### 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 emissi on 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 emissi ons 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 le ading to regional haze (PM, SOx, and NOx)
was the estimation of emissions for the offshore activity, primarily of ocean-going vessels. This activity was notpreviously estimated for the WRAP emission inventory, and has been a subject of concern as vesse l traffic passes out from and along and upwind
of the western coast of the US. The other re vision conducted here wa s to update in-shore deep-draft vessel emissions to reflect changi ng 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 me ters (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 me ters). The plume rise was estimated at 2 meters using standard plume rise models w ith 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 ma rine emissions uses location identification data from a sample of vessels within the re gion of interest, and scaling factors by vessel type to estimate all ships. The ship proxim ity 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/ex it of the Strait of Juan de Fu ca using the scaled proximity method were approximately half of what were predic ted 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 unde restimated 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 woul d reduce the number of ship indicators in areas near land and underestimate the ship traffi c. Therefore, emissions in the first whole grid cell and any partial grid cells near th e coast were zeroed out and replaced with emission estimates derived from the in-por t 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 Colu mbia River where the nearest four grid cells now ha ve 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 inve ntory 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 esti mates 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 en tirely 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 offshor e activity, the states were defined using the
latitudes where the state borders meet the sh ore, as shown in Figure 2. For the near port totals in Table 2, it should be noted that th e 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 23,074 177,068Figure 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 tur nover aspect of the work consid ered the entire replacement of steamships with motorships, especially for projecting future year estimates.

Emission Factors Revisions

Table 3 shows the emission factors estimat ed 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 fr om 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 ba se 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] NO X [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] NO X [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 publ ished 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] NO X [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 cons ider the uncertainty in the PM10 emission rates to be in excess of 50%. This may stem from the me thod of collection, filter handling, or other
factors associated with the hygroscopic nature of the par ticulate formed from diesel engines burning high sulfur fuels. The partic ulate emissions rates a nd sulfur relationship
used for the current WRAP inventories are not intended to be the final word on the subject, but provide a reas onable range of estimates c onsistent 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 andPuget 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 ge neral 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. Th e revised emission estimates are shown in Table 6 for Puget Sound ports to be used to cr oss 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 in clude 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 Canadi an traffic, which significantly increased (by 1.5 to 4 times) the reduced speed zone a nd cruise mode emissions for Washington State emissions exclusively in the transit m odes through the Strait of Juan de Fuca. The scope of the inventory does not include the Canadian traffi c emissions, but it should be
understood that this emission source a ffects Washington marine emissions.

### For geographic allocation, the emissions fo r each port were separated into three segments: cruise, reduced speed zone (RSZ ), maneuvering and hotelling/dwelling. For emissions associated with Grays Harbor vesse l calls, all emissions were considered to occur in Grays Harbor County. For all Puge t Sound vessel calls, the cruise condition
emissions were assumed to occur in Clallu m 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 esti mates of the fraction of time spent in each county. For example, port calls to Olympi a included RSZ emissions in Clallum, Jefferson, Kitsap, Pierce, and Thurston counties; and Port calls to Bellingham included transit through Clallum, San Juan, and What com counties. Because shipping lanes often straddle county boundaries, these county desi gnations 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) esti mates from 1996 to 2002 were updated with freight movement informati on 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 ac cording to the procedure described in EPA (1999b), where a scaling factor was determined w ith a similar port, in this case, the Puget Sound totals. Adjustments were made to the act ual vessel activity su ch as reduced speed
zone load and time in mode based on discussi ons with the River Pilots for the Columbia River ports. Other factors, such as cr uise, maneuvering, and hotelling time and load, were kept the same with the ad justed number of vessel calls.

Vessels arriving near the mout h 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 estim ate that 12 knots is a typical average speed for ships once the pilots take command.

There were five major ports in the Columb ia River for which EPA (1999b) identified and
estimated total vessel visits. These total ve ssel 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 necessari ly 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 indi vidual vessel visits by type of vessel for each Columbia River port were divided by the P uget Sound totals and multiplied by the more detailed estimate of the Puge t 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 cr uise, maneuvering, and hotelling emissions. The resulting emissions adjust ed to eliminate steamships fo r 1996 activity are shown in Table 9; Table 10 shows the emissions projec ted to 2002. Cruise conditions are assumed here to begin 14 miles out from Astoria wh ere the reduce speed zone ends. Reduced speed zone emissions used the ratio of total vi sits 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 ra nges 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 Colu mbia River downstream of each port rather than in the port area. The geographic a llocation for the transit (cruise and RSZ) emissions were to the Washington countie s (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 emissi ons 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 vesse l fleet was made for this work other than using the revised NOx emissi on factors described here.

California Coastal Transit and Ports

Emissions for commercial marine vessels operating near and w ithin the State of California were provided by CARB (CARB, 200 5). 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 z one and a shapefile that defined the shipping lanes within that zone. By overlaying th e 100 mile zone for each county with the shipping lanes and the WRAP grid, it was possibl e to assign a fracti on of the county total emissions to each grid cell. For the remain ing 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 countie s where emissions were assigned to major lakes or rivers. Figure 3 shows the grid cel ls 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 tw o inventories was eliminated. This yielded a comprehensive emission inventory for commer cial 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 emi ssions 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 indivi dual vessels and number of calls of each vessel to the WRAP coastal ports. The histor ic trend shown in Figure 3 does not provide a sufficient number of years to determine the fo rm 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 activ ity, per agreement and discussion with CARB staff, and thereby matching CARB projections (CARB, 2005). Thes e average projection factors are shown in Table 11; th ey were applied to all three west coast states, to all in-

### shore and offshore emissions. No emissi on rate decrease wa s projected because
international standards are not expected to affect emissions.

More detailed discussions of the developmen t 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

R 2 = 6.96E-01 Exponential Correlation

y = 3.98E-47e 0.0633x

R 2 = 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

YearPower Installed (kW)

Figure 5. Commercial marine installed power trend and analysis.

### Emissions Summaries

Summaries of the gridded off-shore sour ce emissions for the Base02b, Plan02c and Base18b inventories by state and county, a nnual and seasonal periods, can be found on
the TSS at: http://vista.cira.colostate.edu/tss/Results/Emissions.aspx

.