Introduction 

Commercial marine emissions comprise a wide variety of vessel types and uses. Table 1 

describes the different types of commercial marine vessel activity. In the previous 

WRAP mobile sources emission inventory work, emissions were estimated for most 

types of vessels (Pollack et al., 2004). Military emissions were not estimated because the 

activity data are not publicly available, and offshore emissions were not considered at 

that time. 

Table 1. Commercial vessel types. 

Source Definition Purpose Geographic Area 

Ocean Traffic Deep draft Ocean-going large vessels Near port 

River Traffic Tow or Push Boats Barge Freight Ocean Traffic 

Tugs Vessel assist and support 

functions Near port 

Ferries River or lake ferrying Regular routes 

Other Commercial 

Vessels 

Smaller support or excursion 

boats Near dock 

Dredges Dredging projects Varies 

Commercial Fishing Market fishing Ocean 

Military Coast Guard and Navy Ocean & Port 

Emissions were estimated for deep draft vessels within shore and near port using port call 

data, and offshore emissions generated from ship location data. The most important 

revision for commercial marine emissions leading to regional haze (PM, SOx, and NOx) 

was the estimation of emissions for the offshore activity, primarily of ocean-going 

vessels. This activity was not previously estimated for the WRAP emission inventory, 

and has been a subject of concern as vessel traffic passes out from and along and upwind 

of the western coast of the US. The other revision conducted here was to update in-shore 

deep-draft vessel emissions to reflect changing fleet mix, especially the retirement of 

steamship powered vessels. 

One issue for modelers was which vertical grid layer to introduce the deep draft 

emissions. The stack height of 34 to 58 meters (Starcrest, 2004) and plume rise for 

ocean-going (deep draft) vessels indicated that the emissions should be placed in the 

second vertical layer (above 36 and below 73 meters). The plume rise was estimated at 2 

meters using standard plume rise models with the vessel speed of 17 to 25 knots as the 

wind speed, exhaust exit rate of 35 to 40 meters per second with an average stack 

diameter of about 1.3 meters (Anderson, 2000). 

Off-Shore Emissions Estimation Methodology 

The method used to estimate the offshore marine emissions uses location identification 

data from a sample of vessels within the region of interest, and scaling factors by vessel 

type to estimate all ships. The ship proximity data and methods used to develop the ship 
population and emissions offshore are described in Pollack, et al., 2006. In short, this 

method uses positioning data generated by a subset of the world’s ships, assumes the 

sample is a random sample, and scales that sample to the entire world fleet. 

 

Emissions estimates using this method were compared with the emissions generated 

using the Puget Sound port activity estimates described below. The grid cell emission 

totals at the entrance/exit of the Strait of Juan de Fuca using the scaled proximity method 

were approximately half of what were predicted using just the US port traffic, ignoring 

the traffic to and from Vancouver, Canada. Using the proximity method it would be 

expected that the emissions would be underestimated as ships near land, because 

positioning systems would be turned off or, if manually operated, would not be actively 

engaged during this period of time. This would reduce the number of ship indicators in 

areas near land and underestimate the ship traffic. Therefore, emissions in the first whole 

grid cell and any partial grid cells near the coast were zeroed out and replaced with 

emission estimates derived from the in-port activity for Oregon and Washington ports 

(Puget Sound, Columbia River, Coos Bay, and Grays Harbor) with remaining near coast 

estimates unchanged, as shown in Figure 1. The most apparent difference can be 

observed in Figure 1 for the grid cells near the mouth of the Columbia River where the 

nearest four grid cells now have higher emissions. There are also higher emissions at the 

entrance to the Strait of Juan de Fuca, but that result is less clear in Figure 1. 

Figure 1. Raw offshore emission estimates and with near port emissions substituted 

(Blue grids indicate no emissions over water). 

The commercial marine vessel emission inventory estimates provided by the California 

Air Resources Board (CARB) includes estimates for ships in transit within 100 miles of 
the coast (CARB, 2005). The transit emissions predicted using the proximity method in 

this work were zeroed out for the zone where CARB estimates were applicable. They 

were replaced by the CARB transit emissions estimates that were spatially assigned to the 

coastal shipping lanes defined by CARB. The result of this replacement along the 

California coast is apparent in the right side of Figure 1. The CARB data also included 

large vessel activity in ports. Therefore, in addition to using the CARB transit emissions 

for the California coastal zone, the CARB in port emissions were used for the California 

ports. 

Because the emissions offshore represent entirely new estimates of emissions in the 

WRAP modeling domain, a summary of emissions is shown in Table 2 by state compared 

with the emissions near the ports and for California within the coastal zone. For purposes 

of preparing state emissions totals for offshore activity, the states were defined using the 

latitudes where the state borders meet the shore, as shown in Figure 2. For the near port 

totals in Table 2, it should be noted that the Columbia River vessel traffic (especially the 

transit up and down the river) was primarily allocated to the State of Washington 

counties. 

Table 2. 2002 Large ocean-going ship emissions by location (tons/year). 

State VOC CO NOx PM10 SO2 

Washington (offshore) 1,451 2,941 44,692 3,247 25,130 

Washington (near port) 103 209 3,467 335 2,483 

Washington (within shore) 277 1,206 10,764 763 5,352 

Oregon (offshore) 1,331 2,706 41,113 2,986 23,119 

Oregon (near port) 22 44 736 72 532 

Oregon (within shore) 23 271 1,415 42 212 

California (offshore) 4,269 8,681 131,930 9,587 74,181 

California (coastal zone) 5,387 14,345 111,550 6,042 46,059 

Total 12,863 30,403 345,667 23,074 177,068 
Figure 2. Offshore emissions by state and grid cells replaced with near port data. 

In-shore Port Revisions 

Ocean-going vessel emissions near ports were revised from the previous WRAP 

estimates (Pollack et al., 2004) to account for fleet turnover and more recent emission 

rate estimates. The fleet turnover aspect of the work considered the entire replacement of 

steamships with motorships, especially for projecting future year estimates. 

Emission Factors Revisions

Table 3 shows the emission factors estimated by the U.S. EPA for Category 2 and 3 

engines (EPA, 1999a, 2003) used in previous WRAP emission inventory estimates. For 

the Category 2 engines, the average values shown in Table 3 were the average values 

used to estimate the emission reductions from the new emission standards (Samulski, 

1999), and are quite similar to the emission factors for the highest power Category 1 

engines. For Category 3 engines, EPA relied on a review of the base emission factors by 

ENVIRON (2002), based on the available data to date when the study was conducted. 
Table 3. US EPA (1999a, 2003) baseline emission factors for marine engines.

Engine Category 

HC 

[g/kW-hr] 

CO 

[g/kW-hr] 

NOX

[g/kW-hr] 

PM 

[g/kW-hr] 

Category 2 

(5-30 l/cylinder) 0.134 2.48 13.36 0.32 low sulfur 

Category 3 

Medium Speed 

(> 300 rpm, > 30 

l/cylinder) 

0.5* 0.7 16.6 Fuel sulfur 

dependence 

Category 3 

Slow speed 0.5* 1.1 23.6 Fuel sulfur dependence 

* Converted from kg/tonne units in Lloyds (1995) using 210 (g/kW-hr) for “medium speed” engines. 

Since the previous WRAP work, additional studies related to marine engine emissions 

have been published, including Cooper (2001 and 2003) and ENTEC (2002). Emission 

estimates from these studies were cited in the Port of Los Angeles (PoLA) emission 

inventory report (Starcrest, 2004). Table 4 summarizes emission factors from these 

studies. 

Table 4. Emission factors for marine engines in the Port of Los Angeles emission 

inventory report. 

Engine Category 

HC 

[g/kW-hr] 

CO 

[g/kW-hr] 

NOX

[g/kW-hr] 

PM 

[g/kW-hr] 

Main Engine 

(Medium Speed – Residual Oil) 0.5 1.1 14.0 0.72 

Main Engine 

(Slow Speed – Residual Oil) 0.6 1.4 18.1 1.92 

Auxiliary Engine 

(Medium Speed – Residual Oil) 0.4 1.1 14.7 0.30 

Auxiliary Engine 

(Medium Speed – Gas Oil) 0.4 1.1 13.9 0.30 

The author of the 2002 ENTEC study later published a report to supplement the emission 

data compiled in the ENTEC study for marine engines (IVL, 2004). The emission data 

used in the IVL 2004 study are summarized in Table 5 for engines built prior to the 

MARPOL NOx emission reduction requirement. Note the dramatic difference in the 

slow speed particulate emission rate estimates of 1.92 or 1.3 g/kW-hr. 

Table 5. Emission factors found in the IVL 2004 report for average 1999 conditions. 

Engine Category 

BSFC 

[g/kW-hr] 

HC 

[g/kW-hr] 

CO 

[g/kW-hr] 

NOX

[g/kW-hr] 

PM 

[g/kW-hr] 

Medium Speed – Residual 

Oil (2.4% sulfur) 215 0.2 1.1 14.0 0.5 

Medium Speed – Gas Oil 

(0.4% sulfur) 205 0.2 1.1 13.2 0.2 

Slow Speed – Residual Oil 

(2.4% sulfur) 195 0.3 0.5 18.1 1.3 

Slow Speed – Gas Oil 

(0.4% sulfur) 185 0.3 0.5 17.0 0.2 
There remains considerable uncertainty about the particulate emissions rates, especially 

for engines using high sulfur fuels. The IVL (2004) and ENTEC (2002) estimates 

indicate that the authors consider the uncertainty in the PM10 emission rates to be in 

excess of 50%. This may stem from the method of collection, filter handling, or other 

factors associated with the hygroscopic nature of the particulate formed from diesel 

engines burning high sulfur fuels. The particulate emissions rates and sulfur relationship 

used for the current WRAP inventories are not intended to be the final word on the 

subject, but provide a reasonable range of estimates consistent with the best 

understanding at this time. 

Revised Estimates for Ocean-Going Vessels

EPA (1999b) reviewed estimates of the ocean-going vessel activity for Coos Bay and 

Puget Sound ports. In this document, a method is also described to extrapolate oceangoing vessel activity for other ports and to allocate activity to individual Puget Sound 

ports. Two data sources existed for the EPA (1999b) report, one of which gathered 

general information about the total number of trips by vessel type for the top 95 US ports, 

and the other gathered more specific information for several ports including the Puget 

Sound ports totals and Coos Bay. The more general information was used to allocate the 

more specific activity information to each of the Puget Sound ports and to extrapolate an 

estimate of the activity of the Columbia River ports. This method was identical to 

previous WRAP emission inventories with the replacement of steamships with 

motorships of the same gross tonnage. The revised emission estimates are shown in 

Table 6 for Puget Sound ports to be used to cross reference to the Columbia River ports. 

Emission rates for motorships are higher for NOx but lower for PM and SOx than for 

steamships. 

Table 6. Emission estimates for Puget Sound (excluding Grays Harbor) ocean-going 

vessels in 2002. 

Estimate 

HC 

(tons/year) 

CO 

(tons/year) 

NOx 

(tons/year) 

PM 

(tons/year) 

SO2 

(tons/year) 

Cruise (25 miles to 

entrance of the 

Strait) 52 106 1,759 170 1,400

Reduced Speed 

Zone 135 275 4,554 440 3,255

Maneuvering 23 67 203 24 164

Hotelling 28 523 2,814 68 302

Total 238 971 9,329 701 5,121

The emissions estimated in Table 6 do not include Canadian vessel traffic, and so may 

underestimate the emissions within and just outside the Strait of Juan de Fuca. A 

Canadian study (Levelton, 2002) added in Canadian traffic, which significantly increased 

(by 1.5 to 4 times) the reduced speed zone and cruise mode emissions for Washington 

State emissions exclusively in the transit modes through the Strait of Juan de Fuca. The 

scope of the inventory does not include the Canadian traffic emissions, but it should be 

understood that this emission source affects Washington marine emissions. 
For geographic allocation, the emissions for each port were separated into three 

segments: cruise, reduced speed zone (RSZ), maneuvering and hotelling/dwelling. For 

emissions associated with Grays Harbor vessel calls, all emissions were considered to 

occur in Grays Harbor County. For all Puget Sound vessel calls, the cruise condition 

emissions were assumed to occur in Clallum County. For all Puget Sound vessel calls, 

the maneuvering and hotelling emissions were allocated to the county of the port of 

interest. The emissions for the reduced speed zone were allocated to the counties along 

the primary shipping channel according to estimates of the fraction of time spent in each 

county. For example, port calls to Olympia included RSZ emissions in Clallum, 

Jefferson, Kitsap, Pierce, and Thurston counties; and Port calls to Bellingham included 

transit through Clallum, San Juan, and Whatcom counties. Because shipping lanes often 

straddle county boundaries, these county designations were made for expedience and 

could be improved by plotting emissions along the actual shipping lanes rather than the 

county in general. 

The basic data for the vessel calls and emission estimates were for 1996, the same as the 

previous emission inventory, but the scaling (growth) estimates from 1996 to 2002 were 

updated with freight movement information for 2002 compared to 1996. These scaling 

factors are shown in Table 7 for each port. 

Table 7. Freight tonnage from 1996 to 2002 by port.

Port 1996 2002 2002/1996 

Seattle Harbor, WA 23,547,000 19,591,009 0.83 

Tacoma Harbor, WA 21,491,000 20,587,109 0.96 

Anacortes Harbor, WA 13,844,000 15,362,650 1.11 

Everett Harbor, WA 4,007,000 3,009,175 0.75 

Port Angeles Harbor, WA 2,780,000 1,673,985 0.60 

Grays Harbor, WA 1,990,000 1,485,991 0.75 

Bellingham Harbor, WA 1,419,000 250,000 0.18 

Olympia Harbor, WA 1,893,000 1,440,439 0.76 

Puget Sound Totals 68,981,000 61,914,367 0.90 

Port of Astoria, OR 324,000 95,000 0.29 

Port of Kalama, WA 8,223,000 6,386,161 0.78 

Port of Longview, WA 5,163,000 4,705,771 0.91 

Port of Portland, OR 29,734,000 26,635,044 0.90 

Port of Vancouver, WA 7,704,000 6,610,345 0.86 

Columbia River Totals 51,148,000 44,432,321 0.87 

Coos Bay 3,322,000 1,706,821 0.51 

Valdez, AK 77,116,000 50,513,074 0.66 

Ketchikan, AK 1,341,000 753,000 0.56 

Nikiski, AK 6,608,630 7,235,098 1.09 

Anchorage, AK 3,401,000 2,983,137 0.88 
Columbia River Ports

The Columbia River ports were estimated according to the procedure described in EPA 

(1999b), where a scaling factor was determined with a similar port, in this case, the Puget 

Sound totals. Adjustments were made to the actual vessel activity such as reduced speed 

zone load and time in mode based on discussions with the River Pilots for the Columbia 

River ports. Other factors, such as cruise, maneuvering, and hotelling time and load, 

were kept the same with the adjusted number of vessel calls. 

Vessels arriving near the mouth of the Columbia River are guided by Bar pilots across 

the Columbia Bar to Astoria (approximately 14 nautical miles), where River pilots begin 

piloting ships to their destination. The River pilots estimate that 12 knots is a typical 

average speed for ships once the pilots take command. 

There were five major ports in the Columbia River for which EPA (1999b) identified and 

estimated total vessel visits. These total vessel visits were compared with the total 

activity for Puget Sound ports (including Grays Harbor) for which a more detailed 

estimate has already been produced, as shown in the Table 8. The port call information 

provided here does not necessarily match the actual deep draft vessel calls because often 

smaller ships are included in the Army Corps estimates than would be included in a port 

specific data of deep draft vessels. The individual vessel visits by type of vessel for each 

Columbia River port were divided by the Puget Sound totals and multiplied by the more 

detailed estimate of the Puget Sound ports totals to produce an estimate of vessel activity 

for each of the Columbia River ports. 

 

Table 8. Port activity totals as presented by EPA (1999b) for 1995. 

Activity Data 

Puget 

Sound 

Ports 

Port of 

Portland, 

OR 

Port of 

Kalama, 

WA 

Port of 

Vancouver, 

WA 

Port of 

Longview, 

WA 

Port of 

Astoria, 

OR 

RSZ Mileage 

(Nautical) -- 93 69 94 59 14

Bulk Carrier 1378 694 378 446 543 397

Container Ship 2667 540 0 7 0 0

General Cargo 428 69 1 111 44 5

Other 82 0 0 0 0 1

Passenger 101 792 0 20 5 9

Reefer 108 10 0 0 13 0

Roll on/Roll off 795 126 0 7 15 4

Tanker 1000 299 20 28 9 0

Vehicle Carrier 1069 247 0 13 0 0

By using the ratio of total visits, the emissions for the Columbia River ports were directly 

calculated from the Puget Sound totals for cruise, maneuvering, and hotelling emissions. 

The resulting emissions adjusted to eliminate steamships for 1996 activity are shown in 

Table 9; Table 10 shows the emissions projected to 2002. Cruise conditions are assumed 

here to begin 14 miles out from Astoria where the reduce speed zone ends. Reduced 

speed zone emissions used the ratio of total visits and the ratios of load and time in mode 

for each Columbia River port. For instance, the reduced speed zone in the Puget Sound 
ranges from the entrance of the Strait of Juan de Fuca to near each port while the reduced 

speed zone for the Columbia River ports ranges from 14 miles out and in the Columbia 

River. Both the vessel speed (which affects the engine load) and the time in mode are 

different between the Puget Sound and Columbia River. 

Table 9. Emission estimates for Columbia River ocean-going vessels in 1996. 

Estimate 

HC 

(tons/year) 

CO 

(tons/year) 

NOx 

(tons/year) 

PM 

(tons/year) 

SO2 

(tons/year) 

Port of Astoria, OR 6 69 395 14 76

Port of Kalama, WA 11 78 555 30 198

Port of Longview, WA 15 114 789 41 264

Port of Portland, OR 62 330 2662 174 1196

Port of Vancouver, WA 20 118 914 56 377

Table 10. Emission estimates for Columbia River ocean-going vessels in 2002. 

Estimate 

HC 

(tons/year) 

CO 

(tons/year) 

NOx 

(tons/year) 

PM 

(tons/year) 

SO2 

(tons/year) 

Port of Astoria, OR 3 22 146 7 44

Port of Kalama, WA 11 65 512 31 212

Port of Longview, WA 18 113 862 51 344

Port of Portland, OR 72 328 2935 209 1470

Port of Vancouver, WA 21 109 920 61 423

Overall emissions for vessels visiting each port are shown in Table 10 for 2002. 

However, transit emissions occur in the Columbia River downstream of each port rather 

than in the port area. The geographic allocation for the transit (cruise and RSZ) 

emissions were to the Washington counties (Pacific for cruise and some RSZ, 

Wahkiakum, Cowlitz, or Clark) below each port according to the fraction of time spent in 

each county. Maneuvering and hotelling emissions were allocated to the county of the 

port, whether Washington or Oregon. 

The port of Coos Bay was determined differently in that no steamships called at this port 

in 1996. Therefore, no adjustment to the vessel fleet was made for this work other than 

using the revised NOx emission factors described here. 

California Coastal Transit and Ports

Emissions for commercial marine vessels operating near and within the State of 

California were provided by CARB (CARB, 2005). Emissions were provided for several 

major categories, labeled by ARB as SHIPS IN-TRANSIT, SHIPS MANEUVERING 

and SHIPS BERTHING. The IN-TRANSIT category was defined as corresponding to 

operations on shipping lanes within 100 miles of the California coast. The 

MANUEVERING and BERTHING categories correspond to operations at ports. 

Emissions were also provided for a category labeled COMMERCIAL BOATS that 

accounts for the activity of smaller vessels near ports and on interior waterways. To 

incorporate the data provided by CARB into the WRAP commercial marine inventory, 
the CARB county level emissions estimates were spatially allocated to the 36 kilometer 

grid. 

One of two methods was used to spatially allocate the California marine vessel emissions, 

depending on the emission category. For the IN-TRANSIT emissions, CARB provided a 

shapefile that defined the 100 mile coastal zone and a shapefile that defined the shipping 

lanes within that zone. By overlaying the 100 mile zone for each county with the 

shipping lanes and the WRAP grid, it was possible to assign a fraction of the county total 

emissions to each grid cell. For the remaining emissions categories, the emissions were 

assigned to the grid cells that encompassed the major port in the county. The exception 

to such port assignments were inland counties where emissions were assigned to major 

lakes or rivers. Figure 3 shows the grid cells to which IN-TRANSIT and the port/inshore 

emissions were assigned by these procedures. 

 

Figure 3. Grid assignment of California in-transit (green) and port/inshore (red) 

emissions. 

The grid cell assigned emissions were then added to the other WRAP offshore and near 

port gridded emissions. Any overlap of the two inventories was eliminated. This yielded 

a comprehensive emission inventory for commercial marine operations on the west coast 

that encompasses all the zones shown in Figure 4. 
Figure 4. Distinct zones included in the WRAP commercial marine emissions inventory. 

2018 Off-shore Emissions 

Projection factors for future year commercial marine emissions were derived from a 

study performed by Corbett and Wang (2005). This projection was based on an 

investigation of the historic trend in the larger vessels’ installed power. The installed 

power combines the propulsion power of individual vessels and number of calls of each 

vessel to the WRAP coastal ports. The historic trend shown in Figure 3 does not provide 

a sufficient number of years to determine the form of the equation to use to project future 

year activity. The fit of the historic data was equivalent whether an exponential fit 

(equivalent to compound annual growth rate (CAGR)) or a linear regression was used. 

For this work, therefore, an average of the exponential and linear regression was used to 

project future year commercial marine activity, per agreement and discussion with CARB 

staff, and thereby matching CARB projections (CARB, 2005). These average projection 

factors are shown in Table 11; they were applied to all three west coast states, to all in-
shore and offshore emissions. No emission rate decrease was projected because 

international standards are not expected to affect emissions. 

More detailed discussions of the development of the off-shore mobile source emissions 

inventories can be found n Pollack, et al., 2006. 

Table 11. Projection factors for ocean-going vessels. 

Future Year Relative to 2002

2008 1.39

2013 1.79

2018 2.30

Linear Correlation

y = 2.46E+07x - 4.88E+10

R2

 = 6.96E-01

Exponential Correlation

y = 3.98E-47e0.0633x

R2 = 0.6949

0.0E+00

2.0E+08

4.0E+08

6.0E+08

8.0E+08

1.0E+09

1.2E+09

1.4E+09

1995 2000 2005 2010 2015 2020

Year

Power Installed (kW)

Figure 5. Commercial marine installed power trend and analysis. 
Emissions Summaries 

Summaries of the gridded off-shore source emissions for the Base02b, Plan02c and 

Base18b inventories by state and county, annual and seasonal periods, can be found on 

the TSS at: http://vista.cira.colostate.edu/tss/Results/Emissions.aspx.