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

Emissions M ethods : 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 tw o 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 un der 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 c ounty for all counties in 1 4

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 o f the four seasons, and for an average annual weekday. Seasons are defined as three

month periods: spring is March through May; summer is June through August; fall is September through November; and winter is December through February.
Emissions were est imated for the 2002 base year and for three future years

– 2008, 2013, and 2018.

Pollutants : Emissions were estimated for primary particulate matter (PM 10 and

PM 2.5 ), nitrogen oxides (NO
x ), sulfur dioxide (SO 2 ), volatile organic compounds (VOCs), carbon m onoxide (CO), ammonia (NH 3 ), 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.
Emission s were summarized for gasoline and diesel

- fueled engines.

Approach For Estimating Mobile S ource Emissions

As with most emissions sources, on

- road and off

- road mobile source emissions are estimated as the products of emission factors and activity estimat es. 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 NONROAD 2004 model was used to
estimate so

- called traditional off

- road sources 1

, all sourc es 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.

E missions Models Used and Additional C alculations for A ir Q uality M odeling

On

- road and off

- road mobile source emissions are estimated as th e 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 mod el was
used to estimate emissions from off

- road mobile sources except for aircraft, commercial marine, and locomotives.

EPA M OBILE6 M odel

The MOBILE model is EPA’s regulatory model for estimating on

- road mobile source gram per mile emission factors fo r VOC (exhaust and evaporative), NO X , CO, PM, NH 3 , and SO 2 . 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 wit h, beginning MY 1996;

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

 Tier 2 light

- duty vehicle standards beginning MY 200 5 , with low sulfur gasoline beginning summer 200 4 ;

 Heavy

- duty vehicle standards beginning MY 2004; a nd

 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 cla sses ar e 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 trucks 1 (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 equipp ed 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

Motorcycles 2 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 so urce 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 estima tes 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 invento ry (Pollack et al., 2004).

EPA NONROAD M odel

Off

- road mobile equipment encompasses a wi de 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 em issions 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 N ONROAD2005 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 mod el 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 min ing equipment; and

 oil field equipment.

The NONROAD model does not include commercial marine, locomotive, and aircraft
emissions. Emissions for these three so urce categories are estimated using other EPA methods and guidance documents (described in Secti ons 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), NO X , carbon monoxide (C O), carbon dioxide (CO 2 ), sulfur oxides (SO X ), 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 incorporat es 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 em ission 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 stan dards 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 estim ating 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 engi ne 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 use d 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.

C alifornia M odels

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 CA RB

on

- road emissions was EMFAC2002 (available at http://www.arb.ca.gov/msei/on

road/latest_version.htm ) , with internal updates for some of the activity data that were not publicly avail able.

For many years, CARB has been developing its own off

- road emissions model, called
OFFROAD. Al t hough 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 prov id ed, not activity data and emission factors.

P ollutants Added for Air Quality Modeling

For CMAQ m odeling, 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 (SO 4 );
and NO X needed to be sp lit into NO and NO 2

.
EC and OC were estimated by applying EC/OC fractions to the PM 10 and P M 2.5 emissions estimates. The EC/OC splits used for these calculations are summarized in
Table 2. Th ese are the same EC/OC fractions used in the previous WRAP mob ile 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. Elem ental carbon/organic carbon fractions.

Pro cess/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

Brak e 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 reference d in Table 2 , there has not been a comparable comprehensive eva luation 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 e ffect 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 compositio nal 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 con stituent of NOx. Aftertreatment devices may begin to perturb the ratio of NO and NO2 as NOx and particulate control are applied to diesel engin es (Tonkyn, 2001, Herndon, 2002, and Chatterjee, 2004). Howev er, these sys tems 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.T emporal 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 emission s processing system. The EPA temporal profiles for on

- road and off

road 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

v ehicle 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 ve hicle class across the days of the week and the hours of the day.

Figure 1. Day of week profiles by vehicle class.Figure 2. Weekday hour of day profiles by vehicle class.

Figure 3

. Day of week profiles by vehicle class.

Locomotive Emis sions 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 a lso revised to combine line

- haul andswitching 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 a ctivity 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 (ASL RRA, 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 fracti on 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 (MG TM)
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 we re 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 open

ended 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 c ounties. 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. Fo r 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 usin g 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 summariz ed 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 SO2 1 NH3 2

2002 Fleet Average 10.7 27.4 248.8 6.8 16.4 0.116

1 Reported as SO 2 and derived from an average sulfur level of 2600 ppm. (EPA, 2004b)

2 EPA (2004a)

One issue was to determine the fraction of the tot al 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 * (SOx fuel - SOx bas ) (2)
where
SOx bas = 0% sulfur for entirely elemental and organic carbon PM
SOx fuel = % 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
SO 3 (with the remainder emitted as SO 2 ), which rapidly hydrolyzes in the humid exhaust to hydrated sulfuric acid [H 2 SO 4 *(7)H 2 O] and condenses on other PM. From this assumption arises the molecular weight adjustment of 7.0 (ratio of hydrated s ulfuric 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 ar eas 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 d erived 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 th e 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 Washingt on, 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 tota ls estimated, so a major line that runs through a small or narrow county may not appear significant, and, likewise, a large county may appear over

weighted compared with a neighboring county with less through mileage.

Figure 5. County level rail NOx em issions (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) railr oads’ 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 consump tion trend over as long a period as possible. Freight transfers (ton

- mile) arenot 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
historica l 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

e ach 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 i n 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

cur rent 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 1.37In addition to projected r ailroad 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 1.0 00 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 Tabl e 5 and the emissi on rate projections in Table 6

.

C alifornia Locomotive Emiss i ons

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
sul fur 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 adj ustment 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 SO X emissions. Yearly fuel consumption estimates were derived based
on SO X emissions and the CARB assu med 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 em issions 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 ED MS are expressed in terms of LTOs, which consist of the fouraircraft 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

- in

modes (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 detail ed 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 da ta for VOC, CO, NOx, and SO 2 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 NO 2 were assumed to be 90%

an d 10%, respectively, which are the default EPA speciations.

 It was assumed that no NH 3 is emitted from air carrier turbine engines, which normally run lean.

 All of the fuel

- bound sulfur was assumed to form SO 2 in the engine exhaust.

 Due to the lack of ot her, 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 PM 2.5 , as is assumed in the NEI2002.

Air Taxi, General Aviation and Military Aircraft

– The NEI2002 invento ry data for VOC, CO, NOx, SO 2 , 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 and10% were assumed to be NO 2

.

 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 estimate d 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
fact or for non

- catalyst light

- duty gasoline engines.

 As for air carriers, all of the fuel

- bound sulfur was assumed to form SO 2 in the engine exhaust.

State Updates

The NEI2002

- based inventory estimates were updated with additional information provided for s ix 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 m inor
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, t he 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 e stimates 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) provide d both base and future year aircraft emissions estimates, discussed below.

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

provided by state and local agencies. To estimate seasonal county

- l evel 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/mai n/atads.asp ). The ATADS is the official source for historical monthly or annual air traffic statistics for airports with FAA

- operated or FAA

contracted traffic control towers. The average seasonal distribution was calculated by state and aircraft type f

rom 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 Californ ia, 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 NO
x 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 promulga ted 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 growt h 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 p rojected 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 numbe r 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

– wint er 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.

Detail ed 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 e missions files were generated in the format needed for SMOKE emissions processing. A nnual 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, NO X , CO, NH3, SO 2 , 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

Ba se18b 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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ASLRRA. 1999. “1999 Annual Data Profile of the American Short Line & Regional
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Benson, D. 2004. Personal communication with Doug Benson, February 19. See also ASLRRA, 1999.

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