Proposal to Designate an Emission 


Control Area for Nitrogen Oxides, 


Sulfur Oxides and Particulate Matter 


Technical Support Document
 

Chapter 2
 

Emission Inventory
 

Assessment and Standards Division 

Office of Transportation and Air Quality 

U.S. Environmental Protection Agency 

. 

EPA-420-R-09-007 

April 2009 
2 Emission Inventory 

2.1 Introduction 

Ships (i.e., ocean-going vessels) are significant contributors to the total United States (U.S.) 

mobile source emission inventory. The U.S. ship inventory reported here focuses on Category 3 

(C3) vessels, which use C3 engines for propulsion. C3 engines are defined as having displacement 

above 30 liters per cylinder (L/cyl). The resulting inventory includes emissions from both 

propulsion and auxiliary engines used on these vessels, as well as those on gas and steam turbine 

vessels. 

Most of the vessels operating in U.S. ports that have propulsion engines less than 30 liters 

per cylinder are domestic and are already subject to strict national standards affecting NOX, PM, 

and fuel sulfur content. As such, the inventory does not include any ships, foreign or domestic, 

powered by Category 1 or Category 2 (i.e., <30 L/cyl) engines. In addition, as discussed in Sections 

2.3.2.3.9 and 2.3.3.3, this inventory is primarily based on activity data for ships that carry foreign 

cargo. Category 3 vessels carrying domestic cargo that operate only between U.S. ports are only 

partially accounted for in this inventory.1

 Emissions due to military vessels are also excluded. 

The regional and national inventories for C3 vessels presented in this chapter are sums of 

independently constructed port and interport emissions inventories. Port inventories were 

developed for 89 deep water and 28 Great Lake ports in the U.S.2 While there are more than 117 

ports in the U.S., these are the top U.S. ports in terms of cargo tonnage. Port-specific emissions 

were calculated with a “bottom-up” approach, using data for vessel calls, emission factors, and 

activity for each port. Interport emissions and emissions for the remaining ports were obtained 

using the Waterway Network Ship Traffic, Energy and Environment Model (STEEM).3,4 STEEM 

also uses a “bottom-up” approach, estimating emissions from C3 vessels using historical North 

American shipping activity, ship characteristics, and activity-based emission factors. STEEM was 

used to quantify and geographically (i.e., spatially) represent interport vessel traffic and emissions 

for vessels traveling generally within 200 nautical miles (nm) of the U.S. 

The detailed port inventories were spatially merged into the STEEM gridded inventory to 

create a comprehensive inventory for Category 3 vessels. For the 117 ports, this involved removing 

the near-port portion of the STEEM inventory and replacing it with the detailed port inventories. 

For the remaining U.S. ports for which detailed port inventories are not available, the near-port 

portion of the STEEM inventory was simply retained. This was done for a base year of 2002. 

Inventories for 2020 were then projected using regional growth rates5,6

 and adjustment factors to 

account for the International Maritime Organization (IMO) Tier I and Tier II NOX standards and 

NOX retrofit program.2 Inventories incorporating additional Tier III NOX and fuel sulfur controls 

within the proposed Emission Control Area (ECA) were also developed for 2020. 

The inventory estimates reported in this chapter include emissions out to 200 nm from the 

U.S. coastline, including Alaska and Hawaii, but not extending into the Exclusive Economic Zone 

(EEZ) of neighboring countries. Inventories are presented for the following pollutants: oxides of 

nitrogen (NOX), particulate matter (PM2.5 and PM10), sulfur dioxide (SO2), hydrocarbons (HC), 

carbon monoxide (CO), and carbon dioxide (CO2). The PM inventories include directly emitted 

PM only, although secondary sulfates and nitrates are taken into account in the air quality modeling. 

2-1 
 

2.2 Modeling Domain and Geographic Regions 

The inventories described in this chapter reflect ship operations that occur within the area that 

extends 200 nautical miles (nm) from the official U.S. baseline but exclude operations in Exclusive 

Economic Zones of other countries. The official U.S. baseline is recognized as the low-water line 

along the coast as marked on the official U.S. nautical charts in accordance with the articles of the 

Law of the Sea. The boundary was mapped using geographic information system (GIS) shapefiles 

obtained from the National Oceanic and Atmospheric Administration, Office of Coast Survey.7 

The accuracy of the NOAA shapefiles was verified with images obtained from the U.S. Geologi cal 

Survey. The confirmed NOAA shapefiles were then combined with a shapefile of the U.S. 

international border from the National Atlas. 8 

The resulting region was further subdivided for this analysis to create regions that were 

compatible with the geographic scope of the regional growth rates, which are used to project 

emission inventories for the years 2020, as described later in this document. 

•	 The Pacific Coast region was split into separate North Pacific and South Pacific regions 

along a horizontal line originating from the Washington/Oregon border (Latitude 46° 15’ 

North). 

•	 The East Coast and Gulf of Mexico regions were divided along a vertical line roughly drawn 

through Key Largo (Longitude 80° 26’ West). 

•	 The Alaska region was divided into separate Alaska Southeast and Alaska West regions 

along a straight line intersecting the cities of Naknek and Kodiak. The Alaska Southeast 

region includes most of the State’s population, and the Alaska West region includes the 

emissions from ships on a great circle route along the Aleutian Islands between Asia and the 

U.S. West Coast. 

•	 For the Great Lakes domain, shapefiles were created containing all the ports and inland 

waterways in the near port inventory and extending out into the lakes to the international 

border with Canada. The modeling domain spanned from Lake Superior on the west to the 

point eastward in the State of New York where the St. Lawrence River parts from U.S. soil. 

•	 The Hawaiian domain was subdivided so that a distance of 200 nm beyond the southeastern 

islands of Hawai’i, Maui, O’ahu, Moloka‘i, Ni’ihau, Kaua’i, Lanai, and Kahoolawe was 

contained in Hawaii East. The remainder of the Hawaiian Region was then designated 

Hawaii West. 

This methodology resulted in nine separate regional modeling domains that are identified 

below and shown in Figure 2-1. U.S. territories are not included in this analysis. 

•	 South Pacific (SP) 

•	 North Pacific (NP) 

•	 East Coast (EC) 

•	 Gulf Coast (GC) 

•	 Alaska Southeast (AE) 

•	 Alaska West (AW) 

•	 Hawaii East (HE) 

2-2 
• Hawaii West (HW) 

• Great Lakes (GL) 

AE AW 

HW 

HE 

SP 

NP 

GL 

GC 

EC 

Figure 2-1 Regional Modeling Domains 

2.3 Development of 2002 Inventories 

This section describes the methodology and inputs, and presents the resulting inventories for 

the 2002 baseline calendar year. The first section describes the general methodology. The second 

section describes the methodology, inputs, and results for near port emissions. The third section 

describes the methodology and inputs for emissions when operating away from port (also referred 

to as “interport” emissions). The fourth section describes the method for merging the interport and 

near port portions of the inventory. Resulting total emissions for the U.S., as well as for nine 

geographic regions within the U.S., are then presented. 

2.3.1 Outline of Methodology 

The total inventory was created by summing emissions estimates for ships while at port 

(near port inventories) and while underway (interport inventories). Near port inventories for 

calendar year 2002 were developed for 117 U.S. commercial ports that engage in foreign trade. 

Based on an analysis of U.S. Government data, these 117 commercial ports encompass nearly all 

U.S. C3 vessel calls.9 

The outer boundaries of the ports are defined as 25 nm from the terminus of the reduced 

speed zone for deep water ports and 7 nm from the terminus of the reduced speed zone for Great 

Lake ports. Port emissions are calculated for different modes of operation and then summed. 

Emissions for each mode are calculated using port-specific information for vessel calls, vessel 

characteristics, and activity, as well as other inputs that vary instead by vessel or engine type (e.g., 

emission factors). 

2-3 
The interport inventory is estimated using the Waterway Network Ship Traffic, Energy, and 

Environmental Model (STEEM).3,4

 The model geographically characterizes emissions from ships 

traveling along shipping lanes to and from individual ports, in addition to the emissions from 

vessels transiting near the ports. The shipping lanes were identified from actual ship positioning 

reports. The model then uses detailed information about ship destinations, ship attributes (e.g., 

vessel speed and engine horsepower), and emission factors to produce spatially allocated (i.e., 

gridded) emission estimates for ships engaged in foreign commerce. 

The 117 near port inventories are an improvement upon STEEM’s near port results in 

several ways. First, the precision associated with STEEM’s use of ship positioning data may be less 

accurate in some locations, especially as the lanes approach shorelines where ships would need to 

follow more prescribed paths. Second, the STEEM model includes a maneuvering operational 

mode (i.e., reduced speed) that is generally assumed to occur for the first and last 20 kilometers of 

each trip when a ship is leaving or entering a port. In reality, the distance when a ship is traveling at 

reduced speeds varies by port. Also, the distance a ship traverses at reduced speeds often consists 

of two operational modes: a reduced speed zone (RSZ) as a ship enters or leaves the port area and 

actual maneuvering at a very low speed near the dock. Third, the STEEM model assumes that the 

maneuvering distance occurs at an engine load of 20 percent, which represents a vessel speed of 

approximately 60 percent of cruise speed. This is considerably faster than ships would maneuver 

near the docks. The single maneuvering speed assumed by STEEM also does not reflect the fact 

that the reduced speed zone, and therefore emissions, may vary by port. Fourth, and finally, the 

STEEM model does not include the emissions from auxiliary engines during hotelling operations at 

the port. The near-port inventories correct these issues. 

The regional emission inventories produced by the current STEEM interport model are most 

accurate for vessels while cruising in ocean or Great Lakes shipping lanes; the near port inventories 

use more detailed local port information and are significantly more accurate near the ports. 

Therefore, the inventories in this analysis are derived by merging together: 1) the near port 

inventories, which extend 25 nautical miles and 7 nautical miles from the terminus of the RSZ for 

deep water ports and Great Lake ports, respectively, and 2) the remaining interport portion of the 

STEEM inventory, which extends from the endpoint of the near port inventories to the 200 nautical 

mile boundary or international border with Canada, as appropriate. Near some ports, a portion of 

the underlying STEEM emissions were retained if it was determined that the STEEM emissions 

included ships traversing the area near a port, but not actually entering or exiting the port. 

2.3.2 Near Port Emissions 

Near port inventories for calendar year 2002 were developed for ocean-going vessels at 89 

deep water and 28 Great Lake ports in the U.S. The inventories include emissions from both 

propulsion and auxiliary engines on C3 vessels. 

This section first describes the selection of the ports for analysis and then provides the 

methodology used to develop the near port inventories. This is followed by a description of the key 

inputs. Total emissions by port and pollutant for 2002 are then presented. 

2-4 
2.3.2.1 Selection of Individual Ports to be Analyzed 

All 150 deep water and Great Lake ports in the Principal Ports of the United States dataset10 

were used as a starting point. Thirty ports which had no foreign traffic were eliminated because the 

dataset used to obtain port calls and other ship characteristics has no information about domestic 

traffic. Several California ports were also used because the California Air Resources Board (ARB) 

provided the necessary data and estimates for those ports. The final list of 117 deep water and 

Great Lake ports, along with their coordinates, is given in Appendix 2A. 

2.3.2.2 Port Methodology 

Near port emissions for each port are calculated for four modes of operation: 1) hotelling, 2) 

maneuvering, 3) reduced speed zone (RSZ), and 4) cruise. Hotelling, or dwelling, occurs while the 

vessel is docked or anchored near a dock, and only the auxiliary engine(s) are being used to provide 

power to meet the ship’s energy needs. Maneuvering occurs within a very short distance of the 

docks. The RSZ varies from port to port, though generally the RSZ would begin and end when the 

pilots board or disembark, and typically occurs when the near port shipping lanes reach 

unconstrained ocean shipping lanes. The cruise mode emissions in the near ports analysis extend 25 

nautical miles beyond the end of the RSZ lanes for deep water ports and 7 nautical miles for Great 

Lake ports. 

Emissions are calculated separately for propulsion and auxiliary engines. The basic 

equation used is as follows: 

Equation 2-1 

Emissionsmode[eng] =(calls)×(P[eng] )×(hrs/ callmode)×(LFmode[eng] )×(EF[eng] )×(Adj)×(10−6 tonnes/ g) 

Where: 

- Emissionsmode [eng] = Metric tonnes emitted by mode and engine type 

- Calls = Round-trip visits (i.e., one entrance and one clearance is considered a call) 

- P[eng] = Total engine power by engine type, in kilowatts 

- hrs/callmode = Hours per call by mode 

- LFmode [eng] = Load factor by mode and engine type (unitless) 

- EF[eng] = Emission factor by engine type for the pollutant of interest, in g/kW-hr 

(these vary as a function of engine type and fuel used, rather than activity mode) 

- Adj = Low load adjustment factor, unitless (used when the load factor is below 0.20) 

- 10-6 = Conversion factor from grams to metric tonnes 

Main engine load factors are calculated directly from the propeller curve based upon the 

cube of actual speed divided by maximum speed (at 100% maximum continuous rating [MCR]). In 

addition, cruise mode activity is based on cruise distance and speed inputs. Appendix 2B provides 

the specific equations used to calculate propulsion and auxiliary emissions for each activity mode. 

2.3.2.3 Inputs for Port Emission Calculations 

The following inputs are required to calculate emissions for the four modes of operation 

(cruise, RSZ, maneuvering, and hotelling): 

2-5 
 

• Number of calls 

• Main engine power 

• Cruise (vessel service) speed 

• Cruise distance 

• RSZ distance for each port 

• RSZ speed for each port 

• Auxiliary engine power 

• Auxiliary load factors 

• Main and auxiliary emission factors 

• Low load adjustment factors for main engines 

• Maneuvering time-in-mode (hours/call) 

• Hotelling time-in-mode (hours/call) 

Note that load factors for main engines are not listed explicitly, since they are calculated as a 

function of mode and/or cruise speed. This section describes the inputs in more detail, as well as 

the sources for each input. 

2.3.2.3.1 Calls and Ship Characteristics (Propulsion Engine Power and Cruise Speed) 

For this analysis, U.S. Army Corps of Engineers (USACE) entrance and clearance data for 

2002,11 together with Lloyd’s data for ship characteristics,12 were used to calculate average ship 

characteristics and calls by ship type for each port. Information for number of calls, propulsion 

engine power, and cruise speed were obtained from these data. 

2.3.2.3.1.1 Bins by Ship Type, Engine Type, and DWT Range 

The records from the USACE entrances and clearances data base were matched with 

Lloyd’s data on ship characteristics for each port. Calls by vessels that have either Category 1 or 2 

propulsion engines were eliminated from the data set. The data was then binned by ship type, 

engine type and dead weight tonnage (DWT) range. The number of entrances and clearances in 

each bin are counted, summed together and divided by two to determine the number of calls (i.e., 

one entrance and one clearance was considered a call). For Great Lake ports, there is a larger 

frequency of ships either entering the port loaded and leaving unloaded (light) or entering the port 

light and leaving loaded. In these cases, there would only be one record (the loaded trip into or out 

of the port) that would be present in the data. For Great Lake ports, clearances were matched with 

entrances by ship name. If there was not a reasonable match, the orphan entrance or clearance was 

treated as a call. 

Propulsion power and vessel cruise speed are also averaged for each bin. Auxiliary engine 

power was computed from the average propulsion power using the auxiliary power to propulsion 

power ratios discussed in section 2.3.2.3.4. 

2.3.2.3.1.2 Removal of Category 1 and 2 Ships 

Since these inventories were intended to cover Category 3 propulsion engine ships only, the 

ships with Category 1 and 2 propulsion engines were eliminated. This was accomplished by 

matching all ship calls with information from Lloyd’s Data, which is produced by Lloyd’s RegisterFairplay Ltd.12 Over 99.9 percent of the calls in the entrances and clearances data were directly 

2-6 
matched with Lloyd’s data. The remaining 0.1 percent was estimated based upon ships of similar 

type and size. 

Engine category was determined from engine make and model. Engine bore and stroke 

were found in the Marine Engine 2005 Guide13 and displacement per cylinder was calculated. 

Ships with Category 1 or 2 propulsion engines were eliminated from the data. 

Many passenger ships and tankers have either diesel-electric or gas turbine-electric engines 

that are used for both propulsion and auxiliary purposes. Both were included in the current 

inventory. 

2.3.2.3.2 Cruise Distance 

Cruise mode emissions are calculated assuming a 25 nautical mile distance into and out of 

the port for deep water ports and 7 nautical miles into and out of the port for Great Lake ports 

outside of the reduced speed and maneuvering zones. 

2.3.2.3.3 RSZ Distances and Speeds by Port 

Reduced speed zone (RSZ) distance and speed were individually determined for each port. 

For deep water ports, the RSZ distances were developed from shipping lane information contained 

in the USACE National Waterway Network.14 The database defines waterways as links or line 

segments that, for the purposes of this study, represent actual shipping lanes (i.e., channels, 

intracoastal waterways, sea lanes, and rivers). The sea-side endpoint for the RSZ was selected as 

the point along the line segment that was judged to be far enough into the ocean where ship 

movements were unconstrained by the coastline or other vessel traffic. The resulting RSZ distance 

was then measured for each deep water port. The final RSZ distances and endpoints for each port 

are listed in Appendix 2C. The RSZ for each Great Lake port was fixed at three nautical miles. 

2.3.2.3.4 Auxiliary Engine Power and Load Factors 

Since hotelling emissions are a large part of port inventories, it is important to distinguish 

propulsion engine emissions from auxiliary engine emissions. In the methodology used in this 

analysis, auxiliary engine maximum continuous rating power and load factors were calculated 

separately from propulsion engines and different emission factors (EFs) applied. All auxiliary 

engines were treated as Category 2 medium-speed diesel (MSD) engines for purposes of this 

analysis. 

Auxiliary engine power is not contained in the USACE database and is only sparsely 

populated in the Lloyd’s database; as a result, it must be estimated. The approach taken was to 

derive ratios of average auxiliary engine power to propulsion power based on survey data. The 

California Air Resources Board (ARB) conducted an Oceangoing Ship Survey of 327 ships in 

January 2005 that was principally used for this analysis.15 Average auxiliary engine power to 

propulsion power ratios were estimated by ship type and are presented in Table 2-1. These ratios by 

ship type were applied to the propulsion power data to derive auxiliary power for the ship types at 

each port. 

2-7 
Table 2-1 Auxiliary Engine Power Ratios (ARB Survey, except as noted) 

Ship Type 

Average 

Propulsion 

Engine (kW) 

Average Auxiliary Engines 

Auxiliary to 

Propulsion 

Number Ratio 

Power 

Each 

(kW) 

Total 

Power 

(kW) Engine Speed 

Auto Carrier 10,700 2.9 983 2,850 Medium 0.266 

Bulk Carrier 8,000 2.9 612 1,776 Medium 0.222 

Container Ship 30,900 3.6 1,889 6,800 Medium 0.220 

Passenger Shipa 39,600 4.7 2,340 11,000 Medium 0.278 

General Cargo 9,300 2.9 612 1,776 Medium 0.191 

Miscellaneousb 6,250 2.9 580 1,680 Medium 0.269 

RORO 11,000 2.9 983 2,850 Medium 0.259 

Reefer 9,600 4.0 975 3,900 Medium 0.406 

Tanker 9,400 2.7 735 1,985 Medium 0.211 a

 Many passenger ships typically use a different engine configuration known as diesel-electric. These vessels use 

large generator sets for both propulsion and ship-board electricity. The figures for passenger ships above are 

estimates taken from the Starcrest Vessel Boarding Program. 

b Miscellaneous ship types were not provided in the ARB methodology, so values from the Starcrest Vessel 

Boarding Program were used. 

Auxiliary engine to propulsion engine power ratios vary by ship type and operating mode 

roughly from 0.19 to 0.40. Auxiliary load, shown in Table 2-2, is used together with the total 

auxiliary engine power to calculate auxiliary engine emissions. Starcrest’s Vessel Boarding 

Program16 showed that auxiliary engines are on all of the time, except when using shoreside power 

during hotelling. 

Table 2-2 Auxiliary Engine Load Factor Assumptions 

Ship-Type Cruise RSZ Maneuver Hotel 

Auto Carrier 0.13 0.30 0.67 0.24 

Bulk Carrier 0.17 0.27 0.45 0.22 

Container Ship 0.13 0.25 0.50 0.17 

Passenger Ship 0.80 0.80 0.80 0.64 

General Cargo 0.17 0.27 0.45 0.22 

Miscellaneous 0.17 0.27 0.45 0.22 

RORO 0.15 0.30 0.45 0.30 

Reefer 0.20 0.34 0.67 0.34 

Tanker 0.13 0.27 0.45 0.67 

2.3.2.3.5 Fuel Types and Fuel Sulfur Levels 

There are primarily three types of fuel used by marine engines: residual marine (RM), 

marine diesel oil (MDO), and marine gas oil (MGO), with varying levels of fuel sulfur.5 MDO and 

MGO are generally described as distillate fuels. For this analysis, RM and MDO fuels are assumed 

2-8 
 

to be used. Since PM and SO2 emission factors are dependent on the fuel sulfur level, calculation of 

port inventories requires information about the fuel sulfur levels associated with each fuel type, as 

well as which fuel types are used by propulsion and auxiliary engines. 

Based on an ARB survey,15 average fuel sulfur level for residual marine was set to 2.5 

percent for the west coast and 2.7 percent for the rest of the country. A sulfur content of 1.5 percent 

was used for MDO.17 While a more realistic value for MDO used in the U.S. appears to be 0.4 

percent, given the small proportion of distillate fuel used by ships relative to RM, the difference 

should not be significant. Sulfur levels in other areas of the world can be significantly higher for 

RM. Table 2-3, based on the ARB survey, provides the assumed mix of fuel types used for 

propulsion and auxiliary engines by ship type. 

Table 2-3 Estimated Mix of Fuel Types Used by Ships 

Ship Type 

Fuel Used 

Propulsion Auxiliary 

Passenger 100% RM 92% RM/8% MDO 

Other 100% RM 71% RM/29% MDO 

2.3.2.3.6 Propulsion and Auxiliary Engine Emission Factors 

An analysis of emission data was prepared and published in 2002 by Entec.17 The resulting 

Entec emission factors include individual factors for three speeds of diesel engines (slow-speed 

diesel (SSD), medium-speed diesel (MSD), and high-speed diesel (HSD)), steam turbines (ST), gas 

turbines (GT), and two types of fuel used here (RM and MDO). Table 2-4 lists the propulsion 

engine emission factors for NOX and HC that were used for the 2002 port inventory development. 

The CO, PM, SO2 and CO2 emission factors shown in the table come from other data sources as 

explained below. 

Table 2-4 Emission Factors for OGV Main Engines using RM, g/kWh 

Engine 

All Ports West Coast Ports Other Ports 

NOX CO HC CO2 PM10 PM2.5 SO2 PM10 PM2.5 SO2 

SSD 18.1 1.40 0.60 620.62 1.4 1.3 9.53 1.4 1.3 10.29 

MSD 14.0 1.10 0.50 668.36 1.4 1.3 10.26 1.4 1.3 11.09 

ST 2.1 0.20 0.10 970.71 1.4 1.3 14.91 1.5 1.4 16.10 

GT 6.1 0.20 0.10 970.71 1.4 1.3 14.91 1.5 1.4 16.10 

CO emission factors were developed from information provided in the Entec appendices 

because they are not explicitly stated in the text. HC and CO emission factors were confirmed with 

a recent U.S. Government review.18 

2-9 
 

 

 

 

 

 

 

 

 

PM10

A values were determined based on existing engine test data in consultation with 

ARB.19 GT PM10 emission factors were not part of the U.S. Government analysis but assumed here 

to be equivalent to ST PM10 emission factors. Test data shows PM10 emission rates as dependent 

upon fuel sulfur levels, with base PM10 emission rates of 0.23 g/kw-hr with distillate fuel (0.24% 

sulfur) and 1.35 g/kw-hr with residual fuel (2.46% sulfur).20 The equation used to generate 

emission factors based on sulfur content is shown below. PM2.5 is assumed to be 92 percent of 

PM10. While the US Government NONROAD model uses 0.97 for such conversion based upon low 

sulfur fuels, a reasonable value seems to be closer to 0.92 because higher sulfur fuels in medium 

and slow speed engines would tend to produce larger particulates than high speed engines on low 

sulfur fuels. 

Equation 2-2 Calculation of PM10 Emission Factors Based on Fuel Sulfur Levels 

PMEF = PMNom + [(SAct – SNom) × BSFC × FSC × MWR × 0.0001] 

 where: 

 PMEF = PM emission factor adjusted for fuel sulfur

 PMNom = PM emission rate at nominal fuel sulfur level 

= 0.23 g/kW-hr for distillate fuel, 1.35 g/kW-hr for residual fuel 

SAct = Actual fuel sulfur level (weight percent) 

SNom = nominal fuel sulfur level (weight percent) 

= 0.24 for distillate fuel, 2.46 for residual fuel 

BSFC = fuel consumption in g/kW-hr 

= 200 g/kW-hr used for this analysis 

FSC = percentage of sulfur in fuel that is converted to direct sulfate PM 

= 2.247% used for this analysis 

MWR = molecular weight ratio of sulfate PM to sulfur 

= 224/32 = 7 used for this analysis 

SO2 emission factors were based upon a fuel sulfur to SO2 conversion formula which was 

supplied by ENVIRON.21 Emission factors for SO2 emissions were calculated using the formula 

assuming that 97.753 percent of the fuel sulfur was converted to SO2.

22 The brake specific fuel 

consumption (BSFC)B that was used for SSDs was 195 g/kWh, while the BSFC that was used for 

MSDs was 210 g/kWh based upon Lloyds 1995. The BSFC that was used for STs and GTs was 

305 g/kWh based upon Entec.17 

Equation 2-3 Calculation of SO2 Emission Factors, g/kWh 

SO2 EF = BSFC x 64/32 x 0.97753 x Fuel Sulfur Fraction 

CO2 emission factors were calculated from the BSFC assuming a fuel carbon content of 86.7 

percent by weight17 and a ratio of molecular weights of CO2 and C at 3.667. 

Equation 2-4 Calculation of CO2 Emission Factors, g/kWh 

CO2 EF = BSFC x 3.667 x 0.867 

A PM10 is particulate matter of 10 micrometers or less. B

 Brake specific fuel consumption is sometimes called specific fuel oil consumption (SFOC). 

2-10 
The most current set of auxiliary engine emission factors also comes from Entec except as 

noted below for PM and SO2. Table 2-5 provides these auxiliary engine emission factors. 

Table 2-5 Auxiliary Engine Emission Factors by Fuel Type, g/kWh 

Engine Fuel 

All Ports West Coast Ports Other Ports 

NOX CO HC CO2 PM10 PM2.5 SO2 PM10 PM2.5 SO2 

MSD RM 14.70 1.10 0.40 668.36 1.4 1.3 10.26 1.4 1.3 11.09 

MDO 13.90 1.10 0.40 668.36 0.6 0.55 6.16 0.6 0.55 6.16 

SO2 emission factors were calculated using Equation 2-3 while PM emissions were 

determined using Equation 2-2. 

Using the ratios of RM versus MDO use15 as given in Table 2-3 together with the emission 

factors shown in Table 2-5, the auxiliary engine emission factor averages by ship type are listed in 

Table 2-6. As discussed above, this fuel sulfur level may be too high for the U.S. However, we do 

not believe this emission factor has a significant effect on the total emission inventory estimates. 

Table 2-6 Auxiliary Engine Emission Factors by Ship Type, g/kWh 

Ship Type 

All Ports West Coast Ports Other Ports 

NOx CO HC CO2 PM10 PM2.5 SO2 PM10 PM2.5 SO2 

Passenger 14.64 1.10 0.40 668.36 1.3 1.2 9.93 1.4 1.3 10.70 

Others 14.47 1.10 0.40 668.36 1.1 1.0 9.07 1.2 1.1 9.66 

2.3.2.3.7 Low Load Adjustment Factors for Propulsion Engines 

Emission factors are considered to be constant down to about 20 percent load. Below that 

threshold, emission factors tend to increase as the load decreases. This trend results because diesel 

engines are less efficient at low loads and the brake specific fuel consumption (BSFC) tends to 

increase. Thus, while mass emissions (grams per hour) decrease with low loads, the engine power 

tends to decrease more quickly, thereby increasing the emission factor (grams per engine power) as 

load decreases. Energy and Environmental Analysis Inc. (EEA) demonstrated this effect in a study 

prepared for the U.S. Government in 2000.23 In the EEA report, equations have been developed for 

the various emissions. The low-load emission factor adjustment factors were developed based upon 

the concept that the BSFC increases as load decreases below about 20 percent load. 

Using these algorithms, fuel consumption and emission factors versus load were calculated. 

By normalizing emission factors to 20% load, low-load multiplicative adjustment factors were 

calculated for propulsion engines and presented in Table 2-7. Due to how they are operated, there is 

no need for a low load adjustment factor for auxiliary engines. 

2-11 
Table 2-7 Calculated Low Load Multiplicative Adjustment Factors 

Load (%) NOX HC CO PM SO2 CO2 

1 11.47 59.28 19.32 19.17 5.99 5.82 

2 4.63 21.18 9.68 7.29 3.36 3.28 

3 2.92 11.68 6.46 4.33 2.49 2.44 

4 2.21 7.71 4.86 3.09 2.05 2.01 

5 1.83 5.61 3.89 2.44 1.79 1.76 

6 1.60 4.35 3.25 2.04 1.61 1.59 

7 1.45 3.52 2.79 1.79 1.49 1.47 

8 1.35 2.95 2.45 1.61 1.39 1.38 

9 1.27 2.52 2.18 1.48 1.32 1.31 

10 1.22 2.20 1.96 1.38 1.26 1.25 

11 1.17 1.96 1.79 1.30 1.21 1.21 

12 1.14 1.76 1.64 1.24 1.18 1.17 

13 1.11 1.60 1.52 1.19 1.14 1.14 

14 1.08 1.47 1.41 1.15 1.11 1.11 

15 1.06 1.36 1.32 1.11 1.09 1.08 

16 1.05 1.26 1.24 1.08 1.07 1.06 

17 1.03 1.18 1.17 1.06 1.05 1.04 

18 1.02 1.11 1.11 1.04 1.03 1.03 

19 1.01 1.05 1.05 1.02 1.01 1.01 

20 1.00 1.00 1.00 1.00 1.00 1.00 

2.3.2.3.8 Use of Detailed Typical Port Data for Other Inputs 

There is currently not enough information to readily calculate time-in-mode (hours/call) for 

all 117 ports during the maneuvering and hotelling modes of operation. As a result, it was 

necessary to review and select available detailed emission inventories that have been estimated for 

selected ports to date. These ports are referred to as typical ports. The typical port information for 

maneuvering and hotelling time-in-mode (as well as maneuvering load factors for the propulsion 

engines) was then used for the typical ports and also assigned to the other modeled ports. A 

modeled port is the port in which emissions are to be estimated. The methodology that was used to 

select the typical ports and match these ports to the other modeled ports is briefly described in 

Appendix 2D, and more fully described in an ICF report.2 

2.3.2.3.9 Port Domestic Traffic 

One of the concerns with using USACE entrances and clearances data is that it only contains 

foreign cargo movements moved by either a foreign flag vessel or a U.S. flag vessel. As a result, 

U.S. flag ships carrying domestic cargo (i.e., Jones Act) ships are not included in the USACE data. 

Determining the contribution of Jones Act ships is difficult as most data sources include Category 1 

and 2 Jones Act ship movements with Category 3 ships and do not provide either enough data or a 

method for separating them. 

Under contract to the U.S. Government, ICF conducted an analysis to estimate the amount 

of Category 3 Jones Act ships calling at the 117 U.S. ports. This was done by analyzing marine 

2-12 
exchange data obtained from port authorities for eleven typical ports and using this information to 

estimate the Jones Act ship contribution for the remaining ports. Based on this limited analysis, 

Jones Act ships are estimated to account for 9.2% of the total installed power calling on U.S. ports. 

Approximately 30% of these ships, largely in the Alaska and Pacific regions, have been included in 

the 2002 baseline inventory. Based on this analysis, Jones Act ships excluded from this inventory 

constitute roughly 6.5% of total installed power.24 This results in an underestimation of the port 

ship inventory and therefore the benefits of the ECA program reported in this chapter are also 

underestimated. 

2.3.2.4 2002 Near Port Inventories 

This section provides a summary of the total port emissions for 2002. Table 2-8 provides a 

breakout of the total port emissions by auxiliary and propulsion engines. Table 2-9 provides the 

breakout by mode of operation, while Table 2-10 provides a summary of port emissions by ship 

type. 

Table 2-8 2002 Port Emissions Summary by Engine and Port Type (metric tonnes) 

Engine Type Port Type 

Metric Tonnes 

NOX PM10 PM2.5 HC CO SO2 CO2 

Deep Water 64,288 5,478 5,034 2,532 6,329 52,676 2,360,435 

Propulsion Great Lakes 248 25 23 11 22 187 11,267 

Total 64,536 5,503 5,057 2,543 6,351 52,863 2,371,702 

Deep Water 57,317 5,052 4,597 1,615 4,306 41,232 2,635,436 

Auxiliary Great Lakes 302 25 23 8 23 202 13,944 

Total 57,619 5,077 4,620 1,624 4,328 41,433 2,649,380 

Deep Water 121,606 10,530 9,631 4,148 10,635 93,908 4,995,871 

All Great Lakes 549 50 46 19 45 389 25,210 

Grand Total 122,155 10,580 9,677 4,167 10,680 94,297 5,021,082 

Auxiliary emissions at ports are responsible for 39-48% of the total port inventory, 

depending on the pollutant. Hotelling, cruise, and RSZ modes of operation are all important 

contributors to emissions. 

2-13 
 

 

Table 2-9 2002 Port Emissions Summary by Mode and Port Type (metric tonnes) 

Mode Port Type 

Metric Tonnes 

NOX PM10 PM2.5 HC CO SO2 CO2 

Deep Water 34,193 2,826 2,623 1,141 2,651 21,186 1,314,146 

Cruise Great Lakes 183 17 16 6 14 137 8,313 

Total 34,376 2,843 2,639 1,148 2,665 21,323 1,322,459 

Deep Water 34,427 2,887 2,657 1,280 3,804 35,148 1,318,897 

RSZ Great Lakes 45 4 4 2 4 33 2,052 

Total 34,472 2,891 2,661 1,281 3,808 35,181 1,320,950 

Deep Water 7,383 758 625 440 724 4,356 266,262 

Maneuvering Great Lakes 70 7 7 4 8 50 3,213 

Total 7,452 765 632 444 732 4,406 269,476 

Deep Water 45,603 4,060 3,726 1,287 3,456 33,218 2,096,566 

Hotelling Great Lakes 252 21 19 7 19 168 11,631 

Total 45,855 4,081 3,745 1,294 3,475 33,386 2,108,197 

Deep Water 121,606 10,530 9,631 4,148 10,635 93,908 4,995,871 

All Great Lakes 549 50 46 19 45 389 25,210 

Grand Total 122,155 10,580 9,677 4,167 10,680 94,297 5,021,082 

Table 2-10 2002 Port Emissions Summary by Ship Type and Port Type (metric tonnes) 

Ship Type Port Type 

Metric Tonnes 

NOX PM10 PM2.5 HC CO SO2 CO2 

Deep Water 5,125 421 384 185 577 3,676 198,637 

Auto Carrier Great Lakes 0 0 0 0 0 0 0 

Total 5,125 421 384 185 577 3,676 198,637 

Deep Water 148 13 12 5 12 141 6,364 

Barge Carrier Great Lakes 0 0 0 0 0 0 0 

Total 148 13 12 5 12 141 6,364 

Self-Unloading 

Bulk Carrier 

Deep Water 0 0 0 0 0 0 0 

Great Lakes 276 27 25 10 23 210 13,273 

Total 276 27 25 10 23 210 13,273 

Other Bulk 

Carrier 

Deep Water 19,373 1,570 1,431 633 1,732 14,945 767,825 

Great Lakes 227 19 17 7 18 147 9,807 

Total 19,600 1,589 1,448 640 1,750 15,092 777,632 

Deep Water 33,990 2,733 2,494 1,282 2,833 22,628 1,288,596 

Container Great Lakes 0 0 0 0 0 0 0 

Total 33,990 2,733 2,494 1,282 2,833 22,628 1,288,596 

Deep Water 7,402 630 576 251 684 6,208 302,338 

General Cargo Great Lakes 22 2 2 1 2 15 969 

Total 7,424 631 578 252 686 6,223 303,307 

Miscellaneous Deep Water 179 16 15 6 35 128 8,209 

Great Lakes 0 0 0 0 0 0 0 

2-14 
Ship Type Port Type 

Metric Tonnes 

NOX PM10 PM2.5 HC CO SO2 CO2 

Total 179 16 15 6 35 128 8,209 

Deep Water 19,165 1,819 1,668 578 1,470 14,184 893,157 

Passenger Great Lakes 0 0 0 0 0 0 0 

Total 19,165 1,819 1,668 578 1,470 14,184 893,157 

Refrigerated 

Cargo 

Deep Water 3,027 247 226 98 313 1,968 130,060 

Great Lakes 0 0 0 0 0 0 0 

Total 3,027 247 226 98 313 1,968 130,060 

Deep Water 3,391 281 259 113 278 2,193 139,396 

Roll-On/Roll-Off Great Lakes 0 0 0 0 0 0 0 

Total 3,391 281 259 113 278 2,193 139,396 

Deep Water 29,758 2,796 2,562 994 2,695 27,802 1,259,107 

Tanker Great Lakes 22 2 2 1 2 15 1,012 

Total 29,780 2,798 2,564 995 2,697 27,817 1,260,119 

Deep Water 48 5 4 2 6 34 2,182 

Ocean Going Tug Great Lakes 0 0 0 0 0 0 0 

Total 48 5 4 2 6 34 2,182 

Integrated TugBarge 

Deep Water 0 0 0 0 0 0 0 

Great Lakes 3 0 0 0 0 2 149 

Total 3 0 0 0 0 2 149 

Deep Water 121,606 10,530 9,631 4,148 10,635 93,908 4,995,871 

All Great Lakes 549 50 46 19 45 389 25,210 

Grand Total 122,155 10,580 9,677 4,167 10,680 94,297 5,021,082 

2.3.3 Interport Emissions 

This section presents our nationwide analysis of the methodology and inputs used to 

estimate interport emissions from main propulsion and auxiliary engines used by Category 3 oceangoing vessels for the 2002 calendar year. The modeling domain for vessels operating in the ocean 

extends from the U.S. coastline to a 200 nautical mile boundary. For ships operating in the Great 

Lakes, it extends out to the international boundary with Canada. The emission results are divided 

into nine geographic regions of the U.S. (including Alaska and Hawaii), and then totaled to provide 

a national inventory. 

The interport emissions described in this section represent total interport emissions prior to 

any adjustments made to incorporate near-port inventories. The approach used to replace the nearport portion of the interport emissions is provided in Section 2.3.4. 

2.3.3.1 Interport Methodology 

The interport emissions were estimated using the Waterway Network Ship Traffic, Energy, 

and Environmental Model (STEEM).3,4 STEEM was developed by the University of Delaware as a 

comprehensive approach to quantify and geographically represent interport ship traffic, emissions, 

and energy consumption from large vessels calling on U.S. ports or transiting the U.S. coastline to 

other destinations, and shipping activity in Canada and Mexico. The model estimates emissions 

from main propulsion and auxiliary marine engines used on Category 3 vessels that engage in 

2-15 
 

 

foreign commerce using historical North American shipping activity, ship attributes (i.e., 

characteristics), and activity-based emission factor information. These inputs are assembled using a 

GIS platform that also contains an empirically derived network of shipping lanes. It includes the 

emissions for all ship operational modes from cruise in unconstrained shipping lanes to 

maneuvering in a port. The model, however, excludes hotelling operations while the vessel is 

docked or anchored, and very low speed maneuvering close to a dock. For that reason, STEEM is 

referred to as an “interport” model, to easily distinguish it from the near ports analysis. 

STEEM uses advanced ArcGIS tools and develops emission inventories in the following 

way. The model begins by building a spatially-defined waterway network based on empirical 

shipping location information from two global ship reporting databases. The first is the 

International Comprehensive Ocean-Atmosphere Data Set (ICOADS), which contains reports on 

marine surface and atmospheric conditions from the Voluntary Observing Ships (VOS) fleet.25 

There are approximately 4,000 vessels worldwide in the VOS system. The ICOADS project is 

sponsored by the National Oceanic and Atmospheric Administration and National Science 

Foundation's National Center for Atmospheric Research (NCAR). The second database is the 

Automated Mutual-Assistance Vessel Rescue (AMVER) system.26 The AMVER data set is based 

on a ship search and rescue reporting network sponsored by the U.S. Coast Guard. The AMVER 

system is also voluntary, but is generally limited to ships over 1,000 gross tons on voyages of 24 

hours or longer. About 8,600 vessels reported to AMVER in 2004. 

The latitude and longitude coordinates for the ship reports in the above databases are used to 

statistically create and spatially define the direction and width of each shipping lane in the waterway 

network. Each statistical lane (route and segment) is given a unique identification number for 

computational purposes. For the current analysis, STEEM used 20 years of ICOADS data (1983­

2002) and about one year of AMVER data (part of 2004 and part of 2005) (Figure 2-2). 

Figure 2-2 AMVER and ICOADS data 

Every major ocean and Great Lake port is also spatially located in the waterway network 

using ArcGIS software. For the U.S., the latitude and longitude for each port is taken from the 

USACE report on vessel entrances and clearances.11 Each port also has a unique identification 

number for computational purposes. 

2-16 
 

As illustrated in Figure 2-3, the waterway network represented by STEEM resembles a 

highway network on land. It is composed of ports, which are origins and destinations of shipping 

routes: junctions where shipping routes intersect, and segments that are shipping lanes between two 

connected junctions. Each segment can have only two junctions or ports, and ship traffic flow can 

enter and leave a segment only through a junction or at a port. The figure represents only a sample 

of the many routes contained in the model. 

Figure 2-3 Illustration of STEEM Modeling Domain and Spatial Distribution of Shipping Lanes 

The STEEM interport model also employs a number of databases to identify the movements 

for each vessel (e.g., trips), individual ship attributes (e.g., vessel size and horsepower), and related 

emission factor information (e.g., emission rates) that are subsequently used in the inventory 

calculations. 

To allocate ships to the statistical lanes, STEEM uses ArcGIS Network Analyst tools along 

with specific information on each individual ship movement to solve the most probable path on the 

network between each pair of ports (i.e., a trip) for a certain ship size. This is assumed to represent 

the least-energy path, which in most cases is the shortest distance unless prevented by weather or 

sea conditions, water depth, channel width, navigational regulations, or other constraints that are 

beyond the model’s capability to forecast. 

After identifying the shipping route and resulting distance associated with each unique trip, 

the emissions are simply calculated for each operational mode using the following generalized 

equation along with information from the ship attributes and emission factor databases: 

Equation 2-5 

Emissions per trip = distance (nautical miles) / speed (nautical miles/hour) x horsepower (kW) x 

fractional load factor x emission factor (g/kW-hour) 

In STEEM, emissions are calculated separately for distances representing cruise and 

maneuvering operational modes. Maneuvering occurs at slower speeds and load factors than 

during cruise conditions. In STEEM, maneuvering is assumed to occur for the first and last 

2-17 
20 kilometers of each trip when a ship is entering or leaving a port. A ship is assumed to 

move at maneuvering speed for an entire trip if the distance is less than 20 kilometers. 

Finally, the emissions along each shipping route (i.e., segment) for all trips are proportioned 

among the respective cells that are represented by the gridded modeling domain. For this work, 

emissions estimates were produced at a cell resolution of 4 kilometers by 4 kilometers, which is 

appropriate for most atmospheric air quality models. The results for each cell are then summed, as 

appropriate, to produce emission inventories for the various geographic regions of interest in this 

analysis. 

2.3.3.2 Inputs for Interport Emission Calculations 

The STEEM model includes detailed information about ship routes and destinations in order 

to provide spatially allocated emissions of ships in transit. The shipping lanes and directions were 

empirically derived from ship positioning data in several datasets. The International 

Comprehensive Ocean-Atmosphere Data Set (ICOADS) contains reports on marine surface and 

atmospheric conditions from the Voluntary Observing Ships (VOS) fleet.27 STEEM also uses a 

dataset derived from the Automated Mutual-Assistance Vessel Rescue (AMVER) system,28 which 

is based on a ship search and rescue reporting network sponsored by the U.S. Coast Guard. Traffic 

along each of these lanes is derived from USACE entrance and clearance data for 2002,29 together 

with Lloyd’s Register-Fairplay Ltd’s data for ship characteristics. Information for number of calls, 

ship characteristics, propulsion engine power, and cruise speed were obtained from these data. 

The emission factors and load factors used as inputs to STEEM are very similar to those 

used for the ports analysis. Additional adjustments were made to interport emission results for 

PM10 and SO2 in order to reflect recent U.S. Government review of available engine test data and 

fuel sulfur levels. Details of the STEEM emission inputs and adjustments are located in Appendix 

2E. 

2.3.3.3 Interport Domestic Traffic 

As previously noted, STEEM includes the emissions associated with ships that are engaged 

in foreign commerce. As a result, U.S. flag vessels carrying domestic cargo (Jones Act ships) are 

not included. The STEEM interport analysis also roughly estimated the emissions associated with 

these ships that are engaged solely in domestic commerce.1,4

 Specifically, the interport analysis 

estimated that the large ocean-going vessels carrying only domestic cargo excluded from STEEM 

represent approximately 2-3 percent of the total U.S. emissions. 

In section 2.3.2.3.9 in the estimation of port inventories, the estimate of excluded installed 

power was roughly 6.5 percent. It is not inconsistent that the STEEM estimate of excluded 

emissions is lower than the excluded power estimated from calls to U.S. ports, since the STEEM 

model includes ships that are transiting without stopping at U.S. ports. Since most of the Jones Act 

ships tend to travel closer along the coast line, most of the Jones Act ship traffic is expected to fall 

within the proposed ECA. Therefore, the results presented in this chapter are expected to 

underpredict the benefits of the proposed ECA. 

2-18 
2.3.4 Combining the Near Port and Interport Inventories 

The national and regional inventories in this study are a combination of the results from the 

near ports analysis described in Section 2.3.2 and the STEEM interport modeling described in 

Section 2.3.3. The two inventories are quite different in form. As previously presented, the 

STEEM modeling domain spans the Atlantic and Pacific Oceans in the northern hemisphere. The 

model characterizes emissions from vessels while traveling between ports. That includes when a 

vessel is maneuvering a distance of 20 kilometers to enter or exit a port, cruising near a port as it 

traverses the area, or moving in a shipping lane across the open sea. For the U.S., STEEM includes 

the emissions associated with 251 ports. The results are spatially reported in a gridded format that 

is resolved to a cell dimension of 4 kilometers by 4 kilometers. 

The near port results, however, are much more geographically limited and are not reported 

in a gridded format. The analysis includes the emissions associated with ship movements when 

entering or exiting each of 117 major U.S. ports. For deep water ports that include when a vessel is 

hotelling and maneuvering in the port, operating in the RSZ that varies in length for each port, and 

cruising 25 nautical miles between the end of the RSZ and an unconstrained shipping lane. For 

Great Lakes ports that includes hotelling and maneuvering, three nautical miles of RSZ operation, 

and cruising 7 nautical miles between the end of the RSZ and open water. The results are reported 

for each port and mode of operation. 

To precisely replace only the portion of the STEEM interport inventory that is represented in 

the near port inventory results, it is necessary to spatially allocate the emissions in a format that is 

compatible with the STEEM 4 kilometers by 4 kilometers gridded output. Once that has been 

accomplished, the two inventories can be blended together. Both of these processes are described 

below. This work was conducted by ENVIRON International as a subcontractor under the U.S. 

Government contract with ICF.2 

2.3.4.1 Spatial Location of the Near Port Inventories 

The hotelling, maneuvering, RSZ, and cruise emissions from the near port inventories were 

spatially located by their respective latitude and longitude coordinates using ArcGIS software. For 

this study, shapefiles were created that depicted the emission locations as described above. 

Additional shapefiles were also obtained to locate other geographic features such as the coastline 

and rivers of the U.S. These shapefiles and the STEEM output can be layered upon each other, 

viewed in ArcMap, and analyzed together. The following sections provide a more detailed 

description of how the shapefiles representing the ports, RSZ lanes, and cruise lanes were 

developed. 

2.3.4.1.1 Ports 

Each port, and thus the designated location for hotelling and maneuvering emissions, is 

modeled as a single latitude/longitude coordinate point using the port center as defined by USACE 

in the Principal Ports of the United States dataset.10 The hotelling and maneuvering emissions 

represented by the latitude/longitude coordinate for each port were subsequently assigned to a single 

cell in the gridded inventory where that point was located. It should be noted that modeling a port 

as a point will over specify the location of the emissions associated with that port if it occupies an 

2-19 
area greater than one grid cell, or 4 kilometers by 4 kilometers. The coordinates of all of the 117 

ports used in this work are shown in Appendix 2A. 

2.3.4.2 Reduced Speed Zone Operation 

The RSZ routes associated with each of the 117 ports were modeled as lines. Line 

shapefiles were constructed using the RSZ distance information described in Section 2.3.2 and the 

USACE National Waterway Network (NWN) geographic database of navigable waterways in and 

around the U.S.14 The coordinates of RSZ endpoints for all of the 117 ports used in this work are 

shown in Appendix 2C. 

The RSZ emissions were distributed evenly along the length of the line. The 

latitude/longitude coordinates for each point along the line were subsequently used to assign the 

emissions to a grid cell based on the proportion of the line segment that occurred in the respective 

cell. 

2.3.4.2.1 Cruise Operations 

The cruise mode links that extend 25 nautical miles for deep water ports or 7 nautical miles 

for Great Lake ports from the end of the RSZ end point were also modeled with line shapefiles. 

These links were spatially described for each port following the direction of the shipping lane 

evident in the STEEM data. Again, as with RSZ emissions, the latitude/longitude coordinates for 

each point along the line were subsequently used to assign the emissions to a grid cell based on the 

proportion of the line segment that occurred in the respective cell. 

2.3.4.3 Combining the Near Port and STEEM Emission Inventories in Port Areas 

After spatially defining the geographic location of the near port emissions, but before 

actually inserting them into the gridded STEEM inventory, it was necessary to determine if all of 

the STEEM emissions within an affected cell should be replaced, or if some of the emissions should 

be retained. In this latter case, ships would be traversing the area near a port, but not actually 

entering or exiting the port. 

The percentage of STEEM emissions that are attributable to a port, and should be removed 

and replaced, were approximated by dividing the STEEM emissions in the isolated portion of the 

route that lead only to the port, with the STEEM emissions in the major shipping lane. 

The actual merging of the two inventories was performed by creating a number of databases 

that identified the fraction of the near port inventory for each pollutant species and operating mode 

that should be added to the grid cells for each port. A similar database was also created that 

identified how much of the original STEEM emissions should be reduced to account for ship 

movements associated directly with a port, while preserving those that represented transient vessel 

traffic. These databases were subsequently used to calculate the new emission results for each 

affected cell in the original STEEM gridded inventory, resulting in the combined inventory results 

for this study. 

In a few cases, the outer edges of the port inventories fell outside the international boundary; 

that portion outside the U.S. boundary was removed. As a result, the port totals presented in the 

2-20 
 

next section are slightly less than those reported in Section 2.3.2.4. The removed portion represents 

less than 2 percent of the total port emissions. 

Since STEEM includes emissions associated with 251 ports, the 117 ports do not cover all 

the ports identified by the shipping lane paths evident in the STEEM data. In the remaining ports, 

the STEEM model output was used. 

2.3.5 2002 Baseline Inventories 

The modeling domain of the new combined emission inventory described above is the same 

as the original STEEM domain, i.e., the Atlantic and Pacific Oceans, the Gulf of Mexico, the Great 

Lakes, Alaska, and Hawaii. Inventories for the nine geographic regions of the U.S. specified in 

Section 2.2 were created using ArcGIS software to intersect the regional shapefiles with the 4 

kilometers by 4 kilometers gridded domain. Any grid cell split by a regional boundary was 

considered to be within a region if over 50 percent of its area was within the region. The emissions 

from the cells within a region were then summed. The final emission inventories for 2002 are shown 

in Table 2-11 for each of the nine geographic regions and the nation. The geographic scope of these 

regions was previously displayed in Figure 2-1. 

Table 2-11 2002 Regional and National Emissions from Category 3 Vessel Main and Auxiliary Engines 

Region 

Metric Tonnes 

NOX PM10 PM2.5 a HC CO SO2 CO2 

Alaska East (AE) 18,051 1,425 1,311 597 1,410 10,618 657,647 

Alaska West (AW) 60,019 4,689 4,313 1,989 4,685 34,786 2,143,720 

East Coast (EC) 219,560 17,501 16,101 7,277 17,231 145,024 8,131,553 

Gulf Coast (GC) 172,897 14,043 12,920 5,757 14,169 104,852 6,342,139 

Hawaii East (HE) 22,600 1,775 1,633 749 1,765 13,182 818,571 

Hawaii West (HW) 31,799 2,498 2,297 1,053 2,484 18,546 1,151,725 

North Pacific (NP) 26,037 2,154 1,982 938 2,090 15,295 990,342 

South Pacific (SP) 104,155 8,094 7,447 3,464 8,437 60,443 3,796,572 

Great Lakes (GL) 15,019 1,179 1,085 498 1,174 8,766 541,336 

Total Metric Tonnes 670,135 53,358 49,089 22,324 53,444 411,511 24,573,605 a

 Estimated from PM10 using a multiplicative adjustment factor of 0.92. 

The relative contributions of the near port and interport emission inventories to the total U.S. 

ship emissions are presented in Table 2-12 and Table 2-13. As expected, based on the geographic 

scope of the two types of inventories, the interport and near port inventories are about 80 percent 

and 20 percent of the total, respectively. 

2-21 
 

 

 

 

Table 2-12 2002 Contribution of Near Port and Interport Emissions to the Total C3 Inventory 

Region 

Metric Tonnes 

NOX PM10 PM2.5 a 

Port Interport Total Port Interport Total Port Interport Total 

Alaska East (AE) 833 17,218 18,051 80 1,345 1,425 74 1,237 1,311 

Alaska West (AW) 0 60,019 60,019 0 4,689 4,689 0 4,313 4,313 

East Coast (EC) 48,313 171,247 219,560 4,126 13,375 17,501 3,796 12,305 16,101 

Gulf Coast (GC) 33,637 139,260 172,897 3,169 10,874 14,043 2,916 10,004 12,920 

Hawaii East (HE) 2,916 19,684 22,600 251 1,524 1,775 231 1,402 1,633 

Hawaii West (HW) 0 31,799 31,799 0 2,498 2,498 0 2,297 2,297 

North Pacific (NP) 14,015 12,022 26,037 1,216 938 2,154 1,119 863 1,982 

South Pacific (SP) 20,079 84,076 104,155 1,525 6,569 8,094 1,403 6,044 7,447 

Great Lakes (GL) 491 14,528 15,019 44 1,135 1,179 41 1,044 1,085 

Total Metric Tonnes 120,285 549,852 670,137 10,413 42,945 53,358 9,580 39,510 49,089 a

 Estimated from PM10 using a multiplicative adjustment factor of 0.92. 

Table 2-13 2002 Contribution of Near Port and Interport Emissions to the Total C3 Inventory (Cont’d)
 

Region 

Metric Tonnes 

HC CO SO2 

Port Interport Total Port Interport Total Port Interport Total 

Alaska East (AE) 27 570 597 66 1,344 1,410 641 9,977 10,618 

Alaska West (AW) 0 1,989 1,989 0 4,685 4,685 0 34,786 34,786 

East Coast (EC) 1,603 5,674 7,277 3,864 13,367 17,231 45,952 99,072 145,024 

Gulf Coast (GC) 1,142 4,615 5,757 3,305 10,864 14,169 24,187 80,665 104,852 

Hawaii East (HE) 96 653 749 230 1,535 1,765 1,891 11,291 13,182 

Hawaii West (HW) 0 1,053 1,053 0 2,484 2,484 0 18,546 18,546 

North Pacific (NP) 540 398 938 1,152 938 2,090 8,329 6,966 15,295 

South Pacific (SP) 678 2,786 3,464 1,876 6,561 8,437 11,715 48,728 60,443 

Great Lakes (GL) 17 481 498 40 1,134 1,174 346 8,420 8,766 

Total Metric Tonnes 4,103 18,219 22,322 10,533 42,912 53,445 93,062 318,450 411,512 

As noted previously, these inventories exclude a portion of traffic from U.S. flag ships 

carrying domestic cargo. Estimates range from roughly 2 to 7 percent of installed power, which 

indicates that the inventories may be underestimated by 2 to 7 percent. 

2.4 Development of 2020 Inventories 

2.4.1 Outline of Methodology 

The emissions from Category 3 ocean-going vessels (main propulsion and auxiliary engines) 

are projected to 2020 by applying certain growth factors to the 2002 emission inventories to account 

for the expected change in ship traffic over these time periods due to growth in trade. 

The remaining sections describe the derivation of the growth adjustment factors for each of 

the modeling regions described in Section 2.2. Emission control program related adjustments to the 

2-22 
2020 inventories are then described. A baseline inventory and an inventory within the proposed 

ECA are then presented. 

2.4.2 Growth Factors by Geographic Region 

The growth factors that are used to estimate future year emission inventories are based on 

the expected demand for marine bunker fuels that is associated with shipping goods, i.e., 

commodities, into and out of the U.S. This section describes the growth factors that are used to 

project the emissions to 2020 for each of the nine geographic regions evaluated in this analysis. 

The use of bunker fuel as a surrogate for estimating future emissions is appropriate because the 

quantity of fuel consumed by C3 engines is highly correlated with the amount of combustion 

products, i.e., pollutants that are emitted from those vessels. The term bunker fuel in this report also 

includes marine distillate oil and marine gas oil that are used in some auxiliary power engines. 

The remainder of this section first summarizes the development of growth rates by RTI 

International (RTI) for five geographic regions of the U.S., as performed under contract to the U.S. 

government.5,6 This is followed by the derivation of the growth factors that are used in this study 

for the nine geographic regions of interest. 

2.4.2.1 Summary of Regional Growth Rate Development 

RTI developed fuel consumption growth rates for five geographic regions of the U.S. These 

regions are the East Coast, Gulf Coast, North Pacific, South Pacific, and Great Lakes. The amount 

of bunker fuel required in any region and year is based on the demand for transporting various types 

of cargo by Category 3 vessels. This transportation demand is in turn driven by the demand for 

commodities that are produced in one location and consumed in another, as predicted by an 

econometric model. The flow of commodities is matched with typical vessels for trade routes 

(characterized according to cargo capacity, engine horsepower, age, specific fuel consumption, and 

engine load factors). Typical voyage parameters are then assigned to the trade routes that include 

average ship speed, round trip mileage, tons of cargo shipped, and days in port. Fuel consumption 

for each trade route and commodity type thus depends on commodity projections, ship 

characteristics, and voyage characteristics. Figure 2-4 illustrates the approach to developing 

baseline projections of marine fuel consumption. 

As a means of comparison, the IMO Secretary General’s Informal Cross 

Government/Industry Scientific Group of Experts presented a growth rate that ranged from 3.3% to 

3.7%.30 RTI’s overall U.S. growth rate was projected at 3.4%, which is consistent with that range. 

2-23 
Figure 2-4 Illustration of Method for Estimating Bunker Fuel Demand 

2.4.2.2 Trade Analysis 

The trade flows between geographic regions of the world, as illustrated by the middle 

portion of Figure 2-4 were defined for the following eight general types of commodities: 

- liquid bulk – crude oil 

- liquid bulk – refined petroleum products 

- liquid bulk – residual petroleum products 

- liquid bulk – chemicals (organic and inorganic) 

- liquid bulk –gas (including LNG and LPG) 

- dry bulk (e.g., grain, coal, steel, ores and scrap) 

- general cargo (e.g., lumber/forest products) 

2-24 
 

 

 

 - containerized cargo 

The analysis specifically evaluated trade flows between 21 regions of the world. Table 2-14
 

shows the countries associated with each region. 

Table 2-14 Aggregate Regions and Associated Countries 

Aggregate Regions Base Countries / Regions 

U.S. Atlantic Coast U.S. Atlantic Coast 

U.S. Great Lakes U.S. Great Lakes 

U.S. Gulf Coast U.S. Gulf Coast 

E. Canadaa Canadaa 

W. Canadaa Canadaa 

U.S. Pacific North U.S. Pacific North 

U.S. Pacific South U.S. Pacific South 

Greater Caribbean Colombia, Mexico, Venezuela, Caribbean Basin, Central America 

South America 

Argentina, Brazil, Chile, Peru, Other East Coast of S. America, Other 

West Coast of S. America 

Africa – West Western Africa 

Africa-North/EastMediterranean Mediterranean Northern Africa, Egypt, Israel, 

Africa-East/South Kenya, Other Eastern Africa, South Africa, Other Southern Africa 

Europe-North 

Austria, Belgium, Denmark, Finland, France, Germany, Ireland, 

Netherlands, Norway, Sweden, Switzerland, United Kingdom 

Europe-South Greece, Italy, Portugal, Spain, Turkey, Other Europe 

Europe-East Bulgaria, Czech Republic, Hungary, Poland, Romania, Slovak Republic 

Caspian Region Southeast CIS 

Russia/FSU The Baltic States, Russia Federation, Other Western CIS 

Middle East Gulf Jordan, Saudi Arabia, UAE, Other Persian Gulf 

Australia/NZ Australia, New Zealand 

Japan Japan 

Pacific-High Growth 

Hong Kong S.A.R., Indonesia, Malaysia, Philippines, Singapore, South 

Korea, Taiwan, Thailand 

China China 

Rest of Asia Viet Nam, India, Pakistan, Other Indian Subcontinent a Canada is treated as a single destination in the GI model. Shares of Canadian imports from 

and exports to regions of the world in 2004 are used to divide Canada trade into shipments 

to/from Eastern Canada ports and shipments to/from Western Canada ports.31 

The overall forecast of demand for shipping services and bunker fuel was determined for 

each of the areas using information on commodity flows from Global Insight’s (GI) World Trade 

Service. Specifically, GI provided a specialized forecast that reports the flow of each commodity 

type for the period 1995–2024, based on a proprietary econometric model. The general structure of 

the GI model for calculating trade flows assumes a country’s imports from another country are 

driven by the importing country’s demand forces (given that the exporting country possesses 

enough supply capacity), and affected by exporting the country’s export price and importing 

country’s import cost for the commodity. The model then estimates demand forces, country-specific 

exporting capacities, export prices, and import costs. 

2-25 
 

 

 

The GI model included detailed annual region-to-region trade flows for eight composite 

commodities from 1995 to 2024, in addition to the total trade represented by the commodities. 

Table 2-15 illustrates the projections for 2012 and 2020, along with baseline data for 2005. In 2005, 

dry bulk accounted for 41 percent of the total trade volume, crude oil accounted for 28 percent, and 

containers accounted for 12 percent. Dry bulk and crude oil shipments are expected to grow more 

slowly over the forecast period than container shipments. By 2020, dry bulk represents 39 percent 

of the total, crude oil is 26 percent, and containers rise to 17 percent. 

Table 2-15 Illustration of World Trade Estimates for Composite Commodities, 2005, 2012, and 2020
 

Commodity Type 

Cargo (millions of tons) 

2005 2012 2020 

Dry Bulk 2,473 3,051 3,453 

Crude Oil 1,703 2,011 2,243 

Container 714 1,048 1,517 

Refined Petroleum 416 471 510 

General Cargo 281 363 452 

Residual Petroleum and Other Liquids 190 213 223 

Chemicals 122 175 228 

Natural Gas 79 91 105 

Total International Cargo Demand 5,979 7,426 8,737 

2.4.2.3 Ship Analysis by Vessel Type and Size 

Different types of vessels are required to transport the different commodities to the various 

regions of the world. Profiles of these ships were developed to identify the various vessel types and 

size categories that are assigned to transport commodities of each type along each route. These 

profiles include attributes such as ship size, engine horsepower, engine load factors, age, and engine 

fuel efficiency. This information was subsequently used to estimate average daily fuel consumption 

for each typical ship type and size category. 

The eight GI commodity categories were mapped to the type of vessel that would be used to 

transport that type of cargo using information from Clarkson’s Shipping Database.32 These 

assignments are shown in Table 2-16. 

2-26 
 

 

 

Table 2-16 Assignment of Commodities to Vessel Types 

Commodity Ship Category Vessel Type 

Liquid bulk – crude oil Crude Oil Tankers Tanker 

Liquid bulk – refined 

petroleum products 

Product Tankers Product Carrier 

Liquid bulk – residual 

petroleum products 

Product Tankers Product Carrier 

Liquid bulk – chemicals 

(organic and inorganic) 

Chemical Tankers Chemical & Oil Carrier 

Liquid bulk – natural gas 

(including LNG and LPG) 

Gas Carriers LNG Carrier, LPG Carrier, Chemical & LPG Carrier, 

Ethylene/LPG, Ethylene/LPG/Chemical, 

LNG/Ethylene/LPG, LNG/Regasification, LPG/Chemical, 

LPG/Oil, Oil & Liquid Gas Carrier 

Dry bulk (e.g. grain, coal, 

steel, ores and scrap) 

Dry Bulk Carriers Bulk Carrier 

General cargo (including 

neobulk, lumber/forest 

products) 

General Cargo General Cargo Liner, Reefer, General Cargo Tramp, Reefer 

Fish Carrier, Ro-Ro, Reefer/Container, Ro-Ro 

Freight/Passenger, Reefer/Fleet Replen., Ro-Ro/Container, 

Reefer/General Cargo, Ro-Ro/Lo-Lo, Reefer/Pallets 

Carrier, Reefer/Pass./Ro-Ro, Reefer/Ro-Ro Cargo 

Containerizable cargo Container Ships Fully Cellular Container 

Each of the vessel types were classified by their cargo carrying capacity or deadweight tons 

(DWT). The size categories were identified based on both industry definitions and natural size 

breaks within the data. Table 2-17 summarizes the size categories that were used in the analysis and 

provides other information on the general attributes of the vessels from Clarkson’s Shipping 

Database. The vessel size descriptions are also used to define shipping routes based on physical 

limitations that are represented by canals or straits through which ships can pass. Very large crude 

oil tankers are the largest by DWT rating, and the biggest container ships (Suezmax) are also very 

large. 

2-27 
 

Table 2-17 Fleet Characteristics 

Ship 

Type 

Size by 

DWT 

Maximum 

Size 

(DWT) 

Maximum 

Size 

(DWT) 

Number 

of Ships 

Total 

DWT 

(millions) 

Total 

Horse 

Power 

(millions) 

Total 

Kilowatts 

(millions) 

Container Suezmax 83,000 140,000 101 9.83 8.56 6.38 

PostPanamax 56,500 83,000 465 30.96 29.3 21.85 

Panamax 42,100 56,500 375 18.04 15.04 11.21 

Intermediate 14,000 42,100 1,507 39.8 32.38 24.14 

Feeder 0 14,000 1,100 8.84 7.91 5.90 

General 

Cargo 

All All 3,214 26.65 27.07 20.18 

Dry Bulk Capesize 79,000 0 715 114.22 13.81 10.30 

Panamax 54,000 79,000 1,287 90.17 16.71 12.46 

Handymax 40,000 54,000 991 46.5 10.69 7.97 

Handy 0 40,000 2,155 58.09 19.58 14.60 

Crude Oil 

Tanker 

VLCC 180,000 0 470 136.75 15.29 11.40 

Suezmax 120,000 180,000 268 40.63 5.82 4.34 

AFRAmax 75,000 120,000 511 51.83 8.58 6.40 

Panamax 43,000 75,000 164 10.32 2.17 1.62 

Handymax 27,000 43,000 100 3.45 1.13 0.84 

Coastal 0 27,000 377 3.85 1.98 1.48 

Chemical 

Tanker 

All All 2,391 38.8 15.54 11.59 

Petroleum 

Product 

Tanker 

AFRAmax 68,000 0 226 19.94 3.6 2.68 

Panamax 40,000 68,000 352 16.92 4.19 3.12 

Handy 27,000 40,000 236 7.9 2.56 1.91 

Coastal 0 27,000 349 3.15 1.54 1.15 

Natural 

Gas 

Carrier 

VLGC 60,000 0 157 11.57 5.63 4.20 

LGC 35,000 60,000 140 6.88 2.55 1.90 

Midsize 0 35,000 863 4.79 3.74 2.79 

Other All All 7,675 88.51 53.6 39.96 

Total -- -­ -­ 26,189 888.4 308.96 230.36 

The average fuel consumption for each vessel type and size category was estimated in a 

multi-step process using individual vessel data on engine characteristics. Clarkson’s Shipping 

Database Register provides each ship’s total installed horsepower (HP), type of propulsion (diesel 

or steam), and year of build. These characteristics are then matched to information on typical 

specific fuel consumption (SFC), which is expressed in terms of grams of bunker fuel burned per 

horsepower-hour (g/HP-hr, which is equivalent to 1.341 g/kW-hr). 

The SFC values are based on historical data from Wartsila Sulzer, a popular manufacturer of 

diesel engines for marine vessels. RTI added an additional 10 percent to the reported “test bed” or 

“catalogue” numbers to account for the guaranteed tolerance level and an in-service SFC 

2-28 
differential. Overall, the 10 percent estimate is consistent with other analyses that show some 

variation between the “test bed” SFC values reported in the manufacturer product catalogues and 

those observed in actual service. This difference is explained by the fact that old, used engines 

consume more fuel than brand new engines and in-service fuels may be different than the test bed 

fuels.33 

Figure 2-5 shows SFC values that were used in the model regarding the evolution of specific 

fuel oil consumption rates for diesel engines over time. Engine efficiency in terms of SFC has 

improved over time, most noticeably in the early 1980s in response to rising fuel prices. However, 

there is a tradeoff between improving fuel efficiency and reducing emissions. Conversations with 

engine manufacturers indicate that it is reasonable to assume SFC will remain constant for the 

projection period of this study, particularly as they focus on meeting NOX emission standard as 

required by MARPOL Annex VI, or other potential pollution control requirements. Post-2000 SFC 

values are constant at approximately 135 g/hp-hr (180 g/kW-hr). 

0 

20 

40 

60 

80 

100 

120 

140 

160 

180 

200 

Specific Fuel Consumption (g/bhp-hour) 

1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020 

Figure 2-5 Diesel Engine Specific Fuel Consumption 

RTI assumed a fixed SFC of 220 g/HP-hr (295 g/kW-hr) for steam engines operating on 

bunker fuel. 

Using the above information, the average daily fuel consumption (AFC), expressed in metric 

tons of fuel at full engine load, for each vessel type and size category is found using the following 

equation: 

Equation 2-6 

1 −6 Fleet AFC = ∑[SFC × HP ×10 tonnes / g] v,s v,s v,s N 

2-29 
 

Where: 

- Fleet AFC = Average daily fuel consumption in metric tonnes at full engine load 

- v = Vessel type 

- s = Vessel size category 

- N = Number of vessels in the fleet 

- SFC = Specific fuel consumption in grams of bunker fuel burned per horsepowerhour in use(g/HP-hr) 

- HP = Total installed engine power, in horsepower (HP) 

- 106

 tonnes/g = Conversion from grams to metric tonnes 

As previously noted, AFC values calculated in the above equation are based on total 

horsepower; therefore, they must be scaled down to reflect typical operation using less than 100 

percent of the horsepower rating, i.e., actual engine load. Table 2-18 shows the engine load factors 

that were used to estimate the typical average daily fuel consumption (tons/day) for the main 

propulsion engine and the auxiliary engines when operated at sea and in port.34
 

Table 2-18 Main and Auxiliary Engine Load Factors 

Vessel Type 

Main 

Engine 

Load Factor 

(%) 

Auxiliary Engine as 

Percent of Main 

Engine 

Auxiliary Engine as 

Percent of Main Engine at 

Sea 

Container Vessels 80 22.0 11.0 

General Cargo Carriers 80 19.1 9.5 

Dry Bulk Carriers 75 22.2 11.1 

Crude Oil Tankers 75 21.1 10.6 

Chemical Tankers 75 21.1 10.6 

Petroleum Product Tankers 75 21.1 10.6 

Natural Gas Carrier 75 21.1 10.6 

Other 70 20.0 10.0 

The RTI analysis also assumed that the shipping fleet changes over time as older vessels are 

scrapped and replaced with newer ships. Specifically, vessels over 25 years of age are retired and 

replaced by new ships of the most up-to-date configuration. This assumption leads to the following 

change in fleet characteristics over the projection period: 

•	 New ships have engines rated at the current SFC, so even though there are no further 

improvements in specific fuel consumption, the fuel efficiency of the fleet as a whole 

will improve over time through retirement and replacement. 

•	 New ships will weigh as much as the average ship built in 2005, so the total cargo 

capacity of the fleet will increase over time as smaller ships retire and are replaced. 

•	 Container ships will increase in size over time on the trade routes between Asia to either 

North America or Europe. 

2.4.2.4 Trade Analysis by Commodity Type and Trade Route 

Determining the total number of days at sea and in port requires information on the relative 

amount of each commodity that is carried by the different ship type size categories on each of the 

trade routes. For example, to serve the large crude oil trade from the Middle East Gulf region to the 

2-30 
 

 

Gulf Coast of the U.S., 98 percent of the deadweight tonnage is carried on very large oil tankers, 

while the remaining 2 percent is carried on smaller Suezmax vessels. After the vessel type size 

distribution was found, voyage parameters were estimated. Specifically, these are days at sea and in 

port for each voyage (based on ports called, distance between ports, and ship speed), and the 

number of voyages (based on cargo volume projected by GI and the DTW from Clarkson’s 

Shipping Database). The length of each voyage and number of voyages were used to estimate the 

total number of days at sea and at port, which is a parameter used later to calculate total fuel 

consumption for each vessel type and size category over each route and for each commodity type. 

(More information on determining the round trip distance for each voyage that is associated with 

cargo demand for the U.S. is provided in Section 2.4.2.5.) 

The days at sea were calculated by dividing the round trip distance by the average vessel 

speed: 

Equation 2-7 

round trip distance route Days at Sea Per Voyage v,s,route = speed × 24 hrs v,s 

Where: 

v = Vessel type 

s = Vessel size category
 

route = Unique trip itinerary 

round trip route distance = Trip length in nautical miles 

speed = Vessel speed in knots or nautical miles per hour 

24 hrs = Number of hours in one day 

Table 2-19 presents the speeds by vessel type that were used in the analysis.34 These values 

are the same for all size categories, and are assumed to remain constant over the forecast period. 

Table 2-19 Vessel Speed by Type 

Vessel Type Speed (knots) 

Crude Oil Tankers 13.2 

Petroleum Product Tankers 13.2 

Chemical Tankers 13.2 

Natural Gas Carriers 13.2 

Dry Bulk Carriers 14.1 

General Cargo Vessels 12.3 

Container Vessels 19.9 

Other 12.7 

The number of voyages along each route for each trade was estimated for each vessel type v
 

and size category s serving a given route by dividing the tons of cargo moved by the amount of 

cargo (DTW) per voyage: 

2-31 
 

 

Equation 2-8 

total metric tonnes of cargo moved Number of Voyagesv,s,trade =
 

fleet average DWT × utilization rate v,s 

Where: 

v = Vessel type 

s = Vessel size category 

trade = Commodity type 

Fleet average DWT = Median dead weight tonnage carrying capacity in metric tons 

Utilization rate = Fraction of total ship DWT capacity used 

The cargo per voyage is based on the fleet average ship size from the vessel profile analysis. 

For most cargo, a utilization rate of 0.9 is assumed to be constant throughout the forecast period. 

Lowering this factor would increase the estimated number of voyages required to move the 

forecasted cargo volumes, which would lead to an increase in estimated fuel demand. 

In addition to calculating the average days at sea per voyage, the average days in port per 

voyage was also estimated by assuming that most types of cargo vessels spend four days in port per 

voyage. RTI notes, however, that this can vary somewhat by commodity and port. 

2.4.2.5 Worldwide Estimates of Fuel Demand 

This section describes how the information from the vessel and trade analyses were used to 

calculate the total annual fuel demand associated with international cargo trade. Specifically, for 

each year y of the analysis, the total bunker fuel demand is the sum of the fuel consumed on each 

route of each trade (commodity). The fuel consumed on each route of each trade is in turn the sum 

of the fuel consumed for each route and trade for that year by propulsion main engines and auxiliary 

engines when operated at sea and in port. These steps are illustrated by the following equations: 

Equation 2-9 

FC y = Σ Σ FCtrade,route,year
 trade route
 

= Σ Σ ⎡

⎣AFC trade,route,yatsea x DaysatSea trade,route,y +AFC trade,route, yat port x Daysat Port trade,route, y ⎤

⎦ trade route 

Where: 

FC = Fuel consumed in metric tonnes 

y = calendar year 

trade = Commodity type 

route = Unique trip itinerary 

AFC = Average daily fuel consumption in metric tonnes 

yatsea = Calendar year main and auxiliary engines are operated at sea 

 yatport = Calendar year main and auxiliary engines are operated in port 

2-32 
Equations 2-10 

AFC trade,route, yatsea = Σ (Percent of tradealong route) v,s ⎡

⎣Fleet AFC v,s x (MELF +AE at sea LF )⎤⎦ v,s,t,r 

AFC trade,route, yat port = Σ (Percent of tradealong route) v,s ⎡Fleet AFC v,s x AE import LF ⎤ ⎣ ⎦ v,s,t,r 

Daysat Sea trade,route,y = Σ (Percent of tradealong route) v,s ⎣

⎡Daysat sea per voyage v,s x Number of voyages v,s ⎦

⎤ v,s,t,r 

Days at Port = Σ (Percent of tradealong route) trade,route,y v,s [Days at port per voyage x Number of voyages ] v,s,t,r 

Where: 

- AFC = Average daily fuel consumption in metric tones 

- trade = Commodity type 

- route = Unique trip itinerary 

- yatsea = Calendar year main and auxiliary engines are operated at sea 

- yatport = Calendar year main and auxiliary engines are operated in port 

- y = calendar year 

- v = Vessel type 

- s = Vessel size category 

- t = Trade 

- r = Route 

- Fleet AFC = Average daily fuel consumption in metric tonnes at full engine load 

- MELF = main engine load factor, unitless 

- AE at sea LF = auxiliary engine at-sea load factor, unitless 

- AE in port LF = auxiliary engine in-port load factor, unitless 

The inputs for these last four equations are all derived from the vessel analysis in Section 

2.4.2.3 and the trade analysis in Section 2.4.2.2. 

2.4.2.6 Worldwide Bunker Fuel Consumption 

Based on the methodology outlined above, estimates of global fuel consumption over time 

were computed, and growth rates determined from these projections. 

2-33 
0 

100 

200 

300 

400 

500 

600 

Million Tons of Fuel 

199520002005201020152020 

Container General Cargo Dry Bulk Crude Oil 

Chemicals Petroleum Natural Gas Other 

Fishing Vessels Passenger Ships Military Vessels 

Figure 2-6 Worldwide Bunker Fuel Consumption 

Figure 2-6 shows estimated world-wide bunker fuel consumption by vessel type. Figure 2-7
 

shows the annual growth rates by vessel-type/cargo that are used in the projections shown in Figure 

2-6. Total annual growth is generally between 2.5 percent and 3.5 percent over the time period 

between 2006 and 2020 and generally declines over time, resulting in an average annual growth of
 

around 2.6 percent. 

2-34 
-2% 

0% 

2% 

4% 

6% 

8% 

10% 

Percent Change in Bunker Fuel 

20062008201020122014201620182020 

Total Container General Cargo Dry Bulk 

Crude Oil Chemicals Petroleum Natural Gas 

Other Fishing Vessels Passenger Ships Military Vessels 

Figure 2-7 Annual Growth Rate in World-Wide Bunker Fuel Use by Commodity Type 

2.4.2.7 Fuel Demand Used to Import and Export Cargo for the United States 

The methodology described above provides an estimate of fuel consumption for 

international cargo worldwide. RTI also estimated the subset of fuel demand for cargo imported to 

and exported from five regions of the U.S. The five regions are: 

• North Pacific 

• South Pacific 

• Gulf 

• East Coast 

• Great Lakes 

For this analysis, the same equations were used, but were limited to routes that carried cargo 

between specific cities in Asia, Europe and Middle East to the various ports in the specific regions 

of the U.S. 

The trip distances for non-container vessel types were developed from information from 

Worldscale Association and Maritime Chain.35,36 The data from Worldscale is considered to be the 

industry standard for measuring port-to-port distances, particularly for tanker traffic. The reported 

distances account for common routes through channels, canals, or straits. This distance information 

was supplemented by data from Maritime Chain, a web service that provides port-to-port distances 

along with some information about which channels, canals, or straits must be passed on the voyage. 

2-35 
 

 

 

 

 

Voyage distances for container vessels are based on information from Containerization 

International Yearbook (CIY)37 and calculations by RTI. That reference provides voyage 

information for all major container services. Based on the frequency of the service, number of 

vessels assigned to that service, and the number of days in operation per year, RTI estimated the 

average length of voyages for the particular bilateral trade routes in the Global Insights trade 

forecasts. 

The distance information developed above was combined with the vessel speeds previously 

shown in Table 2-19 to find the length of a voyage in days. Table 2-20 presents the day lengths for 

non-containerized vessel types and Table 2-21 shows the same information for container vessels. 

Table 2-20 Day Length for Voyages for Non-Container Cargo Ship (approximate average) 

Global Insights Trade Regions 

Days per Voyage 

US South 

Pacific 

US North 

Pacific 

US East 

Coast 

US Great 

Lakes US Gulf 

Africa East-South 68 75 57 62 54 

Africa North-Mediterranean 49 56 37 43 47 

Africa West 56 63 36 46 43 

Australia-New Zealand 48 47 65 81 63 

Canada East 37 46 7 18 19 

Canada West 11 5 40 58 39 

Caspian Region 95 89 41 46 48 

China 41 36 73 87 69 

Europe Eastern 61 68 38 45 46 

Europe Western-North 53 60 24 32 34 

Europe Western-South 54 61 30 37 37 

Greater Caribbean 26 33 16 29 17 

Japan 35 31 65 81 62 

Middle East Gulf 77 72 56 65 83 

Pacific High Growth 52 48 67 76 88 

Rest of Asia 68 64 66 64 73 

Russia-FSU 64 71 38 46 48 

Rest of South America 51 30 41 46 44 

Table 2-21 Day Length for Voyages for Container-Ship Trade Routes 

Origin – Destination Regions 

Days per 

Voyage 

Asia – North America (Pacific) 37 

Europe – North America (Atlantic) 37 

Mediterranean – North America 41 

Australia/New Zealand – North America 61 

South America – North America 48 

Africa South – North America (Atlantic) 54 

Africa West – North America (Atlantic) 43 

2-36 
 

Origin – Destination Regions 

Days per 

Voyage 

Asia – North America (Atlantic) 68 

Europe – North America (Pacific) 64 

Africa South – North America (Pacific) 68 

Africa West – North America (Pacific) 38 

Caspian Region – North America (Atlantic) 42 

Caspian Region – North America (Pacific) 38 

Middle East/Gulf Region – North America (Atlantic) 63 

Middle East/Gulf Region – North America (Pacific) 80 

2.4.2.8 Bunker Fuel Consumption for the United States 

Figure 2-8 and Figure 2-9 present the estimates of fuel use for delivering trade goods to and 

from the U.S. The results in Figure 2-8 show estimated historical bunker fuel use in year 2001 of 

around 47 million tonnes (note: while this fuel is used to carry trade goods to and from the U.S., it is 

not necessarily all purchased in the U.S. and is not all burned in U.S. waters). This amount grows 

to over 90 million tonnes by 2020 with the most growth occurring on trade routes from the East 

Coast and the “South Pacific” region of the West Coast. 

0 

10 

20 

30 

40 

50 

60 

70 

80 

90 

100 

Million Tons of Bunker Fuel 

199520002005201020152020 

US North Pacific US Great Lakes US Gulf US East Coast US South Pacific 

Figure 2-8 Bunker Fuel Used to Import and Export Cargo by Region of the United States 

2-37 
 

Figure 2-9 shows the estimated annual growth rates for the fuel consumption that are used in 

the projections shown in Figure 2-8. Overall, the average annual growth rate in marine bunkers 

associated with future U.S. trade flows is 3.4 percent between 2005 and 2020. 

-2% 

0% 

2% 

4% 

6% 

8% 

10% 

Percent Change in Bunker Fuel 

20062008201020122014201620182020 

United States US South Pacific US North Pacific 

US Great Lakes US Gulf US East Coast 

Figure 2-9 Annual Growth Rates for Bunker Fuel Used to Import and Export Cargo by Region of the 

United States 

2.4.2.9 2020 Growth Factors for Nine Geographic Regions 

The results of the RTI analysis described above are used to develop the growth factors that 

are necessary to project the 2002 base year emissions inventory to 2020. The next two sections 

describe how the five RTI regions were associated with the nine regions analyzed in this report, and 

how the specific growth rates for each of the nine regions were developed. 

2.4.2.9.1 Mapping the RTI Regional Results to the Nine Region Analysis 

The nine geographic regions analyzed in this study were designed to be consistent with the 

five RTI regional modeling domains. More specifically, four of the nine geographic areas in this 

study, i.e., Alaska East, Alaska West, Hawaii East, and Hawaii West are actually subsets of two 

broader regional areas that were analyzed by RTI, i.e., the North Pacific for both Alaska regions and 

South Pacific for Hawaii. Therefore, the growth rate information from the related larger region was 

assumed to be representative for that state. 

2-38 
 

 

The nine geographic regions represented in the emission inventory study are presented in 

Figure 2-1. The association of the RTI regions to the emission inventory regions is shown in Table 

2-22. 

Table 2-22 Association of the RTI Regions to the Nine Emission Inventory 

Regions 

Consumption Region 

Corresponding Emission 

Inventory Region 

North Pacific North Pacific (NP) 

North Pacific Alaska East (AE) 

North Pacific Alaska West (AW) 

South Pacific South Pacific (SP) 

South Pacific Hawaii East (HE) 

South Pacific Hawaii West (HW) 

Gulf Gulf Coast (GC) 

East Coast East Coast (EC) 

Great Lakes Great Lakes (GL) 

2.4.2.9.2 Growth Factors for the Emission Inventory Analysis 

Emission inventories for 2020 are estimated by multiplying the 2002 baseline inventory for 

each region by a corresponding growth factor that was developed from the RTI regional results. 

Specifically, the average annual growth rate from 2002-2020 was calculated for each of the five 

regions. Each regional growth rate was then compounded over the inventory projection time period 

for 2020, i.e., 18 years. The resulting multiplicative growth factors for each emission inventory 

region and the associated RTI average annual growth rates are presented in Table 2-23 for 2020. 

2-39 
 

 

Table 2-23 Regional Emission Inventory Growth Factors for 2020 

Emission 2002-2020 Average Multiplicative Growth 

Inventory Region Annualized Growth Rate (%) Factor Relative to 2002 

Alaska East (AE) 3.3 1.79 

Alaska West (AW) 3.3 1.79 

East Coast (EC) 4.5 2.21 

Gulf Coast (GC) 2.9 1.67 

Hawaii East (HE) 5.0 2.41 

Hawaii West (HW) 5.0 2.41 

North Pacific (NP) 3.3 1.79 

South Pacific (SP) 5.0 2.41 

Great Lakes (GL) 1.7 1.35 

2.4.3 Emission Controls in 2020 Baseline and Control Scenarios 

This section describes the control programs present in the 2020 baseline and control 

scenarios. Section 2.4.4 describes the process of incorporating these programs into the 2020 

emission inventories. 

The baseline scenario includes the International Marine Organization’s (IMO) Tier I NOX 

standard for marine diesel engines that became effective in 2000, as well as the Tier II standard that 

will become effective in 2011. Also included in the baseline inventories is the NOX retrofit 

program for pre-controlled engines proposed by IMO. 

Although the 0.1% fuel sulfur requirement goes into place for all vessels operating in ECAs 

beginning in 2015, the use of 2020 as the analytic year will still provide a representative scenario 

for the impact of the 0.1% fuel sulfur requirement on human health and the environment. This is 

because the fuel requirements of the ECA go into effect all at once; there is no phase-in. So the 

impacts of the fuel requirement in 2020 are expected to be the same as in 2015, with a small 

increase due to growth. With regard to the NOX impacts, while 2020 will include five years of 

turnover to the Tier III standards, the long service lives of engines on ocean-going vessels mean that 

these impacts will be small and affect less than 25% of the total fleet, assuming an average 20-year 

service life. These NOX reductions would not inflate the benefits of the program by very much, if 

any. Note that the global fuel sulfur standard does not go into effect until 2020. We did not include 

this in the 2020 analysis, to provide a better estimate of benefits in the early (pre-2020) years of the 

program 

The effects of these controls are reflected in the 2020 emission inventories by applying 

appropriate adjustment factors that reflect the percentage of the vessel fleet in those years that are 

estimated to comply with the controls. Adjustment factors are ratios of 2020 to 2002 calendar year 

(CY) emission factors (EFs). Adjustment factors are derived separately by engine type for 

propulsion and auxiliary engines. The adjustment factors for propulsion engines are applied to the 

propulsion portion of the port inventory and the interport portion of the inventory. The adjustment 

factors for auxiliary engines are applied to the auxiliary portion of the port inventory. 

The control scenario includes an Emission Control Area (ECA) within a distance of 200 

nautical miles (nm) from shore. Outside this distance, baseline controls were applied (i.e., the Tier I 

2-40 
 

 

and Tier II NOX standards, the NOX retrofit program, and current fuel sulfur content levels). The 

ECA NOX controls include the baseline controls above, plus Tier II NOX standards. Fuel sulfur 

content is also assumed to be controlled to 1,000 ppm within the ECA. Note that gas and steam 

turbine engines are not subject to any of the NOx standards; however, these engines are not a large 

part of the inventory. 

The retrofit program for Tier 0 (pre-control) engines was modeled as 11 percent control 

from Tier 0 for 80 percent of 1990 thru 1999 model year (MY) engines greater than 90 liters per 

cylinder (L/cyl) starting in 2011. The retrofit program was also modeled with a five year phase-in. 

The current Tier I controls, which also are modeled as achieving an 11 percent reduction from Tier 

0, apply to the 2000 thru 2010 MY engines. In 2011 thru 2015, Tier II controls are applied. Tier II 

controls are modeled as a 2.5 g/kW-hr reduction from Tier I. In the ECA area only, for 2016 MY 

engines and beyond, Tier III controls are applied. Tier III controls are modeled as achieving an 80
 

percent reduction from Tier I levels. Control of fuel sulfur content within the ECA area affects SO2
 

and PM emissions. 

ECA controls were applied to the 48 state region as well as Alaska East (AE) and Hawaii 

East (HE). Alaska West (AW) and Hawaii West (HW) are baseline cases only. 

2.4.4 2020 Emission Factors 

This section describes the emission factors that are used in the 2020 scenarios. HC and CO 

emission factors are assumed to remain unchanged from the 2002 scenario. NOx and fuel sulfur 

controls are anticipated to lower NOX, SO2 and PM emission factors. The switch to lower sulfur 

distillate fuel use is also assumed to lower CO2 emissions slightly. 

The NOX emission factors (EFs) by engine/ship type and tier are provided in Table 2-24. 

Tier 0 refers to pre-control. There are separate entries for Tier 0/1/2 base and Tier 0/1/2 control, 

since the control engines would be using distillate fuel, and there are small NOX emission 

reductions assumed when switching from residual to distillate fuel.17 The NOX control EFs by tier 

were derived using the assumptions described in section 2.4.3. 

Table 2-24 Modeled NOX Emission Factors by Tier 

Engine/ 

Ship Type 

NOX EF (g/kW-hr) 

Baseline Control Areas 

Tier 0 

T0 

retrofit Tier I 

Tier 

II Tier 0 

T0 

retrofit Tier I Tier II 

Tier 

III 

Main 

SSD 18.1 16.1 16.1 13.6 17 15.1 15.1 12.6 3 

MSD 14 12.5 12.5 10.0 13.2 11.7 11.7 9.2 2.3 

ST 2.1 n/a n/a n/a 2 n/a n/a n/a n/a 

GT 6.1 n/a n/a n/a 5.7 n/a n/a n/a n/a 

Aux 

Pass 14.6 n/aa 13.0 10.5 14.6 n/aa 13.0 10.5 2.6 

Other 14.5 n/aa 12.9 10.4 14.5 n/aa 12.9 10.4 2.6 a

 The retrofit program applies to engines over 90 L/cyl; auxiliary engines are smaller than 

this cutpoint and would therefore not be subject to the program. 

2-41 
The NOX EFs by tier were then used with the vessel age distributions (Table 2-25 & Table 

2-26) to generate calendar year NOX EFs by engine/ship type for the base and control areas included 

in the scenarios. These calendar year NOX EFs are provided in Table 2-27 below. Since the age 

distributions are different for vessels in the Great Lakes, NOX EFs were determined separately for 

the Great Lakes. 

Table 2-25 Vessel Age Distribution for Deep Sea Ports by Engine Type 

Age Group 

(years old) 

Propulsion Engine Type a (Fraction of Total) All 

Auxiliary 

Engines MSD SSD GT ST 

0 0.00570 0.02667 0.00000 0.00447 0.01958 

1 0.07693 0.07741 0.07189 0.12194 0.07670 

2 0.10202 0.07512 0.14045 0.16464 0.08426 

3 0.08456 0.07195 0.05608 0.05321 0.07489 

4 0.08590 0.05504 0.67963 0.00000 0.07831 

5 0.06427 0.05563 0.04165 0.00000 0.05685 

6 0.06024 0.04042 0.00000 0.00000 0.04455 

7 0.07867 0.07266 0.00626 0.00000 0.07150 

8 0.06730 0.05763 0.00000 0.00000 0.05764 

9 0.04181 0.04871 0.00000 0.00000 0.04475 

10 0.04106 0.04777 0.00000 0.00000 0.04364 

11 0.03100 0.03828 0.00000 0.00000 0.03538 

12 0.04527 0.03888 0.00000 0.04873 0.04160 

13 0.03583 0.02787 0.00000 0.00000 0.02909 

14 0.03519 0.02824 0.00000 0.00000 0.02935 

15 0.02921 0.01466 0.00000 0.00000 0.01869 

16 0.00089 0.01660 0.00000 0.00000 0.01189 

17 0.01326 0.01582 0.00000 0.00000 0.01462 

18 0.00847 0.02414 0.00000 0.00000 0.01966 

19 0.00805 0.01982 0.00000 0.00000 0.01550 

20 0.00566 0.02258 0.00000 0.00000 0.01756 

21 0.00495 0.02945 0.00000 0.00000 0.02260 

22 0.00503 0.01883 0.00000 0.00875 0.01467 

23 0.00676 0.01080 0.00000 0.00883 0.00943 

24 0.00539 0.01091 0.00000 0.00883 0.00900 

25 0.01175 0.01099 0.00000 0.18029 0.01224 

26 0.00803 0.01045 0.00000 0.11065 0.01130 

27 0.00522 0.00835 0.00000 0.01395 0.00738 

28 0.00294 0.00788 0.00000 0.08657 0.00659 

29 0.00285 0.00370 0.00034 0.02907 0.00349 

30 0.00254 0.00106 0.00370 0.05126 0.00193 

31 0.00084 0.00113 0.00000 0.00605 0.00096 

32 0.00023 0.00367 0.00000 0.07105 0.00322 

33 0.00117 0.00582 0.00000 0.00000 0.00419 

34 0.00132 0.00092 0.00000 0.00000 0.00098 

35+ 0.01967 0.00013 0.00000 0.03172 0.00598 a

 MSD is medium speed diesel, SSD is slow speed diesel, GT is gas turbine, ST is steam 

turbine. 

2-42 
Table 2-26 Vessel Age Distribution for Great Lake Ports by Engine Type 

Age Group 

(years old) 

Propulsion Engine Typea

 (Fraction of Total) 

MSD SSD ST All Auxiliary 

Engines 

0 0.01610 0.03913 0.00000 0.02399 

1 0.02097 0.03489 0.00000 0.02243 

2 0.01370 0.04644 0.00000 0.02544 

3 0.02695 0.03040 0.00000 0.02511 

4 0.01571 0.04547 0.00000 0.02497 

5 0.04584 0.01498 0.00000 0.02442 

6 0.01494 0.02180 0.00000 0.01528 

7 0.01327 0.01857 0.00000 0.01391 

8 0.00099 0.04842 0.00000 0.02107 

9 0.00027 0.03376 0.00000 0.01454 

10 0.01085 0.01177 0.00000 0.01076 

11 0.00553 0.01183 0.00000 0.00782 

12 0.00739 0.00546 0.00000 0.00626 

13 0.02289 0.02557 0.00000 0.02242 

14 0.00000 0.00286 0.00000 0.00121 

15 0.00275 0.00510 0.00000 0.00361 

16 0.00069 0.00073 0.00000 0.00078 

17 0.00000 0.00104 0.00000 0.00041 

18 0.00342 0.01967 0.00000 0.01059 

19 0.00219 0.01220 0.00000 0.00645 

20 0.00867 0.06140 0.00000 0.03034 

21 0.00000 0.05638 0.00000 0.02503 

22 0.03375 0.02108 0.00000 0.02279 

23 0.04270 0.02051 0.00000 0.02606 

24 0.08161 0.01010 0.00000 0.03744 

25 0.02935 0.05217 0.00000 0.03480 

26 0.18511 0.00522 0.00000 0.07701 

27 0.01870 0.00389 0.00000 0.01083 

28 0.13815 0.01438 0.00000 0.06181 

29 0.05487 0.01160 0.00000 0.02697 

30 0.00000 0.00114 0.00000 0.00047 

31 0.03986 0.00000 0.00000 0.01611 

32 0.03654 0.00282 0.00000 0.01631 

33 0.03358 0.00000 0.00000 0.01358 

34 0.00295 0.00123 0.00000 0.00165 

35+ 0.06974 0.30796 1.00000 0.31734 a

 MSD is medium speed diesel, SSD is slow speed diesel, GT is gas turbine, ST is steam
 

turbine. 

b

 Fleet average weighted by installed power (ship port calls x main propulsion engine
 

power). 

2-43 
 

 

Table 2-27 Modeled NOX Emission Factors by Calendar Year and Control Type 

Engine/ 

Ship 

Type 

CY NOX EF (g/kW-hr) 

2002 

2020 Base 2020 ECA Control 

DSP GL DSP GL 

Main 

SSD 18.1 14.7 15.9 10.8 13.1 

MSD 14 10.9 13.1 7.7 11.8 

ST 2.1 2.1 2.1 2.0 2.0 

GT 6.1 6.1 n/a 5.7 n/a 

Aux 

Pass 14.6 11.7 13.6 8.6 12.0 

Other 14.5 11.5 13.4 8.6 12.0 

DSP = Deep water ports and areas other than the Great Lakes 

GL = Great Lakes 

The PM and SO2 EFs are a function of fuel sulfur level. For the baseline portions of the 

inventory, there are two residual fuel sulfur levels modeled: 25,000 ppm for the West Coast and 

27,000 ppm for the rest of the U.S. The baseline distillate fuel sulfur level assumed for all areas is 

15,000 ppm. As discussed in section 2.3.2.3.5, for the baseline, main engines use residual fuel and 

auxiliary engines use a mix of residual and distillate fuel. For the control areas, there is one level of 

distillate fuel sulfur assumed to be used by all engines: 1,000 ppm for the ECA control areas. 

Table 2-28 provides the PM10 EFs by engine/ship type and fuel sulfur level. For modeling 

purposes, PM2.5 is assumed to be 92 percent of PM10. The PM EFs are adjusted to reflect the 

appropriate fuel sulfur levels using Equation 2-2. 

Table 2-29 provides the modeled SO2 EFs. SO2 emission reductions are directly 

proportional to reductions in fuel sulfur content. 

CO2 is directly proportional to fuel consumed. Table 2-30 provides the modeled CO2 and 

brake specific fuel consumption (BSFC) EFs. Due to the higher energy content of distillate fuel on 

a mass basis, the switch to distillate fuel for the control areas results in a small reduction to BSFC 

and, correspondingly, CO2 emissions.17 

2-44 
 

 

 

 

 

 

 

 

 

 

Table 2-28 Modeled PM10 Emission Factors 

Engine/ 

Ship Type 

PM10 EF (g/kW-hr) 

Baseline Control Areas 

Other than West Coast 

27,000 ppm S 

West Coasta 

25,000 ppm S 

ECA 

1,000 ppm S 

Main 

SSD 1.40 1.40 0.19 

MSD 1.40 1.40 0.19 

ST 1.50 1.40 0.17 

GT 1.50 1.40 0.17 

Aux 

Pass 1.40 1.30 0.19 

Other 1.20 1.10 0.19 a

 For the base cases, the West Coast fuel is assumed to be used in the following 

regions: Alaska East (AE), Alaska West (AW), Hawaii East (HE), Hawaii West 

(HW), North Pacific (NP), and South Pacific (SP). 

Table 2-29 Modeled SO2 Emission Factors* 

Engine/ 

Ship Type 

SO2 EF (g/kW-hr) 

Baseline Control Areas 

Other than West Coast 

27,000 ppm S 

West Coasta 

25,000 ppm S 

ECA Control 

1,000 ppm S 

Main 

SSD 10.29 9.53 0.36 

MSD 11.09 10.26 0.39 

ST 16.10 14.91 0.57 

GT 16.10 14.91 0.57 

Aux 

Pass 10.70 9.93 0.39 

Other 9.66 9.07 0.39 a

 For the base cases, the West Coast fuel is assumed to be used in the following 

regions: Alaska East (AE), Alaska West (AW), Hawaii East (HE), Hawaii West 

(HW), North Pacific (NP), and South Pacific (SP). 

Table 2-30 Modeled Fuel Consumption and CO2 Emission Factors 

Engine/ 

Ship Type 

EF (g/kW-hr) 

Baseline Control Areas 

BSFC CO2 BSFC CO2 

Main 

SSD 195 620 185 589 

MSD 210 668 200 637 

ST 305 970 290 923 

GT 305 970 290 923 

Aux 

Pass 210 668 200 636 

Other 210 668 200 636 

2-45 
 

 

 

 

2.4.5 Calculation of 2020 Near Port and Interport Inventories 

Based on the emission factors described in Section 2.4.4, appropriate adjustments were 

applied to the NOX, PM (PM10 and PM2.5), SO2, and CO2 inventory of each 2020 scenario. This 

section describes the development and application of the adjustment factors to the port and interport 

inventories, and the methodology for combining the port and interport portions. 

2.4.5.1 Port Methodology 

2.4.5.1.1 Non-California Ports 

For the non-California ports, 2002 emissions for each port are summed by engine/ship type. 

Propulsion and auxiliary emissions are summed separately, since the EF adjustment factors differ. 

The appropriate regional growth factor, as provided in Table 2-23, is then applied, along with EF 

adjustment factors by engine/ship type. The EF adjustment factors are a ratio of the control EF to 

the 2002 EF. Table 2-31 through Table 2-35 provide the EF adjustment factors for each pollutant 

and control area. The ports will be subject to ECA controls in the control scenario. These tables are 

also used as input for the California ports and interport control inventory development, discussed in 

subsequent sections. 

Table 2-31 NOX EF Adjustment Factors by Engine/Ship Type and Control Typea 

Engine/ Ship 

Type 

2020 Base 

2020 ECA 

Control 

DSP GL DSP GL 

Main 

SSD 0.8130 0.8783 0.5967 0.7219 

MSD 0.7804 0.9366 0.5515 0.8423 

ST 1.0000 1.0000 0.9524 0.9524 

GT 1.0000 n/a 0.9344 n/a 

Aux 

Pass 0.7985 0.9296 0.5869 0.8196 

Other 0.7972 0.9292 0.5940 0.8295 a

 NOX adjustment factors are a ratio of future base or control EFs to 2002 EFs 

DSP = deep water ports and areas other than the Great Lakes; GL = Great Lakes 

2-46 
 

 

 

 

 

 

 

Table 2-32 PM10 EF Adjustment Factors by Engine/Ship Type and Control Typea 

Engine/ Ship 

Type 

2020 Base 2020 ECA Control 

Other WC Other WC 

Main 

SSD 1.0000 1.0000 0.1352 0.1352 

MSD 1.0000 1.0000 0.1328 0.1328 

ST 1.0000 1.0000 0.1108 0.1187 

GT 1.0000 1.0000 0.1108 0.1187 

Aux 

Pass 1.0000 1.0000 0.1328 0.1430 

Other 1.0000 1.0000 0.1550 0.1691 

a

 PM10 adjustment factors are a ratio of the control EFs to the 2002 EFs. 

PM is not adjusted for the future baseline because fuel sulfur levels are 

only assumed to change within the ECA. 

Other = Other than West Coast 

WC = Ports/areas within the West Coast. This includes the regions of
 

Alaska, Hawaii, North Pacific, and South Pacific. 

Table 2-33 PM2.5 EF Adjustment Factors by Engine/Ship Type and Control Typea 

Engine/ Ship 

Type 

2020 Base 2020 ECA Control 

Other WC Other WC 

Main 

SSD 1.0000 1.0000 0.1339 0.1339 

MSD 1.0000 1.0000 0.1316 0.1316 

ST 1.0000 1.0000 0.1092 0.1176 

GT 1.0000 1.0000 0.1092 0.1176 

Aux 

Pass 1.0000 1.0000 0.1316 0.1426 

Other 1.0000 1.0000 0.1555 0.1711 a

 PM2.5 adjustment factors are a ratio of the control EFs to the 2002 EFs. 

PM is not adjusted for the future baseline because fuel sulfur levels are 

only assumed to change within the ECA. The PM2.5 adjustment factors are 

slightly different from those for PM10 due to rounding. 

Other = Other than West Coast 

WC = Ports/areas within the West Coast. This includes the regions of 

Alaska, Hawaii, North Pacific, and South Pacific. 

2-47 
 

 

 

 

 

 

 

Table 2-34 SO2 EF Adjustment Factors by Engine/Ship Type and Control Typea 

Engine/ Ship 

Type 

2020 Base 2020 ECA Control 

Other WC Other WC 

Main 

SSD 1.0000 1.0000 0.0351 0.0380 

MSD 1.0000 1.0000 0.0353 0.0381 

ST 1.0000 1.0000 0.0352 0.0380 

GT 1.0000 1.0000 0.0352 0.0380 

Aux 

Pass 1.0000 1.0000 0.0365 0.0394 

Other 1.0000 1.0000 0.0405 0.0431 a

 SO2 adjustment factors are a ratio of the control EFs to the 2002 

EFs. SO2 is not adjusted for the future baseline because fuel sulfur 

levels are only assumed to change within the ECA. 

Other = Other than West Coast 

WC = Ports/areas within the West Coast. This includes the regions 

of Alaska, Hawaii, North Pacific, and South Pacific. 

Table 2-35 CO2 EF Adjustment Factors by Engine/Ship Type and Control Typea 

Engine/ Ship 

Type 

2020 Base 2020 ECA Control 

Other WC Other WC 

Main 

SSD 1.0000 1.0000 0.9488 0.9488 

MSD 1.0000 1.0000 0.9531 0.9531 

ST 1.0000 1.0000 0.9509 0.9509 

GT 1.0000 1.0000 0.9509 0.9509 

Aux 

Pass 1.0000 1.0000 0.9525 0.9593 

Other 1.0000 1.0000 0.9525 0.9683 a

 CO2 adjustment factors are a ratio of the control EFs to the 2002
 

EFs. CO2 is not adjusted for the future baseline because fuel
 

consumption (BSFC) is only assumed to change within the ECA. 

Other = Other than West Coast 

WC = Ports/areas within the West Coast. This includes the regions 

of Alaska, Hawaii, North Pacific, and South Pacific. 

2.4.5.1.2 California Ports 

For the California ports, 2002 emissions for each port are summed by ship type. Propulsion 

and auxiliary emissions are summed separately, since the EF adjustment factors differ. The EF 

adjustment factors by engine/ship type, provided in the previous section, are consolidated by ship 

type, using the CARB assumption that engines on all ships except passenger ships are 95 percent 

slow speed diesel (SSD) engines and 5 percent medium speed diesel engines (MSD) based upon a 

2005 ARB survey.C All passenger ships were assumed to be medium speed diesel engines with 

electric drive propulsion (MSD-ED). Steam turbines (ST) and gas-turbines (GT) are not included in 

C California Air Resources Board, 2005 Oceangoing Ship Survey, Summary of Results, September 2005. 

2-48 
 

 

 

 

the CARB inventory. The EF adjustment factors by ship type are then applied, along with shipspecific growth factors supplied by CARB. The ship-specific growth factors relative to 2002 are 

provided in Table 2-36 below. 

Table 2-36 Growth Factors by Ship Type for California Ports Relative to 2002 

Ship Type 

Calendar Year 

2002 2020 

Auto 1.0000 1.5010 

Bulk 1.0000 0.2918 

Container 1.0000 2.5861 

General 1.0000 0.7331 

Passenger 1.0000 7.5764 

Reefer 1.0000 1.0339 

RoRo 1.0000 1.5010 

Tanker 1.0000 2.0979 

2.4.5.2 Interport Methodology 

The interport portion of the inventory is not segregated by engine or ship type. As a result, 

regional EF adjustment factors were developed based on the assumed mix of main (propulsion) 

engine types in each region. The mix of main engine types by region was developed using the ship 

call data and is presented in Table 2-37. Main engines are considered a good surrogate for interport 

emissions, since the majority of emissions while underway are due to the main engines. The EF 

adjustment factors by main engine type in Section 2.4.5.1were used together with the mix of main 

engine types by region to develop the EF regional adjustment factors for each control area. The 

resulting EF regional adjustment factors for each pollutant and control area are provided in Table 

2-38 through Table 2-42 below. These EF regional adjustment factors, together with the regional 

growth factors in Table 2-23, were applied to calculate the future inventories for each control area. 

Table 2-37 Installed Power by Main Engine Type and Region 

Region 

2020 Installed Power (%) 

MSD SSD GT ST Total 

Alaska East (AE) 19.1% 18.4% 0.3% 62.2% 100% 

Alaska West (AW) 19.1% 18.4% 0.3% 62.2% 100% 

East Coast (EC) 25.6% 72.5% 0.9% 1.0% 100% 

Gulf Coast (GC) 13.7% 85.5% 0.0% 0.8% 100% 

Hawaii East (HE) 66.2% 18.5% 7.4% 8.0% 100% 

Hawaii West (HW) 66.2% 18.5% 7.4% 8.0% 100% 

North Pacific (NP) 5.1% 83.5% 1.6% 9.7% 100% 

South Pacific (SP) 29.2% 70.8% 0.0% 0.0% 100% 

Great Lakes (GL) 48% 44% 0% 8% 100% 

2-49 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Table 2-38 NOX EF Adjustment Factors by Region and Control Typea 

U.S. Region 2002 

2020 

Base ECA Control 

Alaska East (AE) 1.0000 0.9237 0.8104 

Alaska West (AW) 1.0000 0.9237 n/a 

East Coast (EC) 1.0000 0.8082 0.5917 

Gulf Coast (GC) 1.0000 0.8102 0.5935 

Hawaii East (HE) 1.0000 0.8202 0.6201 

Hawaii West (HW) 1.0000 0.8202 n/a 

North Pacific (NP) 1.0000 0.8325 0.6343 

South Pacific (SP) 1.0000 0.8036 0.5837 

Great Lakes (GL) 1.0000 0.8131 0.7989 

Out of Regionb 1.0000 0.8095 n/a a NOX adjustment factors are a ratio of future base or control EFs to 

2002 EFs. These regional adjustment factors are used to adjust the 

interport portion of the 2002 inventory. 

b

 Out of Region refers to areas outside the 200 nm US modeling 

boundary, but within the air quality modeling domain. The out of 

region adjustment factors are derived by weighting the regional 

adjustment factors by the main propulsion power in each region. 

Table 2-39 PM10 EF Adjustment Factors by Region and Control Typea 

U.S. Region 2002 

2020 

Base ECA Control 

Alaska East (AE) 1.0000 1.0000 0.1244 

Alaska West (AW) 1.0000 1.0000 n/a 

East Coast (EC) 1.0000 1.0000 0.1341 

Gulf Coast (GC) 1.0000 1.0000 0.1347 

Hawaii East (HE) 1.0000 1.0000 0.1311 

Hawaii West (HW) 1.0000 1.0000 n/a 

North Pacific (NP) 1.0000 1.0000 0.1332 

South Pacific (SP) 1.0000 1.0000 0.1345 

Great Lakes (GL) 1.0000 1.0000 0.1320 

Out of Regionb 1.0000 1.0000 n/a a

 PM10 adjustment factors are a ratio of future base or control EFs to 

2002 EFs. These regional adjustment factors are used to adjust the 

interport portion of the 2002 inventory. 

b

 Out of Region refers to areas outside the 200 nm US modeling 

boundary, but within the air quality modeling domain. The out of 

region adjustment factors are derived by weighting the regional 

adjustment factors by the main propulsion power in each region. 

2-50 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Table 2-40 PM2.5 EF Adjustment Factors by Region and Control Typea 

U.S. Region 2002 

2020 

Base ECA Control 

Alaska East (AE) 1.0000 1.0000 0.1233 

Alaska West (AW) 1.0000 1.0000 n/a 

East Coast (EC) 1.0000 1.0000 0.1329 

Gulf Coast (GC) 1.0000 1.0000 0.1334 

Hawaii East (HE) 1.0000 1.0000 0.1299 

Hawaii West (HW) 1.0000 1.0000 n/a 

North Pacific (NP) 1.0000 1.0000 0.1320 

South Pacific (SP) 1.0000 1.0000 0.1332 

Great Lakes (GL) 1.0000 1.0000 0.1307 

Out of Regionb 1.0000 1.0000 n/a a

 PM2.5 adjustment factors are a ratio of future base or control EFs 

to 2002 EFs. These regional adjustment factors are used to adjust 

the interport portion of the 2002 inventory. 

b

 Out of Region refers to areas outside the 200 nm US modeling 

boundary, but within the air quality modeling domain. The out of 

region adjustment factors are derived by weighting the regional 

adjustment factors by the main propulsion power in each region. 

Table 2-41 SO2 EF Adjustment Factors by Region and Control Typea 

U.S. Region 2002 

2020 

Base ECA Control 

Alaska East (AE) 1.0000 1.0000 0.0380 

Alaska West (AW) 1.0000 1.0000 n/a 

East Coast (EC) 1.0000 1.0000 0.0352 

Gulf Coast (GC) 1.0000 1.0000 0.0352 

Hawaii East (HE) 1.0000 1.0000 0.0381 

Hawaii West (HW) 1.0000 1.0000 n/a 

North Pacific (NP) 1.0000 1.0000 0.0380 

South Pacific (SP) 1.0000 1.0000 0.0380 

Great Lakes (GL) 1.0000 1.0000 0.0352 

Out of Regionb 1.0000 1.0000 n/a a

 SO2 adjustment factors are a ratio of future base or control EFs to 

2002 EFs. These regional adjustment factors are used to adjust the 

interport portion of the 2002 inventory. 

b

 Out of Region refers to areas outside the 200 nm US modeling 

boundary, but within the air quality modeling domain. The out of 

region adjustment factors are derived by weighting the regional 

adjustment factors by the main propulsion power in each region are 

derived by weighting the regional adjustment factors by the main 

propulsion power in each region. 

2-51 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

Table 2-42 CO2 EF Adjustment Factors by Region and Control Typea 

U.S. Region 2002 

2020 

Base ECA Control 

Alaska East (AE) 1.0000 1.0000 0.9509 

Alaska West (AW) 1.0000 1.0000 n/a 

East Coast (EC) 1.0000 1.0000 0.9499 

Gulf Coast (GC) 1.0000 1.0000 0.9494 

Hawaii East (HE) 1.0000 1.0000 0.9519 

Hawaii West (HW) 1.0000 1.0000 n/a 

North Pacific (NP) 1.0000 1.0000 0.9493 

South Pacific (SP) 1.0000 1.0000 0.9501 

Great Lakes (GL) 1.0000 1.0000 0.9510 

Out of Regionb 1.0000 1.0000 n/a 

a CO2 adjustment factors are a ratio of future base or control EFs 

to 2002 EFs. These regional adjustment factors are used to adjust 

the interport portion of the 2002 inventory. b Out of Region refers to areas outside the 200 nm US modeling 

boundary, but within the air quality modeling domain. The out of 

region adjustment factors are derived by weighting the regional 

adjustment factors by the main propulsion power in each region. 

2.4.5.3 Estimating and Combining the Near Port and Interport Control Inventories 

To produce future year control scenarios, the interport inventories were scaled by a growth 

factor to 2020, as previously described. An ECA boundary line was drawn so that each point on it 

was at a 200 nm distance from the nearest point on land. Adjustment factors, as described in 

section 2.4.4, were then applied to interport emissions within the ECA boundary. 

To create control scenarios in the near port inventories, growth and control factors were 

applied to the 2002 near port inventories (described in sections 2.4.2 and 2.4.4). The near port 

inventories were then converted into a gridded format (section 2.3.4). Using this grid, STEEM 

values were removed from near port cells and near port emissions were used as replacement values. 

In cases where the emissions near ports were only partially attributable to port traffic, the STEEM 

inventory was reduced rather than removed. 

Interport and near port emissions were then aggregated to form regional totals. 

2.4.6 2020 Baseline and Control Inventories and Fuel Consumption 

The baseline emission inventories for 2020 are presented in Table 2-43. 

2-52 
 

Table 2-43 2020 Baseline Inventory 

U.S. Region 

Metric Tonnes per Year 

NOx PM10 PM2.5a HC CO SO2 CO2 

Alaska East (AE) 27,982 2,561 2,356 1,073 2,534 19,084 1,182,047 

Alaska West (AW) 89,826 8,118 7,469 3,444 8,112 60,227 3,711,596 

East Coast (EC) 391,995 39,003 35,882 16,216 38,382 323,038 18,121,202 

Gulf Coast (GC) 232,114 23,403 21,531 9,590 23,628 174,751 10,567,512 

Hawaii East (HE) 42,935 4,185 3,850 1,765 4,161 31,075 1,930,172 

Hawaii West (HW) 60,409 5,888 5,417 2,483 5,855 43,722 2,715,741 

North Pacific (NP) 38,051 3,916 3,603 1,706 3,799 27,807 1,800,743 

South Pacific (SP) 208,294 20,148 18,536 8,585 20,686 149,751 9,490,502 

Great Lakes (GL) 18,768 1,613 1,484 681.914 1,607 11,993 740,624 

Total U.S. Metric Tonnes 1,110,375 108,835 100,128 45,544 108,762 841,447 50,260,140 a Estimated from PM10 using a multiplicative conversion factor of 0.92. 

The ECA control case inventories for each of the nine geographic regions and the U.S. 

domain total are presented in Table 2-44. The regional and total inventories include all emissions 

within the 200 nm US modeling domain. Controls are applied to all regions included in the 

proposed ECA. 

Table 2-44 Category 3 Vessel Inventories for 2020 Proposed ECA Control Casea 

U.S. Region 

Metric Tonnes per Year 

NOx PM10 PM2.5a HC CO SO2 CO2 

Alaska East (AE) 25,978 322 296 1,073 2,534 728 1,124,652 

Alaska West (AW) 89,826 8,118 7,469 3,444 8,112 60,227 3,711,596 

East Coast (EC) 289,671 5,286 4,863 16,216 38,382 11,514 17,233,800 

Gulf Coast (GC) 170,861 3,201 2,945 9,590 23,628 6,255 10,034,946 

Hawaii East (HE) 32,952 551 507 1,765 4,161 1,187 1,838,832 

Hawaii West (HW) 60,409 5,888 5,417 2,483 5,855 43,722 2,715,741 

North Pacific (NP) 29,105 539 496 1,706 3,799 1,076 1,715,210 

South Pacific (SP) 150,461 2,753 2,533 8,585 20,686 5,786 9,009,986 

Great Lakes (GL) 16,420 207 190 681 1,607 420 704,390 

Total U.S. Metric Tonnes 865,684 26,864 24,715 45,544 108,762 130,914 48,089,152 a This scenario assumes ECA controls apply within 200 nautical miles of all U.S. regions. Alaska 

West and Hawaii West are not subject to ECA controls. 

The fuel consumption by fuel type in the baseline and ECA cases is also presented in 

Table 2-45. 

2-53 
 

 

Table 2-45 Fuel Consumption by Category 3 Vessels in Baseline and ECA Scenarios. 

U.S. Region 

Baseline With ECA 

Metric Tonnes Fuel Metric Tonnes Fuel 

Distillate Residual Total Distillate Residual Total 

Alaska East (AE) 3,386 367,977 371,363 353,331 0 353,331 

Alaska West (AW) 0 1,166,068 1,166,068 0 1,166,068 1,166,068 

East Coast (EC) 202,139 5,490,981 5,693,120 5,414,326 0 5,414,326 

Gulf Coast (GC) 96,428 3,223,557 3,319,985 3,152,669 0 3,152,669 

Hawaii East (HE) 10,529 595,871 606,400 577,704 0 577,704 

Hawaii West (HW) 0 853,202 853,202 0 853,202 853,202 

North Pacific (NP) 28,532 537,206 565,738 538,866 0 538,866 

South Pacific (SP) 83,576 2,898,045 2,981,622 2,830,658 0 2,830,658 

Great Lakes (GL) 1,269 231,412 232,681 221,297 0 221,297 

Total U.S. Metric 

Tonnes 425,860 15,364,319 15,790,179 13,088,852 2,019,270 15,108,122 

2.5 Projected Emission Reductions 

The projected reduction (tonnes) for the 2020 control case relative to the 2020 baseline is 

presented in Table 2-46. Reductions by region, for the total U.S., and for the total 48-states, are 

provided by pollutant in each table. 

Table 2-46 Reductions for 2020 Proposed ECA Control Casea 

U.S. Region 

Metric Tonnes per Year 

NOX PM10 PM2.5 a HC CO SO2 CO2 

Alaska East (AE) 2,004 2,239 2,060 0 0 18,356 57,395 

Alaska West (AW) 0 0 0 0 0 0 0 

East Coast (EC) 102,324 33,717 31,019 0 0 311,524 887,402 

Gulf Coast (GC) 61,253 20,202 18,586 0 0 168,496 532,566 

Hawaii East (HE) 9,983 3,634 3,343 0 0 29,888 91,340 

Hawaii West (HW) 0 0 0 0 0 0 0 

North Pacific (NP) 8,946 3,377 3,107 0 0 26,731 85,533 

South Pacific (SP) 57,833 17,395 16,003 0 0 143,965 480,516 

Great Lakes (GL) 2,348 1,406 1,294 0 0 11,573 36,234 

Total U.S. Metric 

Tonnes 244,690 81,971 75,413 0 0 710,534 2,170,987 a

 The emission reductions are relative to the 2020 baseline. 

2.6 Conclusion 

An emission inventory for ships in the U.S. was developed based on the latest state of the art 

models and inputs, using a “bottom-up” methodology. The inventory includes emissions for 117 

ports, as well as emissions for ships while underway in U.S. waters. The analysis clearly 

2-54 
demonstrates that emissions from ships in the proposed ECA are contributing to U.S. air pollution. 

The inventory data were used as an input for the air quality modeling analysis. 

2-55 
 

 

Appendices 

Appendix 2A: Port Coordinates 

Table 2A-1 Port Coordinates 

Port Name 

US ACE 

Code 

Port Coordinates 

Longitude Latitude 

Albany, NY C0505 -73.7482 42.64271 

Alpena, MI L3617 -83.4223 45.0556 

Anacortes, WA C4730 -122.6 48.49617 

Anchorage, AK C4820 -149.895 61.23778 

Ashtabula, OH L3219 -80.7917 41.91873 

Baltimore, MD C0700 -76.5171 39.20899 

Barbers Point, Oahu, HI C4458 -158.109 21.29723 

Baton Rouge, LA C2252 -91.1993 30.42292 

Beaumont, TX C2395 -94.0881 30.08716 

Boston, MA C0149 -71.0523 42.35094 

Bridgeport, CT C0311 -73.1789 41.172 

Brownsville, TX C2420 -97.3981 25.9522 

Brunswick, GA C0780 -81.4999 31.15856 

Buffalo, NY L3230 -78.8953 42.8783 

Burns Waterway Harbor, IN L3739 -87.1552 41.64325 

Calcite, MI L3620 -83.7756 45.39293 

Camden-Gloucester, NJ C0551 -75.1043 39.94305 

Carquinez, CA CCA01 -122.123 38.03556 

Catalina, CA CCA02 -118.496 33.43943 

Charleston, SC C0773 -79.9216 32.78878 

Chester, PA C0297 -75.3222 39.85423 

Chicago, IL L3749 -87.638 41.88662 

Cleveland, OH L3217 -81.6719 41.47852 

Conneaut, OH L3220 -80.5486 41.96671 

Coos Bay, OR C4660 -124.21 43.36351 

Corpus Christi, TX C2423 -97.3979 27.81277 

Detroit, MI L3321 -83.1096 42.26909 

Duluth-Superior, MN and WI L3924 -92.0964 46.77836 

El Segundo, CA CCA03 -118.425 33.91354 

Erie, PA L3221 -80.0679 42.15154 

Escanaba, MI L3795 -87.025 45.73351 

Eureka, CA CCA04 -124.186 40.79528 

Everett, WA C4725 -122.229 47.98476 

Fairport Harbor, OH L3218 -81.2941 41.76666 

Fall River, MA C0189 -71.1588 41.72166 

Freeport, TX C2408 -95.3304 28.9384 

Galveston, TX C2417 -94.8127 29.31049 

Gary, IN L3736 -87.3251 41.61202 

Georgetown, SC C0772 -79.2896 33.36682 

Grays Harbor, WA C4702 -124.122 46.91167 

Gulfport, MS C2083 -89.0853 30.35216 

Hilo, HI C4400 -155.076 19.72861 

2-56 
 

Port Name 

US ACE 

Code 

Port Coordinates 

Longitude Latitude 

Honolulu, HI C4420 -157.872 21.31111 

Hopewell, VA C0738 -77.2763 37.32231 

Houston, TX C2012 -95.2677 29.72538 

Indiana Harbor, IN L3738 -87.4455 41.67586 

Jacksonville, FL C2017 -81.6201 30.34804 

Kahului, Maui, HI C4410 -156.473 20.89861 

Kalama, WA C4626 -122.863 46.02048 

Lake Charles, LA C2254 -93.2221 30.22358 

Long Beach, CA C4110 -118.21 33.73957 

Longview, WA C4622 -122.914 46.14222 

Lorain, OH L3216 -82.1951 41.48248 

Los Angeles, CA C4120 -118.241 33.77728 

Manistee, MI L3720 -86.3443 44.25082 

Marblehead, OH L3212 -82.7091 41.52962 

Marcus Hook, PA C5251 -75.4042 39.81544 

Matagorda Ship Channel, TX C2410 -96.5641 28.5954 

Miami, FL C2164 -80.1832 25.78354 

Milwaukee, WI L3756 -87.8997 42.98824 

Mobile, AL C2005 -88.0411 30.72527 

Morehead City, NC C0764 -76.6947 34.71669 

Muskegon, MI L3725 -86.3501 43.19492 

Nawiliwili, Kauai, HI C4430 -159.353 21.96111 

New Bedford, MA C0187 -70.9162 41.63641 

New Castle, DE C0299 -75.5616 39.65668 

New Haven, CT C1507 -72.9047 41.29883 

New Orleans, LA C2251 -90.0853 29.91414 

New York, NY and NJ C0398 -74.0384 40.67395 

Newport News, VA C0736 -76.4582 36.98522 

Nikishka, AK C4831 -151.314 60.74793 

Oakland, CA C4345 -122.308 37.82152 

Olympia, WA C4718 -122.909 47.06827 

Other Puget Sound, WA C4754 -122.72 48.84099 

Palm Beach, FL C2162 -80.0527 26.76904 

Panama City, FL C2016 -84.1993 30.19009 

Pascagoula, MS C2004 -88.5588 30.34802 

Paulsboro, NJ C5252 -75.2266 39.82689 

Penn Manor, PA C0298 -74.7408 40.13598 

Pensacola, FL C2007 -87.2579 30.40785 

Philadelphia, PA C0552 -75.2022 39.91882 

Plaquemines, LA, Port of C2255 -89.6875 29.48 

Port Angeles, WA C4708 -123.453 48.1305 

Port Arthur, TX C2416 -93.9607 29.83142 

Port Canaveral, FL C2160 -80.6082 28.41409 

Port Dolomite, MI L3627 -84.3128 45.99139 

Port Everglades, FL C2163 -80.1178 26.09339 

Port Hueneme, CA C4150 -119.208 34.14824 

Port Inland, MI L3803 -85.8628 45.95508 

2-57 
Port Name 

US ACE 

Code 

Port Coordinates 

Longitude Latitude 

Port Manatee, FL C2023 -82.5613 27.63376 

Portland, ME C0128 -70.2513 43.64951 

Portland, OR C4644 -122.665 45.47881 

Presque Isle, MI L3845 -87.3852 46.57737 

Providence, RI C0191 -71.3984 41.81178 

Redwood City, CA CCA05 -122.21 37.51306 

Richmond, CA C4350 -122.374 37.92424 

Richmond, VA C0737 -77.4194 37.45701 

Sacramento, CA CCA06 -121.544 38.56167 

San Diego, CA C4100 -117.178 32.70821 

San Francisco, CA C4335 -122.399 37.80667 

Sandusky, OH L3213 -82.7123 41.47022 

Savannah, GA C0776 -81.0954 32.08471 

Searsport, ME C0112 -68.925 44.45285 

Seattle, WA C4722 -122.359 47.58771 

South Louisiana, LA, Port of C2253 -90.6179 30.03345 

St. Clair, MI L3509 -82.4941 42.82663 

Stockton, CA C4270 -121.316 37.9527 

Stoneport, MI L3619 -83.4703 45.28073 

Tacoma, WA C4720 -122.452 47.28966 

Tampa, FL C2021 -82.5224 27.78534 

Texas City, TX C2404 -94.9181 29.36307 

Toledo, OH L3204 -83.5075 41.66294 

Two Harbors, MN L3926 -91.6626 47.00428 

Valdez, AK C4816 -146.346 61.12473 

Vancouver, WA C4636 -122.681 45.62244 

Wilmington, DE C0554 -75.507 39.71589 

Wilmington, NC C0766 -77.954 34.23928 

2-58 
Appendix 2B: Port Methodology and Equations 

Near port emissions for each port are calculated for four modes of operation: 1) hotelling, 2) 

maneuvering, 3) reduced speed zone (RSZ), and 4) cruise. Hotelling, or dwelling, occurs while the 

vessel is docked or anchored near a dock, and only the auxiliary engine(s) are being used to provide 

power to meet the ship’s energy needs. Maneuvering occurs within a very short distance of the 

docks. The RSZ varies from port to port, though generally the RSZ would begin and end when the 

pilots board or disembark, and typically occurs when the near port shipping lanes reach 

unconstrained ocean shipping lanes. The cruise mode emissions in the near ports analysis extend 25 

nautical miles beyond the end of the RSZ lanes for deep water ports and 7 nautical miles for Great 

Lake ports. 

Emissions are calculated separately for propulsion and auxiliary engines. The basic 

equation used is as follows: 

Equation 2B-1 

Emissionsmod e[eng] = (calls)×(P[eng] )×(hrs / call mod e )×(LFmod e[eng] )×(EF[eng ] )×(Adj)×(10−6 tonnes / g) 

Where: 

Emissionsmode [eng] = Metric tonnes emitted by mode and engine type 

Calls = Round-trip visits (i.e., one entrance and one clearance is considered a call) 

P[eng] = Total engine power by engine type, in kilowatts 

hrs/callmode = Hours per call by mode 

LFmode [eng] = Load factor by mode and engine type (unitless) 

EF[eng] = Emission factor by engine type for the pollutant of interest, in g/kW-hr 

(these vary as a function of engine type and fuel used, rather than activity mode) 

Adj = Low load adjustment factor, unitless (used when the load factor is below 0.20) 

10-6 = Conversion factor from grams to metric tonnes 

Main engine load factors are calculated directly from the propeller curve based upon the 

cube of actual speed divided by maximum speed (at 100% maximum continuous rating [MCR]). In 

addition, cruise mode activity is based on cruise distance and speed inputs. The following sections 

provide the specific equations used to calculate propulsion and auxiliary emissions for each activity 

mode. 

Cruise 

Cruise emissions are calculated for both propulsion (main) and auxiliary engines. The basic 

equation used to calculate cruise mode emissions for the main engines is: 

Equation 2B-2 

Emissionscruise[main] = (calls) × (P[main] ) × (hrs / callcruise ) × (LFcruise[main] ) × (EF[main] ) × (10−6 tonnes / g) 

Where: 

Emissionscruise [main] = Metric tonnes emitted from main engines in cruise mode 

Calls = Round-trip visits (i.e., one entrance and one clearance is considered a call) 

2-59 
P[main] = Total main engine power, in kilowatts 

hrs/callcruise = Hours per call for cruise mode 

LFcruise [main] = Load factor for main engines in cruise mode (unitless) 

EF[main] = Emission factor for main engines for the pollutant of interest, in g/kW-hr (these vary 

as a function of engine type and fuel used, rather than activity mode) 

10-6 = Conversion factor from grams to metric tonnes 

In addition, the time in cruise is calculated as follows: 

Equation 2B-3 

Hrs / call = Cruise Dis tance [nmiles ] / Cruise Speed [knots ] × 2 trips / call cruise 

Where: 

Cruise distance = one way distance (25 nautical miles for deep sea ports, and 7 nautical miles 

for Great Lake ports) 

Cruise speed = vessel service speed, in knots 

2 trips/call = Used to calculate round trip cruise distance 

Main engine load factors are calculated directly from the propeller curve based upon the 

cube of actual speed divided by maximum speed (at 100% maximum continuous rating [MCR]): 

Equation 2B-4 

LoadFactor cruise [ main ] = (Cruise Speed [knots ] / Maximum Speed [knots ])

3 

Since cruise speed is estimated at 94 percent of maximum speed38, the load factor for main 

engines at cruise is 0.83. 

Substituting Equation 2B-3 for time in cruise into Equation 2B-2, and using the load factor 

of 0.83, the equation used to calculate cruise mode emissions for the main engines becomes the 

following: 

Equation 2B-5 Cruise Mode Emissions for Main Engines 

Emissionscruise[main] = (calls) ×(P[main]) ×(CruiseDistance/CruiseSpeed) ×(2 trips/call) ×0.83×(EF[main]) ×(10−6 tonne 

Where: 

Emissionscruise [main] = Metric tonnes emitted from main engines in cruise mode 

calls = Round-trip visits (i.e., one entrance and one clearance is considered a call) 

P[main] = Total main engine power, in kilowatts 

Cruise distance = one way distance (25 nautical miles for deep sea ports, and 7 nautical miles 

for Great Lake ports) 

Cruise speed = vessel service speed, in knots 

2 trips/call = Used to calculate round trip cruise distance 

0.83 = Load factor for main engines in cruise mode, unitless 

EF [main] = Emission factor for main engines for the pollutant of interest, in g/kW-hr (these vary 

as a function of engine type and fuel used, rather than activity mode) 

10-6 = Conversion factor from grams to metric tonnes 

2-60 
g

The equation used to calculate cruise mode emissions for the auxiliary engines is: 

Equation 2B-6 Cruise Mode Emissions for Auxiliary Engines 

Emissions cruise [aux ] = (calls ) × (P[aux ]) × (Cruise Distance/Cruise Speed ) × (2trips/call ) × (LFcruise [aux ]) × (EF[aux ]) × (10−6 tonnes / 

Where: 

Emissionscruise[aux] = Metric tonnes emitted from auxiliary engines in cruise mode 

calls = Round-trip visits (i.e., one entrance and one clearance is considered a call) 

P[aux] = Total auxiliary engine power, in kilowatts 

Cruise distance = one way distance (25 nautical miles for deep sea ports, and 7 nautical miles 

for Great Lake ports) 

Cruise speed = vessel service speed, in knots 

2 trips/call = Used to calculate round trip cruise distance 

LFcruise [aux] = Load factor for auxiliary engines in cruise mode, unitless (these vary by ship type 

and activity mode) 

EF[aux] = Emission factor for auxiliary engines for the pollutant of interest, in g/kW-hr (these 

vary as a function of engine type and fuel used, rather than activity mode) 

10-6 = Conversion factor from grams to metric tonnes 

The inputs of calls, cruise distance, and vessel speed are the same for main and auxiliary 

engines. Relative to the main engines, auxiliary engines have separate inputs for engine power, 

load factor, and emission factors. The activity-related inputs, such as engine power, vessel speed, 

and calls, can be unique to each ship calling on a port, if ship-specific information is available. For 

this analysis, these inputs were developed by port for bins that varied by ship type, engine type, and 

dead weight tonnage (DWT) range. 

Reduced Speed Zone 

RSZ emissions are calculated for both propulsion (main) and auxiliary engines. The basic 

equation used to calculate RSZ mode emissions for the main engines is: 

Equation 2B-7 

EmissionsRSZ[main] = (calls)×(P[main])×(hrs/ callRSZ )×(LFRSZ[main])×(EF[main])×(Adj)×(10−6 tonnes/ g) 

Where: 

EmissionsRSZ[main] = Metric tonnes emitted from main engines in RSZ mode 

calls = Round-trip visits (i.e., one entrance and one clearance is considered a call) 

P[main] = Total main engine power, in kilowatts 

hrs/callRSZ = Hours per call for RSZ mode 

LFRSZ [main] = Load factor for main engines in RSZ mode, unitless 

EF[main] = Emission factor for main engines for the pollutant of interest, in g/kW-hr (these vary 

as a function of engine type and fuel used, rather than activity mode) 

Adj = Low load adjustment factor, unitless (used when the load factor is below 0.20) 

10-6 = Conversion factor from grams to metric tonnes 

2-61 
In addition, the time in RSZ mode is calculated as follows: 

Equation 2B-8 

Hrs / call RSZ = RSZ Dis tance [nmiles ] / RSZ Speed [knots ] × 2 trips / call 

Load factor during the RSZ mode is calculated as follows: 

Equation 2B-9 

LoadFactor [ ] = (RSZ Speed / Maximum Speed )

3 

RSZ main 

In addition: 

Equation 2B-10 

Maximum Speed = Cruise Speed / 0.94 

Where: 

0.94 = Fraction of cruise speed to maximum speed 

Substituting Equation 2B-10 into Equation 2B-9, the equation to calculate load factor becomes: 

Equation 2B-11 

LoadFactorRSZ [main] = (RSZ Speed × 0.94 / Cruise Speed )

3 

Where: 

0.94 = Fraction of cruise speed to maximum speed 

Load factors below 2 percent were set to 2 percent as a minimum. 

Substituting Equation 2B-8 for time in mode and Equation 2B-11 for load factor into 

Equation 2B-7 , the expression used to calculate RSZ mode emissions for the main engines 

becomes: 

Equation 2B-12 RSZ Mode Emissions for Main Engines 

Emissions [ aux ] = (calls ) × (P[ ] ) × (RSZ Distance/ RSZ Speed ) × (2 trips /call ) RSZ aux 

3 −6 × (RSZ Speed × 0.94 / Cruise Speed ) × (EF[ aux ] ) × (Adj )× (10 tonnes / g ) 

Where: 

EmissionsRSZ[main] = Metric tonnes emitted from main engines in RSZ mode 

calls = Round-trip visits (i.e., one entrance and one clearance is considered a call) 

P[main] = Total main engine power, in kilowatts 

RSZ distance = one way distance, in nautical miles (specific to each port) 

RSZ speed = speed, in knots (specific to each port) 

2 trips/call = Used to calculate round trip RSZ distance 

Cruise speed = vessel service speed, in knots 

EF[main] = Emission factor for main engines for the pollutant of interest, in g/kW-hr (these vary 

as a function of engine type and fuel used, rather than activity mode) 

Adj = Low load adjustment factor, unitless (used when the load factor is below 0.20) 

2-62 
10-6 = Conversion factor from grams to tons 

0.94 = Fraction of cruise speed to maximum speed 

Emission factors are considered to be relatively constant down to about 20 percent load. 

Below that threshold, emission factors tend to increase significantly as the load decreases. During 

the RSZ mode, load factors can fall below 20 percent. Low load multiplicative adjustment factors 

were developed and applied when the load falls below 20 percent (0.20). If the load factor is 0.20 

or greater, the low load adjustment factor is set to 1.0. 

The equation used to calculate RSZ mode emissions for the auxiliary engines is: 

Equation 2B-13 RSZ Mode Emissions for Auxiliary Engines 

Emissions RSZ [aux] = (calls) × (P[aux] ) × (RSZ Distance/ RSZ Speed ) × (2 trips/call) × (LFRSZ [aux] ) × (EF[aux] ) × (10 −6 tonnes / g) 

Where: 

EmissionsRSZ[aux] = Metric tonnes emitted from auxiliary engines in RSZ mode 

calls = Round-trip visits (i.e., one entrance and one clearance is considered a call) 

P[aux] = Total auxiliary engine power, in kilowatts 

RSZ distance = one way distance, in nautical miles (specific to each port) 

RSZ speed = speed, in knots (specific to each port) 

2 trips/call = Used to calculate round trip cruise distance 

LFRSZ [aux] = Load factor for auxiliary engines in RSZ mode, unitless (these vary by ship type 

and activity mode) 

EF[aux] = Emission factor for auxiliary engines for the pollutant of interest, in g/kW-hr (these 

vary as a function of engine type and fuel used, rather than activity mode) 

10-6 = Conversion factor from grams to metric tonnes 

Unlike main engines, there is no need for a low load adjustment factor for auxiliary engines, 

because of the way they are generally operated. When only low loads are needed, one or more 

engines are shut off, allowing the remaining engines to maintain operation at a more efficient level. 

The inputs of calls, RSZ distance, and RSZ speed are the same for main and auxiliary 

engines. Relative to the main engines, auxiliary engines have separate inputs for engine power, 

load factor, and emission factors. The RSZ distances vary by port rather than vessel or engine type. 

Some RSZ speeds vary by ship type, while others vary by DWT. Mostly, however, RSZ speed is 

constant for all ships entering the harbor area. All Great Lake ports have reduced speed zone 

distances of three nautical miles occurring at halfway between cruise speed and maneuvering speed. 

Maneuvering 

Maneuvering emissions are calculated for both propulsion (main) and auxiliary engines. 

The basic equation used to calculate maneuvering mode emissions for the main engines is: 

Equation 2B-14 

Emissionsman[main] = (calls) ×(P[main]) ×(hrs/ callman ) ×(LFman[main]) ×(EF[main]) ×(Adj) ×(10−6 tonnes/ g) 

Where: 

2-63 
Emissionsman[main] = Metric tonnes emitted from main engines in maneuvering mode 

calls = Round-trip visits (i.e., one entrance and one clearance is considered a call) 

P[main] = Total main engine power, in kilowatts 

hrs/callman = Hours per call for maneuvering mode 

LFman [main] = Load factor for main engines in maneuvering mode, unitless 

EF[main] = Emission factor for main engines for the pollutant of interest, in g/kW-hr (these vary 

as a function of engine type and fuel used, rather than activity mode) 

Adj = Low load adjustment factor, unitless (used when the load factor is below 0.20) 

10-6 = Conversion factor from grams to metric tonnes 

Maneuvering time-in-mode is estimated based on the distance a ship travels from the 

breakwater or port entrance to the pier/wharf/dock (PWD). Maneuvering times also include shifts 

from one PWD to another or from one port within a greater port area to another. Average 

maneuvering speeds vary from 3 to 8 knots depending on direction and ship type. For consistency, 

maneuvering speeds were assumed to be the dead slow setting of approximately 5.8 knots. 

Load factor during maneuvering is calculated as follows: 

Equation 2B-15 

LoadFactor = (Man Speed[knots]/ Maximum Speed[knots])

3 

man[main] 

In addition: 

Equation 2B-16 

Maximum Speed = Cruise Speed[knots]/ 0.94 

Where: 

0.94 = Fraction of cruise speed to maximum speed 

Also, the maneuvering speed is 5.8 knots. Substituting Equation 2B-16 into Equation 2B-15, and 

using a maneuvering speed of 5.8 knots, the equation to calculate load factor becomes: 

Equation 2B-17 

LoadFactor = (5.45/ Cruise Speed )

3 

man[main] 

Load factors below 2 percent were set to 2 percent as a minimum. 

Substituting Equation 2B-17 for load factor into Equation 2B-14, the expression used to 

calculate maneuvering mode emissions for the main engines becomes: 

Equation 2B-18 Maneuvering Mode Emissions for Main Engines 

Emissions man[main ] = (calls) × (P[ main ] ) × (hrs / call man ) × (5.45 / Cruise Speed )

3 × (EF[main ] ) × ( Adj) × (10 −6 tonnes / g) 

Where: 

Emissionsman[main] = Metric tonnes emitted from main engines in maneuvering mode 

calls = Round-trip visits (i.e., one entrance and one clearance is considered a call) 

P[main] = Total main engine power, in kilowatts 

2-64 
hrs/callman = Hours per call for maneuvering mode 

Cruise speed = Vessel service speed, in knots 

EF[main] = Emission factor for main engines for the pollutant of interest, in g/kW-hr (these vary 

as a function of engine type and fuel used, rather than activity mode) 

Adj = Low load adjustment factor, unitless (used when the load factor is below 0.20) 

10-6 = Conversion factor from grams to metric tonnes 

Since the load factor during maneuvering usually falls below 20 percent, low load 

adjustment factors are also applied accordingly. Maneuvering times are not readily available for all 

117 ports. For this analysis, maneuvering times and load factors available for a subset of the ports 

were used to calculate maneuvering emissions for the remaining ports. This is discussed in more 

detail in section 2.3.2.3.8. 

The equation used to calculate maneuvering mode emissions for the auxiliary engines is: 

Equation 2B-19 Maneuvering Mode Emissions for Auxiliary Engines 

Emissions man[aux ] = (calls) × (P[aux ] ) × (hrs / call man ) × (LFman[aux ] ) × (EF[aux ] ) × (10 −6 tonnes / g) 

Where: 

Emissionsman[aux] = Metric tonnes emitted from auxiliary engines in maneuvering mode 

calls = Round-trip visits (i.e., one entrance and one clearance is considered a call) 

P[aux] = Total auxiliary engine power, in kilowatts 

hrs/callman = Hours per call for maneuvering mode 

LFman [aux] = Load factor for auxiliary engines in maneuvering mode, unitless (these vary by ship 

type and activity mode) 

EF[aux] = Emission factor for auxiliary engines for the pollutant of interest, in g/kW-hr (these 

vary as a function of engine type and fuel used, rather than activity mode) 

10-6 = Conversion factor from grams to metric tonnes 

Low load adjustment factors are not applied for auxiliary engines. 

Hotelling 

Hotelling emissions are calculated for auxiliary engines only, as main engines are not 

operational during this mode. The equation used to calculate hotelling mode emissions for the 

auxiliary engines is: 

Equation 2B-20 Hotelling Mode Emissions for Auxiliary Engines 

Emissions hotel[aux] = (calls) × (P[aux] ) × (hrs / callhotel ) × (LFhotel[aux] ) × (EF[aux] ) × (10 −6 tonnes / g) 

Where: 

Emissionshotel[aux] = Metric tonnes emitted from auxiliary engines in hotelling mode 

calls = Round-trip visits (i.e., one entrance and one clearance is considered a call) 

P[aux] = Total auxiliary engine power, in kilowatts 

hrs/callhotel = Hours per call for hotelling mode 

LFhotel [aux] = Load factor for auxiliary engines in hotelling mode, unitless (these vary by ship 

type and activity mode) 

2-65 
EF[aux] = Emission factor for auxiliary engines for the pollutant of interest, in g/kW-hr (these 

vary as a function of engine type and fuel used, rather than activity mode) 

10-6 = Conversion factor from grams to metric tonnes 

Hotelling times are not readily available for all 117 ports. For this analysis, hotelling times 

available for a subset of the ports were used to calculate hotelling emissions for the remaining ports. 

2-66 
 

 

Appendix 2C: Port Reduced Speed Zone (RSZ) Information 

Table 2C-1 Port RSZ Information 

Port Name 

RSZ 

Speed 

(knts) 

RSZ 

distance 

(naut mi) 

Final RSZ End Point(s) 

Longitude Latitude 

Albany, NY c 142.5 -73.8929 40.47993 

Alpena, MI e 3 -83.2037 44.99298 

Anacortes, WA a 108.3 -124.771 48.49074 

Anchorage, AK 14.5 143.6 -152.309 59.5608 

Ashtabula, OH e 3 -80.8097 42.08549 

Baltimore, MD c 157.1 -75.8067 36.8468 

Barbers Point, Oahu, HI 10 5.1 -158.132 21.21756 

-89.4248 28.91161 

Baton Rouge, LA 10 219.8 -89.137 28.98883 

Beaumont, TX 7 53.5 -93.7552 29.55417 

Boston, MA 10 14.3 -70.7832 42.37881 

Bridgeport, CT 10 2 -73.1863 41.13906 

Brownsville, TX 8.8 18.7 -97.0921 26.06129 

-80.9345 31.29955 

Brunswick, GA 13 38.8 -81.1357 30.68935 

Buffalo, NY e 3 -79.0996 42.81683 

Burns Waterway Harbor, IN e 3 -87.1032 41.80625 

Calcite, MI e 3 -83.5383 45.39496 

Camden-Gloucester, NJ c 94 -75.0095 38.79004 

Carquinez, CA 12 39 -122.632 37.76094 

Catalina, CA 12 11.9 -118.465 33.63641 

Charleston, SC 12 17.3 -79.6452 32.62557 

Chester, PA c 78.2 -75.0095 38.79004 

Chicago, IL e 3 -87.4141 41.86971 

Cleveland, OH e 3 -81.765 41.63079 

Conneaut, OH e 3 -80.5639 42.13361 

Coos Bay, OR 6.5 13 -124.359 43.35977 

Corpus Christi, TX d 30.1 -96.8753 27.74433 

Detroit, MI e 3 -83.1384 42.10308 

Duluth-Superior, MN and WI e 3 -91.8536 46.78916 

-118.926 33.91252 

El Segundo, CA 12 23.3 -118.465 33.63641 

Erie, PA e 3 -80.115 42.3151 

Escanaba, MI e 3 -86.9224 45.58297 

Eureka, CA 12 9 -124.347 40.75925 

Everett, WA a 123.3 -124.771 48.49074 

Fairport Harbor, OH e 3 -81.3917 41.91401 

Fall River, MA 9 22.7 -71.3334 41.41708 

Freeport, TX c 2.6 -95.2949 28.93323 

Galveston, TX c 9.3 -94.6611 29.3247 

Gary, IN e 3 -87.2824 41.77658 

Georgetown, SC 12 17.6 -79.0779 33.1924 

2-67 
 

Port Name 

RSZ 

Speed 

(knts) 

RSZ 

distance 

(naut mi) 

Final RSZ End Point(s) 

Longitude Latitude 

Grays Harbor, WA a 4.9 -124.24 46.89509 

Gulfport, MS 10 17.4 -88.9263 30.11401 

Hilo, HI 10 7.1 -154.985 19.76978 

-157.956 21.17658 

Honolulu, HI 10 10 -157.785 21.23827 

Hopewell, VA 10 91.8 -75.8067 36.8468 

Houston, TX c 49.6 -94.6611 29.3247 

Indiana Harbor, IN e 3 -87.4007 41.8401 

Jacksonville, FL 10 18.6 -81.3649 30.39769 

Kahului, Maui, HI 10 7.5 -156.44 21.01066 

Kalama, WA b 68.2 -124.137 46.22011 

Lake Charles, LA 6 38 -93.3389 29.73094 

-118.465 33.63641 

Long Beach, CA 12 18.1 -118.13 33.45211 

Longview, WA b 67.3 -124.137 46.22011 

Lorain, OH e 3 -82.2701 41.64023 

-118.465 33.63641 

Los Angeles, CA 12 20.6 -118.13 33.45211 

Manistee, MI e 3 -86.3819 44.41573 

Marblehead, OH e 3 -82.7293 41.69638 

Marcus Hook, PA c 94.7 -75.0095 38.79004 

Matagorda Ship Channel, TX 7.3 24 -96.2287 28.33472 

Miami, FL 12 3.8 -80.1201 25.75787 

Milwaukee, WI e 3 -87.6718 42.97343 

Mobile, AL 11 36.1 -88.0644 30.1457 

Morehead City, NC 10 2.2 -76.6679 34.68999 

Muskegon, MI e 3 -86.5377 43.29151 

Nawiliwili, Kauai, HI 10 7.3 -159.266 21.87705 

New Bedford, MA 9 22.4 -71.1013 41.38499 

New Castle, DE c 60.5 -75.0095 38.79004 

New Haven, CT 10 2.1 -72.9121 41.26588 

-89.4248 28.91161 

New Orleans, LA 10 104.2 -89.137 28.98883 

New York, NY and NJ c 15.7 -73.8929 40.47993 

Newport News, VA 14 24.3 -75.8067 36.8468 

Nikishka, AK 14.5 90.7 -152.309 59.5608 

Oakland, CA 12 18.4 -122.632 37.76094 

Olympia, WA a 185.9 -124.771 48.49074 

Other Puget Sound, WA a 106 -124.771 48.49074 

Palm Beach, FL 3 3.1 -79.9973 26.77129 

Panama City, FL 10 10 -84.1797 30.0818 

Pascagoula, MS 10 17.5 -88.4804 30.09597 

Paulsboro, NJ c 83.5 -75.0095 38.79004 

Penn Manor, PA c 114.5 -75.0095 38.79004 

Pensacola, FL 12 12.7 -87.298 30.27777 

Philadelphia, PA c 88.1 -75.0095 38.79004 

2-68 
 

 

 

 

Port Name 

RSZ 

Speed 

(knts) 

RSZ 

distance 

(naut mi) 

Final RSZ End Point(s) 

Longitude Latitude 

-89.4248 28.91161 

Plaquemines, LA, Port of 10 52.4 -89.137 28.98883 

Port Angeles, WA a 65 -124.771 48.49074 

Port Arthur, TX 7 21 -93.7552 29.55417 

Port Canaveral, FL 10 4.4 -80.5328 28.41439 

Port Dolomite, MI e 3 -84.2445 45.83181 

Port Everglades, FL 7.5 2.1 -80.082 26.08627 

Port Hueneme, CA 12 2.8 -119.238 34.10859 

Port Inland, MI e 3 -85.6524 45.87553 

Port Manatee, FL 9 27.4 -83.0364 27.59078 

Portland, ME 10 11.4 -70.1077 43.54224 

Portland, OR b 105.1 -124.137 46.22011 

Presque Isle, MI e 3 -87.082 46.5804 

Providence, RI 9 24.9 -71.3334 41.41708 

Redwood City, CA 12 36 -122.632 37.76094 

Richmond, CA 12 22.6 -122.632 37.76094 

Richmond, VA 10 106.4 -75.8067 36.8468 

Sacramento, CA 12 90.5 -122.632 37.76094 

San Diego, CA 12 11.7 -117.315 32.62184 

San Francisco, CA 12 14.4 -122.632 37.76094 

Sandusky, OH e 3 -82.5251 41.56193 

Savannah, GA 13 45.5 -78.0498 33.83598 

Searsport, ME 9 22.2 -68.7645 44.1179 

Seattle, WA a 133.3 -124.771 48.49074 

-89.4248 28.91161 

South Louisiana, LA, Port of 10 142.8 -89.137 28.98883 

St. Clair, MI e 3 -82.5838 42.55923 

Stockton, CA 12 86.9 -122.632 37.76094 

Stoneport, MI e 3 -83.2355 45.25919 

Tacoma, WA a 150.5 -124.771 48.49074 

Tampa, FL 9 30 -83.0364 27.59078 

Texas City, TX c 15.1 -94.6611 29.3247 

Toledo, OH e 3 -83.3034 41.7323 

Two Harbors, MN e 3 -91.4414 46.93391 

Valdez, AK 10 27.2 -146.881 60.86513 

Vancouver, WA b 95.7 -124.137 46.22011 

Wilmington, DE c 65.3 -75.0095 38.79004 

Wilmington, NC 10 27.6 -80.325 31.84669 a Cruise speed through Strait of Juan de Fuca, then varies by ship type for remaining journey 

b

 Inbound on Columbia River at 6.5 knots, outbound at 12 knots 

c

 Speed varies by ship type similar to typical like port d Speed varies by ship DWTs 

e

 All Great Lake ports have reduced speed zone distances of 3 nautical miles with speeds halfway 

between service speed and maneuvering speed. 

2-69 
 

 

 

Appendix 2D: Use of Detailed Typical Port Data for Other Inputs 

There is currently not enough information to readily calculate time-in-mode (hours/call) for 

all 117 ports during the maneuvering and hotelling modes of operation. As a result, it was 

necessary to review and select available detailed emission inventories that have been estimated for 

selected ports to date. These ports are referred to as typical ports. The typical port information for 

maneuvering and hotelling time-in-mode (as well as maneuvering load factors for the propulsion 

engines) was then used for the typical ports and also assigned to the other modeled ports. A 

modeled port is the port in which emissions are to be estimated. The methodology that was used to 

select the typical ports and match these ports to the other modeled ports is briefly described in this 

appendix, and more fully described in the ICF documentation.39 

2.6.1 Selection of Typical Ports 

In 1999, the U.S. Government published two guidance documents40,41 to calculate marine 

vessel activity at ports. These documents contained detailed port inventories of eight deep sea 

ports, two Great Lake ports and two inland river ports. The detailed inventories were developed by 

obtaining ship call data from Marine Exchanges/Port Authorities (MEPA) at the various ports for 

1996 and matching the various ship calls to data from Lloyds Maritime Information Services to 

provide ship characteristics. The ports for which detailed inventories were developed are shown in 

Table 2D-1 for deep sea ports and Table 2D-2 for Great Lake ports along with the level of detail of 

shifts for each port. Most ports provided the ship name, Lloyd’s number, the vessel type, the date 

and time the vessel entered and left the port, and the vessel flag. Inland river ports were developed 

from US Army Corps of Engineers (USACE) Waterborne Commerce Statistics Center data. 

Table 2D-1 Deep Sea MEPA Vessel Movement and Shifting Details 

MEPA Area and Ports MEPA Data Includes 

Lower Mississippi River 

including the ports of New 

Orleans, South Louisiana, 

Plaquemines, and Baton Rouge 

Information on the first and last pier/wharf/dock (PWD) for the 

vessel (gives information for at most one shift per vessel). No 

information on intermediate PWDs, the time of arrival at the first 

destination PWD, or the time of departure from the River. 

Consolidated Port of New York 

and New Jersey and other ports 

on the Hudson and Elizabeth 

Rivers 

All PWDs or anchorages for shifting are named. Shifting arrival 

and departure times are not given. Hotelling time is based upon the 

entrance and clearance times and dates, subtracting out 

maneuvering times. Maneuvering times were calculated based 

upon the distance the ship traveled at a given maneuvering speed. 

Delaware River Ports including 

the ports of Philadelphia, 

Camden, Wilmington and others 

All PWDs or anchorages for shifting are named. Shifting arrival 

and departure times are not given. Hotelling time is based upon the 

entrance and clearance times and dates, subtracting out 

maneuvering times. Maneuvering times were calculated based 

upon the distance the ship traveled at a given maneuvering speed. 

Puget Sound Area Ports including 

the ports of Seattle, Tacoma, 

Olympia, Bellingham, Anacortes, 

and Grays Harbor 

All PWDs or anchorages for shifting are named. Arrival and 

departure dates and times are noted for all movements, allowing 

calculation of maneuvering and hotelling both for individual shifts 

and the overall call on port. 

The Port of Corpus Christi, TX Only has information on destination PWD and date and time in 

and out of the port area. No shifting details. 

2-70 
 

MEPA Area and Ports MEPA Data Includes 

The Port of Coos Bay, OR Only has information on destination PWD and date and time in 

and out of the port area. No shifting details. 

Patapsco River Ports including 

the port of Baltimore Harbor, MD 

All PWDs or anchorages for shifting are named. Shifting arrival 

and departure times are not given. Hotelling time is based upon the 

entrance and clearance times and dates, subtracting out 

maneuvering times. Maneuvering times were calculated based 

upon the distance the ship traveled at a given maneuvering speed. 

The Port of Tampa, FL 

All PWDs or anchorages for shifting are named. Arrival and 

departure dates and times are noted for all movements, allowing 

calculation of maneuvering and hotelling both for individual shifts 

and the overall call. 

Table 2D-2 Great Lake MEPA movements and shifts 

MEPA Area and Ports MEPA Data Includes 

Port of Cleveland, OH 

Information on the first and last PWD for the vessel (gives 

information for at most one shift per vessel). No information on 

intermediate PWDs.. 

Port of Burns Harbor, IN No shifting details, No PWDs listed.. 

Since 1999, several new detailed emissions inventories have been developed and were 

reviewed for use as additional or replacement typical ports: These included: 

• Port of Los Angeles38,42 

• Puget Sound Ports43 

• Port of New York/New Jersey44 

• Port of Houston/Galveston45 

• Port of Beaumont/Port Arthur46 

• Port of Corpus Christi47 

• Port of Portland48 

• Ports of Cleveland, OH and Duluth-Superior, MN&WI49 

Based on the review of these newer studies, some of the previous typical ports were replaced 

with newer data and an additional typical port was added. Data developed for Cleveland and 

Duluth-Superior for LADCO was used in lieu of the previous typical port data for Cleveland and 

Burns Harbor because it provided more detailed information and better engine category definitions. 

The Port of Houston/Galveston inventory provided enough data to add an additional typical port. 

All three port inventories were adjusted to reflect the current methodology used in this study. 

The information provided in the current inventory for Puget Sound Ports43 was used to 

calculate RSZ speeds, load factors, and times for all Puget Sound ports. As described in Section 

2-71 
 

2.6.3.2, an additional modeled port was also added to account for the considerable amount of Jones 

Act tanker ship activity in the Puget Sound area that is not contained in the original inventory. 

The newer Port of New York/New Jersey inventory provided a check against estimates 

made using the 1996 data. All other new inventory information was found to lack sufficient detail 

to prepare the detailed typical port inventories needed for this project. 

The final list of nine deep sea and two Great Lake typical ports used in this analysis and their 

data year is as follows: 

• Lower Mississippi River Ports [1996] 

• Consolidated Ports of New York and New Jersey and Hudson River [1996] 

• Delaware River Ports [1996] 

• Puget Sound Area Ports [1996] 

• Corpus Christi, TX [1996] 

• Houston/Galveston Area Ports [1997] 

• Ports on the Patapsco River [1996] 

• Port of Coos Bay, OR [1996] 

• Port of Tampa, FL [1996] 

• Port of Cleveland, OH on Lake Erie [2005] 

• Duluth-Superior, MN & WI on Lake Michigan [2005] 

The maneuvering and hotelling time-in-modes, as well as the maneuvering load factors for 

these typical ports, were binned by ship type, engine type, and DWT type, using the same bins 

described in the section entitled “Bins by Ship Type, Engine Type, and DWT Range.” 

2.6.2 Matching Typical Ports to Modeled Ports 

The next step in the process was to match the ports to be modeled with the typical port 

which was most like it. Three criteria were used for matching a given port to a typical port: 

regional differencesD, maximum vessel draft, and the ship types that call on a specific port. One 

container port, for instance, may have much smaller bulk cargo and reefer ships number of calls on 

that port than another. Using these three criteria and the eleven typical ports that are suitable for 

port matching, the 89 deep sea ports and 28 Great Lake ports were matched to the typical ports. For 

a typical port, the modeled and typical port is the same (i.e., the port simply represents itself). For 

California ports, we used data provided by ARB as discussed in Section 2.6.3. The matched ports 

for the deep sea ports are provided in Table 2D-3. 

D The region in which a port was located was used to group top ports as it was considered a primary influence on the 

characteristics (size and installed power) of the vessels calling at those ports. 

2-72 
 

 

 

 

 

 

 

 

 

Table 2D-3 Matched Ports for the Deep Sea Ports 

Modeled Port Name Typical Like Port

 Anacortes, WA Puget Sound 

 Barbers Point, HI Puget Sound 

 Everett, WA Puget Sound 

 Grays Harbor, WA Puget Sound 

Honolulu, HI Puget Sound 

Kalama, WA Puget Sound 

Longview, WA Puget Sound 

 Olympia, WA Puget Sound 

 Port Angeles, WA Puget Sound 

 Portland, OR Puget Sound 

Seattle, WA Puget Sound 

Tacoma, WA Puget Sound 

Vancouver, WA Puget Sound 

Valdez, AK Puget Sound 

 Other Puget Sound Puget Sound 

 Anchorage, AK Coos Bay

 Coos Bay, OR Coos Bay

 Hilo, HI Coos Bay

 Kahului, HI Coos Bay

 Nawiliwili, HI Coos Bay

 Nikishka, AK Coos Bay

 Beaumont, TX Houston 

 Freeport, TX Houston 

Galveston, TX Houston 

 Houston, TX Houston 

 Port Arthur, TX Houston 

 Texas City, TX Houston 

 Corpus Christi, TX Corpus Christi 

 Lake Charles, LA Corpus Christi 

 Mobile, AL Corpus Christi 

 Brownsville, TX Tampa 

 Gulfport, MS Tampa 

 Manatee, FL Tampa

 Matagorda Ship Tampa 

 Panama City, FL Tampa

 Pascagoula, MS Tampa 

 Pensacola, FL Tampa 

 Tampa, FL Tampa 

 Everglades, FL Tampa 

 New Orleans, LA Lower Mississippi 

 Baton Rouge, LA Lower Mississippi 

2-73 
 

 

 

 

 

 

 

 

 

Modeled Port Name Typical Like Port

 South Louisiana, LA Lower Mississippi 

 Plaquemines, LA Lower Mississippi 

 Albany, NY New York/New Jersey

 New York/New Jersey New York/New Jersey

 Portland, ME New York/New Jersey

 Georgetown, SC Delaware River 

 Hopewell, VA Delaware River 

Marcus Hook, PA Delaware River 

 Morehead City, NC Delaware River 

Paulsboro, NJ Delaware River 

 Chester, PA Delaware River 

 Fall River, MA Delaware River 

New Castle, DE Delaware River 

 Penn Manor, PA Delaware River 

Providence, RI Delaware River 

Brunswick, GA Delaware River 

 Canaveral, FL Delaware River 

Charleston, SC Delaware River 

 New Haven, CT Delaware River 

 Palm Beach, FL Delaware River 

 Bridgeport, CT Delaware River 

Camden, NJ Delaware River 

 Philadelphia, PA Delaware River 

Wilmington, DE Delaware River 

Wilmington, NC Delaware River 

Richmond, VA Delaware River 

 Jacksonville, FL Delaware River 

 Miami, FL Delaware River 

 Searsport, ME Delaware River 

 Boston, MA Delaware River 

 New Bedford/Fairhaven, MA Delaware River 

 Baltimore, MD Patapsco River 

 Newport News, VA Patapsco River 

 Savannah, GA Patapsco River 

 Catalina, CA ARB Supplied 

 Carquinez, CA ARB Supplied 

El Segundo, CA ARB Supplied 

 Eureka, CA ARB Supplied 

 Hueneme, CA ARB Supplied 

 Long Beach, CA ARB Supplied 

Los Angeles, CA ARB Supplied 

 Oakland, CA ARB Supplied 

2-74 
 

 

 

 

 

 

 

 

 

 

Modeled Port Name Typical Like Port

 Redwood City, CA ARB Supplied 

Richmond, CA ARB Supplied 

 Sacramento, CA ARB Supplied 

 San Diego, CA ARB Supplied 

 San Francisco, CA ARB Supplied 

 Stockton, CA ARB Supplied 

Great Lake ports were matched to either Cleveland or Duluth as shown in Table 2D-4. 

Table 2D-4 Great Lake Match Ports 

Port Name Typical Like Port 

Alpena, MI Cleveland 

 Buffalo, NY Cleveland 

 Burns Waterway, IN Cleveland 

 Calcite, MI Cleveland 

 Cleveland, OH Cleveland 

 Dolomite, MI Cleveland 

 Erie, PA Cleveland 

 Escanaba, MI Cleveland 

Fairport, OH Cleveland 

 Gary, IN Cleveland 

 Lorain, OH Cleveland 

 Marblehead, OH Cleveland 

 Milwaukee, WI Cleveland 

Muskegon, MI Cleveland 

 Presque Isle, MI Cleveland 

 St Clair, MI Cleveland 

Stoneport, MI Cleveland 

 Two Harbors, MN Cleveland 

Ashtabula, OH Duluth-Superior

 Chicago, IL Duluth-Superior

 Conneaut, OH Duluth-Superior

 Detroit, MI Duluth-Superior

 Duluth-Superior, MN&WI Duluth-Superior

 Indiana, IN Duluth-Superior

 Inland Harbor, MI Duluth-Superior

 Manistee, MI Duluth-Superior

 Sandusky, OH Duluth-Superior

 Toledo, OH Duluth-Superior 

2-75 
Once a modeled port was matched to a typical port, the maneuvering and hotelling time-inmode values, as well as the maneuvering load factors by bin for the typical ports, were used directly 

for the modeled ports, with no adjustments. 

2.6.2.1 Bin Mismatches 

In some cases, the specific DWT range bin at the modeled port was not in the typical like 

port data. In those cases, the next nearest DWT range bin was used for the calculations. In a few 

cases, the engine type for a given ship type might not be in the typical like port data. In these cases, 

the closest engine type at the typical like port was used. Also in a few cases, a specific ship type in 

the modeled port data was not in the typical like port data. In this case, the nearest like ship type at 

the typical port was chosen to calculate emissions at the modeled port. 

2.6.3 Stand Alone Ports 

In a few cases, the USACE entrances and clearances data was not used to calculate 

emissions at the modeled port. These include the California ports for which we received data from 

ARB, the Port of Valdez, Alaska, and a conglomerate port within the Puget Sound area, as 

described below. 

2.6.3.1 California Ports 

The California Air Resources Board (ARB) supplied inventories for 14 California ports for 

2002. The data received from ARB for the California ports were modified to provide consistent PM 

and SO2 emissions to those calculated in this report. In addition, cruise and RSZ emissions were 

calculated directly based upon average ship power provided in the ARB methodology document50 

and number of calls, because ARB did not calculate cruise emissions, and transit (RSZ) emissions 

were allocated to counties instead of ports. ARB provided transit distances for each port to 

calculate the RSZ emissions. Ship propulsion and auxiliary engine power were calculated based 

upon the methodology previously described for use in computing cruise and RSZ emissions. For 

maneuvering and hotelling emissions, the ARB values were used and adjusted as discussed below. 

The data supplied by ARB included domestic traffic as well as foreign cargo traffic. 

For PM emission calculations, ARB used an emission factor of 1.5 g/kWh to calculate total 

PM emissions and factors of 0.96 and 0.937 to convert total PM to PM10 and PM2.5 respectively. 

Since an emission factor of 1.4 g/kWh was used in our calculations for PM10 and an emission factor 

of 1.3 g/kWh for PM2.5, ARB PM10 and PM2.5 emissions were multiplied by factors of 0.972 and 

0.925, respectively to get consistent PM10 and PM2.5 emissions for propulsion engines. 

For auxiliary engines, ARB used the same emission factors as above, while we used PM10 

and PM2.5 emission factors of 1.3 and 1.2 g/kWh, respectively for passenger ships and 1.1 and 1.0 

g/kWh, respectively for all other ships. In the ARB inventory, all passenger ships are treated as 

electric drive and all emissions are allocated to auxiliary engines. ARB auxiliary engine emissions 

were thus multiplied by factors of 0.903 and 0.854 respectively for passenger ships and 0.764 and 

0.711 respectively for other ships to provide consistent PM emission calculations. 

2-76 
 

 

 SO2 emissions were also different between the ARB and these analyses. ARB used a 

compositeE

 propulsion engine SO2 emission factor of 10.55 g/kWh while we used a composite SO2 

emission factor of 9.57 g/kWh. Thus, ARB SO2 propulsion emissions were multiplied by a factor 

of 0.907 to be consistent with our emission calculations. For auxiliary engines, ARB used SO2 

emission factors of 11.48 and 9.34 g/kWh, respectively for passenger and other ships, while we use 

emission factors of 9.93 and 9.07 g/kWh, respectively. Thus, ARB auxiliary SO2 emissions were 

multiplied by factors of 0.865 and 0.971, respectively for passenger and other ships to provide 

consistent SO2 emissions. 

2.6.3.2 Port in Puget Sound 

In the newest Puget Sound inventory43, it was found that a considerable amount of tanker 

ships stop at Cherry Point, Ferndale, March Point and other areas which are not within the top 89 

U.S. deep sea ports analyzed in this analysis. In addition, since they are ships carrying U.S. cargo 

(oil from Alaska) from one U.S. port to another, they are not documented in the USACE entrances 

and clearances data. To compensate for this anomaly, an additional port was added which 

encompassed these tanker ships stopping within the Puget Sound area but not at one of the Puget 

Sound ports analyzed in this analysis. Ship calls in the 1996 typical port data to ports other than 

those in the top 89 U.S. deep sea ports were analyzed separately. There were 363 ship calls by 

tankers to those areas in 1996. In the inventory report for 2005, there were 468 calls. For 2002, it 

was estimated there were 432 calls. The same ship types and ship characteristics were used as in 

the 1996 data, but the number of calls was proportionally increased to 432 calls to represent these 

ships. The location of the “Other Puget Sound” port was approximately at Cherry Point near 

Aberdeen. 

2.6.3.3 Port of Valdez 

In a recent Alaska port inventory,51 it was found that significant Category 3 domestic tanker 

traffic enters and leaves the Port of Valdez on destination to West Coast ports. Since the USACE 

entrances and clearances data did not contain any tanker calls at Valdez in 2002, the recent Alaska 

inventory data was used to calculate emissions at that port. In this case, the number of calls and 

ship characteristics for 2002 were taken directly from the Alaska inventory and used in determining 

emissions for the modeled port with the Puget Sound area typical port being used as the like port. 

E

 Based upon ARB assuming 95 percent of the engines were SSD and 5 percent were MSD. The composite SO2 EF of 

9.57 g/kW-hr was calculated using this weighting, along with the SSD and MSD SO2 EFs for the West Coast ports 

reported in Table 2-4. 

2-77 
Appendix 2E: Emission Inputs to STEEM 

The STEEM waterway network model relies on a number of inputs to identify the 

movements for each vessel, individual ship attributes, and related emission factor information. 

Each of these databases is described separately below. 

2.6.4 Shipping Movements 

The shipping activity and routes database provides information on vessel movements or 

trips. It is developed using port entrances and clearances information from the USACE report for 

the U.S. and the Lloyd’s Maritime Intelligence Unit (LMIU) for Canada and Mexico.52 These 

sources contain information for each vessel carrying foreign cargo at each major port or waterway 

that, most importantly for this analysis, includes: 

• Vessel name 

• Last port of call (entrance record) or next port of call (clearance record) 

The database then establishes unique identification numbers for each ship, each port pair, 

and each resulting trip. 

2.6.5 Ship Attributes 

The ship attributes data set contains the important characteristics of each ship that are 

necessary for the STEEM interport model to calculate the emissions associated with each trip. The 

information in this data set is matched to each previously assigned ship identification number. The 

following information comes from the USACE entrances and clearances report for each ship 

identification number: 

• Ship type 

• Gross registered tonnage (GRT) 

• Net registered tonnage (NRT) 

The ship attributes data set contains the following information from Lloyd’s RegisterFairplay for each ship identification number. 

• Main propulsion engine installed power (horsepower) 

• Service speed (cruise speed) 

• Ship size (length, wide, and draft) 

Sometimes data was lacking from the above references for ship speed. In these instances, 

the missing information was developed for each of nine vessel types and the appropriate value was 

applied to each individual ship of that type. Specifically, the missing ship speeds for each ship 

category were obtained from the average speeds used in a Lloyd’s Register study of the Baltic Sea 

and from an Entec UK Limited study for the European Commission.53,54 The resulting vessel 

cruise speeds for ships with missing data are shown in Table 2E-1. 

2-78 
Table 2E-1 Average Vessel Cruise Speed by Ship Type a 

Ship Type Average Cruise Speed (knots) 

Bulk Carrier 14.1 

Container Ship 19.9 

General Cargo 12.3 

Passenger Ship 22.4 

Refrigerated Cargo 16.4 

Roll On-Roll Off 16.9 

Tanker 13.2 

Fishing 11.7 

Miscellaneous 12.7 a Used only when ship specific data were missing from the 

commercial database references. 

The average speed during maneuvering is approximately 60 percent of a ship’s cruise speed 

based on using the propeller law described earlier and the engine load factor for maneuvering that is 

presented later in this section. 

As with vessel cruise speed, main engine installed power was sometimes lacking in the 

Lloyd’s Register-Fairplay data set. Here again, the missing information was developed for nine 

different vessel types and the appropriate value was applied to each individual ship of that type 

when the data were lacking. In this case, the missing main engine horsepower was estimated by 

regressing the relationships between GRT and NRT, and between installed power and GRT for each 

category. This operation is performed internally in the model and the result applied to each 

individual ship, as appropriate. 

The ship attributes database also contains information on the installed power of engines used 

for auxiliary purposes. However, this information is usually lacking in the Lloyds data set, so an 

alternative technique was employed to estimate the required values. In short, the STEEM model 

uses a ratio of main engine horsepower to auxiliary engine horsepower that was determined for 

eight different vessel types using information primarily from ICF International.55 (The ICF report 

attributed these power values to a study for the Port of Los Angeles by Starcrest Consulting.38) The 

auxiliary engine power for each individual vessel of a given ship type is then estimated by 

multiplying the appropriate main power to auxiliary power ratio and the main engine horsepower 

rating for that individual ship. The main and auxiliary power values and the resulting auxiliary 

engine to main engine ratios are shown in Table 2E-2. 

2-79 
 

 

Table 2E-2 Auxiliary Engine Power Ratios 

Vessel Type 

Average Main 

Engine Power 

(kW) 

Average Auxiliary 

Engine Power (kW) 

Auxiliary to Main 

Engine Power 

Ratio 

Bulk Carrier 7,954 1,169 0.147 

Container Ship 30,885 5,746 0.186 

General Cargo 9,331 1,777 0.190 

Passenger Ship 39,563 39,563 a 1.000 

Refrigerated Cargo 9,567 3,900 b 0.136 

Roll On-Roll Off 10,696 c 2,156 c 0.202 

Tanker 9,409 1,985 0.211 

Miscellaneous 6,252 1,680 0.269 a The ICF reference reported a value of 11,000 for auxiliary engines used on passenger 

vessels.55 

b The STEEM used auxiliary engine power as reported in the ARB methodology document.50 

c

 The STEEM purportedly used values for Roll On-Roll Off main and auxiliary engines that 

represent a trip weighted average of the Auto Carrier and Cruise Ship power values from the 

ICF reference. 

Finally, the ship attributes database provides information on the load factors for main 

engines during cruise and maneuvering operation, in addition to load factors for auxiliary marine 

engines. Main engine load factors for cruise operation were taken from a study of international 

shipping for all ship types, except passenger vessels.56 For this analysis, the STEEM model used a 

propulsion engine load factor for passenger ship engines at cruise speed of 55 percent of the total 

installed power. This is based on engine manufacturer data contained in two global shipping 

studies.56,57 During maneuvering, it was assumed that all main engines, including those for 

passenger ships, operate at 20 percent of the installed power. This is consistent with a study done 

by Entec UK for the European Commission. The main engine load factors at cruise speed by ship 

type are shown in Table 2E-3. 

Auxiliary engine load factors, except for passenger ships, were obtained from the ICF 

International study referenced above. These values are also shown in Table 2E-. For cruise mode, 

neither port nor interport portions of the inventory were adjusted for low load operation, as the low 

load adjustments are only applied to propulsion engines with load factors below 20%. 

Table 2E-3 Main and Auxiliary Engine Load Factors at Cruise Speed by Ship Type 

Ship Type 

Average Main Engine 

Load Factor (%) 

Average Auxiliary Engine 

Load Factor (%) 

Bulk Carrier 75 17 

Container Ship 80 13 

General Cargo 80 17 

Passenger Ship 55 25 

Refrigerated Cargo 80 20 

Roll On-Roll Off 80 15 

Tanker 75 13 

Miscellaneous 70 17 

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2.6.6 Emission Factor Information 

The emission factor data set contains emission rates for the various pollutants in terms of 

grams of pollutant per kilowatt-hour (g/kW-hr). The main engine emission factors are shown in 

Table 2E-4. The speed specific factors for NOX, HC, and SO2 were taken from several recent 

analyses of ship emissions in the U.S., Canada, and Europe.50,

55,56, 58 The PM factor was based 

on discussions with the California Air Resources Board (ARB) staff. The fuel specific CO emissio n 

factor was taken from a report by ENVIRON International. 59 The STEEM study used the composite 

emission factors shown in the table because the voyage data used in the model do not explicitly 

identify main engine speed ratings, i.e., slow or medium, or the auxiliary engine fuel type, i.e., 

marine distillate or residual marine. The composite factor for each pollutant is determined by 

weighting individual emission factors by vessel engine population data from a 2005 survey of 

ocean-going vessels that was performed by ARB.60 

Table 2E-4 Main Engine Emission Factors by Ship and Fuel Type 

Engine Type 

Main Engine Emission Factors (g/kW-hr) 

Fuel Type NOX PM10 PM2.5 a HC CO SO2 

Slow Speed 

Residual 

Marine 18.1 1.5 1.4 0.6 1.4 10.5 

Medium Speed 

Residual 

Marine 14 1.5 1.4 0.5 1.1 11.5 

Composite EF 

Residual 

Marine 17.9 1.5 1.4 0.6 1.4 10.6 a Estimated from PM10 using a multiplicative adjustment factor of 0.92. 

The emission factors for auxiliary engines are shown in Table 2E-5. The fuel specific main 

emission factors for NOX and HC were taken from several recent analyses of ship emissions in the 

U.S., Canada, and Europe, as referenced above for the main engine load factors. The PM factor for 

marine distillate was taken from a report by ENVIRON International, which was also referenced 

above. The PM factor for residual marine was based on discussions with the California Air 

Resources Board (ARB) staff. The CO factors are from the Starcrest Consulting study of the Port 

of Los Angeles.38 For SO2, the fuel specific emission factors were obtained from Entec and 

Corbett and Koehler.,56 The composite emission factors displayed in the table are discussed below. 

Table 2E-5 Auxiliary Engine Emission Factors by Ship and Fuel Type 

Engine Type 

Auxiliary Engine Emission Factors (g/kW-hr) 

Fuel Type NOX PM10 PM 2.5 a HC CO SO2 

Medium Speed 

Marine 

Distillate 13.9 0.3 0.3 0.4 1.1 4.3 

Medium Speed 

Residual 

Marine 14.7 1.5 1.4 0.4 1.1 12.3 

Composite EF 

Residual 

Marine 14.5 1.2 1.1 0.4 1.1 ** a Estimated from PM10 using a multiplicative adjustment factor of 0.92. b See Table 2E-6 for composite SO2 emission factors by vessel type. 

2-81 
 

As for main engines, the STEEM study used the composite emission factors for auxiliary 

engines. For all pollutants other than SO2, underlying data used in the model do not explicitly 

identify auxiliary engine voyages by fuel type, i.e., marine distillate or residual marine. Again, the 

composite factor for those pollutants was determined by weighting individual emission factors by 

vessel engine population data from a 2005 survey of ocean-going vessels that was performed by 

ARB.61

 For SO2, composite emission factors for auxiliary engines were calculated for each vessel 

type. These composite factors were determined by taking the fuel specific emission factors from 

Table 2E-5 and weighting them with an estimate of the amount of marine distillate and residual 

marine that is used by these engines. The relative amount of each fuel type consumed was taken 

from the 2005 ARB survey. The relative amounts of each fuel type for each vessel type and the 

resulting SO2 emission factors are shown in Table 2E-6. 

Table 2E-6 Auxiliary Engine SO2 Composite Emission Factors by Vessel Type 

Vessel Type 

Residual Marine 

(%) 

Marine Distillate 

(%) 

Composite 

Emission Factor 

(g/kW-hr) 

Bulk Carrier 71 29 9.98 

Container Ship 71 29 9.98 

General Cargo 71 29 9.98 

Passenger Ship 92 8 11.66 

Refrigerated Cargo 71 29 9.98 

Roll On-Roll Off 71 29 9.98 

Tanker 71 29 9.98 

Miscellaneous 0 100 4.3 

2.6.7 Adjustments to STEEM PM and SO2 Emission Inventories 

The interport emission results contained in this study for PM10 and SO2 were taken from the 

STEEM inventories and then adjusted to reflect the U.S. Government’s recent review of available 

engine test data and fuel sulfur levels for the near port analysis. In the near ports work, a PM 

emission factor of 1.4 g/kW-hr was used for most main engines, e.g., slow speed diesel and medium 

speed diesel engines, all of which are assumed to use residual marine. A slightly higher value was 

used for steam turbine and gas turbine engines, and a slightly lower value was used for most 

auxiliary engines. However, these engines represent only a small fraction of the total emissions 

inventory. As shown in Section 2.6.6, the STEEM study used an emission factor of 1.5 g/kW-hr for 

all main engines and a slightly lower value for auxiliary engines. Here again, the auxiliary engines 

comprise only a small fraction of the total emissions from these ships. Therefore, for simplicity, the 

interport PM inventories were adjusted by multiplying the STEEM results by the ratio of the two 

primary emission factors, i.e., 1.4/1.5 or 0.933, to approximate the difference in fuel effects. 

2-82 
 

Appendix 2F: Inventories Used for Air Quality Modeling 

The emission inventories presented in this chapter are slightly different from the emissions 

inventories used in the air quality modeling presented in Chapter 3. Specifically, the inventories 

used in the air quality modeling reflect a slightly different boundary for the proposed ECA that was 

based on a measurement error. Due to the nature of the measurement error, the corrections to the 

ECA boundaries are not uniform, but are different by coastal area. As seen in Table 2F-1, the 

changes are not expected to have a significant impact on the results of our analysis. The 

measurement error affects only those portions that are farthest from shore. 

The inventories used for air quality modeling also only contain Tier I NOX controls, as 

opposed to the Tier I and Tier II controls contained in the final inventories. 

A comparison of the air quality and final inventories by region for the 2020 baseline 

scenarios is provided in Table 2F-1. Results are provided only for NOX, PM2.5, and SO2, since the 

air quality modeling is focused on ozone and PM2.5. As shown, the inventory provided for air 

quality modeling generally understates the inventory reductions and air quality benefits produced by 

the ECA. 

Table 2F-1 Comparison of Air Quality Inventories vs Final Inventories for 2020 Baseline Case 

U.S. Region 

Metric Tonnes per Year 

NOX PM2.5 SOX 

AQ Final % Diff AQ Final % Diff AQ Final % Diff 

East Coast (EC) 439,713 391,995 12% 35,891 35,882 0% 323,108 323,038 0% 

Gulf Coast (GC) 261,024 232,114 12% 21,669 21,531 1% 175,862 174,751 1% 

North Pacific 

(NP) 42,291 38,051 11% 3,575 3,603 -1% 27,580 27,807 -1% 

South Pacific 

(SP) 216,849 208,294 4% 17,092 18,536 -8% 138,102 149,751 -8% 

Great Lakes 

(GL) 19,842 18,768 6% 1,484 1,484 0% 11,993 11,993 0% 

Total 48-State 979,719 889,222 10% 79,711 81,037 -2% 676,645 687,339 -2% 

2-83 
 

 

 

 

 

 

1 ICF International (March 2009). Inventory Contribution of U.S. Flagged Vessels, prepared for the U.S. 

Environmental Protection Agency, EPA Report Number EPA-420-R-09-005. 

2 ICF International (October 2007). Commercial Marine Port Inventory Development, prepared for the U.S. 

Environmental Protection Agency, EPA Report Number EPA-420-R-07-012c, Docket ID EPA-HQ-OAR-2007-0121­

0063.1. 

3

 Corbett, J. et al. (April 2007). Estimation, Validation and Forecasts of Regional Commercial Marine Vessel 

Inventories, Final Report, prepared by University of Delaware for the California Air Resource Board, Contract Number 

04-346, and the Commission for Environmental Cooperation in North America, Contract Number 113.111, Docket ID 

EPA-HQ-OAR-2007-0121-0063.2. 

4

 Corbett, J. et al. (May 2006). Estimation, Validation and Forecasts of Regional Commercial Marine Vessel 

Inventories, Tasks 1 and 2: Baseline Inventory and Ports Comparison, Final Report, prepared by University of 

Delaware for the California Air Resource Board, Contract Number 04-346, and the Commission for Environmental 

Cooperation in North America, Contract Number 113.111, May 2006, Docket ID EPA-HQ-OAR-2007-0121-0013. 

5

 RTI International (December 2006). Global Trade and Fuels Assessment – Future Trends and Effects of Designation 

Requiring Clean Fuels in the Marine Sector: Task Order No. 1, Draft Report, prepared for the U.S. Environmental 

Protection Agency, EPA Report Number EPA420-D-07-006, Docket ID EPA-HQ-OAR-2007-0121-0063.3. 

6

 RTI International (April 24, 2006). RTI Estimates of Growth in Bunker Fuel Consumption, Memorandum with 

spreadsheet from Michael Gallaher and Martin Ross, RTI, to Barry Garelick and Russ Smith, U.S. Environmental 

Protection Agency, Docket ID EPA-HQ-OAR-2007-0121-0063.4. 

7

 National Oceanic and Atmospheric Administration, Exclusive Economic Zone, Available online at 

http://nauticalcharts.noaa.gov/csdl/eez.htm. 

8

 U.S. Department of Interior, North American Atlas – Political Boundaries, Available online at 

http://www.nationalatlas.gov/mld/bound0m.html. 

9

 US Department of Transportation Maritime Administration, U.S. Water Transportation Statistical Snapshot, May 

2008, available from www.marad.dot.gov 

10 U.S. Army Corps of Engineers Navigation Data Center, Principal Ports of the United States, 2002, available at 

http://www.iwr.usace.army.mil/ndc/db/pport/dbf/pport02.dbf. 

11 U.S. Army Corps of Engineers Navigation Data Center, Vessel Entrances and Clearances, 2002, available at 

http://www.iwr.usace.army.mil/ndc/db/entclrn/data/entrclrn02/ 

12 ICF International (October 2007). Commercial Marine Port Inventory Development, prepared for the U.S. 

Environmental Protection Agency, EPA Report Number EPA-420-R-07-012c, Docket ID EPA-HQ-OAR-2007-0121­

0063.1. 

13 Nexus Media Communications, The Motor Ship’s Guide to Marine Diesel Engines 2005, available at 

http://www.motorship.com/ 

14 U.S. Army Corps of Engineers, National Waterway Network, Available online at 

http://www.iwr.usace.army.mil/ndc/data/datanwn.htm, Downloaded April 2006. 

15 California Air Resources Board (September 2005). 2005 Oceangoing Ship Survey, Summary of Results. 

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16 Starcrest Consulting Group (June 2004). Port-Wide Baseline Air Emissions Inventory, prepared for the Port of Los 

Angeles 

17 Entec UK Limited (2002). Quantification of Emissions from Ships Associated with Ship Movements between Ports in 

the European Community, prepared for the European Commission, Docket ID EPA-HQ-OAR-2007-0121-0059. 

18 U.S. Environmental Protection Agency (January 2009). Main Engine CO and HC Emission Factors in C3 Model and 

Current Literature, Memorandum from Ari Kahan to Docket EPA-HQ-OAR-2007-0121. 

19 U.S. Environmental Protection Agency (September 2007). Estimation of Particulate Matter Emission Factors for 

Diesel Engines on Ocean-Going Vessels, Memorandum from Mike Samulski to Docket EPA-HQ-OAR-2007-0121, 

Docket ID EPA-HQ-OAR-2007-0121-0060. 

20 U.S. Environmental Protection Agency (September 2007). Estimation of Particulate Matter Emission Factors for 

Diesel Engines on Ocean-Going Vessels, Memorandum from Mike Samulski to Docket EPA-HQ-OAR-2007-0121, 

Docket ID EPA-HQ-OAR-2007-0121-0060. 

21 Memo from Chris Lindhjem of ENVIRON, PM Emission Factors, December 5, 2005. 

22 U.S. Environmental Protection Agency, Exhaust and Crankcase Emission Factors for Nonroad Engine Modeling – 

Compression Ignition (April 2004). Appendix C, EPA- 420-P-04-009, available online at 

http://www.epa.gov/otaq/models/nonrdmdl/nonrdmdl2004/420p04009.pdf, Docket ID EPA-HQ-OAR-2003-0190-0411. 

23 Energy and Environmental Analysis Inc. (February 2000). Analysis of Commercial Marine Vessels Emissions and 

Fuel Consumption Data, EPA420-R-00-002, available online at http://www.epa.gov/otaq/models/nonrdmdl/cmarine/r00002.pdf. 

24 ICF International (March 2009). Inventory Contribution of U.S. Flagged Vessels, prepared for the U.S. 

Environmental Protection Agency, EPA Report Number EPA-420-R-09-005. 

25 Corbett, J. et al. (May 2006). Estimation, Validation and Forecasts of Regional Commercial Marine Vessel 

Inventories, Tasks 1 and 2: Baseline Inventory and Ports Comparison, Final Report, prepared by University of 

Delaware for the California Air Resource Board, Contract Number 04-346, and the Commission for Environmental 

Cooperation in North America, Contract Number 113.111, May 2006, Docket ID EPA-HQ-OAR-2007-0121-0013. 

26 Corbett, J. et al. (May 2006). Estimation, Validation and Forecasts of Regional Commercial Marine Vessel 

Inventories, Tasks 1 and 2: Baseline Inventory and Ports Comparison, Final Report, prepared by University of 

Delaware for the California Air Resource Board, Contract Number 04-346, and the Commission for Environmental 

Cooperation in North America, Contract Number 113.111, May 2006, Docket ID EPA-HQ-OAR-2007-0121-0013. 

27 Corbett, J. et al. (May 2006). Estimation, Validation and Forecasts of Regional Commercial Marine Vessel 

Inventories, Tasks 1 and 2: Baseline Inventory and Ports Comparison, Final Report, prepared by University of 

Delaware for the California Air Resource Board, Contract Number 04-346, and the Commission for Environmental 

Cooperation in North America, Contract Number 113.111, May 2006, Docket ID EPA-HQ-OAR-2007-0121-0013. 

28 Corbett, J. et al. (May 2006). Estimation, Validation and Forecasts of Regional Commercial Marine Vessel 

Inventories, Tasks 1 and 2: Baseline Inventory and Ports Comparison, Final Report, prepared by University of 

Delaware for the California Air Resource Board, Contract Number 04-346, and the Commission for Environmental 

Cooperation in North America, Contract Number 113.111, May 2006, Docket ID EPA-HQ-OAR-2007-0121-0013. 

29 U.S. Army Corps of Engineers Navigation Data Center (2002), Vessel Entrances and Clearances available at 

http://www.iwr.usace.army.mil/ndc/db/entclrn/data/entrclrn02/ 

2-85 
 

 

 

 

 

 

30 IMO. Revision of MARPOL Annex VI and the NOx technical code. Input from the four subgroups and individual 

experts to the final report of the Informal Cross Government/Industry Scientific Group of Experts. BLG/INF.10 

12/28/2007 

31 Transport Canada; Transportation in Canada Annual Report 2004. 2004. (Tables 3-26 and 8-27). 

http://www.tc.gc.ca/pol/en/report/anre2004/8F_e.htm. 

32 RTI International (December 2006). Global Trade and Fuels Assessment – Future Trends and Effects of Designation 

Requiring Clean Fuels in the Marine Sector: Task Order No. 1, Draft Report, prepared for the U.S. Environmental 

Protection Agency, EPA Report Number EPA420-D-07-006, Docket ID EPA-HQ-OAR-2007-0121-0063.3. 

33 RTI International (December 2006). Global Trade and Fuels Assessment – Future Trends and Effects of Designation 

Requiring Clean Fuels in the Marine Sector: Task Order No. 1, Draft Report, prepared for the U.S. Environmental 

Protection Agency, EPA Report Number EPA420-D-07-006, Docket ID EPA-HQ-OAR-2007-0121-0063.3. 

34 Corbett, James and Chengfeng Wang (October 26, 2005). Emission Inventory Review SECA Inventory Progress 

Discussion, p 11, memorandum to California Air Resources Board. 

35 RTI International (December 2006). Global Trade and Fuels Assessment – Future Trends and Effects of Designation 

Requiring Clean Fuels in the Marine Sector: Task Order No. 1, Draft Report, prepared for the U.S. Environmental 

Protection Agency, EPA Report Number EPA420-D-07-006, Docket ID EPA-HQ-OAR-2007-0121-0063.3. 

36 RTI International (December 2006). Global Trade and Fuels Assessment – Future Trends and Effects of Designation 

Requiring Clean Fuels in the Marine Sector: Task Order No. 1, Draft Report, prepared for the U.S. Environmental 

Protection Agency, EPA Report Number EPA420-D-07-006, Docket ID EPA-HQ-OAR-2007-0121-0063.3. 

37 RTI International (December 2006). Global Trade and Fuels Assessment – Future Trends and Effects of Designation 

Requiring Clean Fuels in the Marine Sector: Task Order No. 1, Draft Report, prepared for the U.S. Environmental 

Protection Agency, EPA Report Number EPA420-D-07-006, Docket ID EPA-HQ-OAR-2007-0121-0063.3. 

38 Starcrest Consulting Group (June 2004). Port-Wide Baseline Air Emissions Inventory, prepared for the Port of Los 

Angeles 

39 ICF International, Commercial Marine Port Inventory Development, prepared for the U.S. Environmental Protection 

Agency, EPA Report Number EPA-420-R-07-012c, October 2007, Docket ID EPA-HQ-OAR-2007-0121-0063.1. 

40 ARCADIS Geraghty & Miller, Inc. (September 1999). Commercial Marine Activity for Deep Sea Ports in the United 

States, prepared for the U.S. Environmental Protection Agency, EPA Report Number: EPA420-R-99-020, available 

online at http://www.epa.gov/otaq/models/nonrdmdl/c-marine/r99020.pdf. 

41 ARCADIS Geraghty & Miller, Inc. (September 1999). Commercial Marine Activity for Deep Sea Ports in the United 

States, prepared for the U.S. Environmental Protection Agency, EPA Report Number: EPA420-R-99-020, available 

online at http://www.epa.gov/otaq/models/nonrdmdl/c-marine/r99020.pdf. 

42 Starcrest Consulting Group (January 2007). Draft Port of Los Angeles Air Emissions Inventory for Calendar Year 

2005. 

43 Starcrest Consulting Group (April 2007). Puget Sound Maritime Air Forum Maritime Air Emissions Inventory. 

44 Starcrest Consulting Group, LLC (April 2003). The New York, Northern New Jersey, Long Island Nonattainment 

Area Commercial Marine Vessel Emission Inventory, Vol 1 - Report, Prepared for the Port Authority of New York & 

New Jersey, United States and the Army Corps of Engineers, New York District. 

45 Starcrest Consulting Group, LLC (November 2000). Houston-Galveston Area Vessel Emissions Inventory, Prepared 

for the Port of Houston Authority and the Texas Natural Resource Conservation Commission. 

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46 Eastern Research Group and Starcrest Consulting Group, LLC (January 2004). Update To The Commercial Marine 

Inventory For Texas To Review Emissions Factors, Consider A Ton-Mile EI Method, And Revise Emissions For The 

Beaumont-Port Arthur Non-Attainment Area Final Report, Submitted to the Houston Advanced Research Center. 

47 Zuber M. Farooqui and Kuruvilla John (June 2004). Refinement of the Marine Emissions Inventory for the Corpus 

Christi Urban Airshed, Department of Environmental Engineering, Texas A&M University – Kingsville, Proceedings of 

the 97th Annual A&WMA Conf. & Exhibition. 

48 ICF International (October 2007). Commercial Marine Port Inventory Development, prepared for the U.S. 

Environmental Protection Agency, EPA Report Number EPA-420-R-07-012c, October 2007, Docket ID EPA-HQOAR-2007-0121-0063.1. 

49 ENVIRON International Corporation (March 2007). LADCO 2005 Commercial Marine Emissions. 

50 California Air Resources Board (October 2005). Emissions Estimation Methodology for Ocean-Going Vessels. 

51 E.H. Pechan & Associates Inc. (June 2005), Commercial Marine Inventories for Select Alaskan Ports, Final Report, 

Prepared for the Alaska Department of Conservation. 

52 Corbett, J. et al. (May 2006). Estimation, Validation and Forecasts of Regional Commercial Marine Vessel 

Inventories, Tasks 1 and 2: Baseline Inventory and Ports Comparison, Final Report, prepared by University of 

Delaware for the California Air Resource Board, Contract Number 04-346, and the Commission for Environmental 

Cooperation in North America, Contract Number 113.111, May 2006, Docket ID EPA-HQ-OAR-2007-0121-0013. 

53 Lloyd’s Register and International Maritime Organization, Marine Exhaust Emission Quantification Study – Baltic 

Sea, in MEPC 45/INF.7. 1998. 

54 Entec UK Limited (2002). Quantification of Emissions from Ships Associated with Ship Movements between Ports in 

the European Community, prepared for the European Commission, Docket ID EPA-HQ-OAR-2007-0121-0059. 

55 ICF International (January 5, 2006). Current Methodologies and Best Practices in Preparing Port Emission 

Inventories, Final Report, prepared for the U.S. Environmental Protection Agency, available online at 

http://www.epa.gov/sectors/ports/bp_portemissionsfinal.pdf. 

56 Corbett, J.J. and H.W. Koehler (2003). Updated Emissions from Ocean Shipping, Journal of Geophysical Research, 

108(D20); p. 4650. 

57 Corbett, J.J. and H.W. Koehler (2004). Considering Alternative Input Parameters in an Activity-Based Ship Fuel 

Consumption and Emissions Model: Reply to Comment by Oyvind Endresen et al. on “Updated Emissions from Ocean 

Shipping,” Journal of Geophysical Research. 109(D23303). 

58 Levelton Consultants Ltd. (2006). Marine Emission Inventory Study Eastern Canada and Great Lakes – Interim 

Report 4: Gridding Results, prepared for Transportation Development Centre, Transport Canada. 

59 Entec UK Limited (2002). Quantification of Emissions from Ships Associated with Ship Movements between Ports in 

the European Community, prepared for the European Commission, Docket ID EPA-HQ-OAR-2007-0121-0059. 

60 California Air Resources Board (September 2005). 2005 Oceangoing Ship Survey, Summary of Results. 

61 California Air Resources Board (September 2005). 2005 Oceangoing Ship Survey, Summary of Results. 

2-87