APPENDIX D-1

PENNSYLVANIA NONROAD SOURCE EMISSIONS ESTIMATION METHODOLOGY

Bureau of Air Quality

Department of Environmental Protection

Division of Air Information 

  Methodology for estimating nonroad emissions

I.	INTRODUCTION

This following methodology provides a description of the procedures used
to generate 2002, 2004, 2009, and 2018 county-level pollutant emission
estimates for nonroad mobile engines included in the United States
Environmental Protection Agency’s (EPA’s) NONROAD2005 model, as well
as locomotive engines, commercial marine vessel engines, aircraft
operations.  For the NONROAD2005 model engines, emission estimates were
calculated for volatile organic compounds (VOCs), oxides of nitrogen
(NOx), and carbon monoxide (CO).  Revised geographic allocation files
for NONROAD model option files and revised housing unit data used for
the model runs are also included.

II.	2002 NONROAD MODEL SOURCE CATEGORY EMISSIONS

  SEQ CHAPTER \h \r 1 The Department used EPA’s Final NONROAD2005
Model to generate 2002, 2004, 2009, and 2018 summer work weekday
emissions for Greene County in Pennsylvania.  The Department prepared
NONROAD2005 model option files that account for temperatures and
gasoline Reid vapor pressure (RVP) values representative of Greene
County for summer weekdays.  Pennsylvania was separated into four
climate zones for the Maintenance Plans:  East North, East South, West
North, and West South for the inventories for all maintenance plans. 
Greene County is located in the East South region.  The summertime RVP
value used for each region was 9.0.  Minimum, maximum, and average
temperatures for July for each region were obtained from the
Pennsylvania State Climatologist website, Pennsylvania State
Climatologist, Penn State University, 2002 Temperature Data by Weather
Station which is available at   HYPERLINK
"http://pasc.met.psu.edu/PA_Climatologist/cityform.html" 
http://pasc.met.psu.edu/PA_Climatologist/cityform.html .  The RVP and
temperature inputs were applied to all equipment categories to obtain
both summer and annual emissions.  

Table 1 lists the counties included in each of the four regions and the
weather station where the temperature data were obtained for each of the
regions.  Table 2a and 2b presents the RVP and temperature data used in
the model runs for each region of Pennsylvania.



  SEQ CHAPTER \h \r 1 Table 1.  Regions of Pennsylvania and Associated
Maintenance Areas







Region	Weather Station	Counties in Region	FIPSST	FIPSCNTY

East North	Scranton	Lackawanna County	42	069



Luzerne County	42	079



Tioga County	42	117



Wyoming County	42	131

East South	Harrisburg	Adams County	42	001



Berks County	42	011



Cumberland County	42	041



Dauphin County	42	043



Franklin County	42	055



Lancaster County	42	071



Lebanon County	42	075



Monroe County	42	089



Perry County	42	099



York County	42	133

West North	Erie	Erie County 	42	049

West South	Altoona	Blair County	42	013



Cambria County	42	021



Centre County	42	027



Clearfield Count	42	033



Greene County	42	059



Indiana County	42	063



Mercer County	42	085



  SEQ CHAPTER \h \r 1 Table 2a.  Summer Day NONROAD Model Temperature
and RVP Inputs



Temperature*

	Region	Season	Maximum 	Minimum 	Average 	RVP**

  East North	Summer	83	60	72	9.0

East South	Summer	86	66	76	9.0

West North	Summer	80	63	71	9.0

West South	Summer	82	60	71	9.0

*  Temperature in degrees Fahrenheit

** RVP in pounds per square inch (psi)

Table 2b.   Annual Average Day NONROAD Model Temperature and RVP Inputs

	Temperature*

	Region	Maximum 	Minimum 	Average 	RVP**

  East North	61	42	51	9.0

  East South	64	45	55	9.0

  West North	59	43	51	9.0

  West South	60	42	51	9.0

*  Temperature in degrees Fahrenheit

                                ** RVP in pounds per square inch (psi)

EPA-recommended diesel fuel sulfur levels for marine and land equipment
for all years were used as inputs to the model, as outlined in Diesel
Fuel Sulfur Inputs for the Draft NONROAD2004 Model used in the 2004
Nonroad Diesel Engine Fuel Rule, April 27, 2004.  

In past versions of the NONROAD model, state recreational marine vessels
populations were underestimated when compared to boat registrations
tracked by the Pennsylvania Fish and Boat Commission (PFBC).  EPA’s
population of recreational marine vessels in the model now seem more
representative of the number of boat registrations that the Pennsylvania
Fish and Boat Commission tracks.  We used EPA’s default values in the
model runs.  We also examined geographic allocation factors for
residential lawn and garden equipment.  These improvements are discussed
further in the next section.  All other categories rely on default data
included in the model for population and activity estimates.

  SEQ CHAPTER \h \r 1 Residential Lawn and Garden Equipment

The EPA’s NONROAD2005 model uses 2003 U.S. Census data of all housing
units in Pennsylvania to allocate residential lawn and garden equipment,
even though EPA guidance states that emissions should be based only on
the number of single detached, single attached, and double housing
units.  EPA’s method in the NONROAD2005 model alters the allocation of
lawn and garden emissions in some Pennsylvania counties significantly. 
The Department will use data obtained by E.H. Pechan from the 2000
Census, updated to 2002, and used in the state’s 2002 inventory on the
number of single detached, single attached, and double housing units for
both the State and all counties in the state.  Table 3 presents the
Census data (Bureau of the Census, 2003), which can be found in 2000
County and State Housing Units by Unit Type, Census 2000,
http://factfinder.census.gov/servlet/.  The total number of housing
units was incorporated into the NONROAD2005 model geographic allocation
factor file, PA HOUSE.ALO, for use in allocating state-level lawn and
garden equipment populations to all Pennsylvania counties for 2002.

  SEQ CHAPTER \h \r 1 Table 3.  Number of Single and Double-Family
Housing Units from 2002 Census

County	# 1-Unit Detached

Housing Units	# 1-Unit Attached 

Housing Units	# 2-Unit 

Housing Units	Total # of 

Housing Units

Adams	24,549	2,206	1,490	28,245

Allegheny	345,479	46,899	29,002	421,380

Armstrong	22,268	824	1,014	24,106

Beaver	54,418	2,312	2,863	59,593

Bedford	14,684	248	501	15,433

Berks	78,946	32,377	5,803	117,126

Blair	36,919	1,844	2,668	41,431

Bradford	16,861	232	1,224	18,317

Bucks	141,951	30,506	5,425	177,882

Butler	46,271	2,523	2,215	51,009

Cambria	44,453	3,795	2,882	51,130

Cameron	1,749	35	176	1,960

Carbon	14,431	5,104	990	20,525

Centre	27,786	2,691	1,749	32,226

Chester	99,549	25,911	3,155	128,615

Clarion	11,635	118	471	12,224

Clearfield	24,829	412	1,064	26,305

Clinton	10,141	662	711	11,514

Columbia	16,856	1,328	1,388	19,572

Crawford	24,430	397	1,933	26,760

Cumberland	51,934	10,450	2,792	65,176

Dauphin	52,961	20,195	3,848	77,004

Delaware	93,642	64,529	9,361	167,532

Elk	11,355	79	807	12,241

Erie	70,504	2,955	9,504	82,963

Fayette	41,679	3,094	2,473	47,246

Forest	1,660	8	18	1,686

Franklin	34,720	4,292	2,073	41,085

Fulton	4,084	56	133	4,273

Greene	10,387	481	416	11,284

Huntingdon	12,463	331	686	13,480

Indiana	23,215	825	1,232	25,272

Jefferson	14,276	237	729	15,242

Juniata	6,455	355	166	6,976

Lackawanna	53,357	3,328	12,626	69,311

Lancaster	98,364	32,122	7,370	137,856

Lawrence	28,316	799	1,489	30,604

Lebanon	27,272	8,647	2,225	38,144

Lehigh	59,753	29,474	5,118	94,345

Luzerne	82,363	15,404	9,435	107,202

Lycoming	31,568	2,812	2,998	37,378

McKean	13,794	158	972	14,924

Mercer	34,859	735	1,817	37,411

Mifflin	12,327	1,788	966	15,081

Monroe	40,696	1,726	1,457	43,879

Montgomery	163,211	53,370	9,599	226,180

Montour	4,769	645	346	5,760

Northampton	60,344	19,729	4,755	84,828

Northumberland	21,955	9,280	1,657	32,892

Perry	12,209	733	398	13,340

Philadelphia	48,724	359,877	46,425	455,026

Pike	15,501	395	299	16,195

Potter	5,263	62	281	5,606

Schuylkill	32,695	17,989	2,092	52,776

Snyder	10,263	644	526	11,433

Somerset	22,159	1,236	1,312	24,707

Sullivan	2,165	18	84	2,267

Susquehanna	12,139	180	711	13,030

Tioga	11,076	150	753	11,979

Union	9,341	652	527	10,520

Venango	17,197	227	1,080	18,504

Warren	13,031	239	930	14,200

Washington	60,711	3,891	3,187	67,789

Wayne	14,311	249	750	15,310

Westmoreland	113,694	4,839	5,997	124,530

Wyoming	7,858	159	412	8,429

York	95,921	20,218	6,102	122,241

Total	2,724,746	860,086	235,658	3,820,490



  SEQ CHAPTER \h \r 1 After the model runs, model outputs were processed
to develop summer work weekday emissions inventories for NOx, VOC, and
CO.  Nonroad equipment refueling, either by portable container or at the
gasoline pump, is being accounted for under Pennsylvania’s area source
inventory.  As such, spillage and vapor displacement VOC emission
estimates were subtracted from the total VOC emission estimates for all
NONROAD model emissions.  Therefore, only exhaust, crankcase, and
evaporative diurnal components are included in these VOC estimates.

Emissions from nonroad equipment were tabulated in both source
classification code and category format.  Immaterial rounding errors may
exist when comparing the two formats due to different arithmetic
operations performed inside and outside the NONROAD model for the two
formats.

III.	2002 LOCOMOTIVE EMISSIONS

Much of the locomotive emissions were captured from a 1999 survey
conducted by the Department included hydrocarbon (HC) and NOx for the
following locomotive source categories:

2285002006 :	Railroad Equipment, Diesel, Line Haul Locomotives: Class I
Operations

2285002007:	Railroad Equipment, Diesel, Line Haul Locomotives: Class
II/III Operations

2285002008:   Railroad Equipment, Diesel, Line Haul Locomotives:
Passenger Trains (Amtrak)

2285002010:  Railroad Equipment, Diesel, Yard Locomotives

All line haul locomotive emissions were grouped into one SCC category in
the appendix, 2285002005.

Norfolk Southern and CSX Corporations purchased Conrail.  The takeover
of Conrail’s assets occurred in June 1999.  For that reason, it was a
very bad time to develop a representative emission inventory for these
railroads.  Both Conrail and Norfolk Southern suffered major gridlock in
Pennsylvania and beyond during 1999.  Consequently, fuel consumption and
air emissions for these two railroads were greatly reduced in 1999.  The
Department requested and received 2002 fuel usage from these railroad
companies and developed a 2002 emissions inventory for them.  Fuel
consumption increased 60 percent from 1999 to 2002.  Clearly, this was
not due to normal economic growth.  All other emissions from railroad
companies operating in Pennsylvania in 1999 were grown with a growth
factor to obtain 2002 emissions.

Estimating Emissions Growth

To estimate 2004, 2009, and 2018 locomotive emissions, the Department
projected the 2002 inventory to 2004 and beyond using national fuel
consumption information supplied to the Department by the Association of
American Railroads in combination with emissions factors developed by
EPA and presented on the EPA website in the Emissions Factors for
Locomotives, EPA420-F-97-051, December 1997, Table 9, Fleet Average
Emission Factors For All Locomotives.  According to the Association of
American Railroads, national railroad annual fuel consumption has grown
consistently at about 1.6 percent over the last 15 years.  We used the
following normalized emission growth factors for locomotive emissions. 
These numbers compare well with EGAS 5.0 in the near-term.  The only
differences are that EGAS 5.0 forecasts a slightly larger reduction of
NOx emissions in 2018 and a much larger VOC reduction in some future
years.  VOC emissions from locomotives are typically very small.  EGAS
5.0 may be downloaded from http://www.epa.gov/ttn/ecas/egas5.htm.

 

Table 4.  Normalized Growth Factors for Locomotives



Year	Fuel Use Growth	NOx Emission Factors	NOx Emission Growth (Fuel use
growth * emission factor)	HC/VOC Emission Factors	HC/VOC Emission Growth
(Fuel use growth * emission factor)

2002	1.0000	1.0000	1.0000	1.0000	1.0000

2004	1.0323	0.8766	0.9049	1.0000	1.0323

2009	1.1175	0.6765	0.7560	0.8785	0.9817

2018	1.2891	0.5804	0.6553	0.7664	0.9880



  

In the Regulatory Support Document (RSD) for locomotive emission
standards, national emissions account for future, phased-in controls
that will primarily reduce NOx and HC emissions as well (EPA, 1997). 
Emission reductions, which include rule effectiveness and rule
penetration, are estimated based on the percent change in emissions from
the base year to a given projection year.  The Department reduces the
2002 locomotive emissions for NOx and VOC by the percentages shown in
Table 4.   

  SEQ CHAPTER \h \r 1 To estimate VOC emissions from HC, the Department
applied a VOC/HC conversion factor of 1.005 to the HC emissions.  This
conversion factor was obtained from EPA’s Documentation for Aircraft,
Commercial Marine Vessel, Locomotive, and Other Nonroad Components of
the National Emission Inventory, Volume I: Methodology (EPA, 2002).

Estimated annual emissions were divided by 365 to obtain a daily
emission estimate which was assumed to be a good estimate for emissions
during an average summer day.

IV.	Aircraft Emissions

 

Emissions from commercial aircraft for 2002 are estimated using the
Emissions & Dispersion Modeling System (EDMS) 4.20.  Commercial aircraft
operations were insignificant in Greene County and were not modeled by
the EDMS model directly.  General aviation aircraft produced most
aircraft operations in the county.  A description of EDMS calculation
methods is included because some information from the model is used in
estimating emissions from the smaller airports in the county.    

Commercial airport operations data.  Airport specific data on operations
were found at   HYPERLINK "http://www.transtats.bts.gov" 
www.transtats.bts.gov  and also the FAA’s Terminal Area Forecast found
at http://www.apo.data.faa.gov. Operations data on all significantly
large regional airports in Pennsylvania could be found at this website,
although some of the airports modeled supported very few commercial
flights.  We used default values in the EDMS model for aircraft engines
and service equipment for each aircraft type.  

Mixing height in EDMS.  It was determined that a better mixing height
than the model’s default value could be used in EDMS.  Upper air
meteorological data available from Sterling Virginia in combination with
surface data measurements taken at the Philadelphia International
Airport generated 1-hr mixing heights in the meteorological preprocessor
model PCRAMMET, EPA, PCRAMMET (software), Research Triangle Park, NC,
June 1999.  All mixing heights between 10 a.m. and 6 p.m. were not
considered when generating these averages because an extremely small
percentage of flight operations occur between those hours.  The average
summer mixing height was 4,428 feet for airports in the Eastern
Pennsylvania.  The default value of 3000 feet was used for all other
airports.

Small airport emission estimation methodology. Small aircraft emissions
were calculated by using small airport operation statistics, which can
be found at   HYPERLINK "http://www.airnav.com"  www.airnav.com .  An
emissions factor for a typical air general aviation single engine,
multi-engine, and jet engine aircraft were derived by averaging the
emissions factors from a basket of emission factors for common aircraft
of each of the three types of aircraft.  Emission factors and
operational characteristics contained in EDMS were used.  The proportion
of operations between the three groups of aircraft was determined by
examining the number of each aircraft type based at each airport.  For
military operations at small airports, the type of aircraft and its
emission factors are sometimes identifiable.  If not, emission factors
calculated to represent an “average” military aircraft are used. 
Growth was estimated using estimates of small airport activity from
Federal Aviation Administration’s APO Terminal Area Forecast Detailed
Report.  In Greene County, airport operations are projected decline
slightly until 2018.

V. Commercial Marine Vessel Emissions

Greene County contains waterways that are considered part of the Port of
Pittsburgh.  Commercial Marine Vessels (CMV) navigate these waterways in
Greene County and the extended region and produce significant air
emissions.  

All air emissions from commercial vessel traffic in the seven county
area of the Port of Pittsburgh, including Greene County, were estimated
using the methodology outlined in EPA’s publication Commercial Marine
Activity for Great Lakes and Inland River Ports in the United States,
Final Report1.  A comprehensive understanding of the methodology can be
achieved by reviewing this document.  Additional information was
obtained from conversations with tug operators in the port.

The best available EPA methodology for estimating emissions from the
Port of Pittsburgh requires that a typical port be chosen to compare to
the port to be modeled.  A typical port is a port where EPA examined the
emissions and operations in detail.  A typical port contains all the
major elements that can be found in an inland river port to be modeled. 
The modeler can then more easily compare the port being modeled with
known operating characteristics in the typical port to derive trip
durations and movements in the modeled port.  The typical port that was
chosen to compare to Pittsburgh was the Port of St. Louis.  The Port of
St. Louis was chosen because of the presence of locks and the barge to
tug ratio in this port is comparable to the Port of Pittsburgh.

	The next step summarized trip data for the modeled port.  Trip data is
obtained directly from the website of the Waterborne Commerce Statistics
Center of the United States Army Corps of Engineers2.  Trip data under
the heading Port of Pittsburgh in that report totals trips for the
Monongahela River, Allegheny River, and the Ohio River to the state
border with Ohio, along with other trips that occur on the smaller
tributaries in the port.  The total number of trips used for modeling
emissions for the Port of Pittsburgh was obtained here.  

Determining Number of Trips for the Modeled Port Ship Types

	Next, we separated the number of trips into components of upbound and
downbound trips both passing and calling.  The number of trips were
separated into horsepower bins of various groups of tugboats.  The Port
of St. Louis was not used in determining the number of trips for three
reasons: 1) Pittsburgh has no upbound, passing traffic, 2) tugs in St.
Louis generally have more horsepower than tugs in Pittsburgh, and 3)
horsepower data is available for all tugs operating in Pittsburgh. 
Therefore, more accurate and appropriate data was used.  

To complete this step, tugs operating in the Port of Pittsburgh were
separated in five horsepower bins to allow for easier calculation of
fuel usage.  Horsepower information was obtained from the Waterborne
Commerce Statistics Center website3.  All upbound traffic is calling
(i.e. the boat makes a stop somewhere in the Port of Pittsburgh) since
the port is an endpoint for river traffic.  Downbound traffic is
primarily comprised of tugs and coal barges making calls at the
steel-producing facilities and power plants.  Some downbound traffic
passes on to other ports.  No method exists to easily predict the amount
of passing downbound traffic.  By looking at lock data, one lock being
west of the Ohio border and the other one being east, an estimate was
made about the number of tugs that pass over the border.

Determining Trip Durations

	By using formulas supplied in the Commercial Marine Activity for Great
Lakes and Inland River Ports in the United States4, trip times were
calculated for tugboats of different horsepower.  The time of trips
included maneuvering on the river and maneuvering into locks.  A total
time was obtained for all trips by multiplying the time per trip per
horsepower bin by number of trips per horsepower bin.  Similarly, the
total maneuvering time was estimated by using the formulas supplied
below.  Commercial Marine Activity for Great Lakes and Inland River
Ports in the United States5 states that 1.0 hours is needed for
maneuvering into each lock and 0.5 hours for each lock area.  There are
many more locks in Pittsburgh that are distributed in a more complicated
pattern in the port than in the typical port.  A method to determine the
average number of locks traversed for each horsepower bin was developed.
 When trips were averaged for both directions for each horsepower bin,
it offers a reasonable estimate of trip durations based on power rating.
 More powerful tugboats traverse more locks than smaller tugboats, which
is consistent with the results of the EPA methodology, since larger tugs
travel longer distances.  The EPA methodology states that the average
speed of larger tugs is greater.

	Tugboats operators informed us that their boats are pushing freight
essentially non-stop - except for shift changes and maintenance. 
Operators in the port strive to achieve an average speed of five miles
per hour for 20 hours per operating day or about 100 miles a day
including maneuvering time.  Using the methodology in Commercial Marine
Activity for Great Lakes and Inland River Ports in the United States6
generated a speed greater than five miles per hour, but less than the 20
hours per day average that the tug operators strive to achieve, which
reasonably approximates what operators described.

  

	A series of sample calculations are given below for estimating trip
times:	

Allocation of Cruise Time-in-mode to the Modeled Port7

MPCT  = LP * MPRD/LPRD * LPCS/(LPCS +/- LPRC +/-  MPRC)

Where:

		MPCT  = Modeled Port cruise time-in-mode (hr/trip)

LPCT   = Like Port cruise time-in-mode.  

		MPRD  = Modeled Port distance along the river

		LPRD   = Like Port distance along the river

LPCS   = Like Port cruise speed.  Depends on direction. 

LPRC   = Like Port river current (added for upbound vessels or
subtracted for downbound) 

MPRC  = Modeled Port river current (subtracted for upbound vessels or
added for downbound)

River current in the Port of Pittsburgh ranges from rapid flowing to
pooling.  An average current of 3.5 miles per hour was chosen after
talking with tugboat operators in the port.

Sample Calculation of Downbound Trip

	MPCT = 2.7 hours * 120 miles/70 miles  * 6.6 mph/(6.6 mph – 2 mph +
3.5 mph)

           = 3.77 hours

	

Average Number of Locks Traversed

	LTHP BIN = MPCT for HP BIN * (LPCS +/- LPRC +/- MPRC)/13 

	Where:

		LTHP BIN = Locks traversed for each horsepower bin

		MPCT for HP BIN = Modeled port cruise time for each type of horsepower

		LPCS , LPRC , MPRC = As explained above

		13 = Number of locks along typical total length of Pittsburgh waterway

Sample Calculation

	LTHP BIN = 3.77 hours * (6.6 mph - 2 mph + 3.5 mph) = 30.54 miles/13 

		    = 2.35 locks

Total Maneuver Time8    

	TMT = (LTHP BIN * 1.0) + 0.5

	Where:

		TMT = total maneuver time

		1.0 hours = time required to maneuver through a lock

		0.5 hours = time required to maneuver to and from a lock

Sample Calculation

	TMT = 2.35 locks * (1.0 hours + 0.5 hours) 

	          = 3.52 hours

Estimating Emissions

Determining fuel usage and multiplying by an emission factor determined
total emissions.  Fuel usage was estimated for each horsepower bin by
using information from the study Shipboard Marine Engines Emission
Testing for the United States Coast Guard-Final Report9.  Although fuel
usage for every horsepower bin was not available in this report,
examining the available horsepower data could make a reasonable
estimation of the rate of fuel usage.  The names of tugs operating in
the Port of Pittsburgh area and their horsepower ratings are available
from the website of the Waterborne Commerce Statistics Center of the
United States Army Corps of Engineers10.  Barges in the Port of
Pittsburgh are mostly loaded in the downbound direction and unloaded in
the upbound direction.  Tugboats were estimated to run at close to
maximum power, 75 percent load, for calculating fuel usage, since they
push cargo downstream and empty barges upstream against a strong
current.  The following fuel usage was used for the various horsepower
bins:

Table 5.  Tugboat Fleet Characterization in the Port of Pittsburgh Based
on Horsepower Rating



Horsepower Bin	Number of Tugs in the Port	Percentage of Total Tugs and
Trips	Fuel Usage (gal/hr)

0-500	32	33	25

750-1500	51	52	44

1500-3000	9	9	105

3000-5000	4	4	200

5000-8000	2	2	250

Total Tugs	98	100

	

 Emission factors from tugboats and pushboats are nearly nonexistent. 
Commercial Marine Vessels Contributions to Emission Inventories11
suggested that 550 lb of NOx are produced per 1000 gallons of fuel used.
 CMV engines also known as type II marine engines are engineered and
perform similarly to locomotive engines.  Since some locomotive engines
are the same size and operate under similar circumstances as tugboat
engines, emission factors of locomotive engines were used at the
suggestion of Greg Janssen of EPA12.  Emission factors of locomotives
from the U.S. EPA website,   HYPERLINK "http://www.epa.gov"  www.epa.gov
13 show NOx emissions at 609 lb per 1000 gallons of fuel.  Since these
tugs are under load like locomotives, the emission factors of
locomotives seem more appropriate than other emission factors available.
 Other emission factors for hydrocarbons, carbon monoxide, and
particulate matter were 23.6 lb, 60.4 lb, and 15.0 lb, respectively, for
1000 gallons of fuel consumed.

Distributing Emissions to the County Level

We used total emissions for the Port of Pittsburgh and apportioned the
total to the county level by counting the number of piers, wharves, and
docks (PWDs) in each county and calculating the percentage PWDs in the
county relative to the port total of PWDs,14 as shown in Table 6.  The
total emissions from the port for all pollutants were multiplied by the
percentage of PWDs in the county to obtain countywide emissions.

Table 6.  Geographic Distribution Piers, Wharves, and Docks in the Port
of Pittsburgh by County.



County	Number of Piers, Wharves, and Docks in County 	Percentage of
Piers, Wharves, and Docks by County

Allegheny	102	53.7

Armstrong	5	2.6

Beaver	34	17.9

Fayette	7	3.7

Green	11	5.8

Washington	24	12.6

Westmoreland	7	3.7

Total	190	100.0



Example Emission Calculation of Downbound Calling Trips for 0-750
Horsepower Tugboats in Greene County:

Total tug trips in the Port of Pittsburgh in 1999 = 28,584

Downbound calling trips in 1999 = 13,850

Number of tugs in the 0-750 horsepower ranges = 32

Total number of tugs in operation in the port = 98

Percentage of tugs and trips (assumed) in the 0-750 horsepower bin = 33

Downbound calling trips in this horsepower bin= (13,850 * 0.33) = 4,571

Like port cruise times for these tugs on downbound calling trips = 2.7
hours

Modeled port cruise time for these tugs on downbound calling trips =
3.77 hours

Average number of locks traversed for these tugs on these trips in
modeled ports = 2.35

Modeled port maneuver time = 2.35 * (1.0 hours + 0.5 hours) = 3.52 hours

Total time per trip = 3.77 hours + 3.52 hours = 7.29 hours

Total NOx emissions = 4,571 trips * 7.29 hours/trip * 25 gal/hour * 609
lb NOx/1000 gal of fuel used *1 ton/2000 lb = 253.67 tons/year

NOx Emissions in Allegheny County from 0-750 horsepower bin in downbound
direction = 253.67 tons/year * 5.8% = 14.71 tons/year.

Emissions from all horsepower bins and trip types were summed to get an
annual emissions estimate for each county in the Port of Pittsburgh. 
The number of operations and shipping activity are believed to be
relatively constant throughout the year.  An exception may occur when
there is a severe drought.  Therefore, annual emissions were divided by
365 days to determine the average emissions per summer day.

Estimating Emissions CMV Growth

Emissions growth is based on two factors: future fuel consumption and
future emissions standards.  Emissions standards or programs that take
place in the future will greatly lower emissions produced by CMV
engines.  Fuel use growth and future emission reductions used to
calculate total future emissions were based upon information contained
in the Final Regulatory Impact Analysis: Control of Emissions from
Marine Diesel Engines.15  

Fuel use growth for CMV was obtained from Table 5.8 in the regulatory
impact analysis, “Baseline Emissions from Category 2 CI Marine Engines
Operated in U.S. Waters.”16 An average annual fuel use growth of 0.9
percent was estimated in the table, which led to corresponding baseline
emission increases (absent controls) in carbon monoxide (CO), NOx, and
VOC.  Growth was based upon the number of extra CMV expected to enter
service in future years.  

In Table 5-14, which is entitled, “Projected NOx Emission Reductions
from Category 2 CI Marine Engines Operated in U.S. Waters,”17 the
emission reductions for hydrocarbons were obtained for future years. 
The hydrocarbon reduction was applied directly to VOC emissions. 
Emission reductions in NOx were obtained from Table 5-9, “Projected
NOx Emission Reductions from Category 2 CI Marine Engines Operated in
U.S. Waters.” 18 Table 5-9 was used for NOx because it shows emission
reductions of Category 2 CI Marine Engines exclusively.  Percentage
reductions were derived from these two tables for VOC and NOx for use in
the future years contained in our inventory.  The tables gave reductions
for 2010, 2020, and 2030.  We interpolated linearly between years to
obtain reductions for our inventory’s years of 2002, 2004, 2009, and
2018.  Table 7 below shows the growth rates in emissions used to
estimate emissions in the Greene County inventory.

Table 7. Normalized Growth in Emissions for CMV in the U.S.



Normalized Future Emission Factors	Normalized Growth in Emissions (CMV
Growth * Normalized Future Emission Factors)

Year	CMV Fuel Use Growth	CO	NOx	VOC	CO	NOx	VOC

2002	1.0000	1.0000	1.0000	1.0000	1.0000	1.0000	1.0000

2004	1.0181	1.0000	0.9800	0.9900	1.0181	0.9977	1.0079

2009	1.0647	1.0000	0.9600	0.9800	1.0647	1.0221	1.0434

2018	1.1541	1.0000	0.8700	0.9200	1.1541	1.0041	1.0618

         

 PAGE   

 PAGE   14 

1 United States Environmental Protection Agency, Office of Mobile
Sources, Assessment and Modeling Division, Commercial Marine Activity
for Great Lake and Inland River Ports in the United States, Final
Report, Ann Arbor, Michigan, September 1999.

2  United States Army Corps of Engineers, Navigation Data Center,
Waterborne Statistics Center, Waterborne Commerce of the United States,
Waterways and Harbors on the: Gulf Cost, Mississippi River System and
Antilles, New Orleans, Louisiana, available February, 2004, at  
HYPERLINK "http://www.iwr.usace.army.mil/ndc/wcsc/wcsc.htm" 
http://www.iwr.usace.army.mil/ndc/wcsc/wcsc.htm .

3 Waterborne Commerce Statistics Center of the United States, United
States Army Corps of Engineers, Vessel Characteristics, available at the
website http://www.iwr.usace.army.mil/ndc/veslchar/veslcharsearch.htm,
July 19, 2001.

4  United States Environmental Protection Agency, Office of Mobile
Sources, Assessment and Modeling Division, Commercial Marine Activity
for Great Lake and Inland River Ports in the United States, Final
Report, Ann Arbor, Michigan, September 1999, Section 4.

5  Ibid.,  p. 4-9.

6  Ibid., Section 4 

7  Ibid.,  p. 4-24.

8  Ibid.,  p. 4-9.

9 Volpe National Transportation Systems Center and United States Coast
Guard Headquarters Naval Engineering Division, prepared by Environmental
Transportation Consultants, Shipboard Marine Engines Emission Testing
for the United states Coast Guard Final Report, 1995.

10 Website of the Waterborne Commerce Statistics Center of the United
States, United States Army Corps of Engineers,   HYPERLINK
"http://www.iwr.usace.army.mil/ndc/wcsc/wcsc.htm" 
http://www.iwr.usace.army.mil/ndc/wcsc/wcsc.htm , July 19, 2001.

11 Booz-Allen & Hamilton Inc, Transportation Consulting Division, 523
West Sixth Street, Suite 616, Los Angeles, CA 90014, Commercial Marine
Vessel Contributions to Emission Inventories, September 12, 1991.

12  Email exchange with Greg Janssen of U.S. EPA, Office of
Transportation and Air Quality, Assessment and Standards Division, Ann
Arbor Michigan, January 24, 2001.

13  U. S. EPA, Office of Transportation and Air Quality, website at
http://www.epa.gov/otaq/locomotv.htm, July 19, 2001.

14 United States Environmental Protection Agency, Office of Mobile
Sources, Assessment and Modeling Division, Commercial Marine Activity
for Great Lake and Inland River Ports in the United States, Final
Report, Ann Arbor, Michigan, September 1999, p. 4-9.

15  United States Environmental Protection Agency, Office of mobile
Sources, Engines and Compliance Division, Final Regulatory Analysis:
Control of Emissions from Marine Diesel Engines, November 1999.

16  Ibid. , p. 109.

17 Ibid. , p. 115.

18 Ibid. , p. 110. 

