Excerpt from: http://vista.cira.colostate.edu/TSS/Results/Emissions.aspx

Emissions Methods: Mobile Source Emissions

Introduction

Mobile sources include on-road and off-road vehicles and engines. On-road mobile 

sources include vehicles certified for highway use – cars, trucks, and motorcycles. For 

reporting on-road mobile source emissions, vehicles are divided into two major classes –

light-duty and heavy-duty. Light-duty vehicles include passenger cars, light-duty trucks 

(up to 8500 lbs gross vehicle weight [GVW]), and motorcycles. Heavy-duty vehicles are 

trucks of more than 8500 lbs GVW.

Off-road mobile equipment encompasses a wide variety of equipment types that either 

move under their own power or are capable of being moved from site to site. Off-road 

mobile equipment sources are defined as those that move or are moved within a 12-

month period and are covered under the EPA’s emissions regulations for nonroad mobile 

sources. Off-road mobile sources are vehicles and engines in the following categories:

 Agricultural equipment, such as tractors, combines, and balers;

 Aircraft, jet and piston engines;

 Airport ground support equipment, such as terminal tractors;

 Commercial marine vessels, such as ocean-going deep draft vessels;

 Commercial and industrial equipment, such as fork lifts and sweepers;

 Construction and mining equipment, such as graders and back hoes;

 Lawn and garden equipment, such as leaf and snow blowers;

 Locomotives, switching and line-haul trains;

 Logging equipment, such as shredders and large chain saws;

 Pleasure craft, such as power boats and personal watercraft;

 Railway maintenance equipment, such as rail straighteners;

 Recreational equipment, such as all-terrain vehicles and off-road motorcycles; and

 Underground mining and oil field equipment, such as mechanical drilling engines.

Road dust emissions estimates are also included in the mobile source emissions category, 

and are discussed separately with the fugitive dust emissions inventory summary. 

Mobile Source Inventory Scope

The scope of the WRAP mobile sources emission inventories is as follows:

Geographic domain: Emissions were estimated by county for all counties in 14

states: Alaska, Arizona, California, Colorado, Idaho, Montana, Nevada, New 

Mexico, North Dakota, Oregon, South Dakota, Utah, Washington, and Wyoming. 
Temporal resolution: Emissions were estimated for an average day in each of the 

four seasons, and for an average annual weekday. Seasons are defined as threemonth periods: spring is March through May; summer is June through August; 

fall is September through November; and winter is December through February. 

Emissions were estimated for the 2002 base year and for three future years –

2008, 2013, and 2018.

Pollutants: Emissions were estimated for primary particulate matter (PM10 and 

PM2.5), nitrogen oxides (NOx), sulfur dioxide (SO2), volatile organic compounds 

(VOCs), carbon monoxide (CO), ammonia (NH3), elemental and organic carbon 

(EC/OC), and sulfate (SO4). 

Sources: For all pollutants, emissions were estimated separately by vehicle class 

for on-road sources and by equipment type/engine type for off-road sources. 

Emissions were summarized for gasoline and diesel-fueled engines.

Approach For Estimating Mobile Source Emissions

As with most emissions sources, on-road and off-road mobile source emissions are 

estimated as the products of emission factors and activity estimates. Except for 

California, the on-road mobile sources emission factors were derived from EPA’s

MOBILE6 model, available at http://www.epa.gov/OMSWWW/m6.htm. Activity for 

on-road mobile sources is vehicle miles traveled (VMT). State and local agencies were 

provided default modeling inputs and VMT levels for base and future years for review 

and update; all states and several agencies provided updated. The California Air 

Resources Board (CARB) provided on-road emissions estimates by county and vehicle 

class directly; these were based on CARB’s in-house version of their EMFAC model.

For all states except California, EPA’s draft NONROAD2004 model was used to 

estimate so-called traditional off-road sources1, all sources listed above except aircraft, 

commercial marine, and locomotives. The NONROAD model includes estimates of 

emission factors, activity levels, and growth factors for all traditional off-road sources. 

The default activity levels were provided to state agencies for input and update; however, 

no state provided updated off-road activity data. Emissions estimation methods for 

aircraft, commercial marine, and locomotives were similar to approaches EPA has 

recently used in developing national emission inventories. For California, CARB 

provided off-road emissions estimates by source category and county directly.

Emissions Models Used and Additional Calculations for Air Quality Modeling

On-road and off-road mobile source emissions are estimated as the products of emission 

factors and activity estimates. Except for California, the on-road mobile sources 

 

1 The final version of NONROAD (NONROAD2005, available at http://www.epa.gov/otaq/nonrdmdl.htm) 

was released after the work in this project was completed. 
emission factors were derived from the EPA MOBILE6 model. Activity for on-road 

mobile sources is vehicle miles traveled (VMT). EPA’s NONROAD2004 model was 

used to estimate emissions from off-road mobile sources except for aircraft, commercial 

marine, and locomotives. 

EPA MOBILE6 Model

The MOBILE model is EPA’s regulatory model for estimating on-road mobile source 

gram per mile emission factors for VOC (exhaust and evaporative), NOX, CO, PM, NH3, 

and SO2. The current regulatory version of the model is MOBILE6, released in 2002. 

The model and supporting documentation may be found on EPA’s web site at 

http://www.epa.gov/OMSWWW/m6.htm. 

The MOBILE6 model includes the effects of all of the following “on the books” Federal 

regulations for on-road motor vehicles:

 Tier 1 light-duty vehicle standards, beginning with, beginning MY 1996;

 National Low Emission Vehicle (NLEV) standards, beginning MY 2001;

 Tier 2 light-duty vehicle standards beginning MY 2005, with low sulfur gasoline 

beginning summer 2004;

 Heavy-duty vehicle standards beginning MY 2004; and

 Heavy-duty vehicle standards beginning MY 2007, with low sulfur diesel 

beginning summer 2006.

MOBILE6 estimates emissions by vehicle class, for 28 vehicle classes. For the WRAP 

modeling, the emissions were estimated for eight vehicle classes, which are combined 

from these 28. The eight vehicle classes are those that were modeled in the prior 

generation of the mode, MOBILE5, as shown in Table 1.
Table 1. MOBILE5 vehicle classes for which emissions were estimated.

Vehicle Class MOBILE 

Code

Weight Description

Light-duty gasoline vehicles 

(passenger cars)

LDGV Up to 6000 lb gross vehicle weight (GVW)

Light-duty gasoline trucks1

(pick-ups, minivans, passenger 

vans, and sport-utility vehicles)

LDGT1 Up to 6000 lb GVW

LDGT2 6001-8500 lb GVW

Heavy-duty gasoline vehicles HDGV 8501 lb and higher GVW equipped with 

heavy-duty gasoline engines

Light-duty diesel vehicles 

(passenger cars)

LDDV Up to 6000 lb GVW

Light-duty diesel trucks LDDT Up to 8500 lb GVW 

Heavy-duty diesel vehicles HDDV 8501 lb and higher GVW 

Motorcycles2 MC

1 Emissions for light-duty trucks are modeled separately for two weight classes with different emissions standards in the 

Clean Air Act

2 Highway-certified motorcycles only are included in the model. Off-road motorcycles, such as dirt bikes, are modeled as 

a no-road mobile source in EPA’s NONROAD model.

The particulate matter emission factors in MOBILE6 are from an earlier EPA particulates 

emission factor model called PART5. The tire and brake wear estimates from PART5 

used in MOBILE6 are dated, and newer brake wear estimates were available (Garg et al,) 

and were used to develop revised brake wear emission factors, the same as used in the 

previous WRAP mobile sources emission inventory (Pollack et al., 2004). 

EPA NONROAD Model

Off-road mobile equipment encompasses a wide variety of equipment types that either 

move under their own power or are capable of being moved from site to site. Off-road 

mobile equipment sources are defined as those that move or are moved within a 12-

month period and are covered under the EPA’s emissions regulations for nonroad mobile 

sources. Emissions for so-called traditional nonroad sources are estimated by EPA in 

their NONROAD emissions model, available on the NONROAD web page at

http://www.epa.gov/otaq/nonrdmdl.htm. 

At the time that the off-road emissions were estimated for this project, the latest version 

of the model was draft NONROAD2004. In December of 2005 final NONROAD2005 

was released. The web page above provides now only the NONROAD2005 final model.

The NONROAD model includes both emission factors and default county-level 

population and activity data. The model therefore estimates not just emission factors but 

also emissions. Technical documentation of all aspects of the model can be found on the 

EPA NONROAD web page.
The NONROAD model includes more than 80 basic and 260 specific types of nonroad 

equipment, and further stratifies equipment types by horsepower rating and fuel type, in 

the following categories:

 airport ground support, such as terminal tractors;

 agricultural equipment, such as tractors, combines, and balers;

 construction equipment, such as graders and back hoes;

 industrial and commercial equipment, such as fork lifts and sweepers;

 recreational vehicles, such as all-terrain vehicles and off-road motorcycles;

 residential and commercial lawn and garden equipment, such as leaf and 

snowblowers;

 logging equipment, such as shredders and large chain saws;

 recreational marine vessels, such as power boats;

 underground mining equipment; and

 oil field equipment.

The NONROAD model does not include commercial marine, locomotive, and aircraft 

emissions. Emissions for these three source categories are estimated using other EPA 

methods and guidance documents (described in Sections 5-7). However, support 

equipment for aircraft, locomotive, and commercial marine operations and facilities are 

included in the NONROAD model.

The NONROAD model estimates emissions for six exhaust pollutants: hydrocarbons 

(HC), NOX, carbon monoxide (CO), carbon dioxide (CO2), sulfur oxides (SOX), and PM. 

The model also estimates emissions of non-exhaust HC for six modes — hot soak, 

diurnal, refueling, resting loss, running loss, and crankcase emissions.

The NONROAD model used in this study incorporates the effects of all of the following 

“on the books” Federal nonroad equipment regulations:

$ Emission standards for new nonroad spark-ignition engines below 25 hp;

$ Phase 2 emission standards for new spark-ignition hand-held engines below 25 

hp;

$ Phase 2 emission standards for new spark-ignition nonhandheld engines below 25 

hp; 

$ Emission standards for new gasoline spark-ignition marine engines;

$ Tier 1 emission standards for new nonroad compression-ignition engines above 

50 hp;

$ Tier 1 and Tier 2 emission standards for new nonroad compression-ignition 

engines below 50 hp including recreational marine engines;

$ Tier 2 and Tier 3 standards for new nonroad compression-ignition engines of 50 

hp and greater not including recreational marine engines greater than 50 hp; and

$ Tier 4 emissions standards for new nonroad compression-ignition engines above 

50 hp, and reduced nonroad diesel fuel sulfur levels.
The NONROAD model provides emission estimates at the national, state, and county 

level. The basic equation for estimating emissions in the NONROAD model is as 

follows:

 Emissions = (Pop)(Power)(LF)(A)(EF)

where

Pop = Engine Population

Power = Average Power (hp)

LF = Load Factor (fraction of available power)

A = Activity (hrs/yr)

EF = Emission Factor (g/hp-hr) 

The national or state engine population is estimated and multiplied by the average power, 

activity, and emission factors. Equipment population by county is estimated in the model 

by geographically allocating national engine population through the use of econometric 

indicators, such as construction valuation. The manner in which the geographic 

allocation is performed is as follows:

(County Population)i /(National Population)I = (County Indicator)i /(National Indicator)i

where

i is an equipment application like construction or agriculture.

Activity is temporally allocated through the use of monthly, and day of week fractions of 

yearly activity.

The NONROAD model has default estimates for all variables and factors used in the 

calculations. All of these estimates are in model input files, and can be changed by the 

user if data more appropriate to the local area are available. 

California Models

The California Air Resources Board (CARB) provided on-road and off-road emissions 

data for base and future years for use in this project. CARB has developed their own 

models for on-road and off-road emissions estimation. CARB’s on-road model is 

referred to as EMFAC. The version of the model that was used to generate the CARB 

on-road emissions was EMFAC2002 (available at http://www.arb.ca.gov/msei/onroad/latest_version.htm), with internal updates for some of the activity data that were not 

publicly available. 

For many years, CARB has been developing its own off-road emissions model, called 

OFFROAD. Although CARB has developed most of the model inputs as part of their 

analyses in support of their off-road equipment regulations, the model has never been

publicly released. 
For all California emissions, CARB provided their emissions estimates for the base and 

future years. Emissions data only were provided, not activity data and emission factors. 

Pollutants Added for Air Quality Modeling

For CMAQ modeling, additional model species are required beyond what is estimated in 

MOBILE, NONROAD, EMFAC, and OFFROAD. Specifically, particulate matter 

needed to be split into elemental carbon (EC), organic carbon (OC), and sulfate (SO4); 

and NOX needed to be split into NO and NO2.

EC and OC were estimated by applying EC/OC fractions to the PM10 and PM2.5

emissions estimates. The EC/OC splits used for these calculations are summarized in 

Table 2. These are the same EC/OC fractions used in the previous WRAP mobile 

sources emissions estimates; their derivation is described in Pollack et al., 2004. Sulfate 

was then estimated as PM – EC – OC, for both PM10 and PM2.5. Coarse PM is 

calculated as PM10 – PM2.5

Table 2. Elemental carbon/organic carbon fractions.

Process/Pollutant EC OC Source

Gasoline Exhaust 23.9% 51.8% Gillies and Gertler, 2000

Light-Duty Diesel Exhaust 61.3% 30.3% Gillies and Gertler, 2000

Heavy-Duty Diesel Exhaust 75.0% 18.9% Gillies and Gertler, 2000

Tire Wear 60.9% 21.75% Radian, 1988

Brake Wear 2.8% 97.2% Garg et al, 2000

While there have been several studies and reviews of particulate composition (e.g. EPA, 

2001 and Turpin and Lim, 2000) since the time of the work referenced in Table 2, there 

has not been a comparable comprehensive evaluation of particulate composition. Many 

particulate source/receptor statistical modeling efforts have been attempted, but all used 

source profiles that predate those listed in Table 2. A comprehensive evaluation of 

source profiles needs to include the effect of the proper age distribution and maintenance 

history of in-use vehicles. No recent studies have investigated the source profiles using 

such an evaluation, and so could not be used for this work. In addition, the default EPA 

resource for compositional estimates of emissions, SPECIATE, has not provided any 

revised profiles since October 1999. 

The ratio of NO to NO2 for NOx emissions from mobile sources is a result of the 

chemical equilibrium formed during internal combustion with NO the primary constituent 

of NOx. Aftertreatment devices may begin to perturb the ratio of NO and NO2 as NOx 

and particulate control are applied to diesel engines (Tonkyn, 2001, Herndon, 2002, and 

Chatterjee, 2004). However, these systems have not yet been widely employed, so it is 

not possible to judge what the proportion of NOx that NO and NO2 will be in the future. 

For this work the EPA default proportions of NO and NO2 (90/10) were used to 

apportion the NOx emission estimates.
Temporal Profiles

The on-road and off-road emissions are estimated as average day, per season. For use in 

air quality modeling, these average day emissions must be temporally allocated to the 24 

hours of the day for each day of the week. This temporal allocation is done in the 

SMOKE emissions processing system. The EPA temporal profiles for on-road and offroad emissions were reviewed and found to be deficient for on-road sources. The EPA 

defaults for on-road temporal profiles vary only by weekday vs. weekend; for both 

weekdays and weekends the 24-hour profiles do not vary by vehicle class. And there are 

only two day of week profiles – one for light-duty gasoline vehicles and one for all 

vehicle classes.

ENVIRON has analyzed an extremely large database of detailed traffic counter data by 

vehicle class, roadway type, and state under contract to EPA (Lindhjem, 2004). From 

this work using national databases of vehicle activity maintained by the Federal Highway 

Administration (FHWA), revised temporal profiles for on-road sources were developed.

The databases used were the FHWA Traffic Volume Trends 

(http://www.fhwa.dot.gov/policy/ohpi/travel/index.htm) for temporal activity of vehicles, 

and the FHWA Vehicle Travel Information System (VTRIS) 

(http://www.fhwa.dot.gov/ohim/ohimvtis.htm) that identifies individual vehicle classes to 

estimate temporal variation in the vehicle mix. Three sets of profiles were developed: 

day of week profiles by vehicle class (Figure 1); hour of day profiles for weekdays, by 

vehicle class (Figure 2); and hour of day profiles for weekends, by vehicle class (Figure 

3). These temporal profiles show important differences in vehicle activity by vehicle 

class across the days of the week and the hours of the day.

Day of Week Profiles

0.000

0.020

0.040

0.060

0.080

0.100

0.120

0.140

0.160

0.180

0.200

S M T W Th F Sat

LDGV

LDGT1

LDGT2

HDGV

LDDV

LDDT

HDDV

MC

Figure 1. Day of week profiles by vehicle class.
Weekday Diurnal Profiles

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0.09

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

LDGV

LDGT1

LDGT2

HDGV

LDDV

LDDT

HDDV

MC

Figure 2. Weekday hour of day profiles by vehicle class.

Weekend Diurnal Profiles

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

LDGV

LDGT1

LDGT2

HDGV

LDDV

LDDT

HDDV

MC

Figure 3. Day of week profiles by vehicle class.

Locomotive Emissions Estimation Methodology 

County level locomotive emissions estimates were estimated as the product of 

locomotive fuel consumption and average locomotive emission factors. Previous WRAP 

locomotive emissions estimates (Pollack et al., 2004) allocated national fuel consumption 

estimates to counties using emissions data offered by the National Emissions Inventory. 

A detailed revision to that allocation method was developed for allocating 2002 national 

fuel consumption estimates. Emission factors were also revised to combine line-haul and 
switching engines because only national total fuel consumption was available. 

Additional emission factors for ammonia and fuel sulfur provided by EPA were also 

incorporated and form the basis from which sulfur dioxide was estimated.

2002 Locomotive Emissions

Development of the 2002 locomotive emissions involved spatially allocated 2002 

national locomotive activity, in the form of fuel consumption, using historic data of 

freight movements. The 2002 Class I railroad activity data were derived from national 

fuel consumption data reported by the Association of American Railroads (AAR, 2003), 

and the activity data for Class II/III railroads from data reported by the American Short 

Line & Regional Railroad Association (ASLRRA, 1999 and Benson, 2004). To allocate 

this national fuel consumption to the county level, ENVIRON used the most recent 

county level rail activity estimates available. These activity estimates were ton-miles of 

freight movement estimated by the Bureau of Transportation Statistics (2002), using data 

from 1995. The 2002 national activity data were allocated to each county in the WRAP 

states using the fraction of the 1995 national rail activity that occurred in each county and 

then multiplying that fraction by the 2002 national rail activity, as demonstrated in 

equation (1).

CA02 = NA02 * (CA95/NA95) (1)

where 

CA02 = 2002 county locomotive fuel consumption

NA02 = 2002 national locomotive fuel consumption

CA95 = 1995 county million gross ton miles (MGTM) 

NA95 = 1995 national total MGTM

The spatial allocation of the national emissions in this work followed the methods of the 

EPA National Emission Inventory (NEI, 1999 and unchanged for 2002) of allocating 

locomotive activity. The 1995 activity data were obtained as GIS shapefiles containing 

track segments and an associated database of rail density per mile (MGTM/mi) 

corresponding to those segments. The segment-specific rail density estimates were 

provided as ranges. For each segment, the midpoint of the density range was assumed to 

represent the average track loading on that segment. Table 3 shows a list of the ranges 

and the midpoint values used in this study. The top end density was reported as an openended range, greater than 100 MGTM/mi, which was estimated as 120 MGTM/mi. This 

differs from the allocation method used in the NEI 2002, which represented the top end 

traffic density as 100 MGTM/mi. The use of 120 MGTM/mi is expected to more 

accurately reflect the relative importance of those main line track segments than using the 

minimum value of 100 MGTM/mi.
Table 3. Track segment density ranges used for allocation to counties (MGTM/mi).

Density ID Segment Density Range Assumed Segment Density 

0 unknown, abandoned, or dummy 0

1 0.1 to 4.9 2.5

2 5.0 to 9.9 7.45

3 10.0 to 19.9 14.95

4 20.0 to 39.9 29.95

5 40.0 to 59.9 49.95

6 60.0 to 99.9 79.95

7 100.0 and greater 120

To obtain county level rail density from track segment density, a shapefile was first 

created that contained all US counties. Next, the two shapefiles were projected to the 

same map projection so that the counties were overlaid by the BTS track segments. 

Then, track segments were intersected by the county borders so that county-specific track 

segments were created. For each county it was then possible to sum the products of 

segment densities and county-specific segment lengths to obtain the total county activity 

as 1995 ton-miles. The county fraction of 1995 national rail activity was then the sum of 

activity in that county over the sum of activity in all counties. The relative county 

locomotive activity for the western States is shown in Figure 4.

Figure 4. County level rail activity in the WRAP states.
Year 2002 county rail fuel consumption was estimated using the 1995 county fraction of 

national rail activity as demonstrated in equation (1). National locomotive fleet average 

emissions factors with units of grams per gallon of fuel were obtained from the EPA 

(1997). The emission factors for 2002 are summarized in Table 4. County level 

emissions of hydrocarbons (HC), NOx and particulate matter (PM10) were calculated by 

multiplying 2002 county level fuel consumption by these emission factors.

Table 4. National fleet average emission factors (gram per gallon) from EPA (1997).

Engine Type HC CO NOx PM SO21 NH32

2002 Fleet Average 10.7 27.4 248.8 6.8 16.4 0.116

1 Reported as SO2 and derived from an average sulfur level of 2600 ppm. (EPA, 2004b)

2 EPA (2004a)

One issue was to determine the fraction of the total PM emissions that is sulfate. 

Equation (2) was derived from test data from an EPA study that measured the PM weight 

change that resulted from a change in the fuel sulfur level. The percentage of sulfate PM 

was estimated to be 19.4%. The remaining PM was split between EC and OC using the 

historic National Emission Trends report estimate of 80% as elemental carbon and 20% 

as organic carbon.

Sulfate PM (BSFC units) = BSFC * 7.0 * 0.02247 * 0.01 * (SOxfuel - SOxbas) (2)

where 

SOxbas = 0% sulfur for entirely elemental and organic carbon PM

SOxfuel = % sulfur in fuel used (0.26%)

Sulfate PM = 0.0004 (g/gram fuel) or 1.32 (g/gallon) or 19.4% of the PM rate in 

Table 4.

Equation (2) was derived by estimating that the fuel sulfur partially (2.247%) converts to 

SO3 (with the remainder emitted as SO2), which rapidly hydrolyzes in the humid exhaust 

to hydrated sulfuric acid [H2SO4*(7)H2O] and condenses on other PM. From this 

assumption arises the molecular weight adjustment of 7.0 (ratio of hydrated sulfuric acid 

to elemental sulfur). The figure 0.01 in the equation is to adjust values in percent (%) to 

fractional values.

County level locomotive emissions were estimated for all WRAP counties based on the 

procedure described above, except for those areas for which emissions data were supplied 

by local or state agencies. Four states - Alaska, Arizona, Wyoming, and Idaho - and one 

county - Clark County, NV - supplied more detailed locomotive emissions estimates from 

local surveys and other information derived from specific activity in those states. In the 

case of Arizona and Wyoming, ENVIRON performed surveys of all railroad activity 

(Pollack et al, 2004a; Pollack et al, 2004b). The Alaska Department of Environmental 

Conservation (Edwards, 2005) and the Idaho Department of Environmental Quality 

(Reinbold, 2005) supplied their own estimates, as did the Clark County Department of 

Air Quality Management (Li, 2005). 
The spatial allocation of annual locomotive NOx emissions is shown in Figure 5. 

Seasonal emissions were estimated based on an assumption of uniform year-round 

activity. Figure 5 shows the effect of the major east-west corridors from Los Angeles 

through Arizona and New Mexico, Northern California through Nevada, Utah and 

Wyoming, and Washington, Northern Montana and North Dakota; the north-south 

corridor through California, Oregon, and Washington; and the coal mining region of 

eastern Wyoming. Other major and minor routes are also evident though the size of the 

county affects the emission totals estimated, so a major line that runs through a small or 

narrow county may not appear significant, and, likewise, a large county may appear overweighted compared with a neighboring county with less through mileage. 

Figure 5. County level rail NOx emissions (tons per year) in the WRAP states.

2018 Locomotive Emissions

To estimate future year activity, a trend analysis was performed on the historical fuel 

consumption of the activity of the two predominant (in the West, Union Pacific and 

BNSF) railroads’ activity. Figure 6 shows the company-wide fuel consumption 

calculated from historic revenue ton-mile and fuel consumption per revenue ton-mile. 

National freight transfers and the regression of fuel efficiency were used to determine the 

fuel consumption trend over as long a period as possible. Freight transfers (ton-mile) are 
not a sufficient activity indicator alone because the efficiency (ton-miles per gallon of 

fuel consumed) of railroads has been improving over time. AAR (2005) provided 

historical efficiency (gallons per ton-mile) for Burlington Northern (predating the merger 

with the Atchison Topeka and Santa Fe [ATSF] railroad) and Union Pacific (predating 

the merger with Southern Pacific and others). The historic trend in fuel efficiency for 

each company (Union Pacific and Burlington Northern) was combined with the revenue 

ton-mile for Union Pacific and Southern Pacific, and BN and ATSF. A trend in fuel 

consumption for the combined companies was thus estimated from 1990 through 2002 as 

shown in Figure 5-3 despite the merger activity that occurred during this period. The 

future year projected activity was then determined from a linear regression of the fuel 

consumption for the combined company operations of the predominant railroads in their 

current configuration as Union Pacific and BNSF.

Figure 6. Trends in historical rail fuel consumption by railroad.

The resulting future year projection factors are listed in Table 5 for the two major 

railroads and the combined projection. The trends for the two railroads are very similar.

Table 5. Locomotive activity growth projection for this work.

Comparison Years Union Pacific BNSF Combined

2008 / 2002 1.13 1.15 1.14

2013 / 2002 1.24 1.27 1.26

2018 / 2002 1.35 1.40 1.37

y = 0.0894x - 174.92

R

2

 = 0.8837

y = 0.0897x - 175.96

R

2

 = 0.9481

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

1988 1990 1992 1994 1996 1998 2000 2002 2004

Year Fuel Consumption Estimated (Billion Gallons)

Union Pacific Relative Efficiency

Burlington Northern Relative Efficiency

Linear (Union Pacific Relative Efficiency)

Linear (Burlington Northern Relative Efficiency)
In addition to projected railroad activity, the emission rates were projected using EPA 

future year emission rates (1997, Regulatory Support Document), as shown in Table 6.

Table 6. Locomotive emission rate projections.

Comparison Years HC CO NOx PM SO2* NH3

2008/2002 0.892 1.000 0.693 0.882 0.192 1

2013/2002 0.819 1.000 0.627 0.802 0.006 1

2018/2002 0.763 1.000 0.580 0.740 0.006 1

* Fuel sulfur averaged 2600 ppm in 2002, assumed to average 500 ppm in 2008 and 15 ppm in 2013 and 

2018. (EPA, Clean Air Nonroad Diesel Rule Fact Sheet, May, 2004) PM emission rates were not adjusted 

for fuel sulfur level though a reduction should be realized with low sulfur fuel.

The overall emissions from locomotives for future years were then determined by 

combining the activity growth in Table 5 and the emission rate projections in Table 6.

California Locomotive Emissions

CARB provided locomotive emissions for the base and three future years from their 

internal emissions data bases. CARB’s emission estimates assumed 2500 ppm sulfur in 

the fuel for all years, and so adjustments were made to the SO2 and PM emissions to 

reflect the lower mandated levels in future years. Federal requirements are for sulfur 

levels to be 500 ppm in 2008 and 15 ppm in 2013 and 2018. However, ARB expects fuel 

sulfur levels to be 129 in 2008. SO2 emissions were adjusted using a direct scalar of the 

fuel sulfur levels assumed in the emissions estimated by ARB and the regulated levels. 

The PM emissions were adjusted to reflect the lower sulfur levels using a PM adjustment 

derived by ARB staff, as provided to ENVIRON.

The CARB emissions did not include NH3; NH3 was estimated by developing a scaling 

factor based on SOX emissions. Yearly fuel consumption estimates were derived based 

on SOX emissions and the CARB assumed 2500ppm fuel sulfur content. A per-volume 

NH3 emission factor was applied to the estimated fuel consumption to estimate NH3 

emissions for each year at the county level. Lastly, PM was split among sulfate, EC, and 

OC using the same methods as for the other states described above. 

Aircraft Emissions Estimation Methodology

County-level aircraft emissions for 2002 for the WRAP states were obtained from work 

performed for EPA’s 2002 National Emissions Inventory (NEI2002). Activity data for 

aircraft emissions are takeoff cycles (LTOs), and emission factors are primarily from the 

Federal Aviation Administration (FAA) Emissions and Dispersion Modeling System 

(EDMS). The 2002 emissions were projected to future years using forecast LTOs 

available from the FAA. More detailed estimates were provided for some states.

The FAA EDMS model combines specified aircraft and activity levels with default 

emissions factors in order to estimate annual inventories for a specific airport. Aircraft 

activity levels in EDMS are expressed in terms of LTOs, which consist of the four 
aircraft operating modes: taxi and queue, take-off, climb-out, and landing. Default 

values for the amount of time a specific aircraft spends in each mode, or the time-inmodes (TIMs), are coded into EDMS.

Aircraft emissions are estimated for four aircraft categories:

 Air carriers, which are larger turbine-powered commercial aircraft with at least 60 

seats or 18,000 lbs payload capacity;

 Air taxis, which are commercial turbine or piston-powered aircraft with less than 

60 seats or 18,000 lbs payload capacity; 

 General aviation aircraft, which are small piston-powered, non-commercial 

aircraft; and 

 Military aircraft. 

2002 Aircraft Emissions 

For the 2002 aircraft emissions, annual emissions files prepared for the NEI2002 formed 

the basis of the work. These files were sent to ENVIRON by EPA’s contractor, Eastern 

Research Group (Billings, 2005). For this work, ERG ran the EDMS model for about 

1100 towered airports across the U.S. using detailed 2002 aircraft/LTO activity data. 

Additional calculations were performed to estimate the additional pollutants needed for 

WRAP modeling. Key elements of those calculations are described by aircraft type 

below. 

Air Carriers – The NEI2002 inventory data for VOC, CO, NOx, and SO2 for Air Carriers 

were used directly. Additional calculations were made to estimate the emissions of the 

additional pollutants in the WRAP inventory:

 The NOx inventory speciation values for NO and NO2 were assumed to be 90% 

and 10%, respectively, which are the default EPA speciations. 

 It was assumed that no NH3 is emitted from air carrier turbine engines, which 

normally run lean.

 All of the fuel-bound sulfur was assumed to form SO2 in the engine exhaust.

 Due to the lack of other, more recent sources for aircraft particulate emission 

factors, the total suspended particulate (TSP) emissions from the air carriers 

were estimated using a commercial fleet-average emission factor from EPA’s 

1985 National Acid Precipitation Assessment Program (NAPAP). To calculate 

PM2.5, according to the NEI2002, 97.6% of the particulate matter emitted from 

Commercial Aircraft was assumed to be PM2.5, as is assumed in the NEI2002.

Air Taxi, General Aviation and Military Aircraft – The NEI2002 inventory data for VOC, 

CO, NOx, SO2, PM10, and PM2.5 for these Aircraft types were used directly. Additional 

calculations were made to estimate the emissions of the additional pollutants in the 

inventory:

 As for the air carriers, 90% of the NOx emissions were assumed to be NO and 
10% were assumed to be NO2. 

 For ammonia, air taxi and military aircraft were assumed to be dominated by 

turbine-powered aircraft running lean, thus producing a negligible amount of 

ammonia. For general aviation, ammonia was estimated using a fleet-average 

fuel consumption rate from the EDMS data for piston engines, operational 

mode-specific fuel flow rates weighted by the typical time spent in each mode, 

average hours of operation estimated from FAA data, and a g/gallon emission 

factor for non-catalyst light-duty gasoline engines.

 As for air carriers, all of the fuel-bound sulfur was assumed to form SO2 in the 

engine exhaust.

State Updates

The NEI2002-based inventory estimates were updated with additional information 

provided for six areas:

For Alaska, Sierra Research, under contract to the WRAP Emissions Forum, developed 

seasonal aircraft emissions estimates for all aircraft types for Alaska in 2002. These data 

were used instead of the NEI2002 data described above. A number of minor 

modifications needed to be made to the data to make them consistent with the rest of the 

aircraft data. The most significant difference was that air carriers and air taxis were 

lumped into one category. These were then coded as the air carriers SCC, and WRAP 

Alaska air taxi emissions were set to zero. 

For Arizona, the NEI2002-based inventory was updated with emissions estimates from 

the Arizona 2002 inventory work previously done by ENVIRON (Pollack et al., 2004). 

This work included detailed EDMS modeling based on activity data obtained from both 

the FAA and local sources. Further updates were made for specific airports with 

emissions data provided by Pima and Maricopa Counties.

The Idaho DEQ provided 2002 aircraft emissions for all counties for general aviation and 

military aircraft.

Clark County (Nevada) provided 2002 emissions estimates for three airports in the 

county, based on a recent airport emissions study (Ricondo, 2004).

For Wyoming, the NEI2002-based inventory was updated emissions estimates from 

Wyoming 2002 inventory work previously done by ENVIRON (Pollack et al., 2004a). 

This work included detailed EDMS modeling based on activity data obtained from both 

the FAA and local sources.

The California Air Resources Board (CARB) provided both base and future year aircraft 

emissions estimates, discussed below.

Seasonal Emissions Estimates
The NEI2002 aircraft emissions are annual estimates, as were most of the updates 

provided by state and local agencies. To estimate seasonal county-level emission 

inventories, the monthly distribution of activity for airports in the WRAP region was 

obtained from the FAA’s Air Traffic Activity Data System (ATADS) 

(http://www.apo.data.faa.gov/main/atads.asp). The ATADS is the official source for 

historical monthly or annual air traffic statistics for airports with FAA-operated or FAAcontracted traffic control towers. The average seasonal distribution was calculated by 

state and aircraft type from the ATADS dataset. These state-level seasonal distributions 

were then applied to the annual county-level emissions in each state to derive the 

seasonal county-level emissions for each state. 

2018 Aircraft Emissions

For all states except California, aircraft emissions were projected to the three future years 

from the 2002 emissions, by county and aircraft type, using FAA LTO forecasts as the 

activity data. Emission factors were assumed to be unchanged over time. The 

International Civil Aviation Organization (ICAO) has promulgated NOx and CO emission 

standards for commercial aircraft, exempting general aviation and military engines from 

the rule (ICAO, 1998), and the majority of engines are already meeting this standard. 

EPA officially promulgated the ICAO standards for air carriers in a final rule in 

November 0f 2005.

The historic and projected LTO data by airport are available online from the Federal 

Aviation Administration (FAA) Terminal Area Forecast (TAF) database 

(http://www.apo.data.faa.gov/main/taf.asp) for all aircraft categories for which emissions 

were estimated. Projected LTO data for years 2008, 2013 and 2018, and historic data for 

2002 were used to develop future year growth factors for all aircraft types. Growth 

factors were calculated as the ratio of the sum of LTOs by county and aircraft type in 

each future year to the sum of LTOs by county and aircraft type in 2002. These future 

year growth factors were then applied to 2002 emission estimates by county and aircraft 

to develop future year emission inventories. 

A small number of counties had no aircraft LTOs in 2002 and a significant number of 

LTOs in future years. For these counties, emissions were calculated using projected 

future year LTOs and Emission Factors by aircraft type.

California Aircraft Emissions 

CARB provided annual, winter, and summer aircraft emissions estimates by county and 

aircraft type for the 2002 base year and the three future years. A number of processing 

steps were required to generate off-road emissions for California that are similar in 

content and format to the emissions for the remaining WRAP states:
 The CARB aircraft emissions for commercial aircraft and air taxis were 

combined. The SCC for commercial aircraft was assigned to the combined 

emissions, and zero emissions were assigned to the SCC for air taxis.

 Spring and call emissions were calculated at the county and SCC level as

Spring or fall emissions = (4 * annual emissions – winter emissions – summer 

emissions) / 2

 Ammonia emissions were calculated using NH3/SOX scaling factors at the 

county and SCC level.

 The additional pollutants needed for WRAP modeling were calculated using 

speciation factors and appropriate formulas.

Detailed discussions of the development of the mobile source emissions inventories can 

be found n Pollack, et al., 2006.

Generation of SMOKE and NIF Files

All mobile source emissions files were generated in the format needed for SMOKE 

emissions processing. Annual average day county-level locomotive emissions SMOKE 

files were generated, for all WRAP states combined, only for years 2002 and 2018, the 

years for which the WRAP air quality modeling is performed. The pollutants included in 

the SMOKE files are VOC, NOX, CO, NH3, SO2, PM10, EC10, OC10, SO4(10), PM2.5, 

EC2.5, OC2.5, SO4(2.5), coarse PM (PMC), NO, and NO2. Separate files were prepared 

for each year.

Emissions Summaries

Summaries of the gridded mobile source emissions for the Base02b, Plan02c and 

Base18b inventories by state and county, annual and seasonal periods, can be found on 

the TSS at: http://vista.cira.colostate.edu/tss/Results/Emissions.aspx. 

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