estimates of VOC, CO, NOx, and PM. Before actually calculating emissions, the model must 

determine the number of gallons used based on the GTM and fuel usage per GTM. The model 

must also assign the appropriate emission factor to each portion of the fleet based on the fleet 

composition. Following are examples of how the model derives these two variables before 

calculating emissions. 
2-12 

Activity Table

GIS Attribute Table

Fleet Composition Table

Emission Factor Table

• Data Identification Code

• Adjusted Emission Factors

• Emission Estimate

Emissions Table

SCC Matching Table

• Railroad Company

• Year Data Represents

• Data Identification Code

• Yard Identification Code

• Number of Locomotives

• Annual Hours of Operation

• Fuel Usage Data

• Data Identification Code

• Yard Identification Code

• Railroad Company

• Year the Data Represent

• Swticher Fleet Composition

• Weighted Emission Factor

• SCC Code

• Total Hours of Operation

• Total Fuel Usage

• Emission Estimate

Figure 2-2. Switch Locomotive Components of TREIM

• Tier 0

• Tier 1

• Tier 2

• Year Data Represents

Activity Table

GIS Attribute Table

Fleet Composition Table

Emission Factor Table

• Data Identification Code

• Adjusted Emission Factors

• Emission Estimate

Emissions Table

SCC Matching Table

• Railroad Company

• Year Data Represents

• Data Identification Code

• Yard Identification Code

• Number of Locomotives

• Annual Hours of Operation

• Fuel Usage Data

• Data Identification Code

• Yard Identification Code

• Railroad Company

• Year the Data Represent

• Swticher Fleet Composition

• Weighted Emission Factor

• SCC Code

• Total Hours of Operation

• Total Fuel Usage

• Emission Estimate

Figure 2-2. Switch Locomotive Components of TREIM

• Tier 0

• Tier 1

• Tier 2

• Year Data Represents
2-13 

2.3.1 Line Haul Example: What are the NOx Emissions for Segment X? 

Activity Data. The emissions for line haul locomotives are derived from actual fuel usage 

by segment as reported by the railway companies. The railway companies provided fuel usage 

for individual segments within DFW and HGB; however, for non- DFW and HGB counties 

where such detailed data were not provided, fuel usage was automatically calculated by dividing 

the reported GTM with GTM/gal values recommended by the U.S. EPA as noted in the 

following equation: 

Fuel Usage for Segment X = (GTM for Segment X)/(GTM/gal) 

Example activity calculation: 

 Given: GTM for Segment X = 50,000,000 

 GTM/gallon for Segment X = 600 GTM/gal 

Fuel Usage for Segment X = (GTM for Segment X)/(GTM/gal) 

= (50,000,000)/(600 GTM/gal)) 

= 83,333 gallons 

Emission Factors. The emission factors were weighted by the locomotive fleet 

composition for the specified calendar year prior to being applied to the fuel estimates, as noted 

in the following equation: 

Wef = (T0F)(T0EF) + (T1F)(T1EF) + (T2F)(T2EF) 

Where: Wef = Weighted emission factor 

T0F = Tier 0 fraction 

T0EF = Tier 0 line haul emission factor 

T1F = Tier 1 fraction 

T1EF = Tier 1 line haul emission factor 

T2F = Tier 2 fraction 

T2EF = Tier 2 line haul emission factor 

Example emission factor calculation: 

Given: T0F = Tier 0 fraction is 10% of the fleet or 0.10 

T0EF = Tier 0 line haul emission factor for NOx is 

178 grams/gal 

T1F = Tier 1 fraction is 85% of the fleet or 0.85 

T1EF = Tier 1 line haul emission factor for NOx is 

 139 grams/gal 

T0F = Tier 2 fraction is 5% of the fleet or 0.05 

T2EF = Tier 2 line haul emission factor NOx is 

103 grams/gal 
2-14 

Wef for NOx = (T0F) × (T0EF for NOx) + (T1F) × (T1EF for NOx) + (T2F) × (T2EF for 

NOx) 

= 0.10 × 178 + 0.85 × 139 + 0.05 × 103 

= 17.8 + 118.15 + 5.15 

= 141.1 grams of NOx/gal 

Any revisions made to the fuel data or the fleet composition are automatically 

incorporated into the emission estimates for each of the railway segments once the user clicks on 

“calculate emissions.” 

Annual emissions of NOx. Once the activity data and emission factors are derived, the 

annual line haul emissions can be calculated. 

Annual line emissions = (gallons of fuel used) × (grams of pollutant/gallon of fuel) × 

(pounds/gram) × (tons/pounds) 

Given: Fuel Usage for Segment X = 83,333 gallons 

Weighted emission factor = 141.1 grams of NOx/gal 

453.59 grams/pound 

1 ton/2,000 pounds 

Annual line emissions = (83,333 gal) × (141.1 grams NOx/gal) × (1 pound/453.59 grams) × 

(1 ton/2,000 lbs) = 12.96 tons per year NOx

2.3.2 Switch Example: What are the NOx Emissions for Yard X? 

The model multiplies activity data by an emission factor to yield annual emission 

estimates. Before actually calculating emissions, the model must determine the activity data (i.e., 

number of gallons used). In most cases fuel usage values (gallons) were provided by the railway 

companies; however, if fuel usage data were not provided, the fuel usage was calculated based 

on hours of operation and typical notch settings (as discussed in Section 2.1) multiplied by the 

number of yard engines operating at the yard. 

Activity Data. The model assumes that one engine consumes 9.375 gallons of diesel fuel 

per hour (U.S. EPA 1992), as noted in the following equation and example calculation: 

Annual Fuel Usage for Yard X = (Number of Yard Locomotives) × (9.375 gal/hr) × 

 (8,760 hr/year) 

Where: Annual Yard Fuel Usage was obtained from railway companies, or 

were derived from hours of operation 

Example activity calculation: 

 Given: 12 Yard Locomotives 
2-15 

Fuel Usage for Segment X = (Number of Yard Locomotives) × (9.375 gal/hr) × 

(8,760 hr/year) 

 = (12) × (9.375 gal/hr) × (8,760 hr/year) 

 = 985,500 gallons/yr 

Emission Factors. The emission factors were weighted by the locomotive fleet 

composition for the specified calendar year prior to being applied to the fuel estimates, similar to 

the line haul example in Section 2.3.1 and demonstrated in the following equation: 

Wef = (T0F) × (T0EF) + (T1F) × (T1EF) + (T2F) × (T2EF) 

Where: Wef = Weighted emission factor 

T0F = Tier 0 fraction 

T0EF = Tier 0 yard emission factor 

T1F = Tier 1 fraction 

T1EF = Tier 1 yard emission factor 

T2F = Tier 2 fraction 

T2EF = Tier 2 yard emission factor 

Example emission factor calculation: 

Given: T0F = Tier 0 fraction is 5% of the fleet or 0.05 

T0EF = Tier 0 yard emission factor for NOx is 

262 grams/gal 

T1F = Tier 1 fraction is 80% of the fleet or 0.80 

T1EF = Tier 1 yard emission factor for NOx is 

202 grams/gal 

T2F = Tier 2 fraction is 10% of the fleet or 0.10 

T2EF = Tier 2 yard emission factor NOx is 152 grams/gal 

Wef for NOx = (T0F) × (T0EF for NOx) + (T1F) × (T1EF for NOx) + (T2F) × (T2EF for 

NOx) 

= (0.10) × (262) + (0.85) × (202) + (0.05) × (152) 

= 26.2 + 171.7 + 7.6 

= 205.5 grams of NOx/gal 

Annual emissions of NOx. Once the activity data and emission factors are derived, the 

annual yard emissions can be calculated. 

Annual yard emissions = (gallons of fuel used) × (grams of pollutant/gallon of fuel) × 

(pounds/gram) 
2-16 

Given: Fuel Usage for Yard X = 985,500 gallons 

Weighted emission factor = 205.5 grams of NOx/gal 

453.59 grams/pound 

Annual yard emissions = (985,500 gal) × (205.5 grams NOx/gal) × (1 pound/453.59 

grams) = 446,483 pounds NOx. 

2.4 Texas Railroad Emissions Inventory Model 

This section describes how the user interacts with the model. For screen-by-screen 

instructions on how to use the model, refer to the User’s Manual in Appendix B. 

The TREIM uses a pre-populated dataset to calculate emissions of four pollutants from 

railway locomotives in all Texas counties (Figure 2-3). Users do not need additional data to 

calculate emissions. The model provides the following functionality: 

· Calculates state and county level emissions. 

· Calculates emissions for any year between 1999 and 2020 (DFW and HGB areas). 

Figure 2-3. Model Calculates Annual Emissions for all Texas 

Counties Based on Pre-populated Activity Data 
2-17 

· Calculates emissions of specific train types operating in DFW and HGB areas (where 

data have been provided). 

· Uses GIS to view emissions at the county, rail line segment, and yards. 

· Evaluates the effect of replacing older higher-emitting locomotives with newer loweremitting locomotives. 

2.4.1 Model Background 

The database behind the model was originally developed as a repository for organizing 

and storing activity data provided by the railway companies for the DFW and HGB areas. The 

model was developed by applying U.S. EPA emission factors (U.S. EPA 1997) to the activity 

data (BNSF 2007 and UP 2007) to estimate annual emissions of VOC, CO, NOx, and PM. 

To enhance the original model, the geographic scope was expanded to include all 

counties in Texas. Emission estimates were developed at the segment level and applied to GIS 

shape files (DOT/BTS 2002) to show the spatial attributes of the emissions data and to allow for 

county level summaries. The model was further expanded to include emission estimates for the 

years 1999 to 2020 for the DFW and HGB areas. 

For this version of the model, users are expected to be TCEQ staff who are 

authorized to access confidential business information. In order to develop a public version of 

the model, the current model must be modified to aggregate data and block access to confidential 

business information provided by the railroads. The public version of the model can be used by 

railway companies, city and county planners, university researchers, and others who are 

interested in evaluating air quality issues related to railway activities in the state of Texas. 

2.4.2 User Options 

Users can immediately calculate county-level emissions. Alternatively, users can estimate 

emissions for a specific year or railway segment. Also, for railway segments located in the 

counties that constitute the HGB and DFW areas, users can select the train type before 

calculating emissions. It should be noted that not all railway companies provided train type data 

for every year, but the model was developed to include detailed data where provided. 

Users can also modify the pre-populated dataset to evaluate different scenarios such as 

changes in railway traffic, or use of higher Tier locomotives. To evaluate the results of such 

scenarios, users must run the scenario, save the data, then compare to the original data. In 

developing the scenarios, users should save the edited data files as a separate file. The user can 

always return to the original “read only” data, which is provided on the compact disk (CD), 

along with the model. 

2.4.3 Navigating the Model 

The model’s forms are designed to take users step-by-step through the process of 

calculating emissions. This section describes how the model operates based on user input. For 
2-18 

complete screen-by-screen instructions on how to use the model, refer to the User’s Manual in 

Appendix B. 

Data Forms: General and Specific Line Haul 

The General and Specific Line Haul Data forms allow users to view the data set before 

calculating emissions. Using the data forms (Figure 2-4), users can scroll through each record by 

clicking on the arrows at the bottom of the form. Each record represents information for a 

railway segment. 

Figure 2-4. Each Record Represents Data for a Railway Segment

The Specific Line Haul Data form is similar to the General Line Haul Data form, except 

that the train type can be set to any of the 25 train types. The train type code is automatically set 

when the train type is selected. 

Table 2-7 shows the data fields that appear on the General and Specific Line Haul Data 

forms. 

Table 2-7. General and Specific Line Haul Data Forms Contain the Same Fields 

Field Description User can edit? 

Railroad company UP, BNSF, Kansas City Southern (KCS) No 

County County name No 

State/county FIPS 48=Texas; next three digits represent county No 

Segment ID Code Identifies the specific railway segment. No 

Fuel usage 

(gallons) 

The model calculates annual fuel usage by dividing 

GTM by GTM/gallon, which yields gallons per year. 

Baseline only 
2-19 

Table 2-7. General and Specific Line Haul Data Forms Contain the Same Fields (Cont.) 

Field Description User can edit? 

Train type In the General data, the model assigns a generic train 

type. However, in the Specific data, railway traffic is 

defined as one of the 25 specific train types from the pull 

down menu. 

No 

Train type code A shorthand code that corresponds to the train type data 

field. This field is automatically assigned when the Train 

Type is selected. 

No 

Data year Calendar year of activity, which ranges from 2002 to 

2020 

No 

Fuel Usage Rate 

(gallons per 1,000 

GTM) 

Function of train type. The model applies a generic fuel 

usage rate in the General data, and applies a specific fuel 

usage rate to the train type selected in the Specific data. 

No 

Train Count Number of trains that move along the segment No 

GTM Gross ton mileage Yes, but fuel 

usage needs to 

be modified 

separately 

Users can edit several fields, as follows: 

· Fuel Usage (gallons) – Users can modify fuel usage, but the change must be made at 

the segment or rail yard level. 

· Year – Users can select the year for which they are interested in estimating emissions. 

· GTM – Users can adjust the GTM in the base year; for projected years the GTM is 

adjusted by the segment level profiles. 

· Tier profiles – Users can modify Tier profiles, which will weight the emission factors 

for the Tier mix. Note that if hybrids for rail yards are being evaluated, then the fuel 

usage must be adjusted to account for the fact that these engines use 60 percent less 

fuel than conventional engines. 

Data Form: Fleet Composition 

Users can enter or edit the fleet composition for line haul and yard locomotives from any 

railroad company in the data set. Figure 2-5 shows example line haul fleet compositions; the yard 

fleet composition form is similar. The fleet composition values are presented as fractions of each 

locomotive Tier that sum to 1. These fractions can vary from year to year as new locomotives are 

added to the railway roster and older locomotives are removed from service. To allow for back 

casting and forecasting of locomotive emission estimates, the year associated with the Tier split 

must also be provided. 
2-20 

Figure 2-5. Line Haul Fleet Composition Form 

As with the line haul operations, the yard fleet composition values are presented as 

fractions that sum to 1 and can vary from year to year as new yard engines are added and older 

engines are taken out of service. Currently, there is only one SCC for yard locomotives (i.e., 

2285002010). This code was assigned to all yard locomotive emissions estimates. 

Data Forms: Emissions 

The emissions screen provides three views of the calculated emissions. First, emissions 

are presented by segment including the adjusted emission factors used in the estimate; second, 

emissions are presented as pollutant totals for each railway segment; and third, emissions are 

presented summarized to the county level. 

Geographic Information System Component 

An important attribute of the model is the GIS component. The railway companies 

submitted their railway activity data with segment identification codes that were matched to 

railway segments in GIS shape files developed for this model. The advantage to having the 

railway activity and emissions data available in a GIS format is that emissions can be calculated 

at the segment level and viewed at a sub-county level. Prior to the development of GIS tools, 

state and local agencies found it challenging to disaggregate state fuel usage to individual 

counties. Once the data are in a GIS format, it is possible to perform “what if scenarios” and 

identify emission “hot spots.” At this stage of the model development, the number of scenarios 

that can be run is limited to changes in the fleet composition and segment and yard level activity 

data. 

With the data in a GIS format, users can view the attribute data for every segment used in 

the model, whether the data are applied to the generic or specific emission estimating approach. 

Figure 2-6 shows data that were applied to the generic approach to estimate emissions for a 
2-21 

segment of rail line in Tarrant County running north of Fort Worth. The segment is shown in 

bright blue. The activity data, including train counts, total gross tonnage, GTM, and fuel 

consumption associated with that segment is noted in the information box. The information box 

also includes state and county FIP codes, county name, segment code, track length data, and the 

emission estimates. 

Like the general approach data, each specific segment displays the segment-level activity 

data, including train counts, total gross tonnage, GTM and fuel consumption associated with that 

segment is noted in the information box. The information box also includes state and county FIP 

codes, county name, segment code, track length data as well as emission estimates. The 

information box for the specific approach contains a second level of data because the segment 

also contains information broken out by each train type found on that portion of rail line. This 

enables the user to identify how much of the total activity or emissions for that segment can be 

attributed to any individual train type. 

Having the data in a GIS format is particularly useful because specific heavy traffic 

segments can be easily identified and different scenarios can be run showing what future 

emissions look like if the traffic is increased, decreased, or shifted to other segments. Data can 

also be summarized at the county or sub county-level (e.g., metropolitan statistical areas, zip 

codes, census blocks, or modeling grids), by overlaying these maps onto the data. The example 

shown in Figure 2-6 provides county-level mapping. These can be developed and applied in the 

future as they are needed. 

Figure 2-6. Example GIS Screen for General Line Haul Data 
3-1 

3.0 DATA COLLECTION 

Initially the model was developed with GTM data obtained from the BTS (DOT/BTS 

2002) for all counties with line haul operations in Texas. To calculate accurate emission 

estimates, the TCEQ project manager, ERG, and Starcrest staff worked with BNSF, UP, and 

Kansas City Southern (KCS) to provide appropriate activity data for inclusion into the earlier 

versions of the model. BNSF and UP are two largest railway companies servicing the DFW and 

HGB areas. The data provided by the railway companies for the current study documented the 

railway segment or yard location, including mileage markers, segment length, train counts, 

GTM, and fuel consumption (BNSF 2007 and UP 2007). Locomotive engine Tier profiles were 

also developed for line haul and yard operations. The segment and yard locations were provided 

in a GIS format in order to spatially allocate activity and estimates of emissions from 

locomotives. This detailed data set was used to populate the model. 

Both BNSF and UP railway companies submitted activity data (BNSF 2007 and UP 

2007); however, the companies provided their data using significantly different levels of 

disaggregation. For example, BNSF estimated fuel consumption using event recorder data for a 

variety of different railway operations. These event recorder data files contain specific 

information that yields very accurate activity data (BNSF 2007). Thus, the emission estimating 

method for BNSF allows for segment-specific and different railway activities (or train types) to 

be incorporated into the model. On the other hand, the UP data set aggregated all railway 

activities (or train types) together, but the data elements were disaggregated into smaller railway 

segments (UP 2007). 

UP provided GIS shape files directly. Some of the active segments included in their GIS 

files were not included in their activity data files. Thus, emissions could not be estimated for 

these additional segments without additional information about rail traffic or fuel consumption 

for these segments. BNSF was not able to provide GIS shape files in a timely fashion, so their 

activity and fuel usage data were applied to GIS segments developed from other data sources 

(i.e., BTS and DeskMap). 

Locational data for the yards provided by the railroads were often incomplete or 

contained inconsistent data. Only three rail yards included appropriate latitude and longitude 

coordinates. These coordinates were included in the model. All other rail yards were assigned to 

an appropriate county using the data provided by the railway companies. 

In order to revise the model to update the locomotive engine Tier profiles and develop a 

dataset of projected railway activity, ERG developed a series of spreadsheets within an Excel 

Workbook that included all of the data needed for the update. ERG populated the data request 

workbook with data previously submitted by the railway companies. The workbook was color 

coded to facilitate review by the railway companies noting any missing or additional data that 

were needed to implement the revision to the model. These data request workbooks were 

submitted to the Class I railways and followed up with a conference call to explain the data 

request and answer any questions. ERG staff were available throughout the project to facilitate 

submittal of the data. The provided data were compared to previously submitted data to ensure 

that the data were consistent and reasonable. 
4-1 

4.0 RESULTS AND ANALYSIS 

4.1 State Locomotive Emission Summary 

In the state of Texas in 2006, locomotives are estimated to emit approximately 8,843 tons 

per year (29.1 tons per day) of CO, 3,612 tons per year (11.9 tons per day) of VOC, 64,623 tons 

per year (212.5 tons per day) of NOx, and 2,192 tons per year (7.2 tons per day) of PM as noted 

in Table 4-1. These emissions are underestimated relative to actual railway emissions since they 

include yard locomotives for only the DFW and HGB areas. If yard locomotives were included 

for all other counties, then the actual state total locomotive emissions would be greater than 

reported in these estimates. 

Activity data for yard locomotives in other counties could be estimated based on activity 

data for yard locomotives associated with DFW and HGB. However, because these two areas are 

major hubs for railway traffic, their yard activities are considered to be atypical of other areas 

and would yield an unreasonably high estimate of yard activities for the rest of the state. 

Therefore, these estimates are not included in this model. Emission estimates for line haul 

operations are provided in Appendix A for counties not included in the DFW and HGB areas. 

Table 4-1. Total 2006 Total (Line and Yard) Locomotive Contributions 

Tons per Year by Area 

Area CO VOC NOx PM 

Dallas-Fort Worth 1,118 528 9,604 268 

Houston-GalvestonBrazoria 1,701 818 14,678 407 

All Other Counties* 6,024 2,265 40,340 1,517 

TOTAL 8,843 3,612 64,623 2,192 

Tons per Average Ozone day 

Area CO VOC NOx PM 

Dallas-Fort Worth 3.68 1.74 31.57 0.88 

Houston-GalvestonBrazoria 5.59 2.69 48.26 1.34 

All Other Counties* 19.80 7.45 132.62 4.99 

TOTAL 29.07 11.87 212.46 7.21 

Percentage Distribution by Area 

Dallas-Fort Worth 12.64 14.62 14.86 12.21 

Houston-GalvestonBrazoria 19.23 22.66 22.71 18.58 

All Other Counties* 68.13 62.72 62.42 69.21 

TOTAL 100 100 100 100 

* Other counties were not updated in the latest versions so these values represent 2003 emission levels. Note 

that these emissions are for line haul locomotives, only, while the DFW and HGB emissions include both line 

haul and yard locomotives. 
4-2 

4.2 Dallas-Fort Worth and Houston-Galveston-Brazoria Emission Summary

Collectively, the DFW and HGB counties account for approximately one third of Texas 

locomotive emissions, as noted in Table 4-1. As Table 4-2 indicates, between half and two 

thirds of railroad emissions are associated with the operation of yard locomotives. The 

DFW and HGB fractions may be overestimated because the non-DFW and HGB counties were 

not updated in this effort. 

Between DFW and HGB, HGB has more locomotive emissions. The HGB area is 

responsible for approximately 50 percent more locomotive emissions than the DFW area. 

Table 4-2. 2006 Total Locomotive Emissions for the State of Texas (tons per year) 

Area CO VOC NOx PM 

Dallas-Fort Worth 

Line Haul Total 497 178 3,772 121 

Yard Engine Activity 620 350 5,832 146 

 Yard Contribution (percentage) 55.5 66.3 60.7 54.5 

Dallas-Fort Worth Total 1,118 528 9,604 268 

Houston-Galveston-Brazoria 

Line Haul Total 689 247 5,163 168 

Yard Engine Activity 1,011 571 9,515 239 

 Yard Contribution (percentage) 59.4 69.8 64.8 58.7 

Houston-Galveston-Brazoria Total 1,701 818 14,678 407 

All Other Counties (2003) 

Line Haul 6,024 2,265 40,340 1,517 

Grand Total 8,843 3,612 64,623 2,192 

4.3 Line Haul Emission Summary 

As Table 4-3 shows, over 50 percent of DFW line haul rail emissions occur in Tarrant 

County; while most of the line haul rail emissions for the HGB area are associated with Harris 

County where most of the port activities are focused. 

Table 4-3. 2006 Line Haul Emission Estimates for Dallas-Fort Worth and HoustonGalveston-Brazoria Counties (tons per year) 

County CO VOC NOx PM 

Dallas-Fort Worth 

Dallas 60 22 451 15

Denton 163 58 1,244 40

Tarrant 274 98 2,077 67

DFW Total 497 178 3,772 121
4-3 

Table 4-3. 2006 Line Haul Emission Estimates for Dallas-Fort Worth and HoustonGalveston-Brazoria Counties (tons per year) (Cont.) 

Houston-Galveston-Brazoria 

Brazoria 73 26 552 18

Chambers 2 1 16 1

Fort Bend 95 34 724 23

Galveston 14 5 107 3

Harris 327 117 2,442 80

Liberty 93 33 691 23

Montgomery 77 28 576 19

Waller 7 3 55 2

HGA Total 689 247 5,163 168

DFW and HGA Total 1,187 425 8,936 290

Line haul locomotive emission estimates for the remaining 207 Texas counties having 

railway traffic can be found in Appendix A. These estimates are based on the average GTM data 

provided by the BTS. The following nine counties all had slightly elevated emission estimates 

(VOC greater than 35 tpy, CO greater than 100 tpy, PM greater than 25 tpy, and NOx greater than 

670 tpy). These higher estimates are considered reasonable because major rail lines intersect in 

these nine counties, as shown in Figure 4-1. 

· Bell 

· Bexar 

· Carson 

· Hemphill 

· McLennan 

· Milam 

· Parmer 

· Potter 

· Robertson 

As noted above in Section 2 of this report, the emission estimates for the non-DFW and 

HGB counties are based on BTS railway traffic data, which do not incorporate local railway 

provided activity data and are therefore less accurate. More detailed data from the railway 

companies for these additional counties would allow for a more accurate emission estimate and a 

reduction in the uncertainty associated with these estimates. 
4-4 

Figure 4-1. Texas Railway Map with Additional High 

Traffic Counties Highlighted 

4.4 Yard Locomotives Emission Summary 

Individual yard locomotives continuously generate emissions in each county, unlike line 

haul locomotives that only contribute emissions to a location during the period they transit an 

area. Yard locomotives account for between 57 and 72 percent (depending upon the pollutant) of 

the locomotive emissions in the DFW and HGB counties with most of the yard locomotive 

emissions occurring in the HGB area (see Table 4-4 for details). As with line haul operations, 

most of the yard locomotive emissions occur in Tarrant and Harris Counties. 

Table 4-4. 2006 Switch Engine Emission Estimates for Dallas-Fort Worth and HoustonGalveston-Brazoria Counties (tons per year) 

CO VOC NOx PM 

Dallas-Fort Worth 

Dallas 194 109 1,819 46

Denton 0 0 0 0

Tarrant 426 241 4,013 101

DFW Total 620 350 5,832 146
4-5 

Table 4-4. 2006 Switch Engine Emission Estimates for Dallas-Fort Worth and HoustonGalveston-Brazoria Counties (tons per year) (Cont.) 

Houston-Galveston-Brazoria 

Brazoria 30 17 285 7

Galveston 30 17 280 7

Harris 939 530 8,823 222

Liberty 13 8 127 3

HGB Total 1,011 571 9,515 239

DFW and HGB Total 1,632 921 15,347 385

4.5 Emission Projections 

The railway companies provided a limited amount of information about projected activity 

levels or future Tier locomotive data, such that emission estimates in the model are based on the 

2006 Tier data, projection data developed for TCEQ, knowledge of regulatory control programs 

and TERP data. The projected emissions do not take into consideration future control activities 

such as idle reduction (5-10 percent reduction), add on controls (20-95 percent reduction) or fuel 

switching (10-50 percent reduction). The projected emission estimates summarized in Table 4-5 

probably under estimate actual emissions. Table 4-6 summarizes the speciated organic HAP 

emission for the projected years. 

Table 4-5. Projected Total Locomotive Contributions for the State of Texas 

Tons Per Year by Area 

Area Pollutant 2000 2005 2010 2015 2020 

CO 1,143 792 1,142 1,179 1,290

VOC 541 348 465 465 510

NOx 8,029 6,625 7,528 7,545 8,222 Dallas-Fort Worth 

PM 279 193 229 230 248

CO 1,452 1,222 1,664 1,725 1,810

VOC 685 558 683 690 724

NOx 10,433 10,301 11,070 11,230 11,770 Houston-Galveston-Brazoria

PM 356 298 336 343 358

Tons Per Average Ozone Day

CO 3.76 2.60 3.75 3.88 4.24

VOC 1.78 1.14 1.53 1.53 1.68

NOx 26.40 21.78 24.81 24.81 27.03 Dallas-Fort Worth 

PM 0.92 0.63 0.75 0.76 0.82

CO 4.77 4.02 5.47 5.67 5.95

VOC 2.25 1.83 2.25 2.27 2.38

NOx 34.30 33.87 36.39 36.92 38.70 Houston-Galveston-Brazoria

PM 1.17 0.98 1.10 1.13 1.18
4-6 

Table 4-6. Projected HAP Emissions 

Tons per year by area 

Area Pollutant 2000 2005 2010 2015 2020 

1,3-Butadiene 1.15 0.74 1.00 1.00 1.09

2,2,4 Trimethylpentane 1.21 0.78 1.06 1.05 1.15

Acetaldehyde 6.63 4.27 5.78 5.76 6.31

Acrolein 1.10 0.71 0.96 0.96 1.05

Benzene 0.91 0.59 0.79 0.79 0.87

Ethlybenzene 1.08 0.70 0.94 0.94 1.03

Formaldehyde 15.29 9.83 13.31 13.28 14.55

n-Hexane 2.98 1.91 2.59 2.59 2.83

Propionaldehyde 3.30 2.12 2.87 2.87 3.14

Styrene 1.14 0.73 0.99 0.99 1.08

Toluene 1.73 1.11 1.51 1.50 1.65

Dallas/ 

Fort 

Worth 

Xylene 2.60 1.67 2.26 2.26 2.47

1,3-Butadiene 1.41 1.15 1.41 1.42 1.49

2,2,4 Trimethylpentane 1.53 1.25 1.53 1.55 1.62

Acetaldehyde 8.16 6.65 8.14 8.22 8.63

Acrolein 1.36 1.11 1.35 1.37 1.43

Benzene 1.12 0.92 1.12 1.13 1.19

Ethlybenzene 1.37 1.12 1.37 1.38 1.45

Formaldehyde 18.81 15.32 18.76 18.95 19.88

n-Hexane 3.77 3.07 3.76 3.80 3.98

Propionaldehyde 4.18 3.40 4.17 4.21 4.42

Styrene 1.44 1.17 1.43 1.45 1.52

Toluene 2.19 1.79 2.19 2.21 2.32

Houston/ 

Galveston

Xylene 3.29 2.68 3.28 3.31 3.48

Tons per average zone day by area 

Area Pollutant 2000 2005 2010 2015 2020 

1,3-Butadiene 0.0038 0.0024 0.0033 0.0033 0.0036

2,2,4 Trimethylpentane 0.0040 0.0026 0.0035 0.0035 0.0038

Acetaldehyde 0.0218 0.0140 0.0190 0.0190 0.0207

Acrolein 0.0036 0.0023 0.0032 0.0032 0.0034

Benzene 0.0030 0.0019 0.0026 0.0026 0.0029

Ethlybenzene 0.0036 0.0023 0.0031 0.0031 0.0034

Formaldehyde 0.0503 0.0322 0.0438 0.0438 0.0477

n-Hexane 0.0098 0.0063 0.0085 0.0085 0.0093

Propionaldehyde 0.0109 0.0070 0.0095 0.0095 0.0103

Styrene 0.0037 0.0024 0.0033 0.0033 0.0035

Toluene 0.0057 0.0036 0.0050 0.0050 0.0054

Dallas/ 

Fort 

Worth 

Xylene 0.0085 0.0055 0.0074 0.0074 0.0081
4-7 

Table 4-6. Projected HAP Emissions (Cont.) 

Tons per year by area 

Area Pollutant 2000 2005 2010 2015 2020 

1,3-Butadiene 0.0046 0.0038 0.0046 0.0047 0.0049

2,2,4 Trimethylpentane 0.0050 0.0041 0.0050 0.0051 0.0053

Acetaldehyde 0.0268 0.0218 0.0268 0.0271 0.0284

Acrolein 0.0045 0.0036 0.0045 0.0045 0.0047

Benzene 0.0037 0.0030 0.0037 0.0037 0.0039

Ethlybenzene 0.0045 0.0037 0.0045 0.0045 0.0048

Formaldehyde 0.0618 0.0503 0.0618 0.0623 0.0654

n-Hexane 0.0124 0.0101 0.0124 0.0125 0.0131

Propionaldehyde 0.0137 0.0112 0.0137 0.0138 0.0145

Styrene 0.0047 0.0038 0.0047 0.0048 0.0050

Toluene 0.0072 0.0059 0.0072 0.0073 0.0076

Houston/ 

Galveston

Xylene 0.0108 0.0088 0.0108 0.0109 0.0114

4.6 TERP Related Emission Reductions 

The Texas Emissions Reduction Plan (TERP) was established by the 77th Texas 

Legislature in 2001, through enactment of Senate Bill 5, to help improve and maintain good air 

quality in areas throughout the state. Since the start of the program, there have been several 

legislative revisions. HB 2481, 79th Texas Legislature, Regular Session, encouraged costeffective emission reductions for locomotive and marine sector through grant awards. These 

awards include locomotive replacement, locomotive engine replacement, use of add on controls, 

and application of other new technologies.

For this project, the railway companies provided data on current and near term projected 

use of low emitting locomotive engines such as the greengoat hybrids and genset locomotives, 

which comply with stringent nonroad diesel engines standards. Much of this equipment is funded 

by the TERP program. Table 4-7 summarizes data provided by TCEQ that quantify the 

anticipated NOx emission reductions from locomotives purchased made through the TERP 

program (TCEQ 3 August 2007). 
4-8 

Table 4-7. Projected TERP NOx Emission Reductions 

2006 2007 2008 2009 2010 

TERP Emission Reductions (tons per year) 

Dallas-Fort Worth 250 1,281 1,317 1,642 1,642

Houston-GalvestonBrazoria 

176 1,457 1,491 1,979 1,979

Total 2,432 4,745 4,816 5,631 5,632

Estimated emissions without TERP 

Dallas-Fort Worth 9,854 9,823 9,748 9,812 9,244

Houston-GalvestonBrazoria 

14,854 13,704 13,606 13,744 13,049

Percentage of TERP emission reduction relative to total emissions 

Dallas-Fort Worth 2.5 13.0 13.5 16.7 17.8

Houston-GalvestonBrazoria 

1.2 10.6 11.0 14.4 15.2

These data along with data provided by the railway companies were used in developing 

the yard and line haul profiles for the DFW and HGB emission estimates. Note in Table 4-7 that 

TERP emission reductions have the biggest impact on emissions in 2007 when a large number of 

new engines are put into service. TERP impact continues to be significant into the future as 

higher Tier line haul locomotive continue to come on line, further reducing the over all railroad 

emissions. 
5-1 

5.0 COMPARISON TO INDEPENDENT STUDIES 

In this section, results of this 2006 inventory are compared to three Periodic Emissions 

Inventories (PEI) that were prepared for the Texas State Implementation Plan (SIP), beginning 

with the 1996 PEI. Model data for 2000 were selected for this comparison because they fit into 

the range of years considered in the other studies. 

The federal Clean Air Act Amendments of 1990 required a new baseline for SIP emission 

inventories, including nonroad mobile sources such as locomotive engines. This base year 

inventory was updated in 1996 and every three years thereafter. A survey was conducted to 

support the 1990 base year emissions inventory, but the response rate was low and many railway 

companies declined to participate. The 1996 inventory was assembled as best as practicable and 

subsequently approved as part of the SIP. Due to lack of information about locomotive growth 

potentials, the 1996 PEI was reflective of the 1990 baseline, without any significant changes. 

The three State PEIs included in the analysis for this report include the following: 

· 1996 PEI 

· 1999 PEI update 

· 2002 PEI update 

The approach used for each inventory resulted in overall emissions that were not 

consistent over time. Results from the current study confirm the need to recalculate the baseline 

locomotive emissions inventory so that past, present, and future emissions may be adjusted to 

show a more consistent trend, as expected. In addition to the three official PEI sources and this 

report, included are summary locomotive emissions from the ozone SIPs (i.e., “Modeling EI”) 

and summary locomotive emissions from the modeling inventories developed for the DFW and 

HGB ozone SIPs are included for comparison purposes. 

The HGB area is a severe nonattainment area and includes eight counties with the most 

serious ozone problem in Texas. The Dallas area comprises the central four counties in the Metro 

region and is a moderate ozone nonattainment area. The Beaumont-Port Arthur area is a 

marginal ozone nonattainment area. Units are expressed in terms of tons per average ozone 

season day (June-August). 

Tables 5-1 and 5-2 show that in both the Houston and Dallas nonattainment areas the 

1999 PEI has the greatest emissions (with the exception of VOC, which is highest in the current 

inventory), possibly due to the fact that many railway companies purchase diesel fuel in Texas, 

but use it elsewhere (e.g., BNSF is based in Fort Worth but ships locomotive diesel all over the 

country). In all other inventories, except for the modeling inventories, a GTM method was used 

for line haul locomotives and yard activity as opposed to Department of Energy fuel sales data. 

The GTM method estimates fuel usage based on GTM traffic activity; these fuel estimates are 

applied to emission factors to estimate emissions. 
5-2 

Table 5-1. Locomotive Emissions in the Houston Nonattainment Region by Scenario 

(Tons per Day) 

Scenario CO VOC NOx

1996 PEI 3.36 1.36 22.19 

1999 PEI 5.18 2.09 52.74 

2002 PEI 1.93 0.75 18.53 

Modeling EI (2000) n/a 1.47 35.94 

2003 TREIM 3.83 1.62 25.40

2006 TREIM 

(back cast 2000 estimate) 4.77 2.25 34.30 

Table 5-2. Locomotive Emissions in the Dallas Nonattainment Region by Scenario 

(Tons per Day) 

Scenario CO VOC NOx

1996 PEI 1.49 0.64 10.48 

1999 PEI 3.81 1.55 38.65 

2002 PEI 1.68 0.65 16.29 

Modeling EI (2000) n/a 0.93 22.90 

2003 TREIM 2.59 1.07 16.99

2006 TREIM 

(back cast 2000 estimate) 3.75 1.78 26.39 

Adjustments may have been made to the temporal and spatial allocation of the modeling 

emission inventories causing these estimates to be different from the other inventories, but still 

within the range of the 1999 and 2002 PEIs for VOC and NOx. 

Over time, locomotive emissions should have trends that reflect (1) activity growth and 

(2) reductions due to cleaner technology such as from the U.S. EPA Locomotive Rule (See 

http://www.epa.gov/otaq/locomotv.htm) and state initiatives such as the Texas Emission 

Reduction Plan (See http://www.tnrcc.state.tx.us/oprd/sips/terp.html). 

An additional study was examined to judge the reasonableness of the revised model’s 

results. CENRAP commissioned a study entitled “Emission Inventory Development for Mobile 

Sources and Agricultural Dust Sources for the Central States” (CENRAP 2004). As Table 5-3 

indicates, results for the revised model are between 1 (for PM) and 14 percent (for NOx) less than 

the CENRAP study. This is understandable because the CENRAP study was derived from fuel 

sales data, which tends to overestimate emissions because fuel sold in a given area may not 

necessarily be combusted in the same area. 
5-3 

Table 5-3. CENRAP Texas Locomotive Study (tons per year) 

Study CO VOC NOx PM 

CENRAP 9,489 3,753 68,236 2,149 

This Report 8,619 3,491 58,802 2,152 

% Difference 9% 7% 14% <1% 
6-1 

6.0 FUTURE ENHANCEMENTS

When the revision of the TREIM model was completed, an assessment was performed to 

identify enhancements or improvements that could be made in the future. Some of the more 

significant enhancements/improvements focused on resolving data quality issues associated with 

data provided by the railway companies. Following are the major enhancements that may be 

considered in the future: 

· Revise the model structure to make data queries simpler and more user friendly. The 

process should be automated allowing the users to change the level of control, growth 

factors, and activity more easily. A revision to the data structure should also allow for 

emission comparisons to be output directly. As part of this process the compiled data 

should be streamlined, which will make data queries easier and faster to implement. 

Streamlining the data may also be important in developing a public version of the 

model that addresses confidential business information requirements. 

· Develop a method to locate and quantify yard activities in non-DFW and HGB 

counties (i.e., all other remaining counties). 

· Include nonroad cargo handling equipment in the yard locomotive assessment, recent 

studies indicate that nonroad engines and equipment may be significant emission 

sources for rail related activities. 

· Disaggregate the activity/emissions data in non-DFW and HGB counties into 

individual railway segments using railway-provided data. During discussions with 

BNSF, it became apparent that they had a complete GIS data set for the whole State 

of Texas. It is expected that UP has a similar state-wide data set that could be used to 

more accurately estimate activity and emission outside the DFW and HGB areas. 

· Resolve the issue of confidential business data. Currently, the model includes 

information that identifies the location and level of activity for each of the railroad 

company. It is possible to develop a version of the model that removes any railroad 

company identifiers and aggregates the activity and emissions data up to the county 

level, such as version may help address the issue of confidentiality. 

· Expand the projection data to include non-DFW and HGB areas. 

· Conduct special studies that focus on improving the projections data needed to 

provide more accurate estimates of future year emissions. This will be challenging 

because railway companies are resistant to provide such information. 

· Revise the Tier profiles to account for the introduction of Tier 3 and Tier 4 engines. 

At this time the U.S. EPA has not provided any information regarding emission 

standards to be applied to these future Tier locomotives.
7-1 

7.0 CONCLUSIONS AND RECOMMENDATIONS 

This version of the TREIM model updates the locomotive Tier profiles, reflecting 

emission controls required by federal regulations and Texas emission reduction programs. This 

version also projects emissions to 2020 based on growth factors developed in a recent TCEQ 

project and engine fleet data provided by the railway companies. The incorporation of 

projections data has provided an added level of complexity to the model. Furthermore, the 

railroad company data provided for this revision were often inconsistent with data that they had 

provided for the previous version of the model making it particularly challenging to merge the 

two datasets. Despite these limitations the model has generated reasonable emissions estimates 

that are consistent with other Texas railway emissions data. The model estimated that in 2006, 

locomotives emitted approximately 8,843 tons per year (29.1 tons per day) of CO, 3,612 tons per 

year (11.9 tons per day) of VOC, 64,623 tons per year (212.5 tons per day) of NOx and 2,192 

tons per year (7.2 tons per day) of PM. Collectively, the DFW and HGB counties account for 

approximately one third of Texas locomotive emissions. In 2006, the Houston-GalvestonBrazoria area was responsible for approximately 50 percent more locomotive emissions than the 

Dallas-Fort Worth area due to freight movements associated with marine vessel activities. 

Projected emissions estimates suggest that current locomotive standards and TERP 

program will offset emissions associated with railway growth up to 2020. In fact, the TERP 

activities reduce NOx emissions from approximately 2 percent in 2006 to approximately 

16 percent in 2010, taking into consideration reasonable, but probably low growth estimates and 

application of federal locomotive standards. 

Initially the TREIM was developed as a repository for railway data and to estimate 

emissions. The model includes additional functions that allow for a limited number of data 

queries. To make data queries easier and further expand the types of data queries that the model 

can support, it is recommended that the database structure be revaluated and streamlined. It 

should also be noted that modifications made to this version of the model focused on the 

nonattainment counties of DFW and HGB. Tier profiles and projection data were not developed 

for the remaining counties in Texas. Future updates to the model should include these non-DFW 

and HGB counties. 
8-1 

8.0 REFERENCES 

Burlington Northern Santa Fe. September 28, 2007. BNSFHGGTMData.pdf, 

BNSFDFWGTMData.pdf, and BNSFLocoData.pdf. 

CENRAP, October 2004, Emission Inventory Development for Mobile Sources and Agricultural 

Dust Sources for the Central States. 

Texas Commission on Environmental Quality, August 3, 2007, TERC Tier Data.xls. 

Texas Commission on Environmental Quality, August 31, 2007, Development of Commercial 

Marine Vessel, Locomotive, and Air Traffic Activity Growth Factors: Draft Final Report.

Texas Commission on Environmental Quality, 2002, “Locomotive Emissions Inventory: Update 

and Discussion, Version 1.2,” prepared by ERG, August 30, 2002. 

U.S. Environmental Protection Agency/ Office of Transportation and Air Quality. December 

1997. Technical Highlights - Emission Factors for Locomotives. 

U.S. Environmental Protection Agency/ Office of Transportation and Air Quality. April 1998. 

Locomotive Emission Standards Regulatory Support Document. 

U.S. Department of Transportation, Bureau of Transportation Statistics, 2002) National 

Transportation Atlas Database2002 Shape file Format: Rail 100k. 

Union Pacific. September 17 2007. DFW LH-1 & SE-5 2004/2005/2006.pdf and HGA LH-1 & 

SE-5 2004/2005/2006.pdf. 
Appendix A 

Emission Estimates for Non-Dallas-Fort Worth and Houston-Galveston-Brazoria Counties 
A-1 

2003 Line Haul Locomotive Emission Estimates for Texas Counties (tons per year) 

(excluding Dallas-Fort Worth and Houston-Galveston-Brazoria Areas) 

FIPS County Name CO VOC NOX PM 

001 Anderson 43.84 16.49 293.40 11.04

005 Angelina 11.64 4.38 77.87 2.93

007 Aransas 0.61 0.23 4.09 0.15

009 Archer 0.03 0.01 0.20 0.01

011 Armstrong 45.77 17.21 306.29 11.53

013 Atascosa 11.24 4.23 75.25 2.83

015 Austin 69.86 26.27 467.49 17.60

017 Bailey 16.15 6.07 108.04 4.07

021 Bastrop 50.02 18.81 334.72 12.60

023 Baylor 0.05 0.02 0.35 0.01

025 Bee 0.10 0.04 0.66 0.02

027 Bell 108.28 40.72 724.60 27.27

029 Bexar 124.87 46.95 835.61 31.45

035 Bosque 58.15 21.86 389.12 14.65

037 Bowie 69.83 26.26 467.30 17.59

041 Brazos 45.51 17.11 304.56 11.46

043 Brewster 40.45 15.21 270.67 10.19

045 Briscoe 0.02 0.01 0.12 0.00

047 Brooks 0.05 0.02 0.35 0.01

049 Brown 25.93 9.75 173.53 6.53

051 Burleson 98.01 36.86 655.91 24.69

053 Burnet 0.06 0.02 0.41 0.02

055 Caldwell 33.77 12.70 225.99 8.51

057 Calhoun 1.65 0.62 11.04 0.42

059 Callahan 28.21 10.61 188.76 7.10

061 Cameron 20.56 7.73 137.61 5.18

063 Camp 18.33 6.89 122.65 4.62

065 Carson 105.66 39.73 707.09 26.61

067 Cass 75.50 28.39 505.28 19.02

069 Castro 9.78 3.68 65.48 2.46

073 Cherokee 44.22 16.63 295.93 11.14

075 Childress 43.91 16.51 293.84 11.06

077 Clay 49.38 18.57 330.42 12.44

079 Cochran 0.04 0.01 0.24 0.01

081 Coke 0.04 0.02 0.29 0.01

083 Coleman 39.51 14.86 264.38 9.95

087 Collingsworth 1.05 0.40 7.06 0.27

089 Colorado 57.98 21.80 387.98 14.60

091 Comal 32.99 12.40 220.75 8.31

093 Comanche 0.07 0.03 0.47 0.02
A-2 

2003 Line Haul Locomotive Emission Estimates for Texas Counties (tons per year) 

(excluding Dallas-Fort Worth and Houston-Galveston-Brazoria Areas) (Continued) 

FIPS County Name CO VOC NOX PM 

097 Cooke 34.20 12.86 228.84 8.61

099 Coryell 9.30 3.50 62.20 2.34

101 Cottle 0.06 0.02 0.41 0.02

103 Crane 0.50 0.19 3.33 0.13

105 Crockett 0.00 0.00 0.01 0.00

107 Crosby 0.03 0.01 0.19 0.01

109 Culberson 59.35 22.32 397.17 14.95

111 Dallam 82.25 30.93 550.39 20.72

115 Dawson 0.03 0.01 0.18 0.01

117 Deaf Smith 65.50 24.63 438.31 16.50

119 Delta 0.01 0.00 0.08 0.00

123 DeWitt 6.78 2.55 45.39 1.71

127 Dimmit 0.05 0.02 0.36 0.01

129 Donley 59.32 22.31 396.96 14.94

131 Duval 0.07 0.03 0.45 0.02

133 Eastland 28.74 10.81 192.33 7.24

135 Ector 29.20 10.98 195.38 7.35

141 El Paso 96.98 36.47 649.00 24.43

139 Ellis 18.98 7.14 127.00 4.78

143 Erath 0.07 0.03 0.45 0.02

145 Falls 43.95 16.53 294.14 11.07

147 Fannin 0.08 0.03 0.54 0.02

149 Fayette 81.01 30.46 542.12 20.40

151 Fisher 17.39 6.54 116.39 4.38

153 Floyd 0.14 0.05 0.94 0.04

155 Foard 0.03 0.01 0.23 0.01

159 Franklin 2.26 0.85 15.13 0.57

161 Freestone 19.35 7.28 129.51 4.87

163 Frio 33.79 12.70 226.10 8.51

165 Gaines 0.01 0.00 0.06 0.00

169 Garza 39.30 14.78 263.00 9.90

175 Goliad 0.05 0.02 0.31 0.01

177 Gonzales 54.67 20.56 365.84 13.77

179 Gray 59.95 22.54 401.20 15.10

181 Grayson 73.97 27.81 494.98 18.63

183 Gregg 39.01 14.67 261.03 9.82

185 Grimes 20.58 7.74 137.71 5.18

187 Guadalupe 85.43 32.13 571.73 21.52

189 Hale 9.94 3.74 66.53 2.50

191 Hall 24.24 9.12 162.23 6.11
A-3 

2003 Line Haul Locomotive Emission Estimates for Texas Counties (tons per year) 

(excluding Dallas-Fort Worth and Houston-Galveston-Brazoria Areas) (Continued) 

FIPS County Name CO VOC NOX PM 

195 Hansford 0.09 0.03 0.60 0.02

197 Hardeman 47.09 17.71 315.10 11.86

199 Hardin 10.84 4.08 72.57 2.73

203 Harrison 97.18 36.54 650.34 24.48

205 Hartley 87.33 32.84 584.42 22.00

207 Haskell 0.10 0.04 0.64 0.02

209 Hays 27.67 10.41 185.18 6.97

211 Hemphill 100.12 37.65 670.02 25.22

213 Henderson 11.09 4.17 74.24 2.79

215 Hidalgo 1.62 0.61 10.82 0.41

217 Hill 91.36 34.35 611.39 23.01

219 Hockley 6.24 2.35 41.77 1.57

221 Hood 0.04 0.02 0.30 0.01

223 Hopkins 16.56 6.23 110.81 4.17

225 Houston 16.82 6.33 112.57 4.24

227 Howard 30.38 11.42 203.27 7.65

229 Hudspeth 121.03 45.51 809.94 30.48

231 Hunt 7.23 2.72 48.41 1.82

233 Hutchinson 0.05 0.02 0.32 0.01

235 Irion 0.07 0.02 0.44 0.02

239 Jackson 22.53 8.47 150.75 5.67

241 Jasper 14.91 5.61 99.78 3.76

243 Jeff Davis 30.42 11.44 203.55 7.66

245 Jefferson 43.11 16.21 288.51 10.86

247 Jim Hogg 0.02 0.01 0.14 0.01

249 Jim Wells 0.12 0.04 0.80 0.03

251 Johnson 70.01 26.33 468.53 17.63

253 Jones 0.07 0.03 0.48 0.02

255 Karnes 0.05 0.02 0.30 0.01

257 Kaufman 23.67 8.90 158.42 5.96

261 Kenedy 22.34 8.40 149.51 5.63

271 Kinney 43.48 16.35 290.99 10.95

273 Kleberg 9.54 3.59 63.83 2.40

275 Knox 0.07 0.03 0.45 0.02

283 La Salle 40.54 15.25 271.32 10.21

277 Lamar 0.10 0.04 0.66 0.02

279 Lamb 30.65 11.53 205.13 7.72

281 Lampasas 36.63 13.77 245.14 9.23

285 Lavaca 5.66 2.13 37.87 1.43

287 Lee 24.06 9.05 161.03 6.06
A-4 

2003 Line Haul Locomotive Emission Estimates for Texas Counties (tons per year) 

(excluding Dallas-Fort Worth and Houston-Galveston-Brazoria Areas) (Continued) 

FIPS County Name CO VOC NOX PM 

289 Leon 56.03 21.07 374.96 14.11

293 Limestone 16.33 6.14 109.31 4.11

295 Lipscomb 31.40 11.81 210.11 7.91

297 Live Oak 11.03 4.15 73.79 2.78

299 Llano 0.03 0.01 0.20 0.01

303 Lubbock 40.64 15.28 271.95 10.24

305 Lynn 0.57 0.21 3.79 0.14

313 Madison 6.69 2.52 44.77 1.69

315 Marion 38.28 14.40 256.19 9.64

317 Martin 10.71 4.03 71.65 2.70

321 Matagorda 24.72 9.30 165.42 6.23

323 Maverick 13.94 5.24 93.32 3.51

307 McCulloch 0.02 0.01 0.16 0.01

309 McLennan 127.04 47.77 850.18 32.00

325 Medina 50.05 18.82 334.94 12.61

329 Midland 25.96 9.76 173.72 6.54

331 Milam 120.56 45.33 806.80 30.37

333 Mills 30.61 11.51 204.82 7.71

335 Mitchell 28.56 10.74 191.12 7.19

337 Montague 26.91 10.12 180.07 6.78

341 Moore 15.06 5.66 100.75 3.79

343 Morris 14.79 5.56 98.95 3.72

345 Motley 0.05 0.02 0.37 0.01

347 Nacogdoches 15.46 5.81 103.46 3.89

349 Navarro 30.48 11.46 204.00 7.68

351 Newton 5.82 2.19 38.94 1.47

353 Nolan 43.20 16.24 289.11 10.88

355 Nueces 17.68 6.65 118.33 4.45

357 Ochiltree 0.05 0.02 0.34 0.01

359 Oldham 24.52 9.22 164.11 6.18

361 Orange 64.35 24.20 430.61 16.21

363 Palo Pinto 33.29 12.52 222.76 8.38

365 Panola 7.10 2.67 47.53 1.79

367 Parker 14.22 5.35 95.16 3.58

369 Parmer 125.06 47.03 836.92 31.50

371 Pecos 7.18 2.70 48.08 1.81

373 Polk 6.84 2.57 45.76 1.72

375 Potter 122.15 45.93 817.44 30.77

377 Presidio 39.45 14.84 264.03 9.94

381 Randall 96.09 36.13 643.04 24.20
A-5 

2003 Line Haul Locomotive Emission Estimates for Texas Counties (tons per year) 

(excluding Dallas-Fort Worth and Houston-Galveston-Brazoria Areas) (Continued) 

FIPS County Name CO VOC NOX PM 

383 Reagan 0.05 0.02 0.31 0.01

387 Red River 0.05 0.02 0.34 0.01

389 Reeves 42.11 15.84 281.81 10.61

391 Refugio 34.80 13.09 232.91 8.77

393 Roberts 51.38 19.32 343.81 12.94

395 Robertson 101.29 38.09 677.81 25.51

399 Runnels 0.07 0.03 0.45 0.02

401 Rusk 17.60 6.62 117.80 4.43

403 Sabine 4.58 1.72 30.63 1.15

405 San Augustine 4.00 1.50 26.77 1.01

409 San Patricio 26.58 9.99 177.85 6.69

411 San Saba 0.05 0.02 0.35 0.01

415 Scurry 34.18 12.85 228.75 8.61

419 Shelby 20.23 7.61 135.40 5.10

421 Sherman 36.06 13.56 241.31 9.08

423 Smith 23.48 8.83 157.10 5.91

427 Starr 0.03 0.01 0.17 0.01

429 Stephens 4.92 1.85 32.94 1.24

433 Stonewall 0.00 0.00 0.03 0.00

437 Swisher 7.84 2.95 52.45 1.97

441 Taylor 64.94 24.42 434.56 16.36

443 Terrell 54.57 20.52 365.22 13.75

445 Terry 0.06 0.02 0.39 0.01

449 Titus 15.12 5.68 101.15 3.81

451 Tom Green 0.08 0.03 0.53 0.02

453 Travis 25.86 9.73 173.08 6.51

455 Trinity 7.45 2.80 49.84 1.88

457 Tyler 0.06 0.02 0.39 0.01

459 Upshur 36.01 13.54 240.99 9.07

461 Upton 0.05 0.02 0.35 0.01

463 Uvalde 43.72 16.44 292.60 11.01

465 Val Verde 82.80 31.13 554.07 20.85

467 Van Zandt 25.96 9.76 173.75 6.54

469 Victoria 32.02 12.04 214.30 8.07

471 Walker 6.43 2.42 43.05 1.62

475 Ward 39.62 14.90 265.14 9.98

477 Washington 30.99 11.65 207.39 7.81

479 Webb 0.13 0.05 0.90 0.03

481 Wharton 1.71 0.64 11.45 0.43

483 Wheeler 0.09 0.04 0.63 0.02
A-6 

2003 Line Haul Locomotive Emission Estimates for Texas Counties (tons per year) 

(excluding Dallas-Fort Worth and Houston-Galveston-Brazoria Areas) (Continued) 

FIPS County Name CO VOC NOX PM 

485 Wichita 50.48 18.98 337.82 12.72

487 Wilbarger 51.63 19.42 345.53 13.01

489 Willacy 8.56 3.22 57.26 2.16

491 Williamson 67.57 25.41 452.15 17.02

493 Wilson 0.04 0.01 0.26 0.01

495 Winkler 0.04 0.02 0.28 0.01

497 Wise 18.15 6.83 121.49 4.57

499 Wood 25.05 9.42 167.64 6.31

507 Zavala 0.06 0.02 0.40 0.01

Total 6,024.06 2,264.68 40,339.85 1,517.29
Appendix B 

User’s Manual for the Texas Railway Emission Inventory Model (TREIM) 
Texas Railroad Emission 

Inventory 

User Manual 

August 31, 2007
1 

INTRODUCTION

The Texas Railroad Emissions Inventory Model (TREIM) uses a populated dataset to 

calculate emissions of four pollutants from railway locomotives in Texas (Figure 1). You do not 

need additional data to calculate emissions. Using the model, you can do the following: 

· Calculate state and county level emissions; 

· Calculate emissions for any year between 1999 and 2020 (for the Dallas/Fort Worth 

(D/FW) and Houston/Galveston (H/G) areas); 

· Calculate emissions of specific train types operating in D/FW and H/G areas (where 

data have been provided); 

· Use GIS to view emissions at the county, rail line segment, and modeling grid levels; 

and 

· Evaluate the effect of replacing older higher-emitting locomotives with newer loweremitting locomotives; 

Figure 1. Model Calculates Annual Emissions for all Texas Counties 

Based on Populated Activity Data 

User Options 

You can immediately calculate county-level emissions. Alternatively, you can estimate 

emissions for a specific year or railway segment for segments located in the counties that 

constitute the H/G and D/FW areas. 
2 

The model was developed to include detailed data where provided, such that you can 

select the specific train type you are interested in before calculating emissions. Note that not all 

railroad companies provided train type data—and those that provided data did not provide data 

for all of the inventory years. 

You can also modify the populated dataset to evaluate different scenarios such as changes 

in railroad traffic, or use of higher Tier locomotives. To run a scenario, output from the original 

data will need to be compared outside the model with output from the scenario. In developing the 

scenarios, the edited data files should be saved as a separate file. You can always return to the 

original “read only” data, which is provided on the CD. 

Model Background 

The database behind the model was originally developed as a repository for organizing 

and storing activity data provided by the railroad companies for the D/FW and H/G areas. The 

model was developed by applying U.S. EPA emission factors to the activity data to estimate 

annual emissions of volatile organic compounds (VOC), carbon monoxide (CO), nitrogen oxides 

(NOx), and particulate matter (PM). 

To enhance the model, the geographic scope was expanded to include all counties in 

Texas. Emission estimates were developed at the segment level and applied to GIS shapefiles to 

show the spatial attributes of the emissions data and to allow for county level summaries. The 

model was further expanded to include emission estimates for the years 1999 to 2020 for the 

D/FW and H/G areas. 

For this version of the model, users are expected to be Texas Commission on 

Environmental Quality (TCEQ) staff that are authorized to access confidential business 

information. In order to develop a public version of the model, the current model will have to be 

modified to aggregate data and block access to confidential business information provided by the 

railroads. A public version of the model can be used by railroad companies, city and county 

planners, university researchers, and others who are interested in evaluating air quality issues 

related to railroad activities in Texas. 

HOW THE MODEL CALCULATES EMISSIONS 

The model multiplies activity data by an emission factor to yield annual emission 

estimates of VOC, CO, NOx, and PM. Activity data were provided by the railroad companies for 

the D/FW and H/G areas. Activity data for the remaining Texas counties are from Bureau of 

Transportation (BTS). Emission factors were obtained from “U.S. EPA Emission Factors for 

Locomotives” (U.S. EPA/ Office of Mobile Sources, Technical Highlights, Emission Factors for 

Locomotives EPA420-F-97-051, December 1997). 

Equation 1. Activity data × Emission factor = Annual emissions 
3 

Activity Data 

The model is populated with activity data. You can modify some of the data, such as fuel 

consumption rates or engine Tier profiles to evaluate specific scenarios such as increased traffic 

or introduction of engines with lower emissions. 

The model divides locomotives into two types: line haul and switch yard. A line haul 

locomotive hauls freight over railroad lines from one destination to another, while a yard (or 

switch) locomotive moves freight within a rail yard. Line haul data is further broken down into 

General data and Specific data. Activity data in the General data set represents the combined 

activity for all train types, while the Specific line haul data provide disaggregated activity data 

for each specific train type operating on the segment. Note that specific line haul data were not 

provided by all railroad companies, but the model was designed to include detailed data 

submittals. Also note that specific line haul data were provided for only the H/G and D/FW areas 

and for only a limited number of inventory years. The specific train types incorporated into the 

model are listed in Table 1. 

Table 1. Specific Train Type Activities 

Locomotive

Code Locomotive Activity Description 

A Amtrak 

B Baretable Intermodal 

C Loaded Unit Coal 

D Lite Engine 

E Empty Unit Coal 

F Foreign Railroad Trains NSF 

G Loaded Unit Grain 

H Manifest (High Priority) 

I Deadhead Crew Moves 

J Not Used 

K Helper Service 

L Local Service 

M Manifest (Normal Priority) 

N Hours of Service Relief Crews 

O Officer Specials 

P Intermodal Premium 

Q Intermodal Guarantee 

R Road Switcher Service 

S Intermodal D-Stack 

T Transfer Service (Interchange Received/Delivered) 

U Unit (Not Grain or Coal) 

V Vehicle (Auto) 

W Work Train 

X Empty Unit Grain 

Y Yard Engines 

Z Priority Ups Intermodal 
4 

Emission Factors

Emission factors were obtained from “U.S. EPA Emission Factors for Locomotives” as 

noted in Table 2. The model assigns the following emission factors based on the classification of 

the locomotive as provided by the railroad companies. 

Table 2. Locomotive Emission Factors (grams/gal) by Tier 

Locomotive Pollutants 

Type VOC CO NOx PM 

Non Tier (Pre-1973) 

Line Haul 10 26.6 270 6.7

Switch 21 38.1 362 9.2

Tier 0: Controlled emission rates for locomotives manufactured in 1973-2001 

Line Haul 10 26.6 178 6.7 

Switch 21 38.1 262 9.2 

Tier 1: Controlled emission rates for locomotives manufactured in 2002-2004 

Line Haul 9.8 26.6 139 6.7 

Switch 21 38.1 202 9.2 

Tier 2: Controlled emission rates for locomotives manufactured after 2004 

Line Haul 5.4 5.0 103 3.6 

11 7.3 152 4.3 

Hybrid Engines 

Switch 0.046 0.008 0.206 0.005 

Source: U.S. EPA/ Office of Mobile Sources, Technical Highlights, Emission Factors for 

Locomotives EPA420-F-97-051, December 1997. 

Footnote: Locomotives manufactured before 1973 are considered to be uncontrolled. 

Factors for hybrid engines were obtained from the railroad companies and the manufacturers. 

Annual Emissions 

Line Haul Emissions. The model calculates annual emissions for line haul operations by 

multiplying activity data (gallons of fuel used) by an emission factor (grams of pollutant per 

gallon of fuel). The model calculates emissions for VOC, CO, NOx, and PM. After converting 

grams to pounds, the model reports annual emissions in pounds of pollutant per year. 

Equation 2. Annual line emissions = gallons of fuel used × grams of pollutant/gallon of 

fuel × pounds/gram = pounds of pollutant 

Switch Yard Emissions. The model calculates annual emissions for switch operations by 

multiplying activity data (hours of operation) by fuel usage (gallons of fuel per hour of 

operation) by an emission factor (grams of pollutant per gallon of fuel). The model calculates 

emissions for VOC, CO, NOx, and PM. After converting grams to pounds, the model reports 

annual emissions in pounds of pollutant per year. 

Equation 3. Annual switch emissions = hours of operation × gallons of fuel per hour × 

grams of pollutant/gallon of fuel × pounds/gram = pounds of pollutant 
5 

HOW TO USE THE MODEL 

System Requirements 

To use the model, your computer system must have the following: 

· MS Access, Version 2003 or higher (with latest service pack) 

· 1 gb RAM 

· 50 mb of disk memory 

· CD drive 

· To best access all features of the GIS component of the model, ArcView 9.2 is 

recommended. The shapefiles and map document are compatible with virtually all 

different GIS programs, however, some features may not be available in all programs. 

You may operate the model from the source file. However, before you modify the data, 

you should save an unedited version of the model in a separate location. This will enable you to 

compare your results to the unmodified version of the model. 

STEP 1: 

SELECT TRAIN TYPE 

The model opens with the data type screen presented in Figure 2. From here you can select the 

data type for which you want to calculate emissions. 

· General Line Haul Data. Contains data for line haul locomotives that reflects total 

activity per railroad segment, regardless of railroad traffic type. The model links the 

activity with a generic fuel usage rate and emission factors to calculate annual 

emissions. Select this button and you’ll be taken to the General Line Haul Data form 

(see page 6). 

Figure 2. Opening Screen 
6 

· Specific Line Haul Data. Contains data for line haul locomotives that reflects activity 

data per railroad segment, by 25 different railroad activity types (see Table 1), where 

available. The model links a specific train type activity with a specific fuel usage rate 

and emission factor to calculate annual emissions. Select this button and you’ll be 

taken to the Specific Line Haul Data form. 

· Switch Data. Contains data for switch yard locomotives. The model multiplies the 

activity data (hours of operation) by an emission factor to calculate annual emissions. 

Select this button and you’ll be taken to the Switch Data form (see page 9). 

· View Total (Line Haul and Switch) Emissions by County. Sums emissions for both 

line haul and switch data, by county, using the data that is populated into the model. 

Select this button and you’ll be taken to Total Line Haul and Switch Emissions by 

County. 

· Exit. Allows you to exit the program. 

STEP 2: VIEW OR EDIT LINE HAUL ACTIVITY DATA 

Data Forms: General and Specific Line Haul 

The General and Specific Line Haul Data forms allow you to view the data set before 

calculating emissions. Using the data forms (Figure 3), you can scroll through each record of the 

baseline by clicking on the arrows at the bottom of the form. Each record represents information 

for a railway segment. 

The Specific Line Haul Data form is similar to the General Line Haul Data form, except 

that the train type can be set to any of the 25 train types. The train type code is automatically set 

when the train type is selected. 

Figure 3. Baseline General Line Haul Data Form 
7 

Table 3 shows the data fields that appear on the General and Specific Line Haul Baseline 

Data forms. 

Table 3. Baseline General and Specific Line Haul Data Forms Contain the Same Fields 

Field Description User Can Edit? 

Railway company UP BNSF, Kansas City No 

County County name No 

State/county FIPS 48=Texas; next three digits represent county No 

Segment ID Code Identifies the specific railroad segment. No 

Fuel usage 

(gallons) 

The model calculates annual fuel usage by dividing 

GTM by GTM/gallon, which yields gallons per year. 

Baseline only 

Train type In the General data, the model assigns a generic train 

type. However, in the Specific data, railroad traffic is 

defined as one of the 25 specific train types from the pull 

down menu. 

No 

Train type code A shorthand code that corresponds to the train type data 

field. This field is automatically assigned when the Train 

Type is selected. 

No 

Data year Calendar year of activity, which is 2004 for the baseline. Yes 

Fuel Usage Rate 

(gallons per 1,000 

GTM)

Function of train type. The model applies a generic fuel 

usage rate in the General data, and applies a specific fuel 

usage rate to the train type selected in the Specific data. 

No 

Train Count Number of trains that move along the segment No 

GTM Gross ton mileage No 

While the General and Specific Line Haul Data forms instantly display the 2004 Baseline 

data, you also have the option to examine and modify projected activity data for all other years 

between 1999 and 2020. Selecting “Examine Projected Activity” will display a form with 

segment-specific fuel usage and GTM for each year. As with the baseline data, using the 

projected data forms (Figure 4), you can scroll through each record of the baseline by clicking on 

the arrows at the bottom of the form. Each record represents information for a railroad segment. 

Figure 4. Projected General Line Haul Data Form 
8 

Table 4 shows the data fields that appear on the General and Specific Line Haul Projected 

Data forms. 

Table 4. Projected General and Specific Line Haul Data Forms Contain the Same Fields 

Field Description User can edit? 

Railroad company UP BNSF, Kansas City No 

County County name no 

State/county FIPS 48=Texas; next three digits represent county no 

Segment ID Code Identifies the specific railroad segment. no 

CAGR Combined Annual Growth Rate No 

Data year Calendar year of activity, which ranges from 1999 to 

2020. 

No 

Fuel Use (GAL) The model calculates annual fuel usage by dividing 

GTM by GTM/gallon, which yields gallons per year. 

Yes 

Train Count Number of trains that move along the segment No 

Activity (GTM) Gross ton mileage No 

Choosing Between General or Specific Line Haul 

The difference between General and the Specific line haul data is that activity data in the 

General data set are based on a generic train type for all segments, while the Specific line haul 

data are based on a specific train type for a specific segment. The Specific line haul data apply to 

only the D/FW and H/G counties. 

Next Steps: 

Once you have reviewed or edited the activity data, choose one of the following options: 

· View/Edit Fleet Composition. Refer to page 9, Figure 5. 

· Calculate Baseline Emissions. Refer to page 12, Figure 7. 

STEP 3: VIEW OR EDIT SWITCH ACTIVITY DATA 

Data Form: Switch Data 

The Switch Data Form allows you to view the dataset before calculating emissions. Using 

the Switch Data Form (Figure 5), you can scroll through each record by clicking on the arrows at 

the bottom of the form. Each record represents information for a railyard. 
9 

Figure 5. Switch Data 

Table 5 shows the data fields that appear on the Switch Data Form. 

Table 5. General and Specific Line Haul Data Forms Contain the Same Fields 

Field Description User Can Edit? 

Railroad company UP, BNSF, Kansas City no 

Data year Calendar year of activity, which ranges from 

2002 to 2020 

no 

City City name no 

Train Count Number of trains that move along the segment no 

Fuel usage (gallons) The model calculates annual fuel usage by 

dividing the annual hours of operation by a fuel 

usage factor. 

Yes 

Annual hours of 

operation 

Annual hours of operation No 

Data ID Code Identifies the specific data record. no 

Yard ID Code Identifies the specific railyard. no 

Latitude Latitude no 

Longitude Longitude no 
10 

Next Steps: 

Once you have reviewed or edited the activity data, choose one of the following options: 

· If hybrid engines are being evaluated, it is necessary to adjust the fuel usage rate to 

account for the fact that these engines use approximately 60 percent less fuel than 

conventional engines. 

· View/Edit Fleet Composition. Refer to page 10, Figure 6. 

· Calculate Baseline Emissions. Refer to page 13, Figure 8. 

STEP 4: VIEW OR EDIT FLEET COMPOSITION 

Data Forms: Fleet Composition 

The Fleet Composition Form allows you to view how the model assigns emission factors 

across the fleet. The Switch Fleet Composition data form is similar to the Line Haul Fleet 

Composition data form (Figure 6), except the switch fleet data contain information for specific 

yards. 

Figure 6. Line Haul Fleet Composition Form 

The model is populated with fleet composition data provided by the railroad companies. 

The model assigns an emission factor for the portion of the fleet assigned to each Tier. The 

model assigns an emission factor based on when a locomotive was manufactured, which is 

known as a Tier. For example, if 95 percent of a fleet’s locomotives were manufactured in 1975 

and 5 percent were manufactured in 2002, then the model assign a Tier 0 emission factor to 

95 percent of the fleet and a Tier 1 emission factor to 5 percent of the fleet. The Tier definitions 

and associated controlled emission factors are presented in Table 6. 
11 

Table 6. Locomotive Emission Factors (grams/gal) by Tier 

Locomotive Pollutants 

Type VOC CO NOx PM 

Non Tier (Pre-1973) 

Line Haul 10 26.6 270 6.7

Switch 21 38.1 362 9.2

Tier 0: Controlled emission rates for locomotives manufactured in 1973-2001 

Line Haul 10 26.6 178 6.7 

Switch 21 38.1 262 9.2 

Tier 1: Controlled emission rates for locomotives manufactured in 2002-2004 

Line Haul 9.8 26.6 139 6.7 

Switch 21 38.1 202 9.2 

Tier 2: Controlled emission rates for locomotives manufactured after 2004 

Line Haul 5.4 5.0 103 3.6 

11 7.3 152 4.3 

Hybrid Engines 

Switch 0.046 0.008 0.206 0.005 

Source: U.S. EPA Emission Factors for Locomotives – Table 9: Fleet Average Emission Factors 

for all Locomotives BPR 420-F-97-051, 1997. 

Footnote: Locomotives manufactured before 1973 are considered to be uncontrolled. 

Factors for hybrid engines were obtained from the railroad companies and the manufacturers. 

Editing the fleet composition allows you to evaluate the effect of replacing older higheremitting locomotives with newer lower-emitting locomotives which is discussed later in this user 

manual. 

You can edit the fleet composition using the Fleet Composition Form and calculate 

emissions if you want to evaluate possible scenarios, such as the ones listed below. To edit the 

fleet composition, select the year for which you want to edit. If you edit the data, then you can 

compare your results to results generated by the original model data. To return to the original 

data, close the model and reopen it from the original, unedited file on the CD. 

Next Steps: 

Once you have reviewed or edited the fleet composition, choose one of the following 

options: 

· Calculate Baseline Emissions. Refer to page 12, Figure 7 (Line Haul) and page 13, 

Figure 8 (Switch). 
12 

STEP 5: VIEW RESULTS 

Line Haul Emissions

Results are presented in a similar format for line haul and switch emissions, for both 

baseline and projected emissions. Select the data tab to view results as follows: 

Line Haul Emissions (Figure 7) 

· Baseline: View by Segment ID, by County, or by Pollutant 

· Projected: View by Segment ID or by County 

Figure 7. Line Haul Emissions by Segment 

You can also select Print Emissions by Segment ID, Print Emissions by County, or Print 

Emissions by Pollutant to print emissions tables that have been formatted for printing. 

Switch Emissions

Select the data tab to view results by Segment ID or by Pollutant. 

Switch Emissions (Figure 8) 

· Baseline: View by Yard ID, by County, or by Pollutant 

· Projected: View by Yard ID or by County 
13 

You can also select Print Emissions by Yard ID or Print Emissions by Pollutant to print 

emissions tables that have been formatted for printing. 

Figure 8. Switch Emissions by Yard

Each emissions table can be sorted or filtered to customize the display of the data. To 

sort, right-click on the column name of the column you wish to use and then select “Sort 

Ascending” or “Sort Descending” to organize the data in alphabetical or numeric order. You can 

also filter the data to isolate a particular county or pollutant by right clicking on the cell that 

contains the value you wish to view and then selecting “Filter by selection.” There are several 

other table manipulations and filtering options available within MS Access; refer to MS Access 

Help for more information. 

View Total (Line Haul and Switch) Emissions by County 

From the opening screen (Figure 2), select View Total (Line Haul and Switch) Emissions 

by County to view a summary of emissions for both line haul and switch data, by county. 

View Total (Line Haul and Switch) Emissions by County 

This sums baseline emissions for both generic and specific line haul as well as switch 

data, by county. The user is then presented an easy-to-read, consolidated baseline emissions 

report for printing. 

GIS Component 

The model comes with an associated Geographic Information System (GIS) component 

that enables you to take advantage of the spatial element of the dataset. While the MS Access 

interface enables you to view and modify data for specific rail segments and county summaries, 
14 

the GIS interface extends the model to provide powerful visualization of the segments’ 

connectivity and the layout of the railroad network across the state. The GIS component consists 

of three shapefiles and one map document. For best performance, ESRI’s ArcMap software 

version 9.3 or higher should be used to access these files. 

The model contains an ArcMap document that is actively linked to the segment and yard 

data present in the Access database. This portion of the model allows you to better utilize the 

spatial element of the dataset by incorporating the activity data along with a map of all railroad 

segments and intermodal facilities locations. The default view of the data shows the intermodal 

facilities symbolized by combined annual growth rate, which the segment data is symbolized 

using the 2004 GTM activity data. A county boundary data file is included as well. 

In addition to the information readily visible in the Table of Contents window, you may 

access additional segment or yard-level data via the “Identify” tool. Clicking on any railroad 

segment in the network will open a window that lists a number of segment properties, including: 

· Segment ID (the unique identifier that links the segment-specific activity data to the 

line segment indicating the railroad’s location in the GIS) 

· County Name and FIPS 

· Segment length (in miles) 

· Railroad company 

· Density class 

· Combined Annual Growth Rate 

· Activity in GTM for each year from 1999 through 2020. 

Two levels of additional information are available for the intermodal facilities. Using the 

“Identify” tool to click on a yard location will open a new window listing a number of yard 

properties, including the yard name, coordinates, city location, and cumulative average growth 

rate (CAGR), among other properties. Expanding the “+” checkbox on the left of the identify 

window reveals a new set of information specific to that yard for each year between 1999 and 

2020. For each year, this includes but is not limited to the following: 

· DataIDCode (unique identifier that links the yard-specific activity data to the point 

indicating the yard’s location in the GIS) 

· Yard-specific Tier profile 

· Annual Fuel usage (gallons) 

· Annual Hours of operation 

Note that several intermodal facilities were obtained from the BTS data and, as such, lack 

fleet composition and projected activity data. For these yards, a placeholder for 2004 fleet 

information has been included in the model to facilitate future revisions. 

Note: in the event of broken linkages between the GIS and Access components of the model, 

they can be recreated as follows: 
15 

· In ArcMap, go to File/Add Data, and navigate to the Access database and add the 

tables named “LINEHAUL_Activity_Projections” and 

“YARD_Activity_Projections” 

· Intermodal_Rail_Facilities: in ArcMap, right-click on the shapefile in the table of 

contents, then select “Joins and Relates” then “Relate...” Base the relate on the field 

“ID11” and relate it to the table “YARD_Activity_Projections” using the field 

“DataIDCode”, then name the relate “YARD_Activity_Projections_relate”. 

· TXRailroad_FINAL: in ArcMap, right-click on the shapefile in the table of contents, 

then select “Joins and Relates” then join... Choose the field 

“TX_Railroad_FINAL.SegmentID” and join it to the table 

“LINEHAUL_Activity_Projections” based on the field “SegmentIDcode.” 

· Please note: if the GIS files (and changes) are saved to the user’s drive and not 

moved, these linkages persist even after opening and closing the model so that these 

steps need not be repeated. 

Users who do not have ArcGIS can still access and benefit from the GIS data. A free data 

viewer is available through ESRI that allows users to create a map for easy display and query of 

GIS data. Instructions and a free download are available at the following link: 

http://www.esri.com/software/arcexplorer/download9.html

EVALUATING POSSIBLE LINE HAUL SCENARIOS

You can edit the activity data in the Line Haul and Yard Data Forms and calculate 

emissions if you want to evaluate possible scenarios. After editing the data, you can compare 

your results to results generated by the original model data. To return to the original data, close 

the model and reopen it from the original, unedited file on the CD. 

Example 1: If railroad traffic doubles along railroad segment, then how would NOx emissions be 

effected for a specified county? 

· To evaluate this scenario the model should be run for the inventory year of interest. 

· The NOx estimate for segment of interest should be extracted from model output. 

· Fuel usage data for the segment of interest should be doubled to represent a doubling 

in traffic along the segment of interest. 

· Once this change has been made, the model can be rerun. 

· The NOx output for the specified county for the base line can be compared with the 

output for the scenario to evaluate the impact that doubling of traffic has on local air 

quality. 

Example 2: If the number of yard locomotives a Yard X increase by 50 percent, then what would 

the SOx emissions be for a specific county? 

· To evaluate this scenario the model should be run for the inventory year of interest. 
16 

· The SOx estimate for the yard of interest should be extracted from model output. 

· Hours of operation data should be increased by 50 percent to represent an increase in 

yard engine population of this amount. 

· Once this change has been made, the model can be rerun. 

· The SOx output for the specified county for the base line can be compared with the 

output for the scenario to evaluate the impact that increasing the yard fleet by 50 

percent has on SOx emissions. 

Example 3: If higher Tier engines are used at Yard Y, then how would NOx emissions be 

reduced? 

· To evaluate this scenario the model should be run for the inventory year of interest. 

· The NOx estimate for the yard of interest should be extracted from model output. 

· The yard fleet composition should be modified for the yard of interest, increasing the 

fraction of switch engines with higher Tier rating. 

· Once this change has been made, the model can be rerun. 

· The NOx output for the base line can be compared with the output for the scenario to 

evaluate the impact that increasing the number of higher Tier switch engines has on NOx

emissions. 
Attachment B 

Emission Factors for Locomotives 
1

Technical

Highlights

 Printed on Recycled Paper

United States

Environmental Protection

Agency

Air and Radiation EPA420-F-97-051

December 1997

Office of Mobile Sources

Emission Factors for Locomotives

Estimated Baseline Freight Locomotive Emission

Rates

In support of the rulemaking finalizing the locomotive emission standards, EPA has estimated average emission rates, given in grams per

brake horsepower-hour (g/bhp-hr), for current uncontrolled locomotives.

These estimates are shown in Table 1. It is important to note that there is

significant variability in in-use emission rates. Table 2 shows the range

of emission rates that have been reported for NOx and PM.

The Environmental Protection Agency (EPA) has established emission

standards for oxides of nitrogen (NOx), hydrocarbons (HC), carbon

monoxide (CO), particulate matter (PM) and smoke for newly

manufactured and remanufactured diesel-powered locomotives and

locomotive engines, which have previously been unregulated. Three

separate sets of emission standards have been adopted, with

applicability of the standards dependent on the date a locomotive is first

manufactured. The first set of standards (Tier 0) apply to locomotives

and locomotive engines originally manufactured from 1973 through

2001. The second set of standards (Tier 1) apply to locomotives and

locomotive engines originally manufactured from 2002 through 2004.

The final set of standards (Tier 2) apply to locomotives and locomotive

engines originally manufactured in 2005 and later. To analyze the

environmental benefits expected from these new standards, EPA had to

calculate emission factors for locomotives.
2

* Line-haul locomotives over the line-haul duty-cycle

** Switch locomotives over the switch duty-cycle

Conversion to Gram per Gallon Emission Factors

It is often useful to express emission rates as grams of pollutant emitted

per gallon of fuel consumed (g/gal). This can be done by multiplying the

emission rates in Table 1 by a conversion factor. EPA has estimated the

appropriate conversion factor to be 20.8 bhp-hr/gal. These converted

emission factors are shown in Table 3.

Projected Future Emission Factors

With the new national emission standards for both newly manufactured

and remanufactured locomotives originally built after 1972, future

locomotive emission rates are projected to be much lower than the

baseline rates shown above. EPA’s estimates of future emission rates for

setaRnoissimEesU-nIenilesaBdetamitsE-1elbaT

)rh-phb/g(

H O C C x N M O P

Line-Haul* 0 8 .48 1 0 .2 1 2 3. 0.3

Switch** 1 3 .01 1 4 .8 1 4 7. 0.4

Table 2 - Range of NOx and PM Emission Rates (g/bhp-hr)

L e ine-Haul Cycle Switch Cycl

N M Ox P x N M O P

1 1 0.3-18.2 0 1 .22-0.4 9 6 .2-33. 0.22-0.8

Table 3 - Converted Emission Factors (g/gal)

H O C C x N M O P

Line-Haul 1 6 0 2 0 6. 2 7 7 6.

Switch 2 1 1 3 2 8. 3 2 6 9.
3

Tier 0-Tier 2 locomotives are shown in Tables 4-6, respectively. Table 9

gives the expected fleet average emission factors for all locomotives,

which reflects the penetration of the Tier 0-Tier 2 locomotives into the

fleet over time.

rofsetaRnoissimEdellortnoCdetamitsE-4elbaT

)0reiT(1002-3791niderutcafunaMsevitomocoL

H O C C x N M O P

g/bhp

-hr g/gal g/bhp-hr g/gal g/bhp-hr g/gal g/bhp-hr g/gal

Line-Haul 0 0 .48 1 8 1 6 2. 2 6. 6. 8 8 1 2 7 0 7.6 .3

S ctiw h 1 1 0. 1 2 3 1 1 8. 3 6 8. 1 2 2. 2 4 6 0 2.9 .4

Ta elb 5 - E its m eta d Co ellortn d Em si ois n R eta s rof

Locom vito es Man afu erutc d ni 200 2-2 004 reiT( )1

H O C C x N M O P

g/bhp

-hr g/gal g/bhp-hr g/gal g/bhp-hr g/gal g/bhp-hr g/gal

Line-Haul 0 8.9 8 .47 1 6 2. 2 7. 6. 6 9 1 2 3 0 7.6 .3

S ctiw h 1 1 0. 1 2 3 1 1 8. 3 9. 8. 9 2 2 4 0 0 2.9 .4

Ta elb 6 - E its m eta d Co ellortn d Em si ois n R eta s rof

Locom vito es Man afu erutc d retfa 2004 reiT( )2

H O C C x N M O P

g/bhp

-hr g/gal g/bhp-hr g/gal g/bhp-hr g/gal g/bhp-hr g/gal

Line-Haul 0 4.5 8 .26 1 6 2. 2 0. 6. 5 3 1 7 0 0 6.3 .1

S ctiw h 0 1 5. 2 1 3 1 1 8. 3 3. 8. 7 2 1 1 5 0 3.4 .2
4

Emission Inventory Estimation

Total emissions can be calculated by multiplying the emission factors (in g/gal)

by the fuel consumption rates (in million-gal/yr) to give annual emission rates

(in metric tons per year). This metric estimate can be converted to standard tons

(or short tons) per year, by multiplying it by 1.1.

In the United States, the great majority of fuel consumed by locomotives

each year is used in line-haul freight service. Smaller amounts are also

used in switching and passenger service. EPA’s estimates of these fuel

volumes are shown in Table 7. EPA’s estimates of annual emission rates

calculated from these fuel consumption rates are shown in Table 8.

For More Information

For further information on emission factors for locomotives, please write

to:

U.S. Environmental Protection Agency

Engine Programs and Compliance Division

2565 Plymouth Road

Ann Arbor, MI 48105

Additional documents on locomotive emission standards are available

electronically from the EPA Internet server at:

http://www.epa.gov/OMSWWW/locomotv.htm

or by calling (734) 668-4333.

noitpmusnoCleuFevitomocoL6991-7elbaT

)raey/lagnoillim(yrogetaCecivreSyb

luaH-eniLthgierFlanoitaN 133,3

gnihctiwSthgierFlanoitaN 072

gierFlanoigeRdnalacoL th 512

gnessaP re 331

evitomocoLediwnoitaN6991detamitsE-8elbaT

)raeyrepsnottrohsdnasuoht(setaRnoissimE

H O C C x N M O P

4 9 7 1 2 1 1 0 ,20 3
5

srotcaFnoissimEegarevAteelF-9elbaT

sevitomocoLllAroF

( ) )rh-phb/g (g/gal

Y C ear H OC x N M O P CH OC x N M O P

1 2 999 0 2 .5 1 0 .3 1 3 3.3 0 7 .3 1 4 0. 2 7 7. 2 8 76. 6.

2 2 000 0 2 .5 1 6 .3 1 3 3.1 0 7 .3 1 4 0. 2 8 7. 2 8 73. 6.

2 2 001 0 2 .5 1 4 .3 1 3 2.7 0 7 .3 1 4 0. 2 0 7. 2 8 65. 6.

2 2 002 0 2 .5 1 6 .3 1 3 1.9 0 7 .3 1 4 0. 2 8 7. 2 8 48. 6.

2 2 003 0 2 .5 1 2 .3 1 3 1.2 0 7 .3 1 4 0. 2 3 7. 2 8 33. 6.

2 1 004 0 2 .5 1 9 .3 1 3 0.4 0 7 .3 1 4 0. 2 1 7. 2 8 18. 6.

2 0 005 0 2 .5 1 0 .3 9 2 .6 0 4 .3 1 4 0. 2 8 7. 1 6 99. 6.

2 8 006 0 2 .4 1 2 .3 8 1 .9 0 1 .3 1 4 0. 2 6 7. 1 4 85. 6.

2 7 007 0 2 .4 1 1 .3 8 0 .5 0 8 .3 9 4 . 2 0 7. 1 2 77. 6.

2 6 008 0 2 .4 1 9 .3 8 9 .2 0 6 .2 9 4 . 2 5 7. 1 0 72. 6.

2 5 009 0 2 .4 1 9 .3 8 8 .0 0 4 .2 9 4 . 2 3 7. 1 9 68. 5.

2 4 010 0 2 .4 1 4 .3 7 8 .8 0 1 .2 9 4 . 2 0 7. 1 7 63. 5.

2 4 011 0 2 .4 1 4 .3 7 7 .7 0 1 .2 9 4 . 2 1 7. 1 7 61. 5.

2 3 012 0 2 .4 1 2 .3 7 7 .6 0 9 .2 8 4 . 2 5 7. 1 6 58. 5.

2 2 013 0 2 .4 1 0 .3 7 6 .5 0 8 .2 8 4 . 2 9 7. 1 5 55. 5.

2 2 014 0 2 .4 1 7 .3 7 6 .3 0 7 .2 8 4 . 2 4 7. 1 4 53. 5.

2 1 015 0 2 .4 1 6 .3 7 5 .2 0 5 .2 8 4 . 2 0 7. 1 3 51. 5.

2 0 016 0 2 .4 1 4 .3 7 5 .1 0 4 .2 8 4 . 2 5 7. 1 2 48. 5.

2 0 017 0 2 .4 1 4 .3 7 5 .0 0 3 .2 8 4 . 2 5 7. 1 1 46. 5.

2 9 018 0 2 .3 1 4 .3 6 4 .9 0 2 .2 8 4 . 2 4 7. 1 1 44. 5.

2 9 019 0 2 .3 1 4 .3 6 4 .8 0 1 .2 8 4 . 2 4 7. 1 0 42. 5.

2 8 020 0 2 .3 1 5 .3 6 4 .7 0 9 .2 7 4 . 2 3 7. 1 9 40. 4.

2 8 021 0 2 .3 1 5 .3 6 3 .6 0 8 .2 7 4 . 2 3 7. 1 8 38. 4.

2 7 022 0 2 .3 1 6 .3 6 3 .5 0 7 .2 7 4 . 2 4 7. 1 7 36. 4.

2 7 023 0 2 .3 1 6 .3 6 2 .4 0 6 .2 7 4 . 2 4 7. 1 7 34. 4.

2 6 024 0 2 .3 1 7 .3 6 2 .3 0 5 .2 7 4 . 2 5 7. 1 6 32. 4.

2 6 025 0 2 .3 1 9 .3 6 2 .2 0 4 .2 7 4 . 2 7 7. 1 5 30. 4.

2 5 026 0 2 .3 1 0 .3 6 1 .2 0 3 .2 7 4 . 2 0 7. 1 4 29. 4.

2 5 027 0 2 .3 1 2 .3 6 1 .1 0 2 .2 7 4 . 2 2 7. 1 4 27. 4.

2 4 028 0 2 .3 1 4 .3 6 1 .0 0 1 .2 7 4 . 2 6 7. 1 3 25. 4.

2 4 029 0 2 .3 1 6 .3 5 0 .9 0 0 .2 7 4 . 2 0 7. 1 2 24. 4.

2 3 030 0 2 .3 1 8 .3 5 0 .8 0 9 .2 6 4 . 2 3 7. 1 2 22. 4.

2 3 031 0 2 .3 1 0 .3 5 0 .8 0 8 .2 6 4 . 2 7 7. 1 1 20. 4.

2 2 032 0 2 .3 1 3 .3 5 9 .7 0 7 .1 6 4 . 2 2 7. 1 0 19. 4.

2 2 033 0 2 .3 1 6 .3 5 9 .6 0 6 .1 6 4 . 2 6 7. 1 0 17. 4.

2 1 034 0 2 .3 1 8 .3 5 9 .5 0 5 .1 6 4 . 2 1 7. 1 9 16. 3.

2 1 035 0 2 .3 1 4 .3 5 9 .5 0 4 .1 6 4 . 2 3 7. 1 9 15. 3.

2 1 036 0 2 .3 1 2 .3 5 9 .5 0 4 .1 6 4 . 2 9 7. 1 9 14. 3.

2 1 037 0 2 .3 1 9 .3 5 8 .4 0 3 .1 6 4 . 2 3 7. 1 8 14. 3.

2 0 038 0 2 .3 1 7 .3 5 8 .4 0 3 .1 6 4 . 2 7 7. 1 8 13. 3.

2 0 039 0 2 .3 1 4 .3 5 8 .4 0 2 .1 6 4 . 2 2 7. 1 7 13. 3.

2 0 040 0 2 .3 1 1 .3 5 8 .4 0 2 .1 6 4 . 2 6 7. 1 7 12. 3.