# 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 cont ributors 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 re sulting inventory includes emissions from both propulsion and auxiliary engines us ed on these vessels, as well as those on gas and steam turbine vessels.
Most of the vessels operating in U.S. ports that have propul sion engines less than 30 liters per cylinder are domestic and are already subjec t to strict national standards affecting NO X 

### , PM, and fuel sulfur content. As su ch, the inventory does not include any ships, foreign or domestic, powered by Category 1 or Category 2 (i .e., <30 L/cyl) engines. In a ddition, as discussed in Sections 2.3.2.3.9 and 2.3.3.3 , this inventory is primarily based on acti vity data for ships that carry foreign cargo. Category 3 vessels carrying domestic cargo that operate onl y 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 vesse ls presented in this chapter are sums of independently constructed port and interport emi ssions inventories. Po rt 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 vesse l 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 emi ssions from C3 vessels using historical North American shipping activity, ship ch aracteristics, and activ ity-based emission factors. STEEM was used to quantify and geographically (i.e., spatially) represent inter port 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 repl acing it with the detail ed port inventories. For the remaining U.S. ports for which detailed po rt inventories are not available, the near-port portion of the STEEM inventory was simply retain ed. This was done for a base year of 2002. Inventories for 2020 were then projected using regional growth rates 5
, 6 

### and adjustment factors to account for the International Maritime Or ganization (IMO) Tier I and Tier II NO X 

### standards and NO X 

### retrofit program. 2 

### Inventories incorporating additional Tier III NO X 

### and fuel sulfur controls within the proposed Emission Control Ar ea (ECA) were also developed for 2020.
The inventory estimates reported in this chap ter 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 (NO X 

### ), particulate matter (PM 2.5 

### and PM 10 

### ), sulfur dioxide (SO 2 

### ), hydrocarbons (HC), carbon monoxide (CO), and carbon dioxide (CO 2 

### ). 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. nautic al charts in accordance with the articles of the Law of the Sea. The boundary was mapped using ge ographic information system (GIS) shapefiles obtained from the National Oceanic and Atmosp heric 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 th en 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 regi onal 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 separa te 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 vertic al line roughly drawn through Key Largo (Longitude 80° 26’ West).

• The Alaska region was divided into separate Alaska South east and Alaska West regions along a straight line intersecti ng the cities of Naknek and K odiak. The Alaska Southeast region includes most of the State’s populati on, and the Alaska West region includes the emissions from ships on a great circle route al ong the Aleutian Islands between Asia and the U.S. West Coast.

• For the Great Lakes domain, shapefiles were cr eated containing all th e ports and inland waterways in the near port inventory and exte nding out into the lakes to the international border with Canada. The modeling domain span ned 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, Lana i, and Kahoolawe was contained in Hawaii East. The remainder of the Hawaiian Region was then designated Hawaii West.
This methodology resulted in nine separate re gional 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 sectio n 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 secti on describes the method for merging the interport and near port portions of th e 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 emi ssions estimates for ships while at port (near port inventories) and while underway (interport invent ories). Near port inventories for calendar year 2002 were developed for 117 U.S. comme rcial 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 th e terminus of the reduced speed zone for Great Lake ports. Port emissions are calculated fo r different modes of operation and then summed. Emissions for each mode are calculated using po rt-specific information for vessel calls, vessel characteristics, and activity, as we ll as other inputs that vary inst ead 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 la nes were identified from actual ship positioning reports. The model then uses detailed informati on about ship destinations , ship attributes (e.g., vessel speed and engine horsepower), and emissi on factors to produce spa tially allocated (i.e., gridded) emission estimates for sh ips engaged in foreign commerce.
The 117 near port inventories are an improve ment upon STEEM’s near port results in several ways. First, the precisi on associated with STEEM’s use of ship positioning data may be less accurate in some locations, especially as the lane s 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 assume d 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 as sumed by STEEM also does not reflect the fact that the reduced speed zone, and therefore emissi ons, 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 inve ntories correct these issues.
The regional emission inventories produced by th e 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 ar e derived by merging together: 1) the near port inventories, which extend 25 nauti cal miles and 7 nautical miles from the terminus of the RSZ for deep water ports and Great Lake ports, respectivel y, and 2) the remaining in terport portion of the STEEM inventory, which extends from the endpoint of the near port inventor ies to the 200 nautical mile boundary or international borde r 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 we re developed for ocean-going vessels at 89 deep water and 28 Great Lake por ts in the U.S. The inventor ies include emissions from both propulsion and auxiliary engines on C3 vessels.

This section first describes th e selection of the ports for an alysis and then provides the methodology used to develop the near port inventories. This is fo llowed by a description of the key inputs. Total emissions by port and pollu tant 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 dataset 10 

### were used as a starting point. Thirty ports whic h had no foreign traffic were eliminated because the dataset used to obtain port calls and other ship characteristics has no info rmation about domestic traffic. Several California ports were also used because the California Air Resources Board (ARB) provided the necessary data and estimates for thos e ports. The final list of 117 deep water and Great Lake ports, along with their co ordinates, is given in Appendix 2 A.

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 dw elling, occurs while the vessel is docked or anchored near a dock, and only th e auxiliary engine(s) are being used to provide power to meet the ship’s energy needs. Maneuvering occu rs 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 occu rs 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 th e RSZ lanes for deep water ports and 7 nautical miles for Great Lake ports.
Emissions are calculated separately for pr opulsion and auxiliary engines. The basic equation used is as follows:

Equation 2-1



### Emissions mod e [ eng ] 

### = ( calls ) × ( P [ eng ] 

### ) × ( hrs / call mod e 

### ) × ( LF mod e [ eng ] 

### ) × ( EF [ eng ] 

### ) × ( Adj ) × (10 − 6 

### tonnes / g )

Where:

- Emissions mode [eng] 

### = Metric tonnes emitted by mode and engine type

- Calls = Round-trip visits (i .e., one entrance and one clea rance is considered a call) - P [eng] 

### = Total engine power by engine type, in kilowatts

- hrs/call mode 

### = Hours per call by mode

- LF mode [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 di rectly from the propeller curve based upon the cube of actual speed divided by maximum speed (a t 100% maximum continuous rating [MCR]). In addition, cruise mode activity is based on cr uise distance and speed inputs. Appendix 2 B provides the specific equations used to calculate propuls ion 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 li sted explicitly, since th ey are calculated as a function of mode and/or cruise spee d. This section describes the inpu ts in more detail, as well as the sources for each input.

2.3.2.3.1 Calls and Ship Characteristics (Propuls ion Engine Power and Cruise Speed)
For this analysis, U.S. Army Corps of Engin eers (USACE) entrance a nd 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 por t. 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 cl earances 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) ra nge. 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 wa s 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 woul d only be one record (the loaded trip into or out of the port) that would be presen t in the data. For Great Lake por ts, clearances were matched with entrances by ship name. If ther e 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 c over 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 Ll oyd’s Data, which is produced by Lloyd’s RegisterFairplay Ltd. 12 

### Over 99.9 percent of the calls in the entr ances and clearances data were directly

2-6

### matched with Lloyd’s data. The remaining 0.1 pe rcent 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 Guide 13 

### and displacement per cylinder was calculated. Ships with Category 1 or 2 propulsion engi nes were eliminated from the data.
Many passenger ships and tankers have either di esel-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 sp eed 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 deve loped 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 shippi ng lanes (i.e., channels, intracoastal waterways, sea lanes, and rivers). The sea-side endpoi nt 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 ot her 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 2 C. 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 larg e 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 maxi mum continuous rating power and lo ad 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 estimate d. The approach taken was to derive ratios of average auxili ary engine power to propulsion pow er 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 pr opulsion power data to derive auxi liary power for the ship types at each port.

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

Ship Type Average Propulsion Engine (kW) Average Auxiliary Engines Auxiliary to Propulsion Ratio Number 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 Ship a 39,600 4.7 2,340 11,000 Medium 0.278 General Cargo 9,300 2.9 612 1,776 Medium 0.191
Miscellaneous b 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 di fferent 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 A RB 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 calcu late auxiliary engine emissions . Starcrest’s Vessel Boarding Program 16 

### 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 SO 2 

### emission factors are dependent on the fuel sulfur level, calculation of port inventories requires information about the fuel su lfur 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 pe rcent for the rest of the country. A sulfur content of 1.5 percent was used for MDO. 17 

### While a more realistic value for M DO used in the U.S. appears to be 0.4 percent, given the small proportion of distillate fuel used by ship s relative to RM, the difference should not be significant. Sulfur levels in other areas of the worl d 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 pr epared and published in 2002 by Entec. 17 

### The resulting Entec emission factors include indi vidual factors for three speeds of diesel engines (slow-speed diesel (SSD), medium-speed dies el (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 NO X 

### and HC that were used for the 2002 port inventory development. The CO, PM, SO 2 

### and CO 2 

### 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

NO X CO HC CO 2 PM 10 PM 2.5 SO 2 PM 10 PM 2.5 SO 2
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 in formation 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

### PM 10 A 

### values were determined based on existing engine test data in consultation with ARB. 19 

### GT PM 10 

### emission factors were not part of the U. S. Government analysis but assumed here to be equivalent to ST PM 10 

### emission factors. Test data shows PM 10 

### emission rates as dependent upon fuel sulfur levels, with base PM 10 

### emission rates of 0.23 g/kw-h r 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. PM 2.5 

### is assumed to be 92 percent of PM 10 

### . 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 becaus e 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 PM 10 Emission Factors Based on Fuel Sulfur Levels



### PM EF 

### = PM Nom 

### + [(S Act 

### – S Nom 

### ) × BSFC × FSC × MWR × 0.0001]

where: PM EF = PM emission factor adjusted for fuel sulfur PM Nom = PM emission rate at nominal fuel sulfur level = 0.23 g/kW-hr for distillate fuel, 1.35 g/kW-hr for residual fuel S Act = Actual fuel sulfur level (weight percent) S Nom = 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



### SO 2 

### emission factors were based upon a fuel sulfur to SO 2 

### conversion formula which was supplied by ENVIRON. 21 

### Emission factors for SO 2 

### emissions were calculated using the formula assuming that 97.753 percent of the fu el sulfur was converted to SO 2 

### . 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 SO 2 Emission Factors, g/kWh



### SO 2 

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

CO 2 

### emission factors were calculated from th e BSFC assuming a fuel carbon content of 86.7 percent by weight 17 

### and a ratio of molecular weights of CO 2 

### and C at 3.667.

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



### CO 2 

### EF = BSFC x 3.667 x 0.867

A PM 10 is particulate matter of 10 micrometers or less.
B Brake specific fuel consumption is sometime s called specific fuel oil consumption (SFOC).



### 2-10

### The most current set of auxiliary engine emi ssion factors also comes from Entec except as noted below for PM and SO 2 

### . 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

NO X CO HC CO 2 PM 10 PM 2.5 SO 2 PM 10 PM 2.5 SO 2

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



### SO 2 

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

. Using the ratios of RM versus MDO use 15 

### 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 to tal emission inventory estimates.

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

Ship Type All Ports West Coast Ports Other Ports

NO x CO HC CO 2 PM 10 PM 2.5 SO 2 PM 10 PM 2.5 SO 2 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 lo ads, 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 fact or adjustment factors we re developed based upon the concept that the BSFC increases as lo ad decreases below about 20 percent load.
Using these algorithms, fuel consumption and em ission factors versus lo ad were calculated. By normalizing emission factors to 20% load, lo w-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.

2-11Table 2-7 Calculated Low Load Multiplicative Adjustment Factors
Load (%) NO X HC CO PM SO 2 CO 2 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 in formation to readily calculat e time-in-mode (hours/call) for all 117 ports during the maneuveri ng and hotelling modes of opera tion. As a result, it was necessary to review and select av ailable detailed emission inventorie s that have been estimated for selected ports to date. These ports are referred to as typical ports. The ty pical 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 a nd 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 m odeled ports is briefly described in Appendix 2 D, 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 eith er a foreign flag vessel or a U. S. flag vessel. As a result, U.S. flag ships carrying domestic cargo (i.e., Jone s Act) ships are not incl uded in the USACE data. Determining the contribution of Jones Act ships is difficult as most data so urces 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. G overnment, ICF conducted an anal ysis 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 el even typical ports and using this information to estimate the Jones Act ship contribution for the re maining 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 Al aska 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 th e 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 NO X PM 10 PM 2.5 HC CO SO 2 CO 2 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 responsib le for 39-48% of the total port inventory, depending on the pollutant. Hotelling, cruise, and RSZ modes of opera tion are all important contributors to emissions.

2-13Table 2-9 2002 Port Emissions Summary by Mode and Port Type (metric tonnes)

Mode Port Type Metric Tonnes NO X PM 10 PM 2.5 HC CO SO 2 CO 2 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
NO X PM 10 PM 2.5 HC CO SO 2 CO 2 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



### 2-14Ship Type Port Type Metric Tonnes
NO X PM 10 PM 2.5 HC CO SO 2 CO 2 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 analys is 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 mo deling 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 inte rnational boundary with Canada. The emission results are divided into nine geographic regions of th e U.S. (including Alaska and Hawa ii), 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 invent ories. The approach used to replace the nearport portion of the inte rport emissions is pr ovided 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 geographical ly 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 ac tivity in Canada and Mexico. The model estimates emissions from main propulsion and auxiliar y marine engines used on Categor y 3 vessels that engage in

2-15

### foreign commerce using historical North Ameri can shipping activity, sh ip attributes (i.e., characteristics), and activity-based emission factor information. These inputs are assembled using a GIS platform that also c ontains an empirically derived network of shipping lanes. It includes the emissions for all ship operational modes from cruise in unconstrai ned shipping lanes to maneuvering in a port. The model, however, ex cludes hotelling operations while the vessel is docked or anchored, and very low speed maneuveri ng close to a dock. For that reason, STEEM is referred to as an “interport” model, to easil y distinguish it from th e near ports analysis.

STEEM uses advanced ArcGIS tools and deve lops 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 sh ip reporting databases. The first is the International Comprehensive Ocean-Atmosphere Da ta 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 Atmo spheric Administration and National Science Foundation's National Center for Atmospheric Res earch (NCAR). The second database is the Automated Mutual-Assistance Vess el Rescue (AMVER) system. 26 

### The AMVER data set is based on a ship search and rescue re porting network sponsor ed by the U.S. Coast Guard. The AMVER system is also voluntary, but is generally limited to ships ove r 1,000 gross tons on voyages of 24 hours or longer. About 8,600 vesse ls reported to AMVER in 2004.
The latitude and longitude coordinates for the sh ip reports in the above databases are used to statistically create and sp atially 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 da ta (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 en trances 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 orig ins and destinations of shipping routes: junctions where shipping routes intersect, and segments that are sh ipping lanes between two connected junctions. Each segment can have only tw o 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 Doma in and Spatial Distribution of Shipping Lanes


### The STEEM interport model also employs a numbe r of databases to identify the movements for each vessel (e.g., trips), individual ship attrib utes (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 sta tistical lanes, STEEM uses ArcG IS 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, navigati onal regulations, or othe r constraints that are beyond the model’s capability to forecast.
After identifying the shipping route and resultin g 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 em ission 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 S TEEM, 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 resolu tion of 4 kilometers by 4 kilometers, which is appropriate for most atmospheric air quality mode ls. The results for each cell are then summed, as appropriate, to produce emission inve ntories 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 a bout ship routes and destinations in order to provide spatially allocated emi ssions 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 Vol untary Observing Ships (VOS) fleet. 27 

### STEEM also uses a dataset derived from the Automated Mutual -Assistance Vessel Resc ue (AMVER) system, 28 

### which is based on a ship search and rescue reporting ne twork 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, pr opulsion engine power, and cruise sp eed 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 PM 10 

### and SO 2 

### in order to reflect recent U.S. Government review of available engine test data and fuel sulfur levels. Details of the STEEM emi ssion inputs and adjustments are located in Appendix 2 E.

2.3.3.3 Interport Domestic Traffic
As previously noted, STEEM includes the emissi ons associated with sh ips that are engaged in foreign commerce. As a result, U.S. flag ve ssels carrying domestic cargo (Jones Act ships) are not included. The STEEM interpor t analysis also roughl y estimated the emissi ons 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 inventorie s, the estimate of excluded installed power was roughly 6.5 percent. It is not inconsistent that th e STEEM estimate of excluded emissions is lower than the excluded power estimat ed from calls to U.S. ports, since the STEEM model includes ships that are tran siting 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 sh ip traffic is expected to fall within the proposed ECA. Theref ore, 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 invent ories in this study are a combination of the results from the near ports analysis described in Section 2.3.2 and the STEEM interpor t 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 Paci fic 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 por ts. The results are spatially re ported 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 th e RSZ and an unconstrained shipping lane. For Great Lakes ports that includes hotelling and maneuvering, three naut ical 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 toge ther. Both of these processes are described below. This work was conducted by ENVIRON In ternational as a subcon tractor 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 emi ssions from the near port inventories were spatially located by their respective latitude and l ongitude coordinates using ArcGIS software. For this study, shapefiles were created that depicted the emission lo cations 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 shapefile s representing the ports, RSZ la nes, 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 latit ude/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 shoul d be noted that modeling a port as a point will over specify the loca tion of the emissions associated w ith 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 2 A.

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) geographi c database of navigable waterways in and around the U.S. 14 

### The coordinates of RSZ endpoints for a ll of the 117 ports used in this work are shown in Appendix 2 C.
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 occu rred 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 por t following the direction of the shipping lane evident in the STEEM data. Again, as with RSZ emissions, the la titude/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 Em ission Inventories in Port Areas
After spatially defining the geographic locati on of the near port emissions, but before actually inserting them into the gridded STEEM i nventory, 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 ar ea near a port, but not actually entering or exiting the port.
The percentage of STEEM emissions that are a ttributable to a port, and should be removed and replaced, were approximated by dividing the S TEEM 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 inve ntory 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 em issions should be reduced to account for ship movements associated directly wi th a port, while preserving those that represented transient vessel traffic. These databases were subsequently us ed to calculate the new emission results for each affected cell in the original S TEEM 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. bounda ry was removed. As a result, th e 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 emi ssion 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. Inve ntories for the nine geographic re gions of the U.S. specified in Section 2.2 were created using ArcGIS software to in tersect 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 inve ntories for 2002 are shown in Table 2-11 for each of the nine geographic regions and the nation. The geogr aphic 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 NO X PM 10 PM 2.5 a HC CO SO 2 CO 2 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 PM 10 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 inventor ies, the interport and near port inventories are about 80 percent and 20 percent of the total, respectively.

2-21Table 2-12 2002 Contribution of Near Port and Interport Emissions to the Total C3 Inventory

Region Metric Tonnes NO X PM 10 PM 2.5 a Port Interport Total Port In terport 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 PM 10 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 SO 2 Port Interport Total Port In terport 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 r oughly 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 vess els (main propulsion and auxiliary engines) are projected to 2020 by applying certain growth f actors to the 2002 emission inventories to account for the expected change in ship traffic over th ese 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 describe d. 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 th at is associated with shipping goods, i.e., commodities, into and out of the U.S. This sectio n describes the growth factors that are used to project the emissions to 2020 for each of the nine geographic regions ev aluated in this analysis. The use of bunker fuel as a surrogate for estimati ng future emissions is appropriate because the quantity of fuel consumed by C3 engines is highly correlated with th e 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 th at are used in some auxiliary power engines.

The remainder of this section first summari zes the development of growth rates by RTI International (RTI) for five geographic regions of th e 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 fo r five geographic regions of the U.S. These regions are the East Coast, Gulf Coast, North Paci fic, South Pacific, and Great Lakes. The amount of bunker fuel required in any re gion and year is based on the de mand for transporting various types of cargo by Category 3 vessels. This transportati on demand is in turn driven by the demand for commodities that are produced in one location a nd 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). Ty pical voyage parameters are then assigne d to the trade routes that include average ship speed, round trip mil eage, tons of cargo shipped, and days in port. Fuel consumption for each trade route and commodity type t hus 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 S ecretary General’s Informal Cross Government/Industry Scientific Gro up of Experts presented a growth rate that ranged from 3.3% to 3.7%. 30 

### RTI’s overall U.S. growth rate was projecte d at 3.4%, which is consistent with that range.

2-23Figure 2-4 Illustration of Method fo r 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. Canada a Canada a
W. Canada a Canada a
U.S. Pacific North U.S. Pacific North U.S. Pacific South U.S. Pacific South Greater Caribbean Colombia, Mexico, Vene zuela, 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 AfricaAfrica-North/EastMediterranean Mediterranean Northern Africa, Egypt, Israel, Africa-East/South Kenya, Other Eastern A frica, South Africa, Other Southern Africa

Europe-North Austria, Belgium, Denmark, Finl and, France, Germany, Ireland, Netherlands, Norway, Sweden , Switzerland, United Kingdom Europe-South Greece, Italy, Portug al, 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, Mala ysia, 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 se rvices 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 provide d a specialized forecast that repor ts 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 mode l then estimates demand forces, country-specific exporting capacities, export prices, and import costs.

2-25

### The GI model included detailed annual regi on-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 ba seline data for 2005. In 2005, dry bulk accounted for 41 percent of the total trade volume, crude o il accounted for 28 percent, and containers accounted for 12 percen t. Dry bulk and crude oil shipme nts are expected to grow more slowly over the forecast period th an container shipments. By 2020, dry bulk represents 39 percent of the total, crude oil is 26 percent, and containe rs 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 tran sport the different commodities to the various regions of the world. Profiles of these ships were developed to identify th e 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 inform ation from Clarkson’s Shipping Database. 32 

### These assignments are shown in Table 2-16

.

2-26Table 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-R o/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 ba sed 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 attribut es 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 strait s through which ships can pa ss. Very large crude oil tankers are the largest by DWT rating, and the biggest container ships (Suezmax) are also very large.

2-27Table 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 inst alled horsepower (HP), type of propulsion (diesel or steam), and year of build. These characteri stics are then matched to information on typical specific fuel consumption (SFC), which is expres sed 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 fr om Wartsila Sulzer, a popular manufacturer of diesel engines for marine vessels. RTI added an a dditional 10 percent to the reported “test bed” or “catalogue” numbers to account for the guarantee d 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 re ported in the manufacturer product catalogues and those observed in actual service. This differen ce is explained by the fact that old, used engines consume more fuel than brand ne w 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 improvi ng 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, partic ularly as they focus on meeting NO X 

### emission standard as required by MARPOL Annex VI, or other potential pollu tion control requirements. Post-2000 SFC values are constant at approxi mately 135 g/hp-hr (180 g/kW-hr).

0 20 40 60 80 100 120 140 160 180 200Specific 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 fu el 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 consumpti on 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 gram s of bunker fuel burned per horsepowerhour in use(g/HP-hr)

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

- 10 6 

### 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 a uxiliary engines when opera ted 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 shippi ng 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 confi guration. This assumption leads to the following change in fleet characterist ics 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, th e 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 tim e as smaller ships re tire 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 crud e oil trade from the Middle East Gulf region to the

2-30

### Gulf Coast of the U.S., 98 percent of the deadwei ght 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 estimat ed. Specifically, these ar e 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 vol ume 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 di stance 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 calcul ated 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 = Tr ip length in nautical miles speed = Vessel speed in knot s 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 assume d 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:

2-31Equation 2-8 

### total metric tonnes of cargo moved Number of Voyages
v , 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 th e fleet average ship size from th e vessel profile analysis. For most cargo, a utilization rate of 0.9 is assume d to be constant throughout the forecast period. Lowering this factor would incr ease 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 type s 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 dema nd associated with international cargo trade. Specifically, for each year y 

### of the analysis, th e total bunker fuel demand is the su m of the fuel consumed on each route of each trade (commodity). The fuel consum ed on each route of each tr ade 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 illu strated by the following equations:

Equation 2-9



### FC = Σ Σ FC y trade,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 auxiliar y engines are operated at sea yatport = 

### Calendar year main and auxiliary engines are operated in port

2-32Equations 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) v,s 

## [ Days at port per voyage x Number of voyages 

## ] trade,route,y 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 auxiliar y 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 consumpti on 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, estima tes of global fuel consumption over time were computed, and growth rates determined from these projections.

2-330 100 200 300 400 500 600Million Tons of Fuel199520002005201020152020

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 per cent over the time period between 2006 and 2020 and generally declines over ti me, resulting in an average annual growth of around 2.6 percent.

2-34-2% 0% 2% 4% 6% 8% 10%Percent Change in Bunker Fuel20062008201020122014201620182020

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 t ypes 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 po rt-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 fr om Containerization International Yearbook (CIY) 37 

### and calculations by RTI. That reference provides voyage information for all major container services. Ba sed on the frequency of th e 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 wa s combined with the ve ssel 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



### 2-36Origin – 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 us e 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 th is fuel is used to carry trade goods to and from the U.S., it is not necessarily all purchased in th e U.S. and is not all burned in U. S. waters). This amount grows to over 90 million tonnes by 2020 with the most grow th 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 100Million Tons of Bunker Fuel199520002005201020152020

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

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



### 2-37

### Figure 2-9 shows the estimated annual growth rates fo r 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 fl ows is 3.4 percent between 2005 and 2020.

-2% 0% 2% 4% 6% 8% 10%Percent Change in Bunker Fuel20062008201020122014201620182020

US South Pacific US North Pacific 

### United States

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 de velop the growth factors that are necessary to project the 2002 base year emissi ons inventory to 2020. The next two sections describe how the five RTI regions were associated w ith the nine regions analy zed 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 analyz ed in this study were designe d to be consistent with the five RTI regional modeling domains. More specifi cally, 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 an alyzed by RTI, i.e., the North Pa cific for both Alaska regions and South Pacific for Hawaii. Therefor e, the growth rate information from the related larger region was assumed to be representative for that state.

2-38

### The nine geographic regions re presented in the emission inve ntory study are presented in Figure 2-1. The association of the RTI regions to the emission inventor y 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 Sout h 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 Emi ssion 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 fr om 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 multiplicati ve growth factors for each emission inventory region and the associated RTI average annual growth rates are presented in Table 2-23 for 2020.

2-39Table 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 Ba seline and Control Scenarios

This section describes the c ontrol programs present in th e 2020 baseline and control scenarios. Section 2.4.4 describes the process of incorpor ating these programs into the 2020 emission inventories.

The baseline scenario includes the Internat ional Marine Organization’s (IMO) Tier I NO X 

### standard for marine diesel engines that became effec tive in 2000, as well as the Tier II standard that will become effective in 2011. Also include d in the baseline inventories is the NO X 

### retrofit program for pre-controlled engines proposed by IMO.
Although the 0.1% fuel sulfur re quirement goes into place for all vessels operating in ECAs beginning in 2015, the use of 2020 as the analytic y ear will still pr ovide a represen tative scenario for the impact of the 0.1% fuel sulfur requirement on human health and the environment. This is because the fuel requirements of th e ECA go into effect all at once; there is no phase-in. So the impacts of the fuel requirement in 2020 are expect ed to be the same as in 2015, with a small increase due to growth. With regard to the NO X 

### impacts, while 2020 will include five years of turnover to the Tier III standards, the long service lives of engine s 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 NO X 

### reductions would not inflate the benef its of the program by very much, if any. Note that the global fuel sulfur standard does not go into e ffect until 2020. We did not include this in the 2020 analysis, to provide a better estimate of be nefits in the early (p re-2020) years of the program

The effects of these controls are reflected in the 2020 emission i nventories by applying appropriate adjustment factors that reflect the percentage of the ve ssel fleet in those years that are estimated to comply with the c ontrols. Adjustment factors are ratios of 2020 to 2002 calendar year (CY) emission factors (EFs). Adjustment factor s are derived separately by engine type for propulsion and auxiliary engi nes. The adjustment factors for propulsion engines are applied to the propulsion portion of the port invent ory 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 C ontrol Area (ECA) within a distance of 200 nautical miles (nm) from shore. Outside this distan ce, baseline controls were applied (i.e., the Tier I

2-40

### and Tier II NO X 

### standards, the NO X 

### retrofit program, and current fu el sulfur content levels). The ECA NO X 

### controls include the baseline controls above, plus Tier II NO X 

### 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 s ubject to any of the NO x 

### 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 progra m was also modeled with a five year phase-in. The current Tier I controls, which also are mode led as achieving an 11 per cent 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 leve ls. Control of fuel sulfur content within the ECA area affects SO 2 

### and PM emissions.
ECA controls were applied to the 48 state regi on as well as Alaska East (AE) and Hawaii East (HE). Alaska West (AW) and Hawa ii West (HW) are baseline cases only.



## 2.4.4 2020 Emission Factors

This section describes the emi ssion factors that are used in the 2020 scenarios. HC and CO emission factors are assumed to remain unchanged from the 2002 scenario. NO x 

### and fuel sulfur controls are anticipated to lower NO X 

### , SO 2 

### and PM emission factors. The switch to lower sulfur distillate fuel use is also assumed to lower CO 2 

### emissions slightly.

The NO X 

### emission factors (EFs) by engine/shi p 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 w ould be using distillate fuel, and there are small NO X 

### emission reductions assumed when switching fr om residual to distillate fuel. 17 

### The NO X 

### control EFs by tier were derived using the assump tions described in section 2.4.3

.
Table 2-24 Modeled NO X Emission Factors by Tier

Engine/ Ship Type NO X 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/a a 13.0 10.5 14.6 n/a a 13.0 10.5 2.6 Other 14.5 n/a a 12.9 10.4 14.5 n/a a 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 NO X 

### EFs by tier were then used with the vessel age distributions ( Table 2-25 & Table 2-26 ) to generate calendar year NO X 

### EFs by engine/ship type for the base and control areas included in the scenarios. These calendar year NO X 

### EFs are provided in Table 2-27 below. Since the age distributions are different for vessels in the Great Lakes, NO X 

### EFs were determined separately for the Great Lakes.

Table 2-25 Vessel Age Distribution fo r 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 sp eed diesel, GT is gas turbine, ST is steam turbine.



### 2-42Table 2-26 Vessel Age Distribution for Great Lake Ports by Engine Type

Age Group (years old) Propulsion Engine Type a (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 sp eed diesel, GT is gas turbine, ST is steam turbine. b Fleet average weighted by installed power (ship port calls x main propulsion engine power).



### 2-43Table 2-27 Modeled NO X Emission Factors by Calendar Year and Control Type

Engine/ Ship Type CY NO X 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 SO 2 

### EFs are a function of fuel sulfur le vel. For the base line 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 dist illate 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 residu al and distillate fuel. For the control areas, there is one level of distillate fuel sulfur assumed to be used by a ll engines: 1,000 ppm for the ECA control areas.

Table 2-28 provides the PM 10 

### EFs by engine/ship type and fuel sulfur level. For modeling purposes, PM 2.5 

### is assumed to be 92 percent of PM 10 

### . The PM EFs are adjusted to reflect the appropriate fuel sulfur levels using Equation 2-2

.
Table 2-29 provides the modeled SO 2 

### EFs. SO 2 

### emission reductions are directly proportional to reductions in fuel sulfur content.

CO 2 

### is directly proportiona l to fuel consumed. Table 2-30 provides the modeled CO 2 

### 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 th e control areas results in a small reduction to BSFC and, correspondingly, CO 2 

### emissions. 17



### 2-44Table 2-28 Modeled PM 10 Emission Factors

Engine/ Ship Type PM 10 EF (g/kW-hr) Baseline Control Areas Other than West Coast 27,000 ppm S West Coast a 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 SO 2 Emission Factors*

Engine/ Ship Type SO 2 EF (g/kW-hr) Baseline Control Areas Other than West Coast 27,000 ppm S West Coast a 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 CO 2 Emission Factors

Engine/ Ship Type EF (g/kW-hr)
Baseline Control Areas
BSFC CO 2 BSFC CO 2 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 NO X 

### , PM (PM 10 

### and PM 2.5 

### ), SO 2 

### , and CO 2 

### inventory of each 2020 scenario. This section describes the development a nd application of the adjustment factors to the port and interport inventories, and the methodology for comb ining the port and in terport 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 ar e summed separately, since the EF adjustment factors differ. The appropriate regional grow th factor, as provided in Table 2-23 , is then applied, along with EF adjustment factors by engine/ship ty pe. The EF adjustment factors ar e 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 co ntrol scenario. These tables are also used as input for the Califor nia ports and interport control inve ntory development, discussed in subsequent sections.

Table 2-31 NO X EF Adjustment Factors by Engine/Ship Type and Control Type a

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 NO X 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



### 2-46Table 2-32 PM 10 EF Adjustment Factors by Engine/Ship Type and Control Type a

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 PM 10 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 Paci fic, and South Pacific.

Table 2-33 PM 2.5 EF Adjustment Factors by Engine/Ship Type and Control Type a

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 PM 2.5 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 PM 2.5 adjustment factors are slightly different from those for PM 10 due to rounding. Other = Other than West Coast WC = Ports/areas within the West Coast. This includes the regions of Alaska, Hawaii, North Paci fic, and South Pacific.



### 2-47Table 2-34 SO 2 EF Adjustment Factors by Engine/Ship Type and Control Type a

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 SO 2 adjustment factors are a ratio of the control EFs to the 2002 EFs. SO 2 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 Pa cific, and South Pacific.

Table 2-35 CO 2 EF Adjustment Factors by Engine/Ship Type and Control Type a

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 CO 2 adjustment factors are a ratio of the control EFs to the 2002 EFs. CO 2 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 Pa cific, 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 adjust ment factors differ. The EF adjustment factors by engine/ship type, provided in the previous s ection, are consolidated by ship type, using the CARB assumption that engines on all ships except passenger ships are 95 percent slow speed diesel (SSD) engine s 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-tu rbines (GT) are not included in

C California Air Resources Board, 2005 Oceangoing Ship Sur vey, Summary of Results, September 2005.



### 2-48

### the CARB inventory. The EF adjustment factors by ship type are then ap plied, along with shipspecific growth factors supplied by CARB. The ship-specific grow th 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 invent ory 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 e ngine 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 u nderway are due to the main engines. The EF adjustment factors by main engine type in Section 2.4.5.1 were used together with the mix of main engine types by region to develop the EF regional adjustment factor s 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 futu re 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%



### 2-49Table 2-38 NO X EF Adjustment Factors by Region and Control Type a

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 Region b 1.0000 0.8095 n/a

a NO X adjustment factors are a ratio of future base or control EFs to 2002 EFs. These regional adjustme nt 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 PM 10 EF Adjustment Factors by Region and Control Type a

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 Region b 1.0000 1.0000 n/a

a PM 10 adjustment factors are a ratio of future base or control EFs to 2002 EFs. These regional adjustme nt 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.



### 2-50Table 2-40 PM 2.5 EF Adjustment Factors by Region and Control Type a

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 PM 2.5 adjustment factors are a ratio of future base or control EFs to 2002 EFs. These regional adjust ment 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 SO 2 EF Adjustment Factors by Region and Control Type a

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 Region b 1.0000 1.0000 n/a

a SO 2 adjustment factors are a ratio of future base or control EFs to 2002 EFs. These regional adjustme nt 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.



### 2-51Table 2-42 CO 2 EF Adjustment Factors by Region and Control Type a

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 Region b 1.0000 1.0000 n/a

a CO 2 adjustment factors are a ratio of future base or control EFs to 2002 EFs. These regional adjust ment 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 Po rt and Interport Control Inventories


### To produce future year control scenarios, the interport inventories we re 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 i nventories, growth and control factors were applied to the 2002 near port invent ories (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 por t 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 ra ther than removed.
Interport and near port emissions were th en aggregated to form regional totals.



## 2.4.6 2020 Baseline and Control Inventories and Fuel Consumption

The baseline emission inventor ies for 2020 are presented in Table 2-43

.

2-52Table 2-43 2020 Baseline Inventory

U.S. Region Metric Tonnes per Year

NOx PM10 PM2.5 a HC CO SO 2 CO 2 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 PM 10 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 inve ntories 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 Case a

U.S. Region Metric Tonnes per Year

NOx PM10 PM2.5 a HC CO SO 2 CO 2 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 20 0 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 ba seline and ECA cases is also presented in Table 2-45

.

2-53Table 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 cont rol case relative to th e 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 Case a

U.S. Region Metric Tonnes per Year

NO X PM 10 PM 2.5 a HC CO SO 2 CO 2 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 unde rway in U.S. waters. The analysis clearly

2-54

### demonstrates that emissions from ships in the pro posed ECA are contributing to U.S. air pollution. The inventory data were used as an i nput 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 C2 252 -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 C24 17 -94.8127 29.31049 Gary, IN L3736 -87.3251 41.61202 Georgetown, SC C07 72 -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



### 2-56Port 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 C0 187 -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 L38 03 -85.8628 45.95508



### 2-57Port Name US ACE Code Port Coordinates Longitude Latitude Port Manatee, FL C2 023 -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 C42 70 -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 fo r four modes of operation: 1) hotelling, 2) maneuvering, 3) reduced speed zone (RSZ), and 4) cr uise. Hotelling, or dw elling, occurs while the vessel is docked or anchored near a dock, and only the auxiliary engi ne(s) are being used to provide power to meet the ship’s energy needs. Maneuve ring 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 oc curs 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 th e RSZ lanes for deep water ports and 7 nautical miles for Great Lake ports.
Emissions are calculated separately for pr opulsion and auxiliary engines. The basic equation used is as follows:

Equation 2B-1 

### Emissions mod e [ eng ] 

### = ( calls ) × ( P [ eng ] 

### ) × ( hrs / call mod e 

### ) × ( LF mod e [ eng ] 

### ) × ( EF [ eng ] 

### ) × ( Adj ) × (10 − 6 

### tonnes / g )



### Where: Emissions mode [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/call mode 

### = Hours per call by mode LF mode [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 (u sed when the load factor is below 0.20) 10 -6 

### = Conversion factor from grams to metric tonnes
Main engine load factors are calculated di rectly from the prope ller curve based upon the cube of actual speed divided by maximum speed (a t 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 em issions for each activity mode.



## Cruise
Cruise emissions are calculated for both propul sion (main) and auxiliary engines. The basic equation used to calculate cruise mode emissions for the main engines is:

Equation 2B-2 

### Emissions cruise [ main ] 

### = ( calls ) × ( P [ main ] 

### ) × ( hrs / call cruise 

### ) × ( LF cruise [ main ] 

### ) × ( EF [ main ] 

### ) × (10 − 6 

### tonnes / g )

Where: Emissions cruise [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/call cruise 

### = Hours per call for cruise mode LF cruise [main] 

### = Load factor for main engine s in cruise mode (unitless) EF [main] 

### = Emission factor for main engines for the pollu tant of interest, in g/kW-hr (these vary as a function of engine type and fu el 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 

### tan ce 

### [ 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 di rectly from the prope ller 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 speed 38 

### , 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 

### Emissions cruise [ main ] 

### = ( calls ) × ( P [ main ] 

### ) × ( CruiseDis tan ce

/ CruiseSpeed ) × (2 trips / call ) × 0.83 × ( EF [ main ] 

### ) × (10 − 6 

### tonn e

Where: Emissions cruise [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 po llutant of interest, in g/kW-hr (these vary as a function of engine type and fu el 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 Dis tan ce / Cruise Speed ) × (2 trips / call ) × ( LF cruise [ aux ] 

### ) × ( EF [ aux ] 

### ) × (10 − 6 

### tonnes /



### Where: Emissions cruise[aux] 

### = Metric tonnes emitted from auxi liary 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 LF cruise [aux] 

### = Load factor for auxiliary engines in cruise mode, unitle ss (these vary by ship type and activity mode) EF [aux] 

### = Emission factor for auxiliary engines for th e pollutant of interest, in g/kW-hr (these vary as a function of engi ne type and fuel used, ra ther than activity mode) 10 -6 

### = Conversion factor from grams to metric tonnes
The inputs of calls, cruise distance, and vesse l 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 propulsi on (main) and auxiliary engines. The basic equation used to calculate RSZ mode emissions for the main engines is:

Equation 2B-7 

## Emissions RSZ [ main ] 

## = ( calls ) × ( P [ main ] 

## ) × ( hrs / call RSZ 

## ) × ( LF RSZ [ main ] 

## ) × ( EF [ main ] 

## ) × ( Adj ) × (10 − 6 

## tonnes / g )

Where: Emissions RSZ[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/call RSZ 

### = Hours per call for RSZ mode LF RSZ [main] 

### = Load factor for main engines in RSZ mode, unitless EF [main] 

### = Emission factor for main engines for the po llutant of interest, in g/kW-hr (these vary as a function of engine type and fu el used, rather than activity mode) Adj = Low load adjustment factor, unitless (u sed 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 tan ce [ 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 

### LoadFactor RSZ [ 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 Dis tan ce / RSZ Speed ) × (2 trips / call ) RSZ aux
3

− 6 

### × 

## ( RSZ Speed × 0.94 / Cruise Speed 

## ) × ( EF [ aux ] 

### ) × 

## ( Adj 

## ) × (10 tonnes / g )



### Where: Emissions RSZ[main] 

### = Metric tonnes emitted from main engines in RSZ mode calls = Round-trip visits (i.e., one entran ce and one clearance is considered a call) P [main] 

### = Total main engine power, in kilowatts RSZ distance = one way distance, in na utical 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 po llutant of interest, in g/kW-hr (these vary as a function of engine type and fu el used, rather than activity mode) Adj = Low load adjustment factor, unitless (u sed 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 relativ ely constant down to about 20 percent load. Below that threshold, emission factors tend to incr ease significantly as the load decreases. During the RSZ mode, load factors can fall below 20 per cent. 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 adjust ment 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 Dis tan ce / RSZ Speed ) × (2 trips / call ) × ( LF RSZ [ aux ] 

### ) × ( EF [ aux ] 

### ) × (10 − 6 

### tonnes / g )



### Where: Emissions RSZ[aux] 

### = Metric tonnes emitted from auxiliary engines in RSZ mode calls = Round-trip visits (i.e., one entran ce and one clearance is considered a call) P [aux] 

### = Total auxiliary engine power, in kilowatts RSZ distance = one way distance, in na utical miles (specific to each port) RSZ speed = speed, in knots (specific to each port) 2 trips/call = Used to calculate round trip cruise distance LF RSZ [aux] 

### = Load factor for auxiliary engines in RS Z 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 lo w 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 se parate 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 ha lfway between cruise speed and maneuvering speed.


## Maneuvering
Maneuvering emissions are calculated for bot h propulsion (main) and auxiliary engines. The basic equation used to calculate maneuvering mode emissions for the main engines is:

Equation 2B-14 

### Emissions man [ main ] 

### = ( calls ) × ( P [ main ] 

### ) × ( hrs / call
man 

### ) × ( LF man [ main ] 

### ) × ( EF [ main ] 

### ) × ( Adj ) × (10 − 6 

### tonnes / g )



### Where:

2-63

### Emissions man[main] 

### = Metric tonnes emitted from main engines in maneuvering mode calls = Round-trip visits (i.e., one entran ce and one clearance is considered a call) P [main] 

### = Total main engine power, in kilowatts hrs/call man 

### = Hours per call for maneuvering mode LF man [main] 

### = Load factor for main engines in maneuvering mode, unitless EF [main] 

### = Emission factor for main engines for the po llutant of interest, in g/kW-hr (these vary as a function of engine type and fu el used, rather than activity mode) Adj = Low load adjustment factor, unitless (u sed 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 th e pier/wharf/dock (PWD). Maneuv ering times also include shifts from one PWD to another or from one port with in 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 maneuverin g 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 e quation 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: Emissions man[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/call man 

### = Hours per call for maneuvering mode Cruise speed = Vessel service speed, in knots EF [main] 

### = Emission factor for main engines for the po llutant of interest, in g/kW-hr (these vary as a function of engine type and fu el used, rather than activity mode) Adj = Low load adjustment factor, unitless (u sed 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 applie d accordingly. Maneuvering times are not readily available for all 117 ports. For this analysis, maneuvering times and lo ad factors available for a subset of the ports were used to calculate maneuvering emissions for th e remaining ports. This is discussed in more detail in section 2.3.2.3.8

. The equation used to calculate maneuvering m ode emissions for the auxiliary engines is:

Equation 2B-19 Maneuvering Mode Emissions for Auxiliary Engines 

### Emissions
man [ aux ] 

### = ( calls ) × ( P [ aux ] 

### ) × ( hrs / call
man 

### ) × ( LF
man [ aux ] 

### ) × ( EF [ aux ] 

### ) × (10 − 6 

### tonnes / g )

Where: Emissions man[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/call man 

### = Hours per call for maneuvering mode LF man [aux] 

### = Load factor for auxiliary engines in ma neuvering mode, unitless (these vary by ship type and activity mode) EF [aux] 

### = Emission factor for auxiliary engines for th e pollutant of interest, in g/kW-hr (these vary as a function of engi ne 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 auxilia ry engines only, as main engines are not operational during this mode. Th e equation used to calculate hot elling mode emissions for the auxiliary engines is:

Equation 2B-20 Hotelling Mode Emissions for Auxiliary Engines 

### Emissions hotel [ aux ] 

### = ( calls ) × ( P [ aux ] 

### ) × ( hrs / call hotel 

### ) × ( LF hotel [ aux ] 

### ) × ( EF [ aux ] 

### ) × (10 − 6 

### tonnes / g )

Where: Emissions hotel[aux] 

### = Metric tonnes emitted from a uxiliary 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/call hotel 

### = Hours per call for hotelling mode LF hotel [aux] 

### = Load factor for auxiliary engines in hot elling mode, unitless (these vary by ship type and activity mode)

2-65

### EF [aux] 

### = Emission factor for auxiliary engines for th e pollutant of interest, in g/kW-hr (these vary as a function of engi ne type and fuel used, rather than activity mode) 10 -6 

### = Conversion factor from grams to metric tonnes
Hotelling times are not readily available for al l 117 ports. For this analysis, hotelling times available for a subset of the ports were used to ca lculate hotelling emissions for the remaining ports.

2-66

## Appendix 2C: Port Reduced Sp eed 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



### 2-67Port 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



### 2-68Port 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 Typi cal Port Data for Other Inputs There is currently not enough in formation to readily calculate time-in-m ode (hours/call) for all 117 ports during the maneuveri ng and hotelling modes of operati on. As a result, it was necessary to review and select av ailable detailed emission inventorie s 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 a nd 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 a nd match these ports to the other modele d ports is briefly described in this appendix, and more fully desc ribed in the ICF documentation. 39



## 2.6.1 Selection of Typical Ports
In 1999, the U.S. Government published two guidance documents 40
, 41 

### to calculate marine vessel activity at ports. These documents cont ained detailed port inventor ies of eight deep sea ports, two Great Lake ports and two inland river port s. The detailed inventories were developed by obtaining ship call data from Marine Exchanges/Po rt 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. Th e ports for which detailed inventor ies were developed are shown in Table 2D- 1 for deep sea ports and Table 2D-2 for Great Lake ports along w ith 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 rive r ports were developed from US Army Corps of Engineers (USACE) Waterborne Commerce Statistics Center data.

Table 2D-1 Deep Sea MEPA Vesse l 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 shif ting 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 shif ting 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 sh ifting 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.



### 2-70MEPA 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 shif ting 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 sh ifting 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 ME PA 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 Angeles 38
, 42



### • Puget Sound Ports 43



### • Port of New York/New Jersey 44



### • Port of Houston/Galveston 45



### • Port of Beaumont/Port Arthur 46



### • Port of Corpus Christi 47



### • Port of Portland 48



### • Ports of Cleveland, OH and Duluth-Superior, MN&WI 49



### Based on the review of these newer studies, some of the previous typical ports were replaced with newer data and an additiona l typical port was added. Da ta 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 info rmation and better engine category definitions. The Port of Houston/Galveston in ventory provided enough data to a dd an additional typical port. All three port inventories were adjusted to re flect the current methodology used in this study.
The information provided in the cu rrent inventory for Puget Sound Ports 43 

### was used to calculate RSZ speeds, load factors, and times fo r 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 th e 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 in ventories needed for this project.

The final list of nine deep sea and two Great Lake typical ports us ed 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 modele d with the typical port which was most like it. Three criteria were used for matching a given port to a typical port: regional differences D 

### , maximum vessel draft, and the ship t ypes that call on a specific port. One container port, for instance, may have much smalle r 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 prov ided 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.



### 2-72Table 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



### 2-73Modeled 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



### 2-74Modeled 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 eith er 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 typica l port, the maneuvering and hotelling time-inmode values, as well as the maneuve ring load factors by bin for the t ypical 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 ra nge bin was used for the calculations. In a few cases, the engine type for a given sh ip type might not be in the typica l like port data. In these cases, the closest engine type at the typi cal 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 congl omerate port within th e 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 SO 2 

### 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 document 50 

### 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 we re used and adjusted as discussed below. The data supplied by ARB included domestic tr affic as well as foreign cargo traffic.

For PM emission calculations, ARB used an em ission factor of 1.5 g/ kWh to calculate total PM emissions and factors of 0.96 a nd 0.937 to convert total PM to PM 10 

### and PM 2.5 

### respectively. Since an emission factor of 1.4 g/kWh was used in our calculations for PM 10 

### and an emission factor of 1.3 g/kWh for PM 2.5 

### , ARB PM 10 

### and PM 2.5 

### emissions were multiplied by factors of 0.972 and 0.925, respectively to get consistent PM 10 

### and PM 2.5 

### emissions for propulsion engines.
For auxiliary engines, ARB used the same emission factors as above, while we used PM 10 

### and PM 2.5 

### emission factors of 1.3 and 1.2 g/kWh, resp ectively 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 ar e 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 provi de consistent PM emission calculations.

2-76

### SO 2 

### emissions were also different between the ARB and these analyses. ARB used a composite E 

### propulsion engine SO 2 

### emission factor of 10.55 g/kWh while we used a composite SO 2 

### emission factor of 9.57 g/kWh. Thus, ARB SO 2 

### propulsion emissions were multiplied by a factor of 0.907 to be consistent with our emission calc ulations. For auxiliary engines, ARB used SO 2 

### emission factors of 11.48 and 9.34 g/kWh, respectiv ely for passenger and othe r ships, while we use emission factors of 9.93 and 9.07 g/kWh, respectively. Thus, ARB auxiliary SO 2 

### emissions were multiplied by factors of 0.865 and 0.971, respecti vely for passenger and other ships to provide consistent SO 2 

### emissions.

2.6.3.2 Port in Puget Sound


### In the newest Puget Sound inventory 43 

### , it was found that a cons iderable amount of tanker ships stop at Cherry Point, Ferndale, March Poin t and other areas which ar e not within the top 89 U.S. deep sea ports analyzed in this analysis. In addition, since they ar e 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 ar ea but not at one of the Puget Sound ports analyzed in this analysis. Ship calls in the 1996 typical port da ta to ports other than those in the top 89 U.S. deep sea ports were an alyzed 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 characteris tics were used as in the 1996 data, but the number of calls was proportionally increased to 432 cal ls 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 significan t Category 3 domestic tanker traffic enters and leaves the Port of Valdez on de stination to West Coast ports. Since the USACE entrances and clearances data di d not contain any tanker calls at Valdez in 2002, the recent Alaska inventory data was used to calcula te emissions at that port. In this case, the number of calls and ship characteristics for 2002 were ta ken directly from the Alaska i nventory 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 SO 2 EF of 9.57 g/kW-hr was calculated using this weighting, along with the SSD and MSD SO 2 EFs for the West Coast ports reported in Table 2-4.



### 2-77

## Appendix 2E: Emission Inputs to STEEM The STEEM waterway network model relie s on a number of inputs to identify the movements for each vessel, individual ship attribut es, 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 Intellig ence Unit (LMIU) for Canada and Mexico. 52 

### These sources contain information for each vessel carryi ng foreign cargo at each major port or waterway that, most importantly for this analysis, includes:

• Vessel name

• Last port of call (entranc e 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 im portant 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 pr eviously assigned ship id entification number. The following information comes from the USACE entr ances and clearances report for each ship identification number:

• Ship type

• Gross registered tonnage (GRT)

• Net registered tonnage (NRT)
The ship attributes data set contains th e 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 re ferences for ship speed . In these instances, the missing information was developed for each of ni ne vessel types and th e 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.

2-78Table 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 approx imately 60 percent of a ship’s cruise speed based on using the propeller law desc ribed 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 info rmation was developed for nine different vessel types and the appropriate value wa s applied to each individua l ship of that type when the data were lacking. In this case, th e 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 internal ly in the model and the result applied to each individual ship, as appropriate.
The ship attributes database also contains in formation 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 employe d 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 inform ation primarily from ICF International. 55 

### (The ICF report attributed these power values to a study for th e 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 auxili ary power ratio and the main engine horsepower rating for that individual ship. The main and a uxiliary power values and the resulting auxiliary engine to main engine ratios are shown in Table 2E-2

.

2-79Table 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 averag e of the Auto Carrier and Cruise Ship power values from the ICF reference.


### Finally, the ship attributes database provi des information on the load factors for main engines during cruise and maneuveri ng operation, in addition to load factors for auxiliary marine engines. Main engine load factors for cruise operation were taken from a study of in ternational shipping for all ship types, except passenger vessels. 56 

### For this analysis, the STEEM model used a propulsion engine load factor for pa ssenger ship engines at cruise speed of 55 percent of the total installed power. This is based on engine manufacturer data containe d in two global shipping studies. 56
, 57 

### During maneuvering, it was assumed that all main engines, including those for passenger ships, operate at 20 per cent 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 propul sion 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



### 2-80

## 2.6.6 Emission Factor Information

The emission factor data set contains emissi on 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 NO X 

### , HC, and SO 2 

### 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 v oyage data used in the model do not explicitly identify main engine speed ratings , i.e., slow or medium, or the a uxiliary engine fuel type, i.e., marine distillate or residual marine. The com posite factor for each pollutant is determined by weighting individual emission f actors 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 NO X PM 10 PM 2.5 a HC CO SO 2
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 PM 10 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 NO X 

### and HC were taken from several recent analyses of ship emissions in the U.S., Canada, and Europe, as referenced above for th e main engine load factors. The PM factor for marine distillate was taken from a report by ENVI RON International, which was also referenced above. The PM factor for residual marine wa s based on discussions w ith the California Air Resources Board (ARB) staff. The CO factors are from the Starcrest Consu lting study of the Port of Los Angeles. 38 

### For SO 2 

### , the fuel specific emission factor s were obtained from Entec and Corbett and Koehler. , 56 

### The composite emission factors displa yed 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 NO X PM 10 PM 2.5 a HC CO SO 2
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 PM 10 using a multiplicative adjustment factor of 0.92. b See Table 2E-6 for composite SO 2 emission factors by vessel type.



### 2-81

### As for main engines, the STEEM study used the composite emission factors for auxiliary engines. For all po llutants other than SO 2 

### , 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 determ ined 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 SO 2 

### , 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 th e amount of marine di stillate and residual marine that is used by these engines. The rela tive 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 SO 2 

### emission factors are shown in Table 2E-6

.

Table 2E-6 Auxiliary Engine SO 2 Composite Emission Fa ctors 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 SO 2 

## Emission Inventories
The interport emission results contained in this study for PM 10 

### and SO 2 

### were taken from the STEEM inventories and then adjusted to reflect th e U.S. Government’s recent review of available engine test data and fuel sulfur levels for the near port analysis. In th e near ports work, a PM emission factor of 1.4 g/kW-hr was used for most ma in engines, e.g., slow speed diesel and medium speed diesel engines, all of whic h 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 repr esent 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 aux iliary engines. Here agai n, 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 multip lying 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 presente d 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 porti ons that are farthest from shore.
The inventories used for air quality modeling also only contain Tier I NO X 

### controls, as opposed to the Tier I and Tier II contro ls 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 NO X 

### , PM 2.5 

### , and SO 2 

### , since the air quality modeling is focused on ozone and PM 2.5 

### . As shown, the inventory provided for air quality modeling generally understa tes 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

NO X PM 2.5 SO X 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%



### 2-831 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, Valida tion 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). Gl obal 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 Gr owth 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://nauticalch arts.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 (S eptember 2005). 2005 Oceangoing Sh ip Survey, Summary of Results.



### 2-8416 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 Vess els, 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 Vess els, 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, Validati on 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, Validati on 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, Validati on 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, Validati on 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 Cent er (2002), Vessel Entrances and Clearances available at http://www.iwr.usace.army.mil/ndc/db/entclrn/data/entrclrn02/



### 2-8530 IMO. Revision of MARPOL Annex VI and the NO x technical code. Input from the four subgroups and individual experts to the final report of the Informal Cross Govern ment/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). Glob al 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). Glob al 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). Glob al 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). Glob al 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). Glob al 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). Comme rcial Marine Activity for Deep Sea Ports in the United States, prepared for the U.S. Enviro nmental 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). Comme rcial Marine Activity for Deep Sea Ports in the United States, prepared for the U.S. Enviro nmental 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 Co rps 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.



### 2-8646 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). Refine ment of the Marine Emissions Inventory for the Corpus Christi Urban Airshed, Department of Environmental Engin eering, 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, Validati on 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 Organizatio n, 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 Methodolo gies 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 (20 04). Considering Alternative Input Parame ters 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 (S eptember 2005). 2005 Oceangoing Sh ip Survey, Summary of Results.

61 California Air Resources Board (S eptember 2005). 2005 Oceangoing Sh ip Survey, Summary of Results.



### 2-87