1



### Locomotive Emission Inventories for the United Stat es from ERTAC Rail

Michelle S. Bergin
GA Environmental Protection Division Air Protection Branch, Planning and Support 4244 International Pkwy, Suite 136, Atlanta, GA 303 54

michelle.bergin@dnr.state.ga.us

Matthew Harrell Illinois Environmental Protection Agency Air Quality Planning Section 1021 North Grand Avenue East, Springfield, IL 62794 -9276

matthew.harrell@Illinois.gov

Mark Janssen Lake Michigan Air Directors Consortium (LADCO) 9501 W Devon Ave, Suite 701, Rosemont, IL 60302

janssen@ladco.org

ABSTRACT

Three nationwide locomotive emission inventories ha ve been developed in collaboration with twenty-seven state air protection agencies, coordin ated through the Eastern Regional Technical Advisory Committee, with support from railroads and industry associations. The inventories are for Class I line-haul locomotives which travel long dis tances, Class I switcher locomotives which largely operate within railyards, and for Shortline and Reg ional locomotives which generally serve specific industries and complex railyards and intermodal ter minals. This paper presents the data and methodologies used to develop the inventories, the results, and potential improvements.

Locomotives generally utilize very large diesel com bustion engines, resulting in emissions of
NO
x , PM, hydrocarbons, and greenhouse gases. Emission s are regionally distributed from long-distance hauling as well as sometimes being highly concentra ted in railyards. Along with contributing to secondary pollutants such as ozone and PM 2.5 , which can exceed National Ambient Air Quality Standards, locomotive emissions can directly impact sensitive populations near railroad tracks and railyards (e.g. schools, hospitals, ‘Environmental Justice’ communities).

Locomotive emission inventory development tools oft en vary dramatically between states. Except for a few notable exceptions, the resulting inventories generally lack the resolution needed to support air quality modeling or ‘hotspot’ evaluatio n and are difficult to combine for cohesive systema tic analysis. Freight transportation is projected to i ncrease, dramatically in some areas near the East C oast due to the 2014 Panama Canal expansion, and technol ogies and techniques for reducing emissions and/or population exposure are available, making th ese inventories timely and useful to support the sa fe,
efficient advancement of the United States railroad system.

INTRODUCTION2

Rail-related emissions can be important components of inventories used to support atmospheric management practices at local, state, regional, and national levels. Previous air quality studies oft en have not data needed to adequately represent this s ource sector in air quality modeling and planning 1-6

.

Air protection agencies from twenty-seven states, c oordinated through the Eastern Regional Technical Advisory Committee (ERTAC) and headed by the Lake Michigan Air Directors Consortium (LADCO), identified rail as an important, but poorl y characterized emission source so established a subcommittee to better quantify and characterize ra il-related emissions inventories. The ERTAC Rail Subcommittee (ERTAC Rail) had active representation from twelve member states, three regional planning offices, and the US EPA (Appendix A). The subcommittee’s go als were to (1) standardize agencies’ inventory development methods through a c ollaborative effort, (2) improve the quality of dat a received and the resulting emission inventories, an d (3) reduce the administrative burden on railroad companies of providing data. Information on ERTAC Rail, Railroad participation, the Rail industry, an d effects of rail on air quality are available elsewh ere 7

With support from the Rail industry and assistance from the ERTAC Rail Data Workgroup (Appendix A), ERTAC Rail developed 3 inventories of locomotive emissions as summarized in Table 1. The inventories represent locomotive emisisons from Class I line-haul, Class I railyard switchers, and Shortline (Class III) and Regional Railroads (Class II) and are available at http://www.georgiaair.org/airpermit/html/planningsu pport/regdev/locomotives/inventories2012.htm

Table 1. Summary of ERTAC Rail Inventories: U.S. Locomotive Emissions and Fuel Use for either
2007 or 2008*. Fuel Use** (gal/yr) Emissions (tons/yr)
NO
x PM 2.5 HC SO 2 CO NH 3 CO 2 Class I***
line-haul 3,770,914,002 754,443 23,439 37,941 7,836 110,969 347 42,305k

Class I
switcher 301,046,290 74,431 2,042 4,867 624 9,230 28 3,367k

Class II
and III 157,800,000 47,035 1,065 1,737 327 4,631 14 1,765k

*See Appendix B for a description of the year and s ource of data utilized for each inventory. **Locomotive grade diesel ***Excluding Amtrak and including work train fuel u se

Because of the difficulty in obtaining data and dif ferences in states’ needs for inventory years,
data sources from both 2007 and 2008 were utilized (Appendix B.) Due to the variability and uncertainty in much of the data, the results may be considered applicable for either 2007 or 2008 or scaled to more accurately represent a selected year . This paper presents the data and methodologies used to develop the three categories of locomotive emission inventories. This set of railroad emissio n inventories should greatly improve our ability to e stimate rail-related emissions over large areas and where locally-collected data is not available.

The Surface Transportation Board (STB) defines Clas s I Railroads as having had minimum carrier operating revenues of $401.4 million (USD) in 2008. As shown in Table 2, there are 8 Class I Railroads operating in the United States, about 33 Regional Railroads (Class II), and approximately 54 0 Class III Railroads (Shortlines) 8 . While categorized as a Class I Railroad, Amtrak w as excluded from3

these inventories because of significant difference s in equipment and operation characteristics. Clas s I railroads are required to report fuel use and numbe r of locomotives for both switchers and line-haul locomotives to the STB, while Class II and III rail roads do not submit data. Line-haul locomotives ar e usually newer, larger locomotives that travel long distances (e.g. between cities) while switcher locomotives are generally older, smaller locomotive s that mostly operate within railyards, splitting a nd joining rail cars with varying destinations. Shortl ine railroads often utilize smaller, older locomoti ves than Class I line-haul. While duties are generally segregated, there is not always a hard-line distin ction and locomotives are occasionally utilized for both hauling and switching. Passenger and Commuter Rail (including Amtrak), industrial locomotives, and ass ociated non-locomotive equipment are not included
in these inventories. The railroad emission invent ories are segregated by railroad class and function
because of the differences in fleet characteristics , operational patterns (‘duties’), and data availab ility.

Class I line-haul activities are the largest source of rail-related emissions, with estimates of Class I line-haul fuel consumption totals to be from 74 t o 84% of all rail sources combined 4, 5 . For this reason, characterizing Class I line-haul emissions were a f ocal point of the inventory development efforts. However, railyards and local and regional railroads can have important impacts on local scale air qual ity and elevated direct exposures.

Table 2. Class I Railroads, Reported Locomotive Fuel Use, and Railroad Fuel Consumption Index (RFCI) 9

.

Class I Railroads* R-1 Reported Locomotive Fuel Use (gal/yr) RFCI (ton-miles/gal) Line-Haul (2007)** Switcher

(2008) BNSF 1,393,874,954 52,497,057 883.14 Canadian National 93,830,751 12,290,022 1190.79 Canadian Pacific*** 50,320,233 4,594,067 1096.28 CSX 514,687,186 53,717,674 963.81 Kansas City Southern 69,787,071 1,816,759 785.89 Norfolk Southern 463,267,278 32,317,375 865.75 Union Pacific 1,185,146,529 143,470,336 974.64 Total 3,770,914,002 300,492,223 929.47 * Excluding Amtrak
** Includes work trains
*** CP’s line-haul fuel use values include 2008 dat a (rather than 2007) for their Delaware and Hudson subsidiary.



### LOCOMOTIVE EMISSIONS INVENTORY DEVELOPMENT METHODS

Class I Line-Haul Inventory

Earlier efforts to characterize line-haul railroad emissions relied on highly aggregated activity data and generally apportioned annual system-wide f uel use equally across all route miles of track operated by a Class I railroad. However, the major ity of freight tonnage carried by Class I railroads is concentrated on a disproportionately small number o f route miles (Figure 1). In addition, emissions calculations were previously based on an estimate o f annual nationwide-average locomotive fleet mix to
create one set of emissions factors.4

For this inventory, the Class I Railroads allowed E RTAC Rail access under a confidentiality agreement to a link-level (single lengths of track) line-haul GIS layer activity dataset managed by th e Federal Railroad Administration 10 . Each railroad also provided fleet mix informatio n that allowed ERTAC Rail to calculate weighted emission factors b ased on the fraction of their line-haul fleet meeti ng each Tier level category. The use of this data, la rgely following a line-haul inventory methodology recommended by Sierra Research 2, 3 , resulted in a link-level line-haul locomotive emi ssion inventory using railroad-specific emission factors. This seg ment-level inventory is nationwide, aggregated to s tate and county level files and can be converted to grid ded emissions files for use in photochemical and dispersion modeling. Link-level emissions may be p rovided to third parties for special study requests pending approval of any Class I railroads operating in the study domain. The calculations are describ ed below as a two-part process, first calculating rail road-specific factors and then total emissions per rail
link.

Figure 1. US Railroad Traffic Density in 2006. 11 MGT is million gross tons.

1) Calculate Railroad-Specific Factors.

The EPA provides annual default Emission Factors fo r locomotives based on characteristic operating cycles (‘duty cycles’) and the estimated nationwide fleet mixes for both switcher and line-h aul locomotives. However, fleet mixes vary from railro ad to railroad and, as can be seen in Figure 2, Cla ss I railroad activity is highly regionalized in nature and subject to issues of local terrain such as oper ation on plains vs. mountainous areas, which can have a s ignificant impact on fuel consumption and
emissions.

As an alternative approach to using a single nation wide set of emission factors, ERTAC Rail requested each Class I company to provide a descrip tion of their line-haul fleet mix based on Tier rat ing, which each company provided under a confidentiality agreement. An engine’s Tier level is based on the year the engine was built and determines allowable emission limits (Table 3).5

Figure 2. Class I Railroad Territories in the United States 12

.

Table 3. EPA line-haul locomotive Emission Factors by Tier, 1997 standards (grams/gal). Note that the new standards released in 2008 did not apply to fle ets in the year 2008. 13

PM 10 HC NO
x CO Uncontrolled (pre-1973) 6.656 9.984 270.4 26.624 Tier 0 (1973-2001) 6.656 9.984 178.88 26.624 Tier 1 (2002-2004) 6.656 9.776 139.36 26.624 Tier 2 (2005 + ) 3.744 5.408 102.96 26.624 Based on values in EPA Technical Highlights: Emiss ion Factors for Locomotives, EPA Office of Transpor tation and Air Quality, EPA-420-F-09-025, April 2009.

Weighted Emission Factors (EF) per pollutant for ea ch gallon of fuel used (gm/gal or lbs/gal)
were calculated for each Class I railroad fleet bas ed on its fraction of line-haul locomotives at each regulated Tier level (Equation 1; Table 3).

Equation 1 

# ∑

= 

### = 4

1 

### )

* (

T TRR

iT iRR 

### f EF EF

where

EF iRR = Weighted Emission Factor for pollutant i for Cl ass I railroad RR (gm/gal).6

EF

iT = Emission Factor for pollutant i for locomotives in Tier T (gm/gal) (Table 3).
There were 4 Tiers of locomotives in the 2008 f leets. f TRR = Fraction of railroad RR fleet in Tier T.

While engine emissions are variable within Tier cat egories, this approach likely provides better regional estimates than uniformly applying the nati onwide average emission factors. This approach likely provides conservative emission estimates as locomotive engines are certified to meet or exceed the emissions standard for each Tier, although emis sion levels may increase after certification and ac tual operations may vary widely from the line-haul duty- cycle estimation.

Other emission factors are not engine specific. Fo r locomotives, PM 2.5 is assumed to be 97% of
PM 10 13 , and emission factors applied for SO 2 and NH 3 are 1.88 g/gal 13 and 83.3 mg/gal 14 respectively. Greenhouse gases are estimated using emission facto rs shown in Table 4. Note that non-road engine and fuel specific information is sparse for these c onversions and that locomotive and marine engines a re not subject to general non-road fuel or engine stan dards.

Table 4. EPA greenhouse gas emission factors for locomotive diesel fuel (grams/gal). 15

CO 2 N 2 O CH 4 Locomotive diesel 1.015E4 0.26 0.80

A Railroad Fuel Consumption Index (RFCI) was also c alculated for each Class I railroad using their system-wide line-haul fuel consumption (FC) a nd gross ton-mile (GTM) data reported in their annual R-1 reports submitted to the Surface Transpo rtation Board 8 (Equation 2). This value represents the average number of GTM produced per gallon of di esel fuel used over their system in a year and varies between railroad carriers depending on facto rs such as fleet mix, system terrain, speeds, loading/weight of cargo, train type (e.g., intermod al, unit, and manifest), and operating practices. ( Table 2).

Equation (2)

RR RR
RR FC GTM RFCI =

where
RFCI

RR = Railroad Fuel Consumption Index (gross ton-mile s/gal) per Class I railroad (RR).
GTM

RR = Gross Ton-Miles (GTM), annual system-wide gross ton miles of freight transported per RR. (R-1 Report Schedule 755, Li ne 104)
FC RR = Annual system-wide fuel consumption by line-haul and work trains per RR (gal)

(R-1 Report Schedule 750, Lines 1 and 6).

2) Calculate Emissions per Link.

Emissions of pollutant i per link L ( E iL ) are calculated by multiplying the gallons of dies el fuel consumed by each Class I railroad on the link by th at railroad’s weighted Emission Factor for the pollutant, and then summing emissions for all Class I railroads operating on that link (Equation 3). This approach splits the activity on each link (represen ted by MGT) evenly between all railroads operating on the link. Note that the weighted Emission Factors are converted to tons/gal for these calculations, a nd that variables with units in tons may represent ton s of freight hauled (MGT, RFCI) or tons of pollutan ts (EF, E).7

Equation (3) 

## ∑

= 

      

= N

RR iRR

RR L L

iL EF

RFCI l N MGT

E

1 6

*

* 10*

where

E iL

= Emissions of pollutant i per link L (tons/year)

. N = Number of Class I railroads operating on link L.
MGT L = Millions of Gross Tons hauled per link per year from the FRA database (10 6 tons/yr) 9

. l L = Link length from the FRA database (miles).

EF iRR = Weighted Emission Factor for pollutant i per rai lroad RR (Equation 1; tons/gal).
RFCI

RR = Railroad Fuel Consumption Index per railroad RR (Equation 2; gross tonmiles/gal).

Note that approximately 36% of Class I route miles in the United States are shared by more than one Class I carrier, a fraction that drops to 26% w hen neglecting track only shared between one Class I freight railroad and Amtrak. Accurately apportioni ng the specific fractions of tonnage (MGT) per carrier per link was considered, but after comparin g likely worst-case areas, the difficultly of mergi ng carrier-specific MGT with the aggregated FRA MGT da taset was considered too great considering the potential gain in accuracy. Where warranted, MGT d ata may be apportioned more accurately in the
future.

Class I Railyard Inventory (‘Switcher’ Locomotives)

Railyard emissions can be important for air quality management in nonattainment areas and exposure analysis, as well as in regional analysis and for future transportation and freight planning. While there are many unique sources operating in ra ilyards 16 , switcher locomotives are expected to be the largest single source not represented by other emissions accounting techniques. Therefore, as a starting point for a comprehensive railyard invento ry, this Class I switcher emission inventory was developed. It is assumed that estimates for yards of interest, associated equipment and activity, and
smaller railroads could be refined later. This inv entory will be useful for regional and some local modeling, helps identify where railyards need to be better characterized, and provides a strong foundation for future development of a meaningful n ationwide Class I switcher emissions inventory.

The inventory provides a comprehensive overview of where Class I railyards are, who owns them, and gives a geographical allocation of switch er emissions based on number of switchers operating and/or tonnage around/through the yards, and is bou nded by what is reported as nationwide switcher fue l usage by each Class I railroad. Some results were refined based on yard-specific input by the railroa d companies that own the yard or from previous specif ic studies by states.

While ERTAC Rail represents states east of the Miss issippi River, the railroad companies specified they wanted this effort to result in a co nsistent nationwide inventory. ERTAC Rail agreed t o calculate emissions for all states when the data wa s available and when additional significant effort was not required. Because both the dataset of railyard s and switcher fuel use was nationwide in scope, th e resulting initial railyard inventory is a nationwid e, ‘top-down’ derivation. However, railroad compan ies may have different levels and quality of data avail able, and may have interpreted some data requests8

differently. Also, states are requested to update yards they have detailed information on when possib le, and a few states (i.e. California) have unique rail road operations and equipment. Therefore, data for some areas will be more accurate than for others, a nd locally-derived inventories may be more accurate

. Yard-specific operations data would greatly improve the representation of emissions 6, 17

.

The development of this nationwide top-down railyar d switcher inventory consisted of three main
activities: 1) Locate Class I Railyards 2) Select/Calculate Emission Factors 3) Estimate Locomotive Activity 4) Improve Estimates

1) Locate Class I Railyards.

Identification and correct placement of railyards was an important first step, requiring a comprehensive electronic dataset. The confidential FRA database used for the Line-haul inventory 10 was selected because it was the best available database and would provide consistency with the Class I Lin ehaul inventory. A comparable public database compi led by the Bureau of Transportation Statistics is
also available 10 . Data from the public source will not match the c onfidential data used for this inventory exactly but will be very similar. The FRA database has rail links (track lengths) individually identi fied as parts of specific railyards. While there may be discrepancies in how each railroad defined railyar d links, this dataset appears to identify most Class I railyards in the U.S., and shows a high density o f yards in the eastern states (Figure 3a and b). Fo r each railyard link, the database gives length, up to 3 owners and 3 operators, and a Federal Density Code (explained below).

Figure 3. Class I Railyards in the United States and estima tes of (a) Annual NOx emissions and (b) Annual PM2.5 emissions from switcher locomotives (t ons/yr in 2008).

(a)9 (b)

2) Select/Calculate Emission Factors.

As described in the Line-haul section, the EPA prov ides annual default emission factors based on characteristic operating cycles (‘duty cycles’) and an annual estimated nationwide fleet mix for both switcher and line-haul locomotives (i.e. percent of locomotives in each Tier level category). However

, switcher fleet mix is not uniform from company to c ompany and, as can be seen in Figure 2, Class I railroad activity is highly regional.

As an alternative approach to using the default fac tors, ERTAC Rail requested each Class I rail company to provide a description of their switcher fleet mix based on Tier rating, which each company provided under a confidentiality agreement. An eng ine’s Tier level determines allowable emission limits based on the year the engine was built and/o r re-manufactured (Table 5). While engine emission s are variable within Tier categories, this estimate likely provides a better regional estimate than the nationwide average. The company-specific system wi de fleet mix was used to calculate weighted average emissions factors for switchers operated by each Class I railroad, resulting in ranges between fleet emission factors such as from 6.10 to 6.69 g/ gal PM 10 and 212.98 to 264.48 g/gal NO
x . Emission
factors for PM 2.5 , SO 2 , NH 3 , VOC, and GHGs are the same as for line-haul locom otives.

Table 5. EPA switcher locomotive emission factors by Tier, 1997 standards (grams/gal).
PM 10 HC NO
x CO Uncontrolled (pre-1973) 6.688 15.352 264.48 27.816 Tier 0 (1973-2001) 6.688 15.352 191.52 27.816 Tier 1 (2002-2004) 6.536 15.352 150.48 27.816 Tier 2 (2005 + ) 2.888 7.752 110.96 27.816 Listed years apply to the year the engine was built . Table based on values from 13 . Note that the new standards released in 2008 did not apply to existing fleets in the year 2 008.10

Switchers are assumed to operate 24-hour per day 36 5 days per year. An evaluation of the effects on emissions of variability in operations a nd in temporal patterns between railyards would be very useful for future emission estimates.

3) Estimate Locomotive Activity.

The STB R-1 reports used for the line-haul invento ry are also used for the switcher inventory to provide consistency (Table 2). There may be incons istencies between railroads in how fuel use is estimated to be apportioned between line-haul and s witcher locomotive use, and possibly even in the total locomotive fuel use, so these values should b e evaluated and may be adjusted in the future. However, the use of these reported values provides a good starting point for segregating and allocatin g emissions by individual Class I carriers.

The next step for inventory development is to alloc ate switcher fuel use to each railyard. Two methods were first applied, one that relies on publ icly available line-haul activity (the ‘Dencode’ method), and the other using confidential line-haul activity (the ‘MGT’ method.) At this time, Norfol k Southern and Kansas City Southern have provided inp ut for use of the MGT method, and the Dencode method is applied for the other five railroads.

The Dencode Method – based on publicly available da ta

Each link in both the publicly available BTS databa se and the confidential FRA database has a ‘Federal Density Code’ (Dencode) ranging from 1 to 7 assigned based on the cumulative annual freight tonnage hauled on the link (track). Total Switcher Fuel Use in each railyard Y (SFU Y ) is estimated as
follows:

First a Switcher Activity Indicator per yard (SAI Y ) is calculated by multiplying the average dencode of the links within a railyard by the sum o f the length of the links for that railyard (Equati on 4).

Equation (4) SAI Y = ) * ( nY nY FDC l 

## ∑

where
SAI Y = Switcher Activity Indicator in Railyard Y

n Y = number of links identified as part of railyard Y
l nY = length of link n in miles
FDC
n = Federal Density Code (1 to 7) of link n

Next, this value is then weighted (SAI Y ’) based on an ownership factor (OF) set between 0 and 1. The OF depends on the number of owners listed for e ach railyard: if there is one owner the OF is set to 1, if there are two owners the primary owner is set to 0.8 and the secondary is 0.2, and if there are 3 owners the primary is 0.7, the secondary is 0.2, an d the tertiary is 0.1.

Equation (5) SAI Y ’ = OF Y * SAI Y

Next, the SAI Y ’ of all railyards belonging to a Class I railroad (RR) were summed, and the
fraction of the railroads total SAI associated with each railyard was multiplied by the railroads tota l annual switcher fuel use reported in the R-1 (TFU RR ), resulting in the total Switcher Fuel Use for eac h railyard Y (Equation 6).11

Equation (6) SFU Y = RR

RR Y Y TFU SAI SAI *

' ' 

# ∑

where
SFU Y = Switcher Fuel Use at railyard Y

Finally, the SFU Y is multiplied by the emission factors described in the previous section to obtain annual switcher emissions at each railyard.

The MGT Method – based on confidential data

Two railroads, Norfolk Southern and Kansas City Sou thern (KCS), allowed the use of confidential link-level tonnage information and wei ghting factors to correct skewed estimates to improve estimated switcher activity at important ya rds. Other railroads may also allow the use of thi s technique for their inventories in the future. In addition, KCS provided yard-specific activity
information.

The MGT Method also uses the FRA database for raily ard identification and link lengths. However, rather than using the average dencode per link, confidential annual gross tonnage (MGT) hauled per link in the railyard was used to calcula te the railyard switcher activity (SAI Y ). This is calculated by replacing FDC
n in Equation 4 with link-specific tonnage MGT
n (Equation 7).

Equation (7) SAI Y = ) * ( nY nY MGT l 

# ∑

where
SAI Y = Switcher Activity Indicator in Railyard Y

n Y = number of links identified as part of railyard Y
l nY = length of link n in miles
MGT nY = million gross tons on link n

This method provides a more refined comparison betw een railyards than the use of the 7- category dencodes; however, is more susceptible to errors for yards where tonnage is not correlated to switching activity. For example, a yard with large coal trains pulling through used for crews to chan ge over would be assigned an overly high level of emis sions for switching activity. To account for this, a discretionary Switching Activity Factor (SAF) was i ntroduced to allow railroads to roughly weight yard s with clearly higher or lower levels of switching ac tivity than what results from the mathematical allocation. Therefore, SAI Y is weighted based on both the ownership factor (OF ) as well as the SAF (Equation 8). For example, a yard used for crew ch anges and not switching may have an SAF of 0, while a yard at a major interchange between cities may have an SAF of 3.

Equation (8) SAI Y ’ = OF Y *SAF Y * SAI Y

Again, the SAI Y ’ of all railyards belonging to a Class I railroad (RR) are summed, and the
fraction of the railroads total SAI associated with each railyard was multiplied by the railroads tota l annual switcher fuel use reported in the R-1 (TFU RR ), resulting in the total Switcher Fuel Use for eac h railyard Y (Equation 9).12

Equation (9) SFU Y = RR

RR Y Y TFU SAI SAI *

' ' 

# ∑

While the SAF allows estimates of yard-specific emi ssions to be adjusted, the total level of emissions for each railroad, which is based on syst emwide fuel use and systemwide emission factors, remains unchanged. The MGT method SFU Y is also later multiplied by the emission factors d escribed in the previous section to obtain annual switcher e missions at each railyard.

4) Improve estimates.

In addition to the Switching Activity Factor descri bed above, direct input was also used to improve emission estimates for important railyards. Each Class I railroad provided an estimate of annual average switcher fuel use (generally much lo wer than the EPA default of 82,490 gal/yr) as well as the name, location, and number of operating swit chers for railyards with 8 or more switchers operating in ozone or PM 2.5 nonattainment areas. Some railroads, Kansas City S outhern in particular, then provided additional railyard specific data. T his directly reported data was used to overwrite th e dencode or MGT derived emissions estimates for thos e railyards.

The difference in estimated fuel use for those rail yards was re-allocated (added or removed) between the remaining railyards belonging to that C lass I railroad. It is important to note that there are some discrepancies in how this data was reported fo r the large railyards by each railroad. For exampl e, some railroads reported all switchers located at a railyard while others reported ‘full time equivalen t’ switchers, meaning the number of switchers normaliz ed to a full working cycle (24-hours per day yearround.) This process should be standardized for fu ture inventory versions. Variability is also introd uced
because ‘switcher’ locomotives can also be used for ‘road work’, meaning they occasionally haul loads between yards.

States also have the option of updating specific ra ilyard emissions estimates. Because this inventory is derived ‘top-down’, local studies and familiarity with specific railyards is expected to provide better estimates, which can be used to adju st this inventory. Care must be taken to ensure th e other railyard estimates are adjusted to account fo r increases or decreases in estimated fuel use per yard.

Shortline and Regional Railroads (Class II and III)

There are approximately 570 Class II and III Railro ads operating in the United States, around
450 of which are members of the American Shortline and Regional Railroad Association (ASLRRA) 8

. In general, while information on Class II/III link locations and many fleet characteristics are availa ble online, a significant effort would be required to c ompile this data into a usable format and check the data for reliability. In addition, the Class II and III sectors of the railroad industry have been in cons iderable flux since partial deregulation from the 1980 Stagg ers Act. States with relatively large numbers of C lass II and III railroads, such as Maine, Pennsylvania, North Carolina, and Massachusetts, have independent surveys and emission inventory estimates available, but no regional or national level cohesive electro nic datasets have been previously developed.

Class II and III activities account for an estimate d 4% of the total locomotive fuel use in the combined ERTAC inventories and for approximately 32 % of the industry’s national freight rail mileage 18 . These railroads are widely dispersed, though mor e concentrated in the eastern United States13

(Figure 4), and often utilize older, higher emittin g locomotives than their Class I counterparts. The se railroads provide services to industries requiring consistent transportation of large amounts or very heavy freight, small towns, and/or provide switchin g and terminal services at complex rail and intermodal intersections (Figure 5).

Figure 4. Shortline and Regional Railroads (Class III and I I) in the United States (based on GIS data from reference 10).

Figure 5. Carloads shipped by Shortline and Regional Railro ads by commodity group 8

To calculate a nationwide Class II/III emissions in ventory, ERTAC Rail first surveyed existing data sources, compared states’ methods and data use d for existing inventories, and contacted members of the Class II and III Railroad communities for gu idance, including the ASLRRA, the FRA, and14

Genesee & Wyoming, RailAmerica, and Omnitrax (large holding companies of Class II and III railroads). In particular, Mr. Steve Sullivan from ASLRRA and Mr. David Powell from Genesee & Wyoming were able to provide significant assistance in identification of an appropriate nationwide
dataset of Class II and III railroads with links an d total locomotive fuel use, the development of a calculation method, and guidance on appropriate ass umptions such as fleet average locomotive Tier classification. Data sources, calculations, and as sumptions used to develop the resulting inventory a re described below.

This ERTAC Rail Class II/III inventory provides a c ohesive nationwide inventory with data for where Shortline and Regional Railroads operate and who owns them, and provides a comprehensive geographical allocation of their locomotive emissio ns bounded by what is reported as total nationwide Class II/III fuel usage by the ASLRRA. This invent ory will be useful for regional and some local modeling, helps identify where railroads may need t o be better characterized, and provides a strong foundation for future development of a more accurat e nationwide Shortline and Regional railroad emissions inventory.

1) Locate Class II and III Railroads.

Identification and correct placement of Class II a nd III railroads was an important first step, requiring a comprehensive electronic dataset. The F RA database used for the Class I inventories identifies links as owned or operated by specific S hortline or Regional Railroads using “Reporting Marks”, which are brief identification abbreviation s. The full list of Reporting Marks is included wi th the inventory. The locations of these links with a ssociated data including reporting mark, railroad n ame, class, number of links and route miles owned and/or operated, and total route miles of links were extracted by ERTAC Rail (Matt Harrell, Appendix D.) While this dataset contains confidential data for the Class I railroads, the geographical allocation of Class II and III links (Figure 4) and related information is public 10 , as is the similar Bureau of Transportation Statis tics database 10

.

2) Select/Calculate Emission Factors.

While some Class II and III railroads purchase more recent locomotives, locomotives often serve 30 to 40 years in Class I line-haul service before being transferred to switching service or sold to C lass II or III railroads. Through guidance from the Cla ss II/III Railroad community, it was agreed that th e EPA Non-regulated (pre-1973) emission factors 13 best represent most operating Class II and III locomotives. In addition, although the fuel use an d route miles data obtained represent both switchin g and line-haul activities by the Class II and III ra ilroads, the US EPA line-haul duty cycle was select ed as most representative along with the “Small Line-Haul ” conversion factor 13 to obtain gm/gal emission factors for hydrocarbons (HC), oxides of nitrogen ( NOx), particulate matter (PM),and carbon monoxide (CO) (Table 6). Emission factors for PM 2.5 , SO 2 , NH 3 , VOC, and GHGs are the same as for line-haul
locomotives.

Table 6. Emission factors over the standard operating line -haul duty cycle using a conversion factor of 18.2 bhp-hr/gal 13

. Line-Haul (gm/gal)

HC NOx PM CO Non-regulated (pre 1973) 8.736 236.6 5.824 23.29615

3) Calculate Emissions.

The ASLRRA compiles data from the Class II and III railroads every few years 8 , including total industry fuel use for locomotives and total Class I I/III Route Miles. The FRA dataset also provides
route miles. These values can be used to calculate an average Fuel Use Factor (FUF) for the industry (Equation 10).

Equation (10) mile gal

miles gal

les IIIRouteMi II TotalClass tryFuelUse TotalIndus tor FuelUseFac

FRA ASLRRA 64. 2814 367,52 000, 800, 157

/ =

= =

Some data was available from the ASLRRA to distingu ish between the Shortline and Regional Railroad fuel use and miles of track, however the r esulting fuel efficiency factors were not intuitive ly correct so ERTAC Rail chose to keep their emissions inventories combined until the discrepancies can be improved or explained.

ERTAC Rail chose to use the Total Class II/III Rout e Miles values with non-zero activity from the FRA dataset for this calculation, 52,367 miles, as shown. This way, if a railroad is known to hav e ceased operations, those route miles are not counte d while the total fuel used estimate remains the sa me. In addition, this dataset was available in GIS form at for mapping purposes, the data is also used for the Class I line-haul and switcher inventories, and it is part of a more comprehensive, managed inventory.

For comparison, ERTAC Rail has actual fuel use data and route miles for approximately 50 shortline railroads, which result in an overall ave rage FUF of 2560.10 gal/mile, very close to the nationally calculated FUF. The average of the inde pendently calculated FUFs in the inventory is 3001.09 gal/mile, with the maximum being 15953.78, the minimum being 67.97, and a standard deviation of 3393.08 gal/mile. As can be seen, whi le the averages are in good agreement, there is a w ide range of potential individual FUFs per railroad whi ch can depend on many factors such as frequency of trips, tonnage hauled, terrain, distance, locomotiv es used, and others. This wide potential range indicates the importance of using specific fuel use data when possible. There are also discrepancies between the FRA route miles assigned to railroads a nd route miles reported by the railroads. Because of the GIS linkages available with the FRA database, t he FRA route miles rather than reported route miles are used in the inventory. These should also be ve rified when possible and, if necessary, corrected b oth in the FRA database and the emissions inventory.

The Fuel Use Factor calculated for use in this inve ntory (2814.64 gal/mile) was multiplied with
the route miles listed for each Class II and III ra ilroad in the FRA database, resulting an estimate o f gallons of fuel used in 2008 for each railroad. Th e annual gallons of fuel used were then multiplied with pollutant emission factors for a mass of pollutant emitted for the year

.

Further modifications were made to the estimates to reflect actual fuel use collected for specific class II/III railroads, including entries of ‘0’ fo r railroads known to be out of operation. In parti cular, specific fuel use data was entered for railroads ow ned by Genesee & Wyoming and for some Class III railroads operating in Pennsylvania, Massachusetts, and North Carolina. These modifications were accounted for in the Fuel Use Factor calculation ap plied to the remaining railroads so that the nation al total fuel use is equal to the total estimate by th e ASLRRA (157,800,800 gal) and the Fuel Use Factor is calculated only using fuel and track data for railr oads with no known fuel use information.16

Limitations, Conclusions, and Future Work

It must be noted that freight-related rail activity is not always routine and no annual emissions inventory will ever be able to capture the innate v ariability of the source. However, as other large emission sources are reduced, and if rail activity increases as expected, it is important to include t he best emission estimates possible of these sources for ai r quality and epidemiological analysis. In the fut ure, on-line data loggers and other tracking technologie s, combined with ambient studies and detailed modeling, will hopefully provide more insight to th e emissions of locomotives and other railyard

sources.

This is a first generation inventory, and many limi tations exist. For the Class I inventories, early
ERTAC Rail discussions concluded that link-level to nnage was the most important data to obtain, while other variables such as track grade and track speed could not be addressed at this time. ERTAC Rail calculated railroad-specific fleet-averaged emissio n factors rather than applying the estimated nation al average; however, it is recognized that emissions f rom individual engines are highly variable even within Tier categories depending on variables such as the specific locomotive model, operation cycle, and conditions of operation. Future evaluation of e mission variability within Tiers and between certai n types of operation and locations would be valuable. There is also likely significant variability in ac tual switching duty-cycles and, potentially, in the numb er of switchers operating at some railyards at different times of the year. ‘Road-switching’, or the use of what are considered switching locomotive s to move between nearby yards, should be addressed i n either this or the ERTAC line-haul inventory.

Major areas for future improvement of the Class II and III inventory include distinguishing between switching and hauling operations and updati ng fleet Tier characteristics where appropriate. More accurate annual fuel use estimates, rather tha n using the estimated Fuel Use Factor would greatly improve the inventory. For example, 2006 Class II/ III fuel use reported to the ASLRRA was 309 million gallons, almost double what was reported on ly two years later in 2008. It is unlikely that th e industry halved fuel use in two years, but no other activity indicators have been found for comparison

. Another area of uncertainty that can be improved is estimates of temporal variability. Emissions inventory preparation guidance from the U.S. EPA de scribes locomotive activity as constant throughout the year (e.g. no daily, weekly, or seasonal variab ility), though weekly or seasonal variation is high ly likely for the Class II and III lines. States or other areas with local, direct Class II and III dat a including temporal activity, fleet characteristics, or annual fuel use should use that information in place of t his inventory. This inventory was designed to incorpor ate refinements from industry or state agency representatives, and we request that any data colle cted be submitted to ERTAC Rail so that the inventory can be continuously improved.

An uncertainty study on the data used for this inve ntory, including the R-1 reported fuel use for Class I line-hauls and, in particular, switchers, t he confidential link-level tonnage data for Class I linehaul operations, and the duty-cycle estimates for l ine-haul and switcher operations would help in evaluating the quality of this inventory. Addition ally, the uncertainty contributed by the use of ton nage hauled as an indicator of the amount of switching a ctivity. Localized studies should examine how shared tracks are apportioned between multiple carr iers in their domain.

Emissions inventory preparation guidance from the U .S. EPA describes locomotive activity as relatively constant throughout the year (e.g. no da ily, weekly, or seasonal variability); however, act ual activity levels do vary seasonally and annual avera ging may dilute or exaggerate concentrations during pollution episodes. ERTAC Rail and the Class I rai lroad community had some discussions addressing if17

incorporating more specific fleet mix or monthly or seasonal variation may be worthwhile, and these topics should be looked into further.

Finally, it is important to reiterate that the link -level MGT data maintained by the FRA is proprietary and can only be released to agencies/gr oups outside the FRA with the express permission of each Class I railroad. It is possible that one or more Class I railroads could withhold permission fo r access, but data for specialized studies may be pro vided if requested. This database can also be improved by better distinguishing between haulage a nd trackage rights, and by apportioning tonnage hauled on links to specific carriers.



### ACKNOWLEDGMENTS

We would like to thank the ERTAC Rail Data Workgrou p and the ERTAC Rail Subcommittee for their participation in the development of these inventori es (Appendix A), as well as Raquel Wright of the Federal Railroad Administration, Richard Billings o f ERG, and David Powell of Genesee & Wyoming,
Inc.



### REFERENCES

1. “Documentation for Aircraft, Commercial Marine Vessel, Locomotive, and Other Nonroad Components of the National Emissions Inventory, Vol ume I – Methodology”; EPA Contract No.: 68-D-02-063. Prepared for the US EPA Emissions , Monitoring and Analysis Division by Eastern Research Group (ERG) for E.H. Pechan & Asso ciates, Inc. Sept. 2005. ftp://ftp.epa.gov/EmisInventory/2002finalnei/docume ntation/mobile/2002nei_mobile_nonroad_ methods.pdf . Related documents at ftp://ftp.epa.gov/EmisInventory/2002finalnei/docume ntation/mobile/2002nei_mobile_nonroad_tr ain.pdf

2. “Revised Inventory Guidance For Locomotive Emiss ions”; Report No. SR2004-06-01. Prepared for Southeastern States Air Resource Managers (SESA RM) by Sierra Research Inc. June 2004. http://www.metro4-sesarm.org/pubs/railroad/FinalGui dance.pdf

3. “Research Project: Development of Railroad Emis sion Inventory Methodologies”; Report No. SR2004-06-02. Prepared for Southeastern States Air Resource Managers (SESARM) by Sierra Research, Inc. June 2004. http://www.metro4-sesarm .org/pubs/railroad/FinalMethodologies.pdf

4. “Draft LADCO 2005 Locomotive Emissions”. Prepared f or Lake Michigan Air Director Consortium by Environ Feb 2007. http://www.ladco.org/reports/technical_support_docu ment/references/ladco_2005_locomotive_e missions.021406.pdf

5. “NYSERDA Clean Diesel Technology: Non-Road Field De monstration Program, Development of the 2002 Locomotive Survey for New York State”; Agreement Number 8958. Prepared for the New York State Energy Research And Development Auth ority (NYSERDA) by Southern
Research Institute Feb. 2007. http://www.nyserda.org/publications/LocomotiveSurve yReportwithAppendices.pdf

6. Gould, G..and Niemeier, D. “Review of Regional Loco motive Emission Modeling and the Constraints Posed by Activity Data.” Transportation Research Record: Journal of the Transportation Research Board , No. 2117, Transportation Research Board of the Na tional Academies, Washington, D.C., 2009, pp 24-32.18

7. Bergin, M., Harrell, M., McDill, J., Janssen, M, Dr iver, L., Fronczak, R., Nath, R. and D. Seep. "ERTAC Rail: A Collaborative Effort in Building a R ailroad-Related Emissions Inventory Between Eastern States Air Protection Agencies and Participation with the Railroad Industry," 18th Annual International Emission Inventory Confer ence. Baltimore, MD. April 14 - 17, 2009. Paper and presentation available at: http://www.epa.gov/ttn/chief/conference/ei18/sessio n6/bergin.pdf

8. American Short Line and Regional Association (ASLRR A) 2008 Fact Book. Available at www.alsrra.org

. 9. Surface Transportation Board R-1 Reports, available at: http://www.stb.dot.gov/stb/industry/econ_reports.ht ml

. 10. Confidential database was provided with assistance from Raquel Wright of the Federal Railroad Administration. Similar public data providing rang es of tonnage hauled rather than link-level tonnage is available from the Bureau of Transportat ion Statistics in the NTAD 2009 shapefile data (data is representative for the year 2007): http://www.bts.gov/publications/national_transporta tion_atlas_database/2009

. 11. US DOT Bureau of Transportation Statistics’ 2008 Na tional Transportation Atlas Database.

12. Freight Railroads in the United States 2008. Assoc iation of American Railroads. Available at: http://www.aar.org/~/media/AAR/InCongress_Railroads States/2008unitedstates.ashx

. 13. EPA Technical Highlights: Emission Factors for Loc omotives, EPA Office of Transportation and Air Quality, EPA-420-F-09-025, April 2009. http://www.epa.gov/otaq/regs/nonroad/locomotv/420f0 9025.pdf

14. “Estimating Ammonia Emissions From Anthropogenic No nagricultural Sources”, Draft Final Report. Prepared for EPA/STAPPA-ALAPCO Emission In ventory Improvement Program by E.H. Pechan & Assoc. April 2004. Supported by perso nal communication (5/6/2010) with Craig Harvey, US EPA, OTAQ, and Robert Wooten, NC DENR. http://www.epa.gov/ttnchie1/eiip/techreport/volume0 3/eiip_areasourcesnh3.pdf

15. “Inventory of U.S. Greenhouse Gas Emissions and Sin ks: 1990-2005”. EPA 430-R-07-002, Annex 3.2, U.S. Environmental Protection Agency Apr il 2007. Web site: http://www.epa.gov/climatechange/emissions/usinvent oryreport.html

. 16. Douglass, B., Heiken, J., Rubenstein, G. “Characte rization and Breadth of Rail Yard Specific
Inventories”. 19th Annual International Emission In ventory Conference. San Antonio, TX. September 27- 30, 2010.

17. Ireson, R.G., Germer, M.J., and Schmid, L.A. “Devel opment of Detailed Railyard Emissions to Capture Activity, Technology, and Operational Chang es”. U.S. EPA, 14th Annual International Emission Inventory Conference. Las Vagas, NV. Apri l 11 - 14, 2005.

18. Freight Railroads in the United States 2008. Assoc iation of American Railroads (AAR). From: http://www.aar.org/~/media/AAR/InCongress_Railroads States/2008unitedstates.ashx . Also An Overview of America's Freight Railroads, Oct 2011, AAR. From:http://www.aar.org/~/media/aar/BackgroundPapers/Overview%20of%20US%20Freight%20RRs%20October %2019%202011.ashx19 

### APPENDICES

Appendix A

REPRESENTATIVE ORGANIZATION

ERTAC Rail Data Workgroup

Matt Harrell IL EPA Michelle Bergin (Co-Chair) and Byeong Kim GA EPD Mark Janssen (Co-Chair) LADCO
Julie McDill and Patrick Davis MARAMA Laurel Driver US EPA OAQPS
Robert Fronczak AAR
Steven Sullivan ASLRRA
Rick Nath CSX David Seep and Lyle Staley BNSF Ken Roberge CPR
Carl Akins and Peter Conlon KCS
Erika Akkerman CN
M. John Germer UP
Brent Mason and Richard Russell NS
Joanne Maxwell Amtrak

ERTAC Rail Subcommittee

1 Allan Ostrander MI Dept. of Environ. Quality 2 Amanda Carter AL Dept. of Environ. Manag. 3 Ashley Mixon SC Dept. of Health and Environ.Cont rol 4 Bob Wooten NC Dept. of Environ. and Natural Reso urces 5 Carla Bedenbaugh SC Dept. of Health and Environ. Control 6 Chad Wilbanks SC Dept. of Health and Environ. Co ntrol 7 Dennis McGeen MI Dept. of Environ. Quality 8 Douglas Malchenson PA Dept. of Environ. Protecti on 9 Eric Zalewsky NY Dept. of Environ. Conserv. 10 Grant Hetherington WI Dept. of Natural Resource s 11 Jim Boylan GA Environ. Protection Div. 12 Julie McDill (Co-chair) Mid-Atlantic Regional Ai r Management Assoc. (MARAMA) 13 Kelley Matty PA Dept. of Environ. Protection 14 Kevin McGarry NY Dept. of Environ. Conserv. 15 Laurel Driver US EPA OAQPS 16 Lisa Higgins ME Dept. of Environ. Protection 17 Mark Janssen (Co-chair) ERTAC/ Lake Michigan Air Directors Consortium (LADCO) 18 Matthew Harrell (GIS lead) IL Environ. Protectio n Agency 19 Michelle Bergin (Co-chair) GA Environ. Protectio n Division
20 Mike Koerber LADCO 21 Pat Brewer Visibility Improvement State and Tri bal Association of the Southeast (VISTAS) /ASIP20

22 Richard Dalebout MI Dept. of Environ. Quality 23 Sam Long IL Environ. Protection Agency 24 Stacy Allen MO Dept. of Natural Resources 25 Tracy Anderson AL Dept. of Environ. Manag. 26 William Nichols OH Environ. Protection Agency

Appendix B: Source and Year of Data Utilized for E ach Inventory

Data 

### Year Source



### Class I Line-Haul

Annual Line-Haul Fuel Use
and Gross Ton-Miles 2007 STB R-1 Reports (CP data for D&H is for 2008.)

Line-haul fleet mix for
emission factors 2008 Each Class I railroad

Link-level tonnage 2007 FRA confidential database

Class I Railyards (Switcher Locomotives)

Annual Switcher Fuel Use 2008 R-1 Reports

Switcher fleet mix for
emission factors 2008 Each Class I railroad

Link-level tonnage or Density Code (for activity estimate) 2007 FRA confidential database

Class II and III Locomotives

Annual Total Fuel Use 2008 ASLRRA Annual Report (20 08)

Track length and railroad 2007 FRA confidential dat abase

Estimated fleet mix for
emission factors Discussions with ASLRRA and Class II and III representatives.

Appendix C: Railroad Companies Operating in the Un ited States

Railroad


### Class I

BNSF
Canadian National
Canadian Pacific***
CSX Kansas City Southern
Norfolk Southern
Union Pacific
Amtrack *Regional (Class II)21 Mileage Alabama and Gulf Coast Railway 435
Alaska Railroad 506 Buffalo and Pittsburgh Railroad 699 Central Oregon and Pacific Railroad 447 Dakota, Missouri Valley and Western Railroad 534 Florida East Coast Railway 386
Great Lakes Central Railroad 396
Indiana Rail Road 602 Indiana and Ohio Railway 692
Iowa Interstate Railroad 608
Kansas and Oklahoma Railroad 869 Kyle Railroad 554
Missouri and Northern Arkansas Railroad 583
Montana Rail Link 905 Montreal, Maine and Atlantic Railway 527 Nebraska Kansas Colorado Railway 559 New York, Susquehanna and Western Railway 357
Northern Plains Railroad 486 Paducah and Louisville Railway 290 Pan Am Railways 1165
Portland and Western Railroad 587
Providence and Worcester Railroad 568 Red River Valley and Western Railroad 575 San Joaquin Valley Railroad 351
South Kansas and Oklahoma Railroad 398
Texas Northeastern Railroad 665 Texas Pacifico Transportation 393 Utah Railway 430 Wheeling and Lake Erie Railway 878
Wisconsin and Southern Railroad 837

Shortline (Class III)
See www.aslrra.org/our_members/Railroad_Members or http://www.railsusa.com/links/Shortline_Railroads/ * These railroads were classified as regional by th e AAR in 2007 as reported in Wikipedia ( http://en.wikipedia.org/wiki/Regional_railroad#cite _note-2 , accessed Oct. 7, 2011.) Railroads known to have been purchased by Class I railroads after 2007 have been removed from this list.

Appendix D. ERTAC – Class 2/3 Shapefile Documentat ion (Matt Harrell, 13 Jul 2009)

Introduction This document outlines the methods and procedures u sed to compile a shapefile representing the links i n the FRA 1:100,000 railroad dataset that are owned o r operated by Class II and III railroad companies. It is important to note that there is a considerable a mount of overlap between the Class II’s and III’s a nd the Class I and passenger railroads. Class II’s an d III’s can operate on Class I or passenger rail li nks and vice versa. Although the final shapefile specifica lly represents Class II and III links, there are ma ny Class I and passenger railroads represented as well

.22

Procedure
1. Started with all proprietary FRA links where “NET = ‘M’ and “STCNTYFIPS” <> ‘ ‘ (this definition query selects all active mainline links located within the United States).

2. Ran 12 queries, one for each ownership and trackage rights field, to select all links not associated with a Class I freight railroad or Amtra k and not containing a null value (e.g.,

"RROWNER1" <> 'AMTK' AND "RROWNER1" <> 'BNSF' AND " RROWNER1" <> 'CN'

AND "RROWNER1" <> 'CPRS' AND "RROWNER1" <> 'CSXT' A ND "RROWNER1" <>

'KCS' AND "RROWNER1" <> 'NS' AND "RROWNER1" <> 'UP' AND "RROWNER1" <> ' '). The first query was setup as a new selection . Each of the 11 subsequent queries were setup to add records to initial set of records. 26 ,261 links were selected and exported to a new shapefile.

3. Due to the multitude of railroad codes used to repr esent commuter rail operations across the country, additional processing was required to remo ve any links that were not operated by a Class II or III freight railroad. Each commuter ra ilroad was queried out of the new shapefile and the links analyzed to eliminate all links where no Class II or III operations were occurring. The following commuter rail operations were evaluated: NJT (New Jersey Transit), MNCW (Metro-North Commuter Railroad), LI (Long Island Railroad), CDOT (Connecticut DOT), MBTA (Massachusetts Bay Transpor tation Authority), SEPA (Southeastern Pennsylvania Transportation Authority ), MARC (Maryland Area Rail Commuter), VRE (Virginia Railway Express), MTRA (No rtheastern Illinois Regional Commuter Railroad), CSS (Northern Indiana Commuter Transportation District), DART (Dallas Area Rapid Transit), SCRA (Southern Califor nia Regional Rail Authority – including also SCAX, LACM, LAPT, and LATC), TCRA (South Flori da Regional Transportation Authority), PJPB (Caltrain), and ACE (Altamont Comm uter Express). Approximately 1581 links were identified with no Class II or III opera tions and were deleted from the Class 2/3 shapefile.

4. The remaining Class II and III links were then comp ared to the regional maps contained in the July-August issue of The Official Railway Guide to assess the completeness of the Class 2/3 shapefile. Six specific edits were made to the shapefile to correct the most glaring errors: 1) BMLP links deleted (Black Mesa & Lake Powell, an electric coal hauling railway in Arizona); 2) DSNG links deleted (Durango & Silverto n steam tourist railroad in Colorado; 3) CIC haulage rights links on CN from Chicago to Omah a deleted; 4) DMIR links deleted (Duluth, Missabe & Iron Range, now owned and operat ed by CN in Minnesota; 5) EVWR’s
ex-CSXT links coded from Evansville, IN to Okawvill e, IL (Evansville Western Railroad); 6) INRD ex-CP links coded from Chicago, IL to Louisvil le, IN (Indiana Rail Road).

5. During the course of reviewing the FRA dataset, 555 “active” links were found to have no ownership or trackage rights codes. 1005 links hav e no codes listed in the 3 ownership fields. In most cases these links are very short a nd scattered across the country. Only the links representing the EVWR and INRD spanned large distances and were fixed. The other problem links were deemed to be insignificant. A li sting of these links will be provided back to the FRA to assist with their coding in 1:100K ra ilway shapefile.23

KEY WORDS

Locomotive emissions
Railroad emissions Air quality
Nonroad emissions Emissions inventory Air quality modeling